r-statistics.co by Selva Prabhakaran

ggplot2 Quickref

Basics tasks

Legend

Plot text and annotation

Multiple plots

Geom layers

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Basic tasks

Basic plot setup

gg <- ggplot(df, aes(x=xcol, y=ycol))

df must be a dataframe that contains all information to make the ggplot. Plot will show up only after adding the geom layers.

Scatterplot

library(ggplot2)
gg <- ggplot(diamonds, aes(x=carat, y=price)) 
gg + geom_point()

Static - point size, shape, color and boundary thickness

gg + geom_point(size=1, shape=1, color="steelblue", stroke=2)  # 'stroke' controls the thickness of point boundary

Dynamic - point size, shape, color and boundary thickness

Make the aesthetics vary based on a variable in df.

gg + geom_point(aes(size=carat, shape=cut, color=color, stroke=carat))  # carat, cut and color are variables in `diamonds`

Add Title, X and Y axis labels

gg1 <- gg + geom_point(aes(color=color))
gg2 <- gg1 + labs(title="Diamonds", x="Carat", y="Price")  # ggtitle("title") also changes the title.
print(gg2)

Change color of all text

gg2 + theme(text=element_text(color="blue"))  # all text turns blue.

Change title, X and Y axis label and text size

plot.title: Controls plot title. axis.title.x: Controls X axis title axis.title.y: Controls Y axis title axis.text.x: Controls X axis text axis.text.y: Controls y axis text

gg3 <- gg2 + theme(plot.title=element_text(size=25), axis.title.x=element_text(size=20), axis.title.y=element_text(size=20), axis.text.x=element_text(size=15), axis.text.y=element_text(size=15))
print(gg3)

Change title face, color, line height

gg3 + labs(title="Plot Title\nSecond Line of Plot Title") + theme(plot.title=element_text(face="bold", color="steelblue", lineheight=1.2))

Change point color

gg3 + scale_colour_manual(name='Legend', values=c('D'='grey', 'E'='red', 'F'='blue', 'G'='yellow', 'H'='black', 'I'='green', 'J'='firebrick'))

Adjust X and Y axis limits

Method 1: Zoom in

gg3 + coord_cartesian(xlim=c(0,3), ylim=c(0, 5000)) + geom_smooth()  # zoom in

Method 2: Deletes the points outside limits

gg3 + xlim(c(0,3)) + ylim(c(0, 5000)) + geom_smooth()  # deletes the points 
#> Warning messages:
#> 1: Removed 14714 rows containing non-finite values (stat_smooth). 
#> 2: Removed 14714 rows containing missing values (geom_point).

Method 3: Deletes the points outside limits

gg3 + scale_x_continuous(limits=c(0,3)) + scale_y_continuous(limits=c(0, 5000)) + geom_smooth()  # deletes the points outside limits
#> Warning message:
#> Removed 14714 rows containing missing values (geom_point).

Notice the change in smoothing line because of deleted points. This could sometimes be misleading in your analysis.

Change X and Y axis labels

gg3 + scale_x_continuous(labels=c("zero", "one", "two", "three", "four", "five")) + scale_y_continuous(breaks=seq(0, 20000, 4000))  # if Y is continuous, if X is a factor

Use scale_x_discrete instead, if X variable is a factor.

Rotate axis text

gg3 + theme(axis.text.x=element_text(angle=45), axis.text.y=element_text(angle=45))

Flip X and Y Axis

gg3 + coord_flip()  # flips X and Y axis.

Grid lines and panel background

gg3 + theme(panel.background = element_rect(fill = 'springgreen'),
  panel.grid.major = element_line(colour = "firebrick", size=3),
  panel.grid.minor = element_line(colour = "blue", size=1))

Plot margin and background

gg3 + theme(plot.background=element_rect(fill="yellowgreen"), plot.margin = unit(c(2, 4, 1, 3), "cm")) # top, right, bottom, left

Colors

The whole list of colors are displayed at your R console in the color() function. Here are few of my suggestions for nice looking colors and backgrounds:

  • steelblue (points and lines)
  • firebrick (point and lines)
  • springgreen (fills)
  • violetred (fills)
  • tomato (fills)
  • skyblue (bg)
  • sienna (points, lines)
  • slateblue (fills)
  • seagreen (points, lines, fills)
  • sandybrown (fills)
  • salmon (fills)
  • saddlebrown (lines)
  • royalblue (fills)
  • orangered (point, lines, fills)
  • olivedrab (points, lines, fills)
  • midnightblue (lines)
  • mediumvioletred (points, lines, fills)
  • maroon (points, lines, fills)
  • limegreen (fills)
  • lawngreen (fills)
  • forestgreen (lines, fills)
  • dodgerblue (fills, bg)
  • dimgray (grids, secondary bg)
  • deeppink (fills)
  • darkred (lines, points)

If you are looking for consistent colors, the RColorBrewer package has predefined color palettes

