ggalt : Extra Coordinate Systems, Geoms, Statistical Transformations,
Scales & Fonts for ‘ggplot2’
A compendium of ‘geoms’, ‘coords’, ‘stats’, scales and fonts for ‘ggplot2’, including splines, 1d and 2d densities, univariate average shifted histograms, a new map coordinate system based on the ‘PROJ.4’-library and the ‘StateFace’ open source font ‘ProPublica’.
The following functions are implemented:
-
geom_ubar: Uniform width bar charts -
geom_horizon: Horizon charts (modified from https://github.com/AtherEnergy/ggTimeSeries) -
coord_proj: Likecoord_map, only better (prbly shld use this withgeom_cartogramasgeom_map’s new defaults are ugh) -
geom_xspline: Connect control points/observations with an X-spline -
stat_xspline: Connect control points/observations with an X-spline -
geom_bkde: Display a smooth density estimate (usesKernSmooth::bkde) -
geom_stateface: Use ProPublica’s StateFace font in ggplot2 plots -
geom_bkde2d: Contours from a 2d density estimate. (usesKernSmooth::bkde2D) -
stat_bkde: Display a smooth density estimate (usesKernSmooth::bkde) -
stat_bkde2d: Contours from a 2d density estimate. (usesKernSmooth::bkde2D) -
stat_ash: Compute and display a univariate averaged shifted histogram (polynomial kernel) (usesash::ash1/ash::bin1) -
geom_encircle: Automatically enclose points in a polygon -
byte_format: + helpers. e.g. turn10000into10 Kb -
geom_lollipop(): Dead easy lollipops (horizontal or vertical) -
geom_dumbbell(): Dead easy dumbbell plots -
stat_stepribbon(): Step ribbons -
annotation_ticks(): Add minor ticks to identity, exp(1) and exp(10) axis scales independently of each other. -
geom_spikelines(): Instead of geom_vline and geom_hline a pair of segments that originate from same c(x,y) are drawn to the respective axes. -
plotly integration for a few of the ^^ geoms
# you'll want to see the vignettes, trust me
install.packages("ggplot2")
install.packages("ggalt")
# OR: devtools::install_github("hrbrmstr/ggalt")library(ggplot2)
library(gridExtra)
library(ggalt)
# current verison
packageVersion("ggalt")
## [1] '0.6.1'
set.seed(1492)
dat <- data.frame(x=c(1:10, 1:10, 1:10),
y=c(sample(15:30, 10), 2*sample(15:30, 10), 3*sample(15:30, 10)),
group=factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))
)Example carved from: https://github.com/halhen/viz-pub/blob/master/sports-time-of-day/2_gen_chart.R
library(hrbrthemes)
library(ggalt)
library(tidyverse)
sports <- read_tsv("https://github.com/halhen/viz-pub/raw/master/sports-time-of-day/activity.tsv")
sports %>%
group_by(activity) %>%
filter(max(p) > 3e-04,
!grepl('n\\.e\\.c', activity)) %>%
arrange(time) %>%
mutate(p_peak = p / max(p),
p_smooth = (lag(p_peak) + p_peak + lead(p_peak)) / 3,
p_smooth = coalesce(p_smooth, p_peak)) %>%
ungroup() %>%
do({
rbind(.,
filter(., time == 0) %>%
mutate(time = 24*60))
}) %>%
mutate(time = ifelse(time < 3 * 60, time + 24 * 60, time)) %>%
mutate(activity = reorder(activity, p_peak, FUN=which.max)) %>%
arrange(activity) %>%
mutate(activity.f = reorder(as.character(activity), desc(activity))) -> sports
sports <- mutate(sports, time2 = time/60)
ggplot(sports, aes(time2, p_smooth)) +
geom_horizon(bandwidth=0.1) +
facet_grid(activity.f~.) +
scale_x_continuous(expand=c(0,0), breaks=seq(from = 3, to = 27, by = 3), labels = function(x) {sprintf("%02d:00", as.integer(x %% 24))}) +
viridis::scale_fill_viridis(name = "Activity relative to peak", discrete=TRUE,
labels=scales::percent(seq(0, 1, 0.1)+0.1)) +
labs(x=NULL, y=NULL, title="Peak time of day for sports and leisure",
subtitle="Number of participants throughout the day compared to peak popularity.\nNote the morning-and-evening everyday workouts, the midday hobbies,\nand the evenings/late nights out.") +
theme_ipsum_rc(grid="") +
theme(panel.spacing.y=unit(-0.05, "lines")) +
theme(strip.text.y = element_text(hjust=0, angle=360)) +
theme(axis.text.y=element_blank())
ggplot(dat, aes(x, y, group=group, color=group)) +
geom_point() +
geom_line()
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point() +
geom_line() +
geom_smooth(se=FALSE, linetype="dashed", size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(spline_shape=-0.4, size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(spline_shape=0.4, size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(spline_shape=1, size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(spline_shape=0, size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(dat, aes(x, y, group=group, color=factor(group))) +
geom_point(color="black") +
geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
geom_xspline(spline_shape=-1, size=0.5)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# bkde
data(geyser, package="MASS")
ggplot(geyser, aes(x=duration)) +
stat_bkde(alpha=1/2)
