From 55b342bcc8f1638bf51ff7b5febc35adc2978f81 Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Wed, 16 Oct 2024 11:13:23 -0800 Subject: [PATCH 01/15] draft middle slides without formatting --- docs/emma_index.qmd | 426 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 426 insertions(+) create mode 100644 docs/emma_index.qmd diff --git a/docs/emma_index.qmd b/docs/emma_index.qmd new file mode 100644 index 0000000..b232577 --- /dev/null +++ b/docs/emma_index.qmd @@ -0,0 +1,426 @@ +--- +title: "Why you should use a script-based workflow for your survey" + +author: McCrea Cobb | Emma Schillerstorm | Jonah Withers +format: + revealjs: + theme: [custom.scss] + logo: images/FWS-logo.png + footer: "Alaska Data Week" + self-contained: false +editor: source +--- + +```{r setup} +#| echo: false + +knitr::opts_chunk$set(warning = FALSE, + message = FALSE, + fig.retina = 3, + fig.align = "center") +``` + +## The Tetlin Witch Project + +- Witch Background: + - Range: witch trials of the early modern period pushed the North American witch range northward into Alaska and parts of Canada + - Habitat: primarily boreal forest; avoid water bodies + - Threats: deforestation eliminating ability to craft broomsticks; competition for resources with Yetis +- The Survey: + - Annual presence/absence survey conducted for 40 years + - For 8 consecutive days in October, data is collected on witch presence at 100 sites on the refuge + - Occupancy is assessed based on proximity to forest and proximity to water + +::: notes +none +::: + +## Photos from the Field + +Coming soon to your Wild Weekly... + +\[insert photos\] + +::: notes +none +::: + +## Where We Are + +- Completed: data collection, data entry (in a multi-sheet Excel workbook) +- Next: data clean-up +- No automated data processing workflow exists for this dataset, but I have a little bit of prior experience with R, so let's create one! + +\[Insert screenshot of workbook\] + +::: notes +none +::: + +## Step 1: Set Up New Script File + +- Looks like a text file with a "Run" button +- Document code: + - Human readability = key to the foundation of reusability + + - Translate coding language to human language = critical for yourself AND others + + - Adding comments serves as a reminder for what your code accomplishes and why it was included + + - Basic documentation is as easy as a "\#" (shortcut: CTRL-SHIFT-C) + + ```{r} + #| echo: true + #This is how I document my code. This code does not execute. + + #I can add an optional header to the top of my script file to remind myself #what the file accomplishes: + + #This file is a reusable script to tidy and QC occupancy data collected on #witches in Tetlin NWR. + ``` + + - More advanced documentation for creating functions and packages: "roxygen comments" + +::: notes +none +::: + +## Step 1: Set Up New Script File + +- Get started coding by using a "function" -\> takes an input, accomplishes a task, and spits out an output + + - R has built-in functions to accomplish many common tasks + + - However, you can install additional packages (sets of functions) for your specific needs + + - Always includes documentation on how to use their functions + +- Utilizing existing functions allows you to accomplish complex tasks with a single line of code + + - Back-end code is already written for you + + - You can also create your own functions and even bundle your own functions into your own packages + +::: notes +none +::: + +## Step 1: Set Up New Script File + +- To use external functions, we need to load their packages + - First, install packages to be able to load them + + ```{r} + #| echo: true + #| eval: false + install.packages("tidyverse","readxl","janitor") + ``` + + - Use `library()` to load at top of document + + - Continually update while writing rest of code + + ```{r} + #| echo: true + #Load packages + library(tidyverse) + library(readxl) + library(janitor) + ``` + +::: notes +none +::: + +## Step 2: Import Data + +- Built-in and installable functions exist for reading data from various sources +- Can read data from a locally stored file (.csv, .xlsx, etc.) + - Example: `my_data <- read.csv("path/to/filename.csv")` +- Can read data from a provided link (online authoritative source, ServCat, etc.) + - Example: `my_data <- read.csv(download.file("www.url.com","save_as_filename.csv"))` + +::: notes +none +::: + +## Step 2: Import Data + +- For our data: + - Going to use `read_excel()` to import data from a workbook and specify which sheet we want to access + - Set new "dataframe" as a **variable** - the basis of reusability! + +```{r} +#| echo: true +#Import raw data +witch_data <- read_excel("emma_files/witch_survey.xlsx", sheet = 1) +``` + +::: notes +none +::: + +## Step 3: Tidy Data + +- Definition of "tidy data" \[Insert photo\] + +::: notes +none +::: + +## Step 3: Tidy Data + +- Let's take a peak at what we are working with: + +```{r} +#| echo: true +#Peak at data +str(witch_data) +summary(witch_data) +``` + +- Why is our data not tidy? +- Answer: Some column names are values NOT variables + +::: notes +none +::: + +## Step 3: Tidy Data + +- Let's use `tidyverse()` to tidy our data in one line by "pivoting" + +```{r} +#| echo: true +#Tidy data structure +tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) +head(tidy_data) +``` + +::: notes +none +::: + +## Step 3: Tidy Data + +- In tidy data, column names are variables, so they should be structured as such +- Common variable naming conventions: + - camelCase + - snake_case +- We can use the `janitor` package to fix all our column names in a single line of code + +```{r} +#| echo: true +#Clean column names +tidy_data <- clean_names(tidy_data) +head(tidy_data) +``` + +::: notes +none +::: + +## Step 4: Quality Control and Exploratory Data Analysis + +- Exploratory Data Analysis (EDA) = a way to get to know your data before drawing conclusions + + - May consist of: generating summary statistics, plotting variables, etc. + + - Uses: + + - QC - Are there errors in my data? Are my data within the expected range of values? + + - Check assumptions - Does the data follow an expected distribution? How variable is the data? + + - Identify patterns and relationships - Are my variables correlated? + + - Generate hypotheses + + - Example packages: skimr(), corrplot(), summary_tools(), data_explorer(), assertr() + +::: notes +none +::: + +## Step 4: Quality Control and Exploratory Data Analysis + +Notes regarding my field team... + +- **Frank and Stein the Wildlife Biologists** (*Roles: field work, data recording*) + - Frank - records "none" rather than "0" on the data sheets + - Stein - has illegible handwriting (his 0's can look like 6's, and his 1's can look like 2's or 7's) +- **Casper the friendly Biotech** (*Role: data entry/digitization*) + - Enters data exactly as it is written + +::: notes +none +::: + +## Step 4: Quality Control and Exploratory Data Analysis + +- Let's check and see why our "presence" column is non-numeric + +```{r} +#| echo: true +#Find non-numerics - hide warning +tidy_data$presence[which(is.na(as.numeric(tidy_data$presence)))] +``` + +- Let's replace all instances of "none" with a value of 0 and convert the column back to a numeric type + +```{r} +#| echo: true +#Fix non-numerics +tidy_data$presence[which(tidy_data$presence == "none")] <- 0 +tidy_data$presence <- as.integer(tidy_data$presence) +``` + +::: notes +none +::: + +## Preserve Products + +- Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains +- Preserve data, script files, and reports + - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey + +::: notes +none +::: + +## Preserve Products + +- I want to go further and compile all years of data to preserve + - However, all past years are in the same untidy format, and Frank used "none" instead of "0" since the survey first began + - Would be a nightmare to manually fix, but we can reuse the script we just created +- Wrapping script into a function makes this even more streamlined (optional) + +::: notes +none +::: + +## Preserve Products + +- Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step + +```{r} +#| echo: true +#| eval: false + +#Load packages +library(tidyverse) +library(readxl) +library(janitor) + +#Import raw data +witch_data <- read_excel("witch_survey.xlsx", sheet = 1) + +#Tidy data structure +tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + +#Clean column names +tidy_data <- clean_names(tidy_data) + +#Fix non-numerics +tidy_data$presence[which(tidy_data$presence == "none")] <- 0 +tidy_data$presence <- as.integer(tidy_data$presence) + +#Fix typos +tidy_data$presence[which(tidy_data$presence == 6)] <- 0 +tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + +#Export tidy csv +write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) +``` + +::: notes +none +::: + +## Preserve Products + +- OR wrap script into function + +```{r} +#| echo: true +#| eval: false + +#' Tidy and QC witch data +#' +#' @param year The year (YYYY) representing the worksheet name to pull data from +#' +#' @return A tidied dataframe +#' @export +#' +#' @examples +#' clean_witch_data(year = 2024) +clean_witch_data <- function(year){ + #Import raw data + observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) + site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") + + #Relate tables: observation data and site data + witch_data <- left_join(observation_data, site_data) + + #Tidy data structure + tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + + #Clean column names + tidy_data <- clean_names(tidy_data) + + #Fix non-numerics + tidy_data$presence[which(tidy_data$presence == "none")] <- 0 + tidy_data$presence <- as.integer(tidy_data$presence) + + #Fix typos + tidy_data$presence[which(tidy_data$presence == 6)] <- 0 + tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + + #Return tidy dataframe + return(tidy_data) +} +``` + +::: notes +none +::: + +## Preserve Products + +- Can run function for each year and compile data as follows: + +```{r} +#| echo: true +#| eval: false + +#Clean and compile all years of data +compiled_data <- clean_witch_data(2004) +for(val in 2005:2024){ + compiled_data <- full_join(compiled_data, clean_witch_data(year = val)) +} + +#Export +write.csv(compiled_data, file = "compiled_witch_data.csv", row.names = FALSE) +``` + +::: notes +none +::: + +## Reuse + +- Can now repeat all tidying, analyses, and visualizations next year with just the click of a button! +- Medium to high initial investment for dramatically reduced future workload + +::: notes +none +::: + +## Resources + +- Available in the README.md +- If you are in Refuges and interested in developing an automated workflow for your survey but need some assistance, submit an I&M Tech Request + +::: notes +none +::: From 55e0f976018a8533975b4e9ef3df0ac7bef20ff0 Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Mon, 28 Oct 2024 13:44:21 -0800 Subject: [PATCH 02/15] section 2 draft 2 --- docs/emma_index.qmd | 773 +++++++++++++++++++++++++++++++++++++------- 1 file changed, 655 insertions(+), 118 deletions(-) diff --git a/docs/emma_index.qmd b/docs/emma_index.qmd index b232577..4467647 100644 --- a/docs/emma_index.qmd +++ b/docs/emma_index.qmd @@ -1,14 +1,23 @@ --- title: "Why you should use a script-based workflow for your survey" -author: McCrea Cobb | Emma Schillerstorm | Jonah Withers +author: McCrea Cobb | Emma Schillerstrom | Jonah Withers format: revealjs: - theme: [custom.scss] + theme: scss/custom-dark.scss + code-overflow: wrap logo: images/FWS-logo.png footer: "Alaska Data Week" - self-contained: false + chalkboard: + theme: whiteboard + boardmarker-width: 5 + buttons: false + embed-resources: false + code-block-height: 500px +filters: + - output-line-highlight.lua editor: source +highlight-style: a11y --- ```{r setup} @@ -20,94 +29,228 @@ knitr::opts_chunk$set(warning = FALSE, fig.align = "center") ``` -## The Tetlin Witch Project +::: columns +::: {.column width="50%"} +Left column +::: + +::: {.column width="50%"} +Right column +::: +::: + +## Blank Slide -- Witch Background: - - Range: witch trials of the early modern period pushed the North American witch range northward into Alaska and parts of Canada - - Habitat: primarily boreal forest; avoid water bodies - - Threats: deforestation eliminating ability to craft broomsticks; competition for resources with Yetis -- The Survey: - - Annual presence/absence survey conducted for 40 years - - For 8 consecutive days in October, data is collected on witch presence at 100 sites on the refuge - - Occupancy is assessed based on proximity to forest and proximity to water +Content ::: notes -none ::: -## Photos from the Field +## The Tetlin Witch Project {.smaller} + +### Species Background - [*North American Witch*]{.champagne} + +::: columns +::: {.column width="50%"} +#### [History:]{.extrabold .champagne} + +{{< fa up-long >}} Early modern period witch trials shifted range northward + +#### [Habitat:]{.extrabold .champagne} -Coming soon to your Wild Weekly... +{{< fa tree >}} Boreal forest -\[insert photos\] +#### [Threats:]{.extrabold .champagne} + +{{< fa broom >}} Deforestation eliminates ability to craft broomsticks + +{{< fa ghost >}} Competition for resources with ghosts +::: + +::: {.column width="50%"} +![Witch flying over Tetlin NWR in early fall](images/witch_tetlin.png){fig-align="left" width="4.4in"} +::: +::: ::: notes none ::: -## Where We Are +## The Tetlin Witch Project {.smaller} + +### Survey Design - [*an Occurrence Survey*]{.champagne} + +::: columns +::: {.column width="50%"} +\[Insert Map\] +::: -- Completed: data collection, data entry (in a multi-sheet Excel workbook) -- Next: data clean-up -- No automated data processing workflow exists for this dataset, but I have a little bit of prior experience with R, so let's create one! +::: {.column width="50%"} +- Conducted annually 20 years +- Completed over 8 days in August +- Each day, data is collected at 100 sites on the refuge +- Goal: + - understand distribution of witches on Tetlin NWR + - learn about the factors that influence it +::: +::: + +::: notes +lots of biotechs - obviously national funding for witch conservation +::: + +## The Tetlin Witch Project {.smaller} -\[Insert screenshot of workbook\] +### Survey Design - [*an Occupancy Survey*]{.champagne} + +::: columns +::: {.column width="50%"} +\[Insert Map\] +::: + +::: {.column width="50%"} +- Conducted annually 20 years +- Completed over 8 days in August +- Each day, data is collected at 100 sites on the refuge +- Goal: + - understand [distribution of witches]{.champagne} on Tetlin NWR + - learn about the factors that influence it +::: +::: ::: notes none ::: -## Step 1: Set Up New Script File +## The Tetlin Witch Project {.smaller} -- Looks like a text file with a "Run" button -- Document code: - - Human readability = key to the foundation of reusability +### Survey Design - [*an Occupancy Survey*]{.champagne} - - Translate coding language to human language = critical for yourself AND others +::: columns +::: {.column width="50%"} +\[Insert Map\] +::: - - Adding comments serves as a reminder for what your code accomplishes and why it was included +::: {.column width="50%"} +- Conducted annually 20 years +- Completed over 8 days in August +- Each day, data is collected at 100 sites on the refuge +- Goal: + - understand [distribution of witches]{.champagne} on Tetlin NWR + - learn about the [factors that influence it]{.champagne} +::: +::: - - Basic documentation is as easy as a "\#" (shortcut: CTRL-SHIFT-C) +::: notes +none +::: - ```{r} - #| echo: true - #This is how I document my code. This code does not execute. +## Photos from the Field - #I can add an optional header to the top of my script file to remind myself #what the file accomplishes: +{{< fa camera >}} Coming soon to your Wild Weekly... - #This file is a reusable script to tidy and QC occupancy data collected on #witches in Tetlin NWR. - ``` +::: columns +::: {.column width="50%"} +![](images/witch_hiding.png){fig-align="center"} +::: - - More advanced documentation for creating functions and packages: "roxygen comments" +::: {.column width="50%"} +![](images/witch_moose.png){fig-align="center"} +::: +::: ::: notes none ::: -## Step 1: Set Up New Script File +## Where We Are -- Get started coding by using a "function" -\> takes an input, accomplishes a task, and spits out an output +::: columns +::: {.column width="50%"} +**Completed:** - - R has built-in functions to accomplish many common tasks +- Data collection ✓ - - However, you can install additional packages (sets of functions) for your specific needs +- Data entry ✓ - - Always includes documentation on how to use their functions +**Next:** -- Utilizing existing functions allows you to accomplish complex tasks with a single line of code +- Data clean-up ☐ +::: - - Back-end code is already written for you +::: {.column width="50%"} +::: {layout="[[-1], [1], [-1]]"} +![Excel workbook with all years of data](images/workbook_sc.png){fig-align="center"} +::: +::: +::: + +::: notes +*No automated data processing workflow exists for this dataset, so let's create one!