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 = 4, skip = 1, # and 5
col_types = "c")
d %>% write_csv("hadi-coded-data.csv")
d <- read_csv("hadi-coded-data.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("[", ., "]")
tibble(log_odds_coef = x0a, log_odds_ci = x0b,
odds_coef = x1, odds_ci = x2,
prob_coef = x3, prob_ci = x4) %>%
gather(key, val)
}
images
How about faces — we already have this!
m1 <- glm(as.integer(there_is_a_face_in_this_post) ~ 1, data = d,
family = "binomial")
create_table_output(m1)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -1.398
## 2 log_odds_ci [-1.531 , -1.269]
## 3 odds_coef 0.198
## 4 odds_ci [0.178 , 0.22]
## 5 prob_coef 0.165
## 6 prob_ci [0.151 , 0.18]
images + first and/or last name
m2 <- glm(as.integer(is_there_an_identifiable_face_in_this_post) ~ 1, data = d,
family = "binomial")
create_table_output(m2)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -3.301
## 2 log_odds_ci [-3.596 , -3.031]
## 3 odds_coef 0.036
## 4 odds_ci [0.027 , 0.046]
## 5 prob_coef 0.035
## 6 prob_ci [0.026 , 0.044]
and images + ethnic group
m3 <- glm(as.integer(ethnic_group_face) ~ 1, data = d,
family = "binomial")
create_table_output(m3)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -6.555
## 2 log_odds_ci [-8.35 , -5.427]
## 3 odds_coef 0.001
## 4 odds_ci [0 , 0.004]
## 5 prob_coef 0.001
## 6 prob_ci [0 , 0.004]
and images + gender
m4 <- glm(as.integer(gender_face) ~ 1, data = d,
family = "binomial")
create_table_output(m4)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -3.794
## 2 log_odds_ci [-4.171 , -3.457]
## 3 odds_coef 0.022
## 4 odds_ci [0.015 , 0.031]
## 5 prob_coef 0.022
## 6 prob_ci [0.015 , 0.03]
videos
d <- read_sheet("https://docs.google.com/spreadsheets/d/1uq6VCaAxCog3eKHt5rnCLF7bWwOUyLn12UEXSsFDFNQ/edit#gid=1041895723",
sheet = 5, skip = 1, # and 5
col_types = "c")
d %>% write_csv("hadi-coded-data-vid.csv")
d <- read_csv("hadi-coded-data-vid.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")
How about faces — we already have this!
m1 <- glm(as.integer(there_is_a_face_in_this_post) ~ 1, data = d,
family = "binomial")
create_table_output(m1)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef 0.039
## 2 log_odds_ci [-0.512 , 0.593]
## 3 odds_coef 1.04
## 4 odds_ci [0.375 , 1.809]
## 5 prob_coef 0.51
## 6 prob_ci [0.273 , 0.644]
images + first and/or last name
m2 <- glm(as.integer(is_there_an_identifiable_face_in_this_post) ~ 1, data = d,
family = "binomial")
create_table_output(m2)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -1.54
## 2 log_odds_ci [-2.326 , -0.868]
## 3 odds_coef 0.176
## 4 odds_ci [0.089 , 0.296]
## 5 prob_coef 0.15
## 6 prob_ci [0.082 , 0.228]
and images + gender
m4 <- glm(as.integer(gender_face) ~ 1, data = d,
family = "binomial")
create_table_output(m4)
## # A tibble: 6 × 2
## key val
## <chr> <chr>
## 1 log_odds_coef -2.773
## 2 log_odds_ci [-4.187 , -1.77]
## 3 odds_coef 0.059
## 4 odds_ci [0.015 , 0.145]
## 5 prob_coef 0.056
## 6 prob_ci [0.015 , 0.127]