platform_comparison
anjie
4/23/2021
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(here)## here() starts at /Users/caoanjie/Desktop/projects/looking_time/adult_analysism_rt <- read_csv(here("data/platform_comparison_data/mTurk/processed_data/trimmed_RTdata.csv")) %>% mutate(platform = "mTurk")## Parsed with column specification:
## cols(
## subject = col_character(),
## block_number = col_double(),
## block_type = col_character(),
## trial_number = col_double(),
## item_type = col_character(),
## trial_type = col_character(),
## trial_complexity = col_character(),
## item_id = col_character(),
## rt = col_double(),
## exposure_type = col_character(),
## half = col_character(),
## block_deviant_number = col_double(),
## trial_type_index = col_character(),
## first_dev_position = col_double(),
## second_dev_position = col_double()
## )m_demog <- read_csv(here("data/platform_comparison_data/mTurk/processed_data/trimmed_demogdata.csv")) %>% mutate(platform = "mTurk")## Parsed with column specification:
## cols(
## subject = col_character(),
## age = col_double(),
## ethnicity = col_character(),
## gender = col_character(),
## education = col_character()
## )m_complexity <- read_csv(here("data/platform_comparison_data/mTurk/processed_data/trimmed_complexitydata.csv")) %>% mutate(platform = "mTurk")## Parsed with column specification:
## cols(
## subject = col_character(),
## question_type = col_character(),
## stimulus = col_character(),
## rating = col_double()
## )m_similarity <- read_csv(here("data/platform_comparison_data/mTurk/processed_data/trimmed_similaritydata.csv")) %>% mutate(platform = "mTurk")## Parsed with column specification:
## cols(
## subject = col_character(),
## question_type = col_character(),
## stimulus_left = col_character(),
## stimulus_right = col_character(),
## rating = col_double()
## )p_rt <- read_csv(here("data/platform_comparison_data/prolific/processed_data/trimmed_RTdata.csv")) %>% mutate(platform = "prolific")## Parsed with column specification:
## cols(
## subject = col_character(),
## block_number = col_double(),
## block_type = col_character(),
## trial_number = col_double(),
## item_type = col_character(),
## trial_type = col_character(),
## trial_complexity = col_character(),
## item_id = col_character(),
## rt = col_double(),
## exposure_type = col_character(),
## half = col_character(),
## block_deviant_number = col_double(),
## trial_type_index = col_character(),
## first_dev_position = col_double(),
## second_dev_position = col_double()
## )p_demog <- read_csv(here("data/platform_comparison_data/prolific/processed_data/trimmed_demogdata.csv")) %>% mutate(platform = "prolific")## Parsed with column specification:
## cols(
## subject = col_character(),
## age = col_double(),
## ethnicity = col_character(),
## gender = col_character(),
## education = col_character()
## )p_complexity <- read_csv(here("data/platform_comparison_data/prolific/processed_data/trimmed_complexitydata.csv")) %>% mutate(platform = "prolific")## Parsed with column specification:
## cols(
## subject = col_character(),
## question_type = col_character(),
## stimulus = col_character(),
## rating = col_double()
## )p_similarity <- read_csv(here("data/platform_comparison_data/prolific/processed_data/trimmed_similaritydata.csv")) %>% mutate(platform = "prolific")## Parsed with column specification:
## cols(
## subject = col_character(),
## question_type = col_character(),
## stimulus_left = col_character(),
## stimulus_right = col_character(),
## rating = col_double()
## )rt <- bind_rows(m_rt, p_rt) %>%
mutate(looking_time = case_when(
exposure_type == "forced_short" & trial_number == 1 ~ 1000,
exposure_type == "forced_long" & trial_number == 1 ~ 10000,
TRUE ~ 500 + rt
))
demog <- bind_rows(m_demog, p_demog)
complexity <- bind_rows(m_complexity, p_complexity)
