Histograms are used to visualize count data. The chart consists of bars with a y axis representing the count and the x axis representing the data. The following code demonstrates the use of the hist function in R:

# Data Visualisation 
 
library(car) 
library(RColorBrewer) 
 
data("Salaries") 
 
# Histogram of Salaries of Professors from the Salaries dataset 
hist(Salaries$salary/1000, main="Histogram of Salaries", xlab="Salary (in '000s)", ylab="Count") 

The output of the preceding code is as follows:

We will be running the same code with a minor change of col=brewer.pal(8,"Reds"). This will provide you with the same output as the previous one with the color red:

# Same as above with a Brewer Palette 
h1 <- hist(Salaries$salary/1000,main="Histogram of Salaries", xlab="Salary (in '000s)", ylab="Count", col=brewer.pal(8,"Reds")) 

The output of the preceding code is as follows:

The hist function produces several metrics which can be accessed using their respective names (for example, in this case, h1$breaks, h1$counts, and so on), as shown:

names(h1) 
# [1] "breaks"   "counts"   "density"  "mids"     "xname"    "equidist" 
 
h1$counts 
#  [1]   1  50  90 114  60  48  23   8   2   1 
 
h1$breaks 
#  [1]  40  60  80 100 120 140 160 180 200 220 240 
 

The pros and cons of using histograms are as follows:

Pros:

Histograms can be used to get a general overview quickly from large datasets.

Cons:

• Can only be used for numeric values
• The bin width can be changed (if equal bin sizes are not used) which might not be able to capture the important aspects of the data