Loading libraries

library(readxl)
library(tidyverse)
library(naniar)
library(lubridate)
library(ggalluvial)
library(papaja)
library(RVAideMemoire)
library(rcompanion)
library(officer)
library(rvg)
library(kableExtra)

Coding incorrect unit counting

'%!in%' <- Negate('%in%') 

coded <-
  raw %>% 
  replace_with_na(replace = list("N")) %>% 
  select(ID, age, gender, order, PENS, A_B, A_D, A_E, A_G, B_B, B_D, B_E, B_G) %>% 
  #coding cincorrect unit counting for the "How many kinds?" trials in task A
  mutate(A_B_cc = if_else(A_B %!in% c("3", "4", "5") | is.na(A_B), 1, 0), 
         A_D_cc = if_else(A_D %!in% c("3", "4", "5") | is.na(A_D), 1, 0),
         A_E_cc = if_else(A_E == 0, 1, 0),
         A_G_cc = if_else(A_G %!in% c("3", "4", "5") | is.na(A_G), 1, 0)) %>% 
    #coding incorrect unit counting for the "How many colors?" trials in task B
  mutate(B_B_cc = if_else(B_B %!in% c("4", "5", "6") | is.na(B_B), 1, 0), 
         B_D_cc = if_else(B_D %!in% c("4", "5", "6") | is.na(B_D), 1, 0),
         B_E_cc = if_else(B_E == 0, 1, 0),
         B_G_cc = if_else(B_G %!in% c("4", "5", "6") | is.na(B_G), 1, 0)) %>% 
  #assign a unique group id for each child, depending on their response pattern in task A and B
  mutate(B_group = group_indices(., B_B_cc, B_D_cc, B_E_cc, B_G_cc)) %>% 
  mutate(A_group = group_indices(., A_B_cc, A_D_cc, A_E_cc, A_G_cc)) 

