inst/doc/example5_fst.R

In tidyfst: Tidy Verbs for Fast Data Manipulation

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
#  library(tidyfst)
#  
#  # Generate some random data frame with 10 million rows and various column types
#  nr_of_rows <- 1e7
#  
#  df <- data.frame(
#      Logical = sample(c(TRUE, FALSE, NA), prob = c(0.85, 0.1, 0.05), nr_of_rows, replace = TRUE),
#      Integer = sample(1L:100L, nr_of_rows, replace = TRUE),
#      Real = sample(sample(1:10000, 20) / 100, nr_of_rows, replace = TRUE),
#      Factor = as.factor(sample(labels(UScitiesD), nr_of_rows, replace = TRUE))
#    )
#  
#  # write the fst file, make sure you do not have the file with same name in the directory
#  export_fst(df,"fst_test.fst")
#  
#  # remove all variables in the environment
#  rm(list = ls())

## -----------------------------------------------------------------------------
#  parse_fst("fst_test.fst") -> ft
#  ft

## -----------------------------------------------------------------------------
#  ft %>%
#    select_fst(Factor) %>%
#    count_dt(Factor) -> factor_info
#  
#  factor_info

## -----------------------------------------------------------------------------
#  ft %>%
#    select_fst(Integer,Factor) %>%
#    summarise_dt(avg = mean(Integer),by = Factor) -> avg_info
#  
#  avg_info
#  

## -----------------------------------------------------------------------------
#  # delete the output file
#  unlink("fst_test.fst")

## -----------------------------------------------------------------------------
#  iris %>% as_fst() -> iris_fst
#  mtcars %>% as_fst() -> mtcars_fst
#  
#  iris_fst
#  mtcars_fst