library(tidyverse)
I’m a huge fan of R and ggplot for data analysis and visualitation. However, forgetful as I am I frequently find it difficult to remember the exact synatx for a specific visualization. In order to adress this issue this is the place were I collect all the sample code snipptes I tend to be using frequently or that I am considering especially cool and do not want to forget about. Please feel free to also use this ressource if it is of any help to you.
In the examples I’m mainly using the mtcars-dataset that comes with R. Here’s a quick glance on the data:
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 ## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 ## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 ## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 ## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 ## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 ## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 ## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 ## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 ## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 ## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 ## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 ## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 ## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 ## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 ## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 ## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 ## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 ## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 ## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 ## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 ## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 ## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 ## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 ## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 ## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 ## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Basic Graphs
Bar charts
Bar charts showing raw values
mtcars %>% mutate(name = row.names(.)) %>%
ggplot(aes(x=name, y=mpg)) +
geom_col() +
theme(axis.text.x = element_text(angle=45, hjust=1))
Bar chart showing means, errorbars, individual cases & number of cases
mtcars %>%
ggplot(aes(x=cyl, y=mpg)) +
geom_bar(stat="summary", fun.y=mean, fill="orange") +
geom_jitter(height=0, width=.2, color="grey80")+
geom_errorbar(stat="summary", fun.data=mean_cl_normal, width=.2) +
geom_text(aes(label=paste("n",..count..,sep="=")), y=-0.4, stat="count", colour="grey", size=3)
Grouped bar chart with number of cases
mtcars %>% mutate(am1 = as.factor(am)) %>%
ggplot(aes(x=cyl, y=mpg, group=am1, fill=am1)) +
geom_bar(stat="summary", fun.y=mean, position="dodge") +
geom_errorbar(stat="summary", fun.data=mean_cl_normal, width=.2, position=position_dodge(width=1.8)) +
geom_text(aes(label=paste("n=",..count..,sep=""),y=..count..),stat="count", # y must be defined multiple times for it to work
y=0, vjust=1, size=3, color="grey60", position=position_dodge(1.8))
Boxplots
Simple Boxplot
mtcars %>%
ggplot(aes(x=factor(cyl), y=mpg)) +
geom_boxplot()
Boxplot with mean, individual cases and number of cases
mtcars %>%
ggplot(aes(x=factor(cyl), y=mpg)) +
geom_point(color="grey70", position=position_jitter(width=.2, height=0)) +
geom_boxplot(fill="transparent") +
geom_point(stat="summary", fun.y=mean, shape=8, color="red") +
geom_text(aes(label=paste("n =",..count..)), y=0, stat="count", color="grey70") +
expand_limits(y=0)
Violonplot instead of Boxplot
mtcars %>%
ggplot(aes(x=factor(cyl), y=mpg)) +
geom_point(color="grey70", position=position_jitter(width=.2, height=0)) +
geom_violin(fill="transparent") +
geom_point(stat="summary", fun.y=mean, shape=8, color="red") +
geom_text(aes(label=paste("n =",..count..)), y=0, stat="count", color="grey70") +
expand_limits(y=0)
Scatterplots
Scatterplot with linear model
mtcars %>%
ggplot(aes(mpg, disp)) +
geom_smooth(method=lm, se=T, fill="grey90", fullrange=T) +
geom_point(aes(color=factor(gear)))
Scatterplot with non-overlapping text labels
library(ggrepel)
mtcars %>%
mutate(ID = row.names(.)) %>%
ggplot(aes(mpg, disp)) +
geom_point() +
geom_text_repel(aes(label=ID))
Scatterplot with selected cases labeled
library(ggrepel)
mtcars %>%
mutate(ID = row.names(.)) %>%
mutate(IDselect = ifelse(ID %in% c("Chrysler Imperial","Pontiac Firebird"), ID, NA)) %>%
ggplot(aes(mpg, disp)) +
geom_point() +
geom_text_repel(aes(label=IDselect))
