This package provides a set of functions to make working with date and datetime data much easier!
While most time-based packages are designed to work with clean and pre-aggregate data, timeplyr contains a set of tidy tools to complete, expand and summarise both raw and aggregate date/datetime data.
Significant efforts have been made to ensure that grouped calculations are fast and efficient thanks to the excellent functionality within the collapse package.
You can install and load timeplyr using the below code.
# CRAN version
install.packages("timeplyr")
# Development version
remotes::install_github("NicChr/timeplyr")ts, mts, xts, zooand timeSeries objects using ts_as_tibblelibrary(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.4 ✔ readr 2.1.4
#> ✔ forcats 1.0.0 ✔ stringr 1.5.1
#> ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::desc() masks timeplyr::desc()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ dplyr::top_n() masks timeplyr::top_n()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
eu_stock <- EuStockMarkets %>%
ts_as_tibble()
eu_stock
#> # A tibble: 7,440 × 3
#> group time value
#> <chr> <dbl> <dbl>
#> 1 DAX 1991. 1629.
#> 2 DAX 1992. 1614.
#> 3 DAX 1992. 1607.
#> 4 DAX 1992. 1621.
#> 5 DAX 1992. 1618.
#> 6 DAX 1992. 1611.
#> 7 DAX 1992. 1631.
#> 8 DAX 1992. 1640.
#> 9 DAX 1992. 1635.
#> 10 DAX 1992. 1646.
#> # ℹ 7,430 more rowstime_ggplot
For the next examples we use flights departing from New York City in 2013.
time_byflights_monthly <- flights %>%
select(date, arr_delay) %>%
time_by(date, "month")
flights_monthly
#> # A tibble: 336,776 x 2
#> # Time: date [12]
#> # By: month
#> # Span: 2013-01-01 - 2013-12-31
#> date arr_delay
#> <date> <dbl>
#> 1 2013-01-01 11
#> 2 2013-01-01 20
#> 3 2013-01-01 33
#> 4 2013-01-01 -18
#> 5 2013-01-01 -25
#> 6 2013-01-01 12
#> 7 2013-01-01 19
#> 8 2013-01-01 -14
#> 9 2013-01-01 -8
#> 10 2013-01-01 8
#> # ℹ 336,766 more rowsWe can then use this to create a monthly summary of the number of flights and average arrival delay
flights_monthly %>%
summarise(n = n(),
mean_arr_delay = mean(arr_delay, na.rm = TRUE))
#> # A tibble: 12 × 3
#> date n mean_arr_delay
#> <date> <int> <dbl>
#> 1 2013-01-01 27004 6.13
#> 2 2013-02-01 24951 5.61
#> 3 2013-03-01 28834 5.81
#> 4 2013-04-01 28330 11.2
#> 5 2013-05-01 28796 3.52
#> 6 2013-06-01 28243 16.5
#> 7 2013-07-01 29425 16.7
#> 8 2013-08-01 29327 6.04
#> 9 2013-09-01 27574 -4.02
#> 10 2013-10-01 28889 -0.167
#> 11 2013-11-01 27268 0.461
#> 12 2013-12-01 28135 14.9If the time unit is left unspecified, the time functions try to find the highest time unit possible.
