Tag: R

Neat guide to {tidyverse} updates in R, by Mine Çetinkaya-Rundel

This guide is intended for people teaching tidyverse, but it’s relevant to anyone (e.g., me!) trying to keep up with developments.

Everytime I load {tidyverse}, it advises me to use {conflicted}. I didn’t realise it would be this useful:

library(conflicted)
    
penguins |>
  filter(species == "Adelie")
#> Error:
#> ! [conflicted] filter found in 2 packages.
#> Either pick the one you want with `::`:
#> • dplyr::filter
#> • stats::filter
#> Or declare a preference with `conflicts_prefer()`:
#> • `conflicts_prefer(dplyr::filter)`
#> • `conflicts_prefer(stats::filter)`

Another neat update: case_when now doesn’t need type-specific NAs like NA_character_ for default cases.

# now, optionally
df |>
  mutate(
    x = case_when(
       ~ "value 1",
       ~ "value 2",
       ~ "value 3",
      .default = NA
    )
  )

Lots of other tips.

Distinguishing between = and <-

‘Perhaps because of the use of = for assignment in FORTRAN […] assignment is often read as “x equals E”. This causes great confusion. The first author learned to distinguish between = and := while giving a lecture in Marktoberdorf, Germany, in 1975. At one point, he wrote “:=” on the board but pronounced it “equals”. Immediately, the voice of Edsger W. Dijkstra boomed from the back of the room: “becomes!”. After a disconcerted pause, the first author said, “Thank you; if I make the same mistake again, please let me know.”, and went on. Once more during the lecture the mistake was made, followed by a booming “becomes” and a “Thank you”. The first author has never made that mistake again! The second author, having received his undergraduate education at Cornell, has never experienced this difficulty.’

– David Gries and Fred B. Schneider (1993, p. 17, footnote 6) [A logical approach to discrete math. Springer.]

R lets you use both = and <- for assignment, FYI.

Little Miss and Mr Men name binariness

Pownall and Heflick (2023) investigated gender stereotypes in all 47 Mr Men and 34 Little Miss books. One of the studies asked (adult) participants to rate the masculinity/femininity of the character names on a scale from 1 (“entirely feminine”) to 5 (“entirely masculine”). The “Mr” and “Little Miss” were stripped off, so, e.g., participants were asked about the words “Quick”, “Princess”, “Greedy”, etc.

I created a name binariness index for each mean rating, \(r\); the distance between the mean rating and 3 (the mid-point), as a proportion of the maximum that distance could be (2):

\(\displaystyle \frac{|r-3|}{2}\)

Here’s a plot of the names, sorted by name binariness. So good candidates for nonbinary characters would be Mx Quick or Mx Lucky. Alternatively, you could rightly reject the premise that any of them are gendered and go for Mx Princess.

References

Pownall, M., & Heflick, N. (2023). Mr. Active and Little Miss Passive? The Transmission and Existence of Gender Stereotypes in Children’s Books. Sex Roles, 89, 758–773.

Mediation analysis effects

Are you wondering how to extract estimates of the following estimands from linear models underlying mediation analyses…?

  • total average causal effect (ACE) / average treatment effect (ATE)
  • average direct effect (ADE)
  • average causal mediation effect (ACME)
  • proportion mediated effect

You might be interested in this example I put together, using {mediation} in R, showing the arithmetic!

Research Design in the Social Sciences

“This book introduces a new way of thinking about research designs in the social sciences. Our hope is that this approach will make it easier to develop and to share strong research designs.

“At the heart of our approach is the MIDA framework, in which a research design is characterized by four elements: a model, an inquiry, a data strategy, and an answer strategy. We have to understand each of the four on their own and also how they interrelate.”

Uses {DeclareDesign} in R.

Blair, G., Coppock, A., & Humphreys, M. (2023). Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign. Princeton University Press.

Hypothesis testing for categorical predictors

Interesting update to {ggeffects}, by Daniel Lüdecke:

A reason to compute adjusted predictions (or estimated marginal means) is to help understanding the relationship between predictors and outcome of a regression model. In particular for more complex models, for example, complex interaction terms, it is often easier to understand the associations when looking at adjusted predictions instead of the raw table of regression coefficients.

The next step, which often follows this, is to see if there are statistically significant differences. These could be, for example, differences between groups, i.e. between the levels of categorical predictors or whether trends differ significantly from each other.

The ggeffects package provides a function, hypothesis_test(), which does exactly this: testing differences of adjusted predictions for statistical significance. This is usually called contrasts or (pairwise) comparisons. This vignette shows some examples how to use the hypothesis_test() function and how to test whether differences in predictions are statistically significant.

Read more.