expersso

library(tidyverse)

df <-
  "https://rsf.org/sites/default/files/import_good_-_index_2018_pour_import.csv" %>%
  read_csv(locale = locale(decimal_mark = ",")) %>%
  select(`Overall Score 2016`, `Score 2017`, ISO, "country" = EN_country) %>%
  mutate(d = `Score 2017` - `Overall Score 2016`) %>%
  gather(year, value, -ISO, -country, -d) %>%
  mutate(
    year = as.numeric(gsub("[^0-9]", "", year)),

expersso

# inspiration: https://twitter.com/MilesMcBain/status/915448555027828737

accumulate_until <- function(.x, .f, .p, ..., .init = NULL) {
  n   <- length(.x)
  m   <- mode(.x)
  out <- vector(m, n)
  
  if(!is.null(.init)) {
    .x <- c(.init, .x)
  }

expersso

# Motivation: https://twitter.com/zentree/status/905666552925536256

library(tidyverse)
library(rlang)

unnest_dfc <- function(df, var) {
  var <- enquo(var)
  new_df <- df %>% 
    pull(!!var) %>%
    transpose() %>%

expersso

# Uses Fundamental Theorem of Arithmetic to determine if 
# two character strings are anagrams of each other.
# 
# Inspired by:
# https://www.reddit.com/r/math/comments/6hb0xk/
# clever_algorithm_to_determine_whether_or_not_two/

library(primes)
library(purrr)

expersso

library(tidyverse)
library(scales)
library(readxl)
library(forcats)

get_data <- function() {
  tmp <- tempfile(fileext = ".xlsx")
  on.exit(unlink(tmp))
  download.file("http://www.carmenreinhart.com/user_uploads/data/124_data.xlsx",
                tmp, mode = "wb")

expersso

## Nim
next_player <- function(player) if(player == 1) 2 else 1
valid_move  <- function(board, row, num) board[row] >= num
finished    <- function(board) all(board == 0)
update      <- function(board, row, num) `[<-`(board, row, board[row] - num)
disp_row    <- function(row, num) cat(row, ":", rep("*", num), "\n")
disp_board  <- function(board) purrr::walk2(seq_along(board), board, disp_row)

nim <- function(board = 5:1, player = 1) {
  if(finished(board)) {

expersso

library(stringr)
library(purrr)

vowels <- c("a", "e", "i", "o", "u")
is_vowel <- function(l) l %in% c(vowels, toupper(vowels))
is_capitalized <- . %>% substr(1, 1) %>% str_detect("[[:upper:]]")
split_at <- function(x, i) split(x, cumsum(seq_along(x) %in% i))

pigify_word <- function(word) {
  ltrs <- strsplit(word, "")[[1]]

expersso

# Based on https://twitter.com/andrewheiss/status/841655403087822848

library(tidyverse)

url <- paste0("https://gist.githubusercontent.com/andrewheiss/", 
              "db6c981f1032207fd5a1d1a59113c469/raw/", 
              "23768ef3d9dc87a53efd891da4d3d1a448dbca34/long.csv")

df <- read.csv(url, stringsAsFactors = FALSE) %>% 
  tbl_df() %>% 

expersso

library(tidyverse)
library(stringr)

pascal_triangle <- function(n) {
  df <- data_frame(N = 0:n, k = map(N, seq, from = 0)) %>% 
    unnest() %>% 
    mutate(c = choose(N, k)) %>% 
    group_by(N) %>% 
    summarise(c = paste(c, collapse = "  "))
  

expersso

library(tidyverse)
library(scales)
library(OECD)
library(grid)
library(gridExtra)

oecd <- c("AUS","AUT","BEL","CAN","CHL","CZE","DNK","EST","FIN","FRA",
          "DEU","GRC","HUN","ISL","IRL","ISR","ITA","JPN","KOR","LVA",
          "LUX","MEX","NLD","NZL","NOR","POL","PRT","SVK","SVN","ESP",
          "SWE","CHE","TUR","GBR","USA")