After downloading RStudio from here, start it up. You will see an interface that looks approximately like this:

On the left, you will be able to type in R commands. On the lower right, you can get help on all things R, and if you produce a graph, it will be displayed there too. But before you can do that, you need data.

Converting an Excel spreadsheet into a format that R can read

Let's start with a some data that I have extracted from a corpus (a text collection) of U.S. inaugural addresses. Often when you produce data, you first have it in a spreadsheet, such as Excel. So let's first practice getting data out of Excel and into R, if you have Excel. (Otherwise skip this step and go to "Reading data into RStudio".)

You can download the inaugural addresses data as an Excel .xlsx file here. R cannot directly read this file -- the internal Excel format looks like gibberish to it. But you can export the data form Excel in a format called "CSV", or comma separated values:

• In Excel, choose the "File" menu, and from the "File" menu choose "Save As..."
• In the panel that comes up, change "Format" to "Comma Separated Values (.csv)"
  

Reading data into RStudio

If you have skipped the previous step, you find the inaugural data as a .csv file here.

In RStudio, choose "Import Dataset" on the upper right side of the window, and choose "From Text File":

Choose the .csv file with the inaugural addresses. You now see a panel with many choices that you can make about the encoding (you should be fine leaving everything as is), and a preview on the right-hand side. Hit "Import".

Congratulations: you have imported your first dataset into R. You can now see the table in the upper left panel in RStudio:

• Each row describes one speech.
  
• The first column contains column numbers
• The second column has president names
• The third column contains the year the address was given
• The 4th column is the length of the speech in words
• All the rest of the columns are word counts. For example, the column headed "America" says how often this speech contained the word "America".

The lower left panel of RStudio is for typing in R commands. Instead of importing the file by clicking on "Import Dataset", you could also have typed a command in the lower left panel to achieve the same effect. And in fact, RStudio is now showing in the lower left panel the command that you could have typed. In my case, it is

> inaugural <- read.csv("~/Teaching/repeating_classes/stats_intro/materials/data/inaugural.csv")
>   View(inaugural)

The ">" is not something you would type. It is just the prompt that R gives you to show that you can type in a command. "read.csv", somewhat unsurprisingly, is a command that reads a csv file. The contents of the file have been stored under the name "inaugural" using the left-arrow: "inaugural <- read.csv(...)". The command "View(inaugural)" views the contents of "inaugural" in the upper left panel. You can also view the whole thing in the lower left panel by typing "inaugural" and hitting Return. To see just the first few lines of the table, type

> head(inaugural)

(Again, don't type the ">", it's what R shows you automatically.) This should give you the following output:

X  president year length America citizen citizens democracy freedom  I me duties
1 1 Washington 1789   1538       0       0        5         0       0 23  8      1
2 2 Washington 1793    147       1       0        1         0       0  6  1      0
3 3      Adams 1797   2585       5       1        5         0       0 13  5      1
4 4  Jefferson 1801   1935       0       0        7         0       4 21  4      2
5 5  Jefferson 1805   2384       0       0       10         0       2 18  8      4
6 6    Madison 1809   1265       0       0        1         0       1 11  8      2

Note: In this course, we will try to get by with relatively little R. The commands that we do in this worksheet will be about the most complex you will  have to master. R can do much more, and if you are curious for additional R constructions or commands, let me know. But you will not need more for this course.

Inspecting a data frame

inaugural is a table -- R calls this a data frame. A data frame consists of cells. For example, the cell at row 1, column 2 contains the word "Washington". You can get this information from R as follows:

> inaugural[1,2]
[1] Washington
34 Levels: Adams Buchanan Bush Carter Cleveland Clinton Coolidge Eisenhower Garfield Grant Harding ... Wilson

"inaugural[ 1, 2]" says that you want to access row 1, column 2 of the data frame stored in inaugural. You have to use square brackets for this. When you type inaugural[1,2], the first piece of data that R gives you, labeled [1], is "Washington". (In this case it is also the only piece of data. But when you type in a query that lets R give you tens or hundreds of pieces of data as answer, you may appreciate the numbering.) The data that it outputs is "Washington", the entry at row 1 column 2.

Additionally, it tells you that there are 34 different values for column 2, ranging from "Adams" to "Wilson". (You can ignore that information for now.)

