ofchurches

library(tidyverse)
library(tidygraph)
library(ggraph)

grosses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-28/grosses.csv', guess_max = 40000)

# links between theatres that have put on the same show
# create the edge list
edge_list <- grosses %>%
  select(show, theatre) %>%

ofchurches

library(tidyverse)
library(tidytext)
library(wordcloud)
library(reshape2)

# Plot 1: Sentiment wordcloud.

gdpr_text <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-21/gdpr_text.tsv')

#base on https://www.tidytextmining.com/sentiment.html

ofchurches

library(tidyverse)
library(gganimate)
library(tidygraph)
library(ggraph)

#Import the data
polls <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-14/polls.csv')

#Make an edge list with nodes as artists with an edge drawn each time the same critic votes for the two artists
edge_list <- polls %>%

ofchurches

library(tidyverse)
library(gganimate)

tdf_winners <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-07/tdf_winners.csv')

tdf_winningest <- tdf_winners %>%
  count(winner_team) %>%
  top_n(10) %>%
  pull(winner_team)

ofchurches

library(tidyverse)
library(scales)

#First, come up with an idea in static ggplot...

beer_states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_states.csv')

top_states <- beer_states %>%
  filter(state != "total") %>%
  filter(type == "On Premises") %>%

ofchurches

library(readr)
library(tidyverse)
library(gganimate)
library(tidygraph)
library(igraph)
library(ggraph)

season_goals <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-03/season_goals.csv')

top_8 <- season_goals %>%