* +::: + +## Why Clean Up Data in R? {.incremental .smaller} + +::: columns +::: {.column width="50%"} +#### [Limitations of]{.cursive} {.center} + +#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Messy + +- Prone to human error + +- Difficult to correct or reverse changes + +- Inefficient + +- Not reusable +::: +::: - - You can also create your own functions and even bundle your own functions into your own packages +::: {.column width="50%"} +#### [Advantages of]{.cursive} {.center} + +#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Can iterate through large datasets + +- Can manipulate data without overwriting it + +- Can easily undo or reapply changes + +- Changes are documented + +- Avoids repeating steps + +- Allows for automated workflows/pipelines + +- Eliminates future QC workload +::: +::: +::: ::: notes -none +- efficient, reusable, not prone to error, and on top of that... + +- working outside of original file + +- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows + +- great for apps or lab data that comes in a set, untidy format + +- Requires medium initial effort for a future of no effort + +- can apply changes to all rows, all sheets, all files at once ::: -## Step 1: Set Up New Script File +## Set Up New Script File - To use external functions, we need to load their packages - - First, install packages to be able to load them + - First, install packages ```{r} #| echo: true @@ -115,9 +258,9 @@ none install.packages("tidyverse","readxl","janitor") ``` - - Use `library()` to load at top of document + - Next, use `library()` to load - - Continually update while writing rest of code + - Sits at top of script and updated as you go ```{r} #| echo: true @@ -128,144 +271,302 @@ none ``` ::: notes -none +sits at top of script +::: + +## Set Up New Script File + +This is called a [*comment*]{.yellow}. + +```{r} +#| echo: true +#| eval: false +#| code-line-numbers: "1" +#Load packages +library(tidyverse) +library(readxl) +library(janitor) +``` + +::: callout-tip +## Best Practice + +Document every line to few lines of code with a comment. ::: -## Step 2: Import Data +A comment precedes [machine-readable language]{.yellow} and supplies a translation to [human-readable language]{.yellow}. -- Built-in and installable functions exist for reading data from various sources -- Can read data from a locally stored file (.csv, .xlsx, etc.) - - Example: `my_data <- read.csv("path/to/filename.csv")` -- Can read data from a provided link (online authoritative source, ServCat, etc.) - - Example: `my_data <- read.csv(download.file("www.url.com","save_as_filename.csv"))` +::: center +[Human Readability]{.orange .extrabold} {{< fa handshake >}} [Reusability]{.orange .extrabold} +::: ::: notes none ::: -## Step 2: Import Data +## Set Up New Script File + +Basic documentation is as easy as a "`#`" (shortcut: `CTRL-SHIFT-C`) + +```{r} +#| echo: true +#| eval: false +# Any code or text that is commented will not be executed, +but if it is not commented, R will treat it as code, +so this will return an ERROR. +``` + +::: callout-tip +## Tip! + +The `#` can also be used to temporarily disable lines of code for testing purposes. +::: + +::: notes +It serves as a reminder for what your code accomplishes and why it was included. + +Translating coding language to human language is critical for yourself AND others. +::: + +## Import Data + +Can read data from a... + +1. [Locally stored file]{.orange} (*.csv, .xlsx, etc.*) + +::: small +*Example:* +::: + +```{r} +#| echo: true +#| eval: false +my_data <- read.csv("path/to/filename.csv") +``` + +2. [Online source]{.orange} (*download link, API, ServCat, etc.*) + +::: small +*Example:* +::: + +```{r} +#| echo: true +#| eval: false +my_data <- read.csv(download.file("www.url.com","save_as_filename.csv")) +``` + +::: notes +Built-in and installable functions exist for reading data from various sources Like a link to the online authoritative source for a dataset +::: + +## Import Data - For our data: - - Going to use `read_excel()` to import data from a workbook and specify which sheet we want to access - - Set new "dataframe" as a **variable** - the basis of reusability! + - Use `read_excel()` to import data from a specific sheet in a workbook + - Set new "dataframe" as a [*variable*]{.yellow} ```{r} #| echo: true #Import raw data -witch_data <- read_excel("emma_files/witch_survey.xlsx", sheet = 1) +witch_data <- read_excel("data/witch_survey.xlsx", sheet = "2024") ``` +::: callout-note +## Note + +Variables are the basis of reusability! +::: + ::: notes -none +Variables allow us to easily reference complex expressions and enable reusability. If I write all my code referencing the "witch_data" variable and I decide I want to change the input file, I can easily do that in this one line and not have to tamper with any of the rest of my code. ::: -## Step 3: Tidy Data +## Tidy Data -- Definition of "tidy data" \[Insert photo\] +::: {layout="[[-1], [1], [-1]]"} +![](images/tidy_graphic.png){fig-align="center"} +::: ::: notes none ::: -## Step 3: Tidy Data +## Tidy Data -- Let's take a peak at what we are working with: +Let's take a peak at what we are working with: ```{r} -#| echo: true -#Peak at data -str(witch_data) -summary(witch_data) +#| echo: false +#| eval: true +library(gt) +gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') +#%>% tab_caption("First 6 lines of dataset") ``` -- Why is our data not tidy? -- Answer: Some column names are values NOT variables +::: center +[*First 6 lines of dataset*]{.smaller} +::: + +::: callout-caution +## Why is our data not tidy? + +**Answer:** Some column names are values NOT variables +::: + +::: notes +- This means we need to restructure our data +::: + +## Tidy Data + +Let's take a closer look at our data using some R built-in functions + +- First, with `str()` + + ```{r} + #| echo: true + #| eval: true + #| class-output: highlight + #| output-line-numbers: "|5|9|11" + #Glimpse of dataset,including datatypes of columns + str(witch_data) + ``` + +::: notes +- Restructing our data requires a little bit more knowledge on the state of the data because we are going to have to combine columns, and columns of different types cannot be combined +::: + +## Tidy Data {.scrollable chalkboard-buttons="true"} + +- Then, with `summary()` + + ```{r} + #| echo: true + #| eval: true + #| class-output: highlight + #| output-line-numbers: "|3|10|17|14" + #Summary of dataset, including summary stats + summary(witch_data) + ``` ::: notes none ::: -## Step 3: Tidy Data +## Tidy Data -- Let's use `tidyverse()` to tidy our data in one line by "pivoting" +Let's use `tidyverse` to tidy our data in one line by "pivoting" ```{r} #| echo: true #Tidy data structure tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) -head(tidy_data) ``` +::: small +Updated dataframe: +::: + +::: smaller +*Row Count: 800* +::: + +::: {style="height:290px; overflow-y: scroll"} +```{r} +#| echo: false +gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') +``` +::: + ::: notes none ::: -## Step 3: Tidy Data +## Tidy Data {.scrollable} +::: small - In tidy data, column names are variables, so they should be structured as such + - Common variable naming conventions: - - camelCase - - snake_case + + - `camelCase` + - `snake_case` + - We can use the `janitor` package to fix all our column names in a single line of code +::: ```{r} #| echo: true #Clean column names tidy_data <- clean_names(tidy_data) -head(tidy_data) ``` +```{r} +#| echo: false +gt(head(tidy_data,1)) %>% opt_stylize(style = 6, color = 'cyan') +``` + +::: {.smaller .center} +*New column headers* +::: + ::: notes none ::: -## Step 4: Quality Control and Exploratory Data Analysis - -- Exploratory Data Analysis (EDA) = a way to get to know your data before drawing conclusions +## Quality Control and Exploratory Data Analysis {.small} - - May consist of: generating summary statistics, plotting variables, etc. +Exploratory Data Analysis (EDA) = [getting to know your data before drawing conclusions]{.orange}, often through summarization and/or visualization - - Uses: +*Example packages:* `skimr`, `corrplot`, `summary_tools`, `DataExplorer`, `assertr` - - QC - Are there errors in my data? Are my data within the expected range of values? +
- - Check assumptions - Does the data follow an expected distribution? How variable is the data? +::: {.cursive .small} +[Are there errors in my data?]{.fragment} [Are there outliers?]{.fragment} [How variable is my data?]{.fragment} - - Identify patterns and relationships - Are my variables correlated? +[Are my data within their expected range of values?]{.fragment} - - Generate hypotheses +[Are my variables correlated?]{.fragment} [Do my variables follow their expected distributions?]{.fragment} - - Example packages: skimr(), corrplot(), summary_tools(), data_explorer(), assertr() +[What hypotheses can I generate?]{.fragment} [Are the assumptions for my analyses met?]{.fragment} +::: ::: notes none ::: -## Step 4: Quality Control and Exploratory Data Analysis +## {.small} + +#### Frank and Stein the Wildlife Biologists -Notes regarding my field team... +[*Roles: field work, data recording*]{.small} -- **Frank and Stein the Wildlife Biologists** (*Roles: field work, data recording*) - - Frank - records "none" rather than "0" on the data sheets - - Stein - has illegible handwriting (his 0's can look like 6's, and his 1's can look like 2's or 7's) -- **Casper the friendly Biotech** (*Role: data entry/digitization*) - - Enters data exactly as it is written +![](images/frank_stein_graphic.png){fig-align="left"} + +#### Casper the friendly Biotech + +[*Role: data entry/digitization*]{.small} + +![](images/casper_graphic.png){fig-align="left"} ::: notes none ::: -## Step 4: Quality Control and Exploratory Data Analysis +## Quality Control and Exploratory Data Analysis -- Let's check and see why our "presence" column is non-numeric +Check to see why our `presence` column is non-numeric ```{r} #| echo: true -#Find non-numerics - hide warning +#| warning: false +#Find non-numerics tidy_data$presence[which(is.na(as.numeric(tidy_data$presence)))] ``` -- Let's replace all instances of "none" with a value of 0 and convert the column back to a numeric type +Replace all instances of `"none"` with a value of `0` ```{r} #| echo: true @@ -275,37 +576,116 @@ tidy_data$presence <- as.integer(tidy_data$presence) ``` ::: notes -none +and convert the column back to a numeric type ::: -## Preserve Products +## Quality Control and Exploratory Data Analysis {.scrollable} -- Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains -- Preserve data, script files, and reports - - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey +- Explore our data again + +```{r} +#| echo: true +#| eval: true +#Check data +str(tidy_data) +summary(tidy_data$presence) +hist(tidy_data$presence) +``` + +- Let's correct all misread numbers + +```{r} +#| echo: true +#Fix typos +tidy_data$presence[which(tidy_data$presence == 6)] <- 0 +tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 +``` ::: notes none ::: -## Preserve Products +## Quality Control and Exploratory Data Analysis {.small} -- I want to go further and compile all years of data to preserve - - However, all past years are in the same untidy format, and Frank used "none" instead of "0" since the survey first began - - Would be a nightmare to manually fix, but we can reuse the script we just created -- Wrapping script into a function makes this even more streamlined (optional) +::: columns +::: {.column width="50%"} +- Use `DataExplorer` to generate a comprehensive report + - Includes summary statistics, distributions, missing data, PCA, qq plots, and more + +```{r} +#| echo: true +#| eval: false +create_report(tidy_data, output_format = pdf(), output_file = "report.pdf") +``` + +- Endless EDA options! + +```{r} +#| eval: true +library(DataExplorer) +``` +::: + +::: {.column width="50%"} +{{< pdf report.pdf width=100% height=500 >}} +::: +::: ::: notes none ::: -## Preserve Products +## Preserve Data Assets {.small .scrollable} + +[*Should occur several times during data management lifecycle!*]{.champagne} + +To Do List {{< fa pencil >}}: + +1. Write machine-readable metadata, including a data dictionary +2. Preserve raw data and script file (if reusing) in ServCat + +::: callout-tip +## Tip + +Contact your program's data manager for program-specific preservation guidelines. + +- Fisheries and Ecological Services - *Jonah Withers* + +- Migratory Birds Management - *Tammy Patterson* + +- National Wildlife Refuge Program - *Caylen Cummins* + +- Science Applications - *Hilmar Maier* +::: -- Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step +::: notes +- Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains +- Preserve data, script files, and reports + - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey +::: + +## Preserve Data Assets + +- A step further: tidy, compile, and preserve all 20 years of data + - All past year sheets are in the same untidy format + - Frank used "none" instead of "0" since the survey first began +- Wrapping script into a function makes this even more streamlined (*optional*) + +::: notes +- Would be a nightmare to manually fix, but we can reuse the script we just created +::: + +## Preserve Data Assets + +::: panel-tabset +### [Basic: Script]{.smaller} + +Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step ```{r} #| echo: true #| eval: false +#| code-block-height: 200px #Load packages library(tidyverse) @@ -333,17 +713,50 @@ tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) ``` -::: notes -none -::: +### [Advanced: Function]{.smaller} + +OR wrap script into function + +```{r} +#| echo: true +#| eval: false +#| code-block-height: 200px + +clean_witch_data <- function(year){ + #Import raw data + observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) + site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") + + #Relate tables: observation data and site data + witch_data <- left_join(observation_data, site_data) + + #Tidy data structure + tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + + #Clean column names + tidy_data <- clean_names(tidy_data) + + #Fix non-numerics + tidy_data$presence[which(tidy_data$presence == "none")] <- 0 + tidy_data$presence <- as.integer(tidy_data$presence) + + #Fix typos + tidy_data$presence[which(tidy_data$presence == 6)] <- 0 + tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + + #Return tidy dataframe + return(tidy_data) +} +``` -## Preserve Products +### [Advanced: Documented Function]{.smaller} -- OR wrap script into function +Make sure to add proper documentation! ```{r} #| echo: true #| eval: false +#| code-block-height: 200px #' Tidy and QC witch data #' @@ -380,21 +793,26 @@ clean_witch_data <- function(year){ return(tidy_data) } ``` +::: ::: notes none ::: -## Preserve Products +## Preserve Data Assets -- Can run function for each year and compile data as follows: +Can run our function for each year and compile data as follows: ```{r} #| echo: true #| eval: false -#Clean and compile all years of data +# Clean and compile all years of data + +#Tidy first year of data compiled_data <- clean_witch_data(2004) + +#Iterate through each year, tidy data for that year, and join with rest of tidy data for(val in 2005:2024){ compiled_data <- full_join(compiled_data, clean_witch_data(year = val)) } @@ -403,12 +821,77 @@ for(val in 2005:2024){ write.csv(compiled_data, file = "compiled_witch_data.csv", row.names = FALSE) ``` +::: {style="height:190px; overflow-y: scroll"} +```{r} +#| eval: TRUE +gt(tidy_data) %>% opt_stylize(style = 6) +``` +::: + +::: notes +none +::: + +## Preserve Data Assets + +Tidy, compiled data ready to be preserved and shared + +::: smaller +::: {style="height:500px; overflow-y: scroll"} +```{r} +#| eval: TRUE +#gt(tidy_data) %>% opt_stylize(style = 6, color = 'white') +library(DT) +DT::datatable(tidy_data, options = list( + columnDefs = list( + list(targets = "_all", className = "custom-header") + ) + ) +) +``` +::: +::: + +::: notes +none +::: + +## Preserve Data Assets + +Avoid any input files through automated pulls from ServCat! + +```{r} +#| eval: false +#| echo: true +library(httr) + +#Request +url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/255753" +response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) + +#Extract file name from response header +header <- headers(response) +filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) + +#Save file +writeBin(content(response,"raw"),filename) + +# +new_data <- clean_witch_data("witch_data.xlsx", 2025) +old_data <- read.csv("all_witch_data.csv") +combine_data(old_data, new_data) + +analyze_witch_data(new_data) +``` + ::: notes none ::: ## Reuse +Like magic {{\< fa wand-sparkles \>} + - Can now repeat all tidying, analyses, and visualizations next year with just the click of a button! - Medium to high initial investment for dramatically reduced future workload @@ -416,6 +899,40 @@ none none ::: +## Reuse: Example + +Fast forward to 2025 {{< fa forward-fast >}} + +```{r} +#| eval: false +#| echo: true +library(httr) + +#Request +url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/255753" +response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) + +#Extract file name from response header +header <- headers(response) +filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) + +#Save file +writeBin(content(response,"raw"),filename) + +# +new_data <- clean_witch_data("witch_data.xlsx", 2025) +old_data <- read.csv("all_witch_data.csv") +combine_data(old_data, new_data) + +analyze_witch_data(new_data) + +#Maybe code to reupload to ServCat?? +``` + +::: notes +none +::: + ## Resources - Available in the README.md @@ -424,3 +941,23 @@ none ::: notes none ::: + +## Import Data + +- Extra step: merge environmental site data and occurrence data + +```{r} +#| echo: true +#Relate tables: observation data and site data; reorder joined columns +# witch_data <- left_join(observation_data, site_data) %>% relocate(c(12,13), .after = 3) +``` + +::: callout-note +## Suggestion + +You can often replace complex MS Access databases with R scripts. Using R to relate tables also ensures your files are all in an open format. +::: + +::: notes +none +::: From 6de88fce7640fb90b4e09a34cdb845600984952d Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Tue, 29 Oct 2024 11:17:26 -0800 Subject: [PATCH 03/15] Section 2 draft 3 --- docs/_emma.qmd | 1062 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1062 insertions(+) create mode 100644 docs/_emma.qmd diff --git a/docs/_emma.qmd b/docs/_emma.qmd new file mode 100644 index 0000000..db23fbe --- /dev/null +++ b/docs/_emma.qmd @@ -0,0 +1,1062 @@ +--- +title: "Why you should use a script-based workflow for your survey" + +author: McCrea Cobb | Emma Schillerstrom | Jonah Withers +format: + revealjs: + theme: scss/custom-dark.scss + code-overflow: wrap + logo: images/FWS-logo.png + footer: "Alaska Data Week" + chalkboard: + theme: whiteboard + boardmarker-width: 5 + buttons: false + embed-resources: false + code-block-height: 500px +filters: + - output-line-highlight.lua +editor: source +highlight-style: a11y +--- + +```{r setup} +#| echo: false + +knitr::opts_chunk$set(warning = FALSE, + message = FALSE, + fig.retina = 3, + fig.align = "center") +``` + +::: columns +::: {.column width="50%"} +Left column +::: + +::: {.column width="50%"} +Right column +::: +::: + +## Blank Slide + +Content + +::: notes +::: + +# Data Clean-Up + +![](images/witch_data_cleanup.png){fig-align="center"} + +## Why Clean Up Data in R? {.incremental .smaller} + +::: columns +::: {.column width="50%"} +#### [Limitations of]{.cursive} {.center} + +#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Messy + +- Prone to human error + +- Difficult to correct or reverse changes + +- Inefficient + +- Not reusable +::: +::: + +::: {.column width="50%"} +#### [Advantages of]{.cursive} {.center} + +#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Can iterate through large datasets + +- Can manipulate data without overwriting it + +- Can easily undo or reapply changes + +- Changes are documented + +- Avoids repeating steps + +- Allows for automated workflows/pipelines + +- Eliminates future QC workload +::: +::: +::: + +::: notes +- efficient, reusable, not prone to error, and on top of that... + +- working outside of original file + +- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows + +- great for apps or lab data that comes in a set, untidy format + +- Requires medium initial effort for a future of no effort + +- can apply changes to all rows, all sheets, all files at once +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Intro to the Data +::: + +::: notes +what data are we cleaning up? let me introduce you to the tetlin witch project +::: + +## Tetlin Witch Survey {.smaller} + +::: columns +::: {.column width="50%"} +\[Insert Map\] +::: + +::: {.column width="50%"} +
+ +[Type:]{.extrabold .orange} Annual occurrence survey + +[Logistics:]{.extrabold .orange} + +- Completed over 8 days in August + +- Detection (0, 1) recorded at 100 sites/day + +- Conducted for 20 years at Tetlin NWR + +[Goal:]{.extrabold .orange} Estimate probability of witch occurrence across the refuge +::: +::: + +::: notes +the Tetlin Witch project, not the same as the Blair witch project + +lots of biotechs - obviously national funding for witch conservation +::: + +## Witch Background + +::: columns +::: {.column .center width="50%"} +Do NOT like water + +![](images/witch_melting.png){fig-align="center"} +::: + +::: {.column .center width="50%"} +DO favor forest + +![](images/witch_broom.png){fig-align="center"} +::: +::: + +::: notes +Can use to characterize and predict their distribution + +Species Background - North American Witch + +Began inhabiting Tetlin NWR during the early modern period when widespread witch trials shifted their range northward + +Started monitoring them due to concerns surrounding the threat of deforestation, impacting their ability to construct broomsticks +::: + +## Where We Are + +::: columns +::: {.column width="50%"} +**Completed:** + +- Data collection ✓ + +- Data entry ✓ + +**Next:** + +- Data clean-up ☐ +::: + +::: {.column width="50%"} +::: {layout="[[-1], [1], [-1]]"} +![Excel workbook with all years of data](images/workbook_sc.png){fig-align="center"} +::: +::: +::: + +::: notes +I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. +No automated data processing workflow exists for this dataset, so let's create one! +::: + +## Photos from the Field + +{{< fa camera >}} Coming soon to your Wild Weekly... + +::: columns +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_hiding.png){fig-align="center"} +::: + +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_moose.png){fig-align="center"} +::: + +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_tetlin.png){fig-align="center"} +::: +::: + +::: notes +none +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Starting Our Script +::: + +::: notes +::: + +## Getting Set Up + +
+ +✓ Created a new R project + +✓ Set up a standard file directory structure + +
+ +::: center +[Time to get coding!]{.large .yellow} +::: + +::: notes +following Jonah's guidance... +open a new blank R script file and... +::: + +## Setting Up New Script + +- To use external functions, we need to load their packages + - First, install packages + + ```{r} + #| echo: true + #| eval: false + install.packages(c("tidyverse","readxl","janitor")) + ``` + + - For non-CRAN packages, use `devtools::install_github("author/package_name")` + + - Next, use `library()` to load + + - Sits at top of script and updated as you go + + ```{r} + #| echo: true + #Load packages + library(tidyverse) + library(readxl) + library(janitor) + ``` + +::: notes +sits at top of script + +these packages are all in the CRAN (comprehensive R archive network) repository +::: + +## Set Up New Script File + +This is called a [*comment*]{.yellow}. + +```{r} +#| echo: true +#| eval: false +#| code-line-numbers: "1" +#Load packages +library(tidyverse) +library(readxl) +library(janitor) +``` + +A comment precedes [machine-readable language]{.yellow} and supplies a translation to [human-readable language]{.yellow}. + +::: center +[Human Readability]{.orange .extrabold} {{< fa handshake >}} [Reusability]{.orange .extrabold} +::: + +::: callout-tip +## Best Practice + +Document every line to few lines of code with a comment. +::: + +::: notes +none +::: + +## Set Up New Script File + +Basic documentation is as easy as a "`#`" (shortcut: `CTRL-SHIFT-C`) + +```{r} +#| echo: true +#| eval: false +# Any code or text that is commented will not be executed, +but if it is not commented, R will treat it as code, +so this will return an ERROR. +``` + +::: callout-tip +## Tip! + +The `#` can also be used to temporarily disable lines of code for testing purposes. +::: + +::: notes +It serves as a reminder for what your code accomplishes and why it was included. + +Translating coding language to human language is critical for yourself AND others. +::: + +# Data Clean-Up: *Importing* + +## Importing Data + +Can read data from a... + +1. [Locally stored file]{.orange} (*.csv, .xlsx, etc.*) + +::: small +*Example:* +::: + +```{r} +#| echo: true +#| eval: false +my_data <- read.csv("path/to/filename.csv") +``` + +2. [Online source]{.orange} (*download link, API, ServCat, etc.*) + +::: small +*Examples:* +::: + +```{r} +#| echo: true +#| eval: false +my_data <- read.csv(download.file("www.url.com","save_as_filename.csv")) +``` + +```{r} +#| echo: true +#| eval: false +my_data <- GET(url = "api-url.com/input-id", body = list("parameter1","parameter2")) +``` + +::: notes +Built-in and installable functions exist for reading data from various sources Like a link to the online authoritative source for a dataset +::: + +## Importing Data + +Extra Step: merge multiple tables + +```{r} +#| echo: true +#| eval: false +#Join tables by common variables +all_data <- left_join(observation_data, site_data) +``` + +::: callout-note +## Suggestion + +You can often replace complex MS Access databases with R scripts. Using R to join related tables also ensures your files are all in an open format. +::: + +::: notes +none +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Importing Our Data +::: + +::: notes +::: + +## Importing Our Data + +- For our data: + - Use `read_excel()` to import data from a specific sheet in a workbook + - Set new "dataframe" as a [*variable*]{.yellow} + +```{r} +#| echo: true +#Import raw data +witch_data <- read_excel("data/witch_survey.xlsx", sheet = "2024") +``` + +::: callout-note +## Note + +Variables are the basis of reusability! +::: + +::: notes +Variables allow us to easily reference complex expressions and enable reusability. If I write all my code referencing the "witch_data" variable and I decide I want to change the input file, I can easily do that in this one line and not have to tamper with any of the rest of my code. +::: + +# Data Clean-Up: [Tidying]{.orange} + +## Tidy Data + +::: {layout="[[-1], [1], [-1]]"} +![](images/tidy_graphic.png){fig-align="center"} +::: + +::: notes +none +::: + +## Why Tidy Data? + +::: incremental +- Standardization + +- Interpretability + +- Machine readability + +- Ease of use and reuse +::: + +::: notes +By keeping data in a standardized format with clear attributes and values, the data can be easily interpreted by humans and manipulated through computers +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Tidying Our Data +::: + +::: notes +::: + +## Tidying Our Data + +Let's take a peak at what we are working with: + +```{r} +#| echo: false +#| eval: true +library(gt) +gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') +#%>% tab_caption("First 6 lines of dataset") +``` + +::: center +[*First 6 lines of dataset*]{.smaller} +::: + +::: callout-caution +## Why is our data not tidy? + +**Answer:** Some column names are values NOT variables +::: + +::: notes +- This means we need to restructure our data +::: + +## Tidying Our Data + +Let's use `tidyverse` to tidy our data in one line by "pivoting" + +```{r} +#| echo: true +#Tidy data structure +tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) +``` + +::: small +Updated dataframe: +::: + +::: smaller +*Row Count: 800* +::: + +::: {style="height:290px; overflow-y: scroll"} +```{r} +#| echo: false +gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') +``` +::: + +::: notes +none +::: + +## Tidying Our Data {.scrollable} + +::: small +- In tidy data, column names are variables, so they should be structured as such + +- Common variable naming conventions: + + - `camelCase` + - `snake_case` + +- We can use the `janitor` package to fix all our column names in a single line of code +::: + +```{r} +#| echo: true +#Clean column names +tidy_data <- clean_names(tidy_data) +``` + +```{r} +#| echo: false +gt(head(tidy_data,1)) %>% opt_stylize(style = 6, color = 'cyan') +``` + +::: {.smaller .center} +*New column headers* +::: + +::: notes +none +::: + +# Data Clean-Up: [Exploratory Data Analysis (EDA)]{.orange} + +## What is EDA? {.small} + +Exploratory Data Analysis (EDA) = [getting to know your data before drawing conclusions]{.orange}, often through summarization and/or visualization + +*Example packages:* `skimr`, `corrplot`, `summary_tools`, `DataExplorer`, `assertr` + +
+ +::: {.cursive .small} +[Are there errors in my data?]{.fragment} [Are there outliers?]{.fragment} [How variable is my data?]{.fragment} + +[Are my data within their expected range of values?]{.fragment} + +[Are my variables correlated?]{.fragment} [Do my variables follow their expected distributions?]{.fragment} + +[What hypotheses can I generate?]{.fragment} [Are the assumptions for my analyses met?]{.fragment} +::: + +::: notes +Lends itself to quality control +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center} +Exploring Our Data & Performing Quality Control +::: + +::: notes +::: + +## {.small} + +#### Frank and Stein the Wildlife Biologists + +[*Roles: field work, data recording*]{.small} + +![](images/frank_stein_graphic.png){fig-align="left"} + +#### Casper the friendly Biotech + +[*Role: data entry/digitization*]{.small} + +![](images/casper_graphic.png){fig-align="left"} + +![](images/qc_6.jpg){.absolute top="360" right="280" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="360" right="170" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="360" right="60" width="100" height="100"} + +::: notes +none +::: + +## Exploring Our Data (EDA) + +Let's [rewind {{< fa backward-fast >}}]{.orange} and take a closer look at our starting data + +- First, with `str()` + + ```{r} + #| echo: true + #| eval: true + #| class-output: highlight + #| output-line-numbers: "|5|9|11" + #Glimpse of dataset,including datatypes of columns + str(witch_data) + ``` + +::: notes +- (Restructing our data requires a little bit more knowledge on the state of the data because we are going to have to combine columns, and columns of different types cannot be combined) +- using some R built-in functions +::: + +## Exploring Our Data (EDA) {.scrollable chalkboard-buttons="true"} + +- Then, with `summary()` + + ```{r} + #| echo: true + #| eval: true + #| code-block-height: 300px + #| class-output: highlight + #| output-line-numbers: "|3|10|17|14" + #Summary of dataset, including summary stats + summary(witch_data) + ``` + +::: notes +none +::: + +## EDA for Quality Control + +See why our presence data is non-numeric + +```{r} +#| echo: true +#| warning: false +#Find non-numerics +tidy_data$presence[which(is.na(as.numeric(tidy_data$presence)))] +``` + +Replace all instances of `"none"` with `0` + +```{r} +#| echo: true +#Fix non-numerics +tidy_data$presence[which(tidy_data$presence == "none")] <- 0 +tidy_data$presence <- as.integer(tidy_data$presence) +``` + +::: notes +and convert the column back to a numeric type +::: + +## EDA for Quality Control {.scrollable} + +Explore distribution + +```{r} +#| echo: true +#| eval: true +#Plot histogram of "presence" values +hist(tidy_data$presence) +``` + +Correct misread numbers + +```{r} +#| echo: true +#Fix typos +tidy_data$presence[which(tidy_data$presence == 6)] <- 0 +tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 +``` + +::: notes +applies change to all 800 lines of data in one line of code +::: + +## More EDA Options {.small} + +::: columns +::: {.column width="50%"} +- Use `DataExplorer` to generate a comprehensive report + - Includes summary statistics, distributions, missing data, PCA, qq plots, and more + +```{r} +#| echo: true +#| eval: false +create_report(tidy_data, output_format = pdf(), output_file = "report.pdf") +``` + +- Endless options! + +```{r} +#| eval: true +library(DataExplorer) +``` +::: + +::: {.column width="50%"} +{{< pdf report.pdf width=100% height=500 >}} +::: +::: + +::: notes +not necessary for our simple dataset but a great, customizable option +::: + +# Preservation + +\[insert photo\] + +## Preserving Data and Data Assets {.small} + +Yes, **already**! + +Preservation should occur [several times]{.orange} during the data management lifecycle and include all data, metadata, and data assets (e.g., protocols, presentations, reports, code) + +::: callout-tip +## Tip + +Contact your program's data manager for program-specific preservation guidelines. + +- Fisheries and Ecological Services - *Jonah Withers* + +- Migratory Birds Management - *Tammy Patterson* + +- National Wildlife Refuge Program - *Caylen Cummins* + +- Other programs - *Hilmar Maier* +::: + +[Reminder:]{.yellow} Reproducibility and reuse are impossible without *findability* + +::: notes +- In the FWS, we must preserve our data and data assets in a Board approved repository, which is ServCat or ScienceBase +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center} +Preserving Our Data +::: + +::: notes +::: + +## Preserving Our Data + +[To Do List {{< fa pencil >}}:]{.extrabold .yellow} + +1. Write machine-readable metadata, including