knitr::kable(coded) %>% 
  kable_styling() %>%
  scroll_box(width = "1000px", height = "500px")
ID age gender order PENS A_B A_D A_E A_G B_B B_D B_E B_G A_B_cc A_D_cc A_E_cc A_G_cc B_B_cc B_D_cc B_E_cc B_G_cc B_group A_group
1 5.668493 female A 22 8 8 1 8 8 8 1 8 1 1 0 1 1 1 0 1 5 8
2 4.731507 female B 8 4 3 1 4 5 3 1 5 0 0 0 0 0 1 0 0 2 1
4 5.400000 female B 22 9 5 1 9 9 9 1 9 1 0 0 1 1 1 0 1 5 6
2814 6.200000 male A 22 4 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
6252 5.260000 male A 21 4 4 1 4 6 5 1 5 0 0 0 0 0 0 0 0 1 1
3116 5.460000 male B 24 4 4 1 4 4 5 1 5 0 0 0 0 0 0 0 0 1 1
6023 4.310000 female B 19 9 9 0 9 9 9 1 5 1 1 1 1 1 1 0 0 4 10
6066 4.190000 female B 11 9 9 1 8 9 7 0 9 1 1 0 1 1 1 1 1 7 8
6420 4.100000 female A 11 4 9 1 8 9 7 1 9 0 1 0 1 1 1 0 1 5 3
6049 4.410000 male B 8 11 N 1 7 10 6 1 6 1 1 0 1 1 0 0 0 3 8
6134 5.630000 male A 22 4 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
6065 5.570000 male B 19 5 4 1 5 4 5 1 5 0 0 0 0 0 0 0 0 1 1
6147 4.640000 female A 18 10 N 0 9 10 5 1 5 1 1 1 1 1 0 0 0 3 10
6150 4.270000 male B 10 9 9 1 8 8 9 1 9 1 1 0 1 1 1 0 1 5 8
6148 4.240000 male A 7 9 7 1 4 9 5 1 5 1 1 0 0 1 0 0 0 3 7
6100 4.100000 male A 6 8 8 0 9 9 8 0 9 1 1 1 1 1 1 1 1 7 10
3238 5.610000 female B 23 5 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
3350 6.250000 male B 21 N 9 1 9 10 9 1 9 1 1 0 1 1 1 0 1 5 8
6238 4.610000 male A 9 5 8 0 9 15 10 1 10 0 1 1 1 1 1 0 1 5 4
6414 4.500000 male B 23 4 4 1 4 9 N 1 9 0 0 0 0 1 1 0 1 5 1
6075 4.290000 female A 18 4 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
6076 5.260000 female B 24 9 9 1 9 5 5 1 5 1 1 0 1 0 0 0 0 1 8
6059 4.060000 female A 13 9 4 1 4 4 4 1 5 1 0 0 0 0 0 0 0 1 5
6031 4.040000 male B 11 9 9 1 9 9 9 0 9 1 1 0 1 1 1 1 1 7 8
6102 5.190000 male A 18 N 9 1 9 N 5 1 5 1 1 0 1 1 0 0 0 3 8
6112 4.040000 male A NA 9 N 0 4 10 N 1 5 1 1 1 0 1 1 0 0 4 9
6218 4.520000 male B 9 13 10 0 9 12 10 0 10 1 1 1 1 1 1 1 1 7 10
6136 5.010000 male B 24 N N 1 N N N 1 N 1 1 0 1 1 1 0 1 5 8
6253 4.710000 male A 23 4 4 1 4 15 8 1 6 0 0 0 0 1 1 0 0 4 1
6227 4.460000 male A 17 9 N 0 10 9 9 0 9 1 1 1 1 1 1 1 1 7 10
6143 5.580000 female B 22 4 4 1 4 7 6 1 5 0 0 0 0 1 0 0 0 3 1
6024 5.230000 male A 9 9 9 1 9 9 5 1 5 1 1 0 1 1 0 0 0 3 8
6056 5.510000 male B 22 5 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
6050 4.840000 male B 20 5 9 1 4 9 11 0 5 0 1 0 0 1 1 1 0 6 2
3363 5.220000 female A 24 9 N 1 N 5 9 1 5 1 1 0 1 0 1 0 0 2 8
6174 4.840000 male B 16 4 4 1 4 5 5 1 5 0 0 0 0 0 0 0 0 1 1
6002 4.500000 male A 7 4 N 1 9 10 9 1 5 0 1 0 1 1 1 0 0 4 3
6142 5.200000 female B 22 8 8 0 8 8 8 1 8 1 1 1 1 1 1 0 1 5 10
3373 5.200000 male A 24 N N 1 N 6 N 1 5 1 1 0 1 0 1 0 0 2 8
6145 5.930000 male A 22 6 9 1 9 9 9 1 5 1 1 0 1 1 1 0 0 4 8
3306 5.700000 female B 21 5 5 1 5 6 5 1 5 0 0 0 0 0 0 0 0 1 1
3196 5.420000 female A 21 4 4 1 4 8 7 1 9 0 0 0 0 1 1 0 1 5 1
6078 5.190000 female B 22 4 4 1 4 7 5 1 5 0 0 0 0 1 0 0 0 3 1
3122 5.280000 female B 22 9 9 1 9 9 9 1 9 1 1 0 1 1 1 0 1 5 8
6073 4.010000 female A 8 7 8 1 N 9 10 1 N 1 1 0 1 1 1 0 1 5 8
5 3.265753 male A 16 9 4 1 4 9 5 1 5 1 0 0 0 1 0 0 0 3 5

Data visualization of the unique condition responses with ggaluvial package

#dataframe for visualization
grouped <-
  coded %>% 
  select(ID, A_B_cc:A_group, PENS, age) %>% 
  gather(trial, incorrect_unit_count, 2:9) %>% 
  separate(trial, into = c("task", "trial"), extra = "merge", sep = "_") %>% 
  arrange(ID)

