ajstewartlang

library(tidyverse)
library(janitor)
library(countrycode)
library(ggthemes)

waste_vs_gdp <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-plastic-waste-vs-gdp-per-capita.csv")

waste_vs_gdp <- clean_names(waste_vs_gdp)

waste_vs_gdp$continent <- countrycode(sourcevar = waste_vs_gdp$entity,

ajstewartlang

library(tidyverse)
library(janitor)
library(countrycode)
library(ggthemes)

coast_vs_waste <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/coastal-population-vs-mismanaged-plastic.csv")

coast_vs_waste <- clean_names(coast_vs_waste)

coast_vs_waste$continent <- countrycode(sourcevar = coast_vs_waste$entity,

ajstewartlang

# pkgs
pacman::p_load(tidyverse, polite, scales, ggimage, ggforce,
               rvest, glue, extrafont, ggrepel, magick)
loadfonts()

## add_logo function from Thomas Mock
add_logo <- function(plot_path, logo_path, logo_position, logo_scale = 10){

    # Requires magick R Package https://github.com/ropensci/magick

ajstewartlang

library(tidyverse)
library(ggthemes)
library(tools)
library(gganimate)
library(ggimage)

full_trains <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv")

p <- full_trains %>%
  filter(service == "International" & year == 2017) %>%

ajstewartlang

library(tidyverse)
library(broom)
library(Hmisc)

total_samples <- 10000
sample_size <- 128
participant <- rep(1:sample_size)
condition <- c(rep("fast", times = sample_size/2), rep("slow", times = sample_size/2))
all_data <- NULL

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)

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

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

## 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

# 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