R/C2/Introduction-to-ggplot2/English-timed

Time	Narration
00:01	Welcome to this tutorial on Introduction to ggplot2.
00:07	In this tutorial, we will learn about
00:11	Need for data visualization
00:15	Basic plot function in R
00:18	ggplot2 package
00:21	To understand this tutorial, you should know,
00:25	Basics of Statistics and
00:28	Data frames
00:31	If not, please locate the relevant tutorials on R on this website.
00:38	This tutorial is recorded on
00:40	Ubuntu Linux OS version 16.04
00:46	R version 3.4.4
00:50	RStudio version 1.1.463
00:56	Install R version 3.2.0 or higher.
01:02	For this tutorial, we will use,
01:05	A data frame moviesData.csv
01:10	And a script file ggPlots.R.
00:15	Please download these files from the Code files link of this tutorial.
01:21	I have downloaded and moved these files to ggPlots folder.
01:28	This folder is located in myProject folder on my Desktop.
01:33	I have also set ggPlots folder as my Working Directory.
01:40	Now let us learn about visualization.
01:44	Visualization is an important tool for insight generation.
01:50	It is used to understand the data structure, identify outliers and find patterns.
01:57	There are 2 methods of data visualization in R:
02:03	Basics graphics and
02:05	Grammar of graphics (popularly known as ggplot2)
02:11	Let us switch to RStudio.
02:14	Open the script ggPlots.R in RStudio.
02:20	Let us run this script by clicking on the Source button.
02:26	movies data frame is loaded in the workspace.
02:30	This data frame will be used later in this tutorial.
02:35	First, we will plot a sine curve by taking equally spaced samples.
02:42	In the Source window, type the following commands.
02:47	Here, we have used the seq function to generate a sequence.
02:53	This sequence is from minus pi to plus pi with an interval of zero point one.
03:00	In plot command, the first argument is x and the second argument y is Sine of x.
03:12	Save the script and run the last three lines of code by pressing Ctrl + Enter keys simultaneously.
03:23	A plot of sine curve appears in the Plots window.
03:27	In the Plots window, click on the Zoom button to maximize the plot.
03:34	Now we will add some more layers in this plot.
03:39	Click on Close button to close this plot.
03:43	In the Source window, type the following commands.
03:48	Here, we have added main and ylab arguments to the plot function.
03:55	Run the current line.
03:57	In the Plots window, click on Zoom button to maximize the plot.
04:04	The title of the plot and label of Y-axis have been added to the plot.
04:10	Close this plot.
04:13	Now we will learn how to change the type of plot.
04:18	In the Source window, type the following commands.
04:23	Here, we have used the type argument and set it to l.
04:30	It means that the type of plot we need is lines.
04:36	col equal to blue, changes the colour of the plot to blue.
04:42	Run the current line.
04:43	The type and color of the plot have been changed.
04:49	Now, we will plot one more graph on the same plot.
04:53	Let us plot cosine of x along with sine of x on the same plot.
05:01	In the Source window, type the following commands.
05:05	This command plots sine of x using the plot function.
05:12	Next, we use lines function to plot cosine of x.
05:18	After the first line is plotted, the lines function is used.
05:23	It takes an additional vector cos of x as an input to draw the second line in the plot.
05:32	Run the last two lines of code by pressing Ctrl+Enter keys simultaneously.
05:40	The two graphs appear in the same plot window.
05:45	Here we can add a legend to the plot to differentiate between the multiple graphs.
05:52	For this, we will use legend function.
05:56	In the Source window, type the following command.
06:00	I will resize the Source window.
06:04	The first argument refers to the coordinates for placing the legend in our plot.
06:12	We have set the coordinates to topleft.
06:16	The second argument is the names to be given.
06:20	Since we have plotted sine and cosine functions, we will pass these two names as a vector.
06:28	Next, we have used the fill argument to specify the graphs by their colors.
06:35	Recall that, sine function is plotted in blue and cosine function in red.
06:42	I will resize the Plots window.
06:45	Run the last three lines of code by pressing Ctrl+Enter keys simultaneously.
06:54	In the Plots window, click on Zoom button to maximize the plot.
