Base Plotting

Parameters#

plot(x = cars$speed, y = cars$dist, pch = 1, col = 1, 
     main = "Distance vs Speed of Cars", 
     xlab = "Speed", ylab = "Distance")

We can use many other variations in the code to get the same result. We can also change the parameters to obtain different results.

with(cars, plot(dist~speed, pch = 2, col = 3, 
     main = "Distance to stop vs Speed of Cars", 
     xlab = "Speed", ylab = "Distance"))

Additional features can be added to this plot by calling points(), text(), mtext(), lines(), grid(), etc.

plot(dist~speed, pch = "*", col = "magenta", data=cars,
     main = "Distance to stop vs Speed of Cars", 
     xlab = "Speed", ylab = "Distance")
mtext("In the 1920s.")
grid(,col="lightblue")

Matplot

matplot is useful for quickly plotting multiple sets of observations from the same object, particularly from a matrix, on the same graph.

Here is an example of a matrix containing four sets of random draws, each with a different mean.

xmat <- cbind(rnorm(100, -3), rnorm(100, -1), rnorm(100, 1), rnorm(100, 3))
head(xmat)
#          [,1]        [,2]       [,3]     [,4]
# [1,] -3.072793 -2.53111494  0.6168063 3.780465
# [2,] -3.702545 -1.42789347 -0.2197196 2.478416
# [3,] -2.890698 -1.88476126  1.9586467 5.268474
# [4,] -3.431133 -2.02626870  1.1153643 3.170689
# [5,] -4.532925  0.02164187  0.9783948 3.162121
# [6,] -2.169391 -1.42699116  0.3214854 4.480305

One way to plot all of these observations on the same graph is to do one plot call followed by three more points or lines calls.

plot(xmat[,1], type = 'l')
lines(xmat[,2], col = 'red')
lines(xmat[,3], col = 'green')
lines(xmat[,4], col = 'blue')

However, this is both tedious, and causes problems because, among other things, by default the axis limits are fixed by plot to fit only the first column.

Much more convenient in this situation is to use the matplot function, which only requires one call and automatically takes care of axis limits and changing the aesthetics for each column to make them distinguishable.

matplot(xmat, type = 'l')

Note that, by default, matplot varies both color (col) and linetype (lty) because this increases the number of possible combinations before they get repeated. However, any (or both) of these aesthetics can be fixed to a single value…

matplot(xmat, type = 'l', col = 'black')

…or a custom vector (which will recycle to the number of columns, following standard R vector recycling rules).

matplot(xmat, type = 'l', col = c('red', 'green', 'blue', 'orange'))

Standard graphical parameters, including main, xlab, xmin, work exactly the same way as for plot. For more on those, see ?par.

Like plot, if given only one object, matplot assumes it’s the y variable and uses the indices for x. However, x and y can be specified explicitly.

matplot(x = seq(0, 10, length.out = 100), y = xmat, type='l')

In fact, both x and y can be matrices.

xes <- cbind(seq(0, 10, length.out = 100),
             seq(2.5, 12.5, length.out = 100),
             seq(5, 15, length.out = 100),
             seq(7.5, 17.5, length.out = 100))
matplot(x = xes, y = xmat, type = 'l')

Histograms

Histograms allow for a pseudo-plot of the underlying distribution of the data.

hist(ldeaths)

hist(ldeaths, breaks = 20, freq = F, col = 3)

Combining Plots

It’s often useful to combine multiple plot types in one graph (for example a Barplot next to a Scatterplot.) R makes this easy with the help of the functions par() and layout().

par()

par uses the arguments mfrow or mfcol to create a matrix of nrows and ncols c(nrows, ncols) which will serve as a grid for your plots. The following example shows how to combine four plots in one graph:

par(mfrow=c(2,2))
plot(cars, main="Speed vs. Distance")
hist(cars$speed, main="Histogram of Speed")
boxplot(cars$dist, main="Boxplot of Distance")
boxplot(cars$speed, main="Boxplot of Speed")

layout()

The layout() is more flexible and allows you to specify the location and the extent of each plot within the final combined graph. This function expects a matrix object as an input:

layout(matrix(c(1,1,2,3), 2,2, byrow=T))
hist(cars$speed, main="Histogram of Speed")
boxplot(cars$dist, main="Boxplot of Distance")
boxplot(cars$speed, main="Boxplot of Speed")

Density plot

A very useful and logical follow-up to histograms would be to plot the smoothed density function of a random variable. A basic plot produced by the command

plot(density(rnorm(100)),main="Normal density",xlab="x")

would look like

You can overlay a histogram and a density curve with

x=rnorm(100)
hist(x,prob=TRUE,main="Normal density + histogram")
lines(density(x),lty="dotted",col="red")

which gives

Empirical Cumulative Distribution Function

A very useful and logical follow-up to histograms and density plots would be the Empirical Cumulative Distribution Function. We can use the function ecdf() for this purpose. A basic plot produced by the command

plot(ecdf(rnorm(100)),main="Cumulative distribution",xlab="x")

would look like

Getting Started with R_Plots

• Scatterplot

You have two vectors and you want to plot them.

x_values <- rnorm(n = 20 , mean = 5 , sd = 8) #20 values generated from Normal(5,8)
y_values <- rbeta(n = 20 , shape1 = 500 , shape2 = 10) #20 values generated from Beta(500,10)

If you want to make a plot which has the y_values in vertical axis and the x_valuesin horizontal axis, you can use the following commands:

plot(x = x_values, y = y_values, type = "p") #standard scatter-plot
plot(x = x_values, y = y_values, type = "l") # plot with lines
plot(x = x_values, y = y_values, type = "n") # empty plot

You can type ?plot() in the console to read about more options.

• Boxplot

You have some variables and you want to examine their Distributions

#boxplot is an easy way to see if we have some outliers in the data.
   
z<- rbeta(20 , 500 , 10) #generating values from beta distribution
z[c(19 , 20)] <- c(0.97 , 1.05) # replace the two last values with outliers      
boxplot(z) # the two points are the outliers of variable z.

• Histograms

Easy way to draw histograms

hist(x = x_values) # Histogram for x vector
hist(x = x_values, breaks = 3) #use breaks to set the numbers of bars you want

• Pie_charts

If you want to visualize the frequencies of a variable just draw pie

First we have to generate data with frequencies, for example :

P <- c(rep('A' , 3) , rep('B' , 10) , rep('C' , 7) )
t <- table(P) # this is a frequency matrix of variable P
pie(t) # And this is a visual version of the matrix above