#' ---
#' title: "What is vectorization in R?"
#' date: "2021-11-03"
#' author: "Jose https://ajuda.multifarm.top"
#' output:
#'   html_document:
#'    code_folding: show
#'    toc: yes
#'    toc_float:
#'      smooth_scroll: true
#'    df_print: paged
#'    highlight: zenburn
#' ---


#' One operation that is slow in R, and somewhat slow in all languages, is memory allocation. So one of the slower ways to write a for loop is to resize a vector repeatedly, so that R has to re-allocate memory repeatedly, like this:

j <- 1

system.time(for (i in 1:10) {
                j[i] = 10
            })

n <- 1:10

j <- 1

system.time(for (i in seq_along(n)) {
        j[i] = 10
    })

fxn <- function(j){
    for (i in 1:10) {
        j[i] = 10
    }
    return(j)
}

system.time(fxn(j))

#' Here, in each repetition of the for loop, R has to re-size the vector and re-allocate memory. It has to find the vector in memory, create a new vector that will fit more data, copy the old data over, insert the new data, and erase the old vector. This can get very slow as vectors get big.

#' If one pre-allocates a vector that fits all the values, R doesn’t have to re-allocate memory each iteration, and the results can be much faster. Here’s how you’d do that for the above case:

j <- rep(NA, 10)

system.time(for (i in 1:10) {
                j[i] = 10
            })

j <- rep(NA, 10)

system.time(for (i in seq_along(1:10)) {
                j[i] = 10
            })

## There are still situations that it may make sense to use for loops instead of vectorized functions, though. These include:

##     Using functions that don’t take vector arguments
##     Loops where each iteration is dependent on the results of previous iterations

## Note that the second case is tricky. In some cases where the obvious implementation of an algorithm uses a for loop, there’s a vectorized way around it. For instance, here is a good example of implementing a random walk using vectorized code. In these cases, you often want to call functions that are essentially C/FORTRAN implementations of loop operations to avoid the loop in R. Examples of such functions include cumsum (cumulative sums), rle (counting number of repeated value), and ifelse (vectorized if…else statements).

#' ## Using rle
#' Compute the lengths and values of runs of equal values in a vector
#  - or the reverse operation.

#' Building data

x <- c("952345172", "alju12", "amou79", "amou91", "baab81", NA)

code <- rep(x, c(5, 10, 10, 20, 2, 7))

df <- data.frame(id = 1:length(code), code)

rle_code <- rle(df$code)

class(rle_code)

attributes(rle_code)

rle_code$values

rle_code$lengths

rle_code$values > 6

rle_code[rle_code$lengths > 6]

rle_code[[1]] > 6

inverse.rle(rle_code)

#' ## Using 'cumsum'
#'


#' ## Using 'ifelse'

?do.call