Intro to R in Epidemiology (Part Two)

Written by Aashna Uppal @auppal

(The content of this course draws from the EpiR Handbook (Chapters 3-8, 17, 30, 40), which is a free online resource developed by Applied Epi)

The tidyverse is like a package of packages, designed specifically for working with data in a “tidy” way.
You can still manipulate data without it – this is called the “base R” way of doing it.
The difference between the two can be illustrated by a quick example.

put_in_oven(mix_all_ingredients_together_into_a_cake_batter(combine(flour, egg, baking_soda, butter, sugar)))

flour %>%
add(egg) %>%
add(baking_soda) %>%
add(butter) %>%
add(sugar) %>%
mix_all_ingredients_together_into_a_cake_batter() %>%
put_in_oven()

The pipe operater (%>%)

Simply explained, the pipe operator (%>%) passes an intermediate output from one function to the next. You can think of it as saying “then”. Many functions can be linked together with %>%.
Piping emphasizes a sequence of actions, not the object the actions are being performed on
Pipes come from the package magrittr, which is automatically included in packages dplyr and tidyverse
Pipes can make code more clean and easier to read, more intuitive

When you import a “dataset” into R, you are generally creating a new data frame object in your R environment and defining it as an imported file (e.g. Excel, CSV, TSV, RDS) that is located in your folder directories at a certain file path/address. We will see an example of this later.
You can import/export many types of files, including those created by other statistical programs (SAS, STATA, SPSS). You can also connect to relational databases.

The rio package

The R package we recommend for importing data is rio.
Its functions import() and export() can handle many different file types (e.g. .xlsx, .csv, .rds, .tsv). When you provide a file path to either of these functions (including the file extension like “.csv”), rio will read the extension and use the correct tool to import or export the file.
The alternative to using rio is to use functions from many other packages, each of which is specific to a type of file. For example, read.csv() (base R), read.xlsx() (openxlsx package), and write_csv() (readr pacakge), etc.

The here package

The package here and its function here() make it easy to tell R where to find and to save your files – in essence, it builds file paths.
Used in conjunction with an R project, here allows you to describe the location of files in your R project in relation to the R project’s root directory (the top-level folder). This is useful when the R project may be shared or accessed by multiple people/computers.
When the here package is first loaded within the R project, it places a small file called “.here” in the root folder of your R project as a “benchmark” or “anchor”

here::i_am("cleaning_code.R")
linelist <- import(here("data", "ebola_linelist.xlsx"))

Written by Aashna Uppal @auppal