ajstewartlang

library(tidyverse)
library(lubridate)
library(patchwork)
library(ggrepel)
library(ggthemes)

spotify_songs <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv')

arrow <- tibble(x1 = 25, x2 = 12, y1 = 320, y2 = 331.5)

ajstewartlang

library(tidyverse)
library(mapdata)
library(ggthemes)
library(patchwork)

# read in various data files
rainfall <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-07/rainfall.csv')
temperature <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-07/temperature.csv')
nasa_fire <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-07/MODIS_C6_Australia_and_New_Zealand_7d.csv')

ajstewartlang

set.seed(1234)

a <- seq(1:20)
b <- a + runif(20, 0, .25)

my_data <- as.data.frame(c(a, b))

my_model <- lm(a ~ b, data  = my_data)

summary(my_model)

ajstewartlang

# Note, not all of these libraries are needed for the code below

library(tidyverse)
library(openintro)
library(ggthemes)
library(ggmap)
library(maps)
library(mapdata)
library(ggrepel)
library(patchwork)

ajstewartlang

library(tidyverse)
library(ggthemes)

cran_code <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-11-12/loc_cran_packages.csv")

# Plot 1 ####
cran_code %>%
  group_by(language) %>%
  summarise(n = n()) %>%
  arrange(-n) %>%

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)

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(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")