leeolney3

library(tidyverse)
library(janitor)
library(ggtext)
library(geofacet)
library(biscale)
library(cowplot)

nurses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv') %>% clean_names()

nurses$st <- state.abb[match(nurses$state, state.name)]

leeolney3

library(tidyverse)
library(ggtext)
library(janitor)

stock <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/fish-stocks-within-sustainable-levels.csv') %>% clean_names()

theme1 = theme_minimal(base_size = 9, base_family = "Roboto") +
  theme(legend.position = "top",
        panel.grid.minor=element_blank(),
        plot.title=element_text(face="bold", size=14),

leeolney3

library(tidyverse)
library(ggtext)

pumpkins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-19/pumpkins.csv')

df1 = pumpkins %>% filter(place!="EXH") %>%
  mutate(weight = parse_number(weight_lbs), place= as.numeric(place)) %>%
  drop_na(place) %>%
  mutate(id2 = id) %>% separate(id2, c("year", "type"), sep="-") %>% 
  filter(place<=100) %>%

leeolney3

library(tidyverse)
library(ggtext)
library(lubridate)
library(ggmosaic)

ultra_rankings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/ultra_rankings.csv')
race <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/race.csv')

df = ultra_rankings %>% left_join(race, by="race_year_id")

leeolney3

# Load Libraries 
library(tidyverse)
library(gt)
#remotes::install_github("jthomasmock/gtExtras")
library(gtExtras)

# Import data
races <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-09-07/races.csv',show_col_types = FALSE)
driver_standings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-09-07/driver_standings.csv',show_col_types = FALSE)
drivers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-09-07/drivers.csv',show_col_types = FALSE)

leeolney3

#30DayMapChallenge Day 1 Points 

# load libraries 
library(tidyverse)
library(rjson)
library(sf) 
library(ggtext)

# import supermarket licenses .csv file
# source: https://data.gov.sg/dataset/list-of-supermarket-licences?view_id=97a106cf-d9af-4808-9476-1d8bcf8dd78b&resource_id=3561a136-4ee4-4029-a5cd-ddf591cce643

leeolney3

# 30DayMapChallenge Day 2 Lines

# load libraries 
library(tidyverse)
library(sf)
library(tmap)

# import water pipes shp 
# source: https://discover.data.vic.gov.au/dataset/water-supply-main-pipelines
water_pipes_shp = read_sf('Water_Supply_Main_Pipelines-shp/Water_Supply_Main_Pipelines.shp')

leeolney3

#30DayMapChallenge Day 3 Polygons.
# Heritage Conservation Districts in Toronto, data from open.toronto.ca
# Data source: https://open.toronto.ca/dataset/heritage-conservation-districts/ (last refreshed Jul 29, 2021)

# load libraries
library(sf)
library(tmap)

# read Heritage Conservation Districts shp file
heritage = read_sf('heritagehcds20210421_webm/Heritage_Conservation_Districts_WGS84.shp')

leeolney3

#30DayMapChallenge Day 4 Hexagons
#Registered fire departments in the US 2021, data from U.S. Fire Administration.
#Data source: https://apps.usfa.fema.gov/registry/download (registry last updated Nov 1, 2021)
#Reference: https://twitter.com/TamayoLeiva_J/status/1447337916154458117/photo/1
#Reference: https://www.r-graph-gallery.com/hexbin-map.html

# load libraries 
library(tidyverse)
library(broom)
library(geojsonio)

leeolney3

#30DayMapChallenge 05 OSM
#Park Spaces in The Woodlands, U.S.

# load libraries 
library(tidyverse)
library(osmdata)
library(ggtext)

# load fonts
library(sysfonts)