a data dictionary +2. Preserve raw data and script file (if reusing) in ServCat +3. Link new ServCat references to the "Tetlin Witch Survey" ServCat Project + +::: fragment +A step further... + +::: center +Tidy, compile, and preserve all 20 years of data +::: +::: + +::: notes +- Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains +- Preserve data, script files, and reports + - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey +::: + +## Preserve Data Assets + +Uh Oh {{< fa face-frown >}}... + +- All past workbook sheets are in the same untidy format + +- And Frank used `none` instead of `0` since the survey began + +::: fragment +Don't worry, [we can reuse our script]{.yellow}! + +::: callout-note +## Suggestion + +Wrapping the script into a function makes this even more streamlined (*optional*) +::: +::: + +::: notes +- Would be a nightmare to manually fix, but we can reuse the script we just created +::: + +## Preserve Data Assets {.scrollable} + +::: panel-tabset +### [Basic: Script]{.smaller} + +Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step + +```{r .custom-code-block-1} +#| echo: true +#| eval: false + +#Load packages +library(tidyverse) +library(readxl) +library(janitor) + +#Import raw data +witch_data <- read_excel("witch_survey.xlsx", sheet = 1) + +#Tidy data structure +tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + +#Clean column names +tidy_data <- clean_names(tidy_data) + +#Fix non-numerics +tidy_data$presence[which(tidy_data$presence == "none")] <- 0 +tidy_data$presence <- as.integer(tidy_data$presence) + +#Fix typos +tidy_data$presence[which(tidy_data$presence == 6)] <- 0 +tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + +#Export tidy csv +write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) +``` + +### [Advanced: Function]{.smaller} + +OR wrap script into function + +```{r} +#| echo: true +#| eval: false + +clean_witch_data <- function(year){ + #Import raw data + observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) + site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") + + #Relate tables: observation data and site data + witch_data <- left_join(observation_data, site_data) + + #Tidy data structure + tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + + #Clean column names + tidy_data <- clean_names(tidy_data) + + #Fix non-numerics + tidy_data$presence[which(tidy_data$presence == "none")] <- 0 + tidy_data$presence <- as.integer(tidy_data$presence) + + #Fix typos + tidy_data$presence[which(tidy_data$presence == 6)] <- 0 + tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + + #Return tidy dataframe + return(tidy_data) +} +``` + +### [Advanced: Documented Function]{.smaller} + +Make sure to add proper documentation! + +```{r} +#| echo: true +#| eval: false + +#' Tidy and QC witch data +#' +#' @param year The year (YYYY) representing the worksheet name to pull data from +#' +#' @return A tidied dataframe +#' @export +#' +#' @examples +#' clean_witch_data(year = 2024) +clean_witch_data <- function(year){ + #Import raw data + observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) + site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") + + #Relate tables: observation data and site data + witch_data <- left_join(observation_data, site_data) + + #Tidy data structure + tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) + + #Clean column names + tidy_data <- clean_names(tidy_data) + + #Fix non-numerics + tidy_data$presence[which(tidy_data$presence == "none")] <- 0 + tidy_data$presence <- as.integer(tidy_data$presence) + + #Fix typos + tidy_data$presence[which(tidy_data$presence == 6)] <- 0 + tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 + + #Return tidy dataframe + return(tidy_data) +} +``` +::: + +::: notes +none +::: + +## Preserve Data Assets + +Can run our function for each year and compile data as follows: + +```{r} +#| echo: true +#| eval: false + +## Clean and compile all years of data +#Tidy first year of data +compiled_data <- clean_witch_data(2004) + +#Iterate through each year, tidy data for that year, and join with rest of tidy data +for(val in 2005:2024){ + compiled_data <- full_join(compiled_data, clean_witch_data(year = val)) +} + +#Export +write.csv(compiled_data, file = "compiled_witch_data.csv", row.names = FALSE) +``` + +::: notes +none +::: + +## Preserve Data Assets + +Tidy, compiled data ready to be preserved and shared + +[*Rows: 16,000*]{.smaller} + +::: smaller +::: {style="height:520px"} +```{r} +#| eval: TRUE +library(DT) +DT::datatable(tidy_data, options = list( + pageLength = 9, + dom = 't', + columnDefs = list( + list(targets = "_all", className = "custom-header") + ) + ) +) +``` +::: +::: + +::: notes +none +::: + +## Preserve Data Assets + +Eliminate input files through ServCat API requests! + +```{r} +#| eval: false +#| echo: true +library(httr) + +#' Pull complete Tetlin witch survey dataset from ServCat +#' +#' @return dataframe +#' @export +#' +#' @examples +#' pull_witch_data() +pull_witch_data <- function(){ + #Request + url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/1234" + response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) + + #Extract file name from response header + header <- headers(response) + filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) + + #Save file + writeBin(content(response,"raw"), paste0("data/",filename)) + + #Import dataset + return(read.csv(paste0("data/",filename))) +} + +``` + +::: notes +none +::: + +# Break Time + +## Reuse + +Like magic {{\< fa wand-sparkles \>} + +- Can now repeat all tidying, analyses, and visualizations next year with just the click of a button! +- Medium to high initial investment for dramatically reduced future workload + +::: notes +none +::: + +## Resources + +- Available in the README.md +- If you are in Refuges and interested in developing an automated workflow for your survey but need some assistance, submit an I&M Tech Request + +::: notes +none +::: From 86cddf34776b8c81383d8107d0dcc5d283f7d032 Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Tue, 29 Oct 2024 11:25:54 -0800 Subject: [PATCH 04/15] Section 2 draft 3 changes before file name update --- docs/emma_index.qmd | 619 +++++++++++++++++++++++++------------------- 1 file changed, 359 insertions(+), 260 deletions(-) diff --git a/docs/emma_index.qmd b/docs/emma_index.qmd index 4467647..db23fbe 100644 --- a/docs/emma_index.qmd +++ b/docs/emma_index.qmd @@ -46,85 +46,83 @@ Content ::: notes ::: -## The Tetlin Witch Project {.smaller} +# Data Clean-Up -### Species Background - [*North American Witch*]{.champagne} +![](images/witch_data_cleanup.png){fig-align="center"} + +## Why Clean Up Data in R? {.incremental .smaller} ::: columns ::: {.column width="50%"} -#### [History:]{.extrabold .champagne} +#### [Limitations of]{.cursive} {.center} -{{< fa up-long >}} Early modern period witch trials shifted range northward +#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} -#### [Habitat:]{.extrabold .champagne} +::: incremental +- Messy -{{< fa tree >}} Boreal forest +- Prone to human error -#### [Threats:]{.extrabold .champagne} +- Difficult to correct or reverse changes -{{< fa broom >}} Deforestation eliminates ability to craft broomsticks +- Inefficient -{{< fa ghost >}} Competition for resources with ghosts +- Not reusable +::: ::: ::: {.column width="50%"} -![Witch flying over Tetlin NWR in early fall](images/witch_tetlin.png){fig-align="left" width="4.4in"} -::: -::: +#### [Advantages of]{.cursive} {.center} -::: notes -none -::: +#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} -## The Tetlin Witch Project {.smaller} +::: incremental +- Can iterate through large datasets -### Survey Design - [*an Occurrence Survey*]{.champagne} +- Can manipulate data without overwriting it -::: columns -::: {.column width="50%"} -\[Insert Map\] -::: +- Can easily undo or reapply changes -::: {.column width="50%"} -- Conducted annually 20 years -- Completed over 8 days in August -- Each day, data is collected at 100 sites on the refuge -- Goal: - - understand distribution of witches on Tetlin NWR - - learn about the factors that influence it +- Changes are documented + +- Avoids repeating steps + +- Allows for automated workflows/pipelines + +- Eliminates future QC workload +::: ::: ::: ::: notes -lots of biotechs - obviously national funding for witch conservation -::: +- efficient, reusable, not prone to error, and on top of that... -## The Tetlin Witch Project {.smaller} +- working outside of original file -### Survey Design - [*an Occupancy Survey*]{.champagne} +- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows -::: columns -::: {.column width="50%"} -\[Insert Map\] +- great for apps or lab data that comes in a set, untidy format + +- Requires medium initial effort for a future of no effort + +- can apply changes to all rows, all sheets, all files at once ::: -::: {.column width="50%"} -- Conducted annually 20 years -- Completed over 8 days in August -- Each day, data is collected at 100 sites on the refuge -- Goal: - - understand [distribution of witches]{.champagne} on Tetlin NWR - - learn about the factors that influence it +# + +::: scary +Witch Survey ::: + +::: {.cursive .center .larger} +Intro to the Data ::: ::: notes -none +what data are we cleaning up? let me introduce you to the tetlin witch project ::: -## The Tetlin Witch Project {.smaller} - -### Survey Design - [*an Occupancy Survey*]{.champagne} +## Tetlin Witch Survey {.smaller} ::: columns ::: {.column width="50%"} @@ -132,35 +130,52 @@ none ::: ::: {.column width="50%"} -- Conducted annually 20 years +
+ +[Type:]{.extrabold .orange} Annual occurrence survey + +[Logistics:]{.extrabold .orange} + - Completed over 8 days in August -- Each day, data is collected at 100 sites on the refuge -- Goal: - - understand [distribution of witches]{.champagne} on Tetlin NWR - - learn about the [factors that influence it]{.champagne} + +- Detection (0, 1) recorded at 100 sites/day + +- Conducted for 20 years at Tetlin NWR + +[Goal:]{.extrabold .orange} Estimate probability of witch occurrence across the refuge ::: ::: ::: notes -none -::: +the Tetlin Witch project, not the same as the Blair witch project -## Photos from the Field +lots of biotechs - obviously national funding for witch conservation +::: -{{< fa camera >}} Coming soon to your Wild Weekly... +## Witch Background ::: columns -::: {.column width="50%"} -![](images/witch_hiding.png){fig-align="center"} +::: {.column .center width="50%"} +Do NOT like water + +![](images/witch_melting.png){fig-align="center"} ::: -::: {.column width="50%"} -![](images/witch_moose.png){fig-align="center"} +::: {.column .center width="50%"} +DO favor forest + +![](images/witch_broom.png){fig-align="center"} ::: ::: ::: notes -none +Can use to characterize and predict their distribution + +Species Background - North American Witch + +Began inhabiting Tetlin NWR during the early modern period when widespread witch trials shifted their range northward + +Started monitoring them due to concerns surrounding the threat of deforestation, impacting their ability to construct broomsticks ::: ## Where We Are @@ -186,68 +201,65 @@ none ::: ::: notes -*No automated data processing workflow exists for this dataset, so let's create one!* +I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. +No automated data processing workflow exists for this dataset, so let's create one! ::: -## Why Clean Up Data in R? {.incremental .smaller} +## Photos from the Field -::: columns -::: {.column width="50%"} -#### [Limitations of]{.cursive} {.center} +{{< fa camera >}} Coming soon to your Wild Weekly... -#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} +::: columns +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_hiding.png){fig-align="center"} +::: -::: incremental -- Messy +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_moose.png){fig-align="center"} +::: -- Prone to human error +::: {.column width="33%" layout="[[-1], [1], [-1]]"} +![](images/witch_tetlin.png){fig-align="center"} +::: +::: -- Difficult to correct or reverse changes +::: notes +none +::: -- Inefficient +# -- Not reusable -::: +::: scary +Witch Survey ::: -::: {.column width="50%"} -#### [Advantages of]{.cursive} {.center} - -#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} +::: {.cursive .center .larger} +Starting Our Script +::: -::: incremental -- Can iterate through large datasets +::: notes +::: -- Can manipulate data without overwriting it +## Getting Set Up -- Can easily undo or reapply changes +
-- Changes are documented +✓ Created a new R project -- Avoids repeating steps +✓ Set up a standard file directory structure -- Allows for automated workflows/pipelines +
-- Eliminates future QC workload -::: -::: +::: center +[Time to get coding!]{.large .yellow} ::: ::: notes -- efficient, reusable, not prone to error, and on top of that... - -- working outside of original file - -- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows - -- great for apps or lab data that comes in a set, untidy format - -- Requires medium initial effort for a future of no effort - -- can apply changes to all rows, all sheets, all files at once +following Jonah's guidance... +open a new blank R script file and... ::: -## Set Up New Script File +## Setting Up New Script - To use external functions, we need to load their packages - First, install packages @@ -255,9 +267,11 @@ none ```{r} #| echo: true #| eval: false - install.packages("tidyverse","readxl","janitor") + install.packages(c("tidyverse","readxl","janitor")) ``` + - For non-CRAN packages, use `devtools::install_github("author/package_name")` + - Next, use `library()` to load - Sits at top of script and updated as you go @@ -272,6 +286,8 @@ none ::: notes sits at top of script + +these packages are all in the CRAN (comprehensive R archive network) repository ::: ## Set Up New Script File @@ -288,18 +304,18 @@ library(readxl) library(janitor) ``` -::: callout-tip -## Best Practice - -Document every line to few lines of code with a comment. -::: - A comment precedes [machine-readable language]{.yellow} and supplies a translation to [human-readable language]{.yellow}. ::: center [Human Readability]{.orange .extrabold} {{< fa handshake >}} [Reusability]{.orange .extrabold} ::: +::: callout-tip +## Best Practice + +Document every line to few lines of code with a comment. +::: + ::: notes none ::: @@ -328,7 +344,9 @@ It serves as a reminder for what your code accomplishes and why it was included. Translating coding language to human language is critical for yourself AND others. ::: -## Import Data +# Data Clean-Up: *Importing* + +## Importing Data Can read data from a... @@ -347,7 +365,7 @@ my_data <- read.csv("path/to/filename.csv") 2. [Online source]{.orange} (*download link, API, ServCat, etc.