knitr::kable(grouped) %>% 
  kable_styling() %>%
  scroll_box(width = "1000px", height = "500px")
ID B_group A_group PENS age task trial incorrect_unit_count
1 5 8 22 5.668493 A B_cc 1
1 5 8 22 5.668493 A D_cc 1
1 5 8 22 5.668493 A E_cc 0
1 5 8 22 5.668493 A G_cc 1
1 5 8 22 5.668493 B B_cc 1
1 5 8 22 5.668493 B D_cc 1
1 5 8 22 5.668493 B E_cc 0
1 5 8 22 5.668493 B G_cc 1
2 2 1 8 4.731507 A B_cc 0
2 2 1 8 4.731507 A D_cc 0
2 2 1 8 4.731507 A E_cc 0
2 2 1 8 4.731507 A G_cc 0
2 2 1 8 4.731507 B B_cc 0
2 2 1 8 4.731507 B D_cc 1
2 2 1 8 4.731507 B E_cc 0
2 2 1 8 4.731507 B G_cc 0
4 5 6 22 5.400000 A B_cc 1
4 5 6 22 5.400000 A D_cc 0
4 5 6 22 5.400000 A E_cc 0
4 5 6 22 5.400000 A G_cc 1
4 5 6 22 5.400000 B B_cc 1
4 5 6 22 5.400000 B D_cc 1
4 5 6 22 5.400000 B E_cc 0
4 5 6 22 5.400000 B G_cc 1
5 3 5 16 3.265753 A B_cc 1
5 3 5 16 3.265753 A D_cc 0
5 3 5 16 3.265753 A E_cc 0
5 3 5 16 3.265753 A G_cc 0
5 3 5 16 3.265753 B B_cc 1
5 3 5 16 3.265753 B D_cc 0
5 3 5 16 3.265753 B E_cc 0
5 3 5 16 3.265753 B G_cc 0
2814 1 1 22 6.200000 A B_cc 0
2814 1 1 22 6.200000 A D_cc 0
2814 1 1 22 6.200000 A E_cc 0
2814 1 1 22 6.200000 A G_cc 0
2814 1 1 22 6.200000 B B_cc 0
2814 1 1 22 6.200000 B D_cc 0
2814 1 1 22 6.200000 B E_cc 0
2814 1 1 22 6.200000 B G_cc 0
3116 1 1 24 5.460000 A B_cc 0
3116 1 1 24 5.460000 A D_cc 0
3116 1 1 24 5.460000 A E_cc 0
3116 1 1 24 5.460000 A G_cc 0
3116 1 1 24 5.460000 B B_cc 0
3116 1 1 24 5.460000 B D_cc 0
3116 1 1 24 5.460000 B E_cc 0
3116 1 1 24 5.460000 B G_cc 0
3122 5 8 22 5.280000 A B_cc 1
3122 5 8 22 5.280000 A D_cc 1
3122 5 8 22 5.280000 A E_cc 0
3122 5 8 22 5.280000 A G_cc 1
3122 5 8 22 5.280000 B B_cc 1
3122 5 8 22 5.280000 B D_cc 1
3122 5 8 22 5.280000 B E_cc 0
3122 5 8 22 5.280000 B G_cc 1
3196 5 1 21 5.420000 A B_cc 0
3196 5 1 21 5.420000 A D_cc 0
3196 5 1 21 5.420000 A E_cc 0
3196 5 1 21 5.420000 A G_cc 0
3196 5 1 21 5.420000 B B_cc 1
3196 5 1 21 5.420000 B D_cc 1
3196 5 1 21 5.420000 B E_cc 0
3196 5 1 21 5.420000 B G_cc 1
3238 1 1 23 5.610000 A B_cc 0
3238 1 1 23 5.610000 A D_cc 0
3238 1 1 23 5.610000 A E_cc 0
3238 1 1 23 5.610000 A G_cc 0
3238 1 1 23 5.610000 B B_cc 0
3238 1 1 23 5.610000 B D_cc 0
3238 1 1 23 5.610000 B E_cc 0
3238 1 1 23 5.610000 B G_cc 0
3306 1 1 21 5.700000 A B_cc 0
3306 1 1 21 5.700000 A D_cc 0
3306 1 1 21 5.700000 A E_cc 0
