CROSSTABLES IN R | DATA VISUALIZATION

A crosstable, also known as a contingency table, is a table showing the frequency of occurrences of certain events or values within different groups or categories. In R, the table() function can be used to create a crosstable.

For example, if you have a categorical variable "Gender" and another categorical variable "Age group", you can create a crosstable showing the number of occurrences of each age group for each gender by running:

# Create example data Gender <- sample(c("Male", "Female"), 100, replace = TRUE) Age <- sample(18:80, 100, replace = TRUE) Age_Group <- cut(Age, c(18, 30, 40, 50, 60, 70, 80), labels = c("18-29", "30-39", "40-49", "50-59", "60-69", "70-79"))

data <- data.frame(Gender, Age_Group)

table(data$Gender, data$Age_Group)

18-29 30-39 40-49 50-59 60-69 70-79 Female 15 8 6 9 7 10 Male 10 11 7 6 3 8

Once you have created the crosstable, you can visualize it using various R packages such as ggplot2, lattice, or vcd. Here is an example of using ggplot2 to create a bar chart of the crosstable:

install.packages("reshape2")

library(reshape2)

ct <- table(data$Gender, data$Age_Group)

ct_melt <- melt(ct)

ggplot(ct_melt, aes(x=Var1, y=value, fill=Var2)) +

  geom_bar(stat='identity', position='dodge')

The mosaic() function is a function from the vcdExtra library that you've loaded, it creates a mosaic plot, which is a graphical representation of a crosstable. The code you provided will create a mosaic plot of the crosstable "cap_tab" with shaded cells and a legend.
The shade = T argument will color the cells according to the frequency. The legend = T argument will add a legend to the plot that shows the frequency of each cell.

library(vcdExtra)

cap_tab = table(data$Gender, data$Age_Group)

cap_tab

mosaic(cap_tab, shade = T, legend = T )

install.packages("gplots")

library(gplots)

balloonplot(cap_tab)

library(MASS)

str(bacteria)

'data.frame': 220 obs. of  6 variables:

 $ y   : Factor w/ 2 levels "n","y": 2 2 2 2 2 2 1 2 2 2 ...

 $ ap  : Factor w/ 2 levels "a","p": 2 2 2 2 1 1 1 1 1 1 ...

 $ hilo: Factor w/ 2 levels "hi","lo": 1 1 1 1 1 1 1 1 2 2 ...

 $ week: int  0 2 4 11 0 2 6 11 0 2 ...

 $ ID  : Factor w/ 50 levels "X01","X02","X03",..: 1 1 1 1 2 2 2 2 3 3 ...

 $ trt : Factor w/ 3 levels "placebo","drug",..: 1 1 1 1 3 3 3 3 2 2 ...

df = bacteria

ggplot(df, aes(x=trt, y=week, fill=hilo)) + 

  geom_col(position = "dodge")

 Load the Titanic dataset

data(Titanic)

Create a crosstable of Survived by Gender

ct <- table(Titanic$Survived, Titanic$Sex)

Create a stacked bar chart of the crosstable

ggplot(melt(ct), aes(x=Var1, y=value, fill=Var2)) +

  geom_bar(stat='identity', position='dodge') +

  ggtitle("Survival by Gender") +

  xlab("Survived") +

  ylab("Count")

Heatmap

This code creates a heatmap that shows the relationship between the number of gears and cylinders and the number of cars in the mtcars dataset. The intensity of the color in the heatmap represents the value of the number of cars.

Create a crosstable of the number of cars by number of gears and number of cylinders

ct <- table(mtcars$gear, mtcars$cyl)

Create a heatmap of the crosstable

ggplot(melt(ct), aes(x=Var1, y=Var2, fill=value)) +

  geom_tile() +

  scale_fill_gradient(low = "white", high = "blue") +

  ggtitle("Number of Cars by Gears and Cylinders") +

  xlab("Number of Gears") +

  ylab("Number of Cylinders")

Scatter Plots

This code creates a scatter plot that shows the relationship between the number of gears and cylinders, and the miles per gallon (mpg) for the cars in the mtcars dataset.

Create a scatter plot of the number of cars by number of gears and number of cylinders

ggplot(mtcars, aes(x=gear, y=cyl, color=mpg)) +

  geom_point() +

  ggtitle("Number of Cars by Gears and Cylinders") +

  xlab("Number of Gears") +

  ylab("Number of Cylinders")

Pie Charts

load the library

library(tidyverse)

create a dataframe

df <- data.frame(

  category = c("A", "B", "C", "D"),

  value = c(20, 30, 40, 10)

)

create a pie chart

ggplot(df, aes(x = "", y = value, fill = category)) + 

  geom_bar(width = 1, stat = "identity") + 

  coord_polar("y", start = 0) + 

  ggtitle("Pie Chart Example")

 Create a crosstable of the number of cars by number of gears

ct <- table(mtcars$gear)

Create a pie chart of the crosstable

ggplot(data.frame(ct), aes(x="", y=Freq, fill=Var1)) +

  geom_bar(width = 1, stat = "identity") +

  coord_polar("y", start=0) +

  ggtitle("Number of Cars by Number of Gears")