diff --git a/.Rbuildignore b/.Rbuildignore
index 25a6254c..14a226b6 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -23,5 +23,6 @@ webpack.prod.js
^\.eslintrc\.js$
^CODEOWNERS$
^index\.md$
+^index\.Rmd$
^wiki
^\.vscode$
diff --git a/README.Rmd b/README.Rmd
index d6fa099f..f3bd75a6 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -9,8 +9,7 @@ always_allow_html: true
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
- fig.path = "man/figures/README-",
- out.width = "100%"
+ fig.path = "man/figures/README-"
)
library(blockr)
@@ -25,9 +24,13 @@ library(DiagrammeR)
[](https://codecov.io/github/blockr-org/blockr)
-Building blocks for data manipulation and visualization operations.
+> {blockr} is Shiny's WordPress (John Coene, 2024)
-```{r, echo=FALSE}
+## Why blockr?
+
+`{blockr}` is an R package designed to democratize data analysis by providing a flexible, intuitive, and __code-free__ approach to building data pipelines. It allows users to create __powerful__ data workflows using pre-built __blocks__ that can be easily __connected__, all without writing a single line of code.
+
+```{r, echo=FALSE, eval=TRUE}
mermaid("
flowchart TD
subgraph LR workspace[Workspace]
@@ -54,7 +57,7 @@ mermaid("
stack2 ---> |... data| stackn
end
",
- height = "500px"
+ width = "100%"
) |>
htmlwidgets::onRender(
"function(el, x) {
@@ -64,7 +67,7 @@ mermaid("
)
```
-Key features:
+## Key features
1. **User-Friendly Interface**: Build data pipelines with intuitive interface.
2. **Flexibility**: Easily add, remove, or rearrange blocks in your pipeline.
@@ -85,7 +88,7 @@ pak::pak("blockr-org/blockr")
Below is a simple case study involving `{blockr}`. We use the palmerpenguins dataset to find out which
femal species has the largest flippers. We create 2 custom blocks allowing to create our plot block (see the plot vignette for more details). Note that the `{blockr.ggplot2}` package exposes some ready to use blocks.
-```r
+```{r, eval=FALSE}
library(blockr)
library(palmerpenguins)
library(ggplot2)
@@ -126,7 +129,7 @@ new_geompoint_block <- function(color = character(), shape = character(), ...) {
stack <- new_stack(
data_block = new_dataset_block("penguins", "palmerpenguins"),
- filter_block = new_filter_block("sex", "female"),
+ filter_block = new_filter_block("sex", "female"),
plot_block = new_ggplot_block("flipper_length_mm", "body_mass_g"),
layer_block = new_geompoint_block("species", "species")
)
diff --git a/README.md b/README.md
index 7b4ab433..5c69fcf4 100644
--- a/README.md
+++ b/README.md
@@ -9,9 +9,21 @@
[](https://codecov.io/github/blockr-org/blockr)
-Building blocks for data manipulation and visualization operations.
+> {blockr} is Shiny’s WordPress (John Coene, 2024)
-Key features:
+## Why blockr?
+
+`{blockr}` is an R package designed to democratize data analysis by
+providing a flexible, intuitive, and **code-free** approach to building
+data pipelines. It allows users to create **powerful** data workflows
+using pre-built **blocks** that can be easily **connected**, all without
+writing a single line of code.
+
+ #> PhantomJS not found. You can install it with webshot::install_phantomjs(). If it is installed, please make sure the phantomjs executable can be found via the PATH variable.
+
+
+
+## Key features
1. **User-Friendly Interface**: Build data pipelines with intuitive
interface.