3306 1 1 21 5.700000 A G_cc 0
3306 1 1 21 5.700000 B B_cc 0
3306 1 1 21 5.700000 B D_cc 0
3306 1 1 21 5.700000 B E_cc 0
3306 1 1 21 5.700000 B G_cc 0
3350 5 8 21 6.250000 A B_cc 1
3350 5 8 21 6.250000 A D_cc 1
3350 5 8 21 6.250000 A E_cc 0
3350 5 8 21 6.250000 A G_cc 1
3350 5 8 21 6.250000 B B_cc 1
3350 5 8 21 6.250000 B D_cc 1
3350 5 8 21 6.250000 B E_cc 0
3350 5 8 21 6.250000 B G_cc 1
3363 2 8 24 5.220000 A B_cc 1
3363 2 8 24 5.220000 A D_cc 1
3363 2 8 24 5.220000 A E_cc 0
3363 2 8 24 5.220000 A G_cc 1
3363 2 8 24 5.220000 B B_cc 0
3363 2 8 24 5.220000 B D_cc 1
3363 2 8 24 5.220000 B E_cc 0
3363 2 8 24 5.220000 B G_cc 0
3373 2 8 24 5.200000 A B_cc 1
3373 2 8 24 5.200000 A D_cc 1
3373 2 8 24 5.200000 A E_cc 0
3373 2 8 24 5.200000 A G_cc 1
3373 2 8 24 5.200000 B B_cc 0
3373 2 8 24 5.200000 B D_cc 1
3373 2 8 24 5.200000 B E_cc 0
3373 2 8 24 5.200000 B G_cc 0
6002 4 3 7 4.500000 A B_cc 0
6002 4 3 7 4.500000 A D_cc 1
6002 4 3 7 4.500000 A E_cc 0
6002 4 3 7 4.500000 A G_cc 1
6002 4 3 7 4.500000 B B_cc 1
6002 4 3 7 4.500000 B D_cc 1
6002 4 3 7 4.500000 B E_cc 0
6002 4 3 7 4.500000 B G_cc 0
6023 4 10 19 4.310000 A B_cc 1
6023 4 10 19 4.310000 A D_cc 1
6023 4 10 19 4.310000 A E_cc 1
6023 4 10 19 4.310000 A G_cc 1
6023 4 10 19 4.310000 B B_cc 1
6023 4 10 19 4.310000 B D_cc 1
6023 4 10 19 4.310000 B E_cc 0
6023 4 10 19 4.310000 B G_cc 0
6024 3 8 9 5.230000 A B_cc 1
6024 3 8 9 5.230000 A D_cc 1
6024 3 8 9 5.230000 A E_cc 0
6024 3 8 9 5.230000 A G_cc 1
6024 3 8 9 5.230000 B B_cc 1
6024 3 8 9 5.230000 B D_cc 0
6024 3 8 9 5.230000 B E_cc 0
6024 3 8 9 5.230000 B G_cc 0
6031 7 8 11 4.040000 A B_cc 1
6031 7 8 11 4.040000 A D_cc 1
6031 7 8 11 4.040000 A E_cc 0
6031 7 8 11 4.040000 A G_cc 1
6031 7 8 11 4.040000 B B_cc 1
6031 7 8 11 4.040000 B D_cc 1
6031 7 8 11 4.040000 B E_cc 1
6031 7 8 11 4.040000 B G_cc 1
6049 3 8 8 4.410000 A B_cc 1
6049 3 8 8 4.410000 A D_cc 1
6049 3 8 8 4.410000 A E_cc 0
6049 3 8 8 4.410000 A G_cc 1
6049 3 8 8 4.410000 B B_cc 1
6049 3 8 8 4.410000 B D_cc 0
6049 3 8 8 4.410000 B E_cc 0
6049 3 8 8 4.410000 B G_cc 0
6050 6 2 20 4.840000 A B_cc 0
6050 6 2 20 4.840000 A D_cc 1
6050 6 2 20 4.840000 A E_cc 0
6050 6 2 20 4.840000 A G_cc 0
6050 6 2 20 4.840000 B B_cc 1
6050 6 2 20 4.840000 B D_cc 1
6050 6 2 20 4.840000 B E_cc 1
6050 6 2 20 4.840000 B G_cc 0
6056 1 1 22 5.510000 A B_cc 0
