A real world example
In chronicler: Add Logging to Functions

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This is practically the same code you can find on this blog post of mine: https://www.brodrigues.co/blog/2018-11-14-luxairport/ but with some minor updates to reflect the current state of the {tidyverse} packages as well as logging using {chronicler}.

Let's first load the required packages, and the avia dataset included in the {chronicler} package:

library(chronicler)
library(dplyr)
library(tidyr)
library(stringr)
library(lubridate)

# Ensure chronicler version of `pick()` is being used
pick <- chronicler::pick

data("avia")

Now I need to define the needed functions for the analysis. To improve logging, I add the dim() function as the .g argument of each function below. This will make it possible to see how the dimensions of the data change inside the pipeline:

# Define required functions 
# You can use `record_many()` to avoid having to write everything

r_select <- record(select, .g = dim)
r_pivot_longer <- record(pivot_longer, .g = dim)
r_filter <- record(filter, .g = dim)
r_mutate <- record(mutate, .g = dim)
r_separate <- record(separate, .g = dim)
r_group_by <- record(group_by, .g = dim)
r_summarise <- record(summarise, .g = dim)

avia_clean <- avia %>%
  r_select(1, contains("20")) %>% # select the first column and every column starting with 20
  bind_record(r_pivot_longer, -starts_with("unit"), names_to = "date", values_to = "passengers") %>%
  bind_record(r_separate,
              col = 1,
              into = c("unit", "tra_meas", "air_pr\\time"),
              sep = ",")

Let’s focus on monthly data:

avia_monthly <- avia_clean %>%
  bind_record(r_filter,
              tra_meas == "PAS_BRD_ARR",
              !is.na(passengers),
              str_detect(date, "M")) %>%
  bind_record(r_mutate,
              date = paste0(date, "01"),
              date = ymd(date)) %>%
  bind_record(r_select,
              destination = "air_pr\\time", date, passengers)

avia_monthly is an object of class chronicle, but in essence, it is just a list, with its own print method:

avia_monthly

Now that the data is clean, we can read the log:

read_log(avia_monthly)

This is especially useful if the object avia_monthly gets saved using saveRDS(). People that then read this object, can read the log to know what happened and reproduce the steps if necessary.

Let's take a look at the final data set:

avia_monthly %>%
  pick("value")

It is also possible to take a look at the underlying .log_df object that contains more details, and see the output of the .g argument (which was defined in the beginning as the dim() function):

check_g(avia_monthly)

After select() the data has 509 rows and 231 columns, after the call to pivot_longer() 117070 rows and 3 columns, separate() adds two columns, after filter() only 7632 rows remain (mutate() does not change the dimensions) and then select() is used to remove 2 columns.