https://github.com/Greater-London-Authority/gglaplot

Makes graphics in the GLA style using ggplot2
https://github.com/Greater-London-Authority/gglaplot

ggplot2 r visualization

Last synced: 3 months ago
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Makes graphics in the GLA style using ggplot2

• Host: GitHub
• URL: https://github.com/Greater-London-Authority/gglaplot
• Owner: Greater-London-Authority
• Created: 2019-01-14T12:28:38.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-09-08T11:02:37.000Z (about 1 year ago)
• Last Synced: 2024-07-17T06:44:14.086Z (4 months ago)
• Topics: ggplot2, r, visualization
• Language: R
• Homepage: https://greater-london-authority.github.io/gglaplot/
• Size: 6.56 MB
• Stars: 17
• Watchers: 2
• Forks: 7
• Open Issues: 8
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output: github_document

---

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE, fig.path = "man/figures/")

```

# gglaplot 

The package provides several wrappers and tools to use with ggplot2 and plotly to make graphics that follow the GLA [City Intelligence Data Design Guidelines](https://data.london.gov.uk/blog/city-intelligence-data-design-guidelines/).

## Installation

```{r Installation, eval=FALSE}

# To install from github use the devtools function:

# This will install all required dependencies

devtools::install_github("Greater-London-Authority/gglaplot")

```

## Usage

```{r Usage, message=FALSE, warning=FALSE, dpi = 300}

library(ggplot2)

library(gglaplot)

library(dplyr)

library(scales)

library(lubridate)

pal <- gla_pal(gla_theme = "default", palette_type = "highlight", n = c(1, 1))

theme_set(theme_gla(gla_theme = "default"))

plot <- ggplot(data = LDNUK, mapping = aes(x = Year, y = GPG, group = location,

                                   colour = location)) +

  ggla_line(aes(size = location)) +

  scale_size_manual(values = c(4 * mm_to_pt, 2 * mm_to_pt)) +

  scale_colour_manual(values = pal) +

  ggla_highlight(filter_type = "end") +

  ggla_axisat0() +

  scale_y_continuous(expand = c(0, 0), limits = c(0, 32.5),

                      labels = dollar_format(prefix = "", suffix = "%")) +

  scale_x_date(date_breaks = "1 year", date_labels = "'%y",

               expand = expansion(mult = c(0.05, 0.01))) +

  labs(title = "Gender Pay Gap - Total (Median)",

        subtitle = "Gender Pay Gap - Total (Median) - London VS UK",

        caption = "Note: 2017 data is provisional\nChart: GLA City Intelligence  Source: London Datastore")

plot

```

Plots can be incorporated in Rmarkdown/Notebooks or exported to be included in documents/slideshows etc

```{r saving plot, message=FALSE, warning=FALSE, eval=FALSE}

ggsave(plot = plot, path = "example_plot.svg")

```

`.svg` is the best format to export plots, and the size and dpi of the output can be adjusted within `ggsave()`.

## Getting Help

### ggplot2

There are many online resources for ggplot2, including:

* [ggplot2 cheatsheet](https://ggplot2.tidyverse.org/)

* [ggplot2 documentation](https://ggplot2.tidyverse.org/reference/)

* [DataCamp course](https://www.datacamp.com/courses/data-visualization-with-ggplot2-1)

### plotly

* [plotly website](https://plotly.com/r/)

* [plotly bookdown](https://plotly-r.com/)

### gglaplot

For help with gglaplot itself, see the vignettes which are available on the [gglaplot github pages](https://greater-london-authority.github.io/gglaplot/).

The BBC has a similar package for their house style which has some comprehensive help pages [here](https://bbc.github.io/rcookbook/).