stroop effect data.R

#### Source: https://twitter.com/jayvanbavel/status/1106985394883710978
library(tidyverse)
library(janitor)

tibble::tribble(
  ~Participant, ~Age, ~Matching.words, ~`Non-matching.words`,
  1,  7.5,           14.73,               60.28,
  2,   36,           12.76,               22.99,
  3,   65,           18.85,               49.79,
  4,   68,            26.8,                48.1,
  5,   38,            8.26,               34.87,
  6,   41,           17.32,               30.07,
  7,  8.5,           11.35,               62.69,
  8,   43,            10.4,                23.8,
  9,   43,           13.12,               27.06,
  10,    8,            8.59,                26.1,
  11,   67,           25.09,               74.29,
  12,   34,            9.26,               23.79,
  13,   43,            19.6,               32.66
) %>% 
  clean_names() %>% 
  pivot_longer(cols = 3:4, names_pattern = "(.*)_words", names_to = "type", values_to = "sec") -> 
  stroop_effect