~hrbrmstr/uaparserjs

aa2b24e41863fb420049c957162c738831c4dee8 — hrbrmstr 11 months ago acf7136
removed heavy deps
M DESCRIPTION => DESCRIPTION +8 -9
@@ 1,7 1,7 @@
Package: uaparserjs
Type: Package
Title: Parse Browser 'User-Agent' Strings into Data Frames
Version: 0.3.0
Title: Parse 'User-Agent' Strings
Version: 0.3.1
Date: 2020-03-31
Authors@R: c(
    person("Bob", "Rudis", , "bob@rud.is", c("aut", "cre")),


@@ 12,19 12,18 @@ Maintainer: Bob Rudis <bob@rud.is>
Description: Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. A function is provided that will take in user agent
    to parse consistently. Tools are provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
URL: http://github.com/hrbrmstr/uaparserjs
BugReports: https://github.com/hrbrmstr/uaparserjs/issues
License: Apache License
URL: https://git.rud.is/hrbrmstr/uaparserjs
BugReports: https://gitlab.com/hrbrmstr/uaparserjs/issues
License: Apache License 2.0 | file LICENSE
Suggests:
    testthat
Depends:
    R (>= 3.0.0)
    R (>= 3.2.0)
Encoding: UTF-8
Imports:
    purrr,
    dplyr,
    progress,
    V8
RoxygenNote: 7.1.0

A LICENSE => LICENSE +201 -0
@@ 0,0 1,201 @@
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M NAMESPACE => NAMESPACE +1 -4
@@ 3,7 3,4 @@
export(get_cache)
export(ua_parse)
import(V8)
importFrom(dplyr,as_tibble)
importFrom(dplyr,progress_estimated)
importFrom(purrr,map)
importFrom(purrr,map_df)
import(progress)

M R/uaparser.R => R/uaparser.R +32 -10
@@ 1,15 1,23 @@
as_tibble <- function(.x) {

  out <- as.data.frame(.x, stringsAsFactors = FALSE)
  class(out) <- c("tbl_df", "tbl", "data.frame")
  out

}

#' Parse a vector of user agents into a data frame
#'
#' Takes in a character vector of user agent strings and returns a \code{tibble}
#' Takes in a character vector of user agent strings and returns a data frame classed as tibble.
#' of parsed user agents.
#'
#' @param user_agents a character vector of user agents
#' @param .progress if \code{TRUE}, will display a progress bar in interactive mode
#' @param .progress if `TRUE`  will display a progress bar in interactive mode
#' @export
#' @return a \code{tibble} with columns for user agent family, major & minor versions
#'     plus patch level along with os family and major & minor versions plus
#' @return a data frame classed as tibble with columns for user agent family, major & minor versions
#'     plus patch level along with OS family and major & minor versions plus
#'     device brand and model.
#' @references \url{http://www.uaparser.org/}
#' @references <http://www.uaparser.org/>
#' @note The regex YAML import date: 2020-03-31
#' @examples
#' ua_parse(paste0("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.2 (KHTML, ",


@@ 17,19 25,33 @@
#'                 "Chrome/15.0.874.106 Safari/535.2", collapse=""))
ua_parse <- function(user_agents, .progress=FALSE) {

  if (.progress) pb <- progress_estimated(length(user_agents))
  purrr::map_df(user_agents, function(x) {
  if (.progress) pb <- progress::progress_bar$new(length(user_agents))
  if (.progress) pb$tick(0)

  lapply(user_agents, function(x) {

    if (.progress) pb$tick()$print()

    res <- .pkgenv$cache[[x]]

    if (length(res) > 0) {

      res

    } else {
      .pkgenv$cache[[x]] <- dplyr::as_tibble(as.list(unlist(.pkgenv$ctx$call("parser.parse", x))))

      .pkgenv$cache[[x]] <- as_tibble(as.list(unlist(.pkgenv$ctx$call("parser.parse", x))))
      .pkgenv$cache[[x]]

    }
  })

  }) -> out

  out <- bind_rows(out)

  as_tibble(out)

}

#' @export
get_cache <- function() { .pkgenv$cache }
\ No newline at end of file
get_cache <- function() { .pkgenv$cache }

