Creating Horizontal Barplots with Average Values: A Deeper Dive into ggplot2

Horizontal Barplots and Average Values: A Deeper Dive

In this article, we’ll explore the concept of horizontal barplots and how to create them using R. We’ll also discuss the average values table that is often displayed alongside these plots.

Introduction to Barplots

A barplot is a type of chart used to display categorical data. It consists of bars of different lengths, each corresponding to a category in the data. The length of the bar indicates the frequency or value associated with that category. In this article, we’ll focus on creating horizontal barplots, which are particularly useful for displaying data where the x-axis represents categories.

Understanding the Data

Let’s start by examining the data provided in the question:

a1 <- c(5.0, 4.0, 7.0, 5.3, 3.5)
names(a1) <- c(as.character(c("Power", "Strenght", "Price", "Perf.", "CCM")))

In this code, a1 is a vector of numbers representing the average values for each category in our data. The names() function assigns a character string to each element in the vector, which corresponds to a category in our data.

Creating a Barplot

Now that we have our data, let’s create a barplot using the barplot() function:

barplot(a1, main="Distribution",
        xlab="N", col=1:5, horiz=TRUE)
legend("topright", 
       legend = names(a1), 
       fill = 1:5,
       cex = 0.75)

In this code:

barplot() creates the barplot.
The first argument is the data to be plotted (a1).
The main argument specifies the title of the plot (“Distribution”).
The xlab argument labels the x-axis (“N”).
The col argument assigns different colors to each category (1:5).
The horiz=TRUE argument tells R to create a horizontal barplot.
The legend() function adds a legend to the plot, which helps identify the categories.

Creating a Horizontal Barplot with Average Values

The question mentions that it would be nice to have an average values table displayed alongside the barplot. To achieve this, we can use the ggplot2 library, which is a popular data visualization tool in R.

library(ggplot2)
df <- data.frame(x=names(a1), y=a1)

In this code:

We load the ggplot2 library using library(ggplot2).
We create a new dataframe (df) that contains both the categories and their corresponding average values.
The x column corresponds to the categories, and the y column corresponds to the average values.

Now we can use ggplot() to create our horizontal barplot with average values:

ggplot(df, aes(x=y, fill=x)) + geom_bar(stat="identity")+ 
  coord_flip() +
  geom_text(aes(y=max(y)+0.5, label=y), color="black")+
  scale_fill_discrete(guide="none")+
  theme_bw()+theme(panel.grid=element_blank())

In this code:

ggplot() creates the base plot.
The first argument is our dataframe (df).
The aes() function specifies the variables to be used in the plot. In this case, we use x=y for the y-axis and fill=x for the colors.
The geom_bar() function adds the barplot to the base plot.
The stat="identity" argument tells R to use the values as the heights of the bars.
The coord_flip() function flips the x and y axes, turning our horizontal barplot into a vertical one.
The geom_text() function adds text labels to each bar. We use y=max(y)+0.5 for the y-position of the text, which ensures that it’s placed above each bar without overlapping.
The scale_fill_discrete() function removes the color legend, as we’ve already specified the colors in the plot.
The theme_bw() function sets a black and white color scheme for the plot.
The panel.grid=element_blank() argument hides the grid lines.

Finally, we add a legend to the top-right corner of the plot:

+ geom_text(aes(y=max(y)+0.5, label=y), color="black", hjust=0)+
  scale_fill_discrete(name="", guide=guide_legend(reverse=TRUE))+
  theme_bw()+
  theme(panel.grid=element_blank(), axis.title=element_blank(),
        axis.text.y=element_blank(), axis.ticks.y=element_blank())

In this code:

We add hjust=0 to the geom_text() function, which centers the text horizontally.
We set name="" in the scale_fill_discrete() function, which removes the legend title.
We set guide_legend(reverse=TRUE) in the scale_fill_discrete() function, which reverses the order of the legend items. This ensures that the categories appear in reverse alphabetical order.

Example Use Case

The code provided above creates a horizontal barplot with average values for each category. However, you can customize this plot to suit your specific needs. Here are some possible variations:

Add more data: You can add more data points or categories by modifying the df dataframe.
Change colors: You can change the colors used in the barplot and legend by modifying the col argument in the barplot() function.
Add more visual effects: You can add more visual effects, such as shadows or gradients, to make your plot stand out.

Conclusion

In this tutorial, we demonstrated how to create a horizontal barplot with average values for each category using R and the ggplot2 library. We also showed how you can customize this plot to suit your specific needs. With practice and experience, you’ll become proficient in creating visually appealing plots that help communicate complex data insights.

Last modified on 2023-07-31