NickCH-K

library(data.table); library(ggplot2)
dat = as.data.table(readxl::read_excel("Polls-per-user-CEU_R_Python_Stata_2022.xlsx"))
dat = dat[2:nrow(dat), 7:8]
setnames(dat, c('After','Before'))
dat = dat[!is.na(Before) & !(Before == 'Other') & !(After == 'Other')]
results = data.table(Language = c('Python','R','Stata'), Change = sapply(c('Python','R','Stata'), \(x) dat[, sum(After == x) - sum(Before == x)]))
ggplot(results, aes(x = Change, y = 0, label = Language)) +
  geom_hline(yintercept = 0, size = 1) + geom_point(size = 3, color = 'darkblue') + geom_text(vjust = -1) + 
  scale_x_continuous(breaks = c(-5, 0, 5, 10), limits = c(-7, 12)) + 
  ggthemes::theme_economist() + 

NickCH-K

library(tidyverse)
library(purrr)

predfunc = function(x, xdat) x[1] + x[2]*xdat
residsum = function(x, xdat, ydat, pwr) sum(abs((predfunc(x, xdat)-ydat))^pwr)


set.seed(1000)
exdat = bind_rows(
  tibble(label = 'Outlier X',

NickCH-K

library(tidyverse)
library(broom)
library(purrr) 

create_data = function(n, trt) {
  tibble(donut = rnorm(n),
         sleep = rnorm(n)) %>%
    mutate(lifting = rnorm(n) + .1*sleep + .3*donut,
           coffee = rnorm(n) + .2*sleep,