ivelasq

library(tidyverse)
library(ggridges)
library(viridis)
library(extrafont)

# font_import()
# loadfonts(device = "win")

wine_ratings <-
  readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv")

ivelasq

# https://www.eia.gov/electricity/data/state/

library(tidyverse)
library(readxl)
library(janitor)
library(geofacet)
library(extrafont)

dat <-
  read_excel(here::here("data", "annual_generation_state.xls", skip = 1)) %>% 

ivelasq

# Chicago YTD Crime Data Visualization

# libraries ---------------------------------------------------------------

library(tidyverse)
library(ggmap)
# devtools::install_github('thomasp85/gganimate')
library(gganimate)
library(RSocrata)
library(RColorBrewer)

ivelasq

# libraries ---------------------------------------------------------------

library(tidyverse)
library(brolgar)
library(extrafont)
library(directlabels)

# data ----------------------------------------------------------------------

ivelasq

library(tidyverse)
library(readxl)
library(viridis)

endowments <-
  read_excel(here::here("data", "NCSE2017 Endowment Markets Values for Media--Final January 31 2018.xlsx"), skip = 2)

rankings <-
  read_csv(here::here("data", "National Universities Rankings.csv"))

ivelasq

library(tidyverse)
library(ggmap)
library(maps)
library(rgdal)

commute_mode <- 
  readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-11-05/commute.csv")

commute_mode_cities <-
  commute_mode %>% 

ivelasq

library(tidyverse)
library(ggbeeswarm)
library(ggdark)
library(showtext)
library(viridis)

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

spotify_songs %>% 
  filter(playlist_genre == "pop") %>% 

ivelasq

library(tidyverse)
library(ggbeeswarm)
library(viridis)

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

spotify_songs %>% 
  filter(playlist_genre == "pop" & track_popularity > 50) %>% 
  ggplot(aes(x = playlist_subgenre, y = danceability, group = playlist_subgenre, color = playlist_subgenre)) +

ivelasq

# TidyTuesday
# February 11, 2020

# load libraries --------------------------------------------------------

library(tidyverse)
library(viridis)
library(lubridate)

# read data -------------------------------------------------------------

ivelasq

# 2020-02-18 Tidy Tuesday

library(tidyverse)
library(rvest)

food_consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-18/food_consumption.csv')

url <- "https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population"

population <-