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

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

# 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(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

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)

mismanaged_vs_gdp <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-mismanaged-plastic-waste-vs-gdp-per-capita.csv")
mismanaged_vs_gdp <- clean_names(mismanaged_vs_gdp)
mismanaged_vs_gdp$continent <- countrycode(sourcevar = mismanaged_vs_gdp$entity,
                                      origin = "country.name",
                                      destination = "continent")

ajstewartlang

library(tidyverse)
library(gganimate)

source("https://gist.githubusercontent.com/ajstewartlang/6c4cd8ab9e0c27747424acdfb3b4cff6/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")

set.seed(3)
all_data <- NULL
sample_size <- 500

for(sample in 1:10) {

ajstewartlang

library(tidyverse)
library(rtweet)
library(tidytext)
library(grid)
library(igraph)
library(ggraph)
library(stringi)
library(leaflet)
library(ggthemes)

ajstewartlang

# Greenwich Village and Soho Top 5 Pizza Restaurants
library(tidyverse)
library(leaflet)

pizza_barstool <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_barstool.csv")

zip_list <- c(10012, 10013, 10014)

ready_to_plot <- pizza_barstool %>%
  filter(zip %in% zip_list) %>%

ajstewartlang

library(tidyverse)
library(rtweet)
library(tidytext)
library(grid)
library(igraph)
library(ggraph)
library(stringi)
library(leaflet)
library(ggthemes)