*) ::: small -*Example:* +*Examples:* ::: ```{r} @@ -356,11 +374,51 @@ my_data <- read.csv("path/to/filename.csv") my_data <- read.csv(download.file("www.url.com","save_as_filename.csv")) ``` +```{r} +#| echo: true +#| eval: false +my_data <- GET(url = "api-url.com/input-id", body = list("parameter1","parameter2")) +``` + ::: notes Built-in and installable functions exist for reading data from various sources Like a link to the online authoritative source for a dataset ::: -## Import Data +## Importing Data + +Extra Step: merge multiple tables + +```{r} +#| echo: true +#| eval: false +#Join tables by common variables +all_data <- left_join(observation_data, site_data) +``` + +::: callout-note +## Suggestion + +You can often replace complex MS Access databases with R scripts. Using R to join related tables also ensures your files are all in an open format. +::: + +::: notes +none +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Importing Our Data +::: + +::: notes +::: + +## Importing Our Data - For our data: - Use `read_excel()` to import data from a specific sheet in a workbook @@ -382,6 +440,8 @@ Variables are the basis of reusability! Variables allow us to easily reference complex expressions and enable reusability. If I write all my code referencing the "witch_data" variable and I decide I want to change the input file, I can easily do that in this one line and not have to tamper with any of the rest of my code. ::: +# Data Clean-Up: [Tidying]{.orange} + ## Tidy Data ::: {layout="[[-1], [1], [-1]]"} @@ -392,7 +452,36 @@ Variables allow us to easily reference complex expressions and enable reusabilit none ::: -## Tidy Data +## Why Tidy Data? + +::: incremental +- Standardization + +- Interpretability + +- Machine readability + +- Ease of use and reuse +::: + +::: notes +By keeping data in a standardized format with clear attributes and values, the data can be easily interpreted by humans and manipulated through computers +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center .larger} +Tidying Our Data +::: + +::: notes +::: + +## Tidying Our Data Let's take a peak at what we are working with: @@ -418,43 +507,7 @@ gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') - This means we need to restructure our data ::: -## Tidy Data - -Let's take a closer look at our data using some R built-in functions - -- First, with `str()` - - ```{r} - #| echo: true - #| eval: true - #| class-output: highlight - #| output-line-numbers: "|5|9|11" - #Glimpse of dataset,including datatypes of columns - str(witch_data) - ``` - -::: notes -- Restructing our data requires a little bit more knowledge on the state of the data because we are going to have to combine columns, and columns of different types cannot be combined -::: - -## Tidy Data {.scrollable chalkboard-buttons="true"} - -- Then, with `summary()` - - ```{r} - #| echo: true - #| eval: true - #| class-output: highlight - #| output-line-numbers: "|3|10|17|14" - #Summary of dataset, including summary stats - summary(witch_data) - ``` - -::: notes -none -::: - -## Tidy Data +## Tidying Our Data Let's use `tidyverse` to tidy our data in one line by "pivoting" @@ -483,7 +536,7 @@ gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') none ::: -## Tidy Data {.scrollable} +## Tidying Our Data {.scrollable} ::: small - In tidy data, column names are variables, so they should be structured as such @@ -515,7 +568,9 @@ gt(head(tidy_data,1)) %>% opt_stylize(style = 6, color = 'cyan') none ::: -## Quality Control and Exploratory Data Analysis {.small} +# Data Clean-Up: [Exploratory Data Analysis (EDA)]{.orange} + +## What is EDA? {.small} Exploratory Data Analysis (EDA) = [getting to know your data before drawing conclusions]{.orange}, often through summarization and/or visualization @@ -534,7 +589,20 @@ Exploratory Data Analysis (EDA) = [getting to know your data before drawing conc ::: ::: notes -none +Lends itself to quality control +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center} +Exploring Our Data & Performing Quality Control +::: + +::: notes ::: ## {.small} @@ -551,13 +619,53 @@ none ![](images/casper_graphic.png){fig-align="left"} +![](images/qc_6.jpg){.absolute top="360" right="280" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="360" right="170" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="360" right="60" width="100" height="100"} + ::: notes none ::: -## Quality Control and Exploratory Data Analysis +## Exploring Our Data (EDA) + +Let's [rewind {{< fa backward-fast >}}]{.orange} and take a closer look at our starting data + +- First, with `str()` + + ```{r} + #| echo: true + #| eval: true + #| class-output: highlight + #| output-line-numbers: "|5|9|11" + #Glimpse of dataset,including datatypes of columns + str(witch_data) + ``` + +::: notes +- (Restructing our data requires a little bit more knowledge on the state of the data because we are going to have to combine columns, and columns of different types cannot be combined) +- using some R built-in functions +::: + +## Exploring Our Data (EDA) {.scrollable chalkboard-buttons="true"} + +- Then, with `summary()` + + ```{r} + #| echo: true + #| eval: true + #| code-block-height: 300px + #| class-output: highlight + #| output-line-numbers: "|3|10|17|14" + #Summary of dataset, including summary stats + summary(witch_data) + ``` + +::: notes +none +::: -Check to see why our `presence` column is non-numeric +## EDA for Quality Control + +See why our presence data is non-numeric ```{r} #| echo: true @@ -566,7 +674,7 @@ Check to see why our `presence` column is non-numeric tidy_data$presence[which(is.na(as.numeric(tidy_data$presence)))] ``` -Replace all instances of `"none"` with a value of `0` +Replace all instances of `"none"` with `0` ```{r} #| echo: true @@ -579,20 +687,18 @@ tidy_data$presence <- as.integer(tidy_data$presence) and convert the column back to a numeric type ::: -## Quality Control and Exploratory Data Analysis {.scrollable} +## EDA for Quality Control {.scrollable} -- Explore our data again +Explore distribution ```{r} #| echo: true #| eval: true -#Check data -str(tidy_data) -summary(tidy_data$presence) +#Plot histogram of "presence" values hist(tidy_data$presence) ``` -- Let's correct all misread numbers +Correct misread numbers ```{r} #| echo: true @@ -602,10 +708,10 @@ tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] ``` ::: notes -none +applies change to all 800 lines of data in one line of code ::: -## Quality Control and Exploratory Data Analysis {.small} +## More EDA Options {.small} ::: columns ::: {.column width="50%"} @@ -618,7 +724,7 @@ none create_report(tidy_data, output_format = pdf(), output_file = "report.pdf") ``` -- Endless EDA options! +- Endless options! ```{r} #| eval: true @@ -632,17 +738,18 @@ library(DataExplorer) ::: ::: notes -none +not necessary for our simple dataset but a great, customizable option ::: -## Preserve Data Assets {.small .scrollable} +# Preservation -[*Should occur several times during data management lifecycle!*]{.champagne} +\[insert photo\] -To Do List {{< fa pencil >}}: +## Preserving Data and Data Assets {.small} -1. Write machine-readable metadata, including a data dictionary -2. Preserve raw data and script file (if reusing) in ServCat +Yes, **already**! + +Preservation should occur [several times]{.orange} during the data management lifecycle and include all data, metadata, and data assets (e.g., protocols, presentations, reports, code) ::: callout-tip ## Tip @@ -655,7 +762,42 @@ Contact your program's data manager for program-specific preservation guidelines - National Wildlife Refuge Program - *Caylen Cummins* -- Science Applications - *Hilmar Maier* +- Other programs - *Hilmar Maier* +::: + +[Reminder:]{.yellow} Reproducibility and reuse are impossible without *findability* + +::: notes +- In the FWS, we must preserve our data and data assets in a Board approved repository, which is ServCat or ScienceBase +::: + +# + +::: scary +Witch Survey +::: + +::: {.cursive .center} +Preserving Our Data +::: + +::: notes +::: + +## Preserving Our Data + +[To Do List {{< fa pencil >}}:]{.extrabold .yellow} + +1. Write machine-readable metadata, including a data dictionary +2. Preserve raw data and script file (if reusing) in ServCat +3. Link new ServCat references to the "Tetlin Witch Survey" ServCat Project + +::: fragment +A step further... + +::: center +Tidy, compile, and preserve all 20 years of data +::: ::: ::: notes @@ -666,26 +808,36 @@ Contact your program's data manager for program-specific preservation guidelines ## Preserve Data Assets -- A step further: tidy, compile, and preserve all 20 years of data - - All past year sheets are in the same untidy format - - Frank used "none" instead of "0" since the survey first began -- Wrapping script into a function makes this even more streamlined (*optional*) +Uh Oh {{< fa face-frown >}}... + +- All past workbook sheets are in the same untidy format + +- And Frank used `none` instead of `0` since the survey began + +::: fragment +Don't worry, [we can reuse our script]{.yellow}! + +::: callout-note +## Suggestion + +Wrapping the script into a function makes this even more streamlined (*optional*) +::: +::: ::: notes - Would be a nightmare to manually fix, but we can reuse the script we just created ::: -## Preserve Data Assets +## Preserve Data Assets {.scrollable} ::: panel-tabset ### [Basic: Script]{.smaller} Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step -```{r} +```{r .custom-code-block-1} #| echo: true #| eval: false -#| code-block-height: 200px #Load packages library(tidyverse) @@ -720,7 +872,6 @@ OR wrap script into function ```{r} #| echo: true #| eval: false -#| code-block-height: 200px clean_witch_data <- function(year){ #Import raw data @@ -756,7 +907,6 @@ Make sure to add proper documentation! ```{r} #| echo: true #| eval: false -#| code-block-height: 200px #' Tidy and QC witch data #' @@ -807,8 +957,7 @@ Can run our function for each year and compile data as follows: #| echo: true #| eval: false -# Clean and compile all years of data - +## Clean and compile all years of data #Tidy first year of data compiled_data <- clean_witch_data(2004) @@ -821,13 +970,6 @@ for(val in 2005:2024){ write.csv(compiled_data, file = "compiled_witch_data.csv", row.names = FALSE) ``` -::: {style="height:190px; overflow-y: scroll"} -```{r} -#| eval: TRUE -gt(tidy_data) %>% opt_stylize(style = 6) -``` -::: - ::: notes none ::: @@ -836,13 +978,16 @@ none Tidy, compiled data ready to be preserved and shared +[*Rows: 16,000*]{.smaller} + ::: smaller -::: {style="height:500px; overflow-y: scroll"} +::: {style="height:520px"} ```{r} #| eval: TRUE -#gt(tidy_data) %>% opt_stylize(style = 6, color = 'white') library(DT) DT::datatable(tidy_data, options = list( + pageLength = 9, + dom = 't', columnDefs = list( list(targets = "_all", className = "custom-header") ) @@ -858,36 +1003,44 @@ none ## Preserve Data Assets -Avoid any input files through automated pulls from ServCat! +Eliminate input files through ServCat API requests! ```{r} #| eval: false #| echo: true library(httr) -#Request -url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/255753" -response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) - -#Extract file name from response header -header <- headers(response) -filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) - -#Save file -writeBin(content(response,"raw"),filename) - -# -new_data <- clean_witch_data("witch_data.xlsx", 2025) -old_data <- read.csv("all_witch_data.csv") -combine_data(old_data, new_data) +#' Pull complete Tetlin witch survey dataset from ServCat +#' +#' @return dataframe +#' @export +#' +#' @examples +#' pull_witch_data() +pull_witch_data <- function(){ + #Request + url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/1234" + response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) + + #Extract file name from response header + header <- headers(response) + filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) + + #Save file + writeBin(content(response,"raw"), paste0("data/",filename)) + + #Import dataset + return(read.csv(paste0("data/",filename))) +} -analyze_witch_data(new_data) ``` ::: notes none ::: +# Break Time + ## Reuse Like magic {{\< fa wand-sparkles \>} @@ -899,40 +1052,6 @@ Like magic {{\< fa wand-sparkles \>} none ::: -## Reuse: Example - -Fast forward to 2025 {{< fa forward-fast >}} - -```{r} -#| eval: false -#| echo: true -library(httr) - -#Request -url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/255753" -response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) - -#Extract file name from response header -header <- headers(response) -filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) - -#Save file -writeBin(content(response,"raw"),filename) - -# -new_data <- clean_witch_data("witch_data.xlsx", 2025) -old_data <- read.csv("all_witch_data.csv") -combine_data(old_data, new_data) - -analyze_witch_data(new_data) - -#Maybe code to reupload to ServCat?? -``` - -::: notes -none -::: - ## Resources - Available in the README.md @@ -941,23 +1060,3 @@ none ::: notes none ::: - -## Import Data - -- Extra step: merge environmental site data and occurrence data - -```{r} -#| echo: true -#Relate tables: observation data and site data; reorder joined columns -# witch_data <- left_join(observation_data, site_data) %>% relocate(c(12,13), .after = 3) -``` - -::: callout-note -## Suggestion - -You can often replace complex MS Access databases with R scripts. Using R to relate tables also ensures your files are all in an open format. -::: - -::: notes -none -::: From 8b5b938dabf0c719b66e42390ec34d6145a9d22d Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Tue, 29 Oct 2024 14:00:31 -0800 Subject: [PATCH 05/15] Section 2 draft 4 --- docs/_emma.qmd | 155 +++++++++++++++++++++++++++---------------------- 1 file changed, 87 insertions(+), 68 deletions(-) diff --git a/docs/_emma.qmd b/docs/_emma.qmd index db23fbe..f352588 100644 --- a/docs/_emma.qmd +++ b/docs/_emma.qmd @@ -201,8 +201,7 @@ Started monitoring them due to concerns surrounding the threat of deforestation, ::: ::: notes -I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. -No automated data processing workflow exists for this dataset, so let's create one! +I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. No automated data processing workflow exists for this dataset, so let's create one! ::: ## Photos from the Field @@ -255,42 +254,48 @@ Starting Our Script ::: ::: notes -following Jonah's guidance... -open a new blank R script file and... +following Jonah's guidance... open a new blank R script file and... ::: -## Setting Up New Script +## Starting a New Script -- To use external functions, we need to load their packages - - First, install packages +- Install packages - ```{r} - #| echo: true - #| eval: false - install.packages(c("tidyverse","readxl","janitor")) - ``` + ```{r} + #| echo: true + #| eval: false + install.packages(c("tidyverse","readxl","janitor")) + ``` - - For non-CRAN packages, use `devtools::install_github("author/package_name")` + - For non-CRAN packages, use `devtools::install_github("author/package_name")` - - Next, use `library()` to load +- Use `library()` to load - - Sits at top of script and updated as you go + - Sits at top of script and updated as you go - ```{r} - #| echo: true - #Load packages - library(tidyverse) - library(readxl) - library(janitor) - ``` + ```{r} + #| echo: true + #Load packages + library(tidyverse) + library(readxl) + library(janitor) + ``` ::: notes -sits at top of script +To use external functions, we need to load their packages these packages are all in the CRAN (comprehensive R archive network) repository + +you can also install non-CRAN packages, such as those developed by your regional biometrician, directly from GitHub using a package called devtools + +once we... can go ahead and load them. only have to install once (so we can remove the installation code), but need to reload in every script + +sits at top of script - + +tells user what packages they need to have installed ::: -## Set Up New Script File +## Starting a New Script This is called a [*comment*]{.yellow}. @@ -320,7 +325,7 @@ Document every line to few lines of code with a comment. none ::: -## Set Up New Script File +## Starting a New Script Basic documentation is as easy as a "`#`" (shortcut: `CTRL-SHIFT-C`) @@ -344,7 +349,9 @@ It serves as a reminder for what your code accomplishes and why it was included. Translating coding language to human language is critical for yourself AND others. ::: -# Data Clean-Up: *Importing* +# Data Clean-Up: [Importing]{.orange} + +![](images/witch_import.jpg){fig-align="center"} ## Importing Data @@ -420,9 +427,9 @@ Importing Our Data ## Importing Our Data -- For our data: - - Use `read_excel()` to import data from a specific sheet in a workbook - - Set new "dataframe" as a [*variable*]{.yellow} +Use `read_excel()` to import data from a specific sheet in a workbook + +- Set new "dataframe" as a [*variable*]{.yellow} ```{r} #| echo: true @@ -442,6 +449,8 @@ Variables allow us to easily reference complex expressions and enable reusabilit # Data Clean-Up: [Tidying]{.orange} +![](images/witch_tidying.jpg){fig-align="center"} + ## Tidy Data ::: {layout="[[-1], [1], [-1]]"} @@ -454,7 +463,10 @@ none ## Why Tidy Data? -::: incremental +::: columns +::: {.column .incremental width="50%"} +