flights %>%
time_by(time_hour)
#> # A tibble: 336,776 x 20
#> # Time: time_hour [6,936]
#> # By: hour
#> # Span: 2013-01-01 05:00:00 - 2013-12-31 23:00:00
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 1 517 515 2 830 819
#> 2 2013 1 1 533 529 4 850 830
#> 3 2013 1 1 542 540 2 923 850
#> 4 2013 1 1 544 545 -1 1004 1022
#> 5 2013 1 1 554 600 -6 812 837
#> 6 2013 1 1 554 558 -4 740 728
#> 7 2013 1 1 555 600 -5 913 854
#> 8 2013 1 1 557 600 -3 709 723
#> 9 2013 1 1 557 600 -3 838 846
#> 10 2013 1 1 558 600 -2 753 745
#> # ℹ 336,766 more rows
#> # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> # hour <dbl>, minute <dbl>, time_hour <dttm>, date <date>time_complete()flights %>%
count(time_hour) %>%
time_complete(time_hour)
#> Assuming a time granularity of 1 hour(s)
#> # A tibble: 8,755 × 2
#> time_hour n
#> <dttm> <int>
#> 1 2013-01-01 05:00:00 6
#> 2 2013-01-01 06:00:00 52
#> 3 2013-01-01 07:00:00 49
#> 4 2013-01-01 08:00:00 58
#> 5 2013-01-01 09:00:00 56
#> 6 2013-01-01 10:00:00 39
#> 7 2013-01-01 11:00:00 37
#> 8 2013-01-01 12:00:00 56
#> 9 2013-01-01 13:00:00 54
#> 10 2013-01-01 14:00:00 48
#> # ℹ 8,745 more rowstime_count()flights %>%
time_count(date, time_by = "quarter")
#> # A tibble: 4 × 2
#> date n
#> <date> <int>
#> 1 2013-01-01 80789
#> 2 2013-04-01 85369
#> 3 2013-07-01 86326
#> 4 2013-10-01 84292time_floor = TRUEstart <- dmy("17-Jan-2013")
flights %>%
time_count(date,
time_by = "week", from = start, time_floor = TRUE)
#> # A tibble: 52 × 2
#> date n
#> <date> <int>
#> 1 2013-01-14 3311
#> 2 2013-01-21 6049
#> 3 2013-01-28 6063
#> 4 2013-02-04 6104
#> 5 2013-02-11 6236
#> 6 2013-02-18 6381
#> 7 2013-02-25 6444
#> 8 2013-03-04 6546
#> 9 2013-03-11 6555
#> 10 2013-03-18 6547
#> # ℹ 42 more rows
flights %>%
time_count(date,
time_by = "month", from = start, time_floor = TRUE)
#> # A tibble: 13 × 2
#> date n
#> <date> <int>
#> 1 2013-01-01 13001
#> 2 2013-02-01 24951
#> 3 2013-03-01 28834
#> 4 2013-04-01 28330
#> 5 2013-05-01 28796
#> 6 2013-06-01 28243
#> 7 2013-07-01 29425
#> 8 2013-08-01 29327
#> 9 2013-09-01 27574
#> 10 2013-10-01 28889
#> 11 2013-11-01 27268
#> 12 2013-12-01 28135
#> 13 NA 14003To check for regularity use time_is_regular
hours <- sort(flights$time_hour)
time_is_regular(hours, time_by = "hours")
#> [1] TRUE
time_is_regular(hours, time_by = "hours", allow_gaps = FALSE)
#> [1] FALSE
time_is_regular(hours, time_by = "hours", allow_dups = FALSE)
#> [1] FALSE
# By-group
time_num_gaps(flights$time_hour, g = flights$origin, time_by = "hours")
#> EWR JFK LGA
#> 2489 1820 2468
time_is_regular(flights$time_hour, g = flights$origin, time_by = "hours")
#> EWR JFK LGA
#> FALSE FALSE FALSEtime_expand()Here we create monthly sequences for each destination that accounts for the start and end dates of each destination
flights %>%
group_by(dest) %>%
time_expand(date, time_by = "month") %>%
summarise(n = n(), start = min(date), end = max(date))
#> # A tibble: 105 × 4
#> dest n start end
#> <chr> <int> <date> <date>
#> 1 ABQ 9 2013-04-22 2013-12-22
#> 2 ACK 6 2013-05-16 2013-10-16
#> 3 ALB 12 2013-01-01 2013-12-01
#> 4 ANC 2 2013-07-06 2013-08-06
#> 5 ATL 12 2013-01-01 2013-12-01
#> 6 AUS 12 2013-01-01 2013-12-01
#> 7 AVL 12 2013-01-01 2013-12-01
#> 8 BDL 12 2013-01-01 2013-12-01
#> 9 BGR 10 2013-03-02 2013-12-02
#> 10 BHM 12 2013-01-02 2013-12-02
#> # ℹ 95 more rowsTo create the same grid of months for each dest, we can do the following
flights %>%
time_expand(date, dest, time_by = "month") %>%
summarise(n = n(), start = min(date), end = max(date), .by = dest)
#> # A tibble: 105 × 4
#> dest n start end
#> <chr> <int> <date> <date>
#> 1 ABQ 12 2013-01-01 2013-12-01
#> 2 ACK 12 2013-01-01 2013-12-01
#> 3 ALB 12 2013-01-01 2013-12-01
#> 4 ANC 12 2013-01-01 2013-12-01
#> 5 ATL 12 2013-01-01 2013-12-01
#> 6 AUS 12 2013-01-01 2013-12-01
#> 7 AVL 12 2013-01-01 2013-12-01
#> 8 BDL 12 2013-01-01 2013-12-01
#> 9 BGR 12 2013-01-01 2013-12-01
#> 10 BHM 12 2013-01-01 2013-12-01
#> # ℹ 95 more rowsThe ability to create time sequences by group is one of the most powerful features of timeplyr.
