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df02_files.qmd
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# 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.
```{r}
#| eval: false
#| echo: true
getwd()
setwd("path/to/directory")
```
## 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:
```{r, eval = FALSE}
## Linux
"/home/marvin/projects/rcourse/session02/data/file.csv"
```
```{r, eval = FALSE}
## 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.
```{r, eval = FALSE}
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")
```
:::{.callout-tip}
## 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
```
:::{.callout-warning}
## 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 `dec`imal points and `sep`arators.
```{r, eval = FALSE}
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](https://www.r-bloggers.com/2020/01/rstudio-projects-and-working-directories-a-beginners-guide/).