+ - Standardization - Interpretability @@ -462,6 +474,15 @@ none - Machine readability - Ease of use and reuse + +- Conducive to metadata +::: + +::: {.column width="50%"} +::: {layout="[[-1], [1], [-1]]"} +![](images/why_tidy_data.jpg){fig-align="center"} +::: +::: ::: ::: notes @@ -525,7 +546,7 @@ Updated dataframe: *Row Count: 800* ::: -::: {style="height:290px; overflow-y: scroll"} +::: {style="height:320px; overflow-y: scroll"} ```{r} #| echo: false gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') @@ -570,6 +591,8 @@ none # Data Clean-Up: [Exploratory Data Analysis (EDA)]{.orange} +![](images/witch_eda.jpg){fig-align="center"} + ## What is EDA? {.small} Exploratory Data Analysis (EDA) = [getting to know your data before drawing conclusions]{.orange}, often through summarization and/or visualization @@ -605,26 +628,6 @@ Exploring Our Data & Performing Quality Control ::: notes ::: -## {.small} - -#### Frank and Stein the Wildlife Biologists - -[*Roles: field work, data recording*]{.small} - -![](images/frank_stein_graphic.png){fig-align="left"} - -#### Casper the friendly Biotech - -[*Role: data entry/digitization*]{.small} - -![](images/casper_graphic.png){fig-align="left"} - -![](images/qc_6.jpg){.absolute top="360" right="280" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="360" right="170" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="360" right="60" width="100" height="100"} - -::: notes -none -::: - ## Exploring Our Data (EDA) Let's [rewind {{< fa backward-fast >}}]{.orange} and take a closer look at our starting data @@ -649,15 +652,34 @@ Let's [rewind {{< fa backward-fast >}}]{.orange} and take a closer look at our s - Then, with `summary()` - ```{r} - #| echo: true - #| eval: true - #| code-block-height: 300px - #| class-output: highlight - #| output-line-numbers: "|3|10|17|14" - #Summary of dataset, including summary stats - summary(witch_data) - ``` +```{r} +#| echo: true +#| eval: true +#| class-output: highlight +#| output-line-numbers: "|3|10|17|14" +#Summary of dataset, including summary stats +summary(witch_data) +``` + +::: notes +let me see what might be the cause +::: + +## {.small} + +#### Frank and Stein the Wildlife Biologists + +[*Roles: field work, data recording*]{.small} + +![](images/frank_stein_graphic.png){fig-align="left"} + +#### Casper the friendly Biotech + +[*Role: data entry/digitization*]{.small} + +![](images/casper_graphic.png){fig-align="left"} + +![](images/qc_6.jpg){.absolute top="360" right="280" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="360" right="170" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="360" right="60" width="100" height="100"} ::: notes none @@ -743,7 +765,7 @@ not necessary for our simple dataset but a great, customizable option # Preservation -\[insert photo\] +![But don't do it this way\...](images/witch_preserve.jpg){fig-align="center"} ## Preserving Data and Data Assets {.small} @@ -796,7 +818,7 @@ Preserving Our Data A step further... ::: center -Tidy, compile, and preserve all 20 years of data +Tidy, QC, compile, and preserve all 20 years of data ::: ::: @@ -804,6 +826,7 @@ Tidy, compile, and preserve all 20 years of data - Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains - Preserve data, script files, and reports - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey +- Feeling ambitious... ::: ## Preserve Data Assets @@ -835,7 +858,7 @@ Wrapping the script into a function makes this even more streamlined (*optional* Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step -```{r .custom-code-block-1} +```{r} #| echo: true #| eval: false @@ -845,7 +868,7 @@ library(readxl) library(janitor) #Import raw data -witch_data <- read_excel("witch_survey.xlsx", sheet = 1) +witch_data <- read_excel("data/witch_survey.xlsx", sheet = "2024") #Tidy data structure tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) @@ -876,10 +899,6 @@ OR wrap script into function clean_witch_data <- function(year){ #Import raw data observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) - site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") - - #Relate tables: observation data and site data - witch_data <- left_join(observation_data, site_data) #Tidy data structure tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) From 62967f39e43a61672d37eb4dcc72813b33a2900b Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Tue, 29 Oct 2024 17:10:39 -0800 Subject: [PATCH 06/15] Reordered slides --- docs/_emma.qmd | 175 +++++++++++++++++++++++-------------------------- 1 file changed, 81 insertions(+), 94 deletions(-) diff --git a/docs/_emma.qmd b/docs/_emma.qmd index f352588..bea1082 100644 --- a/docs/_emma.qmd +++ b/docs/_emma.qmd @@ -1,7 +1,4 @@ --- -title: "Why you should use a script-based workflow for your survey" - -author: McCrea Cobb | Emma Schillerstrom | Jonah Withers format: revealjs: theme: scss/custom-dark.scss @@ -14,6 +11,7 @@ format: buttons: false embed-resources: false code-block-height: 500px + width: 1280 filters: - output-line-highlight.lua editor: source @@ -29,100 +27,21 @@ knitr::opts_chunk$set(warning = FALSE, fig.align = "center") ``` -::: columns -::: {.column width="50%"} -Left column -::: - -::: {.column width="50%"} -Right column -::: -::: - -## Blank Slide - -Content - -::: notes -::: - -# Data Clean-Up - -![](images/witch_data_cleanup.png){fig-align="center"} - -## Why Clean Up Data in R? {.incremental .smaller} - -::: columns -::: {.column width="50%"} -#### [Limitations of]{.cursive} {.center} - -#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} - -::: incremental -- Messy - -- Prone to human error - -- Difficult to correct or reverse changes - -- Inefficient - -- Not reusable -::: -::: - -::: {.column width="50%"} -#### [Advantages of]{.cursive} {.center} - -#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} - -::: incremental -- Can iterate through large datasets - -- Can manipulate data without overwriting it - -- Can easily undo or reapply changes - -- Changes are documented - -- Avoids repeating steps - -- Allows for automated workflows/pipelines - -- Eliminates future QC workload -::: -::: -::: - -::: notes -- efficient, reusable, not prone to error, and on top of that... - -- working outside of original file - -- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows - -- great for apps or lab data that comes in a set, untidy format - -- Requires medium initial effort for a future of no effort - -- can apply changes to all rows, all sheets, all files at once -::: - # ::: scary -Witch Survey +The Tetlin Witch Project ::: ::: {.cursive .center .larger} -Intro to the Data +An Introduction ::: ::: notes what data are we cleaning up? let me introduce you to the tetlin witch project ::: -## Tetlin Witch Survey {.smaller} +## The Survey {.smaller} ::: columns ::: {.column width="50%"} @@ -226,6 +145,80 @@ I am the survey coordinator, but this is my first year on the survey. For the la none ::: +# Data Wrangling + +![](images/witch_data_cleanup.png){fig-align="center"} + +## Why Wrangle Data in R? {.incremental .smaller} + +::: columns +::: {.column width="50%"} +#### [Limitations of]{.cursive} {.center} + +#### [Manual Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Messy + +- Prone to human error + +- Difficult to correct or reverse changes + +- Inefficient + +- Not reusable +::: +::: + +::: {.column width="50%"} +#### [Advantages of]{.cursive} {.center} + +#### [Scripted Workflow]{.cursive .extrabold .yellow} {.center} + +::: incremental +- Can iterate through large datasets + +- Can manipulate data without overwriting it + +- Can easily undo or reapply changes + +- Changes are documented + +- Avoids repeating steps + +- Allows for automated workflows/pipelines + +- Eliminates future QC workload +::: +::: +::: + +::: notes +- efficient, reusable, not prone to error, and on top of that... + +- working outside of original file + +- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows + +- great for apps or lab data that comes in a set, untidy format + +- Requires medium initial effort for a future of no effort + +- can apply changes to all rows, all sheets, all files at once +::: + +## What is Data Wrangling? + +*Definition* + +Our Workflow: + +1. Importing Data + +2. Tidying Data + +3. Exploratory Data Analysis & Quality Control + # ::: scary @@ -738,7 +731,7 @@ applies change to all 800 lines of data in one line of code ::: columns ::: {.column width="50%"} - Use `DataExplorer` to generate a comprehensive report - - Includes summary statistics, distributions, missing data, PCA, qq plots, and more + - Includes summary statistics, distributions, missing data, correlations, PCA, qq plots, and more ```{r} #| echo: true @@ -851,13 +844,11 @@ Wrapping the script into a function makes this even more streamlined (*optional* - Would be a nightmare to manually fix, but we can reuse the script we just created ::: -## Preserve Data Assets {.scrollable} +## [Preserve Data Assets]{.small} ::: panel-tabset ### [Basic: Script]{.smaller} -Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step - ```{r} #| echo: true #| eval: false @@ -890,8 +881,6 @@ write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) ### [Advanced: Function]{.smaller} -OR wrap script into function - ```{r} #| echo: true #| eval: false @@ -921,8 +910,6 @@ clean_witch_data <- function(year){ ### [Advanced: Documented Function]{.smaller} -Make sure to add proper documentation! - ```{r} #| echo: true #| eval: false @@ -965,7 +952,7 @@ clean_witch_data <- function(year){ ::: ::: notes -none +[Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step]{.smaller} [OR wrap script into function]{.smaller} [Make sure to add proper documentation!]{.smaller} ::: ## Preserve Data Assets From e440f07e13d07a02108df4b1a39f970e370a1645 Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Thu, 31 Oct 2024 12:25:23 -0800 Subject: [PATCH 07/15] Final changes to section 2. Added notes. --- docs/_emma.qmd | 527 ++++++++++++++++++++++++++----------------------- 1 file changed, 281 insertions(+), 246 deletions(-) diff --git a/docs/_emma.qmd b/docs/_emma.qmd index bea1082..7cc0c8d 100644 --- a/docs/_emma.qmd +++ b/docs/_emma.qmd @@ -2,9 +2,9 @@ format: revealjs: theme: scss/custom-dark.scss - code-overflow: wrap + code-overflow: scroll logo: images/FWS-logo.png - footer: "Alaska Data Week" + footer: "![](images/FWS-logo.png) [Alaska Data Week 2024](https://doimspp.sharepoint.com/sites/fws-FF07S00000-data/SitePages/Alaska-Data-Week-2024.aspx){.author}" chalkboard: theme: whiteboard boardmarker-width: 5 @@ -12,8 +12,9 @@ format: embed-resources: false code-block-height: 500px width: 1280 + height: 720 filters: - - output-line-highlight.lua + - lua/output-line-highlight.lua editor: source highlight-style: a11y --- @@ -33,27 +34,23 @@ knitr::opts_chunk$set(warning = FALSE, The Tetlin Witch Project ::: -::: {.cursive .center .larger} -An Introduction -::: - ::: notes -what data are we cleaning up? let me introduce you to the tetlin witch project +going to introduce the tetlin witch project, not to be confused with the horror movie the Blair witch project ::: ## The Survey {.smaller} ::: columns ::: {.column width="50%"} -\[Insert Map\] +![Site Map](images/base_map.png){fig-align="center"} ::: ::: {.column width="50%"}
-[Type:]{.extrabold .orange} Annual occurrence survey +[**Type:**]{.extrabold .orange} Annual occurrence survey -[Logistics:]{.extrabold .orange} +[**Logistics:**]{.extrabold .orange} - Completed over 8 days in August @@ -61,14 +58,20 @@ what data are we cleaning up? let me introduce you to the tetlin witch project - Conducted for 20 years at Tetlin NWR -[Goal:]{.extrabold .orange} Estimate probability of witch occurrence across the refuge +[**Goal:**]{.extrabold .orange} Estimate probability of witch occurrence across the refuge ::: ::: ::: notes -the Tetlin Witch project, not the same as the Blair witch project +survey we conduct annually + +each year in the late summer, we spend 8 days in the field -lots of biotechs - obviously national funding for witch conservation +go out to the same 100 sites each day, recording whether a witch is detected there or not - 0 recorded if not detected, 1 recorded if detected + +this survey has been going on... + +and the goal of the project... ::: ## Witch Background @@ -77,30 +80,36 @@ lots of biotechs - obviously national funding for witch conservation ::: {.column .center width="50%"} Do NOT like water -![](images/witch_melting.png){fig-align="center"} +![](images/witch_melting.png){fig-align="center" height="500"} ::: ::: {.column .center width="50%"} DO favor forest -![](images/witch_broom.png){fig-align="center"} +![](images/witch_broom.png){fig-align="center" height="500"} ::: ::: ::: notes -Can use to characterize and predict their distribution +I am sure you all already know everything about the North American Witch because witch conservation is a national priority -Species Background - North American Witch +but I'll provide some species background Began inhabiting Tetlin NWR during the early modern period when widespread witch trials shifted their range northward Started monitoring them due to concerns surrounding the threat of deforestation, impacting their ability to construct broomsticks + +As I am sure you know from the W of Oz, + +...because of the shelter it provides and the resources it offers for broomstick crafting + +Can use this information to characterize and predict their distribution ::: ## Where We Are ::: columns -::: {.column width="50%"} +::: {.column width="35%"} **Completed:** - Data collection ✓ @@ -109,18 +118,22 @@ Started monitoring them due to concerns surrounding the threat of deforestation, **Next:** -- Data clean-up ☐ +- Data wrangling ☐ ::: -::: {.column width="50%"} -::: {layout="[[-1], [1], [-1]]"} +::: {.column width="65%"} ![Excel workbook with all years of data](images/workbook_sc.png){fig-align="center"} ::: ::: -::: ::: notes -I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. No automated data processing workflow exists for this dataset, so let's create one! +finished our data collection and data entry for the season + +on to data wrangling - or getting our data whipped into shape + +in an ideal world, we would have used a data collection app or an entry form with data validation or otherwise performed quality assurance prior to this step, but in this case, we did not + +I am the survey coordinator, but this is my first year on the survey. For the last 20 years, the data has been stored in an excel workbook (shown here) with each year's data on a separate sheet. I inherited a bit of a messy dataset, but I can work with it. No automated data processing workflow exists for this dataset, so let's create one! ::: ## Photos from the Field @@ -142,12 +155,44 @@ I am the survey coordinator, but this is my first year on the survey. For the la ::: ::: notes -none +but first, + +what's a trip to the field without sharing photos? + +I brought my good camera out this time +::: + +## [Data Wrangling]{.cursive} + +![](images/witch_data_cleanup.png){.absolute top="100" right="150" height="600"} ![](images/workflow_data_wrangling.png){.absolute top="80" left="300" height="650"} + +## What is Data Wrangling? + +::: center +*the process of transforming raw data into a usable form* +::: + +
+ +### Our Workflow: + +::: small +1. Importing Data [➝ Obtain]{.orange .fragment} + +2. Tidying Data [➝ Restructure]{.orange .fragment} + +3. Exploratory Data Analysis & Quality Control [➝ Clean, Correct, and Understand]{.orange .fragment} + +![](images/data_wrangle.jpg){fig-align="center" height="200"} ::: -# Data Wrangling +::: notes +can consist of a lot of different types of data manipulation + +... -![](images/witch_data_cleanup.png){fig-align="center"} +goal is for our data to be interpretable, machine readible, error-free, and ready for analyses +::: ## Why Wrangle Data in R? {.incremental .smaller} @@ -188,51 +233,24 @@ none - Allows for automated workflows/pipelines -- Eliminates future QC workload +- Eliminates future workload ::: ::: ::: ::: notes -- efficient, reusable, not prone to error, and on top of that... - -- working outside of original file - -- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows - -- great for apps or lab data that comes in a set, untidy format - -- Requires medium initial effort for a future of no effort - -- can apply changes to all rows, all sheets, all files at once -::: - -## What is Data Wrangling? +- not just slow, but tedious and tiring -*Definition* - -Our Workflow: - -1. Importing Data - -2. Tidying Data - -3. Exploratory Data Analysis & Quality Control - -# +- IS efficient, reusable, not prone to error, and on top of that... -::: scary -Witch Survey -::: +- working outside of original file... -::: {.cursive .center .larger} -Starting Our Script -::: +- can apply change to hundreds of thousands of rows of data in a single step - without ever even having to scroll through the rows -::: notes +- requires medium initial effort for a future of no effort ::: -## Getting Set Up +## Getting Started
@@ -240,109 +258,27 @@ Starting Our Script ✓ Set up a standard file directory structure -
- -::: center -[Time to get coding!]{.large .yellow} -::: - -::: notes -following Jonah's guidance... open a new blank R script file and... -::: - -## Starting a New Script - -- Install packages - - ```{r} - #| echo: true - #| eval: false - install.packages(c("tidyverse","readxl","janitor")) - ``` - - - For non-CRAN packages, use `devtools::install_github("author/package_name")` - -- Use `library()` to load - - - Sits at top of script and updated as you go - - ```{r} - #| echo: true - #Load packages - library(tidyverse) - library(readxl) - library(janitor) - ``` - -::: notes -To use external functions, we need to load their packages - -these packages are all in the CRAN (comprehensive R archive network) repository - -you can also install non-CRAN packages, such as those developed by your regional biometrician, directly from GitHub using a package called devtools - -once we... can go ahead and load them. only have to install once (so we can remove the installation code), but need to reload in every script +✓ Set up version control (*optional, good practice*) -sits at top of script - +✓ Initialized a new R script file (.R) -tells user what packages they need to have installed -::: - -## Starting a New Script - -This is called a [*comment*]{.yellow}. - -```{r} -#| echo: true -#| eval: false -#| code-line-numbers: "1" -#Load packages -library(tidyverse) -library(readxl) -library(janitor) -``` +✓ Installed and loaded our packages -A comment precedes [machine-readable language]{.yellow} and supplies a translation to [human-readable language]{.yellow}. +
