Histograms using Plotly for R

Introduction

Histograms are used to visualize the spread in the data point values of a continuous numerical variable. The Plotly histogram divides the numerical variable up into equally sized bins, ranging from the lowest to the highest value for the variable. It then counts the number of data point values in each bin.

The histogram can also be normalized so as to show a frequency distribution.

A simple histogram

The code below creates a variable called wcc. It takes \(200\) data point values from a normal distribution with \(\mu = 15\) and \(\sigma = 4\).

wcc <- rnorm(100,
             15, 4)
summary(wcc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   5.617  11.419  13.461  14.373  16.885  25.196

A simple histogram is generated below.

p1 <- plot_ly(x = ~wcc,
              type = "histogram")
p1

Adding title

The layout() command added in the pipeline creates titles.

p2 <- plot_ly(x = ~wcc,
              type = "histogram") %>% 
  layout(title = "Histogram of white cell count",
         xaxis = list(title = "White cell count",
                      zeroline = FALSE),
         yaxis = list(title = "Count",
                      zeroline = FALSE))
p2

Normalized histogram

A normalized histogram shows the frequency distribution. It divides the count for each bin by the total number of data point values in the sample. It is achieved by using the histnorm = argument.

p3 <- plot_ly(x = ~wcc,
              type = "histogram",
              histnorm = "probability") %>% 
  layout(title = "Frequency distribution of white cell count",
         xaxis = list(title = "White cell count",
                      zeroline = FALSE),
         yaxis = list(title = "Frequency",
                      zeroline = FALSE))
p3

Horizontal histogram

Flipping the plot to horizontal is as easy as setting the variable, wcc to the y = argument. Remember to change the axes titles as well.

p4 <- plot_ly(y = ~wcc,
              type = "histogram",
              histnorm = "probability") %>% 
  layout(title = "Frequency distribution of white cell count",
         yaxis = list(title = "White cell count",
                      zeroline = FALSE),
         xaxis = list(title = "Frequency",
                      zeroline = FALSE))
p4

Displaying a histogram for more than one categorical group

A histogram for the same variable can be shown for more than one group. The code below creates a data.frame and then splits this into two data.frame objects using the dplyr library.

df <- data.frame(Group = sample(c("A", "B"),
                                200,
                                replace = TRUE),
                 WCC = wcc)
groupA <- df %>% filter(Group == "A")
groupB <- df %>% filter(Group == "B")

The code below creates a histogram of the WCC variable for each group. Since an overlay is going to occur, the opacity is set using the alpha = argument. Each individual histogram is added using the add_histogram() command. The barmode = argument is added to the layout() command.

p5 <- plot_ly(alpha = 0.7) %>% 
  add_histogram(x = ~groupA$WCC,
                name = "Group A") %>% 
  add_histogram(x = ~groupB$WCC,
                name = "GroupB") %>% 
  layout(barmode = "overlay",
         title = "Histogram of white cell count for groups A and B",
         xaxis = list(title = "White cell count",
                      zeroline = FALSE),
         yaxis = list(title = "Count",
                      zeroline = FALSE))
p5