time_summarise()We can shortcut the time aggregation and then additional summary by using time_summarise().
flights %>%
time_summarise(date, # Time variable
across(c(arr_time, dep_time), # By-month summaries
~ mean(.x, na.rm = TRUE)),
time_by = "month",
time_floor = TRUE, # Full months
include_interval = TRUE) # Time interval
#> # A tibble: 12 × 4
#> date interval arr_time dep_time
#> <date> <Interval> <dbl> <dbl>
#> 1 2013-01-01 2013-01-01 UTC--2013-02-01 UTC 1523. 1347.
#> 2 2013-02-01 2013-02-01 UTC--2013-03-01 UTC 1522. 1348.
#> 3 2013-03-01 2013-03-01 UTC--2013-04-01 UTC 1510. 1359.
#> 4 2013-04-01 2013-04-01 UTC--2013-05-01 UTC 1501. 1353.
#> 5 2013-05-01 2013-05-01 UTC--2013-06-01 UTC 1503. 1351.
#> 6 2013-06-01 2013-06-01 UTC--2013-07-01 UTC 1468. 1351.
#> 7 2013-07-01 2013-07-01 UTC--2013-08-01 UTC 1456. 1353.
#> 8 2013-08-01 2013-08-01 UTC--2013-09-01 UTC 1495. 1350.
#> 9 2013-09-01 2013-09-01 UTC--2013-10-01 UTC 1504. 1334.
#> 10 2013-10-01 2013-10-01 UTC--2013-11-01 UTC 1520. 1340.
#> 11 2013-11-01 2013-11-01 UTC--2013-12-01 UTC 1523. 1344.
#> 12 2013-12-01 2013-12-01 UTC--2013-12-31 UTC 1505. 1357.eu_stock <- eu_stock %>%
mutate(date = date_decimal(time))
eu_stock %>%
mutate(month_mean = time_roll_mean(value, window = months(3),
time = date,
g = group)) %>%
time_ggplot(date, month_mean, group)
# Prerequisite: Create Time series with missing values
x <- ts(c(NA, 3, 4, NA, 6, NA, NA, 8))
g <- seq_id(c(3, 5)) # Two groups of size 3 + 5
.roll_na_fill(x) # Simple locf fill
#> Time Series:
#> Start = 1
#> End = 8
#> Frequency = 1
#> [1] NA 3 4 4 6 6 6 8
roll_na_fill(x, fill_limit = 1) # Fill up to 1 NA
#> Time Series:
#> Start = 1
#> End = 8
#> Frequency = 1
#> [1] NA 3 4 4 6 6 NA 8
roll_na_fill(x, g = g) # Very efficient on large data too
#> Time Series:
#> Start = 1
#> End = 8
#> Frequency = 1
#> [1] NA 3 4 NA 6 6 6 8year_month and year_quartertimeplyr has its own lightweight ‘yearmonth’ and `yearquarter’ classes inspired by the excellent ‘zoo’ and ‘tsibble’ packages.
The underlying data for a year_month is the number of months since 1 January 1970 (epoch).
