AlbertRapp

library(gt)
gt::towny |> # new data set in {gt} 0.9.0
  dplyr::select(name, land_area_km2) |> 
  gt() |>
  cols_label(name = 'Name', land_area_km2 = 'Area (sq.km)') |> 
  opt_interactive(
    use_search = TRUE, use_filters = TRUE, 
    use_compact_mode = TRUE, page_size_default = 5
  ) |> 
  tab_options(table.width = '500px', container.width = '500px') 

AlbertRapp

library(tidyverse)


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

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

```{r}
library(tidyverse)


#   https://www.produce.co.nz/seasonality-chart/

fruits <- tibble::tribble(
  ~ fruit,       ~start,      ~ end,
  "Blueberry",   "October",    "April",

AlbertRapp

library(tidyverse)

mpg_2008 <- mpg |> 
  filter(
    year == 2008,
    !(class %in% c('2seater', 'minivan'))
  ) |>  
  mutate(
    class = case_when(
      class %in% c('compact', 'subcompact') ~ '(Sub-)Compact',

AlbertRapp

```{r}
library(tidyverse)
manufacturers <- mpg |> 
  mutate(manufacturer = str_to_title(manufacturer))
```


```{r}

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.                         

AlbertRapp

library(tidyverse)
color_palette <- thematic::okabe_ito(8)
flights <- nycflights13::flights

counts <- flights %>% 
  mutate(
    date = lubridate::make_date(year = year, month = month, day = day)
  ) %>% 
  count(date) %>% 
  mutate(

AlbertRapp

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

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

AlbertRapp

library(tidyverse)

dat <- gapminder::gapminder

####### For loop approach
slopes <- numeric(5)
names(slopes) <- unique(dat$continent)

for (selected_continent in unique(dat$continent)) {
  filtered_data <- dat |>