R/C2/Introduction-to-Data-Frames-in-R/English-timed

Time	Narration
0:00	Welcome to the spoken tutorial on introduction to data frames in R.
00:05	I am Kannan Moudgalya.
00:07	In this tutorial, we will explain
00:10	how to create a data frame in R
00:13	how to work with it
00:15	how to save it into a file and
00:17	how to read data from a file into a data frame
00:21	We will also learn about the data sets that come with the R software
00:27	To understand this tutorial,
00:29	One needs to know elementary maths
00:32	for example, the meaning of mean, row and column
00:35	One needs to know how to edit a text file and plotting
00:39	No programming background is required, however
00:42	Anyone with a simple exposure to statistics can understand this tutorial
00:46	Please locate this tutorial on our web page, spoken hyphen tutorial dot org
00:53	Prerequisite spoken tutorials, if any, will be mentioned in this page
00:57	I am using version 3 of R RStudio 0.97 and Mac OS X 10.7
01:05	But it should work in other versions and also other operating systems.
01:12	Let us switch to Rstudio.
01:14	Let us begin with the “c” command to create a vector
01:23	Notice that I am using the assignment operator, that is, less than followed by hyphen.
01:31	I will use the word get to denote this assignment.
01:35	This is the way to create a vector of three elements.
01:38	c stands for create or concatenate.
01:42	Although the equal to sign works most of the time, one usually uses this operator in R.
01:49	Let us view what the vector sample contains.
01:56	Let us now build a data set.
01:58	We will also call it as a data frame in this tutorial.
02:03	Let me create a vector of Indian Cricket Captains, old and new.
02:10	names gets c of Mahi, Sourav, Azhar, Sunny, Pataudi, Dravid
02:46	Each name has to be entered in between double quotes.
02:50	Let us view what the variable names contains.
02:57	I will create a vector to store the number of matches they captained.
03:15	One can see that all the variables got entered in the workspace.
03:21	Now, let us put these together and create a data frame as I do now.
03:44	It is data dot frame.
03:50	Let us understand what the command data frame does.
03:55	To do this, let us View brackets captaincy.
04:02	The contents of the data frame appear nicely in the window above.
04:09	What will happen if you just type captaincy, without the View command?
04:14	Would you want to try it now?
04:16	In statistics, one works with such data sets.
04:19	Remember, the commands and variables in R are case sensitive.
04:24	The View command begins with a capital V.
04:29	It is easy to access the parts of a data frame.
04:32	For example, this is how I will get the names in the captaincy data frame.
04:39	Let us similarly get the number of matches won.
04:45	Let us find the ratio of matches won.
04:54	When we divide a vector by another, R carries out component-wise division.
04:59	Let us include this ratio also in the captaincy data frame.
05:04	captaincy dollar victory gets ratio.
05:13	What is in ratio gets transferred to victory, a variable in the data frame captaincy.
05:19	We can check that ratio is included in captaincy by the View command.
05:26	We can see that the ratio is included in victory.
05:31	Let us reduce the number of digits displayed in the ratio to 2.
05:35	We do this with the command options within brackets digits=2.
05:48	Let us view captaincy
05:51	We can see that it has been reduced to two digits.
05:56	Let us see the number of matches played by all the captains.
06:03	Let us find the mean of these matches.
06:07	By direct calculation, we find that mean does exactly what it should.
06:16	These two are equal.
06:18	Let us now get some plots with this data frame.
06:22	Let us plot the ratio of victories vs. the year of captaincy.
06:32	Remember that the x axis variable has to be listed first in the plot command.
06:40	Let us plot the number of matches captained versus the names of the captains.
06:50	Drag this a little bit. The graph expands. Put it back.
06:58	We will now explain how to write this data frame to a comma separated variable file or C S V.
07:06	We will use the command write.csv, as we do now.
07:15	Let me move this to the right.
07:23	This writes the captaincy data frame into the file NewCaptaincy.csv
07:29	Let me open this file using my favourite text editor, emacs.
07:34	You may use any other text editor, such as notepad or vi or gedit.
07:40	Please do not, however, use Microsoft office or LibreOffice Writer for this purpose.
07:46	Because, you may not be able to load these files into R.
07:50	Let us now see what the file NewCaptaincy.csv contains.
07:56	This csv file contains everything that we created.
08:00	It also contains row numbers in every row, at the beginning.
08:06	How will you get rid of these row numbers?
08:09	Add an additional flag called row dot names equals FALSE.
08:32	Let us read the new version of this file in emacs.
08:40	In emacs, I just have to refresh it.
08:46	In other editors, you may have to re-open the file.
08:50	We see that the row numbers no longer appear.
08:53	Let us make a small change in this csv file and read it.
08:57	Let us change Mahi to Dhoni and save the file.
09:09	Let us click Import Dataset.
09:19	There are two options:
09:21	from the text file and from the web.
09:25	Reading from a local file is a fool-proof method.
09:28	It works even if Internet is not available.
09:31	All one has to do is to download the required files ahead of time.
09:36	Let us click the File option.
09:40	Let us select NewCaptaincy.
09:47	In order to see this, let's maximise this, let's Import this, let manimise this.
09:57	You will not have this problem, of course, because, you will be using the whole screen.
10:03	For this tutorial, I am using only a small portion of the screen.
10:08	And that’s why we have had this problem.
10:11	The appearance of a data frame called NewCaptaincy here, and also here, shows that it has been read.
10:20	It also has Dhoni as the name.
10:23	The row numbers have also disappeared.
10:26	It is also possible to read excel and ods files into R.
10:31	This involves a little more work and hence not covered in this tutorial.
10:35	It is easy to export excel and ods files into csv format.
10:40	So, for now, please use the csv route to import excel or ods files.
10:47	Let us take up the final topic for this tutorial.
10:50	R comes with several data frames.
10:54	To access them, issue the command data brackets.
11:03	R shows a list of available data frames.
11:07	We can carry out all sorts of calculations on these data sets.
11:12	We will just view one data set, called C O 2.
11:18	We will conclude this tutorial here.
11:20	In this tutorial, we learnt the following:
11:23	We created vectors
11:25	We put the vectors into a data frame
11:27	We operated on the data frame
11:29	We saved the data frame into a file
11:32	Imported the file into R
11:34	Examined the data frames that came with R
11:38	We now suggest an assignment.
11:40	Find out how to calculate median using the help button.
11:48	Repeat the above using web search.
11:51	Calculate the mean and median of the data frame C O 2.
11:57	Take R’s help for all the commands shown in this tutorial.
12:03	Import a data set from the Internet directly or through a file.
12:08	This video summarises the Spoken Tutorial project.
12:13	If you do not have good bandwidth, you may download and watch it.
12:17	We conduct workshops using Spoken Tutorials; give Certificates.
12:22	Please contact us.
12:24	The Spoken Tutorial project is funded by MHRD, Govt. of India
12:30	Thanks for joining, goodbye.

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