AlbertRapp

library(dplyr)
library(ggplot2)
library(patchwork)
library(ggiraph)

dat <- gapminder::gapminder |> 
  janitor::clean_names() |> 
  mutate(
    # ID that is shared for boxplots (this one uses factors, i.e. numbers, as ID instead of continents)

AlbertRapp

library(shiny)
library(bslib)
library(bsicons)

ui <- fluidPage(
  theme = bslib::bs_theme(),
  h1('One Box'),
  fluidRow(
    column(

AlbertRapp

library(shiny)
library(bs4Dash)

ui <- dashboardPage(
  header = dashboardHeader(),
  sidebar = dashboardSidebar(),
  body = dashboardBody(
    h1('One Box'),
    fluidRow(
      actionButton('btn', 'Think of this button as some cool interactive element that reveals important information', width = '400px'),

AlbertRapp

```{r}
setwd(here::here('connected_scatterplots'))

library(tidyverse)
library(lubridate)
library(patchwork)
library(showtext)

AlbertRapp

library(shiny)
library(bs4Dash)

ui <- dashboardPage(
  header = dashboardHeader(),
  sidebar = dashboardSidebar(skin = 'light'),
  body = dashboardBody(
    fluidRow(
      actionButton('btn', 'Click me', width = '200px'),
      bs4ValueBoxOutput('click_box', width = 4)

AlbertRapp

library(shiny)
library(bslib)
library(bsicons)


ui <- fluidPage(
  theme = bs_theme(),
  title = 'Testapp',
  h3('Test App'),
  sidebarLayout(

AlbertRapp

library(tidyverse)


manufacturers <- mpg |> 
  count(manufacturer, sort = TRUE) |> 
  mutate(
    manufacturer = str_to_title(manufacturer),
    manufacturer = fct_reorder(manufacturer, n) 
  )

AlbertRapp

library(tidyverse)

# Helper function to do one single step
move_one_step <- function(head_tail_list, dir) {
  n <- length(head_tail_list)
  for (i in seq_along(head_tail_list)[-n]) {
    # Get coordinates of current head-tail-pair
    head_x <- head_tail_list[[i]]$x[length(head_tail_list[[i]]$x)]
    head_y <- head_tail_list[[i]]$y[length(head_tail_list[[i]]$y)]
    tail_x <- head_tail_list[[i + 1]]$x[length(head_tail_list[[i + 1]]$x)]

AlbertRapp

# Most of the code has been adapted from 
# https://nrennie.rbind.io/blog/2022-12-03-how-to-make-your-own-rstats-wrapped/

setwd(here::here('blog_fcts/'))

base_path <- 'Your_own_file_path_to_Blog_directory'
files <- list.files(
  path = base_path,
  recursive = TRUE
) 

AlbertRapp

library(tidyverse)
calories_input <- read_lines('data/2022_day01.txt')

total_calories_per_elf <- tibble(
  calories = parse_number(calories_input)
) |> 
  mutate(elf = cumsum(if_else(is.na(calories), 1, 0))) |> 
  group_by(elf) |> 
  summarise(calories = sum(calories, na.rm = T))