getwd()
setwd("path/to/directory")Filepaths and data I/O
Once you deal with external files (e.g. loading a .csv), saving outputs or reports, you have to deal with directory and file structure. For beginners, this is often harder than the actual R code itself. To keep everything easy and consistent within R (at least for this course), I recommend setting a working directory either manually or with a Rstudio project and relative file paths to data.
The working directory
The working directory in R is a valuable feature that can saves a lot of nerves and time to navigate to files on your computer. You can check your current working directory with the getwd() function. In RStudio, you can also see your working directory as a path above the R console next to your R version number. The file browser of Rstudio also starts in your working directory by default. Use setwd() in order to change the working directory to some location (i.e. folder) on you computer. R and Rstudio know about the files in your working directory. You can think of it as the starting point of file paths.
Relative file paths
To import data into R, you have to work with its file path. You could use the absolute file path, i.e. starting with the root of your file system:
## Linux
"/home/marvin/projects/rcourse/session02/data/file.csv"## Windows
"C:\Users\Marvin\Dokumente\rcourse\session02\data\file.csv"Way more convenient and less prone to errors is the use of relative paths by setting your working directory.
setwd("/home/marvin/projects/rcourse/session02")
# If the R project is set up in the "session02" directory we can use relative paths:
# relative path to project root
data = read.csv("data/file.csv")
File path auto-completion
Use the TAB Key in RStudio for navigation and auto-completion of file paths.
.csv
Once you deal with actual data you want to analyse, it is rare that you want to build a data.frame from scratch like the example above. Instead, you have some file prepared on your computer e.g. a .csv file you want to get into R. Most likely, this data comes from a spreadsheet application like Excel or Google Sheets that have their own file formats, e.g. a .xlsx file. While it is possible to import .xlsx files into R with external packages, the more elegant way is to use a much simpler file format: comma separated values - .csv.
As the name suggests, a .csv file contains values separated by commas ,:
plotID, soil_ph, soil_temperature, forest_type
1, 5.5, 10, coniferous
2, 5.4, 11, coniferous
3, 6.1, 12, deciduous
csv files in Germany
In Germany, , is used as the decimal point. You will often find csv files where the delimiter is a semicolon ;.
To get a .csv file into R, use the read.csv() function. Here you can also specify the dec and sep arguments to the correct symbols for decimal points and separators.
soil = read.csv("data/soil_samples.csv", sep = ",", dec = ".")R studio Projects
If you want to use RStudio Projects, there is a good blog entry in r-bloggers.com.
