expersso

# SPECIFIC PROBLEM: Extract an html table and include all links
# (even when there are no links or multiple links per cell)
#
# GENERAL PROBLEM: Extract an html table and include all attributes
# for any given tag and attribute
#
# Example usage at bottom of the page
#
# Motivation: https://twitter.com/daattali/status/717582654476914688

expersso

library(httr)
library(purrr)
library(dplyr)
library(ggplot2)
library(scales)
library(ggthemes)

with_msg <- function(msg, f) {
  
  function(...) {

expersso

library(ggplot2)

df <- data.frame(
  "sector" = c("a", "b", "c"),
  "date" = rep(as.Date(c("1998-01-01", "1999-01-01", "2000-01-01")), each = 3),
  "value" = c(-1, 1, 1, 1, -1, -3, 1.3, 1, 2)
)

p <- ggplot(df, aes(x = date, y = value, fill = sector))

expersso

x2 <- c("apple", "banana", "orange", "pineapple", "kiwi")

# Original function
charsub <- function(x, i, value) {
  out <- lapply(strsplit(x, ""), function(z) {
    z <- `[<-`(z, i, value)
    z[is.na(z)] <- " "
    paste0(z, collapse = "")
  })
  unlist(out)

expersso

library(xml2)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)

url <- "http://www.bls.gov/opub/ted/2016/employment-by-industry-1910-and-2015.htm"

page <- read_html(url)

expersso

library(dplyr)
library(tidyr)
library(ggplot2)

df <- anscombe %>% 
  gather() %>% 
  mutate(axis = substr(key, 1, 1)) %>% 
  mutate(number = substr(key, 2, 2)) %>% 
  group_by(axis, number) %>% 
  mutate(key = 1:n()) %>%

expersso

library(tools)
library(dplyr)
library(readxl)
library(ggplot2)
library(countrycode)
library(ggthemes)

# Function to download and open Excel files
get_data <- function(url, ...) {

expersso

library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(stringr)
library(rvest)
library(xml2)
library(OECD)

# Get budget and box office data

expersso

library(OECD)

# Retrieve data from OECD for all countries
df <- get_dataset("HEALTH_STAT", list("CICDHOCD.TXCMFETF+TXCMHOTH+TXCMILTX")) %>% 
    tbl_df() %>% 
    setNames(tolower(names(.)))

# Get list of dataframes with human-readable descriptions of variables and units
dims <- get_data_structure("HEALTH_STAT")

expersso

library(ggplot2)
library(dplyr)

probs <- seq(0.05, 0.95, each = 0.05)

df <- lapply(probs, rbinom, n = 100, size = 100) %>%
  unlist() %>%
  data.frame("successes" = .) %>%
  mutate(probs = rep(probs, each = 100))