tidyverse / ggplot2 Goto Github PK

View Code? Open in Web Editor NEW

6.3K 6.3K 2.0K 1.08 GB

An implementation of the Grammar of Graphics in R

Home Page: https://ggplot2.tidyverse.org

License: Other

R 100.00%

data-visualisation r visualisation

ggplot2's Introduction

tidyverse

Overview

The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command.

If you’d like to learn how to use the tidyverse effectively, the best place to start is R for Data Science (2e).

Installation

# Install from CRAN
install.packages("tidyverse")

# Install the development version from GitHub
# install.packages("pak")
pak::pak("tidyverse/tidyverse")

If you’re compiling from source, you can run pak::pkg_system_requirements("tidyverse"), to see the complete set of system packages needed on your machine.

Usage

library(tidyverse) will load the core tidyverse packages:

ggplot2, for data visualisation.
dplyr, for data manipulation.
tidyr, for data tidying.
readr, for data import.
purrr, for functional programming.
tibble, for tibbles, a modern re-imagining of data frames.
stringr, for strings.
forcats, for factors.
lubridate, for date/times.

You also get a condensed summary of conflicts with other packages you have loaded:

library(tidyverse)
#> ── Attaching core tidyverse packages ─────────────────── tidyverse 2.0.0.9000 ──
#> ✔ dplyr     1.1.3     ✔ readr     2.1.4
#> ✔ forcats   1.0.0     ✔ stringr   1.5.0
#> ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

You can see conflicts created later with tidyverse_conflicts():

library(MASS)
#> 
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#> 
#>     select
tidyverse_conflicts()
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ✖ MASS::select()  masks dplyr::select()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

And you can check that all tidyverse packages are up-to-date with tidyverse_update():

tidyverse_update()
#> The following packages are out of date:
#>  * broom (0.4.0 -> 0.4.1)
#>  * DBI   (0.4.1 -> 0.5)
#>  * Rcpp  (0.12.6 -> 0.12.7)
#>  
#> Start a clean R session then run:
#> install.packages(c("broom", "DBI", "Rcpp"))

Packages

As well as the core tidyverse, installing this package also installs a selection of other packages that you’re likely to use frequently, but probably not in every analysis. This includes packages for:

Working with specific types of vectors:
- hms, for times.
Importing other types of data:
- feather, for sharing with Python and other languages.
- haven, for SPSS, SAS and Stata files.
- httr, for web apis.
- jsonlite for JSON.
- readxl, for .xls and .xlsx files.
- rvest, for web scraping.
- xml2, for XML.
Modelling
- modelr, for modelling within a pipeline
- broom, for turning models into tidy data

Code of Conduct

Please note that the tidyverse project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

ggplot2's People

Contributors

Stargazers

Watchers

Forkers

strongh kohske jrandall fabrices briandiggs davidmorrison gsk3 briandk jthetzel wch wligtenberg jiho djmurphy420 kdaily cardillo tyf0n yoni sxfmol jlegewie cscheid charlescara jdiggans ecacarva huftis abresler eshilts jrnold anitameh stefan-pdx jimhester cswanghan exl022 psibre tarekrached alicederyn apontejosea crowding bart1 milktrader joshbode aaronwolen dsonnamaker quincysmiith nickreich brent-dickinson casunlight kairana honglongwu hal2001 jpritikin duqi varnerac thierryo yigu404 thomaskern kornl tdhock dlizcano smc-dta rpietro jcaton31 eejd robbieyeah lully6 lehy amackey dengyishuo chansonz johnsonhsieh tsieger kemey188 meren incas56 alstat janeshdev dannyarends jkruppa chibuisimaduka nicholasehamilton jofrhwld imclab agstudy geotheory lucentcosmos microbe rpruim stefanedwards dirkduellmann imgeek rasmusab fmitha kejun2013 hjmjohnson kevinushey xiuying nietzsche1993 abdugadir brodieg sebkopf superxroot

ggplot2's Issues

Problem with coord_polar, geom_path and x limits

x <- seq(-5,5,by=0.1)
d <- data.frame(x = x, y = dnorm(x))

base <- ggplot(d, aes(x, y)) + geom_path() + geom_point() + ylim(0, 0.4)

base + coord_polar()
base + coord_polar() + xlim(-1, 1)

hcl colour scale

That takes hue, chroma and luminance as inputs and outputs colour.

