loading, setting up
library(googlesheets4) # install.packages("googlesheets4")
library(tidyverse)
library(janitor)
library(jmRtools)
d <- read_sheet("https://docs.google.com/spreadsheets/d/1uq6VCaAxCog3eKHt5rnCLF7bWwOUyLn12UEXSsFDFNQ/edit#gid=1041895723",
sheet = 3, skip = 1, # and 5
col_types = "c")
# d %>% write_csv("hadi-coded-data-all.csv")
#
# d <- read_csv("hadi-coded-data-all.csv")
d <- d %>% clean_names()
# d <- d %>%
# filter(number_of_student_first_names_in_post != "DNE",
# number_of_student_first_names_in_post != "n/a")
create_table_output <- function(m1) {
x0a <- coef(m1) %>% round(3)
x0b <- confint(m1) %>%
round(3) %>%
paste0(collapse = " , ") %>%
str_c("[", ., "]")
x1 <- convert_log_odds(coef(m1))
x2 <- confint(m1) %>%
convert_log_odds() %>%
round(3) %>%
paste0(collapse = ", ") %>%
str_c("[", ., "]")
x3 <- coef(m1) %>%
convert_log_odds() %>%
convert_odds() %>%
round(3)
x4 <- confint(m1) %>%
convert_log_odds() %>%
convert_odds() %>%
round(3) %>%
paste0(collapse = ", ") %>%
str_c("[", ., "]")
x5 <- coef(m1) %>%
convert_log_odds() %>%
convert_odds() %>%
round(3)
x5 <- x5 * 20622917
x6 <- confint(m1) %>%
convert_log_odds() %>%
convert_odds() %>%
round(3)
x6 <- x6 * 20622917
x6 <- x6 %>%
paste0(collapse = ", ") %>%
str_c("[", ., "]")
tibble(log_odds_coef = x0a, log_odds_ci = x0b,
odds_coef = x1, odds_ci = x2,
prob_coef = x3, prob_ci = x4,
extrap_coef = x5,
extrap_ci = x6) %>%
gather(key, val)
}
d <- d %>%
mutate(vid_or_not =
if_else(is_there_a_video_1_yes_0_no_only_count_embedded_videos_do_not_count_links_that_include_video == "1", "video",
if_else(is_there_a_video_1_yes_0_no_only_count_embedded_videos_do_not_count_links_that_include_video == "0", "not-video", NA))) %>%
mutate(there_is_a_face_in_this_post_video = if_else(vid_or_not == "video" & there_is_a_face_in_this_post == "1", 1, 0)) %>%
mutate(there_is_a_face_in_this_post_image = if_else(vid_or_not == "not-video" & there_is_a_face_in_this_post == "1", 1, 0)) %>%
mutate(is_there_an_identifiable_face_in_this_post_video = if_else(vid_or_not == "video" & is_there_an_identifiable_face_in_this_post == "1", 1, 0)) %>%
mutate(is_there_an_identifiable_face_in_this_post_image = if_else(vid_or_not == "not-video" & is_there_an_identifiable_face_in_this_post == "1", 1, 0)) %>%
mutate(ethnic_group_face_video = if_else(vid_or_not == "video" & ethnic_group_face == "1", 1, 0)) %>%
mutate(ethnic_group_face_image = if_else(vid_or_not == "not-video" & ethnic_group_face == "1", 1, 0)) %>%
mutate(gender_face_video = if_else(vid_or_not == "video" & gender_face == "1", 1, 0)) %>%
mutate(gender_face_image = if_else(vid_or_not == "not-video" & gender_face == "1", 1, 0))
videos
faces
d %>% janitor::tabyl(there_is_a_face_in_this_post_video)
## there_is_a_face_in_this_post_video n percent
## 0 1473 0.982
## 1 27 0.018
m1 <- glm(as.integer(there_is_a_face_in_this_post_video) ~ 1, data = d,
family = "binomial")
create_table_output(m1)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -3.999
## 2 log_odds_ci [-4.405 , -3.64]
## 3 odds_coef 0.018
## 4 odds_ci [0.012, 0.026]
## 5 prob_coef 0.018
## 6 prob_ci [0.012, 0.025]
## 7 extrap_coef 371212.506
## 8 extrap_ci [247475.004, 515572.925]
faces + first and/or last name
d %>%
janitor::tabyl(is_there_an_identifiable_face_in_this_post_video)
## is_there_an_identifiable_face_in_this_post_video n percent
## 0 1490 0.993333333
## 1 10 0.006666667
m2 <- glm(as.integer(is_there_an_identifiable_face_in_this_post_video) ~ 1, data = d,
