hadley

LLM for code generation

Currently three main styles:

1. Autocomplete
   • e.g. github copilot, windsurf
   • Inline "ghost text" as you type
   • Sometimes amazingly good; often pretty useless
   • Need to train yourself to ignore spurious suggestions

hadley

Convert <ingredients> to JSON using the following format:

• If an ingredient has both weight and volume, extract only the weight:

  ¾ cup (150g) dark brown sugar
  [{"name": "dark brown sugar", "quantity": 150, "unit": "g"}]
  

• If an ingredient only lists a volume, extract that.

hadley

library(tidyverse)

# https://twitter.com/buddyherms/status/1576966150680121344 --------------

# PROs: at, by, and regexp examples
# CONs: quite simple

vt_census <- tidycensus::get_decennial(
  geography = "block",
  state = "VT",

hadley

f1 <- function(n) {
  x <- numeric()
  for (i in 1:n) {
    x <- c(x, i)
  }
  x
}

f1.5 <- function(n) {
  x <- numeric()

hadley

data(diamonds, package = "ggplot2")

# Most straightforward
diamonds$ppc <- diamonds$price / diamonds$carat

# Avoid repeating diamonds 
diamonds$ppc <- with(diamonds, price / carat)

# The inspiration for dplyr's mutate
diamonds <- transform(diamonds, ppc = price / carat)

hadley

library(rtweet)
library(tidyverse)

auth_setup_default()
json <- search_tweets("#rstats", n = 5000, include_rts = FALSE, parse = FALSE)[1:5]

tweets <- tibble(json = json) %>% 
  unnest_wider(json) %>% 
  select(statuses) %>% 
  unnest_longer(statuses) %>% 

hadley

library(tidyverse)

simple_data <- tibble(
  group = factor(rep(c("A", "B"), each = 15)),
  subject = 1:30,
  score = c(rnorm(15, 40, 20), rnorm(15, 60, 10))
)

simple_data_se &lt;- simple_data %&gt;% 

hadley

library(ggplot2)

x <- c("بقرة", "دجاج", "حصان")
df <- data.frame(x = x, y = 1:3)

labels_rtl <- function(x) paste0("\u202B", x)

ggplot(df, aes(x, y)) + 
  geom_point() +
  scale_x_discrete(labels = labels_rtl) + 

hadley

# need + validate -----------------------------------------------------------
validate(
  need(
    mzfinder::check_mzr_object(ms_object$mzr_connection),
    "Wasn't able to connect to MS file"
  )
)
validate(
  need(!is.null(input$ppm_input), "ppm_input must not be null"),
  need(input$ppm_input > 0L, "ppm_input must be > 0")

hadley

# Code for quick exploration of
# https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-05-26
# Video at https://youtu.be/kHFmtKCI_F4

library(tidyverse)

cocktails <- readr::read_csv("boston_cocktails.csv")


# Are name and row_id equivalent? -----------------------------------------