| f1 <- function(n) { | |
| x <- numeric() | |
| for (i in 1:n) { | |
| x <- c(x, i) | |
| } | |
| x | |
| } | |
| f1.5 <- function(n) { | |
| x <- numeric() |
| 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) %>% |
| #!/bin/bash | |
| # | |
| # Installs the latest RStudio daily desktop build for OSX/macOS and Ubuntu(amd64) | |
| # | |
| # https://support.rstudio.com/hc/en-us/articles/203842428-Getting-the-newest-RStudio-builds | |
| set -e | |
| install_macos_daily() { | |
| REDIRECT_URL="https://www.rstudio.org/download/latest/daily/desktop/mac/RStudio-latest.dmg" | |
| echo "Discovering daily build from: ${REDIRECT_URL}" | |
| # Perform a HEAD request to find the redirect target. We use the name of the |
R is like:
| # lazy data frame | |
| # | |
| # like idata.frame, but exploits lazy evaluation + macro code generation | |
| # instead of active bindings | |
| library(vadr) | |
| lazy.frame <- function(df, enclos=parent.frame(), ...) UseMethod("lazy.frame") | |
| lazy.frame.lazy.frame <- function(df, ...) df |
| # 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? ----------------------------------------- |
| library(memoise) | |
| library(stringr) | |
| git_prompt <- function() { | |
| git <- find_git() | |
| if (is.null(git)) stop("Git not installed", call. = FALSE) | |
| if (!in_git_repo(git)) stop("Not in git repo", call. = FALSE) | |
| update <- function(...) update_prompt(git) |
The key C function that powers both tryCatch() and withCallingHandlers() is do_addCondHands(). It creates handler object with mkHandlerEntry() then stores in the handler stack for the current frame. (More precisely it writes to R_HandlerStack, a global variable that is an alias to c->handlerstack)
The five R arguments to do_addCondHands() are classes, handlers, parentenv, target, and calling. These are combined with a result object (a list of length 4, returned by the exiting handler to doTryCatch()) to create the handler objects which have five components:
class, accessed with ENTRY_CLASS(e). A string given a class name;
the handler will match all conditions that contain this component in their
class vector.
| ks.default <- function(rows) seq(2, max(3, rows %/% 4)) | |
| many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) { | |
| ldply(seq_along(ks), function(i) { | |
| cl <- kmeans(x, centers = ks[i], ...) | |
| data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster) | |
| }) | |
| } | |
| all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") { |
Notes:
I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.
Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.