I’m a huge fan of R and ggplot for data analysis and visualitation. However, I frequently find it difficult to remember the exact synatx for a specific visualization. So this is the place to help me out and were I collect code snipptes I like to use but keep forgetting the syntax details.
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. At the end of the page you can have a quick glance on the raw data.
Bar charts showing raw data with axis caption rotated
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 and errorbars
mtcars %>% mutate(am = as.factor(am)) %>%
ggplot(aes(x=cyl, y=mpg, group=am, fill=am)) +
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=0,
# y must be defined twice as ..count.. and as value for this to work
vjust=1, size=3, color="grey60", position=position_dodge(1.8))
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="grey") +
expand_limits(y=0)
Faceted Violonplot including y=0
mtcars %>%
ggplot(aes(x=factor(vs), 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="grey") +
expand_limits(y=0) +
facet_grid(.~am)
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
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(aes(label=IDselect), hjust = 0, nudge_x = 0.3)
Scatterplot With trendline, annotations and encircling
library(ggalt)
mtcars_select 320 & mtcars$mpg > 18, ]
ggplot(mtcars, aes(x=mpg, y=disp)) +
geom_point(aes(color=factor(cyl))) +
geom_smooth(method="loess", se=F) +
geom_encircle(data=mtcars_select,
aes(x=mpg, y=disp),
color="red",
size=2,
s_shape=0.5,
spread=0.002,
expand=0.04) +
labs(subtitle=" Fuel economy versus engine size",
y="Displacement",
x="Miles per Gallon",
color="Cylinder",
title="Scatterplot + Trendline + Encircling",
caption="Data: mtcars") +
annotate("text", x=21.5, y=400, label="Encircling", color="red")
Secondary y-axis
mtcars %>%
ggplot(aes(cyl, mpg)) +
geom_point() +
scale_y_continuous("Miles/Gallon",
sec.axis = sec_axis(~./2.352, name = "Kilometer/Liter"))
Cleveland Dot Plot
mtcars %>% mutate(name = row.names(.)) %>%
ggplot(aes(x=reorder(name, mpg), y=mpg)) +
geom_point() +
coord_flip() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(size = .5, linetype=2))
Dumbbell Plot
library(ggplot2)
library(ggalt)
theme_set(theme_classic())
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$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
library(leaflet)
leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=16.426711, lat=48.2685289, popup="AIT Austrian Institute of Technology GmbH")
Heatmap
# normalize data
data % modify(~as.vector(scale(.x)))
data$Model <- row.names(data)
# transform data into long format
data % gather(mpg:carb, key="Variable", value="Value")
data %>%
ggplot(aes(x=Variable, y=Model, fill=Value)) +
geom_tile()
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()\naxis.line.x=element_line()", "axis.ticks=\nelement_line()")) +
labs(x="axis.title.x=element_text(color= , size= , angle= , hjust= , vjust= )",
y=NULL,
title="plot.title=element_text()\n",
subtitle="plot.subtitle=element_text()\n plot.background=element_rect()",
caption="plot.caption=element_text()\n",
color="legend.title=\nelement_text()") +
scale_color_discrete(labels=c("legend.text=\nelement_text()", "legend.text=\nelement_text()")) +
annotate("text", x= 30, y= 100, label="") +
annotate("text", x= 30, y= 500, label="panel.border=element_rect()") +
annotate("text", x= 29, y= 470, label="panel.background=element_rect()") +
annotate("text", x= 28, 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"))
The data
The mtcars dataset that comes build into base-R.
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
Johann Schrammel 