ursulams

# first star
df <- read.table(text = puzzle_input, header = FALSE, sep = "\n")
df$digits <- as.numeric(paste0(sub(".*?(\\d).*", "\\1", df$V1), sub(".*(\\d+).*$", "\\1", df$V1)))
sum_cal_values <- sum(df$digits)

# second star
df$V2 <- sapply(df$V1, function(x){
  gsub("one", "o1e", 
        gsub("two", "t2o", 
              gsub("three", "thr3e",

KaiAragaki

Almost all - if not all - of my new R projects are targets projects.

I'm usually bouncing between computers and occasionally have a collaborator, so targets makes it super super simple to spin up
my analyses at another computer.

Nowadays, I don't just use a targets project, but I like having multiple targets projects within one targets project. This
keeps the pipelines from getting to unweildly, at which point I usually get pretty overwhelmed. 
(From here on, assume that when I say 'project' I mean a targets project, not an RStudio project - all of this happens in a 
single RStudio project)

AlbertRapp

library(tidyverse)
big_tech_companies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-02-07/big_tech_companies.csv')
big_tech_companies
# # A tibble: 14 × 2
#    stock_symbol company                                  
#    <chr>        <chr>                                    
#  1 AAPL         Apple Inc.                               
#  2 ADBE         Adobe Inc.                               
#  3 AMZN         Amazon.com, Inc.                         
#  4 CRM          Salesforce, Inc.                         

strengejacke

// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

edonnachie

library(dplyr)
library(arrow)
library(duckdb)

# install.packages(c("dplyr", "arrpw", "duckdb", "nycflights13"))


## Export the nycflights13 dataset to arrow format ----

nycflights13::flights |>

AlbertRapp

---
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r}
setwd(here::here('stacked_bar_alternative/'))
camcorder::gg_record(
  dir = 'img',

z3tt

library(palmerpenguins)
library(ggtext)
library(colorspace)
library(ragg)

url <- "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png"
img <- magick::image_read((url))
pic <- grid::rasterGrob(img, interpolate = TRUE)

pal <- c("#FF8C00", "#A034F0", "#159090")

b-rodrigues

options(reactable.theme = reactable::reactableTheme(
          color = "hsl(233, 9%, 87%)",
          backgroundColor = "#002b36",
          borderColor = "#eee8d5",
          stripedColor = "#586e75",
          highlightColor = "#6c71c4",
          inputStyle = list(backgroundColor = "hsl(233, 9%, 25%)"),
          selectStyle = list(backgroundColor = "hsl(233, 9%, 25%)"),
          pageButtonHoverStyle = list(backgroundColor = "hsl(233, 9%, 25%)"),
          pageButtonActiveStyle = list(backgroundColor = "hsl(233, 9%, 28%)")

edonnachie

library(sf)
library(ggplot2)

# Data from https://www.geoboundaries.org/index.html#getdata
de2 <- read_sf("data/geoBoundaries-DEU-ADM2-all/geoBoundaries-DEU-ADM2_simplified.shp")

# Extract Munich and Berlin to separate objects
munich <- dplyr::filter(de2, DistrictCo == "09162")
berlin <- dplyr::filter(de2, DistrictCo == "11000")

abmathewks

CreateProjectFiles <- function(PROJECT_PATH = rstudioapi::getActiveProject(),
                               FOLDERS_TO_CREATE = c("data", "docs", "figs", "logs",
                                                     "output", "queries", "R", "tests"),
                               OPEN_NEW_SESSION = TRUE,
                               DEBUG = TRUE){

    if(DEBUG) message("CreateProjectFiles: Function initialized  \n")
  
    if(!dir.exists(PROJECT_PATH)){