juliasilge

library(tidyverse)
library(corrr)

bob_ross <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-02-21/bob_ross.csv')
#> Rows: 403 Columns: 27
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (5): img_src, painting_title, youtube_src, colors, color_hex
#> dbl  (4): painting_index, season, episode, num_colors

juliasilge

library(tidyverse)
theme_set(silgelib::theme_plex())
radio_stations <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-08/state_stations.csv')
#> Rows: 17186 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): call_sign, frequency, city, licensee, format, state
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.

juliasilge

library(tidyverse)
library(tidytext)
library(ggraph)
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#> 
#>     as_data_frame, groups, union

juliasilge

First in R:

library(tidymodels)
data(Sacramento, package = "modeldata")

rf_spec <- rand_forest(mode = "regression")
rf_form <- price ~ type + sqft + beds + baths

rf_fit <-

juliasilge

library(tidyverse)
library(tidytext)
library(stm)
#> stm v1.3.6 successfully loaded. See ?stm for help. 
#>  Papers, resources, and other materials at structuraltopicmodel.com

austen_sparse <- janeaustenr::austen_books() %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words) %>%

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')

juliasilge

library(tidyverse)
library(silgelib)
theme_set(theme_plex())

babynames <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-22/babynames.csv')
#> Rows: 1924665 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (2): sex, name

juliasilge

library(tidyverse)
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#> 
#>     as_data_frame, groups, union
#> The following objects are masked from 'package:purrr':
#> 

juliasilge

Install native ARM R: https://cloud.r-project.org/

Install RStudio (probably preview) which is not native as of today: https://www.rstudio.com/products/rstudio/download/preview/

This blog post is helpful for getting Tensorflow working, but it is older, before the ARM version of R and the RStudio version that supports it was available.

This other blog post has specific steps outlined, but some of it has also been superseded.

juliasilge

library(tidyverse)
library(tidymodels)
library(textrecipes)
library(finetune)
library(vip)

## data from here: https://www.kaggle.com/c/sliced-s01e05-WXx7h8/data
train_raw <- read_csv("train.csv")