MERGED_DATA_PATH <- here("data/2_merged/merged_all.csv")
d <- read_csv(MERGED_DATA_PATH)
clean_d <- read_csv(here("writeups/CCRR_CogSci/cogsci_data/tidy_main.csv"))
order_d <- d %>%
rowwise() %>%
mutate(task_name = case_when(
variable_type == "raven" ~ "RV",
grepl("ebb", trial_type) ~ "EBB",
grepl( "free-description", trial_type) ~ "FD",
grepl("RMTS", trial_type) ~ "RMTS",
grepl("attribution", trial_type) ~ "CA",
grepl("pen-choice", trial_type) ~ "PC",
grepl( "konva",trial_type) ~ "SI",
grepl( "horizon",trial_type) ~ "HZ"
)) %>%
select(subject, task_name) %>%
distinct(subject, task_name) %>%
filter(!is.na(task_name)) %>%
group_by(subject) %>%
mutate(
task_order = row_number()
)
order_info_d <- order_d %>%
left_join(order_d %>%
filter(task_name == "FD") %>%
rename(fd_position = task_order) %>%
select(subject, fd_position), by = "subject") %>%
mutate(location_relative_to_fd = case_when(
task_order < fd_position ~ "before",
task_order > fd_position ~ "after",
task_order == fd_position ~ "FD"
)) %>%
select(-fd_position) %>%
mutate(task_name = case_when(
task_name == "PC" ~ "CP",
TRUE ~ task_name
))
d_with_order_info <- clean_d %>%
left_join(order_info_d %>%
ungroup() %>%
filter(subject %in% order_info_d$subject),
by = c("subject", "task_name"))
a remidner that the pen choice task can be either 6 or 7 because we package SI and HZ together
d_with_order_info %>%
filter(task_order == 7)
## # A tibble: 421 x 10
## X1 subject culture task_name task_info trial_info resp_type resp
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 91 SS1609057… CN SI SI SI inflation_sc… 1.29
## 2 92 SS1609057… CN SI SI SI inflation_sc… 35.2
## 3 93 SS1609062… CN SI SI SI inflation_sc… 1.02
## 4 94 SS1609062… CN SI SI SI inflation_sc… 2.23
## 5 115 SS1609139… CN SI SI SI inflation_sc… 0.425
## 6 116 SS1609139… CN SI SI SI inflation_sc… -51.1
## 7 135 SS1609214… CN SI SI SI inflation_sc… 1.04
## 8 136 SS1609214… CN SI SI SI inflation_sc… 2.16
## 9 141 SS1609229… CN SI SI SI inflation_sc… 0.956
## 10 142 SS1609229… CN SI SI SI inflation_sc… -3.75
## # … with 411 more rows, and 2 more variables: task_order <int>,
## # location_relative_to_fd <chr>
d_with_order_info %>%
filter(task_name == "CA") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_sum = sum(resp),
subject_n = n()) %>%
ggplot(aes(x = task_order, y = resp_sum, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
facet_wrap(~ resp_type) +
theme_classic()
d_with_order_info %>%
filter(task_name == "CA") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_sum = sum(resp),
subject_n = n()) %>%
ggplot(aes(x = location_relative_to_fd, y = resp_sum, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
facet_wrap(~ resp_type) +
theme_classic()
d_with_order_info %>%
filter(task_name == "RV") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_acc = mean(resp)) %>%
ggplot(aes(x = task_order, y = resp_acc, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
geom_smooth(method = "lm") +
theme_classic()
d_with_order_info %>%
filter(task_name == "RV") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_acc = mean(resp),
subject_n = n()) %>%
ggplot(aes(x = location_relative_to_fd, y = resp_acc, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
theme_classic()
d_with_order_info %>%
filter(task_name == "EBB") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_acc = mean(resp)) %>%
ggplot(aes(x = task_order, y = resp_acc, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
geom_smooth(method = "lm") +
theme_classic()
d_with_order_info %>%
filter(task_name == "EBB") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_acc = mean(resp),
subject_n = n()) %>%
ggplot(aes(x = location_relative_to_fd, y = resp_acc, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
theme_classic()
d_with_order_info %>%
filter(task_name == "HZ") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
filter(resp_type == "hz_height") %>%
summarise(
horizon_height = mean(resp)) %>%
ggplot(aes(x = task_order, y = horizon_height, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
geom_smooth(method = "lm") +
theme_classic()
d_with_order_info %>%
filter(task_name == "HZ") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
filter(resp_type == "hz_height") %>%
summarise(
horizon_height = mean(resp)) %>%
ggplot(aes(x = location_relative_to_fd, y = horizon_height, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
theme_classic()
d_with_order_info %>%
filter(task_name == "SI") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
filter(resp_type == "inflation_score_ratio") %>%
summarise(
inflation_score_ratio = mean(resp)) %>%
ggplot(aes(x = task_order, y = inflation_score_ratio, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
geom_smooth(method = "lm") +
theme_classic()
d_with_order_info %>%
filter(task_name == "SI") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
filter(resp_type == "inflation_score_ratio") %>%
summarise(
inflation_score_ratio = mean(resp)) %>%
ggplot(aes(x = location_relative_to_fd, y = inflation_score_ratio, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
theme_classic()
d_with_order_info %>%
filter(task_name == "FD") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
resp_sum = sum(resp),
subject_n = n()) %>%
filter(!grepl("full", resp_type)) %>%
ggplot(aes(x = task_order, y = resp_sum, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
facet_wrap(~ resp_type) +
theme_classic()
d_with_order_info %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
proportion_relation_match = mean(resp),
subject_n = n()) %>%
ggplot(aes(x = task_order, y = proportion_relation_match, color = culture)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
facet_wrap(~ resp_type) +
theme_classic()
d_with_order_info %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture, resp_type, task_order, location_relative_to_fd) %>%
summarise(
proportion_relation_match = mean(resp),
subject_n = n()) %>%
ggplot(aes(x = location_relative_to_fd, y = proportion_relation_match, color = culture)) +
geom_jitter(alpha = .3) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .3)) +
facet_wrap(~ resp_type) +
theme_classic()