6056 1 1 22 5.510000 A D_cc 0
6056 1 1 22 5.510000 A E_cc 0
6056 1 1 22 5.510000 A G_cc 0
6056 1 1 22 5.510000 B B_cc 0
6056 1 1 22 5.510000 B D_cc 0
6056 1 1 22 5.510000 B E_cc 0
6056 1 1 22 5.510000 B G_cc 0
6059 1 5 13 4.060000 A B_cc 1
6059 1 5 13 4.060000 A D_cc 0
6059 1 5 13 4.060000 A E_cc 0
6059 1 5 13 4.060000 A G_cc 0
6059 1 5 13 4.060000 B B_cc 0
6059 1 5 13 4.060000 B D_cc 0
6059 1 5 13 4.060000 B E_cc 0
6059 1 5 13 4.060000 B G_cc 0
6065 1 1 19 5.570000 A B_cc 0
6065 1 1 19 5.570000 A D_cc 0
6065 1 1 19 5.570000 A E_cc 0
6065 1 1 19 5.570000 A G_cc 0
6065 1 1 19 5.570000 B B_cc 0
6065 1 1 19 5.570000 B D_cc 0
6065 1 1 19 5.570000 B E_cc 0
6065 1 1 19 5.570000 B G_cc 0
6066 7 8 11 4.190000 A B_cc 1
6066 7 8 11 4.190000 A D_cc 1
6066 7 8 11 4.190000 A E_cc 0
6066 7 8 11 4.190000 A G_cc 1
6066 7 8 11 4.190000 B B_cc 1
6066 7 8 11 4.190000 B D_cc 1
6066 7 8 11 4.190000 B E_cc 1
6066 7 8 11 4.190000 B G_cc 1
6073 5 8 8 4.010000 A B_cc 1
6073 5 8 8 4.010000 A D_cc 1
6073 5 8 8 4.010000 A E_cc 0
6073 5 8 8 4.010000 A G_cc 1
6073 5 8 8 4.010000 B B_cc 1
6073 5 8 8 4.010000 B D_cc 1
6073 5 8 8 4.010000 B E_cc 0
6073 5 8 8 4.010000 B G_cc 1
6075 1 1 18 4.290000 A B_cc 0
6075 1 1 18 4.290000 A D_cc 0
6075 1 1 18 4.290000 A E_cc 0
6075 1 1 18 4.290000 A G_cc 0
6075 1 1 18 4.290000 B B_cc 0
6075 1 1 18 4.290000 B D_cc 0
6075 1 1 18 4.290000 B E_cc 0
6075 1 1 18 4.290000 B G_cc 0
6076 1 8 24 5.260000 A B_cc 1
6076 1 8 24 5.260000 A D_cc 1
6076 1 8 24 5.260000 A E_cc 0
6076 1 8 24 5.260000 A G_cc 1
6076 1 8 24 5.260000 B B_cc 0
6076 1 8 24 5.260000 B D_cc 0
6076 1 8 24 5.260000 B E_cc 0
6076 1 8 24 5.260000 B G_cc 0
6078 3 1 22 5.190000 A B_cc 0
6078 3 1 22 5.190000 A D_cc 0
6078 3 1 22 5.190000 A E_cc 0
6078 3 1 22 5.190000 A G_cc 0
6078 3 1 22 5.190000 B B_cc 1
6078 3 1 22 5.190000 B D_cc 0
6078 3 1 22 5.190000 B E_cc 0
6078 3 1 22 5.190000 B G_cc 0
6100 7 10 6 4.100000 A B_cc 1
6100 7 10 6 4.100000 A D_cc 1
6100 7 10 6 4.100000 A E_cc 1
6100 7 10 6 4.100000 A G_cc 1
6100 7 10 6 4.100000 B B_cc 1
6100 7 10 6 4.100000 B D_cc 1
6100 7 10 6 4.100000 B E_cc 1
6100 7 10 6 4.100000 B G_cc 1
6102 3 8 18 5.190000 A B_cc 1
6102 3 8 18 5.190000 A D_cc 1
6102 3 8 18 5.190000 A E_cc 0
6102 3 8 18 5.190000 A G_cc 1
6102 3 8 18 5.190000 B B_cc 1
6102 3 8 18 5.190000 B D_cc 0
6102 3 8 18 5.190000 B E_cc 0