## Bandwidth not specified. Using '0.14', via KernSmooth::dpik.
ggplot(geyser, aes(x=duration)) +
geom_bkde(alpha=1/2)
## Bandwidth not specified. Using '0.14', via KernSmooth::dpik.
ggplot(geyser, aes(x=duration)) +
stat_bkde(bandwidth=0.25)
ggplot(geyser, aes(x=duration)) +
geom_bkde(bandwidth=0.25)
set.seed(1492)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)),
rating = c(rnorm(200),rnorm(200, mean=.8)))
ggplot(dat, aes(x=rating, color=cond)) + geom_bkde(fill="#00000000")
## Bandwidth not specified. Using '0.36', via KernSmooth::dpik.
## Bandwidth not specified. Using '0.31', via KernSmooth::dpik.
ggplot(dat, aes(x=rating, fill=cond)) + geom_bkde(alpha=0.3)
## Bandwidth not specified. Using '0.36', via KernSmooth::dpik.
## Bandwidth not specified. Using '0.31', via KernSmooth::dpik.
# ash
set.seed(1492)
dat <- data.frame(x=rnorm(100))
grid.arrange(ggplot(dat, aes(x)) + stat_ash(),
ggplot(dat, aes(x)) + stat_bkde(),
ggplot(dat, aes(x)) + stat_density(),
nrow=3)
## Estimate nonzero outside interval ab.
## Bandwidth not specified. Using '0.43', via KernSmooth::dpik.
cols <- RColorBrewer::brewer.pal(3, "Dark2")
ggplot(dat, aes(x)) +
stat_ash(alpha=1/3, fill=cols[3]) +
stat_bkde(alpha=1/3, fill=cols[2]) +
stat_density(alpha=1/3, fill=cols[1]) +
geom_rug() +
labs(x=NULL, y="density/estimate") +
scale_x_continuous(expand=c(0,0)) +
theme_bw() +
theme(panel.grid=element_blank()) +
theme(panel.border=element_blank())
## Estimate nonzero outside interval ab.
## Bandwidth not specified. Using '0.43', via KernSmooth::dpik.
m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
geom_point() +
xlim(0.5, 6) +
ylim(40, 110)
m + geom_bkde2d(bandwidth=c(0.5, 4))
m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon")
world <- map_data("world")
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
world <- world[world$region != "Antarctica",]
gg <- ggplot()
gg <- gg + geom_cartogram(data=world, map=world,
aes(x=long, y=lat, map_id=region))
gg <- gg + coord_proj("+proj=wintri")
gg
# Run show_stateface() to see the location of the TTF StateFace font
# You need to install it for it to work
set.seed(1492)
dat <- data.frame(state=state.abb,
x=sample(100, 50),
y=sample(100, 50),
col=sample(c("#b2182b", "#2166ac"), 50, replace=TRUE),
sz=sample(6:15, 50, replace=TRUE),
stringsAsFactors=FALSE)
gg <- ggplot(dat, aes(x=x, y=y))
gg <- gg + geom_stateface(aes(label=state, color=col, size=sz))
gg <- gg + scale_color_identity()
gg <- gg + scale_size_identity()
gg
d <- data.frame(x=c(1,1,2),y=c(1,2,2)*100)
gg <- ggplot(d,aes(x,y))
gg <- gg + scale_x_continuous(expand=c(0.5,1))
gg <- gg + scale_y_continuous(expand=c(0.5,1))
gg + geom_encircle(s_shape=1, expand=0) + geom_point()
gg + geom_encircle(s_shape=1, expand=0.1, colour="red") + geom_point()
gg + geom_encircle(s_shape=0.5, expand=0.1, colour="purple") + geom_point()
gg + geom_encircle(data=subset(d, x==1), colour="blue", spread=0.02) +
geom_point()
gg +geom_encircle(data=subset(d, x==2), colour="cyan", spread=0.04) +
geom_point()
gg <- ggplot(mpg, aes(displ, hwy))
gg + geom_encircle(data=subset(mpg, hwy>40)) + geom_point()
ss <- subset(mpg,hwy>31 & displ<2)
gg + geom_encircle(data=ss, colour="blue", s_shape=0.9, expand=0.07) +
geom_point() + geom_point(data=ss, colour="blue")
x <- 1:10
df <- data.frame(x=x, y=x+10, ymin=x+7, ymax=x+12)
gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
stat="stepribbon", fill="#b2b2b2")
gg <- gg + geom_step(color="#2b2b2b")
gg
gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
stat="stepribbon", fill="#b2b2b2",
direction="vh")
gg <- gg + geom_step(color="#2b2b2b")
gg
df <- read.csv(text="category,pct
Other,0.09
South Asian/South Asian Americans,0.12
Interngenerational/Generational,0.21
S Asian/Asian Americans,0.25
Muslim Observance,0.29
Africa/Pan Africa/African Americans,0.34
Gender Equity,0.34
Disability Advocacy,0.49
European/European Americans,0.52
Veteran,0.54
Pacific Islander/Pacific Islander Americans,0.59
Non-Traditional Students,0.61
Religious Equity,0.64