M R/uaparserjs-package.R => R/uaparserjs-package.R +9 -4
@@ 1,9 1,14 @@
#' Parse Browser User Agent Strings into Data Frames
#' Parse 'User-Agent' Strings
#'
#' Despite there being a section in RFC 7231
#' <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
#' structure for 'User-Agent' headers this data is notoriously difficult
#' to parse consistently. Tools are provided that will take in user agent
#' strings and return structured R objects. This is a 'V8'-backed package
#' based on the 'ua-parser' project <https://github.com/ua-parser>.
#'
#' @name uaparserjs
#' @docType package
#' @author Bob Rudis (@@hrbrmstr)
#' @importFrom purrr map map_df
#' @import V8
#' @importFrom dplyr as_tibble progress_estimated
#' @import V8 progress
NULL

A R/utils-mappers.R => R/utils-mappers.R +320 -0
@@ 0,0 1,320 @@
# NOTE: At the bottom of this source file show the equivalents to purrr mappers
#
# NOTE these aren't 100% equivalent to the purrr mappers but cover very common use-cases
#
# NOTE formula function (e.g. ~{}) are 100% supported
#
# NOTE: THESE DO NOT SUPPORT list EXTRACTORS

set_names <- function(object = nm, nm) {
  names(object) <- nm
  object
}

map <- function(.x, .f, ..., .default) {

  default_exists <- !missing(.default)

  if (inherits(.f, "formula")) {
    .body <- dimnames(attr(terms(.f), "factors"))[[1]]
    .f <- function(.x, . = .x) {}
    body(.f) <- as.expression(parse(text=.body))
  }

  nm <- names(.x)

  if (inherits(.f, "function")) {

    lapply(.x, function(x) {
      res <- .f(x, ...)
      if ((length(res) == 0) & default_exists) res <- .default
      res
    }) -> out

  } else if (is.numeric(.f) | is.character(.f)) {

    lapply(.x, function(x) {
      res <- try(x[[.f]], silent = TRUE)
      if (inherits(res, "try-error")) res <- NULL
      if ((length(res) == 0) & default_exists) res <- .default
      res
    }) -> out

  }

  if (length(nm) > 0) out <- set_names(out, nm)

  out

}

map2 <- function(.x, .y, .f, ..., .default) {

  default_exists <- !missing(.default)

  if (inherits(.f, "formula")) {
    .body <- dimnames(attr(terms(.f), "factors"))[[1]]
    .f <- function(.x, .y, . = .x) {}
    body(.f) <- as.expression(parse(text=.body))
  }

  if (inherits(.f, "function")) {
    mapply(
      function(x, ...) {
        res <- .f(x, ...)
        if ((length(res) == 0) & default_exists) res <- .default
        res
      },
      .x, .y,
      ...,
      SIMPLIFY=FALSE, USE.NAMES=FALSE
    )
  }

}

map_chr <- function(.x, .f, ...) {
  nm <- names(.x)
  out <- as.character((map(.x, .f, ..., .default = .default)))
  if (length(nm) > 0) set_names(out, nm) else out
}

map2_chr <- function(.x, .y, .f, ...) {
  as.character(unlist(map2(.x, .y, .f, ..., .default = .default)))
}

map_lgl <- function(.x, .f, ...) {
  nm <- names(.x)
  out <- as.logical(unlist(map(.x, .f, ..., .default = .default)))
  if (length(nm) > 0) set_names(out, nm) else out
}

map2_lgl <- function(.x, .y, .f, ...) {
  as.logical(unlist(map2(.x, .y, .f, ..., .default = .default)))
}

map_dbl <- function(.x, .f, ...) {
  nm <- names(.x)
  out <- as.double(unlist(map(.x, .f, ..., .default = .default)))
  if (length(nm) > 0) set_names(out, nm) else out
}

map2_dbl <- function(.x, .y, .f, ...) {
  as.double(unlist(map2(.x, .y, .f, ..., .default = .default)))
}

map_int <- function(.x, .f, ..., .default) {
  nm <- names(.x)
  out <- as.integer(unlist(map(.x, .f, ..., .default = .default)))
  if (length(nm) > 0) set_names(out, nm) else out
}

map2_int <- function(.x, .y, .f, ...) {
  as.integer(unlist(map2(.x, .y, .f, ..., .default = .default)))
}