6102 3 8 18 5.190000 B G_cc 0
6112 4 9 NA 4.040000 A B_cc 1
6112 4 9 NA 4.040000 A D_cc 1
6112 4 9 NA 4.040000 A E_cc 1
6112 4 9 NA 4.040000 A G_cc 0
6112 4 9 NA 4.040000 B B_cc 1
6112 4 9 NA 4.040000 B D_cc 1
6112 4 9 NA 4.040000 B E_cc 0
6112 4 9 NA 4.040000 B G_cc 0
6134 1 1 22 5.630000 A B_cc 0
6134 1 1 22 5.630000 A D_cc 0
6134 1 1 22 5.630000 A E_cc 0
6134 1 1 22 5.630000 A G_cc 0
6134 1 1 22 5.630000 B B_cc 0
6134 1 1 22 5.630000 B D_cc 0
6134 1 1 22 5.630000 B E_cc 0
6134 1 1 22 5.630000 B G_cc 0
6136 5 8 24 5.010000 A B_cc 1
6136 5 8 24 5.010000 A D_cc 1
6136 5 8 24 5.010000 A E_cc 0
6136 5 8 24 5.010000 A G_cc 1
6136 5 8 24 5.010000 B B_cc 1
6136 5 8 24 5.010000 B D_cc 1
6136 5 8 24 5.010000 B E_cc 0
6136 5 8 24 5.010000 B G_cc 1
6142 5 10 22 5.200000 A B_cc 1
6142 5 10 22 5.200000 A D_cc 1
6142 5 10 22 5.200000 A E_cc 1
6142 5 10 22 5.200000 A G_cc 1
6142 5 10 22 5.200000 B B_cc 1
6142 5 10 22 5.200000 B D_cc 1
6142 5 10 22 5.200000 B E_cc 0
6142 5 10 22 5.200000 B G_cc 1
6143 3 1 22 5.580000 A B_cc 0
6143 3 1 22 5.580000 A D_cc 0
6143 3 1 22 5.580000 A E_cc 0
6143 3 1 22 5.580000 A G_cc 0
6143 3 1 22 5.580000 B B_cc 1
6143 3 1 22 5.580000 B D_cc 0
6143 3 1 22 5.580000 B E_cc 0
6143 3 1 22 5.580000 B G_cc 0
6145 4 8 22 5.930000 A B_cc 1
6145 4 8 22 5.930000 A D_cc 1
6145 4 8 22 5.930000 A E_cc 0
6145 4 8 22 5.930000 A G_cc 1
6145 4 8 22 5.930000 B B_cc 1
6145 4 8 22 5.930000 B D_cc 1
6145 4 8 22 5.930000 B E_cc 0
6145 4 8 22 5.930000 B G_cc 0
6147 3 10 18 4.640000 A B_cc 1
6147 3 10 18 4.640000 A D_cc 1
6147 3 10 18 4.640000 A E_cc 1
6147 3 10 18 4.640000 A G_cc 1
6147 3 10 18 4.640000 B B_cc 1
6147 3 10 18 4.640000 B D_cc 0
6147 3 10 18 4.640000 B E_cc 0
6147 3 10 18 4.640000 B G_cc 0
6148 3 7 7 4.240000 A B_cc 1
6148 3 7 7 4.240000 A D_cc 1
6148 3 7 7 4.240000 A E_cc 0
6148 3 7 7 4.240000 A G_cc 0
6148 3 7 7 4.240000 B B_cc 1
6148 3 7 7 4.240000 B D_cc 0
6148 3 7 7 4.240000 B E_cc 0
6148 3 7 7 4.240000 B G_cc 0
6150 5 8 10 4.270000 A B_cc 1
6150 5 8 10 4.270000 A D_cc 1
6150 5 8 10 4.270000 A E_cc 0
6150 5 8 10 4.270000 A G_cc 1
6150 5 8 10 4.270000 B B_cc 1
6150 5 8 10 4.270000 B D_cc 1
6150 5 8 10 4.270000 B E_cc 0
6150 5 8 10 4.270000 B G_cc 1
6174 1 1 16 4.840000 A B_cc 0
6174 1 1 16 4.840000 A D_cc 0
6174 1 1 16 4.840000 A E_cc 0
6174 1 1 16 4.840000 A G_cc 0
