ex03_row-wise-iteration-are-you-sure.R

#' ---
#' title: "Are you absolutely sure that you, personally, need to iterate over rows?"
#' author: "Jenny Bryan"
#' date: "`r format(Sys.Date())`"
#' output: github_document
#' ---

#+ setup, include = FALSE, cache = FALSE
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  error = TRUE
)
options(tidyverse.quiet = TRUE)

#+ body
# ----
library(tidyverse)

# ----
#' ## Function to give my example data frame
new_df <- function() {
  tribble(
    ~ name, ~ age,
    "Reed", 14,
    "Wesley", 12,
    "Eli", 12,
    "Toby", 1
  )
}

# ----
#' ## Single-row example can cause tunnel vision
#'
#' Sometimes it's easy to fixate on one (unfavorable) way of accomplishing
#' something, because it feels like a natural extension of a successful
#' small-scale experiment.
#'
#' Let's create a string from row 1 of the data frame.
df <- new_df()
paste(df$name[1], "is", df$age[1], "years old")

#' I want to scale up, therefore I obviously must ... loop over all rows!
n <- nrow(df)
s <- vector(mode = "character", length = n)
for (i in seq_len(n)) {
  s[i] <- paste(df$name[i], "is", df$age[i], "years old")
}
s

#' HOLD ON. What if I told you `paste()` is already vectorized over its
#' arguments?
paste(df$name, "is", df$age, "years old")

#' A surprising number of "iterate over rows" problems can be eliminated by
#' exploiting functions that are already vectorized and by making your own
#' functions vectorized over the primary argument.
#'
#' Writing an explicit loop in your code is not necessarily bad, but it should
#' always give you pause. Has someone already written this loop for you? Ideally
#' in C or C++ and inside a package that's being regularly checked, with high
#' test coverage. That is usually the better choice.

# ----
#' ## Don't forget to work "inside the box"
#'

#' For this string interpolation task, we can even work with a vectorized
#' function that is happy to do lookup inside a data frame. The [glue
#' package](https://glue.tidyverse.org) is doing the work under the hood here,
#' but its Greatest Functions are now re-exported by stringr, which we already
#' attached via `library(tidyverse)`.

str_glue_data(df, "{name} is {age} years old")

#' You can use the simpler form, `str_glue()`, inside `dplyr::mutate()`, because
#' the other variables in `df` are automatically available for use.

df %>%
  mutate(sentence = str_glue("{name} is {age} years old"))

#' The tidyverse style is to manage data holistically in a data frame and
#' provide a user interface that encourages self-explaining code with low
#' "syntactical noise".