@@ -82,17 +94,21 @@ new_geompoint_block <- function(color = character(), shape = character(), ...) {
stack <- new_stack(
data_block = new_dataset_block("penguins", "palmerpenguins"),
- filter_block = new_filter_block("sex", "female"),
+ filter_block = new_filter_block("sex", "female"),
plot_block = new_ggplot_block("flipper_length_mm", "body_mass_g"),
layer_block = new_geompoint_block("species", "species")
)
serve_stack(stack)
```
-``` r
-appdir <- system.file("examples", "penguins", package = "blockr")
-appshot(appdir, delay = 3)
-```
+
+
+
+
+Penguins app demo
+
+
+
## Contribute
diff --git a/index.Rmd b/index.Rmd
new file mode 100644
index 00000000..bad642f5
--- /dev/null
+++ b/index.Rmd
@@ -0,0 +1,157 @@
+---
+output: github_document
+always_allow_html: true
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+ collapse = TRUE,
+ comment = "#>",
+ fig.path = "man/figures/README-"
+)
+
+library(blockr)
+library(bslib)
+library(DiagrammeR)
+```
+
+
+
+
+# blockr
+
+
+
+[](https://github.com/blockr-org/blockr/actions/workflows/ci.yml)
+[](https://codecov.io/github/blockr-org/blockr)
+
+
+> {blockr} is Shiny's WordPress (John Coene, 2024)
+
+## Why blockr?
+
+`{blockr}` is an R package designed to democratize data analysis by providing a flexible, intuitive, and __code-free__ approach to building data pipelines. It allows users to create __powerful__ data workflows using pre-built __blocks__ that can be easily __connected__, all without writing a single line of code.
+
+```{r, echo=FALSE}
+mermaid("
+ flowchart TD
+ subgraph LR workspace[Workspace]
+ subgraph stack1[Stack]
+ direction LR
+ subgraph input_block[Block 1]
+ input(Data: dataset, browser, ...)
+ end
+ subgraph transform_block[Block 2]
+ transform(Transform block: filter, select ...)
+ end
+ subgraph output_block[Block 3]
+ output(Result/transform: plot, filter, ...)
+ end
+ input_block --> |data| transform_block --> |data| output_block
+ end
+ subgraph stack2[Stack 2]
+ stack1_data[Stack 1 data] --> |data| transform2[Transform]
+ end
+ stack1 --> |data| stack2
+ subgraph stackn[Stack n]
+ stacki_data[Stack i data] --> |data| transformn[Transform] --> |data| Visualize
+ end
+ stack2 ---> |... data| stackn
+ end
+ ",
+ width = "100%"
+) |>
+ htmlwidgets::onRender(
+ "function(el, x) {
+ el.classList.add('text-center')
+ }
+ "
+ )
+```
+
+## Key features
+
+1. **User-Friendly Interface**: Build data pipelines with intuitive interface.
+2. **Flexibility**: Easily add, remove, or rearrange blocks in your pipeline.
+3. **Extensibility**: Developers can create custom blocks to extend functionality.
+4. **Reproducibility**: Pipelines created with `blockr` are easily shareable and reproducible, with exportable code.
+5. **Interactivity**: Real-time feedback as you build and modify your pipeline.
+
+## Installation
+
+You can install the development version of blockr from [GitHub](https://github.com/) with:
+
+```r
+pak::pak("blockr-org/blockr")
+```
+
+## Example: palmer penguins case study
+
+Below is a simple case study involving `{blockr}`. We use the palmerpenguins dataset to find out which
+femal species has the largest flippers. We create 2 custom blocks allowing to create our plot block (see the plot vignette for more details). Note that the `{blockr.ggplot2}` package exposes some ready to use blocks.
+
+
+## Why blockr?
-`{blockr}` has been built for webR (wasm) and is available for download
-with
-`webr::install("blockr", repos = "https://blockr-org.github.io/webr-repos")`.
+`{blockr}` is an R package designed to democratize data analysis by
+providing a flexible, intuitive, and **code-free** approach to building
+data pipelines. It allows users to create **powerful** data workflows
+using pre-built **blocks** that can be easily **connected**, all without
+writing a single line of code.
-``` r
-library(blockr)
-library(blockr.data)
+
-data_block <- new_dataset_block(selected = "lab", package = "blockr.data")
+## Key features
-stack <- new_stack(data_block)
-serve_stack(stack)
-```
+1. **User-Friendly Interface**: Build data pipelines with intuitive
+ interface.
+2. **Flexibility**: Easily add, remove, or rearrange blocks in your
+ pipeline.
+3. **Extensibility**: Developers can create custom blocks to extend
+ functionality.
+4. **Reproducibility**: Pipelines created with `blockr` are easily
+ shareable and reproducible, with exportable code.
+5. **Interactivity**: Real-time feedback as you build and modify your
+ pipeline.