6174 1 1 16 4.840000 B B_cc 0
6174 1 1 16 4.840000 B D_cc 0
6174 1 1 16 4.840000 B E_cc 0
6174 1 1 16 4.840000 B G_cc 0
6218 7 10 9 4.520000 A B_cc 1
6218 7 10 9 4.520000 A D_cc 1
6218 7 10 9 4.520000 A E_cc 1
6218 7 10 9 4.520000 A G_cc 1
6218 7 10 9 4.520000 B B_cc 1
6218 7 10 9 4.520000 B D_cc 1
6218 7 10 9 4.520000 B E_cc 1
6218 7 10 9 4.520000 B G_cc 1
6227 7 10 17 4.460000 A B_cc 1
6227 7 10 17 4.460000 A D_cc 1
6227 7 10 17 4.460000 A E_cc 1
6227 7 10 17 4.460000 A G_cc 1
6227 7 10 17 4.460000 B B_cc 1
6227 7 10 17 4.460000 B D_cc 1
6227 7 10 17 4.460000 B E_cc 1
6227 7 10 17 4.460000 B G_cc 1
6238 5 4 9 4.610000 A B_cc 0
6238 5 4 9 4.610000 A D_cc 1
6238 5 4 9 4.610000 A E_cc 1
6238 5 4 9 4.610000 A G_cc 1
6238 5 4 9 4.610000 B B_cc 1
6238 5 4 9 4.610000 B D_cc 1
6238 5 4 9 4.610000 B E_cc 0
6238 5 4 9 4.610000 B G_cc 1
6252 1 1 21 5.260000 A B_cc 0
6252 1 1 21 5.260000 A D_cc 0
6252 1 1 21 5.260000 A E_cc 0
6252 1 1 21 5.260000 A G_cc 0
6252 1 1 21 5.260000 B B_cc 0
6252 1 1 21 5.260000 B D_cc 0
6252 1 1 21 5.260000 B E_cc 0
6252 1 1 21 5.260000 B G_cc 0
6253 4 1 23 4.710000 A B_cc 0
6253 4 1 23 4.710000 A D_cc 0
6253 4 1 23 4.710000 A E_cc 0
6253 4 1 23 4.710000 A G_cc 0
6253 4 1 23 4.710000 B B_cc 1
6253 4 1 23 4.710000 B D_cc 1
6253 4 1 23 4.710000 B E_cc 0
6253 4 1 23 4.710000 B G_cc 0
6414 5 1 23 4.500000 A B_cc 0
6414 5 1 23 4.500000 A D_cc 0
6414 5 1 23 4.500000 A E_cc 0
6414 5 1 23 4.500000 A G_cc 0
6414 5 1 23 4.500000 B B_cc 1
6414 5 1 23 4.500000 B D_cc 1
6414 5 1 23 4.500000 B E_cc 0
6414 5 1 23 4.500000 B G_cc 1
6420 5 3 11 4.100000 A B_cc 0
6420 5 3 11 4.100000 A D_cc 1
6420 5 3 11 4.100000 A E_cc 0
6420 5 3 11 4.100000 A G_cc 1
6420 5 3 11 4.100000 B B_cc 1
6420 5 3 11 4.100000 B D_cc 1
6420 5 3 11 4.100000 B E_cc 0
6420 5 3 11 4.100000 B G_cc 1
#for task A "How many kinds?"
flow_A <-
  grouped %>% 
    filter(task == "A") %>% 
    mutate(incorrect_unit_count = as.factor(incorrect_unit_count),
           A_group = as.factor(A_group)) %>% 
  ggplot(aes(x = trial, stratum = incorrect_unit_count, alluvium = ID,
           fill = A_group, label = incorrect_unit_count)) +
  scale_fill_brewer(type = "qual", palette = "Set3") +
  geom_flow(stat = "alluvium", lode.guidance = "rightleft",
            color = "darkgray") +
  geom_stratum() +
  theme_apa() 