Legend

Hide legend

gg3 + theme(legend.position="none")  # hides the legend

Change legend title

gg3 + scale_color_discrete(name="")  # Remove legend title (method1)
p1 <- gg3 + theme(legend.title=element_blank())  # Remove legend title (method)
p2 <- gg3 + scale_color_discrete(name="Diamonds")  # Change legend title
library(gridExtra)
grid.arrange(p1, p2, ncol=2)  # arrange

Change legend and point color

gg3 + scale_colour_manual(name='Legend', values=c('D'='grey', 'E'='red', 'F'='blue', 'G'='yellow', 'H'='black', 'I'='green', 'J'='firebrick'))

Change legend position

Outside plot

p1 <- gg3 + theme(legend.position="top")  # top / bottom / left / right

Inside plot

p2 <- gg3 + theme(legend.justification=c(1,0), legend.position=c(1,0))  # legend justification is the anchor point on the legend, considering the bottom left of legend as (0,0)
gridExtra::grid.arrange(p1, p2, ncol=2)

Change order of legend items

df$newLegendColumn <- factor(df$legendcolumn, levels=c(new_order_of_legend_items), ordered = TRUE)

Create a new factor variable used in the legend, ordered as you need. Then use this variable instead in the plot.

Legend title, text, box, symbol

  • legend.title - Change legend title
  • legend.text - Change legend text
  • legend.key - Change legend box
  • guides - Change legend symbols
gg3 + theme(legend.title = element_text(size=20, color = "firebrick"), legend.text = element_text(size=15), legend.key=element_rect(fill='steelblue')) + guides(colour = guide_legend(override.aes = list(size=2, shape=4, stroke=2)))  # legend title color and size, box color, symbol color, size and shape.

Plot text and annotation

Add text in chart

#> Not Run: gg + geom_text(aes(xcol, ycol, label=round(labelCol), size=3))  # general format
gg + geom_text(aes(label=color, color=color), size=4)

Annotation

#> gg3 + annotate("mytext", x=xpos, y=ypos, label="My text")  # Not run: General Format
library(grid)
my_grob = grobTree(textGrob("My Custom Text", x=0.8, y=0.2, gp=gpar(col="firebrick", fontsize=25, fontface="bold")))
gg3 + annotation_custom(my_grob)

Multiple plots

Multiple chart panels

p1 <- gg1 + facet_grid(color ~ cut)  # arrange in a grid. More space for plots.

Free X and Y axis scales

By setting scales='free', the scales of both X and Y axis is freed. Use scales='free_x' to free only X-axis and scales='free_y' to free only Y-axis.

p2 <- gg1 + facet_wrap(color ~ cut, scales="free")  # free the x and y axis scales.

Arrange multiple plots

library(gridExtra)
grid.arrange(p1, p2, ncol=2)

Geom layers

Add smoothing line

gg3 + geom_smooth(aes(color=color))  # method could be - 'lm', 'loess', 'gam'

Add horizontal / vertical line

p1 <- gg3 + geom_hline(yintercept=5000, size=2, linetype="dotted", color="blue") # linetypes: solid, dashed, dotted, dotdash, longdash and twodash
p2 <- gg3 + geom_vline(xintercept=4, size=2, color="firebrick")
p3 <- gg3 + geom_segment(aes(x=4, y=5000, xend=4, yend=10000, size=2, lineend="round"))
p4 <- gg3 + geom_segment(aes(x=carat, y=price, xend=carat, yend=price-500, color=color), size=2) + coord_cartesian(xlim=c(3, 5))  # x, y: start points. xend, yend: end points
gridExtra::grid.arrange(p1,p2,p3,p4, ncol=2)

Add bar chart

# Frequency bar chart: Specify only X axis.
gg <- ggplot(mtcars, aes(x=cyl))
gg + geom_bar()  # frequency table

gg <- ggplot(mtcars, aes(x=cyl))
p1 <- gg + geom_bar(position="dodge", aes(fill=factor(vs)))  # side-by-side
p2 <- gg + geom_bar(aes(fill=factor(vs)))  # stacked
gridExtra::grid.arrange(p1, p2, ncol=2)

# Absolute bar chart: Specify both X adn Y axis. Set stat="identity"
df <- aggregate(mtcars$mpg, by=list(mtcars$cyl), FUN=mean)  # mean of mpg for every 'cyl'
names(df) <- c("cyl", "mpg")
head(df)
#>   cyl    mpg
#> 1   4  26.66
#> 2   6  19.74
#> 3   8  15.10

gg_bar <- ggplot(df, aes(x=cyl, y=mpg)) + geom_bar(stat = "identity")  # Y axis is explicit. 'stat=identity'
print(gg_bar)

Distinct color for bars

gg_bar <- ggplot(df, aes(x=cyl, y=mpg)) + geom_bar(stat = "identity", aes(fill=cyl))
print(gg_bar)

Change color and width of bars

df$cyl <- as.factor(df$cyl)
gg_bar <- ggplot(df, aes(x=cyl, y=mpg)) + geom_bar(stat = "identity", aes(fill=cyl), width = 0.25)
gg_bar + scale_fill_manual(values=c("4"="steelblue", "6"="firebrick", "8"="darkgreen"))

