Introduction Bar Charts in R
This article focus on the creation of Bar Charts in R. Bar chart helps to compare the data’s visually and one of the most essential parts of graphics. They are easy to create and useful for categorizing data so that the user can grasp the information using a bar (each entity is considered as bars). Most of the statistics information written in the text is difficult to understand, that’s why an effective tool known as column charts were widely used. Bar charts are used when a comparison between data over time happens. The bar height is decided by the given values as input. In bar charts, the data are represented by rectangular bars and even it does multiple comparisons. In some places, to tabulate a data bar plot make use table () function in our examples.
Syntax
The Basic syntax to create a Bar charts in R is shown below.
barplot (H, xlab, ylab, main, names.arg, col)
Description of the Parameters are:
H denotes height (vector or matrix). If H is a vector the values determine the heights of the bars. If it is a matrix with option false corresponds to sub bars, and true denotes to create a horizontal bar.
- xlab: Label for X-axis
- ylab: Label for Y-axis
- main: Heading of the bar chart
- names. arg: Label to the bars a character vector.
- col: It gives color to the bars in the chart.
How to create a simple Bar Chart in R?
Here we shall discuss how to create Bar charts using function barplot () in R which is very easy to implement with vertical and horizontal bars. In the below example we will see creating charts using vectors.
temp <- c(20, 25, 27, 23, 22, 26, 29)
barplot(temp)
Output:
The bar Plot should look like this:
Next example comes with initializing some vector of numbers and creating a table () command to count them. The width of the bar can be adjusted using a parameter width () and space by space () in barplot.
// Vector numbers are created using function c ()
x<- c (1,2,2,2,3,5,5,5,5,4)
cnt <- table(x)
cnt
x
barplot (cnt , space =1.0)
Creating a Bar chart using R built-in data set with Horizontal bar. To do so make horiz = TRUE or else vertical bars are drawn when horiz= FALSE (default option).
We shall consider a R data set as:
Rural Male Rural Female Urban Male Urban Female
## 50-54 11.7 8.7 15.4 8.4
## 55-59 18.1 11.7 24.3 13.6
## 60-64 26.9 20.3 37.0 19.3
## 65-69 41.0 30.9 54.6 35.1
## 70-74 66.0 54.3 71.1 50.0
Here comes an example to plot the built-in data set of R.
a<- VADeaths [2:5, "Urban Male"]
barplot(a)
# Horizontal bar plot
barplot (a, horiz = TRUE)
Output:
Creating a Bar Chart with Labels, title
The bar chart could look more elegant by adding more parameters to the bar plot.
4.8 (2,701 ratings)
View Course
-
Assigning titles and labels
Titles here are assigned using main arguments as “ Km per distance” and x-axis as “km and y-axis as “ count” (labels) and the parameter col is for adding colors to the bar( either in hexadecimal or RGB format) also care should be taken number of bars should be equal to the number of colours assigned in character vector if not the colors get repeated, density is for shading lines on the bars. Titles and labels can be modified and added for the bar charts.
The following example plots kilometer per count using different parameters.
km <- c(11,14,14,16,17,19,17,16,17,18)
table (km)
km
barplot(table(km),
main="km per distance",
xlab="km",
ylab="Count",
border="brown",
col="yellow",
density=5)
-
Assigning and changing colors
x <- VADeaths [2:4, "Rural Male"]
barplot (x, col = "orange", border = "blue")
The bar chart for the above code is given here:
And each of the bars can be assigned different colors. Here, we will fix some labels.
H <- c (6,11,27,2,44)
D <- c("Jan","feb","Mar","Apr","May")
barplot(H,names.arg=D,xlab="Month",ylab="sale",col="Red",main="Salechart",border="yellow")
When executed we get the following Output:
-
Using various Arguments:
B <- c (1, 3, 21, 35, 22, 37, 17)
barplot (B, col="green")
barplot (B, main="BARPLOT", xlab="LETTERS", ylab="VALUES", names.arg=c("A","B","C","D","E","F","G"),
border="yellow", density=c (90, 70, 50, 40, 30, 20, 10))
-
Using Matrix:
mt <- c (3, 1, 10, 12, 14, 7, 9, 11, 18)
val <- matrix (mt, nrow = 3, ncol = 3)
val
barplot (val, col = c ("pink", "yellow", "violet"))
-
Multiple comparisons:
In the below example we have created a matrix for three vectors representing five points and a comparison between them is done using a bar chart. Here, we are using the legend function to display the legends. Bty argument is meant for legend borders. The data is been plotted as follows.
A <- c (2,3,6,4,9)
B <- c (3,5,3,4,11)
C <- c (5,5,7,7,15)
data<- data.frame(A, B, C)
names(data)<- c("Tom","Harry","Gilf")
barplot(height=as.matrix(data),main="Analysis-1",ylab="Vaccine", beside=TRUE,col=rainbow (5))
legend ("topleft",c("Week1","Week2","Week3","Week4","Week5"),cex=2.0,bty="n",fill=rainbow (5))
-
Grouped Bar Plots:
Bar charts are created for all the columns. (columns are grouped together). Group chart make use of matrix as input values.
barplot (VADeaths,col = c("blue", "green", "lightcyan","lavender", "magenta"),
legend = rownames(VADeaths),beside = TRUE)
// Now Making beside = FALSE
barplot (VADeaths, col = c("blue","green","light cyan","lavender","magenta"),
legend = rownames(VADeaths), beside = FALSE)
-
Stacked Bar Plot:
Instead of assigning the bars continuously it is effective to stack them in order.
Example:
counts <- table (VADeaths)
barplot(counts, main="Distribution",
xlab="Rural Female",col=c("darkblue","yellow"), legend = rownames(counts))
Conclusion
Hence, we have discussed basics on creating bar charts in R. this will help you to understand real-time concepts for quantitative comparison. Bar charts play an essential role in data visualizations. We have seen some real-time scenarios on bar charts for categorical values and monitoring variation of a process for the given data set. New variations of bar charts include plotting using dots. Bar charts help in grouping values at several levels.
