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library(rscopus)
library(tidyverse)
library(igraph)
library(GGally)

faculty <- read_csv("faculty.csv") 

faculty$au_id <- NA
for (i in 1:nrow(faculty)) {
  print(i)

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library(tidyverse)
library(BFpack)

# function to simulate bayes factors in Kat's experiments
# takes:
#  - n subjects in each condition
#  - p1 for first condition success probability
#  - p2 for second condition success probability
sim_bfs <- function(n, p1, p2) {
  

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library(tidyverse)

# let's define hunter and ames functions as a family of quadratic functions
# where y = beta_1 * x + beta_2 * x^2
# and beta_1 < 0 and beta_2 > 0 

t <- seq(0, 10, .1)

sim <- expand_grid(b1 = seq(-1, 0, .2), 
            b2 = seq(0, .1, .02), 

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library(tidyverse)
library(BFpack)

n <- 12
kat_data <- tibble(condition = c(rep("reliable", n), 
                                 rep("unreliable", n)),
                   harder = c(rbernoulli(n, p = .8), 
                              rbernoulli(n, p = .3)))

kat_data %>% group_by(condition) %>% summarise(mean_harder = mean(harder))

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library(tidyverse)

probs <- c(.6, .8)
df <- expand_grid(subid = 1:10, 
                  condition = 1:2, 
                  trial_num = 1:12) %>%
  group_by(condition) %>%
  mutate(correct = rbinom(n = n(), size = 1, prob = probs[condition][1]))

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library(tidyverse)
d <- read_csv("Replication_Race_For_R_012519.csv")

correct <- tribble(~name, ~correct_value, ~longname,
                   "SocialMotherLanguage", 0, "prefer individuals who speak their mother’s primary language",
                   "SocialGenderGroup", 24, "start identifying as members of a particular gender group",
                   "SocialFaceNonface", 0, "can tell the difference between faces and things",
                   "SocialFat", 36, "begin associating being fat with negative traits",
                   "RaceRace-Status", 47, "associating particular racial groups with status",
                   "RaceLowStatusNegative", 36, "associate low-status racial groups with negative traits",

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tibble(y = c(1,2,3,4), 
       x = c("E","C","E","C"), 
       subid = c(1,1,2,2)) %>% 
  group_by(subid) %>% 
  do(broom::tidy(lm(y ~ x, data = .))) %>% 
  group_by(term) %>% 
  do(broom::tidy(lm(estimate ~ 1, data = .)))

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library(tidyverse)
library(BayesFactor)

trials_per_kid <- 12

sim_data <- function (n) {
d <- expand.grid(subid = 1:n, 
                 condition = c("pred","unpred","consistent")) %>%
  rowwise %>%
  mutate(correct = mean(rbinom(n = trials_per_kid, 

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library(wordbankr)
library(tidyverse)

possess_data <- get_instrument_data(language = "English (American)", 
                                    form = "WS", 
                                    items = "item_687") # note that 687 is the s-possess item
admin_data <- get_administration_data(language = "English (American)", 
                                      form = "WS")

left_join(possess_data, admin_data) %>% 

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library(tidyverse) 
n_sim <- 100
sims <- expand_grid(n_total = seq(50,500,25), 
                    i = 1:n_sim) %>%
  mutate(idx = 1:n()) %>%
  split(.$idx) %>%
  map_df(function (df) {
    cntl_sim <- tibble(choice = c(rbinom(n = df$n_total/2, size = 1, p = .68), 
                                  rbinom(n = df$n_total/2, size = 1, p = .5)), 
                       condition = c(rep("social", df$n_total/2),