## Installation
@@ -42,8 +40,83 @@ You can install the development version of blockr from
[GitHub](https://github.com/) with:
``` r
-# install.packages("devtools")
-devtools::install_github("blockr-org/blockr")
+pak::pak("blockr-org/blockr")
+```
+
+## Example: palmer penguins case study
+
+Below is a simple case study involving `{blockr}`. We use the
+palmerpenguins dataset to find out which femal species has the largest
+flippers. We create 2 custom blocks allowing to create our plot block
+(see the plot vignette for more details). Note that the
+`{blockr.ggplot2}` package exposes some ready to use blocks.
+
+
+
+
+
+
+
+
+
+
+Expand
+
+
+
+
+
+
+``` r
+library(blockr)
+library(palmerpenguins)
+library(ggplot2)
+
+new_ggplot_block <- function(col_x = character(), col_y = character(), ...) {
+
+ data_cols <- function(data) colnames(data)
+
+ new_block(
+ fields = list(
+ x = new_select_field(col_x, data_cols, type = "name"),
+ y = new_select_field(col_y, data_cols, type = "name")
+ ),
+ expr = quote(
+ ggplot(mapping = aes(x = .(x), y = .(y)))
+ ),
+ class = c("ggplot_block", "plot_block"),
+ ...
+ )
+}
+
+new_geompoint_block <- function(color = character(), shape = character(), ...) {
+
+ data_cols <- function(data) colnames(data$data)
+
+ new_block(
+ fields = list(
+ color = new_select_field(color, data_cols, type = "name"),
+ shape = new_select_field(shape, data_cols, type = "name")
+ ),
+ expr = quote(
+ geom_point(aes(color = .(color), shape = .(shape)), size = 2)
+ ),
+ class = c("plot_layer_block", "plot_block"),
+ ...
+ )
+}
+
+stack <- new_stack(
+ data_block = new_dataset_block("penguins", "palmerpenguins"),
+ filter_block = new_filter_block("sex", "female"),
+ plot_block = new_ggplot_block("flipper_length_mm", "body_mass_g"),
+ layer_block = new_geompoint_block("species", "species")
+)
+serve_stack(stack)
```
## Contribute
diff --git a/man/figures/README-unnamed-chunk-2-1.png b/man/figures/README-unnamed-chunk-2-1.png
new file mode 100644
index 00000000..8a47c897
Binary files /dev/null and b/man/figures/README-unnamed-chunk-2-1.png differ
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
index 097b2416..9e2bd63c 100644
--- a/vignettes/.gitignore
+++ b/vignettes/.gitignore
@@ -1,2 +1,4 @@
*.html
*.R
+
+/.quarto/
diff --git a/vignettes/blockr_examples.Rmd b/vignettes/blockr_examples.Rmd
index 160ebba9..8784c459 100644
--- a/vignettes/blockr_examples.Rmd
+++ b/vignettes/blockr_examples.Rmd
@@ -1,29 +1,18 @@
---
-title: "blockr: case studies"
+title: "Case studies"
output: rmarkdown::html_vignette
vignette: >
- %\VignetteIndexEntry{blockr: case studies}
+ %\VignetteIndexEntry{Case studies}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include=FALSE}
+library(blockr)
library(bslib)
knitr::opts_chunk$set(echo = TRUE)
```
-## Why blockr?
-
-`blockr` is an R package designed to democratize data analysis by providing a flexible, intuitive, and code-free approach to building data pipelines. It allows users to create powerful data workflows using pre-built blocks that can be easily connected, all without writing a single line of code.
-
-### Key Features
-
-1. **User-Friendly Interface**: Build data pipelines using a drag-and-drop interface.
-2. **Flexibility**: Easily add, remove, or rearrange blocks in your pipeline.
-3. **Extensibility**: Developers can create custom blocks to extend functionality.
-4. **Reproducibility**: Pipelines created with `blockr` are easily shareable and reproducible, with exportable code.
-5. **Interactivity**: Real-time feedback as you build and modify your pipeline.
-
## blockr Across Industries
The flexibility of `blockr` makes it valuable across various industries. Let's explore how it can be applied in different sectors with detailed examples. Somex examples require to create a new field such as the `new_slider_field` described in the corresponding vignette.
