extras/pipeline_example.md

In WinVector/rquery: Relational Query Generator for Data Manipulation at Scale

pipeline_example

library("dplyr")

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library("rqdatatable")

## Loading required package: rquery

## Loading required package: wrapr

## 
## Attaching package: 'wrapr'

## The following object is masked from 'package:dplyr':
## 
##     coalesce

library("microbenchmark")

batting <- Lahman::Batting


# dplyr pipeline example from R for Data Science.
fn_dplyr <- function(batting) {
  # http://r4ds.had.co.nz/transform.html
  batting %>% 
    group_by(playerID) %>% 
    summarise(
      ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
      ab = sum(AB, na.rm = TRUE)
    ) %>%
    filter(ab > 100) 
}

system.time(
  batters <- fn_dplyr(batting)
)

##    user  system elapsed 
##   0.525   0.018   0.556

batters <- arrange(batters, playerID)

# translation of above example into an rquery pipeline
fn_rquery <- function(batting) {
  rq_pipeline <- local_td(batting) %.>%
    project_nse(., groupby = "playerID",
                ba = sum(H) / sum(AB),
                ab = sum(AB)) %>%
    select_rows_nse(., ab > 100) %>%
    orderby(., "playerID")
  ex_data_table(rq_pipeline)
}

system.time({
  batters2 <- fn_rquery(batting)
})

##    user  system elapsed 
##   0.118   0.004   0.078

assertthat::are_equal(as.data.frame(batters),
                      as.data.frame(batters2))

## [1] TRUE

microbenchmark(
  dplyr = nrow(fn_dplyr(batting)),
  rquery = nrow(fn_rquery(batting))
)

## Unit: milliseconds
##    expr       min        lq      mean    median        uq      max neval
##   dplyr 449.70200 482.96252 552.90256 544.00514 571.20214 902.9251   100
##  rquery  48.95125  66.52097  75.26825  76.43914  80.62677 160.0422   100

WinVector/rquery documentation built on Aug. 24, 2023, 11:12 a.m.