map_df <- function(.x, .f, ..., .id=NULL) {

  res <- map(.x, .f, ...)
  out <- bind_rows(res, .id=.id)
  out

}

map_dfr <- map_df

map_dfc <- function(.x, .f, ...) {

  res <- map(.x, .f, ...)
  out <- bind_cols(res)
  out

}

map2_df <- function(.x, .y, .f, ..., .id=NULL) {

  res <- map2(.x, .y, .f, ...)
  out <- bind_rows(res, .id = .id)
  out

}


map2_dfc <- function(.x, .y, .f, ...) {

  res <- map2(.x, .y, .f, ...)
  out <- bind_cols(res)
  out

}

# this has limitations and is more like 75% of dplyr::bind_rows()
# this is also orders of magnitude slower than dplyr::bind_rows()
bind_rows <- function(..., .id = NULL) {

  res <- list(...)

  if (length(res) == 1) res <- res[[1]]

  cols <- unique(unlist(lapply(res, names), use.names = FALSE))

  if (!is.null(.id)) {
    inthere <- cols[.id %in% cols]
    if (length(inthere) > 0) {
      .id <- make.unique(c(inthere, .id))[2]
    }
  }

  id_vals <- if (is.null(names(res))) 1:length(res) else names(res)

  saf <- default.stringsAsFactors()
  options(stringsAsFactors = FALSE)
  on.exit(options(stringsAsFactors = saf))

  idx <- 1
  do.call(
    rbind.data.frame,
    lapply(res, function(.x) {
      x_names <- names(.x)
      moar_names <- setdiff(cols, x_names)
      if (length(moar_names) > 0) {
        for (i in 1:length(moar_names)) {
          .x[[moar_names[i]]] <- rep(NA, length(.x[[1]]))
        }
      }
      if (!is.null(.id)) {
        .x[[.id]] <- id_vals[idx]
        idx <<- idx + 1
      }
      .x
    })
  ) -> out

  rownames(out) <- NULL

  class(out) <- c("tbl_df", "tbl", "data.frame")

  out

}

bind_cols <- function(...) {

  res <- list(...)

  row_mismatch <- lapply(res, nrow) != nrow(res[[1]])

  if (any(row_mismatch)) {
    first_mismatch_pos <- which(row_mismatch)[1]
    stop(paste0("Argument ", first_mismatch_pos,
                " must be length ", nrow(res[[1]]),
                ", not ", nrow(res[[first_mismatch_pos]])))
    }

  if (length(res) == 1) res <- res[[1]]

  col_names <- unlist(lapply(res, names), use.names = FALSE)
  col_names <- make.unique(col_names, sep = "")

  saf <- default.stringsAsFactors()
  options(stringsAsFactors = FALSE)
  on.exit(options(stringsAsFactors = saf))

  out <- do.call(cbind.data.frame, res)

  names(out) <- col_names
  rownames(out) <- NULL

  class(out) <- c("tbl_df", "tbl", "data.frame")