@@ -206,20 +195,19 @@ serve_stack(stock_forecast_stack)
```
```{r blockr-stock, echo=FALSE, fig.cap='Stock model demo', fig.align = 'center', out.width='100%'}
-knitr::include_graphics("vignettes/figures/blockr-stock-demo.png")
+knitr::include_graphics("figures/blockr-stock-demo.png")
```
### 2. Pharmaceutical: Clinical Trial Analysis
#### 2.1 AE Forest Plot
-This forest plot visualizes the relative risk of adverse events between two treatment arms in a clinical trial. In this case, it compares "Xanomeline High Dose" to "Xanomeline Low Dose" starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adae` dataset.
+This forest plot visualizes the relative risk of adverse events between two treatment arms in a clinical trial. In this case, it compares "Xanomeline High Dose" to "Xanomeline Low Dose" starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adae` dataset. As you may notice, the `new_forest_plot_block` is a quite complex block. Part of the code is isolated in a function `create_ae_forest_plot` so that the main block constructor is more readable.
```{r, eval=TRUE, echo=FALSE}
card(
create_app_link(
"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-kLMhl8sVysdhPYGAI4ITPr4sronAcTnIfHrdAVBuVqVTwabgpbbY76Jq3ZcfdIA7gfCnmontfZuv1hubsTnZU7i57K7XG51eqIY63cNrGZ4BCc8YJjlJ2NyRfzdBqMiId5y3lPVzRkNgyQTLRCXKVNjR7OVQOUGdnXdVNbTqbhQI3XgWUAvgNGHUdDVVcpDVSS00NrW0GhYPs0VIPhKLddCZGsBgiCcVAWToPYEw49h2Bw7g8PeDcyPHVJXRY6jgJgCpKzqPhCzUcVklkOwOK4tpAjABoONQFN1kfGR02fV98VJABBABRKl5AARycCgCBYb5CQgBo6QMqwMQ4ScOVleTdmbVCZNtW12M47jeI3JklCEngRPwjRNy1XcbzvbgKOY1igoUuj12NU0yB041aPo9E-QDYAiMbTUw3UmRNOinShH0ohDLAXLZKw94eTlIg2GUlkEKQ2ReNUGD1XHaTWJgAdLL4VAfIYPypuKikwMMIsH1tREAGU4Cw9EsU6mQADkZBgJQc3eRznMpWzlH4hLCV4aaa1tbFuPZZChXZUb8wUnY5tkqKuJ4vj4sE4TRKvPdb3IqSqIiuSCqUr8eBNLadKCkaSvNJqWu0qb2oMlIwbRn6WXFTazXYWQsZEwmLSpiLUFffgkvhjL90k9JU0RABxOAc1RFgnM-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-jwyBN0U4kJrjaERONAEDOvjs42LDJfVIvC0a2i4qgGowjRHiMkdI2R9iZAKJUeozR2ignOMMRPah4SPGsyiVYzhtiHEJOCS41pxj3FCDMeabxGSs49ICUE20zTQltJSR4gh3TYkyHiQUgp8zkkmNGRMkgmTpmF02duCBgsTIxl8GUUkzpqTlxpDmekqYuScBZM6GcgMe7s1tNApal94x-m4FNYZ9D+kLNPllMMsghAyE0PkzZLTkGLN2ZEzpKzHgxOyTw3ShgAgLJBcs1h0TrFcKhbpGQlRlDSCCfi9pey0WsMmX4rF0KwC4tYbSpZez0kHKmbE1laQwC5NYntNMplmSkheY812zyvg-FdjotGALjrkC9jmdodA2ANEVQUqVby1YABJlUqkvuwFabBSBiSELPDA5tdY2rteYB17xBWyD1faA18C3Vyp5B6gRJTXLeoefqrgBqikBpYOAiKL9ZLwCgCpe53IQ1QE9VgeF-CiCCKmu6w1-qhGBtYuG3uagc2hqLcI9N70WDWATR0MA3BphgCkN1St7E0BaG3g0RwU0JaMWUeg1l8kUiVsTF7Ka6ClGVs2BAOAWg4DVp0D2iADg+AAA5B0wGHaxOgRAvbsBYAAL1JGoCdABmAArCOzU2bUCZlELUfYu6yZNqIDsHgIhnIBGnce3STQGDiA-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-76IhsteBxFUHNkFs2XTAAeSW3gIJq3RSI-R7mbgqOkdfaga+bH93kSXeu7L76DhFfc5kPbGo99KfvZpyc2XYyyp9YgPmGohn2dA6aQOoJMAIJqDMxUMdGmB3lnIIh8zt31fE7ABdX8-5AJa-OjXMsC26IXCgDsD7tORWVrEStKaTk-jrmtpspk9RWSAz1fSPbGkhwIQPsTY25F88QtL81P+DNTkRQjhjgg8ZBLGc0ESKEHJCaFc3gcRt9JitmBkxFKrYdaxZfOYiLj9haG9w-OoGU2J77d4gPISfPo+8SB1bl-Lwbe8HHhTt59YWIukmiyHJEPqZBxj+LDu03wmQnfWmB9MErCSJtpHyIPreSRkhi8QGAtGqz+yBxk45jyzX4N6xB1A6R1qf51CeJkCd48jd6sIMJFZIG2ZsCczqq-pqwYD3TXzX6cR6ADjPpPI04PgZjP4z6cAJidxojTqGbYxBwsDYzUESDNib6sHiAb59YwjqgNas7kBeRgYwhAYtpCyejeglJsBr5cG2ab456oEghMTpAPh1AMCxycEET0H1wiRMEsFcASDJBgARwhhAA", # nolint
