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

This part of vignette has referred to `dplyr`

's vignette in https://dplyr.tidyverse.org/articles/dplyr.html. We'll try to reproduce all the results. First load the needed packages.

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

`filter_dt()`

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

Note that comma could not be used in the expressions. Which means `filter_dt(flights, month == 1,day == 1)`

would return error.

`arrange_dt()`

```
arrange_dt(flights, year, month, day)
```

Use `-`

(minus symbol) to order a column in descending order:

arrange_dt(flights, -arr_delay)

`select_dt()`

```
select_dt(flights, year, month, day)
```

`select_dt(flights, year:day)`

and `select_dt(flights, -(year:day))`

are not supported. But I have added a feature to help select with regular expression, which means you can:

select_dt(flights, "^dep")

The rename process is almost the same as that in `dplyr`

:

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

`mutate_dt()`

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

However, if you just create the column, please split them. The following codes would not work:

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

Instead, use:

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

If you only want to keep the new variables, use `transmute_dt()`

:

transmute_dt(flights, gain = arr_delay - dep_delay )

`summarise_dt()`

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

`sample_n_dt()`

and `sample_frac_dt()`

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

For the below `dplyr`

codes:

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)

We could get it via:

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

`summarise_dt`

(or `summarize_dt`

) has a parameter "by", you can specify the group.
We could find the number of planes and the number of flights that go to each possible destination:

# 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)

If you need to group by many variables, use:

# 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))

*tidyfst* provides a tidy syntax for *data.table*. For such design, *tidyfst* never runs faster than the analogous *data.table* codes. Nevertheless, it facilitate the dplyr-users to gain the computation performance in no time and guide them to learn more about data.table for speed.
Below, we'll compare the syntax of `tidyfst`

and `data.table`

(referring to Introduction to data.table). This could let you know how they are different, and let users to choose their preference. Ideally, *tidyfst* will lead even more users to learn more about *data.table* and its wonderful features, so as to design more extentions for *tidyfst* in the future.

Because we want a more stable data source, here we'll use the flight data from the above `nycflights13`

package.

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)

In the above examples, we could learn that in *tidyfst*, you could still use the methods in data.table, such as `.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))

Note that currently `keyby`

is not used in *tidyfst*. This featuer might be included in the future for better performance in order-independent tasks. Moreover, `count_dt`

is sorted automatically by the counted number, this could be controlled by the parameter "sort".

# 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))

Now let's try a more complex example:

# 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) )

Let me explain what happens here, especially in `group_dt`

. First filter by condition `carrier == "AA"`

, then group by three variables, which are `origin, dest, month`

. Last, summarise by columns with "_delay" in the column names and get the mean value of all such variables(with "_delay" in their column names). This is a very creative design, utilizing `.SD`

in *data.table* and upgrade the `group_by`

function in *dplyr* (because you never need to `ungroup`

now, just put the group operations in the `group_dt`

). And **you can pipe in the group_dt function**. Let's play with it a little bit further:

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

However, I don't recommend using it if you don't acutually need it for group computation (just start another pipe follows `group_dt`

).
Now let's end with some easy examples:

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

Deep inside, *tidyfst* is born from *dplyr* and *data.table*, and use *stringr* to make flexible APIs, so as to bring their superiority into full play.

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