Most basic scatterplot
The plot() function of R allows to build a
scatterplot. Both numeric variables
of the input dataframe must be specified in the x and
y argument.
# Create data
data = data.frame(
x=seq(1:100) + 0.1*seq(1:100)*sample(c(1:10) , 100 , replace=T),
y=seq(1:100) + 0.2*seq(1:100)*sample(c(1:10) , 100 , replace=T)
)
# Basic scatterplot
plot(x=data$x, y=data$y)Customizations
Here is a description of the most common customization:
cex: circle size-
xlimandylim: limits of the X and Y axis -
pch: shape of markers. See all here. -
xlabandylab: X and Y axis labels col: marker colormain: chart title
# Create data
data = data.frame(
x=seq(1:100) + 0.1*seq(1:100)*sample(c(1:10) , 100 , replace=T),
y=seq(1:100) + 0.2*seq(1:100)*sample(c(1:10) , 100 , replace=T)
)
# Basic scatterplot
plot(data$x, data$y,
xlim=c(0,250) , ylim=c(0,250),
pch=18,
cex=2,
col="#69b3a2",
xlab="value of X", ylab="value of Y",
main="A simple scatterplot"
)Map the marker color to a categorical variable
ss
# the iris dataset is provided by R natively
# Create a color palette
library(paletteer)
colors <- paletteer_c(package = "ggthemes", palette = "Green-Blue-White", n = 3)
# Scatterplot with categoric color scale
plot(
x = iris$Petal.Length,
y = iris$Petal.Width,
bg = colors[ unclass(iris$Species) ],
cex = 3,
pch=21
)Map the marker color to a numeric variable
ss
# the iris dataset is provided by R natively
# Create a color palette
library(paletteer)
nColor <- 20
colors <- paletteer_c(package = "viridis", palette = "inferno", n = nColor)
# Transform the numeric variable in bins
rank <- as.factor( as.numeric( cut(iris$Petal.Width, nColor)))
# Scatterplot with color gradient
plot(
x = iris$Petal.Length,
y = iris$Petal.Width,
bg = colors[ rank ],
cex = 3,
pch=21
)