- "app",
mode = "editor"
),
full_screen = TRUE
@@ -401,16 +389,15 @@ serve_stack(clinical_trial_stack)
#### 2.2 Demographics Table
-This demographics table is taken from the `cardinal` package of [FDA Safety Tables and Figures](https://github.com/pharmaverse/cardinal) and demonstrates `gt` and `rtables` outputs starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adsl` dataset.
-
+This demographics table is taken from the `{cardinal}` package of [FDA Safety Tables and Figures](https://github.com/pharmaverse/cardinal) and demonstrates `{gt}` and `{rtables}` outputs starting from the [pharmaverseadam](https://github.com/pharmaverse/pharmaverseadam) `adsl` dataset. As a side note, the below block requires some extra helpers to work properly which you can find [here](https://github.com/blockr-org/blockr.cardinal/blob/72e6e5518d8226d4fdeb599b6e2e31ba03dc7c82/R/methods.R) in the `{blockr.cardinal}` package.
-```{r cardinal_tables}
+```{r cardinal_tables, eval=FALSE}
library(shiny)
library(blockr)
-library(blockr.falcon)
+library(cardinal)
library(blockr.pharmaverseadam)
-cardinal02_block <- function(...) {
+new_cardinal02_block <- function(...) {
all_cols <- function(data) colnames(data)
fields <- list(
@@ -441,7 +428,7 @@ cardinal02_block <- function(...) {
)
})
- blockr::new_block(
+ new_block(
expr = expr,
fields = fields,
...,
@@ -449,8 +436,8 @@ cardinal02_block <- function(...) {
)
}
-blockr::register_block(
- cardinal02_block,
+register_block(
+ new_cardinal02_block,
"Cardinal 02",
"A Cardinal 02 table",
input = "data.frame",
@@ -462,26 +449,28 @@ blockr::register_block(
# Create the stack
rtables_stack <- new_stack(
new_adam_block(selected = "adsl"),
- cardinal02_block()
+ new_cardinal02_block()
)
serve_stack(rtables_stack)
```
+```{r blockr-cardinal, echo=FALSE, fig.cap='Cardinal demo', fig.align = 'center', out.width='100%'}
+knitr::include_graphics("figures/blockr-cardinal-demo.png")
+```
### 3. Environmental Science: Air Quality Analysis and Prediction
This example demonstrates a pipeline for analyzing air quality data and predicting future pollution levels using actual data from the [openair](https://github.com/cran/openair) package.
-This pipeline imports actual air quality data from the `openair` package and forecasts future pollution levels using an ARIMA model.
+This pipeline imports actual air quality data from the `{openair}` package and forecasts future pollution levels using an ARIMA model.
