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Coding at home

Cédric Scherer z3tt

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Coding at home
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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
``` 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",
## 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 <-
@z3tt
z3tt / cedrics_theme.R
Created May 22, 2019 18:02
My ggplot theme
##----------------------------------------------------------------------------------------------------------##
## Cedric Scherer ([email protected]) ##
## Function for a custom ggplot theme ##
## 2017-05-27 ##
##----------------------------------------------------------------------------------------------------------##
library(ggplot2)
if ("extrafont" %in% rownames(installed.packages()))
{
@z3tt
z3tt / data-ggplot-evolution-vol1.r
Last active December 9, 2023 05:16
Data for the blog post "The Evolution of a ggplot (Vol. 1)"
library(tidyverse)
df_students <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-07/student_teacher_ratio.csv")
df_world_tile <- readr::read_csv("https://gist.githubusercontent.com/maartenzam/787498bbc07ae06b637447dbd430ea0a/raw/9a9dafafb44d8990f85243a9c7ca349acd3a0d07/worldtilegrid.csv") %>%
mutate(
## Namibias two-digit country code is handled as `NA` - let us fix that
alpha.2 = if_else(name == "Namibia", "NA", alpha.2),
## We are going to split "Americas" into "North America" and "Sout America"
region = if_else(region == "Americas", sub.region, region),
@z3tt
z3tt / data.R
Last active August 28, 2018 19:38
Raincloudplots - movement model versus number of infection
dat <- structure(list(Move = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,