flow_A 

#print alluvial plot for task "How many kinds?" 
#four bars represent binary responses to count_array, count_sorted, give_blocks and _count_withblocks
#children with the same color responded in the same way
#for task B "How many colors?"
flow_B <-
  grouped %>% 
    filter(task == "B") %>% 
    mutate(incorrect_unit_count = as.factor(incorrect_unit_count),
           B_group = as.factor(B_group)) %>% 
  ggplot(aes(x = trial, stratum = incorrect_unit_count, alluvium = ID,
           fill = B_group, label = incorrect_unit_count)) +
  scale_fill_brewer(type = "qual", palette = "Set3") +
  geom_flow(stat = "alluvium", lode.guidance = "rightleft",
            color = "darkgray") +
  geom_stratum() +
  theme_apa()

flow_B

#print alluvial plot for task "How many colors?" 
#four bars represent binary responses to count_array, count_sorted, give_blocks and _count_withblocks
#children with the same color responded in the same way

Overlap between group membership in task a and task b:

A1 “always correct” (n=16) -> B1 “always correct” (n=12) = overlap n=10 A8 “only blocks” (n=15) -> B5 “only blocks” (n=12) = overlap n=6 A10 “always wrong” (n=6) -> B7 “always wrong” (n=5) = overlap n=3 A5 “only wrong in randon” (n=2) -> B3 “only wrong in random” (n=8) = overlap n=1

in total 20/46 children (43%) of children demonstrated the exact same pattern of performance on the different tasks

Pre-registered: Cochran’s Q test analyses using the RVAideMemoire package

#Cochran's Q test for task A "How many kinds?"
cochran_A <-
  coded %>% 
  select(ID, 14:17) %>% 
  gather("trial", "incorrect_unit_count", 2:5) %>% 
  separate(trial, into = c("task", "trial"), extra = "merge", sep = "_")

print(cochran_A_results <- cochran.qtest(incorrect_unit_count ~ trial | ID,
              data = cochran_A))
## 
##  Cochran's Q test
## 
## data:  incorrect_unit_count by trial, block = ID 
## Q = 38.6842, df = 3, p-value = 2.025e-08
## alternative hypothesis: true difference in probabilities is not equal to 0 
## sample estimates:
## proba in group             <NA>            <NA>            <NA> 
##       0.5652174       0.5869565       0.1739130       0.5434783
#Q = 38.6842, df = 3, p-value = 2.025e-08 with probabilities 57% --> 59% --> 17% --> 54%


#Cochran's Q test for task B "How many colors?"
cochran_B <-
  coded %>% 
  select(ID, 18:21) %>% 
  gather("trial", "incorrect_unit_count", 2:5) %>% 
  separate(trial, into = c("task", "trial"), extra = "merge", sep = "_")

print(cochran_B_results <- cochran.qtest(incorrect_unit_count ~ trial | ID,
              data = cochran_B))
## 
##  Cochran's Q test
## 
## data:  incorrect_unit_count by trial, block = ID 
## Q = 47.2174, df = 3, p-value = 3.125e-10
## alternative hypothesis: true difference in probabilities is not equal to 0 
## sample estimates:
## proba in group             <NA>            <NA>            <NA> 
##       0.6739130       0.5652174       0.1304348       0.3695652
#Q = 47.2174, df = 3, p-value = 3.125e-10 with probabilities 67% --> 57% --> 13% --> 37%

Post-hoc McNemar tests using rcompanion package

#post-hoc for task A "kinds"

pairwiseMcnemar(incorrect_unit_count ~ trial|ID, data = cochran_A, test = "exact", method = "none", digits = 3)
## $Test.method
##    Test
## 1 exact
## 
## $Adustment.method
##   Method
## 1   none
## 
## $Pairwise
##        Comparison Successes Trials  p.value p.adjust
## 1 B_cc - D_cc = 0         3      7        1 1.00e+00
## 2 B_cc - E_cc = 0        19     20 4.01e-05 4.01e-05
## 3 B_cc - G_cc = 0         4      7        1 1.00e+00
## 4 D_cc - E_cc = 0        19     19 3.81e-06 3.81e-06
## 5 D_cc - G_cc = 0         3      4    0.625 6.25e-01
## 6 E_cc - G_cc = 0         1     19 7.63e-05 7.63e-05
pairwiseMcnemar(incorrect_unit_count ~ trial|ID, data = cochran_B, test = "exact", method = "none", digits = 3)
## $Test.method
##    Test
## 1 exact
## 
## $Adustment.method
##   Method
## 1   none
## 
## $Pairwise
##        Comparison Successes Trials  p.value p.adjust
## 1 B_cc - D_cc = 0         8     11    0.227 2.27e-01
## 2 B_cc - E_cc = 0        25     25 5.96e-08 5.96e-08
## 3 B_cc - G_cc = 0        14     14 0.000122 1.22e-04
## 4 D_cc - E_cc = 0        20     20 1.91e-06 1.91e-06
## 5 D_cc - G_cc = 0         9      9  0.00391 3.91e-03
## 6 E_cc - G_cc = 0         1     13  0.00342 3.42e-03