::: center -[Human Readability]{.orange .extrabold} {{< fa handshake >}} [Reusability]{.orange .extrabold} -::: - -::: callout-tip -## Best Practice - -Document every line to few lines of code with a comment. +[Time to get coding!]{.large .yellow} ::: ::: notes -none -::: +with the help of Jonah, we've already... -## Starting a New Script - -Basic documentation is as easy as a "`#`" (shortcut: `CTRL-SHIFT-C`) - -```{r} -#| echo: true -#| eval: false -# Any code or text that is commented will not be executed, -but if it is not commented, R will treat it as code, -so this will return an ERROR. -``` - -::: callout-tip -## Tip! - -The `#` can also be used to temporarily disable lines of code for testing purposes. +which means it is time to import our data ::: -::: notes -It serves as a reminder for what your code accomplishes and why it was included. - -Translating coding language to human language is critical for yourself AND others. -::: +# Data Wrangling: [Importing]{.orange} -# Data Clean-Up: [Importing]{.orange} +
![](images/witch_import.jpg){fig-align="center"} @@ -381,28 +317,38 @@ my_data <- GET(url = "api-url.com/input-id", body = list("parameter1","parameter ``` ::: notes -Built-in and installable functions exist for reading data from various sources Like a link to the online authoritative source for a dataset +Built-in and installable functions exist for reading data from various sources + +... + +Like a link to the online authoritative source for a dataset or maybe an online database ::: ## Importing Data -Extra Step: merge multiple tables +Extra step: merge multiple tables, if necessary ```{r} #| echo: true #| eval: false #Join tables by common variables -all_data <- left_join(observation_data, site_data) +all_data <- left_join(observation_data, site_data, join_by(c("lat", "lon"))) ``` ::: callout-note ## Suggestion -You can often replace complex MS Access databases with R scripts. Using R to join related tables also ensures your files are all in an open format. +You can sometimes replace the need for MS Access databases through R scripts, depending on the functionality you desire. Using R to join related tables allows for automation and ensures your files are in an open format. ::: ::: notes -none +when you first bring in your data, you may want to include the extra step of merging multiple tables if you have relational data + +in this example,... + +I briefly want to mention that you can replicate some of the functionality of a survey Access database in R + +databases for surveys...w/o relying on a ::: # @@ -416,6 +362,7 @@ Importing Our Data ::: ::: notes +let's go ahead and import the data for our survey ::: ## Importing Our Data @@ -424,6 +371,14 @@ Use `read_excel()` to import data from a specific sheet in a workbook - Set new "dataframe" as a [*variable*]{.yellow} +```{r} +#| echo: false +#Load packages +library(tidyverse) +library(readxl) +library(janitor) +``` + ```{r} #| echo: true #Import raw data @@ -437,13 +392,23 @@ Variables are the basis of reusability! ::: ::: notes -Variables allow us to easily reference complex expressions and enable reusability. If I write all my code referencing the "witch_data" variable and I decide I want to change the input file, I can easily do that in this one line and not have to tamper with any of the rest of my code. +To import our data, we can use a function to read a specific sheet from our workbook and convert it to a "dataframe", which is just a datatable in R + +We are also going to create a "witch_data" variable and define it as this dataframe + +Variables allow us to easily reference complex expressions and enable reusability. If I write all my code referencing the "witch_data" variable and I decide I want to change the input file, I can easily do that in this one line and do not have to tamper with any of the rest of my code. ::: -# Data Clean-Up: [Tidying]{.orange} +# Data Wrangling: [Tidying]{.orange} + +
![](images/witch_tidying.jpg){fig-align="center"} +::: notes +and with that one line of code, we are already onto tidying +::: + ## Tidy Data ::: {layout="[[-1], [1], [-1]]"} @@ -451,7 +416,7 @@ Variables allow us to easily reference complex expressions and enable reusabilit ::: ::: notes -none +is a rectangular format for a dataset, specifically where ::: ## Why Tidy Data? @@ -479,7 +444,11 @@ none ::: ::: notes -By keeping data in a standardized format with clear attributes and values, the data can be easily interpreted by humans and manipulated through computers +...really great for... + +Related to interpretability - but how can i make a data dictionary defining my columns if my columns aren't variables? + +We tidy data because, by keeping data in a standardized format with clear attributes and values, the data can be easily interpreted by humans and manipulated through computers ::: # @@ -503,12 +472,12 @@ Let's take a peak at what we are working with: #| echo: false #| eval: true library(gt) -gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') +gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') %>% tab_options(table.font.size = 20) %>% cols_align(align = "right", columns = everything()) #%>% tab_caption("First 6 lines of dataset") ``` ::: center -[*First 6 lines of dataset*]{.smaller} +[*First few lines of dataset*]{.smaller} ::: ::: callout-caution @@ -518,7 +487,15 @@ gt(head(witch_data, 6)) %>% opt_stylize(style = 6, color = 'cyan') ::: ::: notes -- This means we need to restructure our data +...these are the first few lines of our dataset - let's go through it column by column + +First, we have "Site Number" which is a variable, and all the cells in that column are values of that variable. So that looks good + +Same with longitude and latitude + +But then we get to... that's not a variable. That's a value of the variable "Date", and it does not tell us anything about what these 0s and 1s are values of - we are missing the variable "presence" + +This means we need to restructure our data ::: ## Tidying Our Data @@ -542,12 +519,18 @@ Updated dataframe: ::: {style="height:320px; overflow-y: scroll"} ```{r} #| echo: false -gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') +gt(tidy_data) %>% opt_stylize(style = 6, color = 'cyan') %>% tab_options(table.font.size = 22, table.width = "60%") %>% cols_align(align = "right", columns = everything()) ``` ::: ::: notes -none +Conveniently, R has package (it's actually a package of packages, so super comprehensive) called "tidyverse" made for all things "data tidying" + +Instead of manually transforming the presence data column by column and entering 800 lines of dates, we can tidy our data in one line of code + +and BAM, here is our organized, interpretable, and skinnier but longer dataset + +*scroll* ::: ## Tidying Our Data {.scrollable} @@ -571,30 +554,40 @@ tidy_data <- clean_names(tidy_data) ```{r} #| echo: false -gt(head(tidy_data,1)) %>% opt_stylize(style = 6, color = 'cyan') +gt(head(tidy_data,0)) %>% opt_stylize(style = 6, color = 'cyan') %>% tab_options(table.font.size = 25, table.width = "60%") %>% cols_align(align = "right", columns = everything()) ``` ::: {.smaller .center} -*New column headers* +*New column headers: spaces removed and snake case used* ::: ::: notes -none +... + +Our column names are fairly tidy, but we do have a space in our "Site Number" header, and you cannot have spaces in a variable name because that is not machine readable + +Typically, you want to follow one the common variable naming conventions, such as camel case or snake case demonstrated below + +And we can convert all of our columns to snake case in one line using a package conveniently designed to do so - and these are our new column headers ::: -# Data Clean-Up: [Exploratory Data Analysis (EDA)]{.orange} +# Data Wrangling: [Exploratory Data Analysis (EDA)]{.orange} ![](images/witch_eda.jpg){fig-align="center"} +::: notes +moving on to ex... +::: + ## What is EDA? {.small} Exploratory Data Analysis (EDA) = [getting to know your data before drawing conclusions]{.orange}, often through summarization and/or visualization -*Example packages:* `skimr`, `corrplot`, `summary_tools`, `DataExplorer`, `assertr` +*Example packages:* `skimr`, `corrplot`, `summarytools`, `DataExplorer`, `assertr`
-::: {.cursive .small} +::: {.cursive .small .center} [Are there errors in my data?]{.fragment} [Are there outliers?]{.fragment} [How variable is my data?]{.fragment} [Are my data within their expected range of values?]{.fragment} @@ -605,7 +598,15 @@ Exploratory Data Analysis (EDA) = [getting to know your data before drawing conc ::: ::: notes -Lends itself to quality control +... + +though there are many, many options + +EDA can look a LOT of different ways, and can help us answer quite a few different types of questions, including (but not limited to) + +, and are the assumptions... + +Exploring these questions lends itself to quality control, which is what we are going to use EDA for ::: # @@ -619,6 +620,7 @@ Exploring Our Data & Performing Quality Control ::: ::: notes +let's go ahead and apply this to our survey ::: ## Exploring Our Data (EDA) @@ -631,31 +633,43 @@ Let's [rewind {{< fa backward-fast >}}]{.orange} and take a closer look at our s #| echo: true #| eval: true #| class-output: highlight - #| output-line-numbers: "|5|9|11" + #| output-line-numbers: "|6|7|12" #Glimpse of dataset,including datatypes of columns str(witch_data) ``` ::: notes -- (Restructing our data requires a little bit more knowledge on the state of the data because we are going to have to combine columns, and columns of different types cannot be combined) -- using some R built-in functions +let's take a few steps back and look at our original data, so the "witch_data" variable instead of our "tidy_data" variable + +can use the str() function to get a quick snapshot of our data + +and looking at the output here, I notice a few things + +str() shows us the datatypes of our columns ::: ## Exploring Our Data (EDA) {.scrollable chalkboard-buttons="true"} - Then, with `summary()` +::: small ```{r} #| echo: true #| eval: true #| class-output: highlight -#| output-line-numbers: "|3|10|17|14" +#| output-line-numbers: "|10|17|14|21" #Summary of dataset, including summary stats summary(witch_data) ``` +::: ::: notes -let me see what might be the cause + +something is going on here + +(*turn off pen*) + +and I may know the cause ::: ## {.small} @@ -664,18 +678,18 @@ let me see what might be the cause [*Roles: field work, data recording*]{.small} -![](images/frank_stein_graphic.png){fig-align="left"} +![](images/frank_stein_graphic.png){fig-align="left" height="200"} #### Casper the friendly Biotech [*Role: data entry/digitization*]{.small} -![](images/casper_graphic.png){fig-align="left"} +![](images/casper_graphic.png){fig-align="left" height="200"} -![](images/qc_6.jpg){.absolute top="360" right="280" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="360" right="170" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="360" right="60" width="100" height="100"} +![](images/qc_6.jpg){.absolute top="300" right="580" width="100" height="100"} ![](images/qc_2.jpg){.absolute top="300" right="470" width="100" height="100"} ![](images/qc_7.jpg){.absolute top="300" right="360" width="100" height="100"} ::: notes -none +Here are a few observations I have had of my team. I work with... ::: ## EDA for Quality Control @@ -689,6 +703,8 @@ See why our presence data is non-numeric tidy_data$presence[which(is.na(as.numeric(tidy_data$presence)))] ``` +
+ Replace all instances of `"none"` with `0` ```{r} @@ -699,10 +715,14 @@ tidy_data$presence <- as.integer(tidy_data$presence) ``` ::: notes -and convert the column back to a numeric type +let's go back to our tidy data and see which values in our presence column are non-numeric + +we can see that there are 3 "none"s in our presence/absence data. Thanks, Frank. + +I know these are supposed to be 0, so I am going to go ahead and convert all "none" values to 0, without having to worry about tracking down each error manually ::: -## EDA for Quality Control {.scrollable} +## EDA for Quality Control Explore distribution @@ -723,7 +743,15 @@ tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] ``` ::: notes -applies change to all 800 lines of data in one line of code +now that our data is numeric, I can use a super simple built-in function to plot a histogram + +we know that all of the data should be either 0 or 1, but we have some values outside of the expected range + +I see some 2's, 6's, and 7's and know Stein's handwriting is most likely the root cause + +I know those 6's are supposed to be... + +so I can go ahead and convert those to the correct values and apply the changes to all 800 lines of data with just these 2 lines of code ::: ## More EDA Options {.small} @@ -731,7 +759,7 @@ applies change to all 800 lines of data in one line of code ::: columns ::: {.column width="50%"} - Use `DataExplorer` to generate a comprehensive report - - Includes summary statistics, distributions, missing data, correlations, PCA, qq plots, and more + - Includes summary statistics, distributions, missing data insights, correlation analysis, PCA, qq plots, and more ```{r} #| echo: true @@ -748,17 +776,23 @@ library(DataExplorer) ::: ::: {.column width="50%"} -{{< pdf report.pdf width=100% height=500 >}} +{{< pdf images/report.pdf width=100% height=500 >}} ::: ::: ::: notes -not necessary for our simple dataset but a great, customizable option +While I got away with just a few built in functions, there are many more advanced packages for EDA. One cool example is... + +which can generate... + +not really necessary for our simple dataset, but a great, customizable option that can be used for any data with no work required + +The point I really want to make here... ::: -# Preservation +## [Preservation]{.cursive} -![But don't do it this way\...](images/witch_preserve.jpg){fig-align="center"} +![But don't do it this way\...](images/witch_preserve.jpg){.absolute top="100" right="150" height="600"} ![](images/workflow_preserve_data.png){.absolute top="80" left="300" height="650"} ## Preserving Data and Data Assets {.small} @@ -783,7 +817,13 @@ Contact your program's data manager for program-specific preservation guidelines [Reminder:]{.yellow} Reproducibility and reuse are impossible without *findability* ::: notes -- In the FWS, we must preserve our data and data assets in a Board approved repository, which is ServCat or ScienceBase +Yes, we are already at this step + +... + +In the FWS, we must preserve our data and data assets in a Board approved repository, which means ServCat or ScienceBase. If you have questions about guidelines for your specific program, reach out to your data manager + +Why does this step matter in our reproducible workflow? Well, nothing can be reproduced or reused if it is not accessible, so this is arguably THE most important step ::: # @@ -816,15 +856,16 @@ Tidy, QC, compile, and preserve all 20 years of data ::: ::: notes -- Write machine-readable metadata for the tidied data file, including a data dictionary defining each of the variables (columns) and their domains -- Preserve data, script files, and reports - - In ServCat, create separate reference for each and link all to the project reference corresponding to our witch survey -- Feeling ambitious... +- ... for the tidied data file, including a data dictionary defining each of the variables and their domains + +... + +- I am feeling ambitious so... ::: ## Preserve Data Assets -Uh Oh {{< fa face-frown >}}... +Uh oh {{< fa face-frown >}}... - All past workbook sheets are in the same untidy format @@ -841,7 +882,9 @@ Wrapping the script into a function makes this even more streamlined (*optional* ::: ::: notes -- Would be a nightmare to manually fix, but we can reuse the script we just created +- Unfortunately, all of the 19 other sheets in the workbook are in the same untidy format, and because Frank has been around since the start of the survey,we have some of the same errors throughout + +- This would be a nightmare to fix manually, but we can reuse the script we just created ::: ## [Preserve Data Assets]{.small} @@ -876,7 +919,7 @@ tidy_data$presence[which(tidy_data$presence == 6)] <- 0 tidy_data$presence[which((tidy_data$presence == 2) | (tidy_data$presence == 7))] <- 1 #Export tidy csv -write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) +write.csv(tidy_data, file = "data/tidy_witch_data.csv", row.names = FALSE) ``` ### [Advanced: Function]{.smaller} @@ -885,9 +928,9 @@ write.csv(tidy_data, file = "tidy_witch_data.csv", row.names = FALSE) #| echo: true #| eval: false -clean_witch_data <- function(year){ +wrangle_witch_data <- function(year){ #Import raw data - observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) + witch_data <- read_excel("data/witch_survey.xlsx", sheet = paste0(year)) #Tidy data structure tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) @@ -912,7 +955,7 @@ clean_witch_data <- function(year){ ```{r} #| echo: true -#| eval: false +#| eval: true #' Tidy and QC witch data #' @@ -923,13 +966,9 @@ clean_witch_data <- function(year){ #' #' @examples #' clean_witch_data(year = 2024) -clean_witch_data <- function(year){ +wrangle_witch_data <- function(year){ #Import raw data - observation_data <- read_excel("witch_survey.xlsx", sheet = paste0(year)) - site_data <- read_excel("witch_survey.xlsx", sheet = "Site Info") - - #Relate tables: observation data and site data - witch_data <- left_join(observation_data, site_data) + witch_data <- read_excel("data/witch_survey.xlsx", sheet = paste0(year)) #Tidy data structure tidy_data <- pivot_longer(witch_data, cols = starts_with("08"), names_to = "Date", values_to = "Presence", names_transform = list(Date = mdy), values_transform = list(Presence = as.character)) @@ -952,7 +991,11 @@ clean_witch_data <- function(year){ ::: ::: notes -[Can reuse and rerun script for other years simply by replacing sheet name in "Import Data" step]{.smaller} [OR wrap script into function]{.smaller} [Make sure to add proper documentation!]