R/C2/Aesthetic-Mapping-in-ggplot2/English

Title of the script: Aesthetic Mapping in ggplot2

Author: Varshit Dubey (CoE Pune) and Sudhakar Kumar (IIT Bombay)

Keywords: R, RStudio, ggplot, aesthetic, mapping, video tutorial.

Visual Cue	Narration
Show slide Opening Slide	Welcome to this tutorial on Aesthetic Mapping in ggplot2.
Show slide Learning Objective	In this tutorial, we will learn, What is aesthetic How to create plots using aesthetic Tuning parameters in aesthetic
Show slide Pre-requisites	To understand this tutorial, you should know, Basics of statistics Basics of ggplot2 package Data frames If not, please locate the relevant tutorials on R on this website.
Show slide System Specifications	This tutorial is recorded on Ubuntu Linux OS version 16.04 R version 3.4.4 RStudio version 1.1.463 Install R version 3.2.0 or higher.
Show slide Download Files	For this tutorial, we will use A data frame moviesData.csv, and A script file aesPlots.R. Please download these files from the Code files link of this tutorial.
[Computer screen] Highlight moviesData.csv and aesPlots.R in the folder aesPlots Point to aesPlots folder.	I have downloaded and moved these files to aesPlots folder. This folder is located in myProject folder on my Desktop. I have set aesPlots folder as my Working Directory.
	Now let us see what is Aesthetic?
Show slide What is Aesthetics	Aesthetic is a visual property of the objects in a plot. It includes lines, points, symbols, colors, and position. It is used to add customization to our plots.
Cursor on the interface.	Let us switch to RStudio.
Highlight aesPlots.R in the Files window of RStudio	Open the script aesPlots.R in RStudio.
Highlight ggplot function in the Source window	Here, we are plotting a scatter plot between critics_score and audience_score of movies.
Highlight the Source button	Run this script by clicking on the Source button.
Highlight Plots window	Scatter plot appears in the Plots window.
Highlight movies in the Source window	movies data frame opens in the Source window.
Highlight the plot in the Plots window	In this scatter plot, each point refers to a particular movie. Suppose we want to color these points according to the genre of the movies.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right.
Highlight genre in the Source window	As we can see that there are different genres like Drama Comedy Horror Documentary, etc. So, we will assign a unique color to each genre.
Highlight the script aesPlots.R in the Source window	Click on the script aesPlots.R
[RStudio] ggplot(data = movies, mapping = aes(x = critics_score, y = audience_score, color = genre)) + geom_point()	In the Source window, type the following commands.
Highlight aes in the Source window	Inside aes, we have added color argument and set it to genre.
Highlight run button in the Source window	Save the script and run the current line by pressing Ctrl+ Enter keys simultaneously.
Highlight Plots window	Modified scatter plot appears in the Plots window.
Click on Zoom button to maximize the plot. Highlight Plots window	In the Plots window, click on the Zoom button to maximize the plot.
Highlight the plot	We can see that each point is assigned a unique color according to its genre. In the right side of the plot, we can view the mapping of genres with their colors.
Click on X button to close the plot.	Close this plot.
Highlight ggplot in the Source window	Now, we will learn how to draw a bar chart using ggplot function.
Highlight movies in the Source window	In the Source window, click on movies.
Highlight the scroll bar in the Source window	In the Source window, scroll from left to right.
Highlight mpaa_rating in the Source window	Let us inspect the object named mpaa underscore rating in movies.
Highlight the script aesPlots.R in the Source window	Click on the script aesPlots.R
[RStudio] str(movies$mpaa_rating) levels(movies$mpaa_rating)	In the Source window, type the following commands.
Highlight run button in the Source window	Run the last two lines of code.
Highlight output in the Console window	mpaa_rating is a factor. It has 6 levels like G NC-17 PG PG-13 R, and Unrated.
Highlight output in the Console window	So, our bar chart will have 6 different bars. Each bar will represent the number of movies in each level.
[RStudio] ggplot(data = movies, mapping = aes(x = mpaa_rating)) + geom_bar()	In the Source window, type the following command.
Highlight aes in the Source window	Here, we have mapped mpaa underscore rating on X-axis.
Highlight geom_bar in the Source window	Next, we have used geom underscore bar as we are plotting a bar chart. Similarly, we can use geom_line to draw a line chart geom_boxplot to draw a box plot
Highlight run button in the Source window	Run the current line.
Highlight Plots window	The bar chart appears in the Plots window.
Highlight plot in the Plots window	Now, we will learn how to add labels to this bar chart.
Point to geom_bar(). Type Space plus sign >> press Enter.	In the Source window, after geom_bar(), type space plus sign and press Enter.
[RStudio] labs(y = "Rating count", title = "Count of mpaa_rating")	Now type the following commands.
Highlight labs in the Source window	Here, we have used labs argument to add label and title to the bar chart.
Highlight run button in the Source window	Run the current line.
Highlight Plots window	The modified bar chart appears in the Plots window.
Highlight fifth bar in the Plots window	We can see that most of the movies have been rated as R in mpaa_rating. Suppose, in this bar chart, we want to view the distribution of movies by genre.
[RStudio] ggplot(data = movies, mapping = aes(x = mpaa_rating, fill = genre)) + geom_bar() + labs(y = "Rating count", title = "Count of mpaa_rating by genre")	In the Source window, type the following command.
Highlight fill in the Source window	Inside aes, we have added fill argument and set it to genre.
Highlight run button in the Source window	Run the current line.
Highlight Plots window	The modified bar chart appears in the Plots window.
Highlight Plots window	In the Plots window, click on the Zoom button to maximize the plot.
Highlight fifth bar in the Plots window	There are seven different colors in each bar. Besides the plot, the meaning of each color has been given.
Click on X button to close the plot.	Close the plot.
Highlight movies in the Source window	In the Source window, click on movies data frame.
Highlight runtime in the Source window	Now we will plot a histogram for the object named as runtime in movies. Recall that, we have already learned how to plot a histogram using hist function. Now we will use ggplot2 package to plot a histogram.
Highlight the script aesPlots.R in the Source window.	Click on the script aesPlots.R
[RStudio] ggplot(data = movies, mapping = aes(x = runtime)) + geom_histogram() + labs(x = "Runtime of movies", title = "Distribution of movies' runtime")	In the Source window, type the following command.
Highlight run button in the Source window	Save the script and run the current line.
Highlight output in the Console window	There are some warning messages, which we will ignore for now.
Highlight Plots window	The histogram appears in the Plots window.
	Let us summarize what we have learnt.
Show slide Summary	In this tutorial, we have learnt, What is aesthetic How to create plots using aesthetic Tuning parameters in aesthetic
Show slide Assignment	We now suggest an assignment. Using built-in data set mtcars, draw a bar chart from the object cyl. Add suitable labels to this bar chart.
Show slide About the Spoken Tutorial Project	The video at the following link summarises the Spoken Tutorial project. Please download and watch it.
Show slide Spoken Tutorial Workshops	We conduct workshops using Spoken Tutorials and give certificates. Please contact us.
Show Slide Forum to answer questions	Please post your timed queries in this forum.
Show Slide Forum to answer questions	Please post your general queries in this forum.
Show Slide Textbook Companion	The FOSSEE team coordinates the TBC project. For more details, please visit these sites.
Show Slide Acknowledgment	The Spoken Tutorial project is funded by NMEICT, MHRD, Govt. of India
Show Slide Thank You	The script for this tutorial was contributed by Varshit Dubey (CoE Pune). This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

Contributors and Content Editors

Madhurig, Nancyvarkey, Sudhakarst