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R in 3 Months (Spring 2025)

Bring It All Together (Advanced Data Wrangling)

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View code shown in video 
# Load Packages -----------------------------------------------------------

library(tidyverse)
library(janitor)

# Import Data -------------------------------------------------------------

survey_data_raw <- read_tsv("data-raw/2020-combined-survey-final.tsv") |> 
  clean_names() |> 
  mutate(id = row_number())


# Exploration -------------------------------------------------------------

survey_data_raw |> 
  glimpse()

survey_data_raw
count(qr_learning_path) |> 
  arrange(desc(n))


# Tidying -----------------------------------------------------------------

other_coding_languages <- 
  survey_data_raw |> 
  select(id, qcoding_languages) |> 
  separate_longer_delim(qcoding_languages,
                        delim = ", ")

demographics <- survey_data_raw |> 
  select(id, qyear_born:qcountry)


# Export ------------------------------------------------------------------

other_coding_languages |> 
  write_rds("data/other_coding_languages.rds")

demographics |> 
  write_rds("data/demographics.rds")

Learn More

For your reference:

Bring it All Together (Data Wrangling) [video]

Bring it All Together (Data Visualization) [video]

Bring it all Together Quarto [video]

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Marina Gimenez

Marina Gimenez • April 4, 2025

I do not understand by instead of working with the whole file you first make 2 files out of it to bring them back together? Or was it just for the sake of practicing? Or because the file was so big that indeed you just only picked what you needed, but then still you could have picked it to make a single file?

Gracielle Higino

Gracielle Higino Coach • April 4, 2025

Hi Marina! All of these are good reasons to split your dataset! Very often, splitting a dataset makes it clearer and easier to transpose the parts, when you need to pivot longer or wider. Then you can use joins to combine the pivoted datasets, using only the variables you want.

That's what David is demonstrating here: notice how the raw data in this example contains 53 variables, while the selected and cleaned datasets only have 6 and 2 variables each. He cleaned and selected the data he'd need, and then used them separately and joined as he needed.

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