14  Scripts

14.0.1 Save scripts

  • Make sure you have saved your script 💾 and given it the filename 01_import_penguins_data.R it should be saved in your scripts folder 
#___________________________----
# SET UP ----
# An analysis of the bill dimensions of male and female 
# Adelie, Gentoo and Chinstrap penguins

# Data first published in  Gorman, KB, TD Williams, and WR Fraser. 
# 2014. 
# “Ecological Sexual Dimorphism and Environmental Variability 
# Within a Community of Antarctic Penguins (Genus Pygoscelis).” 
# PLos One 9 (3): e90081.
# https://doi.org/10.1371/journal.pone.0090081. 
#__________________________----

# PACKAGES ----
library(tidyverse) # tidy data packages
library(here) # organised file paths
library(janitor) # cleans variable names
#__________________________----

# IMPORT DATA ----
penguins_raw <- read_csv(here("data", "raw", "penguins_raw.csv"))

# check the data has loaded, prints first 10 rows of dataframe
penguins_raw
#__________________________----

# CLEAN DATA ----

# clean all variable names to snake_case 
# using the clean_names function from the janitor package
# note we are using assign <- 
# to overwrite the old version of penguins 
# with a version that has updated names
# this changes the data in our R workspace 
# but NOT the original csv file

# clean the column names
# assign to new R object
penguins_clean_names <- janitor::clean_names(penguins_raw) 

# quickly check the new variable names
colnames(penguins_clean_names) 

# shorten the variable names for N and C isotope blood samples

penguins_clean_names <- rename(penguins_clean_names,
         "delta_15n"="delta_15_n_o_oo",  # use rename from the dplyr package
         "delta_13c"="delta_13_c_o_oo")

# use mutate and case_when for a statement that conditionally changes the names of the values in a variable
penguins <- penguins_clean_names |> 
  mutate(species = case_when(species == "Adelie Penguin (Pygoscelis adeliae)" ~ "Adelie",
                             species == "Gentoo penguin (Pygoscelis papua)" ~ "Gentoo",
                             species == "Chinstrap penguin (Pygoscelis antarctica)" ~ "Chinstrap"))

# use mutate and if_else
# for a statement that conditionally changes 
# the names of the values in a variable
penguins <- penguins |> 
  mutate(sex = if_else(
    sex == "MALE", "Male", "Female"
  )
  )

# use lubridate to format date and extract the year
penguins <- penguins |>
  mutate(date_egg = lubridate::dmy(date_egg))

penguins <- penguins |> 
  mutate(year = lubridate::year(date_egg))

# Set body mass ranges
penguins <- penguins |> 
  mutate(mass_range = case_when(
    body_mass_g <= 3500 ~ "smol penguin",
    body_mass_g >3500 & body_mass_g < 4500 ~ "mid penguin",
    body_mass_g >= 4500 ~ "chonk penguin",
    .default = NA)
  )

# Assign these to an ordered factor

penguins <- penguins |> 
  mutate(mass_range = fct_relevel(mass_range, 
                                  "smol penguin", 
                                  "mid penguin", 
                                  "chonk penguin")
         )

# WRITE CLEAN DATA ----
## Optional ----

write_csv(penguins, here::here("data", "clean", "penguins_clean.csv"))
  • Does your workspace look like the below?

My neat project layout

My scripts and file subdirectory
  • Does your script run from a blank slate without errors?

14.0.2 Checklist for data checking

  • Is our dataframe in a tidy format?

  • Is each column assigned to the correct data type?

    • Are dates formatted correctly?
    • Are factors set where needed, are the levels in the correct order?

  • Are variables consistently named (e.g. using a naming convention such as snake_case)?

  • Are text values in an appropriate format?

  • Do we have any data duplication?

  • Are there any typos or mistakes in character strings?

14.1 Running a script from console

Now that we have made an import and cleaning script, we can check our blank slates set-up and that our script runs without errors:

Your turn

source("scripts/01_import_penguins_data.R") # must specify dir

# source(here::here("scripts", "01_import_penguins_data.R"))
# using here

14.2 Essential shortcuts

  • Comment/uncomment - Ctrl/Cmd + Shift + c

    • Highlight text and use shortcut keys to commment out text

  • Rename in scope - Ctrl/Cmd + Alt + Shift + M

    • Highlight an R object then use this shortcut key to rename all examples in the script

14.3 Layout

  • Define logical regions of scripts with either ---- or ==== after #

  • This uses markdown logic, #, ##, ### to define titles, headers and subheaders

  • Use styler to help format code and keep it neat

14.4 Reading