ajstewartlang

# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {

ajstewartlang

devtools::install_github('thomasp85/gganimate')

library(tidyverse)
library(gganimate)

#plotting two groups when no difference exists
df <- NULL
set.seed(1111)
sample_size=20

ajstewartlang

# Usimg @micahgallen's nice raincloud plot code 
# https://wellcomeopenresearch.org/articles/4-63
# and fork of benmarwick/geom_flat_violin.R code
# gganimate by Thomas Lin Pedersen - @thomasp85

devtools::install_github('thomasp85/gganimate')

library(tidyverse)
library(gganimate)
library(RColorBrewer)

ajstewartlang

#Slightly messy code - could easily be improved upon
#new version of gganimate not yet on CRAN so need to download from github with devtools package 
#devtools::install_github('thomasp85/gganimate')

library(tidytext)
library(tidyverse)
library(rtweet)
library(gganimate)
library(lubridate)

ajstewartlang

library(tidyverse)
library(gganimate)

data <- NULL
sample <- NULL
d <- NULL

for (i in (1:100)) {
  set.seed(i+1234)
  a <- rnorm(25, 1000, 50)

ajstewartlang

# Note, this uses the new version of gganimate by Thomas Lin Pedersen (@thomasp85) available from:
# https://github.com/thomasp85/gganimate
# Apologies for clunkiness of code below...

library(tidyverse)
library(gganimate)

data <- NULL
sample <- NULL
d <- NULL

ajstewartlang

## Written by Victor Yu (@VictorYuEpi)

## From https://pastebin.com/44c6GsDM
## playing around with some sports data - tracking the rank of athletes who completed the decathlon at the 2016 Rio Olympics #r #ggplot2 #gganimate #dataviz #Datavisualization


## install/load required packages ####

library(tidyverse)
library(RColorBrewer)

ajstewartlang

library(tidyverse)

df <- NULL
set.seed(1111)
sample_size = 20

for (i in 1:100000) {
  a <- rnorm(sample_size, mean = 10, sd = 2)
  b <- rnorm(sample_size, mean = 10, sd = 2)
  a <- cbind(a, rep ("A", sample_size), rep (i, sample_size))

ajstewartlang

# Note this uses the new version of gganimate by Thomas Lin Pedersen - @thomasp85 
# it is not yet on CRAN so need to use devtools to install it
# devtools::install_github('thomasp85/gganimate')
library(gganimate) # New version of gganimate
library(tidyverse) # All praise the tidyverse
library(MASS) # Needed to sample from multivariate distribution

# Simulating multivariate data with specific covariance structure
# Use the mvrnorm() function from the MASS package

ajstewartlang

# From @nnstats on Twitter
# Imagine a hockey game where we know that Team A scores exactly 1 goal for sure and Team B takes 20 shots, 
# each with a 5.5% chance of going in.
# Which team would you rather be?
# (nothing additional happens if you tie.)

library(tidyverse)
library(gganimate)

set.seed(1234)