scale_colour_gradientn ignores labels

scale does not throw error if length of breaks and labels different

e.g. scale_x_continuous(breaks = 1:4, labels = 1:2)

scale_date and scale_datetime should warn if dates not in correct format

scale_shape and scale_size are named inconsistently

This makes it impossible to set defaults set_default_scale("shape","discrete", "manual",value=c(1)) and adds ugly special case code to Scales$add_defaults.

facet_grid free space calculations incorrect

da <- data.frame(
  x = rep(1:5, 2),
  y = c(1+(0:4), 1+(0:4)/10),
  z = rep(1:2, each=5))

qplot(x, y, data=da, group=z, geom="line") +
  facet_grid(z ~ ., scales="free_y", space="free")

Panel two is about half the size it should be

geom_abline doesn't work with scale_log10

myd <- data.frame(
  myvar = c(1,5,10,50,100,500,1000,5000,10000),
  myvarb = c(1:9)
)

base <- ggplot(myd, aes(myvarb, myvar))+
  geom_point()+
  geom_abline(intercept = 1, slope = 100)

base + scale_y_log10()

stat_ecdf

Create stat for computing ecdf. Work with geom_step to create plot of ecdf.

coord_map and geom_tile

library(maps)
data <- data.frame(
  lat = c(-41,-42,-41,-42),
  lon = c(170,170,171,171),
  var = c(1,2,3,4))
coast <- data.frame(map("nz", plot=FALSE)[c("x","y")])

plot <- ggplot(data,aes(lon, lat)) + 
  geom_tile(aes(fill = var)) +
  geom_polygon(data = coast,aes(x, y))
plot #OK
plot + coord_equal() #OK
plot + coord_map()

Legend with three constraints doesn't work

data <- data.frame(x = 1:10, y1 = 1:10, y2 = 1:10)
ggplot(data=data, aes(x=x)) + geom_smooth(aes(y=y1, linetype="y1"))
ggplot(data=data, aes(x=x)) + geom_smooth(aes(y=y1, color="y1"))
ggplot(data=data, aes(x=x)) + geom_smooth(aes(y=y1, color="a", linetype="b"))

Problem with end collision detection in coord_polar

df <- data.frame(x = runif(100) * 360, y = runif(100))
base <- ggplot(df, aes(x, y)) + geom_point() + coord_polar()

base
base + scale_x_continuous(breaks = c(0, 180, 270))

Legend size calculated incorrectly

dummy <- data.frame(
       x = rnorm(1000),
       y = rnorm(1000),
       z = gl(2, 500, labels = c("A rather long label", "An even longer
label than the first label"))
)
ggplot(dummy, aes(x = x, y = y, colour = z)) + geom_point()
last_plot() + theme_gray(base_size = 4)

Reported by Thierry Onkelinx

Faster stat_smooth

Exercise 11 of chapter 4 of Simon Woods' GAM book - having obtained the model matrix and penalty for the smooth from the object returned by e.g. smooth.construct2(s(x,bs="cr"),data=list(x=x),knots=NULL)

Should be faster for the special case needed for stat_smooth.

