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# This is an effort to emulate Stata's `lookfor` in R

lookfor <- function(what, ls_opts = list(), ...){
    s <- do.call("ls", ls_opts)
    d <- lapply(s, lookin, what = what, ...)
    
    # return value should be a list with the string matching the search, along with details of its position
    # big challenge is doing this recursively because, e.g., lists of lists of dataframes would be really difficult to search
    
    class(d) <- 'lookfor'

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# https://www.federalregister.gov/developers/api/v1

# NAMESPACE
# importFrom(RJSONIO, fromJSON)
# importFrom(RCurl, curlPerform, basicTextGatherer)
# S3method(print,fedreg_document)
# S3method(print,fedreg_agency)

fr_search <- function(version='v1', ...) {

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# call package easyio

library('foreign')
library('tools')
# library('haven') # for spss (sav, por), stata (dta), sas
#library('memisc') # for `spss.portable.file` and `spss.system.file`
#library('hmisc') # for `spss.get`
#library('openxlsx') # for `read.xlsx`
#library('data.table') # for fread

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require('XML')

tess_studies_list <- function(){
    x <- xmlParse('http://www.tessexperiments.org/previousstudies.html')
}

get_tess_study <- function(id, file){
    download.file()
}

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# function definition
`%by%` <- function(a,b){
    s <- substitute(a)
    l <- as.list(s)
    p <- parse(text=s)
    if(length(p)>2) {
        l <- l[3:length(l)]
        do.call("by", c(list(data=eval.parent(p[2]), 
                             INDICES=b, 
                             FUN=eval.parent(p[1])), 

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rp <- function() {
    stack <- numeric()
    push <- function(values) stack <<- c(stack, values)
    pop <- function() {
        a <- stack[length(stack)]
        stack <<- stack[-length(stack)]
        return(a)
    }
    reverse <- function() {
        r <- readline()

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 1. Install GNU gettext (on Windows, this can be obtained from the Rtools site: http://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip)
 2. Run `update_pkg_po()` (https://stat.ethz.ch/R-manual/R-devel/library/tools/html/update_pkg_po.html)
   a. Create `/po` if it does not exist
   b. Call `xgettext2pot()` to create/update `po/R-pkgname.pot` file
   c. All `R-lang.po` files in `/po` are updated from `po/R-pkgname.pot` using `msgmerge`
   d. `checkPoFiles()` is called on updated files
   e. If check is successful, messages are compiled using `msgfmt` system call and installed in `/inst/po`.
   f. In a UTF-8 locale, a ‘translation’ ‘R-en@quot.po’ is created with UTF-8 directional quotes, compiled and installed under ‘inst/po’.
   g. If `po/pkg.pot` exists:
     i. `/src` is examined to create `/po/pkg.pot`

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#' Parse a codebook file with variable and level information.
#' 
#' Parses a codebook file where lines starting at column zero (far left) represet
#' variable information (e.g. name, description, type) and indented lines 
#' (i.e. lines beginning with white space, either tabs or spaces, etc.) represent factor
#' levels and labels. 
#' 
#' Note that white space at the beginning and end of each line is stripped before
#' processing that line.
#' 

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read.fwf2 <- function (file, widths, header = FALSE, sep = "\t", skip = 0, 
    row.names, col.names, n = -1, ...) 
{
    doone <- function(x) {
        x <- substring(x, first, last)
        x[!nzchar(x)] <- NA_character_
        paste0(x, collapse = sep)
    }
    if (is.list(widths)) {
        recordlength <- length(widths)

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z <- function(data, FUN = mean, ...) aggregate(. ~ 1, data = data, FUN = FUN, ...)
y <- function(data, FUN = mean, ...) data.frame(t(apply(data, 2, FUN = FUN, ...)))
x <- function(data, FUN = mean, ...) data.frame(t(sapply(data, mean, ...)))

d1 <- data.frame(matrix(1e3, ncol=10))
d2 <- data.frame(matrix(1e6, ncol=10))
d3 <- data.frame(matrix(1e6, ncol=100))


# MEAN