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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)] |
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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), |
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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) %>% |
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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") |
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# 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) |
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#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 |
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# 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') |
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#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') |
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#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) |
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#30DayMapChallenge 05 OSM | |
#Park Spaces in The Woodlands, U.S. | |
# load libraries | |
library(tidyverse) | |
library(osmdata) | |
library(ggtext) | |
# load fonts | |
library(sysfonts) |
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