example1_intro.R
In tidyfst: Tidy Verbs for Fast Data Manipulation

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

## -----------------------------------------------------------------------------
#  library(tidyfst)
#  library(nycflights13)
#  library(data.table)
#  
#  data.table(flights)

## -----------------------------------------------------------------------------
#  filter_dt(flights, month == 1 & day == 1)

## -----------------------------------------------------------------------------
#  arrange_dt(flights, year, month, day)

## -----------------------------------------------------------------------------
#  arrange_dt(flights, -arr_delay)

## -----------------------------------------------------------------------------
#  select_dt(flights, year, month, day)

## -----------------------------------------------------------------------------
#  select_dt(flights, "^dep")

## -----------------------------------------------------------------------------
#  select_dt(flights, tail_num = tailnum)
#  rename_dt(flights, tail_num = tailnum)

## -----------------------------------------------------------------------------
#  mutate_dt(flights,
#    gain = arr_delay - dep_delay,
#    speed = distance / air_time * 60
#  )

## ----eval=FALSE---------------------------------------------------------------
#  mutate_dt(flights,
#    gain = arr_delay - dep_delay,
#    gain_per_hour = gain / (air_time / 60)
#  )

## -----------------------------------------------------------------------------
#  mutate_dt(flights,gain = arr_delay - dep_delay) %>%
#    mutate_dt(gain_per_hour = gain / (air_time / 60))

## -----------------------------------------------------------------------------
#  transmute_dt(flights,
#    gain = arr_delay - dep_delay
#  )

## -----------------------------------------------------------------------------
#  summarise_dt(flights,
#    delay = mean(dep_delay, na.rm = TRUE)
#  )

## -----------------------------------------------------------------------------
#  sample_n_dt(flights, 10)
#  sample_frac_dt(flights, 0.01)

## ----eval=FALSE---------------------------------------------------------------
#  by_tailnum <- group_by(flights, tailnum)
#  delay <- summarise(by_tailnum,
#    count = n(),
#    dist = mean(distance, na.rm = TRUE),
#    delay = mean(arr_delay, na.rm = TRUE))
#  delay <- filter(delay, count > 20, dist < 2000)

## -----------------------------------------------------------------------------
#  flights %>%
#    summarise_dt( count = .N,
#    dist = mean(distance, na.rm = TRUE),
#    delay = mean(arr_delay, na.rm = TRUE),by = tailnum)

## -----------------------------------------------------------------------------
#  # the dplyr syntax:
#  # destinations <- group_by(flights, dest)
#  # summarise(destinations,
#  #   planes = n_distinct(tailnum),
#  #   flights = n()
#  # )
#  
#  summarise_dt(flights,planes = uniqueN(tailnum),flights = .N,by = dest) %>%
#    arrange_dt(dest)
#  

## -----------------------------------------------------------------------------
#  # the dplyr syntax:
#  # daily <- group_by(flights, year, month, day)
#  # (per_day   <- summarise(daily, flights = n()))
#  
#  flights %>%
#    summarise_dt(by = .(year,month,day),flights = .N)
#  
#  # (per_month <- summarise(per_day, flights = sum(flights)))
#  flights %>%
#    summarise_dt(by = .(year,month,day),flights = .N) %>%
#    summarise_dt(by = .(year,month),flights = sum(flights))
#  
#  # (per_year  <- summarise(per_month, flights = sum(flights)))
#  flights %>%
#    summarise_dt(by = .(year,month,day),flights = .N) %>%
#    summarise_dt(by = .(year,month),flights = sum(flights)) %>%
#    summarise_dt(by = .(year),flights = sum(flights))

## -----------------------------------------------------------------------------
#  library(tidyfst)
#  library(data.table)
#  library(nycflights13)
#  
#  flights = data.table(flights) %>% na.omit()

## -----------------------------------------------------------------------------
#  # data.table
#  head(flights[origin == "JFK" & month == 6L])
#  flights[1:2]
#  flights[order(origin, -dest)]
#  
#  # tidyfst
#  flights %>%
#    filter_dt(origin == "JFK" & month == 6L) %>%
#    head()
#  flights %>% slice_dt(1:2)
#  flights %>% arrange_dt(origin,-dest)

## -----------------------------------------------------------------------------
#  # data.table
#  flights[, list(arr_delay)]
#  flights[, .(arr_delay, dep_delay)]
#  flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)]
#  
#  # tidyfst
#  flights %>% select_dt(arr_delay)
#  flights %>% select_dt(arr_delay, dep_delay)
#  flights %>% transmute_dt(delay_arr = arr_delay, delay_dep = dep_delay)