  out

}


# set.seed(1)
# 1:10 %>%
#   map(rnorm, n = 10) %>%
#   map_dbl(mean)
#
# set.seed(1)
# 1:10 %>%
#   purrr::map(rnorm, n = 10) %>%
#   purrr::map_dbl(mean)
#
#
# # Or use an anonymous function
# set.seed(1)
# 1:10 %>%
#   map(function(x) rnorm(10, x))
#
# set.seed(1)
# 1:10 %>%
#   purrr::map(function(x) rnorm(10, x))
#
# # Or a formula
# set.seed(1)
# 1:10 %>%
#   map(~ rnorm(10, .x))
#
# set.seed(1)
# 1:10 %>%
#   purrr::map(~ rnorm(10, .x))
#
# # Extract by name or position
# # .default specifies value for elements that are missing or NULL
# l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))
# l1 %>% map("a", .default = "???")
# l1 %>% purrr::map("a", .default = "???")
#
# l1 %>% map_int("b", .default = NA)
# l1 %>% purrr::map_int("b", .default = NA)
#
# l1 %>% map_int(2, .default = NA)
# l1 %>% purrr::map_int(2, .default = NA)
#
# # Supply multiple values to index deeply into a list
# l2 <- list(
#   list(num = 1:3,     letters[1:3]),
#   list(num = 101:103, letters[4:6]),
#   list()
# )
# l2 %>% map(c(2, 2))
# l2 %>% purrr::map(c(2, 2))
#
#
# # A more realistic example: split a data frame into pieces, fit a
# # model to each piece, summarise and extract R^2
# mtcars %>%
#   split(.$cyl) %>%
#   map(~ lm(mpg ~ wt, data = .x)) %>%
#   map(summary) %>%
#   map_dbl("r.squared")
#
# mtcars %>%
#   split(.$cyl) %>%
#   purrr::map(~ lm(mpg ~ wt, data = .x)) %>%
#   purrr::map(summary) %>%
#   purrr::map_dbl("r.squared")
#
#
# # Use map_lgl(), map_dbl(), etc to reduce to a vector.
# # * list
# mtcars %>% map(sum)
# mtcars %>% purrr::map(sum)
# # * vector
# mtcars %>% map_dbl(sum)
# mtcars %>% purrr::map_dbl(sum)
#
# # If each element of the output is a data frame, use
# # map_dfr to row-bind them together:
# mtcars %>%
#   split(.$cyl) %>%
#   map(~ lm(mpg ~ wt, data = .x)) %>%
#   map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))
#
# mtcars %>%
#   split(.$cyl) %>%
#   purrr::map(~ lm(mpg ~ wt, data = .x)) %>%
#   purrr::map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))

M README.Rmd => README.Rmd +10 -2
@@ 39,12 39,20 @@ hrbrpkghelpr::install_block()
```{r}
library(uaparserjs)

# current verison
# current version
packageVersion("uaparserjs")

dplyr::glimpse(ua_parse("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.2 (KHTML, like Gecko) Ubuntu/11.10 Chromium/15.0.874.106 Chrome/15.0.874.106 Safari/535.2"))

dplyr::glimpse(ua_parse(readLines("tests/agents.txt")))
agents <- readLines("tests/agents.txt")

dplyr::glimpse(ua_parse(agents))

set.seed(100)
batch_100 <- sample(agents, 100)
microbenchmark::microbenchmark(
  ua_parse(batch_100)
)

```


M README.md => README.md +25 -8
@@ 5,7 5,7 @@ developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.re
[![Signed
by](https://img.shields.io/badge/Keybase-Verified-brightgreen.svg)](https://keybase.io/hrbrmstr)
![Signed commit
%](https://img.shields.io/badge/Signed_Commits-14%25-lightgrey.svg)
%](https://img.shields.io/badge/Signed_Commits-100%25-lightgrey.svg)
[![Linux build
Status](https://travis-ci.org/hrbrmstr/uaparserjs.svg?branch=master)](https://travis-ci.org/hrbrmstr/uaparserjs)
[![Coverage


@@ 15,19 15,19 @@ checks](https://cranchecks.info/badges/worst/uaparserjs)](https://cranchecks.inf
[![CRAN
status](https://www.r-pkg.org/badges/version/uaparserjs)](https://www.r-pkg.org/pkg/uaparserjs)
![Minimal R
Version](https://img.shields.io/badge/R%3E%3D-3.0.0-blue.svg)
Version](https://img.shields.io/badge/R%3E%3D-3.2.0-blue.svg)
![License](https://img.shields.io/badge/License-Apache-blue.svg)

# uaparserjs

Parse Browser ‘User-Agent’ Strings into Data Frames
Parse ‘User-Agent’ Strings

## Description

Despite there being a section in RFC 7231
<https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
structure for ‘User-Agent’ headers this data is notoriously difficult to
parse consistently. A function is provided that will take in user agent
parse consistently. Tools are provided that will take in user agent
strings and return structured R objects. This is a ‘V8’-backed package
based on the ‘ua-parser’ project <https://github.com/ua-parser>.



