Julia Silge juliasilge

library(tidyverse)
library(broom)
theme_set(silgelib::theme_plex())

raw_hikes <- read_rds(url('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-24/hike_data.rds'))

hikes <- raw_hikes %>%
    mutate(across(c(length, gain, highpoint, rating), parse_number))

library(tidyverse)
library(tidylo)
library(tidytext)
theme_set(silgelib::theme_plex())

ninja_raw <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-15/ninja_warrior.csv')
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(

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

games <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-16/games.csv')
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   gamename = col_character(),

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(baguette)

set.seed(123)
car_folds <- bootstraps(mtcars, times = 5)

library(tidyverse)
library(tidycensus)
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1
library(viridis)
#> Loading required package: viridisLite
library(patchwork)
theme_set(silgelib::theme_plex())

library(rlang)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

rmsle <- function(data, ...) {
    UseMethod("rmsle")
}

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This other blog post has specific steps outlined, but some of it has also been superseded.

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':
#>

	library(tidymodels)
	library(textrecipes)

	data("small_fine_foods")
	sparse_bp <- hardhat::default_recipe_blueprint(composition = "dgCMatrix")

	text_rec <-
	recipe(score ~ review, data = training_data) %>%
	step_tokenize(review) %>%
	step_stopwords(review) %>%

	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")