w1_first_data_set.Rmd

---
title: "Week 1 - Exploring our first data set"
author: "Nicholas Good (n.good@colostate.edu)"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    theme: yeti
---

```{r global_options, include=FALSE}
  knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE, results = 'hide')
```

# ARCTAS

In our first example we'll look at some data from the [ARCTAS campaign](https://www.nasa.gov/mission_pages/arctas/smoke_plumes.html).

---

# Libraries

We'll be using the some of the `tidyverse` packages for this exercise. Include the `tidyverse` library in your notebook:

```{r}
  library(tidyverse)
```

---

# Source files

Often we'll want to access code we've saved elsewhere. We can use the `source()` function to include r scripts. For today's exercise you'll need to download a script called `r_functions`. Navigate to the course's [shared google drive folder](https://tinyurl.com/R-for-fire-data) and download the scripts folder. Make a copy of the scripts folder in your working directory. Then run the following line of code:

```{r}
 source("scripts/r_functions.R")
```

R will look for the script `r_functions.R` in the folder `scripts`. Check the console for any error messages.

---

# Loading the data

We're going to look at some data from the [Arctas](https://www-air.larc.nasa.gov/cgi-bin/arcstat-c) project. You'll need to download the [dataset](https://tinyurl.com/R-for-fire-data).

![](images/arctas.PNG)
<br>

* Look at the code below. Where do you think R is going to look for the dataset when loading?

* Let's check out the help documentation for some of the functions used in the code segment below (`lapply`, `bind_rows`).

* Try running the code. An `object` named `arctas_data` should appear in your working environment.

```{r}
  arctas_data <- lapply(list.files("../data/arctas",
                                   pattern = "_R14.ict$",
                                   full.names = TRUE),
                        read_ict_file) %>%
                 bind_rows()
```

---

# Working with dataframes

* Determine the class of the object `arctas_data`.

```{r}
  class(arctas_data)
```


Data frames are a common way to stored data in R. Each column has a name, each column is the same length, each column element has the same class.

* Determine the class of the first column:

```{r}
  class(arctas_data$utc)
```

* How many rows and columns are there in the dataset?

```{r}
  nrow(arctas_data)
  ncol(arctas_data)
```

* extract the names of each colum

```{r}
  colnames(arctas_data)
```

* extract the first row

```{r}
  first_row <- arctas_data[1,]
```

* extract the first column

```{r}
  first_col <- arctas_data[,1]
```

* extract the `no` mixing ratio in the fourth column

```{r}
  no_col_4 <- arctas_data$no[4]
```

* extract the `no` mixing ratios for flight number 10

```{r}
  no_flight_10 <- arctas_data$no[which(arctas_data$flight == 10)]
```

* Try typing `arctas_data` into your console and then press `enter`. What happens?

---

# Summary statistics

* Calculate the minimum and maximum `no` values. What do you notice?

```{r}
  no_min <- min(arctas_data$no)
  no_max <- max(arctas_data$no)
```

* Let's clean up the data

```{r}
  arctas_data[arctas_data < -1e+8] <- NA

  plot(arctas_data$no)
```

* Recalculate the minimum and maximum `no` values. What do you notice?

* Let's try this instead:

```{r}
  no_min <- min(arctas_data$no, na.rm = TRUE)
  no_max <- max(arctas_data$no, na.rm = TRUE)
```

* And finally this:

```{r}
  summary(arctas_data$no)
```
---