Transformations of scale_colour_gradient don't work

The following two plots are identical:

qplot(carat, price, data = diamonds, geom = "bin2d") +
  scale_colour_gradient()

qplot(carat, price, data = diamonds, geom = "bin2d") +
  scale_colour_gradient(trans = "log10")

coord_polar incorrectly combines expression tick marks

df <- data.frame(x = runif(10) * 2 * pi)

base <- ggplot(df, aes(x, 1)) + 
  geom_point() + 
  ylim(0,1.1) + 
  coord_polar()

breaks <- seq(0, 2 * pi, pi/2)
base + scale_x_continuous(limits = range(breaks), breaks = breaks)

labels <- c("0", expression(frac(pi,2)), expression(pi),
   expression(frac(3*pi,2)), expression(pi))
base + scale_x_continuous(limits = range(breaks), 
  breaks = breaks, labels = labels)
#0 point converted to string

facet_wrap needs labeller parameter like facet_grid

Break names should be used as labels

If breaks is a named vector, the names should be used as labels.

geom_contour

geom_contour generates errors and does not correctly draw contours for density_2d

qplot(rating, length, data = movies, geom = "density2d",
  colour = factor(Comedy), ylim = c(0, 150))

facet_wrap messes up data order

Because of use of merge. Replace with join function from devel version of plyr.

Scales don't train on set values

qplot(mpg, wt, data=mtcars) + geom_point(x = 0)
qplot(mpg, wt, data=mtcars) + geom_point(x = 20)

scale should have option not to produce legend

Dates not correctly transformed when used as colour scale

a <- structure(list(date = structure(c(1227633710, 1227633725, 1227633740, 
1227633756, 1227633771, 1227633786, 1227633802, 1227633818, 1227633833, 
1227633848, 1227633863, 1227633878), class = c("POSIXt", "POSIXct"
), tzone = ""), x = c(10.2516422, 11.1053127, 11.6594761, 13.2709195, 
14.0127093, 14.7598268, 14.6802017, 15.2590637, 15.5089719, 15.5101305, 
15.1716798, 14.9050077), y = c(-11.5090033, -10.8087639, -10.0120475, 
-7.9856268, -6.5551218, -5.1593914, -4.1750486, -2.3780065, -0.5245896, 
1.3263083, 3.3031301, 4.5791333)), .Names = c("date", "x", "y"
), row.names = c(NA, -12L), class = "data.frame")

ggplot(a) + geom_point(aes(x=x, y=y, colour=date))

coord_flip and geom_hline/geom_vline

qplot(mpg, wt, data = mtcars) + geom_vline(xintercept = 20) + coord_flip()
qplot(mpg, wt, data = mtcars) + geom_hline(yintercept = 5) + coord_flip()

Add geom_curve to match book

Should be geom_line + stat_function

only display diagonal line on rect border if size or colour varies

Order aesthetic does not affect order of dodged groups

In the following example, the groups should be displayed in alphabetical order: gain on the left and loss on the right.

df <- data.frame( 
  FRAME = factor(c("Loss", "Gain", "Loss", "Gain")), 
  Mean = c(5.28, 5.23, 4.95, 5.12), 
  FOCUS = factor(c("Prevention", "Prevention", "Promotion", "Promotion"))
)

dodge <- position_dodge(width=0.90)
ggplot(df, aes(FOCUS, Mean, order=FRAME, colour=FRAME)) + 
  geom_point(position=dodge, size = 4)

Store labels in options

By default, scale names should be stored in options. This will allow simplification of the current default scale production so that scales only need to be added when the plot is drawn, and fix the following bugs:

can not use ylab() to set the label for histograms
annotate overrides the scale defaults

coord_equal does not use space efficiently

In the following example, it expands the y-axis too much - it should change the aspect ratio instead.

qplot(mpg, wt, data = mtcars) + coord_equal()

should be able to set n in stat_density_2d

Ordinal scales

For integers with limited range and ordered factors, ggplot2 should use ordinal scales. In particular, colour should default to an sequential brewer palette, and shape should use shapes with increasing number of edges.