Change color palette

library(RColorBrewer)
display.brewer.all(n=20, exact.n=FALSE)  # display available color palettes
ggplot(mtcars, aes(x=cyl, y=carb, fill=factor(cyl))) + geom_bar(stat="identity") + scale_fill_brewer(palette="Reds")  # "Reds" is palette name

Line chart

# Method 1:
gg <- ggplot(economics, aes(x=date))  # setup
gg + geom_line(aes(y=psavert), size=2, color="firebrick") + geom_line(aes(y=uempmed), size=1, color="steelblue", linetype="twodash")  # No legend
# available linetypes: solid, dashed, dotted, dotdash, longdash and twodash

# Method 2:
library(reshape2)
df_melt <- melt(economics[, c("date", "psavert", "uempmed")], id="date")  # melt by date. 
gg <- ggplot(df_melt, aes(x=date))  # setup
gg + geom_line(aes(y=value, color=variable), size=1) + scale_color_discrete(name="Legend")  # gets legend.

Line chart from timeseries

# One step method.
library(ggfortify)
autoplot(AirPassengers, size=2) + labs(title="AirPassengers")

Ribbons

Filled time series can be plotted using geom_ribbon(). It takes two compulsory arguments ymin and ymax.

# Prepare the dataframe
st_year <- start(AirPassengers)[1]
st_month <- "01"
st_date <- as.Date(paste(st_year, st_month, "01", sep="-"))
dates <- seq.Date(st_date, length=length(AirPassengers), by="month")
df <- data.frame(dates, AirPassengers, AirPassengers/2)
head(df)
#>        dates AirPassengers AirPassengers.2
#> 1 1949-01-01           112            56.0
#> 2 1949-02-01           118            59.0
#> 3 1949-03-01           132            66.0
#> 4 1949-04-01           129            64.5
#> 5 1949-05-01           121            60.5
#> 6 1949-06-01           135            67.5
# Plot ribbon with ymin=0
gg <- ggplot(df, aes(x=dates)) + labs(title="AirPassengers") + theme(plot.title=element_text(size=30), axis.title.x=element_text(size=20), axis.text.x=element_text(size=15))
gg + geom_ribbon(aes(ymin=0, ymax=AirPassengers)) + geom_ribbon(aes(ymin=0, ymax=AirPassengers.2), fill="green")

gg + geom_ribbon(aes(ymin=AirPassengers-20, ymax=AirPassengers+20)) + geom_ribbon(aes(ymin=AirPassengers.2-20, ymax=AirPassengers.2+20), fill="green")

Area

geom_area is similar to geom_ribbon, except that the ymin is set to 0. If you want to make overlapping area plot, use the alpha aesthetic to make the top layer translucent.

# Method1: Non-Overlapping Area
df <- reshape2::melt(economics[, c("date", "psavert", "uempmed")], id="date")
head(df, 3)
#>         date variable value
#> 1 1967-07-01  psavert  12.5
#> 2 1967-08-01  psavert  12.5
#> 3 1967-09-01  psavert  11.7
p1 <- ggplot(df, aes(x=date)) + geom_area(aes(y=value, fill=variable)) + labs(title="Non-Overlapping - psavert and uempmed")

# Method2: Overlapping Area
p2 <- ggplot(economics, aes(x=date)) + geom_area(aes(y=psavert), fill="yellowgreen", color="yellowgreen") + geom_area(aes(y=uempmed), fill="dodgerblue", alpha=0.7, linetype="dotted") + labs(title="Overlapping - psavert and uempmed")
gridExtra::grid.arrange(p1, p2, ncol=2)

Boxplot and Violin

The oulier points are controlled by the following aesthetics: * outlier.shape * outlier.stroke * outlier.size * outlier.colour

If the notch is turned on (by setting it TRUE), the below boxplot is produced. Else, you would get the standard rectangular boxplots.

p1 <- ggplot(mtcars, aes(factor(cyl), mpg)) + geom_boxplot(aes(fill = factor(cyl)), width=0.5, outlier.colour = "dodgerblue", outlier.size = 4, outlier.shape = 16, outlier.stroke = 2, notch=T) + labs(title="Box plot")  # boxplot
p2 <- ggplot(mtcars, aes(factor(cyl), mpg)) + geom_violin(aes(fill = factor(cyl)), width=0.5, trim=F) + labs(title="Violin plot (untrimmed)")  # violin plot
gridExtra::grid.arrange(p1, p2, ncol=2)

Density

ggplot(mtcars, aes(mpg)) + geom_density(aes(fill = factor(cyl)), size=2) + labs(title="Density plot")  # Density plot

Tiles

corr <- round(cor(mtcars), 2)
df <- reshape2::melt(corr)
gg <- ggplot(df, aes(x=Var1, y=Var2, fill=value, label=value)) + geom_tile() + theme_bw() + geom_text(aes(label=value, size=value), color="white") + labs(title="mtcars - Correlation plot") + theme(text=element_text(size=20), legend.position="none")

library(RColorBrewer)
p2 <- gg + scale_fill_distiller(palette="Reds")
p3 <- gg + scale_fill_gradient2()
gridExtra::grid.arrange(gg, p2, p3, ncol=3)

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