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https://github.com/briandconnelly/ggplot2bdc
Collection of themes and tools for modifying ggplot2 plots
https://github.com/briandconnelly/ggplot2bdc
ggplot2 plot r theme
Last synced: 3 months ago
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Collection of themes and tools for modifying ggplot2 plots
- Host: GitHub
- URL: https://github.com/briandconnelly/ggplot2bdc
- Owner: briandconnelly
- License: other
- Created: 2014-04-23T15:10:19.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2017-03-16T20:54:11.000Z (over 7 years ago)
- Last Synced: 2024-05-21T02:54:34.295Z (6 months ago)
- Topics: ggplot2, plot, r, theme
- Language: R
- Homepage:
- Size: 7.67 MB
- Stars: 52
- Watchers: 5
- Forks: 7
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
title: ggplot2bdc README
author: Brian Connelly
output: md_document
---
```{r global_options, echo=FALSE}
knitr::opts_chunk$set(fig.path="figure/", warning=FALSE, message=FALSE,
error=FALSE)
```
# ggplot2bdc
ggplot2bdc is a collection of themes and other things that can be used when creating plots with [ggplot2](http://ggplot2.org/).
For those using the [development version of ggplot](https://github.com/hadley/ggplot2), subtitles and captions are supported.
- Themes
- `theme_bdc_grey`
- `theme_bdc_paneled`
- `theme_bdc_microtiter`
- `theme_bdc_lattice_population`
- Modifying Plots
- Removing Plot Elements
- `gg_remove_titles`, `gg_remove_title`, `gg_remove_subtitle`, `gg_remove_caption`, `gg_remove_title.x`, `gg_remove_title.y`
- `gg_remove_ticks`, `gg_remove_ticks_x`, `gg_remove_ticks_y`
- `gg_remove_grid`, `gg_remove_grid_major`, `gg_remove_grid_minor`, `gg_remove_grid_x`, `gg_remove_grid_y`
- `gg_remove_legend`, `gg_remove_legend_title`, `gg_remove_legend_key`, `gg_remove_legend_text`, `gg_remove_legend_background`
- `gg_remove_background`, `gg_remove_background_canvas`, `gg_remove_background_legend`, `gg_remove_background_facet`
- Rescaling
- `gg_rescale`
- `gg_rescale_golden`
- `gg_rescale_square`
- Modifying Files Containing Plots
- `trim_whitespace`
- Misc
- `ggsave_golden`
- `span`
## Installation
`ggplot2bdc` is not on [CRAN](http://cran.r-project.org/), but you can use [devtools](http://cran.r-project.org/web/packages/devtools/index.html) to install the latest and greatest version.
To do so:
```{r Installation, eval=FALSE}
if(!require("devtools")) install.packages("devtools")
devtools::install_github("briandconnelly/ggplot2bdc")
```
## Examples
To gain access to the following themes, etc. provided, first load the `gplot2bdc` package.
We'll also load `ggplot`, `dplyr`, and `magrittr` for these examples.
```{r}
library(ggplot2bdc)
library(ggplot2)
library(dplyr)
library(magrittr)
```
### Themes
#### theme_bdc_grey
`theme_bdc_grey` is a clean theme that uses thin grey panels on a white background.
Legends are placed above.
Facet labels are displayed as black text on a grey background.
```{r theme_bdc_grey}
pUnemploy <- ggplot(data = economics, aes(x = date, y = unemploy)) +
geom_line() +
scale_x_date() +
labs(x = "Year", y = "Thousands of Persons",
title = "Monthly Unemployment", subtitle = "Seasonally Adjusted",
caption = "Source: Federal Reserve Bank of St. Louis") +
theme_bdc_grey()
pUnemploy
```
Subtle grid lines can be added to the panels with the `grid.x` and `grid.y` parameters.
These also affect minor grid lines, which can further be controlled with the `gridmin.x` and `gridmin.y` parameters.