Should be possible to set right = FALSE in stat_bin

geom_path & geom_line should have lineEnd parameter

To give control over low-level grid params.

coord_polar should have theta and r limits

coord_map should apply same transformation as coord_equal

To ensure that map aspect ratios are correct

geom_text should be able to produce expressions by parsing text

mydata <- data.frame(x = 1:10, y = 1:10)

ggplot(mydata, aes(x, y)) +
  geom_point() +
  annotate("text", x = 6, y = 7, label = "beta[1] == 1", parse = T)

p = qplot(runif(5),runif(5)) + opts(title="A dumb plot")

# this is the default
theme_set(theme_grey(12))
quartz(width=5, height=4, pointsize=12)
p

# this has display issues (overwriting in legends etc.) because the
# font size is too large compared to what the device "expects"
theme_set(theme_grey(25))
quartz(width=5, height=4, pointsize=8)
p

# when the pointsize is set accordingly, the proportions are
# "harmonious" again (well, it still does not look great that's because
#25 is much to big for this size of plot)
theme_set(theme_grey(25))
quartz(width=5, height=4, pointsize=25)
p

# Ahhh, that's better
theme_set(theme_grey(10))
quartz(width=5, height=4, pointsize=10)
p

For data, NULL != data.frame()

For example,

qplot(mpg, wt, data = mtcars) + 
  geom_point(data = data.frame(), colour = "red")

should display black points.

This can occur when subsetting and is annoying.

Layer with all coordinates set does not work

ggplot(mtcars, aes(mpg, wt)) + 
  geom_point() + 
  geom_rect(xmin = 15, xmax = 20, ymin = 3, ymax = 5)

vs.

ggplot() + 
  geom_point(aes(mpg, wt), data = mtcars) + 
  geom_rect(xmin = 15, xmax = 20, ymin = 3, ymax = 5)

scale_***_ should not have trans argument

Because setting it is confusing.

scales should generate compatible legend keys

set.seed(1)
d = data.frame(x=1:10, y=runif(10), z=runif(10))
ggplot(d) + geom_point(aes(x,y, size=z, colour=z)) + scale_area()
# fails

set.seed(7)
d = data.frame(x=1:10, y=runif(10), z=runif(10))
ggplot(d) + geom_point(aes(x,y, size=z, colour=z)) + scale_area()
# works

Ordering across geoms is inconsistent

df <- data.frame( 
  FRAME = factor(c("Loss", "Gain", "Loss", "Gain")), 
  Mean = c(5.28, 5.23, 4.95, 5.12), 
  FOCUS = factor(c("Prevention", "Prevention", "Promotion", "Promotion"))
)

dodge <- position_dodge(width=0.90)
base <- ggplot(df, aes(FOCUS, Mean, fill=FRAME, colour=FRAME)) + 
  geom_point(position=dodge, size = 4)

# Do work:
base + geom_text(aes(label = Mean), position = dodge)
base + geom_bar(position=dodge, stat="identity")
# Don't work
base + geom_bar(position=dodge)
base + geom_boxplot(position=dodge)

facet_grid doesn't work with multiple variables

 qplot(mpg, wt, data = mtcars) + facet_grid(vs + am ~ .)

Facetting doesn't work with partial margins

qplot(mpg, wt, data = mtcars) + facet_grid(vs ~ am, margins = "grand_col")

legend should respect layer data subset

In this example, points and lines are used for both values.

df.actual <- data.frame(x = 1:10, y = 1:10)
df.approx <- data.frame(x = 1:15, y = 1:15 + rnorm(15, sd = 3))
ggplot(df.actual, aes(x, y)) +
  geom_point(aes(colour="Actual")) +
  geom_line(aes(colour="Approximate"), data=df.approx)

geom_rect and geom_line should support infinite coordinates

This would be useful for annotations, and for geom_vline and geom_hline. The main complication would be ensuring correct translation in coordinate systems.

  ggplot(data.frame(x=0:1,y=0:1)) +
    geom_rect(aes(x=x,y=y),xmin=.1,xmax=.2,ymin=-Inf,ymax=Inf)

coord_equal does not work with log scales

df <- data.frame(a = 2 ^ (1:10), b = 3 ^ (1:10))
myplot <- ggplot(df, aes(a,b)) + 
  geom_line() + 
  scale_x_log10() + scale_y_log10()

myplot
myplot + coord_equal()

coord_trans doesn't respect expand

qplot(rlnorm(100), rlnorm(100)) + 
  coord_trans(x="log") + 
  scale_y_continuous(limits=c(0,10),expand=c(0,0))