load packages

library(tidyverse)
library(here)
library(janitor)
library(patchwork)
library(papaja)
library(beepr)

the back story

Students in group1 were told that BabyA is preterm and BabyB is fullterm; students in group 2 were told that BabyA is fullterm and BabyB is preterm.

Both BabyA and BabyB were actually full term.

questions

  1. Does knowing the preterm status of an infant impact how we judge their behaviour?
  2. Does failing to code behaviour blind to preterm status (or any other condition we are trying to measure the effect of) bias coding results?

read the data

# fine motor
fineA <- read_csv(here("smartsparrow", "motor_data", "data2019", "fine_A_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 1", condition = "fine", assignment = "A_pre_B_full")

fineB <- read_csv(here("smartsparrow", "motor_data", "data2019", "fine_B_2019.csv")) %>%
clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 2", condition = "fine", assignment = "A_full_B_pre") 

# add id using nrow 
fineA$id <- 1:nrow(fineA) 
# add id manually to make it start at the end of fineA
fineB$id <- 216:414
  
# pull id to the front  
fine_motor <- rbind(fineA, fineB) %>%
  select(id, everything())


# gross motor

grossA <- read_csv(here("smartsparrow", "motor_data", "data2019", "gross_A_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 1", condition = "gross", assignment = "A_pre_B_full")

grossB <- read_csv(here("smartsparrow", "motor_data", "data2019", "gross_B_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 2", condition = "gross", assignment = "A_full_B_pre")

# add id using nrow 
grossA$id <- 1:nrow(grossA) 
# add id manually to make it start at the end of fineA
grossB$id <- 216:416


gross_motor <- rbind(grossA, grossB) %>%
  select(id, everything())


# play

playA <- read_csv(here("smartsparrow", "motor_data", "data2019", "play_A_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 1", condition = "play", assignment = "A_pre_B_full")

playB <- read_csv(here("smartsparrow", "motor_data", "data2019", "play_B_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 2", condition = "play", assignment = "A_full_B_pre") 

# add id using nrow 
playA$id <- 1:nrow(playA) 
# add id manually to make it start at the end of fineA
playB$id <- 226:437


play_motor <- rbind(playA, playB) %>%
  select(-a_duplo_apart, -b_duplo_apart) %>%
  select(id, everything()) # drop extra duplo, it makes the pivot complicated

# self

selfA <- read_csv(here("smartsparrow", "motor_data", "data2019", "self_A_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 1", condition = "self", assignment = "A_pre_B_full")

selfB <- read_csv(here("smartsparrow", "motor_data", "data2019", "self_B_2019.csv")) %>%
  clean_names() %>%
  select(starts_with("A"), starts_with("B")) %>%
  mutate(group = "group 2", condition = "self", assignment = "A_full_B_pre")

# add id using nrow 
selfA$id <- 1:nrow(selfA) 
# add id manually to make it start at the end of fineA
selfB$id <- 211:411

self_motor <- rbind(selfA, selfB) %>%
  select(id, everything())

make things long

fine_plot <- fine_motor %>%
  pivot_longer(names_to = c("infant", "task"), names_sep = "_", values_to = "rating", 2:5) 

gross_plot <- gross_motor %>%
  pivot_longer(names_to = c("infant", "task"), names_sep = "_", values_to = "rating", 2:5)
## Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [1, 2, 3,
## 4].
play_plot <- play_motor %>%
  pivot_longer(names_to = c("infant", "task"), names_sep = "_", values_to = "rating", 2:7)
## Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [1, 2, 4,
## 6].
self_plot <- self_motor %>%
  pivot_longer(names_to = c("infant", "task"), names_sep = "_", values_to = "rating", 2:5)
## Warning: Expected 2 pieces. Additional pieces discarded in 2 rows [2, 4].

fix factor labels

fine_plot$infant <- factor(fine_plot$infant, levels = c("a", "b"),
                        labels = c("BabyA", "BabyB"))

gross_plot$infant <- factor(gross_plot$infant, levels = c("a", "b"),
                           labels = c("BabyA", "BabyB"))

play_plot$infant <- factor(play_plot$infant, levels = c("a", "b"),
                           labels = c("BabyA", "BabyB"))

self_plot$infant <- factor(self_plot$infant, levels = c("a", "b"),
                           labels = c("BabyA", "BabyB"))

compute summary stats

fine_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE))
## # A tibble: 4 x 3
## # Groups:   group [2]
##   group   infant  mean
##   <chr>   <fct>  <dbl>
## 1 group 1 BabyA   3.03
## 2 group 1 BabyB   2.48
## 3 group 2 BabyA   3.27
## 4 group 2 BabyB   2.39
gross_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE))
## # A tibble: 4 x 3
## # Groups:   group [2]
##   group   infant  mean
##   <chr>   <fct>  <dbl>
## 1 group 1 BabyA   3.19
## 2 group 1 BabyB   3.70
## 3 group 2 BabyA   3.43
## 4 group 2 BabyB   3.55
play_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE))
## # A tibble: 4 x 3
## # Groups:   group [2]
##   group   infant  mean
##   <chr>   <fct>  <dbl>
## 1 group 1 BabyA   2.56
## 2 group 1 BabyB   3.69
## 3 group 2 BabyA   2.91
## 4 group 2 BabyB   3.47
self_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE))
## # A tibble: 4 x 3
## # Groups:   group [2]
##   group   infant  mean
##   <chr>   <fct>  <dbl>
## 1 group 1 BabyA   2.89
## 2 group 1 BabyB   3.81
## 3 group 2 BabyA   3.32
## 4 group 2 BabyB   3.61

Plots

plot fine and gross motor

fplot <- fine_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE)) %>%
  ggplot(aes(x = group, y = mean, fill = group)) +
  geom_col() +
  facet_wrap(~ infant) +
  labs(subtitle = "fine motor ratings") +
  theme(legend.position = "none") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 4))


gplot <- gross_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE)) %>%
  ggplot(aes(x = group, y = mean, fill = group)) +
  geom_col() +
  facet_wrap(~ infant)  +
  labs(subtitle = "gross motor ratings") +
  theme(legend.position = "none") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 4))

#combine fplot and gplot 

patchplot1 <- fplot + gplot +
  plot_annotation(
    title = "group1 thought BabyA was preterm \ngroup2 thought BabyB was preterm")

Ratings of fine and gross motor skills

#save as png
ggsave(here("smartsparrow", "plots", "patchplot1.png"))
## Saving 7 x 5 in image

plot play and self help

pplot <- play_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE)) %>%
  ggplot(aes(x = group, y = mean, fill = group)) +
  geom_col() +
  facet_wrap(~ infant)  +
  labs(subtitle = "play ratings") +
  theme(legend.position = "none") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 4))


splot <- self_plot %>%
  group_by(group, infant) %>%
  summarise(mean = mean(rating,  na.rm = TRUE)) %>%
  ggplot(aes(x = group, y = mean, fill = group)) +
  geom_col() +
  facet_wrap(~ infant)  +
  labs(subtitle = "self help ratings") +
  theme(legend.position = "none") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 4))
  

#combine pplot and splot 

patchplot2 <- pplot + splot +
  plot_annotation(
    title = "group1 thought BabyA was preterm \ngroup2 thought BabyB was preterm")

Ratings of play and self help skills

# save as png
ggsave(here("smartsparrow", "plots", "patchplot2.png"))
## Saving 7 x 5 in image