expersso

library(dplyr); library(httr); library(rvest)
addr <- "Big Ben"
GET("http://maps.google.com/maps/api/geocode/xml", 
    query = list(address = addr)) %>% 
    content() %>% 
    html_nodes(xpath = "//location//lat | //location//lng") %>% 
    html_text() %>% 
    setNames(c("lng", "lat"))

expersso

library(eurostat)
library(dplyr)
library(ggplot2)
library(scales)

#### Original version (not using %+%) ####
# Originally posted at http://www.r-bloggers.com/european-debt-and-interest/ on June 7, 2015

r1 <- get_eurostat('gov_10dd_edpt1')

expersso

# Using ldply returns a clean data frame, so avoids 
# the lapply + do.call(rbind, ...) idiom
# Also, the .progress argument is very useful

df <- ldply(c(2002:2014, 52, 26, 13, 4, 1), function(x) {
        sprintf("http://distrowatch.com/index.php?dataspan=%d", x) %>%
        html() %>% 
        html_nodes(xpath = 
        "//table[@class = 'News' and @style = 'direction: ltr'][2]") %>%
        .[[1]] %>%

expersso

lapply(c("dplyr", "xml2", "rvest", "stringr", "ggplot2"), library, 
       character.only = TRUE)

url <- "https://en.wikipedia.org/wiki/Fatal_dog_attacks_in_the_United_States"

page <- read_html(url)

# Each year is represented by its own table
tbls <- page %>% 
  xml_find_all("//table") %>% 

expersso

lapply(c("WDI", "dplyr", "stringr", "ggplot2", "tidyr", "ggthemes"), library,
       character.only = TRUE)

library(wvs) # local package

# Get shares who mentioned specific groups when asked who they would not want
# as a neighbour
values <- wvs %>%
  filter(wave %in% c("2005-2009", "2010-2014")) %>%
  group_by(country.abbreviation) %>%

expersso

library(lubridate)
library(ggplot2)

period_name <- c(month.name, as.character(wday(1:7, label = TRUE, abbr = FALSE)))
name_length <- sapply(strsplit(period_name, ""), length)

df <- data.frame(period_name, name_length, period_order = c(1:12, 1:7),
                 type = rep(c("Months", "Weekdays"), c(12, 7)))

ggplot(df, aes(x = period_order, y = name_length, label = period_name)) +

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))

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

# Get budget and box office data

expersso

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

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