{.smaller} +this is what our script look like up until this point. it seems like we did a lot, but we actually don't have too scary of a script here. I can go ahead and refresh you on the steps we followed + +... + +very feasible and realistic example of a simple, but super useful script a biologist could make (or request - if you want something like this, contact me) ::: ## Preserve Data Assets @@ -961,23 +1004,24 @@ Can run our function for each year and compile data as follows: ```{r} #| echo: true -#| eval: false +#| eval: true -## Clean and compile all years of data #Tidy first year of data -compiled_data <- clean_witch_data(2004) +compiled_data <- wrangle_witch_data(2005) #Iterate through each year, tidy data for that year, and join with rest of tidy data -for(val in 2005:2024){ - compiled_data <- full_join(compiled_data, clean_witch_data(year = val)) +for(val in 2006:2024){ + compiled_data <- full_join(compiled_data, wrangle_witch_data(year = val)) } #Export -write.csv(compiled_data, file = "compiled_witch_data.csv", row.names = FALSE) +write.csv(compiled_data, file = "data/compiled_witch_data.csv", row.names = FALSE) ``` ::: notes -none +Now, we can... + +The ability to use code for iteration is life changing because, now, in just 4 lines, we have iterated through 20 years of messy data and compiled 16000 lines of clean data ::: ## Preserve Data Assets @@ -991,7 +1035,7 @@ Tidy, compiled data ready to be preserved and shared ```{r} #| eval: TRUE library(DT) -DT::datatable(tidy_data, options = list( +DT::datatable(compiled_data, options = list( pageLength = 9, dom = 't', columnDefs = list( @@ -1004,36 +1048,41 @@ DT::datatable(tidy_data, options = list( ::: ::: notes -none +Here is our... + +If I sort the columns, you can see we have all years of data in this table now + +off screen, I am going to get this into ServCat as promised ::: ## Preserve Data Assets +::: small Eliminate input files through ServCat API requests! +::: ```{r} #| eval: false #| echo: true -library(httr) +library(httr2) -#' Pull complete Tetlin witch survey dataset from ServCat +#' Pull complete Tetlin Witch Survey dataset from ServCat #' -#' @return dataframe +#' @return dataframe of all years witch observation data #' @export #' #' @examples #' pull_witch_data() pull_witch_data <- function(){ - #Request - url <- "https://ecos.fws.gov/ServCatServices/servcat-secure/v4/rest/DownloadFile/1234" - response <- GET(url = url, config = authenticate(":",":","ntlm"), add_headers("Content-Type" = "application/json"), verbose()) + #API request + url <- "https://ecos.fws.gov/ServCatServices/servcat/v4/rest/DownloadFile/1234" + response <- httr2::req_perform(request(url)) #Extract file name from response header - header <- headers(response) - filename <- sub('.*filename="([^"]+)".*',"\\1", header$`content-disposition`) + filename <- sub('.*filename="([^"]+)".*',"\\1", response$headers$`content-disposition`) #Save file - writeBin(content(response,"raw"), paste0("data/",filename)) + writeBin(response$body, paste0("data/",filename)) #Import dataset return(read.csv(paste0("data/",filename))) @@ -1041,28 +1090,14 @@ pull_witch_data <- function(){ ``` -::: notes -none +::: small +From now on, get all data with just: `my_data <- pull_witch_data()` ::: -# Break Time - -## Reuse - -Like magic {{\< fa wand-sparkles \>} - -- Can now repeat all tidying, analyses, and visualizations next year with just the click of a button! -- Medium to high initial investment for dramatically reduced future workload - ::: notes -none -::: - -## Resources - -- Available in the README.md -- If you are in Refuges and interested in developing an automated workflow for your survey but need some assistance, submit an I&M Tech Request +I can even go a step further and write a function to pull the authoritative copy of the dataset from servcat, so we can have a fully automated workflow for our subsequent steps - anyone wanted to run or reproduce analyses does not even need any inputs- they can just run "pull_witch_data()" shown at the bottom here, and have the most up-to-date copy -::: notes -none +and with that, everything is perfectly set up for analyses, and we will get to that after the break ::: + +# Break Time From d83d67104ba2816f3cd6ca81650a957e71040c7f Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Thu, 31 Oct 2024 12:26:30 -0800 Subject: [PATCH 08/15] Added embed pdf Quarto extension. --- docs/_extensions/jmgirard/embedpdf/.DS_Store | Bin 0 -> 6148 bytes .../jmgirard/embedpdf/_extension.yml | 8 + .../jmgirard/embedpdf/embedpdf.lua | 65 + .../quarto-ext/fontawesome/_extension.yml | 7 + .../quarto-ext/fontawesome/assets/css/all.css | 7971 +++++++++++++++++ .../fontawesome/assets/css/latex-fontsize.css | 30 + .../FontAwesome6Brands-Regular-400.ttf | Bin 0 -> 180104 bytes .../FontAwesome6Brands-Regular-400.woff2 | Bin 0 -> 92140 bytes .../webfonts/FontAwesome6Free-Regular-400.ttf | Bin 0 -> 76072 bytes .../FontAwesome6Free-Regular-400.woff2 | Bin 0 -> 25124 bytes .../webfonts/FontAwesome6Free-Solid-900.ttf | Bin 0 -> 384736 bytes .../webfonts/FontAwesome6Free-Solid-900.woff2 | Bin 0 -> 133940 bytes .../assets/webfonts/fa-brands-400.ttf | Bin 0 -> 181852 bytes .../assets/webfonts/fa-brands-400.woff2 | Bin 0 -> 105536 bytes .../assets/webfonts/fa-regular-400.ttf | Bin 0 -> 60520 bytes .../assets/webfonts/fa-regular-400.woff2 | Bin 0 -> 23940 bytes .../assets/webfonts/fa-solid-900.ttf | Bin 0 -> 388460 bytes .../assets/webfonts/fa-solid-900.woff2 | Bin 0 -> 154228 bytes .../assets/webfonts/fa-v4compatibility.ttf | Bin 0 -> 10556 bytes .../assets/webfonts/fa-v4compatibility.woff2 | Bin 0 -> 4960 bytes .../quarto-ext/fontawesome/fontawesome.lua | 84 + 21 files changed, 8165 insertions(+) create mode 100644 docs/_extensions/jmgirard/embedpdf/.DS_Store create mode 100644 docs/_extensions/jmgirard/embedpdf/_extension.yml create mode 100644 docs/_extensions/jmgirard/embedpdf/embedpdf.lua create mode 100644 docs/_extensions/quarto-ext/fontawesome/_extension.yml create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/css/all.css create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/css/latex-fontsize.css create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Brands-Regular-400.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Brands-Regular-400.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Free-Regular-400.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Free-Regular-400.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Free-Solid-900.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/FontAwesome6Free-Solid-900.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-brands-400.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-brands-400.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-regular-400.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-regular-400.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-solid-900.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-solid-900.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.ttf create mode 100644 docs/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.woff2 create mode 100644 docs/_extensions/quarto-ext/fontawesome/fontawesome.lua diff --git a/docs/_extensions/jmgirard/embedpdf/.DS_Store b/docs/_extensions/jmgirard/embedpdf/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0=1.3.0" +contributes: + shortcodes: + - embedpdf.lua + diff --git a/docs/_extensions/jmgirard/embedpdf/embedpdf.lua b/docs/_extensions/jmgirard/embedpdf/embedpdf.lua new file mode 100644 index 0000000..f9f2bbb --- /dev/null +++ b/docs/_extensions/jmgirard/embedpdf/embedpdf.lua @@ -0,0 +1,65 @@ +function pdf(args, kwargs) + local data = pandoc.utils.stringify(args[1]) or pandoc.utils.stringify(kwargs['file']) + local width = pandoc.utils.stringify(kwargs['width']) + local height = pandoc.utils.stringify(kwargs['height']) + local border = pandoc.utils.stringify(kwargs['border']) + local class = pandoc.utils.stringify(kwargs['class']) + local image = pandoc.utils.stringify(kwargs['image']) + local image_force = pandoc.utils.stringify(kwargs['image_force']) + local image_width = pandoc.utils.stringify(kwargs['image_width']) + local image_height = pandoc.utils.stringify(kwargs['image_height']) + local image_border = pandoc.utils.stringify(kwargs['image_border']) + local image_class = pandoc.utils.stringify(kwargs['image_class']) + + if width ~= '' then + width = 'width="' .. width .. '" ' + end + + if height ~= '' then + height = 'height="' .. height .. '" ' + end + + if border ~= '' then + border = 'border="' .. border .. '" ' + end + + if class ~= '' then + class = 'class="' .. class .. '" ' + end + + if image_width ~= '' then + image_width = 'width="' .. image_width .. '" ' + end + + if image_height ~= '' then + image_height = 'height="' .. image_height .. '" ' + end + + if image_border ~= '' then + image_border = 'border="' .. image_border .. '" ' + end + + if image_class ~= '' then + image_class = 'class="' .. image_class .. '" ' + end + + -- detect html + if quarto.doc.isFormat("html:js") then + if image_force == 'TRUE' then + return pandoc.RawInline('html', '') + end + if image ~= '' then + return pandoc.RawInline('html', '') + else + return pandoc.RawInline('html', 'Download PDF file.') + end + else + return pandoc.Null() + end + +end + +-- Add alias shortcode +function embedpdf(...) + return pdf(...) +end diff --git a/docs/_extensions/quarto-ext/fontawesome/_extension.yml b/docs/_extensions/quarto-ext/fontawesome/_extension.yml new file mode 100644 index 0000000..c0787a8 --- /dev/null +++ b/docs/_extensions/quarto-ext/fontawesome/_extension.yml @@ -0,0 +1,7 @@ +title: Font Awesome support +author: Carlos Scheidegger +version: 1.1.0 +quarto-required: ">=1.2.269" +contributes: + shortcodes: + - fontawesome.lua diff --git a/docs/_extensions/quarto-ext/fontawesome/assets/css/all.css b/docs/_extensions/quarto-ext/fontawesome/assets/css/all.css new file mode 100644 index 0000000..3e24980 --- /dev/null +++ b/docs/_extensions/quarto-ext/fontawesome/assets/css/all.css @@ -0,0 +1,7971 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands, +.fal, +.fa-light, +.fat, +.fa-thin, +.fad, +.fa-duotone { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; 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calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: 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animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: 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content: "\f7f3"; } + +.fa-child::before { + content: "\f1ae"; } + +.fa-lira-sign::before { + content: "\f195"; } + +.fa-satellite::before { + content: "\f7bf"; } + +.fa-plane-lock::before { + content: "\e558"; } + +.fa-tag::before { + content: "\f02b"; } + +.fa-comment::before { + content: "\f075"; } + +.fa-cake-candles::before { + content: "\f1fd"; } + +.fa-birthday-cake::before { + content: "\f1fd"; } + +.fa-cake::before { + content: "\f1fd"; } + +.fa-envelope::before { + content: "\f0e0"; } + +.fa-angles-up::before { + content: "\f102"; } + +.fa-angle-double-up::before { + content: "\f102"; } + +.fa-paperclip::before { + content: "\f0c6"; } + +.fa-arrow-right-to-city::before { + content: "\e4b3"; } + +.fa-ribbon::before { + content: "\f4d6"; } + +.fa-lungs::before { + content: "\f604"; } + +.fa-arrow-up-9-1::before { + content: "\f887"; } + +.fa-sort-numeric-up-alt::before { + content: "\f887"; } + +.fa-litecoin-sign::before { + content: "\e1d3"; } + +.fa-border-none::before { + 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content: "\f068"; } + +.fa-chess::before { + content: "\f439"; } + +.fa-arrow-left-long::before { + content: "\f177"; } + +.fa-long-arrow-left::before { + content: "\f177"; } + +.fa-plug-circle-check::before { + content: "\e55c"; } + +.fa-street-view::before { + content: "\f21d"; } + +.fa-franc-sign::before { + content: "\e18f"; } + +.fa-volume-off::before { + content: "\f026"; } + +.fa-hands-asl-interpreting::before { + content: "\f2a3"; } + +.fa-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-asl-interpreting::before { + content: "\f2a3"; } + +.fa-hands-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-gear::before { + content: "\f013"; } + +.fa-cog::before { + content: "\f013"; } + +.fa-droplet-slash::before { + content: "\f5c7"; } + +.fa-tint-slash::before { + content: "\f5c7"; } + +.fa-mosque::before { + content: "\f678"; } + +.fa-mosquito::before { + content: "\e52b"; } + +.fa-star-of-david::before { + content: "\f69a"; } + 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{ + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } 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"\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: 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{'assets/css/all.css', 'assets/css/latex-fontsize.css'} + }) +end + +local function isEmpty(s) + return s == nil or s == '' +end + +local function isValidSize(size) + local validSizes = { + "tiny", + "scriptsize", + "footnotesize", + "small", + "normalsize", + "large", + "Large", + "LARGE", + "huge", + "Huge" + } + for _, v in ipairs(validSizes) do + if v == size then + return size + end + end + return "" +end + +return { + ["fa"] = function(args, kwargs) + + local group = "solid" + local icon = pandoc.utils.stringify(args[1]) + if #args > 1 then + group = icon + icon = pandoc.utils.stringify(args[2]) + end + + local title = pandoc.utils.stringify(kwargs["title"]) + if not isEmpty(title) then + title = " title=\"" .. title .. "\"" + end + + local label = pandoc.utils.stringify(kwargs["label"]) + if isEmpty(label) then + label = " aria-label=\"" .. icon .. "\"" + else + label = " aria-label=\"" .. label .. "\"" + end + + local size = pandoc.utils.stringify(kwargs["size"]) + + -- detect html (excluding epub which won't handle fa) + if quarto.doc.is_format("html:js") then + ensureHtmlDeps() + if not isEmpty(size) then + size = " fa-" .. size + end + return pandoc.RawInline( + 'html', + "" + ) + -- detect pdf / beamer / latex / etc + elseif quarto.doc.is_format("pdf") then + ensureLatexDeps() + if isEmpty(isValidSize(size)) then + return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") + else + return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") + end + else + return pandoc.Null() + end + end +} From 8d75cc394745112f12f3bd4b396851dd69457d75 Mon Sep 17 00:00:00 2001 From: Schillerstrom Date: Thu, 31 Oct 2024 12:27:39 -0800 Subject: [PATCH 09/15] Section 2 images and pdf. --- docs/images/base_map.png | Bin 0 -> 464959 bytes docs/images/casper_graphic.png | Bin 0 -> 25593 bytes docs/images/data_wrangle.jpg | Bin 0 -> 240733 bytes docs/images/frank_stein_graphic.png | Bin 0 -> 51017 bytes docs/images/qc_2.jpg | Bin 0 -> 61189 bytes docs/images/qc_6.jpg | Bin 0 -> 60012 bytes docs/images/qc_7.jpg | Bin 0 -> 59645 bytes docs/images/report.pdf | Bin 0 -> 300631 bytes docs/images/tidy_graphic.png | Bin 0 -> 422250 bytes docs/images/why_tidy_data.jpg | Bin 0 -> 275257 bytes docs/images/witch_broom.png | Bin 0 -> 386380 bytes docs/images/witch_data_cleanup.png | Bin 0 -> 279695 bytes docs/images/witch_eda.jpg | Bin 0 -> 338242 bytes docs/images/witch_hiding.png | Bin 0 -> 2001424 bytes docs/images/witch_import.jpg | Bin 0 -> 323106 bytes docs/images/witch_melting.png | Bin 0 -> 404252 bytes docs/images/witch_moose.png | Bin 0 -> 2250245 bytes docs/images/witch_preserve.jpg | Bin 0 -> 328305 bytes docs/images/witch_tetlin.png | Bin 0 -> 1971260 bytes docs/images/witch_tidying.jpg | Bin 0 -> 271073 bytes docs/images/workbook_sc.png | Bin 0 -> 112147 bytes docs/images/workflow_analyze.png | Bin 0 -> 49169 bytes docs/images/workflow_complete.png | Bin 0 -> 59269 bytes docs/images/workflow_data_wrangling.png | Bin 0 -> 49111 bytes docs/images/workflow_preserve_data.png | Bin 0 -> 49234 bytes docs/images/workflow_preserve_products.png | Bin 0 -> 49228 bytes docs/images/workflow_project_setup.png | Bin 0 -> 49314 bytes docs/images/workflow_report.png | Bin 0 -> 48942 bytes docs/images/workflow_simple.png | Bin 0 -> 48463 bytes docs/images/workflow_summarize_results.png | Bin 0 -> 49284 bytes 30 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/images/base_map.png create mode 100644 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