z3tt

library(ggplot2)

gm_2023 <- readr::read_csv("https://www.cedricscherer.com/data/gapminder-2023.csv")
pal <- c(Africa = "#00D9EE", Americas = "#4CF101", Asia = "#FF4670", Europe = "#FFE702", Oceania = "#CA4ADC")

# base plot
g <-
  ggplot(gm_2023, aes(x = gdp_pcap, y = life_exp, size = pop)) +
  geom_point(
    color = "white", 

hadley

data(diamonds, package = "ggplot2")

# Most straightforward
diamonds$ppc <- diamonds$price / diamonds$carat

# Avoid repeating diamonds 
diamonds$ppc <- with(diamonds, price / carat)

# The inspiration for dplyr's mutate
diamonds <- transform(diamonds, ppc = price / carat)

juliasilge

library(tidyverse)
library(widyr)
library(maps)
#> 
#> Attaching package: 'maps'
#> The following object is masked from 'package:purrr':
#> 
#>     map
eurovision_votes <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-05-17/eurovision-votes.csv')

jthomasmock

library(gt)
library(gtExtras)
library(dplyr)
library(htmltools)

# original source: https://www.bloomberg.com/graphics/2021-german-election-results/

party_df <- tibble(
  Party = c("SPD", "CDU/CSU", "Greens", "FDP", "AfD", "Left", "Other"),
  Seats = c(206, 196, 118, 92, 83, 39, 1),

hadley

# What's the most natural way to express this code in base R?
library(dplyr, warn.conflicts = FALSE)
mtcars %>%
  group_by(cyl) %>%
  summarise(mean = mean(disp), n = n())
#> # A tibble: 3 x 3
#>     cyl  mean     n
#>   <dbl> <dbl> <int>
#> 1     4  105.    11
#> 2     6  183.     7

ghuertaramos

library(ggplot2)
library(ggtree)
library(emojifont)

  tree_text <- "(evergreen_tree,(avocado,((tulip, ( palm_tree, ( banana, ( pineapple, (corn, ( tanabata_tree, ear_of_rice )))))),((grapes, (((peanuts, shamrock), ((( rose, strawberry ), ((apple, pear ), ( peach, (cherry_blossom, cherries) ))), (chestnut, (jack_o_lantern, (watermelon, (cucumber, melon)))))), (hibiscus, (( tangerine, lemon ), (maple_leaf))))),(cactus, (kiwi_fruit, ((sweet_potato, (hot_pepper, (eggplant, (potato, tomato)))), (carrot,(sunflower, blossom)))))))));"
x <- read.tree(text=tree_text)
ggtree(x, layout="circular") +
  xlim(NA, 13) + ylim(NA, 39) +
  geom_tiplab(aes(color=label), parse='emoji', size=6, vjust=0.5, hjust = 0.5, offset = 0.6) +

tylermorganwall

#theme and ggplot derived from David Kretch, his code: https://github.com/davidkretch/london_cholera_map/blob/master/london_cholera_map.R

library(HistData)
library(ggplot2)
library(ggpointdensity)
library(rayshader)

deaths = Snow.deaths
streets = Snow.streets

brfitzpatrick

Job script:

#!/bin/bash

#SBATCH --job-name=rfee
#SBATCH --workdir=/home/user.name/rfee/
#SBATCH --output=r_foreach_example_console_output.txt
#SBATCH --mem-per-cpu=100 # specify RAM per CPU here in Mb
#SBATCH --time=0:02:00

lnalborczyk

############################################################################
# estimation via the base R optim function
# code adapted from Farrell & Lewandowsky (2018, ch.3)
# https://github.com/psy-farrell/computational-modelling
#################################################################

library(hrbrthemes)
library(tidyverse)
library(gganimate)
library(magick)

darencard

layout	title	date	excerpt
posts	Visualizing Genome Annotations	2019-01-25	Creating a UCSC Genome Track for Viewing Genome Annotations

Creating a UCSC Genome Track for Viewing Genome Annotations

When creating a custom, in-house genome annotation, there is no straight-forward way to share it upon publication. NCBI does not accept and archive these, so most users just end up depositing the text files in an online repository. The UCSC Genome Browser team has fortunately come up with a decent way to share an annotation in a graphical manner, so readers can browse the assembly and annotation at some level without a lot of work.