francisbarton

{

    "breadcrumbs.enabled": false,

    "diffEditor.codeLens": true,
    
    "editor.acceptSuggestionOnEnter": "off",
    "editor.bracketPairColorization.enabled": false,
    "editor.cursorBlinking": "phase",
    "editor.cursorStyle": "line",

francisbarton

# Apr 2024: stripped out several packages from the list that were loaded as dependencies anyway
pkgs <- c(
  "usethis",
  "tidyterra",
  "devtools",
  "tmap",
  "janitor",
  "afcolours",
  "arrow",
  "assertthat",

francisbarton

[
  {
    "key": "ctrl+d",
    "command": "editor.action.deleteLines",
    "when": "textInputFocus && !editorReadonly"
  },
  {
    "key": "ctrl+shift+k",
    "command": "-editor.action.deleteLines",
    "when": "textInputFocus && !editorReadonly"

francisbarton

options(
  usethis.full_name = "Fran Barton",
  usethis.protocol  = "ssh",
  usethis.description = list(
    `Authors@R` = 'person(
    "Fran", "Barton",
    email = "[email protected]",
    role = c("aut", "cre"),
    comment = c(ORCID = "0000-0002-5650-1176")
    )',

francisbarton

# originally written at
# https://community.rstudio.com/t/unlist-columns-with-lists-of-different-length/
# with grateful acknowledgement of those who worked it out before me :-)

## improved (well, single pipe) version, to summarise values in a df into a summary table
rectangularise <- function(df) {
  df %>%
    purrr::map(unique) %>%
    purrr::map(sort) %>%
    purrr::map(~ `length<-`(., max(lengths(map(df, ~ unique(.)))))) %>%

francisbarton

# https://gist.github.com/francisbarton/a5907ec17154a59d5c9058d64b3012f5

# I use this pattern regularly in chatty packages
snippet inf
	usethis::ui_info(
		stringr::str_glue("")
	)

# set up a testthat block
snippet testt

francisbarton

options(conflicts.policy = list(warn.conflicts = FALSE))
library(dplyr) # A Grammar of Data Manipulation

summarise_groups <- function(df, grouping_var, column_name){
  df %>%
    group_by({{grouping_var}}) %>%  
    summarise("{{column_name}}_mean" := mean({{column_name}}, na.rm = TRUE))
}

dplyr::storms %>% 

francisbarton

# Get postcode metadata from postcodes.io

get_postcode_metadata <- function(postcodes) {
  endpoint <- "https://api.postcodes.io/postcodes"
  body <- list(postcodes = postcodes)
  
  out <- httr::POST(url = endpoint, body = body, encode = "json")
  
  # thanks to https://github.com/ropensci/PostcodesioR/blob/master/R/postcode_lookup.R for setting a good example with httr
  httr::warn_for_status(out)

francisbarton

# Use the doogal.co.uk API to get data about a postcode
# Doesn't accept a vector of codes all at once, so use with purrr::map_dfr()
# along a vector to combine results into a data frame

get_doogal_data <- function(postcode) {

  data_names <- c(
    "postcode",
    "latitude",
    "longitude",

francisbarton

``` r
library(dplyr, warn.conflicts = FALSE)
library(purrr)
library(rlang, warn.conflicts = FALSE)
library(stringr)

filenames <- c("coronavirus_cases_202007061134.csv", "coronavirus_cases_202007071134.csv", "coronavirus_cases_202007081134.csv")
cases <- c(1000, 1500, 2000)

# couple of functions