---
title: "Creating Flourish graphs in R"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Creating Flourish graphs in R}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, message=F}
library(dplyr)
library(tidyr)
library(flourishcharts)
```


Flourish primarily depends on two main functions in R/Python. They are `flourish` and a `bind_..._data()` function. The `...` depends on the chart type. An R user can specify their preferred chart type and bind data columns to the graph. In R, A list of Flourish functions can be found by running `flourishcharts::`, typing `?flourishcharts` in your console, or heading over to the references page in this site. 

The following chunks show some example charts using Gapminder data, with my accompanying notes.

The `flourishcharts` package provides the data `flourish_api_documentation` frame with metadata about each graph (template ID, version, a simplified chart name for end users, and a URL to Flourish's API documentation). 


### R 

```{r}
head(flourish_api_documentation)
```


`flourishcharts` works by requiring two functions to plot Flourish graphs:

 * `flourish()` in R or `Flourish()` in Python:
    * `chart_type` **(mandatory arg)** -- a string that must match a value in `flourish_api_documentation["chart_type"]`.
    * `api_key` **(mandatory arg)** -- Flourish API key, defaults to `Sys.getenv("FLOURISH_API_KEY")` or `os.environ.get("FLOURISH_API_KEY")`.
    * `chart_description` **(optional arg)** -- a string with the chart description for screen readers.

And secondly, a binding data function which performs the same function as in flourish.studio, whereby the user matches data columns to the graph properties. 

To start with, a scatter plot is laid out as follows. If a user wants to add additional details they can by running `set_..._details()`--in this case, `set_scatter_details()`.

Each function argument pipes into the other and takes on the additional arguments to the graph. The pipe can be the base pipe `|>` or the maggrittr pipe `%>%`. In Python, users can append new functions to the assigned graph.

## Scatterplot

```{r scatter, eval = F}
scatterplot <- flourish(
  chart_type = "scatter"
) |>
  bind_scatter_data(
    data = gapminder,
    x = "gdpPercap",
    y = "lifeExp",
    slider = "year",
    size = "pop",
    color = "continent",
    metadata = c("country", "year")
  )
scatterplot
```


## Line graph

### R

```{r, eval = F}
line_data <- gapminder |>
  filter(country %in% c(
    "Australia",
    "New Zealand",
    "United States",
    "Rwanda",
    "Sierra Leone",
    "Indonesia",
    "Brazil"
  )) |>
  select("country", "year", "lifeExp") |>
  pivot_wider(id_cols = "year", names_from = "country", values_from = "lifeExp")

line_chart <- flourish(
  chart_type = "line"
) |>
  bind_line_bar_pie_data(
    data = line_data,
    label = "year",
    value = colnames(line_data[, c(2:8)])
  ) |>
  set_line_bar_pie_details(
    chart_layout_title = "Life expectancy from the 1950s to 2007",
    chart_layout_subtitle = "Selected countries include Australia, New Zealand, the US, Rwanda, Indonesia, Sierra Leone, and Brazil."
  )
line_chart
```


## Bar chart race


```{r, eval = F}
bcr_data <- gapminder |>
  filter(country %in% c(
    "Australia",
    "New Zealand",
    "United States",
    "Rwanda",
    "Sierra Leone",
    "Indonesia",
    "Brazil"
  )) |>
  select(c("country", "continent", "year", "lifeExp")) |>
  pivot_wider(id_cols = c("country", "continent"), names_from = "year", values_from = "lifeExp")

bcr <- flourish("bar_race") |>
  bind_bar_chart_race_data(
    data = bcr_data,
    label = "country",
    values = colnames(bcr_data[, c(3:14)]),
    category = "continent"
  ) |>
  set_bar_chart_race_details(
    chart_layout_title = "Life expectancy from the 1950s to 2007",
    chart_layout_subtitle = "Selected countries include Australia, New Zealand, the US, Rwanda, Indonesia, Sierra Leone, and Brazil."
  )
bcr
```

