debruine

library(dplyr) # for data processing
library(tidyr) # for data processing
library(magick) # for image reading and OCR
library(tesseract) # for OCR

# read image
dataimg <- magick::image_read("https://pbs.twimg.com/media/FBv8P8XXEBgCBvS?format=jpg&name=medium")

# convert image to text
text <- magick::image_ocr(dataimg)

debruine

---
title: "True Plots"
author: "Lisa DeBruine"
date: "12/10/2021"
output: html_document
---

```{r setup, include=FALSE}
library(ggplot2)
library(dplyr)

debruine

library(dplyr)
library(faux)
library(tidyr)
library(purrr)
library(ggplot2)
library(glue)

within_t <- function(n = 100, mu1 = 0, mu2 = 0, sd1 = 1, sd2 = 1, r = 0, 
         alternative = c("two.sided", "less", "greater")) {
  x <- faux::rnorm_multi(n = n,

debruine

library(tidyverse)
library(truncnorm)

# transcribed table
table2 <- tribble(
  ~question, ~non_mean, ~non_sd, ~aro_mean, ~aro_sd, ~p,
  "shoes", 42, 5.9, 65, 4.06, .001,
  "girl12", 23, 4.11, 46, 6.08, .001,
  "woman40", 58, 3.32, 77, 2.07, .001,
  "woman50", 28, 4.80, 55, 4.69, .001,

debruine

library(faux)
library(tidyverse)

# simulate null data with realistic correlations
dat <- sim_design(
  between = list(B = 0:1),
  within = list(W = 0:1),
  n = 100,
  r = 0.25,
  long = TRUE,

debruine

library(googlesheets4)
library(googledrive)
library(testthat)
library(dplyr)

#' Append rows to a sheet safely
#'
#' Adds one or more new rows after the last row with data in a (work)sheet, 
#' handling data with new columns, missing columns, columns in a different order, 
#' or columns with a different data type.

debruine

library(faux)
library(afex)
library(tidyverse)

sd = 1.5
n = 95
r = .7
nsims = 1e2 
  
roderingel <- sim_design(

debruine

library(tidyverse)

dat <- crossing(v = 1:10, r = seq(0, .9, .1)) %>%
  mutate(normies = map2_dbl(v, r, function(v, r) {
    faux::rnorm_multi(n = 10000, vars = v, r = r, empirical = TRUE) %>%
      filter_all(~(abs(.x) < 1)) %>%
      nrow() / 10000
  }),
r = as.character(r))

debruine

library(tidyverse)
library(splithalf)
library(faux)


n_participants = 60 ## sample size
n_trials = 80
n_blocks = 2

# your method ----

debruine

#' Get named item when unsure of name
#' 
#' If a user gives you a named vector or list and there are a few possibilities for the item you want to get, list the possible names in order, using integers for indices.
#'
#' @param x named vector or list
#' @param ... possible names in x in priority order
#' @param .default default value if no names found
#'
#' @return list or vector item
#' @export