Caribbean/Caribbean Americans,0.67
Latino/Latina,0.69
Middle Eastern Heritages and Traditions,0.73
Trans-racial Adoptee/Parent,0.76
LBGTQ/Ally,0.79
Mixed Race,0.80
Jewish Heritage/Observance,0.85
International Students,0.87", stringsAsFactors=FALSE, sep=",", header=TRUE)
library(ggplot2)
library(ggalt)
library(scales)
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
gg <- ggplot(df, aes(y=reorder(category, pct), x=pct))
gg <- gg + geom_lollipop(point.colour="steelblue", point.size=2, horizontal=TRUE)
gg <- gg + scale_x_continuous(expand=c(0,0), labels=percent,
breaks=seq(0, 1, by=0.2), limits=c(0, 1))
gg <- gg + labs(x=NULL, y=NULL,
title="SUNY Cortland Multicultural Alumni survey results",
subtitle="Ranked by race, ethnicity, home land and orientation\namong the top areas of concern",
caption="Data from http://stephanieevergreen.com/lollipop/")
gg <- gg + theme_minimal(base_family="Arial Narrow")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(axis.line.y=element_line(color="#2b2b2b", size=0.15))
gg <- gg + theme(axis.text.y=element_text(margin=margin(r=0, l=0)))
gg <- gg + theme(plot.margin=unit(rep(30, 4), "pt"))
gg <- gg + theme(plot.title=element_text(face="bold"))
gg <- gg + theme(plot.subtitle=element_text(margin=margin(b=10)))
gg <- gg + theme(plot.caption=element_text(size=8, margin=margin(t=10)))
gg
library(dplyr)
library(tidyr)
library(scales)
library(ggplot2)
library(ggalt) # devtools::install_github("hrbrmstr/ggalt")
health <- read.csv("https://rud.is/dl/zhealth.csv", stringsAsFactors=FALSE,
header=FALSE, col.names=c("pct", "area_id"))
areas <- read.csv("https://rud.is/dl/zarea_trans.csv", stringsAsFactors=FALSE, header=TRUE)
health %>%
mutate(area_id=trunc(area_id)) %>%
arrange(area_id, pct) %>%
mutate(year=rep(c("2014", "2013"), 26),
pct=pct/100) %>%
left_join(areas, "area_id") %>%
mutate(area_name=factor(area_name, levels=unique(area_name))) -> health
setNames(bind_cols(filter(health, year==2014), filter(health, year==2013))[,c(4,1,5)],
c("area_name", "pct_2014", "pct_2013")) -> health
gg <- ggplot(health, aes(x=pct_2014, xend=pct_2013, y=area_name, group=area_name))
gg <- gg + geom_dumbbell(colour="#a3c4dc", size=1.5, colour_xend="#0e668b",
dot_guide=TRUE, dot_guide_size=0.15)
gg <- gg + scale_x_continuous(label=percent)
gg <- gg + labs(x=NULL, y=NULL)
gg <- gg + theme_bw()
gg <- gg + theme(plot.background=element_rect(fill="#f7f7f7"))
gg <- gg + theme(panel.background=element_rect(fill="#f7f7f7"))
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.major.x=element_line())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(legend.position="top")
gg <- gg + theme(panel.border=element_blank())
gg
library(hrbrthemes)
df <- data.frame(trt=LETTERS[1:5], l=c(20, 40, 10, 30, 50), r=c(70, 50, 30, 60, 80))
ggplot(df, aes(y=trt, x=l, xend=r)) +
geom_dumbbell(size=3, color="#e3e2e1",
colour_x = "#5b8124", colour_xend = "#bad744",
dot_guide=TRUE, dot_guide_size=0.25) +
labs(x=NULL, y=NULL, title="ggplot2 geom_dumbbell with dot guide") +
theme_ipsum_rc(grid="X") +
theme(panel.grid.major.x=element_line(size=0.05))
p <- ggplot(msleep, aes(bodywt, brainwt)) + geom_point()
# add identity scale minor ticks on y axis
p + annotation_ticks(sides = 'l')
## Warning: Removed 27 rows containing missing values (geom_point).
# add identity scale minor ticks on x,y axis
p + annotation_ticks(sides = 'lb')
## Warning: Removed 27 rows containing missing values (geom_point).
# log10 scale
p1 <- p + scale_x_log10()
# add minor ticks on both scales
p1 + annotation_ticks(sides = 'lb', scale = c('identity','log10'))
## Warning: Removed 27 rows containing missing values (geom_point).
mtcars$name <- rownames(mtcars)
p <- ggplot(data = mtcars, aes(x=mpg,y=disp)) + geom_point()
p +
geom_spikelines(data = mtcars[mtcars$carb==4,],aes(colour = factor(gear)), linetype = 2) +
ggrepel::geom_label_repel(data = mtcars[mtcars$carb==4,],aes(label = name))
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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