07:00	The two plots with their names appear in the same graph.
07:05	Close the plot.
07:07	So far, we have discussed the basic graphics in R language.
07:13	Now, we will learn about the grammar of graphics by using ggplot2 package.
07:20	ggplot2 package was created by Hadley Wickham in 2005.
07:26	It offers a powerful graphics language for creating elegant and complex plots.
07:34	Let us switch to RStudio.
07:37	I will resize the Plots window.
07:40	To use any package in R, we need to install and then load it.
07:45	As I have already installed ggplot2 package, I will load this directly.
07:52	If you have not installed the package, please use install dot packages function.
07:59	Please make sure that you are connected to the Internet while installing the packages.
08:05	To load this package, we will add the library at the top of the script.
08:11	In the Source window, scroll up to the top of the script.
08:16	Now, at the top of the script, type library and ggplot2 in parentheses.
08:26	Save the script and run this line.
08:31	Now, in the Source window, click on the next line after the legend function.
08:38	We will use movies data frame for exploring ggplot2 package.
08:44	Let us view the objects available in movies data frame.
08:50	In the Source window, type View and movies in parentheses.
08:56	Run the current line.
08:58	movies data frame opens in the Source window.
09:02	Now, we will create a simple scatter plot with two different objects of movies.
09:10	Remember, a scatter plot is a graph in which the values of two variables are plotted along the axes.
09:19	In the Source window, scroll from left to right to see the remaining objects of movies data frame.
09:28	Suppose, we want to visualize the correlation between critics_score and audience_score.
09:37	In the Source window, click on the script ggPlots.R
09:43	In the Source window, type the following command.
09:47	ggplot function takes three basic arguments:
09:52	Data Aesthetics Geometry
09:56	In ggplot function, we have used the following arguments:
10:01	data, which refers to the data set to be used for plotting.
10:06	We have set data equal to movies.
10:10	mapping, which is used to apply aesthetics mapping to the plot.
10:16	aes, which is used to specify the mapping of objects on X and Y axes.
10:23	We will learn more about aesthetics mapping later in this series.
10:28	geom underscore point is used to draw points defined by X and Y coordinates.
10:36	Run the current line.
10:39	Scatter plot appears in the Plots window.
10:42	In the Plots window, click on the Zoom button to maximize the plot.
10:49	We can see that there is a positive correlation between critics_score and audience_score.
10:56	Now we will learn how to save a plot generated by ggplot function.
11:03	Close this plot.
11:05	For saving the plots, there is a function named ggsave in ggplot2 package.
11:13	To know the syntax of ggsave function, we will access the Help section in RStudio.
11:20	In the Console window, type question mark ggsave and press Enter.
11:27	I will resize the Help window.
11:30	The first argument in this function is the filename.
11:35	Next, there is the argument named plot which means the plot to be saved.
11:42	By default, it will save the last plot.
11:46	Click on the Plots window.
11:49	Let us save our scatter plot with a name scatter underscore plot in png format.
11:58	In the Source window, type the following command.
12:02	Save the script and run the current line.
12:07	Click on the Files tab.
12:10	The plot has been saved in our current working directory.
12:15	Let us summarize what we have learnt.
12:19	In this tutorial, we have learnt about,
12:23	Need for data visualization
12:26	Basic plot function in R
12:29	ggplot2 package
12:32	We now suggest an assignment.
12:36	Consider the built-in data set mtcars. Find the numerical variables in this data set.
12:43	Make a scatter plot from the objects named mpg and wt in this data set.
12:51	Save the plot in .jpeg format.
12:56	The video at the following link summarises the Spoken Tutorial project.
13:01	Please download and watch it.
13:04	We conduct workshops using Spoken Tutorials and give certificates.
13:10	Please contact us.
13:13	Please post your timed queries in this forum.
13:17	Please post your general queries in this forum.
13:21	The FOSSEE team coordinates the TBC project.
13:24	For more details, please visit these sites.
13:29	The Spoken Tutorial project is funded by , MHRD, Govt. of India
13:36	The script for this tutorial was contributed by Varshit Dubey (CoE Pune).
13:43	This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.

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