## -----------------------------------------------------------------------------
#  # data.table
#  flights[, sum( (arr_delay + dep_delay) < 0)]
#  flights[origin == "JFK" & month == 6L,
#                 .(m_arr = mean(arr_delay), m_dep = mean(dep_delay))]
#  flights[origin == "JFK" & month == 6L, length(dest)]
#  flights[origin == "JFK" & month == 6L, .N]
#  
#  # tidyfst
#  flights %>% summarise_dt(sum( (arr_delay + dep_delay) < 0))
#  flights %>%
#    filter_dt(origin == "JFK" & month == 6L) %>%
#    summarise_dt(m_arr = mean(arr_delay), m_dep = mean(dep_delay))
#  flights %>%
#    filter_dt(origin == "JFK" & month == 6L) %>%
#    nrow()
#  flights %>%
#    filter_dt(origin == "JFK" & month == 6L) %>%
#    count_dt()
#  flights %>%
#    filter_dt(origin == "JFK" & month == 6L) %>%
#    summarise_dt(.N)

## -----------------------------------------------------------------------------
#  # data.table
#  flights[, c("arr_delay", "dep_delay")]
#  
#  select_cols = c("arr_delay", "dep_delay")
#  flights[ , ..select_cols]
#  flights[ , select_cols, with = FALSE]
#  
#  flights[, !c("arr_delay", "dep_delay")]
#  flights[, -c("arr_delay", "dep_delay")]
#  
#  # returns year,month and day
#  flights[, year:day]
#  # returns day, month and year
#  flights[, day:year]
#  # returns all columns except year, month and day
#  flights[, -(year:day)]
#  flights[, !(year:day)]
#  
#  # tidyfst
#  flights %>% select_dt(c("arr_delay", "dep_delay"))
#  
#  select_cols = c("arr_delay", "dep_delay")
#  flights %>% select_dt(cols = select_cols)
#  
#  flights %>% select_dt(-arr_delay,-dep_delay)
#  
#  flights %>% select_dt(year:day)
#  flights %>% select_dt(day:year)
#  flights %>% select_dt(-(year:day))
#  flights %>% select_dt(!(year:day))

## -----------------------------------------------------------------------------
#  # data.table
#  flights[, .N, by = .(origin)]
#  flights[carrier == "AA", .N, by = origin]
#  flights[carrier == "AA", .N, by = .(origin, dest)]
#  flights[carrier == "AA",
#          .(mean(arr_delay), mean(dep_delay)),
#          by = .(origin, dest, month)]
#  
#  # tidyfst
#  flights %>% count_dt(origin) # sort by default
#  flights %>% filter_dt(carrier == "AA") %>% count_dt(origin)
#  flights %>% filter_dt(carrier == "AA") %>% count_dt(origin,dest)
#  flights %>% filter_dt(carrier == "AA") %>%
#    summarise_dt(mean(arr_delay), mean(dep_delay),
#                 by = .(origin, dest, month))

## -----------------------------------------------------------------------------
#  # data.table
#  flights[carrier == "AA", .N, by = .(origin, dest)][order(origin, -dest)]
#  flights[, .N, .(dep_delay>0, arr_delay>0)]
#  
#  # tidyfst
#  flights %>%
#    filter_dt(carrier == "AA") %>%
#    count_dt(origin,dest,sort = FALSE) %>%
#    arrange_dt(origin,-dest)
#  flights %>%
#    summarise_dt(.N,by = .(dep_delay>0, arr_delay>0))

## -----------------------------------------------------------------------------
#  # data.table
#  flights[carrier == "AA",
#          lapply(.SD, mean),
#          by = .(origin, dest, month),
#          .SDcols = c("arr_delay", "dep_delay")]
#  
#  # tidyfst
#  flights %>%
#    filter_dt(carrier == "AA") %>%
#    group_dt(
#      by = .(origin, dest, month),
#      at_dt("_delay",summarise_dt,mean)
#             )

## -----------------------------------------------------------------------------
#  flights %>%
#    filter_dt(carrier == "AA") %>%
#    group_dt(
#      by = .(origin, dest, month),
#      at_dt("_delay",summarise_dt,mean) %>%
#        mutate_dt(sum = dep_delay + arr_delay)
#             )

## -----------------------------------------------------------------------------
#  # data.table
#  flights[, head(.SD, 2), by = month]
#  
#  # tidyfst
#  flights %>%
#    group_dt(by = month,head(2))