Probability with R. Jane M. Horgan

and arch1, prog1, arch2, prog2, which are abbreviations for Architecture and Programming from Semester 1 and Semester 2, respectively. The remaining rows are the marks (%) obtained for each student. NA denotes that the marks are not available in this particular case.

      The construct for reading this type of data into a data frame is read.table.

      results <- read.table ("F:/data/results.txt", header = T)

      assuming that your data file

is stored in the

folder on the F drive. This command causes the data to be assigned to a data frame called results. Here header = T or equivalently header = TRUE specifies that the first line is a header, in this case containing the names of the variables. Notice that the forward slash (

) is used in the filename, not the backslash (\) which would be expected in the windows environment. The backslash has itself a meaning within R, and cannot be used in this context: / or \\ are used instead. Thus, we could have written

      results <- read.table ("F:\\data\\results.txt", header = TRUE)

      with the same effect.

      The contents of the file results may be listed on screen by typing

      results

      which gives

       gender arch1 prog1 arch2 prog2 1 m 99 98 83 94 2 m NA NA 86 77 3 m 97 97 92 93 4 m 99 97 95 96 5 m 89 92 86 94 6 m 91 97 91 97 7 m 100 88 96 85 8 f 86 82 89 87 9 m 89 88 65 84 10 m 85 90 83 85 11 m 50 91 84 93 12 m 96 71 56 83 13 f 98 80 81 94 14 m 96 76 59 84 ....

Notice that the gender variable is a factor with two levels “f” and “m,”while the remaining four variables are numeric. The figures in the first column on the left are the row numbers, and allows us to access individual elements in the data frame.

      While we could list the entire data frame on the screen, this is inconvenient for all but the smallest data sets. R provides facilities for listing the first few rows and the last few rows.

      head(results, n = 4)

      gives the first four rows of the data set.

      gender arch1 prog1 arch2 prog2 1 m 99 98 83 94 2 m NA NA 86 77 3 m 97 97 92 93 4 m 99 97 95 96

      and

      tail(results, n = 4)

      gives the last four lines of the data set.

       gender arch1 prog1 arch2 prog2 116 m 16 27 25 7 117 m 73 51 48 23 118 m 56 54 49 25 119 m 46 64 13 19

      The convention for accessing the column variables is to use the name of the data frame followed by the name of the relevant column. For example,

      results$arch1[5]

      returns

      [1] 89

      which is the fifth observation in the column labeled arch1.

      Usually, when a new data frame is created, the following two commands are issued.

      attach(results) names(results)

which give

      [1] "gender" "arch1" "prog1" "arch2" "prog2"

      indicating that the column variables can be accessed without the prefix results. For example,

      arch1[5]

      gives

      [1] 89

      The command read.table assumes that the data in the text file are separated by spaces. Other forms include:

      read.csv, used when the data points are separated by commas;

      read.csv2, used when the data are separated by semicolons.

      It is also possible to enter data into a spreadsheet and store it in a data frame, by writing

      newdata <- data.frame() fix(newdata)

      which brings up a blank spreadsheet called newdata, and the user may then enter the variable labels and the variable values.

      Right click and close creates a data frame newdata in which the new information is stored.

      If you subsequently need to amend or add to this data frame write

      fix(newdata)

      which retrieves the spreadsheet with the data. You can then edit the data as required. Right click and close saves the amended data frame.

1.7 Missing Values

      R allows vectors to contain a special

value to indicate that the data point is not available. In the second record in

, notice that

appears for arch1 and prog1. This means that the marks for this student are not available in Architecture and Programming in the first semester; the student may not have sat these examinations. The absent marks are referred to as

, and are not included at the analysis stage.

1.8 Editing

It is possible to edit data that have been already entered and to edit and invoke commands that have been previously used.

      1.8.1 Data Editing

      The data you have read and stored may be edited and changed interactively during your R session. Simply click on Edit on the toolbar to get access to the Data Editor, which allows you to bring up any data frame as a spreadsheet. You can edit its entries as you wish.

      It is also possible to change particular entries of a data frame. For example,

      arch1[7] <- 10

      changes the mark for the seventh student in

in the data frame

from 100 to 10. It may have been entered as 100 in error.

      1.8.2 Command Editing

      The command

      history()

      brings up the previous 25 commands on a separate screen. These can be edited and/or used again as you wish.

      history(max.show = Inf)

      retrieves all previous commands that you have used.

1.9 Tidying Up

      As your R session continues, you may find that the set of objects you have used has become unwieldy, and you may want to remove some. To see what the workspace contains write

      ls()

      or equivalently

      objects()

which causes all objects in the workspace to appear on the screen. If you have run the preceding examples in this chapter, the following should appear.