-```{r environment}
+```{r environment, eval=FALSE}
library(blockr)
library(openair)
library(forecast)
-
# Custom block for air quality data import
-air_quality_block <- function(...) {
+new_air_quality_block <- function(...) {
new_block(
fields = list(
site = new_select_field(
@@ -515,7 +504,7 @@ air_quality_block <- function(...) {
# Custom block for pollution forecasting
-pollution_forecast_block <- function(columns = character(), ...) {
+new_pollution_forecast_block <- function(columns = character(), ...) {
all_cols <- function(data) setdiff(colnames(data), c("date", "site", "source"))
new_block(
@@ -542,7 +531,7 @@ pollution_forecast_block <- function(columns = character(), ...) {
# Register custom blocks
register_block(
- air_quality_block,
+ new_air_quality_block,
name = "Air Quality Data",
description = "Import air quality data",
classes = c("air_quality_block", "data_block"),
@@ -551,7 +540,7 @@ register_block(
)
register_block(
- pollution_forecast_block,
+ new_pollution_forecast_block,
name = "Pollution Forecast",
description = "Forecast pollution levels",
classes = c("pollution_forecast_block", "plot_block"),
@@ -561,18 +550,19 @@ register_block(
# Create the stack
air_quality_stack <- new_stack(
- air_quality_block(),
- pollution_forecast_block(columns = "no2")
+ new_air_quality_block(),
+ new_pollution_forecast_block(columns = "no2")
)
serve_stack(air_quality_stack)
```
+
### 4. Marketing: Causal Impact Analysis of Marketing Interventions
This example demonstrates how to use [CausalImpact](https://google.github.io/CausalImpact/CausalImpact.html) to analyze the effect of marketing interventions on sales data.
-This pipeline generates dummy marketing data with an intervention, then uses CausalImpact to analyze the effect of the intervention on sales.
+This pipeline generates dummy marketing data with an intervention, then uses `{CausalImpact}` to analyze the effect of the intervention on sales. This requires to define a new date field as shown below.
-```{r date_field, include=FALSE}
+```{r date_field}
library(shiny)
new_date_field <- function(value = Sys.Date(), min = NULL, max = NULL, ...) {
blockr::new_field(
@@ -591,10 +581,8 @@ date_field <- function(...) {
#' @method ui_input date_field
#' @export
ui_input.date_field <- function(x, id, name) {
- ns <- NS(input_ids(x, id))
-
shiny::dateInput(
- blockr:::input_ids(x, id),
+ blockr::input_ids(x, id),
name,
value = blockr::value(x, "value"),
min = blockr::value(x, "min"),
@@ -613,8 +601,8 @@ validate_field.date_field <- function(x, ...) {
ui_update.date_field <- function(x, session, id, name) {
updateDateInput(
session,
- blockr:::input_ids(x, id),
- blockr:::get_field_name(x, name),
+ blockr::input_ids(x, id),
+ blockr::get_field_name(x, name),
value = blockr::value(x),
min = blockr::value(x, "min"),
max = blockr::value(x, "max")
@@ -622,10 +610,7 @@ ui_update.date_field <- function(x, session, id, name) {
}
```
-
-
-
-```{r intervention_impact}
+```{r intervention_impact, eval=FALSE}
library(blockr)
library(CausalImpact)
library(dplyr)
@@ -707,12 +692,12 @@ new_causal_impact_block <- function(columns = character(), ...) {
),
expr = quote({
data <- data.frame(
+ date = data$date,