```{r theme_bdc_grey_grid}
midwest$inmetro <- factor(midwest$inmetro, levels = c(0, 1),
labels = c("Rural", "Metro"))
ggplot(data = midwest,
aes(x = inmetro, y = percbelowpoverty, color = state, shape = state)) +
stat_summary(fun.y = "mean", geom = "line", aes(group = state)) +
stat_summary(fun.y = "mean", geom = "point") +
scale_color_hue(name = "State") +
scale_shape_discrete(name = "State") +
labs(x = NULL, y = "Percent",
title = "Poverty in the Midwest",
subtitle = "Poverty rates in rural and metro communities") +
coord_equal(ratio = 0.5) +
theme_bdc_grey(ticks.x = FALSE, grid.y = TRUE)
```
Some people prefer to display categorical data without tick marks along the axis.
These are included by default, but can be removed by setting either the `ticks.x` or `ticks.y` parameters to `FALSE`.
We can also use facets to divide the data by state:
```{r theme_bdc_grey-facets1}
ggplot(data = midwest, aes(x = inmetro, y = percbelowpoverty)) +
facet_grid(. ~ state) +
stat_summary(fun.y = "mean", geom = "line", aes(group = state)) +
stat_summary(fun.y = "mean", geom = "point") +
labs(x = NULL, y = "Percent",
title = "Poverty in the Midwest",
subtitle = "Poverty rates in rural and metro communities") +
coord_equal(ratio = 0.5) +
theme_bdc_grey(grid.y = TRUE)
```
```{r theme_bdc_grey-facets2}
txcities <- txhousing %>%
filter(city %in% c("Austin", "Dallas", "El Paso", "Fort Worth", "Houston",
"San Antonio"))
pTX <- ggplot(data = txcities, aes(x = year, y = median / 1000)) +
facet_grid(city ~ .) +
stat_summary(fun.data = "mean_cl_boot", geom = "ribbon",
color = NA, alpha = 0.3) +
stat_summary(fun.y = "mean", geom = "line") +
labs(x = "Year", y = "Thousands of Dollars",
title = "Home Prices are Getting Bigger in Texas",
subtitle = "Median Home Prices in Texas' Largest Cities",
caption = "Source: TAMU Real Estate Center")
pTX + theme_bdc_grey()
```
#### theme_bdc_paneled
`theme_bdc_paneled` is a clean theme that displays plots without borders on grey panels.
Legends are placed above.
Facet labels are displayed as plan black text.
Subtle grid lines can be added to the panels with the `grid.x` and `grid.y` parameters, and tick marks can be managed with `ticks.x` and `ticks.y`.
By default, grid lines are used along the Y axis.
This theme is best suited for plots with multiple facets.
```{r theme_bdc_paneled}
pTX + theme_bdc_paneled()
```
#### theme_bdc_microtiter
`theme_bdc_microtiter` is a specialized theme for use in creating figures that represent 96-well microtiter plates.
```{r loadplatemap,echo=FALSE}
library(dplyr)
platemap <- read.csv("~/Dropbox/Research/Documents/Blog/Blog Post-Analyzing Bacterial Growth/data/platemap.csv") %>%
mutate(Row = as.numeric(match(toupper(substr(Well,1,1)), LETTERS)), Column = as.numeric(substr(Well,2,5)))
platemap$Environment <- as.factor(platemap$Environment)
```
```{r theme_bdc_microtiter}
pPlate <- ggplot(data = platemap, aes(x = Column, y = Row)) +
geom_point(data = expand.grid(Column = seq(1,12), Row = seq(1,8)),
color = "grey90", fill = "white", shape = 21, size = 8) +
geom_point(aes(shape = Environment, color = Strain), size = 9) +
coord_fixed(ratio = (13/12)/(9/8), xlim = c(0.5, 12.5), ylim = c(0.6, 8.4)) +
scale_y_reverse(breaks = seq(1, 8), labels = LETTERS[1:8]) +
scale_x_continuous(breaks = seq(1, 12), position = "top") +
labs(title = "Plate Layout for My Experiment", subtitle = "25 March 2016") +
guides(shape = guide_legend(override.aes = list(size = 3)),
color = guide_legend(override.aes = list(size = 3))) +
theme_bdc_microtiter()
pPlate
```
For more information, see the [Plotting Microtiter Plate Maps](http://bconnelly.net/2014/05/plotting-microtiter-plate-maps/).
#### theme_bdc_lattice_population
`theme_bdc_lattice_population` is a specialized theme intended to display populations of individuals structured spatially in a lattice.
