3 ways to make a barplot : From Basic To Interactive

Introduction

Nowadays we all talk about the power of graphs. The purpose of a graph is to present data effectively and in a simple way. A graph is more effective than a text. In this article, we will show you how to create a barplot using R in three different ways. A barplot is used to visualize counts of unique data point values in the sample space of a categorical variable. Let’s get started.

Basic Barplot

First, we will create a basic barplot using barplot() function:

counts <- table(diamonds$clarity)
barplot(counts, main="Distribution of the clarity of Diamonds",
        xlab="Clarity")

Barplot using ggplot2

Now, let’s make the graph look prettier using ggplot2. The geom_bar function we can determine the color of the bars. With ggtitle() you can add the title of the graph and the title of the axes.

#loding ggplot2 package
library(ggplot2)

ggplot(diamonds, aes(x=as.factor(clarity) )) +
  geom_bar(color="blue", fill=rgb(0.1,0.4,0.5,0.7) )+
  ggtitle("Distribution of the clarity of Diamonds") +
  xlab("Clarity type") + ylab("Count")

Barplot using plotly

Now let’s make the barplot interactive using plotly. An interactive graph is very useful when we work with dashboards.

#loding plotly package
library(plotly)

p1 <- plot_ly(x = names(table(diamonds$clarity)),
              y = as.numeric(table(diamonds$clarity)),
              name = "clarity",
              type = "bar")%>% 
  layout(title = "Distribution of the clarity of Diamonds",
         xaxis = list(title = "clarity",
                      zeroline = FALSE),
         yaxis = list(title = "Number",
                      zeroline = FALSE))
p1