Objectives

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Chaining dplyr Verbs Using %>%

Load Packages

library(tidyverse)

Define Data Frame as a Tibble

iris_tibble <- tibble(iris)

Advanced Example: The goal of this example is to transform the iris dataset by computing the ratio of Petal.Length to Sepal.Length for observations belonging to the “setosa” species.

iris_tibble %>%  
  # rule 1: choose only the "setosa" species
  filter(Species == "setosa") %>%  
  # rule 2: pick the columns Sepal.Length and Petal.Length
  select(Sepal.Length,Petal.Length) %>%   
  # rule 3: create a new column called length_ratio
  mutate(length_ratio = Petal.Length/Sepal.Length)

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Visualizing Numerical Data using ggplot2

Example: Plotting lengths by species in the iris data set.

# establish data and variables
ggplot(iris_tibble, 
       aes(x = Sepal.Length, 
           y = Petal.Length,
           color = Species)) +
  # draw scatter plot
  geom_point() + 
  # add theme layer
  theme_grey()

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Descriptive statistics

It involves organizing, summarizing, and presenting data in an informative way. It Focuses on describing and understanding the main features of a dataset.

Case Study 1: Titanic Data Set

The the Titanic Data Set is a popular dataset in data science. It contains information about the passengers aboard the Titanic and is often used for survival prediction.

The sinking of the Titanic illustration by Willy Stöwer

The Titanic Data Matrix

The number of observations in the data set is \(2207\) passengers.

Contingency Tables

A table that summarizes data for two categorical variables is called a contingency table.

Example: The contingency table below shows the distribution of survival and different classes of passengers on the Titanic.

Contingency Tables: Using R

Load Packages

library(tidyverse)
library(knitr)

Producing and presenting Contingency Tables in R

titanic_surv_class <- titanic %>% 
  select(survived,class) %>% 
  group_by(survived,class) %>% 
  summarise(total = n(),
            .groups = 'drop')
kable(addmargins(xtabs(total ~ survived + class, titanic_surv_class)))

Row Proportions

For those who survived, what is the proportion of passenger class?

Column Proportions

Does there appear to be a relationship between class and survival for passengers on the Titanic?

Bar Plots

A bar plot is a common way to display a single categorical variable. A bar plot where proportions instead of frequencies are shown is called a relative frequency bar plot.

Bar Plots: Using R

Load Packages

library(tidyverse)
library(gridExtra)

Plotting Bar Plots (raw Counts and Croportions)

p1 <- ggplot(titanic,aes(x=survived)) + 
  geom_bar(fill=COL[1,1]) + 
  ggtitle("Frequencies") + 
  theme_minimal()
p2 <- ggplot(titanic,aes(x=survived)) + 
  geom_bar(aes(y=..count../sum(..count..)),fill=COL[1,1]) + 
  ylab("proportion") + 
  ggtitle("Relative Frequencies") + 
  theme_minimal()
grid.arrange(p1, p2, ncol=2)

Bar Plots vs Histograms

How are bar plots different than histograms?

Bar Plots with Two Variables

Bar plots are graphical representations of categorical data using rectangular bars of varying heights.

Examples of Bar Plots

The following bar plots still uses the geom_bar() layer but with defined parameter option position.

Mosaic Plots

A mosaic plot is a graphical representation of categorical data, displaying proportions and relationships between multiple categorical variables using a tiled area chart.

Mosaic Plots: Using R

Load Packages

library(tidyverse)
library(ggmosiac)

Visualizing Categorical Variables using a Mosaic Plot

ggplot(titanic) + 
  geom_mosaic(aes(x=product(class,survived), fill = survived)) + 
  scale_fill_brewer(palette = 1) +
  theme_mosaic() + 
  theme(legend.position = "none")

Pie Charts

A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions of a whole.

Pie Charts: Using R

Load Packages

library(tidyverse)

Visualizing Categorical Variables using Pie Charts

ggplot(titanic %>% filter(class=="1st"), aes(x="", y=survived, fill=survived)) +
  geom_bar(stat="identity", width=1) +
  scale_fill_brewer(palette = 1) +
  coord_polar("y", start=0) + 
  ggtitle("1st Class") + 
  theme_void()

Comparing Categorical Data to Numerical Data

Example: Age distribution among class passengers and survival.

Comparing Categorical Data to Numerical Data: Using R

Load Packages

library(tidyverse)

Visualizing Multiple Variables

ggplot(titanic,aes(y=class,x=age, fill=survived)) + 
  geom_boxplot() + 
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()

Activity: Visualize Multiple Variables

1. Log-in to Posit Cloud and open the R Studio assignment W 2/26 - Visualize Multiple Variables Techniques.
2. Make sure you are in the current working directory. Rename the .Rmd file by replacing [name] with your name using the format [First name][Last initial]. Then, open the .Rmd file.
3. Change the author in the YAML header.
4. Read the provided instructions.
5. Answer all exercise problems on the designated sections.

References