Data transformation with dplyr

Research Beyond the Lab: Open Science and Research Methods for a Global Engineer

Lars Schöbitz

2025-03-06

Learning Objectives (for this week)

  1. Learners can apply ten functions from the dplyr R Package to generate a subset of data for use in a table or plot.

Data wrangling with dplyr

A grammar of data wrangling…

… based on the concepts of functions as verbs that manipulate data frames

  • select: pick columns by name
  • arrange: reorder rows
  • filter: pick rows matching criteria
  • relocate: changes the order of the columns
  • mutate: add new variables
  • summarise: reduce variables to values
  • group_by: for grouped operations
  • … (many more)

dplyr rules

Rules of dplyr functions:

  • First argument is always a data frame
  • Subsequent arguments say what to do with that data frame
  • Always return a data frame
  • Don’t modify in place

Functions & Arguments

library(dplyr)

filter(.data = gapminder, 
       year == 2007)
  • Function: filter()
  • Argument: .data =
  • Arguments following: year == 2007 What to do with the data

Objects

library(dplyr)

gapminder_2007 <- filter(.data = gapminder, 
                            year == 2007)
  • Function: filter()
  • Argument: .data =
  • Arguments following: year == 2007 What to do with the data
  • Data (Object): gapminder_2007

Operators

library(dplyr)

gapminder_2007 <- gapminder |> 
  filter(year == 2007)
  • Function: filter()
  • Argument: .data =
  • Arguments following: year == 2007 What to do with the data
  • Data (Object): gapminder_2007
  • Assignment operator: <-
  • Pipe operator: |>

Plot

library(dplyr)

gapminder_2007 <- gapminder |> 
  filter(year == 2007) 

ggplot(data = gapminder_2007,
       mapping = aes(x = continent,
                     y = lifeExp,
                     fill = continent)) +
  geom_boxplot(outlier.shape = NA)

Our turn: SDG 6.2.1

Data

head(sanitation)


name iso3 year region_sdg varname_short varname_long residence percent
Afghanistan AFG 2000 Central and Southern Asia san_bas basic sanitation services national 21.9
Afghanistan AFG 2000 Central and Southern Asia san_bas basic sanitation services rural 19.3
Afghanistan AFG 2000 Central and Southern Asia san_bas basic sanitation services urban 30.9
Afghanistan AFG 2000 Central and Southern Asia san_lim limited sanitation services national 5.6
Afghanistan AFG 2000 Central and Southern Asia san_lim limited sanitation services rural 3.1
Afghanistan AFG 2000 Central and Southern Asia san_lim limited sanitation services urban 14.5 


ncol(sanitation)
[1] 8


nrow(sanitation)
[1] 73710

Data

sanitation |> 
  count(varname_short, varname_long)
varname_short varname_long n
san_bas basic sanitation services 14742
san_lim limited sanitation services 14742
san_od no sanitation facilities 14742
san_sm safely managed sanitation services 14742
san_unimp unimproved sanitation facilities 14742

Our turn: md-03-exercises

  1. Open posit.cloud in your browser.
  2. Open the rbtl-fs25 workspace for the course.
  3. Click Start next to md-03-exercises.
  4. In the File Manager in the bottom right window, locate the md-03a-data-transformation.qmd file and click on it to open it in the top left window.
15:00

Take a break

Please get up and move! Let your emails rest in peace.

10:00

Image generated with DALL-E 3 by OpenAI

Your turn: md-03b-your-turn-filter.qmd

  1. Open posit.cloud in your browser.
  2. Open the rbtl-fs25 workspace for the course.
  3. In the File Manager in the bottom right window, locate the md-03b-your-turn-filter.qmd file and click on it to open it in the top left window.
  4. Follow instructions in the file
15:00

R Terminology

library(dplyr)

sanitation_national_2020_sm <- sanitation |> 
  filter(residence == "national",
         year == 2020,
         varname_short == "san_sm")
  • Function: filter()
  • Arguments following: residence == "national", etc. What to do with the data
  • Data (Object): sanitation_national_2020_sm
  • Assignment operator: <-
  • Pipe operator: |>

Task 1.2

  1. Use the filter() function to create a subset from the sanitation data containing urban and rural estimates for Nigeria.
  2. Store the result as a new object in your environment with the name sanitation_nigeria_urban_rural
sanitation_nigeria_urban_rural <- sanitation |> 
  filter(name == "Nigeria", residence != "national")

Task 1.3 - Connected scatterplot

Great for timeseries data

  1. Use the ggplot() function to create a connected scatterplot with geom_point() and geom_line() for the data you created in Task 1.2.

  2. Use the aes() function to map the year variable to the x-axis, the percent variable to the y-axis, and the varname_short variable to color and group aesthetic.

  3. Use facet_wrap() to create a separate plot urban and rural populations.

  4. Change the colors using scale_color_colorblind().

ggplot(data = sanitation_nigeria_urban_rural,
       mapping = aes(x = year, 
                     y = percent, 
                     group = varname_short, 
                     color = varname_short)) +
  geom_point() +
  geom_line() +
  facet_wrap(~residence) +
  scale_color_colorblind()

Task 1.3 - Connected scatterplot

Our turn: back to md-03a-data-transformation.qmd

  1. Open posit.cloud in your browser.
  2. Open the rbtl-fs25 workspace for the course.
  3. Click Start next to md-03-exercises.
  4. In the File Manager in the bottom right window, locate the md-03a-data-transformation.qmd file and click on it to open it in the top left window.
25:00

Take a break

Please get up and move! Let your emails rest in peace.

15:00

Image generated with DALL-E 3 by OpenAI

Your turn: md-03c-your-turn-summarise.qmd

  1. Open posit.cloud in your browser.
  2. Open the rbtl-fs25 workspace for the course.
  3. In the File Manager in the bottom right window, locate the md-03c-your-turn-summarise.qmd file and click on it to open it in the top left window.
  4. Follow instructions in the file
30:00

Homework assignments module 3

Module 3 documentation

rbtl-fs25.github.io/website/modules/md-03.html

Homework due date

  • Homework assignment due: Wednesday, March 12th
  • Correction & feedback phase up to: Tuesday, March 18th

Wrap-up

Thanks! 🌻

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rbtl-fs25.github.io/website/