```{r theme_bdc_lattice_population}
ggplot(data = filter(rockpaperscissors, Time == 10),
aes(x = X, y = Y, color = Type)) +
geom_point(shape=15) +
coord_equal(ratio=1) +
scale_color_hue() +
theme_bdc_lattice_population()
```
We can also show multiple population states:
```{r theme_bdc_lattice_population-facets}
ggplot(data = filter(rockpaperscissors, Time %in% c(0, 200, 400, 600)),
aes(x = X, y = Y, color = Type)) +
facet_wrap(~Time, nrow = 1, ncol = 4) +
geom_point(shape = 15) +
coord_equal(ratio = 1) +
scale_color_hue(guide = FALSE) +
labs(title = "Rock, Paper, Scissors",
subtitle = "Wait, is it ON three or AFTER three?",
caption = "Data from simulation on a 50x50 lattice") +
theme_bdc_lattice_population()
```
This is also a great opportunity to create an animation.
We can use [gganimate](https://github.com/dgrtwo/gganimate) to create an animated GIF showing the population for the first 10 time steps.
```{r lattice-animation, eval=FALSE}
library(gganimate)
p <- ggplot(data = filter(rockpaperscissors, Time < 10),
aes(x = X, y = Y, color = Type, frame = Time)) +
geom_point(shape = 15, size = 3) +
coord_equal(ratio = 1) +
scale_color_hue(guide = FALSE) +
labs(title = "Rock, Paper, Scissors at t=") +
theme_bdc_lattice_population()
gg_animate(p = p, saver = "gif", interval = 0.2)
```
```{r lattice-animation-save, echo=FALSE}
library(gganimate)
p <- ggplot(data = filter(rockpaperscissors, Time < 10),
aes(x = X, y = Y, color = Type, frame = Time)) +
geom_point(shape = 15, size = 3) +
coord_equal(ratio = 1) +
scale_color_hue(guide = FALSE) +
labs(title = "Rock, Paper, Scissors at t=") +
theme_bdc_lattice_population()
gg_animate(p = p, filename = "figure/lattice-anim.gif", interval = 0.2)
```
### Modifying Plots
Although you can always change a plot's properties using ggplot2's `theme` function, it's often difficult to remember which elements control a plot's visual characteristics.
These and other `gg_` functions make it a bit easier to quickly alter a plot.
#### gg_rescale_golden
`gg_rescale_golden` adjusts the proportions of the axes of the given plot object so that follow the golden ratio (horizontally).
If no plot is provided, the last plot that was displayed is used.
```{r gg_rescale_golden}
ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_smooth(method = "loess") +
geom_point(shape = 1) +
labs(x = "Horsepower", y = "Fuel Efficiency (mpg)") +
theme_bdc_grey()
```
```{r gg_rescale_golden2}
gg_rescale_golden()
```
#### gg_rescale_square
`gg_rescale_square` adjusts the proportions of the axes of the given plot object so that they are equal.
If no plot is provided, the last plot that was displayed is used.
```{r gg_rescale_square}
ggplot(data = economics, aes(x = date, y = unemploy)) +
geom_line() +
labs(x = "Date", y = "Thousands of Unemployed Persons") +
theme_bdc_grey()
```
```{r gg_rescale_square2}
gg_rescale_square()
```
#### gg_rescale
`gg_rescale` adjusts the proportions of the axes of the given plot object to the given ratio.
If no plot is provided, the last plot that was displayed is used.
```{r gg_rescale}
ggplot(data = economics, aes(x = date, y = unemploy)) +
geom_line() +
labs(x = "Date", y = "Unemployed Persons (x1000)") +
theme_bdc_grey()
gg_rescale(ratio = 16 / 9)
```
#### ggsave_golden
`ggsave_golden` saves the given plot to the given filename.
The dimensions of the resulting image file's canvas will follow the golden ratio, which is useful in conjunction with `gg_rescale_golden`.
If no plot is specified, the most recently displayed plot is used.
```{r ggsave_golden,eval=FALSE}
# Save our plot of unemployment from earlier
ggsave_golden(filename = "monthly_unemployment.pdf", plot = pUnemploy)
```