expersso

library(tidyverse)
library(scales)
library(stringr)
library(grid)
library(gridExtra)
library(wikipediatrend)

df <- wp_trend(c("R_(programming_language)", "Python_(programming_language)"),
               from = "2007-12-01", to = "2017-01-12")

expersso

library(tidyverse)

inv <- function(m) {
  n <- nrow(m)
  stopifnot(n == ncol(m))
  
  cofactor <- function(m, i, j) (-1)^(i+j) * det(m[-i, -j, drop = FALSE])
  adjoint <- function(m) {
    cofactor_matrix <- outer(seq_len(n), seq_len(n), 
                             Vectorize(function(i, j) cofactor(m, i, j)))

expersso

library(dplyr)
library(ggplot2)
library(ggmap)
library(ropenaq)
library(gridExtra)

# Get pollution data
df <-
  aq_measurements(
    country = "US",

expersso

## Purpose: illustrate use of magick, gganimate, and purrr
## Inspiration:
## https://rud.is/b/2016/07/27/u-s-drought-animations-with-the-witchs-brew-purrr-broom-magick/
## and subsequent discussion on magick vs gganimate:
## https://twitter.com/hrbrmstr/status/758304420224466944
library(purrr)
library(ggplot2)
library(gganimate)
library(animation)
library(magick)

expersso

library(tidyr)
library(plyr)
library(dplyr)
library(stringr)
library(ggplot2)
library(scales)
library(ggthemes)

data <- 
"

expersso

# Replicating https://t.co/Jq1QfFGpjA

library(rvest)
library(stringr)
library(dplyr)
library(tidyr)
library(purrr)
library(lubridate)

get_and_clean_table <- function(url) {

expersso

# Remake of http://blogs.worldbank.org/opendata/measuring-surgical-systems-new-paradigm-health-systems-strengthening

library(dplyr)
library(purrr)
library(ggplot2)
library(scales)
library(ggthemes)
library(readxl)
library(tidyr)
library(wbstats)

expersso

# A remake of https://twitter.com/DataIsBeautiful/status/742593686509346816
# using log scales

library(dplyr)
library(purrr)
library(readr)
library(stringr)
library(ggplot2)
library(scales)
library(ggrepel)

expersso

library(purrr)
library(dplyr)
library(rvest)
library(stringr)
library(geoparser)

w_msg <- function(f) {
  force(f)
  function(...) {
    args <- list(...)

expersso

read_sheet <- function(file, sheet) {
  readxl::read_excel(file, sheet, skip = 6) %>% clean_data()
}

fix_names <- function(df) {
  names(df)[1] <- "location"
  names(df)[-1] <- paste(rep(c(2000, 2014), each = length(names(df)[-1]) / 2), 
                         names(df)[-1], sep = "_")
  df
}