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 : Like coord_map, only better (prbly shld use this with geom_cartogram as geom_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 (uses KernSmooth::bkde)
geom_stateface: Use ProPublica’s StateFace font in ggplot2 plots
geom_bkde2d : Contours from a 2d density estimate. (uses KernSmooth::bkde2D)
stat_bkde : Display a smooth density estimate (uses KernSmooth::bkde)
stat_bkde2d : Contours from a 2d density estimate. (uses KernSmooth::bkde2D)
stat_ash : Compute and display a univariate averaged shifted histogram (polynomial kernel) (uses ash::ash1/ash::bin1)
geom_encircle: Automatically enclose points in a polygon
byte_format: + helpers. e.g. turn 10000 into 10 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

Installation

# you'll want to see the vignettes, trust me
install.packages("ggplot2")
install.packages("ggalt")
# OR: devtools::install_github("hrbrmstr/ggalt")

Usage

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)))
)

Horzon Chart

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())

Splines!

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'

Alternate (better) density plots

# 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.

Alternate 2D density plots

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")

`coord_proj` LIVES! (still needs a teensy bit of work)

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

ProPublica StateFace

# 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

Encircling points automagically

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")

Step ribbons

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

Lollipop charts

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))

Code of Conduct

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README.md

README.md

Installation

Usage

Horzon Chart

Splines!

Alternate (better) density plots

Alternate 2D density plots

`coord_proj` LIVES! (still needs a teensy bit of work)

ProPublica StateFace

Encircling points automagically

Step ribbons

Lollipop charts

Code of Conduct

Files

README.md

Latest commit

History

README.md

File metadata and controls

Installation

Usage

Horzon Chart

Splines!

Alternate (better) density plots

Alternate 2D density plots

coord_proj LIVES! (still needs a teensy bit of work)

ProPublica StateFace

Encircling points automagically

Step ribbons

Lollipop charts

Code of Conduct

`coord_proj` LIVES! (still needs a teensy bit of work)