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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# sccthemes <img src="inst/figures/scc-hex.png" align="right" width="120"/>
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->
The goal of sccthemes is to provide a consistent styling framework and helper functions for graphics made in ggplot at the Suffolk County Council.
This package has been heavily inspired by [bbplot](https://github.com/bbc/bbplot), the BBC's R package for creating and exporting graphics made in ggplot.
## Installation
You can install the development version of sccthemes from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("thomaszwagerman/sccthemes")
```
## SCC Cookbook
You can find the full [SCC Cookbook](https://thomaszwagerman.github.io/sccthemes/). This is a work in progress.
## Suffolk County Council brand and styling
The font and colour codes have been sourced from the [Suffolk County Council's website](https://www.suffolk.gov.uk/), and Google Chrome's Inspect functionality (Ctrl + Shift + i).
The style can be called to any ggplot2 object using `scc_style()`
```{r style_example, warning = FALSE, message = FALSE}
library(ggplot2)
library(gapminder)
library(dplyr)
library(sccthemes)
barchart_data <- gapminder |>
filter(year == 2007 & continent == "Europe") |>
arrange(desc(gdpPercap)) |>
head(5)
barchart_data |>
ggplot(aes(x = country, y = gdpPercap)) +
geom_bar(
stat = "identity",
position = "identity",
color = "#333333",
fill = "#428bca"
) +
geom_hline(
yintercept = 0,
size = 1,
colour = "#333333"
) +
scc_style() +
labs(
title = "Europe's Richest Countries",
subtitle = "GDP per Capita, 2007"
)
```
## Helper functions for ggplot2
Helper functions for certain types of plots are also provided in this repo.
Instead of the code above for instance, this package also contains helper functions which are rough and ready:
```{r helper_example, warning = FALSE, message = FALSE}
library(ggplot2)
library(gapminder)
library(dplyr)
library(sccthemes)
barchart_data <- gapminder |>
filter(year == 2007 & continent == "Europe") |>
arrange(desc(gdpPercap)) |>
head(5)
scc_barchart(
barchart_data,
x = "country",
y = "gdpPercap",
title = "Europe's Richest Countries",
subtitle = "GDP per Capita, 2007"
)
```
Currently, there are helper functions for the following plot types:
- Barplots
- Piecharts (but why you [should discourage their use if you can](https://www.data-to-viz.com/caveat/pie.html))
The BBC's ["ggplot cookbook"](https://bbc.github.io/rcookbook/#make_a_bar_chart) is a good starting point to use as a reference.
More helper functions will be added as I need to make them.
## Publishing your plots
To publish your work, so will need to save your plot. Saving plots in ggplot2 can be a bit of a hassle, so we're porting bbplot's `finalise_plot()` function. Some adjustments have been made, for example we might not want a logo on every plot. Also, because this package does not just deal with ggplot, I've renamed the function `finalise_ggplot()`, to specify this is for `ggplot2` object only.
Once you have created your plot and are happy with it, you can use `finalise_ggplot()` so that you can look at it outside RStudio. Note that the position of the text and other elements do not render accurately in the RStudio Plots panel, so saving it out and opening up the files give you an accurate representation of how the graphic looks.
Also you might want to use the same plot for different purpose: the size of a plot in a presentation will need to be a different size from one in a report. `finalise_ggplot()` allows for this without having to change the original plot.
```{r finalise_ggplot, warning = FALSE, message = FALSE, fig.show = "hide"}
library(ggplot2)
library(gapminder)
library(dplyr)
library(sccthemes)
barchart_data <- gapminder |>
filter(year == 2007 & continent == "Europe") |>
arrange(desc(gdpPercap)) |>
head(5)
plot_to_save <- scc_barchart(
barchart_data,
x = "country",
y = "gdpPercap",
title = "Europe's Richest Countries",
subtitle = "GDP per Capita, 2007"
)
finalise_ggplot(
plot_to_save,
source = "Jennifer Bryan, 2017, Gapminder R package, https://github.com/jennybc/gapminder",
save_filepath = "inst/plots/publish_plot_example.png",
width_pixels = 225,
height_pixels = 165
)
```
<p align="center"><img src="inst/plots/publish_plot_example.png"></p>
## A note on colour palettes, branding and accessibility
### Putting thought into requirements and responsibilities
The goal of colour palettes is to improve storytelling with data, whether it is to show continuous change in a value, differences between distinct categories or to highlight a specific category that you want to highlight.
Some questions we need to think about when choosing colour:
- Is it accessible for everyone, specifically for those with colour blindness/color vision deficiency?
- Is there an association with the colour we use (ie green is good/positive, red is bad/negative)
- Does it fit within the SCC brand?
### Sticking to the Suffolk County Council brand, where possible
The Suffolk County Council has its own colour schemes, the recognisable blues and orange found on the [website](https://www.suffolk.gov.uk/).
This <span style = "color:#2d6ca2">**blue (#2d6ca2)**</span> is used as the primary colour in graphs, with <span style = "color:#e8850c">**orange (#e8850c)**</span> as the secondary colour. Where applicable, the <span style = "color:#e2eefa">**light blue (#e2eefa)**</span> can used as a tertiary colour - but note that this not easily visible against white background (consider using a black outline).
Blue and Orange are colours that are distinct for most common forms of colour blindness/color vision deficiency. However, different types of graphs have vastly different requirements to tell a story, and where the number of categories exceeds three colours the SCC palette is not sufficient.
### No need to reinvent the wheel - use **Viridis**
Therefore, instead of designing our own colour palette, I'd recommend the use of [viridis](https://github.com/sjmgarnier/viridis), for graphs with more than three categories. This package has visually appealing colour palettes, and improve graph readability for readers with **common** forms of color blindness and/or color vision deficiency. Note that for some specific forms, you may have to choose another palette within viridis (for example [seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html), or [turbo](https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html)).
```{r viridis_example, warning = FALSE, message = FALSE}
library(sccthemes)
library(gapminder)
library(dplyr)
asia_pop <- gapminder |>
filter(year == 1997 & continent == "Asia") |>
select(country, lifeExp, pop, gdpPercap) |>
top_n(5)
asia_pop$country <- forcats::fct_drop(asia_pop$country)
scc_piechart(
asia_pop,
asia_pop$gdpPercap,
asia_pop$country,
title = "GDP per Capita contribution percentage by county",
subtitle = "Five largest countries only"
)
```
## Future additions to this package
If there is a further need in the future, other styling options can be added.
For example:
- [bslib](https://rstudio.github.io/bslib/) themes for Shiny, see this example from [UKCEH](https://github.com/NERC-CEH/UKCEH_shiny_theming).
- gt table themes