jrosen48

f <- list.files('~/downloads/Todoist backup 2020-01-18', full.names = T)

library(tidyverse)

l <- map(f, read_csv)
nr <- l %>% map(nrow)

fn <- f[rep(1:18, times = unlist(nr))]
fn <- fn %>% 
    str_split("/") %>% 

jrosen48

library(tidyverse)
library(quanteda)
library(quanteda.classifiers)
library(shiny)
library(shinythemes)

a <- read_csv("all-new-data.csv") %>%
    mutate(Why = text) %>% 
    mutate(Why = str_c(Why, " ", lesson, " ", gen_or_spec)) %>%
    mutate(row_num = row_number(),

jrosen48

``` r
utils::data(anorexia, package = "MASS")

m1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                family = gaussian, data = anorexia)

summary(m1)
#> 
#> Call:
#> glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, 

jrosen48

texPreview::tex_preview(obj = xtable::xtable(head(iris,10)))
#>   [1] "This is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=pdflatex 2019.5.30)  30 MAY 2019 22:24"
#>   [2] "entering extended mode"                                                                                                 
#>   [3] " restricted \\write18 enabled."                                                                                         
#>   [4] " %&-line parsing enabled."                                                                                              
#>   [5] "**/private/var/folders/zn/9_h784cn6t5b2tchsgx4v2p00000gn/T/Rtmp12qz1x/tex_tempD"                                        
#>   [6] "oc.tex"                                                                                                                 
#>   [7] ""                                                                                                                       
#>   [8] "(/private/var/folders/zn/9_h78

jrosen48

library(tidyverse)
library(rtweet)
library(beepr

f <- "replace-with-path-to-file"

d <- read_csv(f)

d$id_str <- str_split(d$status_url, "/") %>% 
  map_chr(~.[6])

jrosen48

library(rscopus)
library(tidyverse)

issns <- c("0022-0663", "1532-6985", "1532-690X", "1532-7809", "1873-782X", "1556-6501", "8756-3894", "0959-4752", "1090-2384")
query <- str_c("ISSN(", issns, ")")

f <- function(query) {
  res <- scopus_search(query = query, max_count = 100000, count = 25, wait_time = 7)
  gen_entries_to_df(res$entries)
}

jrosen48

library(tidyverse)
library(googlesheets)

g <- gs_title("Ed Psych Journal Editorial Boards 2018")
d <- gs_read(g)

m <- d %>% 
    count(Seniority, Gender) %>% 
    spread(Gender, n)

jrosen48

library(tidyverse)

d <- read_csv("Downloads/choice_interest_plot.csv")

sd_val <- 1

lower_cut <- -sd_val*sd(d$interest_c)
upper_cut <- sd_val*sd(d$interest_c)

d <- d %>% 

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f <- function (object)
{
    se.bygroup <- ranef(object, postVar = TRUE)
    n.groupings <- length(se.bygroup)
    for (m in 1:n.groupings) {
        vars.m <- attr(se.bygroup[[m]], "postVar")
        K <- dim(vars.m)[1]
        J <- dim(vars.m)[3]
        se.bygroup[[m]] <- array(NA, c(J, K))
        for (j in 1:J) {

jrosen48

d <- as.data.frame(cbind(c(2, 3, 4, 1, 7, 4, 3, NA, 4), c(2, 3, 4)))
d

detect_multivariate_outliers <- function(data, df, alpha = .997) {
  require(dplyr)
  data$row_id = 1:nrow(data)
  data_ss <- data[complete.cases(data), ]
  mah_dist <- mahalanobis(data_ss, colMeans(data_ss), cov(data_ss))
  crit_val <- qchisq(alpha, df)
  id_and_logical <- data.frame(row_id = data_ss$row_id, is_outlier = (mah_dist > crit_val))