Kcnarf

license: mit

MilesMcBain

f <- function(...) {
  function(df) with(df, ...)
}

footate <- function(.data, ...) {
  dots <- list(...)

  for (column in names(dots)) {
    .data[[column]] <- dots[[column]](.data)
  }

romainfrancois

library(purrr)
library(dplyr)

row_count <- function(.x, .f){ 
  .f <- as_mapper(.f)
  reduce(.x, function(x, y) x + .f(y), .init = 0 ) 
}

data <- tribble( 
  ~x, ~y, ~z, 

thomasp85

library(gganimate) # thomasp85/gganimate
library(cartogram)
library(geogrid) # Need github version jbaileyh/geogrid
library(rnaturalearth)
library(sf)
library(scico)

us <- ne_states('united states of america', returnclass = 'sf')
us <- us[!us$woe_name %in% c('Alaska', 'Hawaii'), ]
us <- st_transform(us, '+proj=eqdc +lat_0=39 +lon_0=-96 +lat_1=33 +lat_2=45 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs')

benmarwick

#------------------------------------------------------
# Exploring the PCA published by Prentiss (1998) to understand the 
# usefulness of the Sullivan and Rozen debitage typology


# read in & tidy the data -----------------------------------------------
library(tidyverse)
# got these data from table 7 (p. 644) of https://www.jstor.org/stable/2694112
# OCR'd using https://tabula.technology/
prentiss <- readr::read_csv("tabula-Prentiss 1988.csv")

timelyportfolio

license: mit
height: 100

DavisVaughan

library(slide)
library(dplyr, warn.conflicts = FALSE)

# be careful not to `library(tsibble)` as it will overwrite slide()

# quarterly data to start with
df <- tibble(
  dates = seq(as.Date("2019-01-01"), as.Date("2021-01-01"), by = "1 quarter"),
  yq = tsibble::yearquarter(dates)
)

ikashnitsky

#===============================================================================
# 2019-11-30 -- gist
# Safe and efficient palette of five colors, print and colorblind friendly
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#===============================================================================

library(magrittr)
library(prismatic)

# a function to test color paletes quickly 

yutannihilation

library(ggplot2)

# functions -----------------------------------------------

geom_liquid_area <- function(mapping = NULL, data = NULL, stat = "identity",
                             position = "stack", na.rm = FALSE, show.legend = NA,
                             inherit.aes = TRUE, ...) {
  layer(
    data = data,

moodymudskipper

# run from R GUI or you might have methods registered by IDE

# first method scrape S3 method tables, classes with no methods are not found
# second method parses code to find `class(foo) <- bar` lines and extracts string litterals if found, we could be a bit smarter there
#   and find more, but that will still not be exhaustive, because we have things like `class(x) <- cl` and we'd have to check the code
#   to see what `cl` is.
# we could also check if some objects are built with `structure`
# calls to `inherits` might also be checked
# The C code should also be inspected or we won't find for instance the "error" or "try-error" classes.