## Warning: Removed 30 rows containing missing values (geom_text_repel).
Scatterplot With Trendline, Annotations and Encircling
library(ggplot2)
library(ggalt)
midwest_select <- midwest[midwest$poptotal > 350000 &
midwest$poptotal <= 500000 & midwest$area > 0.01 &
midwest$area < 0.1, ]
# Plot
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) + # draw points
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) + # draw smoothing line
geom_encircle(aes(x=area, y=poptotal),
data=midwest_select,
color="red",
size=2,
expand=0.08) + # encircle
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot + Encircle",
caption="Source: midwest")
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
Secondary Axis
Cleveland Dot Plot
mtcars %>% mutate(name = row.names(.)) %>% select(name, mpg) %T>% print() %>%
ggplot(aes(x=reorder(name, mpg), y=mpg)) +
geom_point() +
coord_flip() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(size = .5, linetype=2))
## name mpg ## 1 Mazda RX4 21.0 ## 2 Mazda RX4 Wag 21.0 ## 3 Datsun 710 22.8 ## 4 Hornet 4 Drive 21.4 ## 5 Hornet Sportabout 18.7 ## 6 Valiant 18.1 ## 7 Duster 360 14.3 ## 8 Merc 240D 24.4 ## 9 Merc 230 22.8 ## 10 Merc 280 19.2 ## 11 Merc 280C 17.8 ## 12 Merc 450SE 16.4 ## 13 Merc 450SL 17.3 ## 14 Merc 450SLC 15.2 ## 15 Cadillac Fleetwood 10.4 ## 16 Lincoln Continental 10.4 ## 17 Chrysler Imperial 14.7 ## 18 Fiat 128 32.4 ## 19 Honda Civic 30.4 ## 20 Toyota Corolla 33.9 ## 21 Toyota Corona 21.5 ## 22 Dodge Challenger 15.5 ## 23 AMC Javelin 15.2 ## 24 Camaro Z28 13.3 ## 25 Pontiac Firebird 19.2 ## 26 Fiat X1-9 27.3 ## 27 Porsche 914-2 26.0 ## 28 Lotus Europa 30.4 ## 29 Ford Pantera L 15.8 ## 30 Ferrari Dino 19.7 ## 31 Maserati Bora 15.0 ## 32 Volvo 142E 21.4
Dumbbell Plot
library(ggplot2)
library(ggalt)
theme_set(theme_classic())
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area)) # for right ordering of the dumbells
# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) +
geom_dumbbell(color="#a3c4dc",
size=0.75,
point.colour.l="#0e668b") +
labs(x=NULL,
y=NULL,
title="Dumbbell Chart",
subtitle="Pct Change: 2013 vs 2014",
caption="Source: https://github.com/hrbrmstr/ggalt") +
theme(plot.title = element_text(hjust=0.5, face="bold"),
plot.background=element_rect(fill="#f7f7f7"),
panel.background=element_rect(fill="#f7f7f7"),
panel.grid.minor=element_blank(),
panel.grid.major.y=element_blank(),
panel.grid.major.x=element_line(),
axis.ticks=element_blank(),
legend.position="top",
panel.border=element_blank())
## Warning: Ignoring unknown parameters: point.colour.l
plot(gg)
Likert
# library
library(likert)
# Use a provided dataset
data(pisaitems)
items28 <- pisaitems[, substr(names(pisaitems), 1, 5) == "ST24Q"]
# Realize the plot
l28 <- likert(items28)
summary(l28)
## Item low neutral high mean sd ## 10 ST24Q10 41.07516 0 58.92484 2.604913 0.9009968 ## 5 ST24Q05 46.93475 0 53.06525 2.466751 0.9446590 ## 8 ST24Q08 50.39874 0 49.60126 2.484616 0.9089688 ## 7 ST24Q07 51.21231 0 48.78769 2.428508 0.9164136 ## 3 ST24Q03 54.99129 0 45.00871 2.328049 0.9090326 ## 11 ST24Q11 55.54115 0 44.45885 2.343193 0.9609234 ## 2 ST24Q02 56.64470 0 43.35530 2.344530 0.9277495 ## 1 ST24Q01 58.72868 0 41.27132 2.291811 0.9369023 ## 4 ST24Q04 65.35125 0 34.64875 2.178299 0.8991628 ## 9 ST24Q09 76.24524 0 23.75476 1.974736 0.8793028 ## 6 ST24Q06 82.88729 0 17.11271 1.810093 0.8611554
plot(l28)
Maps
Heatmap
Theme-Elemente
mtcars %>%
ggplot(aes(mpg, disp, color=factor(am))) +
geom_point() +
scale_x_continuous(breaks=c(20,32), labels = c("axis.text.x=element_text(color= , size= , angle= , hjust= , vjust= )", "axis.ticks")) +
labs(x="axis.title.x=element_text(color=...)",
y=NULL,
title="plot.title",
subtitle="plot.subtitle plot.background=element_rect()",
caption="plot.caption",
color="legend.title") +
scale_color_discrete(labels=c("legend.text", "legend.text")) +
annotate("text", x= 30, y= 100, label="") +
annotate("text", x= 30, y= 500, label="panel.border=element_rect(...)") +
annotate("text", x= 30, y= 470, label="panel.background=element_rect(...)") +
annotate("text", x= 30, y= 300, label="panel.grid.major.x=element_line(...)\npanel.grid.minor.y=element_blank(...)") +
theme(plot.title = element_text(hjust=0.5, face="bold", color="red"),
plot.subtitle = element_text(hjust=1, face="italic", color="red"),
plot.caption = element_text(hjust=1, face="bold", color="green"),
plot.background=element_rect(fill="#f7f7f7"),
panel.background=element_rect(fill="#f7f7f7"),
panel.grid.minor=element_line(color="grey80"),
panel.grid.major.y=element_blank(),
panel.grid.major.x=element_line(color="grey80"),
axis.title.x=element_text(color="red"),
axis.ticks=element_line(color="blue"),
legend.position="right",
legend.title = element_text(color="green"),
legend.text= element_text(color="blue"))
Johann Schrammel 