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# Convert a 95% confidence interval to a p-value (two-sided).
# Based on:
# How to obtain the P value from a confidence interval
# BMJ 2011; 343 doi: https://doi.org/10.1136/bmj.d2304 

# From estimate to p-value.
estimate.to.pvalue <- function(estimate, lower, upper) {
  stderror = (upper - lower) / (2 * 1.96)      # Get standard error.
  z = estimate / stderror                      # Get test statistic.
  p.value = exp(-0.717 * z - 0.416 * (z ^ 2))  # Get p-value.

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# Create data.
set.seed(1341)
original <- data.frame(x = rnorm(10), y = rnorm(10))
change <- original
change[5, 1] <- 6
change[7, 2] <- 4

# Function to compare data frames for similarity.
compare_data_frames <- function(df1, df2) {
  if (identical(df1, df2)) {

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Include a DiagrammeR grViz() diagram into your RMarkdown document without the need to create a graph PDF file separately.

Based on the code from https://github.com/rich-iannone/DiagrammeR/issues/133#issuecomment-284370996.

```{r}
# Install packages.
install.packages(c("DiagrammeR", "DiagrammeRsvg", "rsvg", "magrittr"))
```

```{r}

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@echo off
REM From https://stackoverflow.com/questions/568564/how-can-i-view-live-mysql-queries
mysqladmin -u username -ppassword --verbose -i 1 processlist

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library(tidyverse)

n.samples = 25

set.seed(5)
rerun(20,rnorm(n.samples)) %>% 
  map_dfr(~data_frame(data = list(.x)), .id = 'samples') %>% 
  mutate(mu = map_dbl(data,mean), 
         se = map_dbl(data,~sd(.x)/sqrt(length(.x))), 
         top = mu +1.96*se,

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library(gganimate)
library(ggrepel)
library(lubridate)
library(tidyverse)

# The order is FUBAR.
partycolors <- c("#006AB3", "#000077", "#52BDEC", "#E8112d",
                 "#83CF39", "#009933", "#DA291C", "#E8AC41")

# Mediearkivet keywords:

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library(tidyverse)

df <- read.csv("https://raw.githubusercontent.com/peterdalle/effectsizes/master/soc-psych.tsv",
               header=TRUE, sep="\t", stringsAsFactors=FALSE)

df.comm <- read.csv("https://raw.githubusercontent.com/peterdalle/effectsizes-comm/master/effectsizes.csv",
               header=TRUE, sep=",", stringsAsFactors=FALSE)

ggplot() +
  geom_density(aes(df$r), fill="red", alpha=0.3) +

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favorite_packages <- c(
  # Package development
  "devtools", "Rcpp", "roxygen2", "testthat", "covr",
  
  # Handle data
  "rio", "tidyverse", "broom", "lubridate", "stringr",
  
  # Database
  "RMySQL",
  

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create_correlation <- function(n=1000, r=0.5) {
  # Based on caracal:s answer at
  # https://stats.stackexchange.com/questions/15011/generate-a-random-variable-with-a-defined-correlation-to-an-existing-variables/15040#15040
  theta <- acos(r)               # corresponding angle
  x1    <- rnorm(n, 1, 1)        # fixed given data
  x2    <- rnorm(n, 2, 0.5)      # new random data
  X     <- cbind(x1, x2)         # matrix
  Xctr  <- scale(X, center=TRUE, scale=FALSE)   # centered columns (mean 0)
  Id   <- diag(n)                               # identity matrix
  Q    <- qr.Q(qr(Xctr[ , 1, drop=FALSE]))      # QR-decomposition, just matrix Q

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REM https://stackoverflow.com/questions/14304480/batch-resize-images-and-output-images-to-new-folder-with-imagemagick

magick mogrify -resize 1024 -quality 100 -path ./new *.jpg