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set.seed(2021) | |
data <- tibble( | |
group = factor(c(rep("Group 1", 100), rep("Group 2", 250), rep("Group 3", 25))), | |
value = c( | |
## Group 1 | |
seq(0, 20, length.out = 100), | |
## Group 2 | |
c(rep(0, 5), rnorm(30, 2, .1), rnorm(90, 5.4, .1), rnorm(90, 14.6, .1), rnorm(30, 18, .1), rep(20, 5)), | |
## Group 3 |
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#' Download data to draw Berlin template map | |
#' @examples download_data_berlin() | |
download_data_berlin <- function() { | |
## SETUP --------------------------------------------------------------------- | |
## output dir for raw geo files | |
dir <- here::here("data-raw", "geo-raw") | |
if (!dir.exists(dir)) dir.create(dir, showWarnings = TRUE, recursive = TRUE) | |
## DISTRICTS ----------------------------------------------------------------- |
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library(tidyverse) | |
library(igraph) | |
library(ggraph) | |
df <- readRDS("data.Rds") | |
graph <- graph_from_data_frame(df) | |
sorting <- c(25, 24, 1, 2, 3, 4, 5, 6, 7, 8, 26, 27, c(9:23)) |
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library(tidyverse) | |
library(gganimate) | |
library(systemfonts) | |
df_mac_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-22/big-mac.csv') | |
df_mac <- | |
df_mac_raw %>% | |
mutate(year = lubridate::year(date)) %>% | |
dplyr::select(date, year, iso_a3, currency_code, name, local_price, |
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library(shiny) | |
library(tmap) | |
library(tidyverse) | |
library(fullPage) | |
data(World) | |
world_vars <- setdiff(names(World), c("iso_a3", "name", "sovereignt", "geometry")) | |
ui <- pagePiling( | |
sections.color = c('#ebebeb', '#f1f1f1'), |
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library(shiny) | |
library(tmap) | |
data(World) | |
world_vars <- setdiff(names(World), c("iso_a3", "name", "sovereignt", "geometry")) | |
ui <- fluidPage( | |
tmapOutput("map"), | |
selectInput("var", "Variable", world_vars) |
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library(tidyverse) | |
library(patchwork) | |
df <- tibble(x = 1, y = 1) | |
g <- ggplot(df, aes(x, y)) + | |
scale_x_continuous(expand = c(0, 0)) + | |
scale_y_continuous(expand = c(0, 0)) + | |
coord_cartesian(clip = "off") + | |
theme_void() + |
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library(tidyverse) | |
library(lubridate) | |
locations <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-23/locations.csv') | |
movement <- locations %>% | |
filter(study_site != "Hart Ranges") %>% | |
mutate( | |
season = fct_rev(season), # reverse seasons | |
longitude = round(longitude, 2), # round long, lat to reduce number of points |
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``` r | |
library(tidyverse) | |
library(raster) | |
library(sf) | |
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3 | |
oceans <- | |
rnaturalearth::ne_download(scale = 10, | |
category = "physical", | |
type = "ocean", |
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## from Hugo Toscano: https://github.com/toscano84/TidyTuesday/blob/master/Week23/TT_week23_fastfood.R | |
"%||%" <- function(a, b) { | |
if (!is.null(a)) | |
a | |
else | |
b | |
} | |
geom_flat_violin <- |