This file was created by running the commands below.
setwd("G:/repos/PkgTestR")
usethis::use_vignette("ffv_gen_data_rda", title = "Generate a R data file")See the guide from R Packages (2e), Chapter 8. See Hadley’s babynames.rda generation file.
Create a data-raw folder
Following Hadley advice:
Often, the data you include in data/ is a cleaned up version of raw data you’ve gathered from elsewhere. I highly recommend taking the time to include the code used to do this in the source version of your package. This will make it easy for you to update or reproduce your version of the data. I suggest that you put this code in data-raw/.
To set-up the data-raw/ folder, we will run the routine use_data_raw(). Note that this is NOT added to gitignore, so the contents will store to git repo. See example data-raw folder from Hadley: babynames.
usethis::use_data_raw(name = "DATASET")
# ✔ Creating 'data-raw/'
# ✔ Adding '^data-raw$' to '.Rbuildignore'
# ✔ Writing 'data-raw/DATASET.R'
# • Modify 'data-raw/DATASET.R'
# • Finish the data preparation script in 'data-raw/DATASET.R'
# • Use `usethis::use_data()` to add prepared data to packageAnd inside the data-raw/DATASET.R file, we have a line of code that uses use_data to properly store rda files.
## code to prepare `DATASET` dataset goes here
usethis::use_data(DATASET, overwrite = TRUE)Write a DATASET.R function
In the data-raw folder, we develop a script that will generate a simulated dataset. Using the R code from R Do Anything Function over Dataframe Rows Expansion, (Mx1 by N) to (MxQ by N+1), we will create a dataset with five variables.
We will run the following script, data-raw/simu_income_v5n9.R, which generates the readable data-raw/simu_income_v5n9.csv and the binary simu_income_v5n9.rda files. This is a simulated dataset with five variable and nine observations. Here is the generated CSV file
# can initialize the file with this:
# usethis::use_data_raw(name = "simu_income_v5n9")
# Load library
library(tibble)
library(dplyr)
library(tidyr)
library(readr)
library(usethis)
# Code below is from:
# https://fanwangecon.github.io/R4Econ/function/dof/htmlpdfr/fs_funceval_expand.html
# https://github.com/FanWangEcon//R4Econ/blob/master/function/dof//htmlpdfr/fs_funceval_expand.R
# Parameter Setups
it_M <- 3
it_Q_max <- 5
fl_rnorm_mu <- 1000
ar_rnorm_sd <- seq(0.01, 200, length.out = it_M)
set.seed(123)
ar_it_q <- sample.int(it_Q_max, it_M, replace = TRUE)
# N by Q varying parameters
mt_data <- cbind(ar_it_q, ar_rnorm_sd)
tb_M <- as_tibble(mt_data) %>%
rowid_to_column(var = "ID") %>%
rename(sd = ar_rnorm_sd, Q = ar_it_q) %>%
mutate(mean = fl_rnorm_mu)
# normal draw expansion dot dollar
# Generate $Q_m$ individual specific incomes, expanded different
# number of times for each m
tb_income <- tb_M %>%
group_by(ID) %>%
do(income = rnorm(.$Q, mean = .$mean, sd = .$sd)) %>%
unnest(c(income))
# Merge back with tb_M
tb_income_full_dd <- tb_income %>% left_join(tb_M)
# Final name
simu_income_v5n9 <- tb_income_full_dd
# Write to CSV and write to rda
write_csv(simu_income_v5n9, "data-raw/simu_income_v5n9.csv")
# this saves in the data folder
usethis::use_data(simu_income_v5n9, overwrite = TRUE)Running the code above, we get the following outputs:
# Adding 'R' to Depends field in DESCRIPTION
# ✔ Creating 'data/'
# ✔ Setting LazyData to 'true' in 'DESCRIPTION'
# ✔ Saving 'simu_income_v5n9' to 'data/simu_income_v5n9.rda'
# • Document your data (see 'https://r-pkgs.org/data.html')Document dta files
In the R folder, create a single file data.R, that will document datasets included in the data/ folder.
#' Simulated unbalanced income panel
#'
#' Generate a panel with M individuals, each individual is observed
#' for different spans of times. Before expanding, generate individual
#' specific normal distribution standard deviation. All individuals
#' share the same mean, but have increasing standard deviations.
#'
#'
#' @format A data frame with 9 rows and 5 variables:
#' \describe{
#' \item{ID}{Individual ID}
#' \item{income}{Simulated income}
#' \item{Q}{Number of observed periods for each individual}
#' \item{sd}{Individual-specific income standard deviation}
#' \item{mean}{Individual-specific income mean}
#' }
#' @source \href{R Do Anything Function over Dataframe Rows Expansion, (Mx1 by N) to (MxQ by N+1)}{https://fanwangecon.github.io/R4Econ/function/dof/htmlpdfr/fs_funceval_expand.html}
#' @examples
#' data(simu_income_v5n9)
"simu_income_v5n9"Above, we document variable names in the @format tag. Initially, put in “sd” twice, and did not put in “mean”. Upon running devtools::check(), received the following warning which indicates that the documentation is checked against actual dataset, using codoc.
W checking for code/documentation mismatches (576ms)
Data codoc mismatches from documentation object 'simu_income_v5n9':
Variables in data frame 'simu_income_v5n9'
Code: ID Q income mean sd
Docs: ID Q income sd sdcodoc does the following:
Find inconsistencies between actual and documented ‘structure’ of R objects in a package. codoc compares names and optionally also corresponding positions and default values of the arguments of functions. codocClasses and codocData compare slot names of S4 classes and variable names of data sets, respectively.