@@ 51,8 51,14 @@ The following functions are implemented:
## Installation

``` r
remotes::install_git("https://git.rud.is/hrbrmstr/uaparserjs.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/uaparserjs")
# or
remotes::install_gitlab("hrbrmstr/uaparserjs")
# or
remotes::install_bitbucket("hrbrmstr/uaparserjs")
# or
remotes::install_github("hrbrmstr/uaparserjs")
```



@@ 66,7 72,7 @@ library(uaparserjs)

# current verison
packageVersion("uaparserjs")
## [1] '0.3.0'
## [1] '0.3.1'

dplyr::glimpse(ua_parse("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.2 (KHTML, like Gecko) Ubuntu/11.10 Chromium/15.0.874.106 Chrome/15.0.874.106 Safari/535.2"))
## Observations: 1


@@ 81,7 87,9 @@ dplyr::glimpse(ua_parse("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.2 (KHTM
## $ os.minor      <chr> "10"
## $ device.family <chr> "Other"

dplyr::glimpse(ua_parse(readLines("tests/agents.txt")))
agents <- readLines("tests/agents.txt")

dplyr::glimpse(ua_parse(agents))
## Observations: 1,091
## Variables: 13
## $ userAgent     <chr> "Mozilla/5.0 (Windows; U; en-US) AppleWebKit/531.9 (KHTML, like Gecko) AdobeAIR/2.5.1", "Mozill…


@@ 97,14 105,23 @@ dplyr::glimpse(ua_parse(readLines("tests/agents.txt")))
## $ os.minor      <chr> NA, NA, NA, NA, NA, "0", "2", "3", "3", "3", "0", "0", "0", "0", "0", NA, NA, NA, NA, "1", "0",…
## $ os.patch      <chr> NA, NA, NA, NA, NA, "3", "2", "3", "4", "5", "1", "3", "3", "3", "4", NA, NA, NA, NA, "1", "6",…
## $ os.patchMinor <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

set.seed(100)
batch_100 <- sample(agents, 100)
microbenchmark::microbenchmark(
  ua_parse(batch_100)
)
## Unit: milliseconds
##                 expr      min      lq     mean   median       uq      max neval
##  ua_parse(batch_100) 19.44652 20.2158 21.42222 20.66315 22.56606 31.78299   100
```

## uaparserjs Metrics

| Lang | \# Files |  (%) | LoC |  (%) | Blank lines |  (%) | \# Lines |  (%) |
| :--- | -------: | ---: | --: | ---: | ----------: | ---: | -------: | ---: |
| R    |        5 | 0.83 |  35 | 0.78 |          13 | 0.39 |       26 | 0.46 |
| Rmd  |        1 | 0.17 |  10 | 0.22 |          20 | 0.61 |       30 | 0.54 |
| R    |        6 | 0.86 | 202 | 0.93 |          94 | 0.81 |      125 | 0.81 |
| Rmd  |        1 | 0.14 |  16 | 0.07 |          22 | 0.19 |       30 | 0.19 |

## Code of Conduct


M man/ua_parse.Rd => man/ua_parse.Rd +5 -5
@@ 9,15 9,15 @@ ua_parse(user_agents, .progress = FALSE)
\arguments{
\item{user_agents}{a character vector of user agents}

\item{.progress}{if \code{TRUE}, will display a progress bar in interactive mode}
\item{.progress}{if `TRUE`  will display a progress bar in interactive mode}
}
\value{
a \code{tibble} with columns for user agent family, major & minor versions
    plus patch level along with os family and major & minor versions plus
a data frame classed as tibble with columns for user agent family, major & minor versions
    plus patch level along with OS family and major & minor versions plus
    device brand and model.
}
\description{
Takes in a character vector of user agent strings and returns a \code{tibble}
Takes in a character vector of user agent strings and returns a data frame classed as tibble.
of parsed user agents.
}
\note{


@@ 29,5 29,5 @@ ua_parse(paste0("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.2 (KHTML, ",
                "Chrome/15.0.874.106 Safari/535.2", collapse=""))
}
\references{
\url{http://www.uaparser.org/}
<http://www.uaparser.org/>
}

M man/uaparserjs.Rd => man/uaparserjs.Rd +9 -1
@@ 3,7 3,15 @@
\docType{package}
\name{uaparserjs}
\alias{uaparserjs}
\title{Parse Browser User Agent Strings into Data Frames}
\title{Parse 'User-Agent' Strings}
\description{
Despite there being a section in RFC 7231
<https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
structure for 'User-Agent' headers this data is notoriously difficult
to parse consistently. Tools are provided that will take in user agent
strings and return structured R objects. This is a 'V8'-backed package
based on the 'ua-parser' project <https://github.com/ua-parser>.
}
\author{
Bob Rudis (@hrbrmstr)
}