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Some of the functions used in this section are described below.

read.table(). If the data set for a project is not small, it is most convenient to enter the data into R from a tabular data file in which each row corresponds to an individual and columns contain various measurements associated with each individual. These files must be plain text (not created by a document processor such as Word). If the data comes from a database or spreadsheet, the simplest way to have R read the data is to have the database or spreadsheet export the data into a comma-separated values file (csv). An example is given by the file

The first argument is the name of the data file. This must be a string that contains the full path to the file if it is not in the startup directory, or it may be an internet address if the file is on a remote server.
The first row of the crabs.csv file contains names for the columns. This row is referred to as a header and requires use of the
The values in each row are separated by a comma. The default separator is white space, so the argument
is needed for the crabs data file. The following R code performs this task.
Crabs = read.table("",header=TRUE,sep=",")
Note: read.table() will return an error message if it finds that the rows don't all contain the same number of values. This can occur, for example, if a csv file was created from an Excel file that had some extraneous blank cells. Otherwise, read.table() returns a data frame that is assigned to the name Crabs.

Note that the first two columns, named Species and Sex, respectively, contain strings, not numeric values. In such cases, read.table() assumes these are categorical variables and then converts each of them automatically to a factor. The unique values of a factor are referred to as its levels. The levels of Species are B,O (for blue and orange), and the levels of Sex are M,F.

A particular column of a data frame can be accessed by name of the data frame followed by a dollar sign followed by the name of the column. So, for example,

refers to the column named FL within the Crabs data frame. We can obtain a histogram of that column by

Smoke-Cancer data

The file
contains data related to smoking and cancer rates by state for 2010.

  1. Import this data into R using the first column as row names. This requires adding the argument,
    within read.table().
  2. Create a new data frame that contains the following variables:
    CigSalesRate = FY2010 Sales per 100,000 population,
    CigYouthRate, CigAdultRate, LungCancerRate, EsophCancerRate
  3. Create all pairwise plots of the variables in this data frame. Add an informative main title and note on the plot that the data includes all states and D.C. Used filled circles for the plot character.
  4. Create a new plot of LungCancerRate vs CigSalesRate with informative title. Note on the plot that CigSalesRate is cigarette sales per 100,000 population.
  5. Repeat this plot but now use red for Texas, black for others, and add the text TX next to the point corresponding to Texas in this plot.
  6. Repeat previous plot but use CigYouthRate instead of CigSalesRate.
  7. Repeat but use CigAdultRate instead of CigYouthRate.
  8. Once you are happy with how these plots look, save them in a pdf document.

Crabs data

Some of the graphical tools available in R are illustrated in the script file

next up previous
Next: Example script Up: Graphical tools Previous: R functions