The tidyverse

The tidyverse is an opinonated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
(www.tidyverse.org)

These are the main packages from the tidyverse that we will use:

Workflow data analysis

The tidyverse

Install the tidyverse once with:

install.packages("tidyverse")

Then load and attach the packages at the beginning of your script:

library(tidyverse)

Readr

readr is a tidyverse package. To use it, you can load the tidyverse:

library(tidyverse) # or library(readr) 

Read files with read_*()

All read_* functions take a path to the data file as a first argument:

dat <- read_csv("data/your_data.csv") # comma delimiter

dat <- read_tsv("data/your_data.txt") # tab delimiter

dat <- read_delim("data/your_data.csv", delim = ";") # semicolon delimiter

dat <- read_delim("data/your_data.txt", delim = "----") # ---- delimiter

Read files with read_*()

The read functions provide several options to modify the reading of data.

Have a look at ?read_delim for all options.

Read files with read_*()

# without skipping first lines
read_csv(file = "data/meta_data_top.csv")

#> # A tibble: 6 × 1
#>   Metadata              
#>   <chr>                 
#> 1 Date: June, 12, 1989  
#> 2 Author: Selina Baldauf
#> 3 Temperature, Rainfall 
#> 4 1.5, 2                
#> 5 1, 0                  
#> 6 0.5, 0.6

# skip meta data lines
read_csv(
  file = "data/meta_data_top.csv",
  skip = 4
)

#> # A tibble: 3 × 2
#>   Temperature Rainfall
#>         <dbl>    <dbl>
#> 1         1.5      2  
#> 2         1        0  
#> 3         0.5      0.6

Read files with read_*()

# First line expected to be column names
read_csv(file = "data/no_col_names.csv")

#> # A tibble: 2 × 2
#>   `1.5`   `2`
#>   <dbl> <dbl>
#> 1   1     0  
#> 2   0.5   0.6

# Default column names are given
read_csv(
  file = "data/no_col_names.csv",
  col_names = FALSE
)

#> # A tibble: 3 × 2
#>      X1    X2
#>   <dbl> <dbl>
#> 1   1.5   2  
#> 2   1     0  
#> 3   0.5   0.6

Read files with read_*()

# First line expected to be column names
read_csv(file = "data/no_col_names.csv")

#> # A tibble: 2 × 2
#>   `1.5`   `2`
#>   <dbl> <dbl>
#> 1   1     0  
#> 2   0.5   0.6

# Specify custom column names
read_csv(
  file = "data/no_col_names.csv",
  col_names = c("Temperature", "Rainfall")
)

#> # A tibble: 3 × 2
#>   Temperature Rainfall
#>         <dbl>    <dbl>
#> 1         1.5      2  
#> 2         1        0  
#> 3         0.5      0.6

Write files with write_*()

Every read_* has a corresponding write_* function to export data from R.

Write files with write_*()

All write_* functions take the data to write as the first and the file to write to as the second argument:

write_csv(dat, file = "data-clean/your_data.csv") # comma delimiter

write_tsv(dat, file = "data-clean/your_data.txt") # tab delimiter

write_delim(dat, file = "data-clean/your_data.csv", delim = ";") # semicolon delimiter

write_delim(dat, file = "data-clean/your_data.txt", delim = "----") # ---- delimiter

Readxl

The readxl package is part of the tidyverse, but you need to load it explicitly

library(readxl)

dat <- read_excel(path = "data/your_data.xlsx")

dat <- read_excel(path = "data/your_data.xlsx", sheet = "sheetName") # via sheet name
dat <- read_excel(path = "data/your_data.xlsx", sheet = 2) # via sheet number

Readxl

A little warning:

• Reading from a text file (.txt or .csv) is more reliable
• Be careful with complicated excel sheets with formulas etc.
• Always double check the data that you imported, e.g. by using the summary function and checking if the number of rows etc. is correct

Absolute vs. relative paths in R

Absolute paths

C:/Users/Selina/folder1/folder2/data/file_to_read.csv

Relative paths

data/file_to_read.csv

• Relative paths are interpreted relative to the working directory
• Check out where your working directory is with getwd()
• In RStudio projects, the working directory is always the project root

Absolute vs. relative paths

Working with R and RStudio, the best way is to:

• Organize your work in an RStudio project
  • The project root is automatically the working directory
  • All your files (also your data) are in one place
• Use paths relative to the project root

Data format

Follow these guidelines to make data import to R easier and less frustrating

• In general: prefer machine-readable file formats (.csv, .txt instead of .xlsx)

• No white space in column headers
  • Use a character as separator, e.g. species_name instead of species name
  • If this is unpractical, have a look at the function janitor::clean_names() from the janitor package
• No special characters in column headers (ä,, ß, é, ê, %, °C, µ …)
• Use . as a decimal separator (not ,)

Paths and file names

• Avoid white space in paths and file names
  • data-raw/my_data.csv instead of data raw/my data.csv
• Avoid special characters in paths