Try it yourself:

• What is the name of the president who gave the 3rd inaugural address? (For now, look in the upper left panel to find out what the column number is for the "president" row. We will discuss a simpler way of doing this later.)
• What was the year in which the 10th inaugural address was given?
• In the 20th inaugural address, how often was the word "democracy" mentioned?

Helpful hint: If you hit the "uparrow" in the R panel (the lower left panel of RStudio), you see the previous command you entered, and you can edit it. When you are done, hit "Return" to execute it.

If you want to see a whole row, just give the number of the row and nothing for the column (but don't forget the comma). For example, to bring up the whole entry for the 5th inaugural address you write inaugural[5, ]: row 5, then a comma, then nothing for the column.

The same way, if you want to see all entries for a column, just give the number of the column but no number for the row. So to see all years in which inaugural addresses were given, you put inaugural[, 3]: nothing for the row, then a comma, then column 3.

Plots

• Try
  plot(inaugural$duties, type="l")
   (that's a lowercase ell). What is different?
  
  
• Other possible types: p, b, h. (not a complete list, there are more). What does each option do? And what option do you find most appropriate for the data?
  
  
• Try
  plot(inaugural$duties, type="p", col="red")
   What other colors work? If you want to see a complete list, try
  
  > colors()
  
  
• The plot( ) command has many parameters. To see more information, type
  
  ?plot
  in the R panel, or choose "Help" in the lower right-hand panel and type "plot" into the search window.
  

Sorting and ordering

You can sort a vector using the command sort( ). Here is how you sort speech lengths from the shortest to the longest:

You can use this sorted vector, for example, to determine the 10 shortest speeches:

For data frames, you usually want to do something slightly different. For example, you might not want to sort just the lengths of the inaugural speeches, but sort all the rows in the order of their speech lengths. The command for this is order( ), and you put it where you would put a filtering constraint. The following command says "I want the inaugural table, in the order of the speech lengths:

Both sort( ) and order( ) can optionally be given another parameter, "decreasing = TRUE", which makes them sort from largest to smallest instead of smallest to largest.

Try it for yourself:

• Sort the speech lengths (by themselves, not the whole data frame) from largest to smallest, and determine the lengths of the 3 longest speeches.
  
• Sort the whole inaugural data frame by lengths of speeches, longest to shortest.
  
• Sort the whole inaugural data frame by how often each speech mentions "democracy"

Merging data frames

Our inaugural data frame does not contain the party affiliation of the presidents. If we had that, we could do a number of additional analyses, for speech length or the degree to which presidents use terms like "freedom" or "democracy". This is a good opportunity to demonstrate how to merge data frames in R.

For convenience, we will use a smaller amount of data. Above we made a "recent inaugural addresses" data frame inaug.new using

It contains 13 entries. We would like to make a new data frame that maps inaugural address years to the affiliation of the president. But how do you make a new data frame by hand (rather than reading one in from file)?

Here is how: You use the function data.frame( ). Inside the parentheses, you specify each column of the data frame. For example, to make a data frame with one column called "A" and one called "B", you could type:

So the column A contains the sequence of values 1,2,3 (in that order), and the column B contains the sequence 20, 30, 40.

Now we are ready for the data frame mapping inaugural address years to the affiliation of each president:

The "year" column in this data frame is a sequence of numbers that are the same as in inaug.new, and the "party" column is a sequence of either "D" or "R".

Now we want to merge the two data frames by their common column "year". And that is exactly what we say:

What merge( ) has done is to link every row in inaug.new to the matching row in affiliation.

We could have saved ourselves some work if we had defined party affiliations for presidents, not years, as some presidents appear multiple times in our data frame. (And we even have two different presidents with the same name and same affiliation in our data.) So we could also say:

But now affiliation is a data frame with 9 rows, and inaug.new has 13 rows. What will merge( ) do?

Reading and writing data in the console

You can always read data using the "Import Dataset" button in RStudio, and that is probably the most convenient route. But you can also read and write data using commands in the console.

• read.csv() reads CSV files, and write.csv() writes data to a file in CSV format
• read.table() reads files that contain a table with whitespace between values, and write.table() writes data to a file with whitespace between values.
  

Watch out: When you use read.table(), and the table has header information in the file, you have to add the parameter header=T, or your data will be mangled.

After reading in new data, say to a data frame named my.df, I recommend always visualizing the first few lines of the data frame to make sure it has been read in without any trouble:

The biggest issue with using read.csv() and read.table() is usually figuring out the locations of the files.