During our last webinar, we covered some of the basic ideas behind ggplot2, the R Visualization package by Dr. Hadley Wickham. In this blog post I will walk through the example that I covered during the webinar.

In order to carry out the examples yourself, you may download the dummy datasets from this link

Creating visualizations is an iterative process. You start with a data set, generate some quick graphs that best depict the insights and keep on adding components/data to the graph to finally produce a viz that you can show in your reports. The idea behind ggplot2 is to make this process simpler and more effective at the same time.

Diving into our example, we want to explore how Transactions for a hypothetical ecommerce store have fared for a particular calendar year. The function which we used for plotting is ggplot() and we need to mention the data frame as one of the arguments. Now for a bit of ggplot2 terminology which I am quoting verbatim from the documentation.

• aes stands for aesthetics and this controls how variables are mapped to the axis. In our example we map month to the x-axis and transactions to the y-axis.
• geoms, short for geometric objects, describe the type of plot you will produce. In our case, we are plotting the data as a bar graph
• stat stands for statistics which help us transform the data prior to plotting. In our case, its an identity transform so the data remains unchanged.

Here’s the R code :

require(ggplot2)
# Load the dataframe
mydata <- read.csv("./datasets/dataset1.csv")
head(mydata)
# Append a new column that maps month numbers to month names
mydata$monthf <- factor(mydata$month,levels=as.character(1:12),
labels=c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep","Oct","Nov","Dec"),
ordered=TRUE)
# Plot Transactions vs Month
ggplot(mydata,aes(monthf,transactions)) +
geom_bar(stat="identity")
# Which month shows the highest transactions ?

You may now be on your way to follow the rest of the code and keep on improving our first visualization using the rest of the R code :

# Load data frame that includes Medium as a dimension
mydata_1 <- read.csv("./data/dataset2.csv")
mydata_1$monthf <- factor(mydata_1$month,levels=as.character(1:12),
labels=c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep","Oct","Nov","Dec"),
ordered=TRUE)
# Facet the Transactions by medium
ggplot(mydata_1,aes(monthf,transactions)) +
geom_bar(stat="identity") +
facet_wrap(~medium)
# What is the problem with this plot ?
# Exclude the mediums having zero transactions
fresh_data <- subset(mydata_1,medium %in% c("cpc","organic","referral","(none)"))
# Re-plot
ggplot(fresh_data,aes(monthf,transactions)) +
geom_bar(stat="identity") +
facet_wrap(~medium)
# Stack the plots vertically for easier comparison
ggplot(fresh_data,aes(monthf,transactions)) +
geom_bar(stat="identity") +
facet_wrap(~medium,ncol=1)
# Which medium performed best w.r.t transactions ?
# Load the data frame including an additional dimension Visitor Type
mydata_2 <- read.csv("./data/dataset3.csv")
mydata_2$monthf <- factor(mydata_2$month,levels=as.character(1:12),
labels=c("Jan","Feb","Mar","Apr","May","Jun",
"Jul","Aug","Sep","Oct","Nov","Dec"),
ordered=TRUE)
# Map a color to Visitor Type Variable
ggplot(mydata_2,aes(monthf,transactions,fill=visitorType)) +
geom_bar(stat="identity") +
facet_wrap(~medium,ncol=1)
# Stack the bar graphs side by side for easier comparison
ggplot(mydata_2,aes(monthf,transactions,fill=visitorType)) +
geom_bar(stat="identity",position="dodge") +
facet_wrap(~medium,ncol=1)
# Strip the grey background and add a plot title
ggplot(mydata_2,aes(monthf,transactions,fill=visitorType)) +
geom_bar(stat="identity",position="dodge") +
facet_wrap(~medium,ncol=1) +
theme_bw() +
ggtitle("MoM transactions split by Visitor Type")

If you followed the code correctly, you might end up with something like this:

There is a lot that can be still improved with the viz but let us stop here and quickly sum up what we just learnt. We understood the basic idea behind ggplot2, gained some knowledge about its terminology and saw how we could generate interesting visualizations in a matter of minutes. Of course, there is a fair bit of programming overhead involved but once you get the hang of ggplot2, it is time well spent in learning to code. If you’re in for something advanced you may want to have a look at our other blog posts on ggplot2 here

Kushan Shah

Kushan is a Web Analyst at Tatvic. His interests lie in getting the maximum insights out of raw data using R and Python.

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