similarity <- bind_rows(m_similarity, p_similarity)basic descriptive
rt %>%
group_by(platform) %>%
distinct(subject, .keep_all = TRUE) %>%
count()## # A tibble: 2 x 2
## # Groups: platform [2]
## platform n
## <chr> <int>
## 1 mTurk 23
## 2 prolific 21demog %>%
ggplot(aes(x = age, fill = platform)) +
geom_density(alpha = .5)
rt %>%
ggplot(aes(x = rt, fill = platform)) +
geom_density(alpha = .3) +
scale_x_log10() +
facet_wrap(~block_type) +
xlab("rt(log)") +
labs(title = "reaction time" )
rt %>%
ggplot(aes(x = looking_time, fill = platform)) +
geom_density(alpha = .3) +
scale_x_log10() +
facet_wrap(~block_type) +
xlab("looking time(log)") +
labs(title = "looking time")
rt %>%
filter(exposure_type == "self_paced") %>%
ggplot(aes(x = looking_time, fill = platform)) +
geom_density(alpha = .3) +
scale_x_log10() +
facet_wrap(~block_type) +
xlab("looking time(log)") +
labs(title = "looking time at the self-paced block only")
rt %>%
filter(exposure_type == "self_paced") %>%
filter(trial_number == 1) %>%
ggplot(aes(x = block_number,
y = looking_time,
color = platform)) +
stat_summary(position = position_dodge(width = 0.1)) +
stat_summary(geom = "line") +
xlab("Block") +
ylab("Looking time") +
labs(title = "Looking time at self-paced blocks at first self-paced trial")## No summary function supplied, defaulting to `mean_se()`
## No summary function supplied, defaulting to `mean_se()`
#facet_wrap(~exposure_type)ggplot(rt,
aes(x=trial_number, y=rt, colour=item_type)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
geom_smooth(method = "lm",
formula = y ~ I(exp(1)**(-x)), se = FALSE) +
facet_wrap(~platform) +
langcog::scale_color_solarized(name = "Item Type") +
theme(legend.position = "bottom") +
ylab("log RT (ms)") +
xlab("Trial Number") +
labs(title = "RT change")
ggplot(rt,
aes(x=trial_number, y=log(rt), colour=item_type)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
geom_smooth(method = "lm",
formula = y ~ I(exp(1)**(-x)), se = FALSE) +
facet_wrap(~platform) +
langcog::scale_color_solarized(name = "Item Type") +
theme(legend.position = "bottom") +
ylab("log RT (ms)") +
xlab("Trial Number") +
labs(title = "RT change log")
ggplot(rt,
aes(x=trial_number, y=looking_time, colour=item_type)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
geom_smooth(method = "lm",
formula = y ~ I(exp(1)**(-x)), se = FALSE) +
#facet_wrap(~platform) +
facet_grid(exposure_type ~ platform) +
langcog::scale_color_solarized(name = "Item Type") +
theme(legend.position = "bottom") +
ylab("looking time (ms)") +
xlab("Trial Number") +
labs(title = "Looking time change (first trial forced)")
ggplot(rt,
aes(x=trial_number, y=log(looking_time), colour=item_type)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
geom_smooth(method = "lm",
formula = y ~ I(exp(1)**(-x)), se = FALSE) +
#facet_wrap(~platform) +
facet_grid(exposure_type ~ platform) +
langcog::scale_color_solarized(name = "Item Type") +
theme(legend.position = "bottom") +
ylab("log looking time (ms)") +
xlab("Trial Number") +
labs(title = "Log looking time change")
Zoom in on the first trial and the second trial
ggplot(rt %>% filter(trial_number %in% c(1,2)),
aes(x=as.factor(trial_number), y=looking_time, colour=exposure_type, shape = item_type)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
facet_grid(exposure_type~platform)+
langcog::scale_color_solarized(name = "Item Type") +
theme(legend.position = "bottom") +
ylab("Looking time (ms)") +
xlab("Trial Number") 
across block?
ggplot(rt %>% mutate(block_number = block_number + 1),
aes(x=as.factor(block_number), y=looking_time, colour=trial_complexity)) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = .2)) +
geom_smooth(method = "lm",
formula = y ~ I(exp(1)**(-x)), se = FALSE) +
facet_grid(~platform)+
langcog::scale_color_solarized(name = "Trial Complexity") +
theme(legend.position = "bottom") +
ylab("Looking time (ms)") +
xlab("Block Number") 