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
xlim and ylim: limits of the X and Y axis
pch: shape of markers. See all here.
xlab and ylab: X and Y axis labels
col: marker color
main: 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

# 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

# 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
)

Related chart types

Scatter

Heatmap

Correlogram

Bubble

Connected scatter

Density 2d