(1) Browsing to find data on a computer and reading it into R.

When trying to find data and import it into R, the commands from the previous tutorial are used; the arguments for each are covered there. The only difference will be the replacement of the file location with the argument 'file.choose()'. The examples below use the same functions for actually reading the data file as was done in the previous tutorial.

Finding a text (.txt) file:

data <- read.table(file.choose(), header = TRUE, sep = " ", dec = ".")

Finding a comma separated values (.csv) file:

data <- read.csv(file.choose(), header = TRUE, sep = ",", dec = ".")

Finding an SPSS (.sav) file; note you must load the 'foreign' library in order to import an SPSS file to R:

library(foreign)

data <- read.spss(file.choose(), use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)

And that's it.

In future tutorial notes, we will be using R console and script files; but remember all scripts can be copied and pasted into the R Console. The script files can also be downloaded and then opened with the R Console or in R Commander using ‘File’, ‘Open script file…’ in the Console or Rcmdr top task bar.

When reading the script files, you'll notice the common convention of using # to start a comment line (which is not working code), while lines without # are working code.