efficient-R: Becoming an Efficient R Programmer

#' @name clock_speed
#' @title CPU clock speeds
#' @description The year and clock speed of CPUs. The clock speed is measured in million of instructions
#' per second. This data set was collected from forums, wikipedia and blog posts. 
#' @docType data
#' @format A two column data frame
#' @source https://csgillespie.wordpress.com/2011/01/25/cpu-and-gpu-trends-over-time/
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#' @name die
#' @title MIPS per die
#' @description The year, clock speed and brief description of CPUs. 
#' The clock speed is measured in million of instructions
#' per second per die. This data set was taken from
#' http://en.wikipedia.org/wiki/List_of_Intel_microprocessors
#' Each CPU is the best in it's class
#' @docType data
#' @format A three column data frame
#' @source https://csgillespie.wordpress.com/2011/01/25/cpu-and-gpu-trends-over-time/
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#' @name ex_mat
#' @title An example data frame and matrix
#' @aliases ex_df
#' @description Example data frame and matrix. Each object has 1000 rows and 100 columns. The data
#' is 10^5 uniform random numbers.
#' @docType data
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#' @name land_df
#' @title UK land use prices
#' @description Prices paid for land in the UK, open data.
#' See \url{https://data.gov.uk/dataset/land-registry-monthly-price-paid-data} for details
#' @docType data
#' @format A sixteen column data frame.
#' See \url{https://www.gov.uk/guidance/about-the-price-paid-data} for column descriptions
#' @examples
#' \dontrun{
#' url = paste0("http://prod.publicdata.landregistry.gov.uk",
#' ".s3-website-eu-west-1.amazonaws.com/pp-monthly-update.txt")
#' download.file(url = url, destfile = "pp-monthly-update.txt")
#' land_df = readr::read_csv("pp-monthly-update.txt", col_names = FALSE)
#' # Get column names
#' library(rvest)
#' land_html = read_html("https://www.gov.uk/guidance/about-the-price-paid-data")
#' land_colnames = html_nodes(land_html, "td:nth-child(1)", )
#' land_colnames = gsub(pattern = "<td>", replacement = "", land_colnames)
#' land_colnames = gsub(pattern = "</td>", replacement = "", land_colnames)
#' land_colnames = abbreviate(land_colnames, minlength = 10)
#' land_colnames = tolower(land_colnames)
#' names(land_df) = land_colnames
#' land_df = land_df[1:10000,]
#' devtools::use_data(land_df, overwrite = TRUE)
#' }
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#' @name ghg_ems
#' @title Worldwide emissions of greenhouse gases (ghg) by sector, country and year.
#' 
#' @description Emissions data from the World Resources Institute,
#' from their website:
#' \url{http://www.wri.org/resources/data-sets/cait-country-greenhouse-gas-emissions-data}.
#' @docType data
#' @format A seven column data frame.
#' @examples
#' \dontrun{
#' # After completing the form,
#' # and downloading the .zip file into the working directory:
#' unzip("CAIT_Country_GHG_Emissions_-_csv_0.zip")
#' f <- "CAIT_Country_GHG_Emissions_-_All_Data.xlsx"
#' ghg_ems <- readxl::read_excel(f, sheet = 4)
#' head(ghg_ems)
#' names(ghg_ems) = stringr::word(names(ghg_ems), sep = " |/")
#' devtools::use_data(ghg_ems, overwrite = TRUE)
#' }
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#' @name miniaa
#' @title Dataset from National Provider Indentifier (NPI) dataset.
#' @description
#' Sample health care providers in the USA. Health plans were
#' required to obtain and use an NPI by May 23, 2008. See 
#' \url{http://www.nber.org/data/npi.html} for more information
#' 
#' @examples
#' \dontrun{ 
#' url <- "http://download.cms.gov/nppes/NPPES_Data_Dissemination_Aug_2015.zip"
#' # download a large dataset - don't run
#' download.file(url, "data/largefile.zip")
#' ## 550600K .......... ...... 100% 1.26M=6m53s
#' # unzip the compressed file, measure time
#' system.time( 
#'   unzip("data/largefile.zip", exdir = "data")
#' )
#' ##    user  system elapsed 
#' ##  34.380  22.428 193.145
#' 
#' bigfile <- "data/npidata_20050523-20150809.csv"
#' file.info(bigfile) # file info (not all shown)
#' ##       size: 5647444347
#' 
#' # bash scripts to generate the file:
#' cd data # change directory
#' split -b100m npidata_20050523-20150809.csv
#' system.time(df3 <- read.csv("data/xaa"))
#' system.time(df4 <- read_csv("data/xaa"))
#' split -l 10 aa mini # further split chunk 'aa' into 10 lines
#' cp miniaa ../data # copy the first into 'sample-data'
#' }
#' @docType data
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#' @name wb_ineq
#' @aliases wb_ineq_renamed
#' @title Global inequality by, country and year.
#' 
#' @description Estimates of the level of income inequality, using the Gini index,
#' from World Bank data. See
#' \url{http://data.worldbank.org/indicator/SI.POV.GINI?locations=AT}
#' for further information. \code{wb_ineq_renamed} is a version of the dataset with updated names.
#' @docType data
#' @format A nine column data frame.
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#' @name pew
#' @title pew dataset from the reshape paper dataset in JSS inequality by, country and year.
#' 
#' @description Dataset collected by Pew with counts of people cross-tabulated by religion and income band.
#' See \url{https://www.jstatsoft.org/article/view/v059i10} for more information.
#' @docType data
#' @format A data frame with 18 rows by 10 columns
#' @examples 
#' \dontrun{
#' # Download data from its original source - an academic paper
#' downloader::download("http://www.jstatsoft.org/v59/i10/supp/4",
#'  destfile = "v59i10-data.zip")
#' # The source code associated with the paper
#' #'downloader::download("http://www.jstatsoft.org/v59/i10/supp/3",
#'  destfile = "extdata/reshape/v59i10.R")
#' # After running the R script...
#' dir.create("extdata/reshape")
#' unzip("v59i10-data.zip", exdir = "extdata/reshape/")
#' # write.csv(raw, "extdata/reshape-pew.csv")
#' # generate pew dataset
#' pew = read_csv("extdata/reshape-pew.csv")
#' pew = pew[-c(1, ncol(pew))] # remove excess cols
#' names(pew) = c("religion", "<$10k", "$10--20k", "$20--30k", "$30--40k",
#'                "$40--50k", "$50--75k", "$75--100k", "$100--150k", ">150k")
#' # write_csv(pew, "extdata/pew.csv")
#' }
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#' @name lnd_geo_df
#' @title Simple geographical/population data of boroughs in London with 
#' 
#' @description Generated from the \url{https://github.com/Robinlovelace/Creating-maps-in-R} tutorial using
#' the simplify function from the mapshaper package and the \code{fortify} function from the ggplot2 package.
#' @docType data
#' @format A data frame with 4 variables and 211 rows
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