How to do data visualization with ggplot2?

R Programming
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Data Visualization is transforming data in the visual format to discover patterns present in it. For data visualization, we are going to use ggplot2. According to ggplot2.com, “ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts” http://ggplot2.org/.

First we have to install ggplot2 and then import it.

install.packages("ggplot2")
library(ggplot2)

We are going to load Titanic data for analysis. If we run View function, the spreadsheet view will open.

titanic <- read.csv("titanic.csv", stringsAsFactors = FALSE)
View(titanic)

view

Then, we will convert all non numerical variables into factors using as.factors function.

titanic$Pclass <- as.factor(titanic$Pclass)
titanic$Survived <- as.factor(titanic$Survived)
titanic$Sex <- as.factor(titanic$Sex)
titanic$Embarked <- as.factor(titanic$Embarked)

We’ll start our visual analysis of the data focusing on questions related to survival rates. Specifically, these questions will use the factor (i.e., categorical) variables in the data. Factor data is very common in the business context and ggplot2 offers many powerful features for visualizing factor data.

First question - What was the survival rate?

As Survived is a factor (i.e., categorical) variable, a bar chart
is a great visualization to use.

ggplot(titanic, aes(x = Survived)) + 
  geom_bar()

Rplot

If you really want percentages.

prop.table(table(titanic$Survived))

Capture

Add some customization for labels and theme.

ggplot(titanic, aes(x = Survived)) + 
  theme_bw() +
  geom_bar() +
  labs(y = "Passenger Count",
       title = "Titanic Survival Rates")

Rplot07

Second question - What was the survival rate by gender?

We can use color to look at two aspects (i.e., dimensions)
of the data simultaneously.

ggplot(titanic, aes(x = Sex, fill = Survived)) + 
  theme_bw() +
  geom_bar() +
  labs(y = "Passenger Count",
       title = "Titanic Survival Rates by Sex")

Rplot08

Third question - What was the survival rate by class of ticket?

ggplot(titanic, aes(x = Pclass, fill = Survived)) + 
  theme_bw() +
  geom_bar() +
  labs(y = "Passenger Count",
       title = "Titanic Survival Rates by Pclass")

Rplot09

Fourth question - What was the survival rate by class of ticket
and gender?

We can leverage facets to further segment the data and enable
“visual drill-down” into the data.

ggplot(titanic, aes(x = Sex, fill = Survived)) + 
  theme_bw() +
  facet_wrap(~ Pclass) +
  geom_bar() +
  labs(y = "Passenger Count",
       title = "Titanic Survival Rates by Pclass and Sex")

Rplot10

Next, we’ll move on to visualizing continuous (i.e., numeric)
data using ggplot2. We’ll explore visualizations of single numeric variables
(i.e., columns) and also illustrate how
ggplot2 enables visual drill-down on numeric data.

Fifth Question - What is the distribution of passenger ages?

The histogram is a staple of visualizing numeric data as it very
powerfully communicates the distribution of a variable (i.e., column).

ggplot(titanic, aes(x = Age)) +
  theme_bw() +
  geom_histogram(binwidth = 5) +
  labs(y = "Passenger Count",
       x = "Age (binwidth = 5)",
       title = "Titanic Age Distribtion")

Rplot11

Sixth Question - What are the survival rates by age?

ggplot(titanic, aes(x = Age, fill = Survived)) +
  theme_bw() +
  geom_histogram(binwidth = 5) +
  labs(y = "Passenger Count",
       x = "Age (binwidth = 5)",
       title = "Titanic Survival Rates by Age")

Rplot13
Another great visualization for this question is the box-and-whisker
plot.

ggplot(titanic, aes(x = Survived, y = Age)) +
  theme_bw() +
  geom_boxplot() +
  labs(y = "Age",
       x = "Survived",
       title = "Titanic Survival Rates by Age")

Rplot14

Seventh Question - What is the survival rates by age when segmented
by gender and class of ticket?

A related visualization to the histogram is a density plot. Think of a
density plot as a smoothed version of the histogram. Using ggplot2
we can use facets to allow for visual drill-down via density plots.

ggplot(titanic, aes(x = Age, fill = Survived)) +
  theme_bw() +
  facet_wrap(Sex ~ Pclass) +
  geom_density(alpha = 0.5) +
  labs(y = "Age",
       x = "Survived",
       title = "Titanic Survival Rates by Age, Pclass and Sex")

Rplot15
If you prefer histograms, no problem!

ggplot(titanic, aes(x = Age, fill = Survived)) +
  theme_bw() +
  facet_wrap(Sex ~ Pclass) +
  geom_histogram(binwidth = 5) +
  labs(y = "Age",
       x = "Survived",
       title = "Titanic Survival Rates by Age, Pclass and Sex")

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#2