Summarizing a Data Frame for Graphing in ggplot2

In this article, we will explore the process of summarizing a data frame to prepare it for graphing using ggplot2 in R. We will discuss how to use the stat_summary function and dplyr’s group_by functionality to summarize the data and create a line graph.

Introduction

ggplot2 is a powerful data visualization library in R that allows users to create high-quality, publication-ready graphics with ease. One of the key steps in creating an effective graph with ggplot2 is summarizing the data by grouping it by relevant variables. In this article, we will focus on how to summarize a data frame using ggplot2.

Data Preparation

To begin, let’s assume that we have a data frame mydf2 containing the yearly cost of four different spending scenarios each with three years:

mydf2 = data.frame(Scenario = c(1,1,1,2,2,2,3,3,3,4,4,4),
                   Year   = c(1,2,3,1,2,3,1,2,3,1,2,3),
                   Cost    = c(140,445,847,948,847,143,554,30,44,554,89,45))

Using stat_summary to Summarize the Data

One way to summarize the data frame is by using the stat_summary function from ggplot2. This function allows us to calculate a summary statistic for each group of observations in the data.

ggplot(mydf2, aes(x = Year, y= Cost)) + stat_summary(fun.y = sum, geom = "line")

In this example, we are calculating the total cost for each year by using the sum function. The resulting graph will display a line plot of the total cost over time.

Using dplyr to Summarize the Data

Another way to summarize the data frame is by using the group_by functionality from the dplyr library. This approach allows us to group the data by year and calculate the sum of costs for each year.

library(dplyr); 
library(ggplot2)

mydf2 %>% group_by(Year) %>% summarise(Cost = sum(Cost)) %>% 
  ggplot(., aes(x = Year, y = Cost)) + geom_line(stat = "identity")

In this example, we are grouping the data by year using group_by, then calculating the sum of costs for each group using summarise. The resulting graph will display a line plot of the total cost over time.

Understanding the Pipe Operator (%>%)

The pipe operator (%>%) is used to pass the output of one operation as the input to another. In this example, we are piping the result of the group_by and summarise operations into ggplot.

mydf2 %>% group_by(Year) %>% summarise(Cost = sum(Cost)) %>% 
  ggplot(., aes(x = Year, y = Cost)) + geom_line(stat = "identity")

Faceting the Plot

One common use case for summarizing data is to create separate plots for each scenario. We can achieve this using the facet_wrap function from ggplot2.

ggplot(mydf2, aes(x = Year, y= Cost)) + 
  geom_line(stat = "identity") + 
  facet_wrap(~ Scenario)

In this example, we are creating a line plot of costs over time for each scenario. The facet_wrap function is used to create separate panels for each scenario.

Plotting Each Scenario with a Separate Line

Another common use case for summarizing data is to plot each scenario on the same graph but with different colors. We can achieve this using the color aesthetic from ggplot2.

ggplot(mydf2, aes(x = Year, y= Cost, color = factor(Scenario))) + 
  geom_line(stat = "identity")

In this example, we are creating a line plot of costs over time for each scenario. The color aesthetic is used to assign different colors to each scenario.

Conclusion

Summarizing data frames is an essential step in preparing the data for graphing using ggplot2. By using the stat_summary function and dplyr’s group_by functionality, we can summarize the data and create effective line plots. Additionally, we can use faceting to create separate panels for each scenario and plotting multiple scenarios on the same graph with different colors.

Last modified on 2024-02-17