#install.packages('ggplot2')
library(ggplot2)Program_4
4.Develop a script in R to produce a bar graph displaying the frequency distribution of categorical data in a given data set, grouped by a specific variable, using ggplot2.
##Steps In this program,we will follow the following steps. - Load the required libraries. - Load and inspect the data set. - perform EDA(Exploratory Data Analysis). - Convert the numerical variables to factors. - Examine the frequency distribution. - Convert the numerical variable into categorical variable. - Examine and interpret the graph.
Step 1:Load Required libraries
-We load ggplot2 ,used for data visualization in R.
Step 2: Load and inspect the dataset
We load the built in mtcars and view the first few rows to understand its structure.
data=mtcars
head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Step 3: Exploratory data analysis
Before creating any visualization, we explore the data set to understand the variable and types.
str(data)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...summary(data) mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000 str(data)helps us to identify the datatype of each variable.summary(data)provides statistical summaries.
Step 4: Covert the numerical variable into factors.
To correctly visualize categorical data, we convert relevant variables into factors.
data$cyl [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4table(data$cyl)
4 6 8
11 7 14 data$gear [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4table(data$gear)
3 4 5
15 12 5 class(data$cyl)[1] "numeric"class(data$gear)[1] "numeric"data$cyl=as.factor(data$cyl)
data$gear=as.factor(data$gear)class(data$cyl)[1] "factor"data$cyl [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
Levels: 4 6 8class(data$gear)[1] "factor"data$gear [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
Levels: 3 4 5summary(data) mpg cyl disp hp drat
Min. :10.40 4:11 Min. : 71.1 Min. : 52.0 Min. :2.760
1st Qu.:15.43 6: 7 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
Median :19.20 8:14 Median :196.3 Median :123.0 Median :3.695
Mean :20.09 Mean :230.7 Mean :146.7 Mean :3.597
3rd Qu.:22.80 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
Max. :33.90 Max. :472.0 Max. :335.0 Max. :4.930
wt qsec vs am gear
Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000 3:15
1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000 4:12
Median :3.325 Median :17.71 Median :0.0000 Median :0.0000 5: 5
Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062
3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000
carb
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :2.812
3rd Qu.:4.000
Max. :8.000 str(data)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...Step 5: Examine the frequency distribution
Before plotting we analye how the data is distributed across categories
table(data$cyl)
4 6 8
11 7 14 table(data$gear)
3 4 5
15 12 5 table(data$cyl,data$gear)
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2-Helps us to understand the count of each category. -provide insight into relationships btw variables. -prepares us for interpreting the visualization.
Step:6 Create the bar graph.
ggplot(data, aes(x=cyl,fill=gear))
ggplot(data, aes(x=cyl,fill=gear))+geom_bar()
ggplot(data, aes(x=cyl,fill=gear))+geom_bar(position="dodge")
ggplot(data, aes(x=cyl,fill=gear))+
geom_bar(position="dodge")+
theme_minimal()+
theme(legend.position='top')
ggplot(data, aes(x=cyl,fill=gear))+
geom_bar(position="dodge")+
theme_minimal()+
theme(legend.position='top')+
labs(title="bar graph displaying the frequency distribution of categorical data", y="Count",x="Number of cylinders")