family = "binomial")
create_table_output(m2)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -5.004
## 2 log_odds_ci [-5.697 , -4.44]
## 3 odds_coef 0.007
## 4 odds_ci [0.003, 0.012]
## 5 prob_coef 0.007
## 6 prob_ci [0.003, 0.012]
## 7 extrap_coef 144360.419
## 8 extrap_ci [61868.751, 247475.004]
faces + ethnic group
d %>%
janitor::tabyl(ethnic_group_face_video)
## ethnic_group_face_video n percent
## 0 1500 1
m3 <- glm(as.integer(ethnic_group_face_video) ~ 1, data = d,
family = "binomial")
create_table_output(m3)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -26.566
## 2 log_odds_ci [-9243.342 , -7890.816]
## 3 odds_coef 0
## 4 odds_ci [0, 0]
## 5 prob_coef 0
## 6 prob_ci [0, 0]
## 7 extrap_coef 0
## 8 extrap_ci [0, 0]
faces + gender
d %>% janitor::tabyl(gender_face_video)
## gender_face_video n percent
## 0 1497 0.998
## 1 3 0.002
m4 <- glm(as.integer(gender_face_video) ~ 1, data = d,
family = "binomial")
create_table_output(m4)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -6.213
## 2 log_odds_ci [-7.605 , -5.259]
## 3 odds_coef 0.002
## 4 odds_ci [0, 0.005]
## 5 prob_coef 0.002
## 6 prob_ci [0, 0.005]
## 7 extrap_coef 41245.834
## 8 extrap_ci [0, 103114.585]
images
faces
d %>% janitor::tabyl(there_is_a_face_in_this_post_image)
## there_is_a_face_in_this_post_image n percent
## 0 1221 0.814
## 1 279 0.186
m1 <- glm(as.integer(there_is_a_face_in_this_post_image) ~ 1, data = d,
family = "binomial")
create_table_output(m1)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -1.476
## 2 log_odds_ci [-1.608 , -1.348]
## 3 odds_coef 0.186
## 4 odds_ci [0.167, 0.206]
## 5 prob_coef 0.157
## 6 prob_ci [0.143, 0.171]
## 7 extrap_coef 3237797.969
## 8 extrap_ci [2949077.131, 3526518.807]
faces + first and/or last name
d %>%
janitor::tabyl(is_there_an_identifiable_face_in_this_post_image)
## is_there_an_identifiable_face_in_this_post_image n percent
## 0 1449 0.9660000000
## 1 50 0.0333333333
## NA 1 0.0006666667
## valid_percent
## 0.96664443
## 0.03335557
## NA
m2 <- glm(as.integer(is_there_an_identifiable_face_in_this_post_image) ~ 1, data = d,
family = "binomial")
create_table_output(m2)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -3.367
## 2 log_odds_ci [-3.662 , -3.096]
## 3 odds_coef 0.033
## 4 odds_ci [0.025, 0.043]
## 5 prob_coef 0.032
## 6 prob_ci [0.024, 0.041]
## 7 extrap_coef 659933.344
## 8 extrap_ci [494950.008, 845539.597]
faces + ethnic group
d %>%
janitor::tabyl(ethnic_group_face_image)
## ethnic_group_face_image n percent
## 0 1498 0.998666667
## 1 2 0.001333333
m3 <- glm(as.integer(ethnic_group_face_image) ~ 1, data = d,
family = "binomial")
create_table_output(m3)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -6.619
## 2 log_odds_ci [-8.414 , -5.49]
## 3 odds_coef 0.001
## 4 odds_ci [0, 0.004]
## 5 prob_coef 0.001
## 6 prob_ci [0, 0.004]
## 7 extrap_coef 20622.917
## 8 extrap_ci [0, 82491.668]
faces + gender
d %>% janitor::tabyl(gender_face_image)
## gender_face_image n percent
## 0 1469 0.97933333
## 1 31 0.02066667
m4 <- glm(as.integer(gender_face_image) ~ 1, data = d,
family = "binomial")
create_table_output(m4)
## # A tibble: 8 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -3.858
## 2 log_odds_ci [-4.236 , -3.522]
## 3 odds_coef 0.021
## 4 odds_ci [0.014, 0.029]
## 5 prob_coef 0.021
## 6 prob_ci [0.014, 0.028]
## 7 extrap_coef 433081.257
## 8 extrap_ci [288720.838, 577441.676]