Dates and character strings – exercises

Day 3, A

Understanding the pitfalls of dealing with dates, times, and manipulating strings with regular expressions
Author

Michael C Sachs

Learning objectives

In this lesson you will

  1. Manipulate dates and times with lubridate
  2. Learn some basic regular expressions for matching ICD-10/ATC codes

Read in the data

From the data, download and read in the file “med-2005-ex.rds”.

library(here)
here() starts at /home/micsac/Teaching/Courses/r-programming
med2005 <- readRDS(here("data", "med-2005-ex.rds"))

Dates

  1. Use the lubridate package (try the function wday) to calculate the day of the week when the dispensation occurred. Do dispensations occur less frequently on weekends?
  2. Calculate the month of the dispensation. Is there a seasonal trend on the number of dispensations?
  3. Use dplyr or data.table to create a new variable that is the last dispensation for each individual during the year (using group_by then mutate or := with by). Then calculate the number of days between each dispensation and the last one. What is the average number of days?

Regular expressions

The “atc” variable contains the ATC code for the drug that was dispensed. This is a standardized classification system for drugs, see https://www.whocc.no/atc/structure_and_principles/.

  1. Count how many dispensations there were for drugs in the class “J01”. Use the grepl function with a pattern.
  2. Are there any “invalid” ATC codes in the dataset?