y = data[[.(response_var)]],
x = data[[.(covariate_var)]]
)
-
- pre_period <- c(min(data$date), as.Date(.(pre_period_end)))
- post_period <- c(as.Date(.(post_period_start)), max(data$date))
+ pre_period <- c(min(as.Date(data$date)), as.Date(.(pre_period_end)))
+ post_period <- c(as.Date(.(post_period_start)), max(as.Date(data$date)))
impact <- CausalImpact(data, pre_period, post_period)
plot(impact)
@@ -749,6 +734,9 @@ marketing_impact_stack <- new_stack(
serve_stack(marketing_impact_stack)
```
+```{r blockr-marketing, echo=FALSE, fig.cap='Marketing demo', fig.align = 'center', out.width='100%'}
+knitr::include_graphics("figures/blockr-marketing-demo.png")
+```
### 5. Dynamical systems
@@ -758,7 +746,6 @@ In the below example, we implemented the Lorenz attractor and solve it with the
card(
create_app_link(
"NobwRAdghgtgpmAXGKAHVA6ASmANGAYwHsIAXOMpMAdzgCMAnRRASwgGdSoAbbgCgA6YOtyIEA1gyG4ABAzioi7GQF4ZBQWAAWpUqnaIA9IZFjJAWiIMA5hmstSWgK50MLIodqNz8xe2kyQjp6Bsa+RBgM5tRQ7DAYVtZCAJTJAhBeTKwcXLyaphIMdtaooqQATAHhymoaQbr6RiaihZY2dg7Oru6e9FHVAfUhTeGR0bHxiSlpEOncLIxQDACefAWSM-OLK3yoDFAEMFCbC-s760XWJWXlM+kQcNQA+kQAJnBP6zIAPOYyAGZOCAEUjuCB8DCQ5IyEDpdIyGSieQQABePz+gOBoJIMj4pFky1kqCWMHY0NhEARCLqlKpCIAGqoZMSGKTgABGAC6MgAVDJlhzuQBqfnAcrcvkCgDMnNw8LpMgAmkyWWzxbzcQL1X9pZzknLaXSAFpM8wCrkarXC5kk9jAGWWsXcnX2znyhEzBEAXzhtP+LDg3Feyl+iJYnEEhpkUCZD2eECc8AYLAIT39gdefHMAA5DFLZOZ2QAGWTlIv690yOixx5PBNJlNpgNBrPFgsAVhLMmL5YNdIINfjibgydT6Zb5WzsnZ06L5fld1pcc+LXEkbp4+DTM37D7VLgAA89kz2C5OA4nOR1wrYhhXlAuBh-vt4NeFTI3nAACztt-vgFArIlbvssRZMhojJqDOSpMtBJpQRWUbvqQYFqCWwEKqQ-rQNwsFznu-42qyNTqBCfDHLIGBrPqMhUQQqQYR6GGIe+8wRpiTJIhQKKelSLFUpCGAEeo3CxCRdRgJ+K5mIMYD3lw0kSCkC7pD6sw5Ac4jojIy6cJp17LlJ6xAUutZXKURCkIpa6VpiA61JoB6ycsKQmXS7z-FATjcFZxDcImHDgZoywYOyzkYJUYC8TR8rLtYcBEDAihsFZ6zpDM7AjgAbh8ekSHweXiMkYBepyQA", # nolint
- "app",
mode = "editor"
),
full_screen = TRUE
diff --git a/vignettes/figures/blockr-cardinal-demo.png b/vignettes/figures/blockr-cardinal-demo.png
new file mode 100644
index 00000000..1e014bbd
Binary files /dev/null and b/vignettes/figures/blockr-cardinal-demo.png differ
diff --git a/vignettes/figures/blockr-marketing-demo.png b/vignettes/figures/blockr-marketing-demo.png
new file mode 100644
index 00000000..fee9ad30
Binary files /dev/null and b/vignettes/figures/blockr-marketing-demo.png differ
diff --git a/vignettes/new-field.Rmd b/vignettes/new-field.Rmd
index 87d021dd..98b12762 100644
--- a/vignettes/new-field.Rmd
+++ b/vignettes/new-field.Rmd
@@ -112,7 +112,6 @@ We can test our newly created field in a custom block.
card(
create_app_link(
"NobwRAdghgtgpmAXGKAHVA6ASmANGAYwHsIAXOMpMAdzgCMAnRRASwgGdSoAbbgCgA6YOtyIEA1gyG4ABAzioi7GQF4ZBQWAAWpUqnaIA9IZFjJAWiIMA5hmstSWgK50MLIodqNz8xe2kyQjp6Bsa+RBgM5tRQ7DAYVtZCAJTJAhDp3CyMUAwAnnymEgxpGRAQcNQA+uxZACZwDFUAZixw3HUyADzmMs1OEASk7hCCEDITMgBuPE5wqjIQTvAMLBrJuOmTMjBsC0sra3wbW5MwUAAe+8uNRyfjk5wK14frmw8TGF-JMiCnRZJmBVqq12nUxtsZtw5gsoXN3ttduM1EiEWdLgtzhc0RMnqgFnicTIvhgiQRuLFlGohLUWA0mqCOkJTqUAL7pdJOFhVNioJykDC0+ktNodbq9fqDYYkPjYmR02TQeA-P4PdhaNh5ZhCxoASQgfNIEMmAKYrAN-J5dXYstkdPu2yVcCJcPmalNzFdtsCYFdKSJSIWHsQXrlQiR-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", # nolint
- "app",
mode = "editor"
),
full_screen = TRUE
+