To create a sequence of ‘year_months’, one can use base arithmetic
year_month(today) + 0:12
#> [1] "2023 Dec" "2024 Jan" "2024 Feb" "2024 Mar" "2024 Apr" "2024 May"
#> [7] "2024 Jun" "2024 Jul" "2024 Aug" "2024 Sep" "2024 Oct" "2024 Nov"
#> [13] "2024 Dec"
year_quarter(today) + 0:4
#> [1] "2023 Q4" "2024 Q1" "2024 Q2" "2024 Q3" "2024 Q4"time_elapsed()Let’s look at the time between consecutive flights for a specific flight number
set.seed(42)
flight_201 <- flights %>%
distinct(time_hour, flight) %>%
filter(flight %in% sample(flight, size = 1)) %>%
arrange(time_hour)
top_n_tbl(time_elapsed(flight_201$time_hour, "hours"))
#> # A tibble: 5 × 2
#> value n
#> <dbl> <int>
#> 1 24 218
#> 2 18 34
#> 3 6 33
#> 4 48 4
#> 5 25 3Flight 201 seems to depart mostly consistently every 24 hours
We can efficiently do the same for all flight numbers
# We use fdistinct with sort as it's much faster and simpler to write
all_flights <- flights %>%
fdistinct(flight, time_hour, sort = TRUE)
all_flights <- all_flights %>%
mutate(elapsed = time_elapsed(time_hour, g = flight, fill = 0))
#> Assuming a time granularity of 1 hour(s)
# Flight numbers with largest relative deviation in time between flights
all_flights %>%
q_summarise(elapsed, .by = flight) %>%
mutate(relative_iqr = p75 / p25) %>%
arrange(desc(relative_iqr))
#> flight p0 p25 p50 p75 p100 relative_iqr
#> <int> <num> <num> <num> <num> <num> <num>
#> 1: 3664 0 12 24 3252.0 6480 271.0000
#> 2: 5709 0 12 24 3080.5 6137 256.7083
#> 3: 513 0 12 24 2250.5 4477 187.5417
#> 4: 3364 0 12 24 2204.5 4385 183.7083
#> 5: 1578 0 24 48 4182.5 8317 174.2708
#> ---
#> 3840: 6114 0 0 0 0.0 0 NaN
#> 3841: 6140 0 0 0 0.0 0 NaN
#> 3842: 6165 0 0 0 0.0 0 NaN
#> 3843: 6171 0 0 0 0.0 0 NaN
#> 3844: 8500 0 0 0 0.0 0 NaNtime_seq_id() allows us to create unique IDs for regular sequences A new ID is created every time there is a gap in the sequence
flights %>%
select(time_hour) %>%
arrange(time_hour) %>%
mutate(time_id = time_seq_id(time_hour)) %>%
filter(time_id != lag(time_id)) %>%
count(hour(time_hour))
#> Assuming a time granularity of 1 hour(s)
#> # A tibble: 2 × 2
#> `hour(time_hour)` n
#> <int> <int>
#> 1 1 1
#> 2 5 364We can see that the gaps typically occur at 11pm and the sequence resumes at 5am.
add_calendar()Now that gaps in time have been filled and we have joined our date table, it is easy to count by any time dimension we like
flights_calendar %>%
fcount(isoyear, isoweek)
#> # A tibble: 53 × 3
#> isoyear isoweek n
#> <int> <int> <int>
#> 1 2013 1 5166
#> 2 2013 2 6114
#> 3 2013 3 6034
#> 4 2013 4 6049
#> 5 2013 5 6063
#> 6 2013 6 6104
#> 7 2013 7 6236
#> 8 2013 8 6381
#> 9 2013 9 6444
#> 10 2013 10 6546
#> # ℹ 43 more rows
flights_calendar %>%
fcount(isoweek = iso_week(time_hour))
#> # A tibble: 53 × 2
#> isoweek n
#> <chr> <int>
#> 1 2013-W01 5166
#> 2 2013-W02 6114
#> 3 2013-W03 6034
#> 4 2013-W04 6049
#> 5 2013-W05 6063
#> 6 2013-W06 6104
#> 7 2013-W07 6236
#> 8 2013-W08 6381
#> 9 2013-W09 6444
#> 10 2013-W10 6546
#> # ℹ 43 more rows
flights_calendar %>%
fcount(month_l)
#> # A tibble: 12 × 2
#> month_l n
#> <ord> <int>
#> 1 Jan 27004
#> 2 Feb 24951
#> 3 Mar 28834
#> 4 Apr 28330
#> 5 May 28796
#> 6 Jun 28243
#> 7 Jul 29425
#> 8 Aug 29327
#> 9 Sep 27574
#> 10 Oct 28889
#> 11 Nov 27268
#> 12 Dec 28135.time_unitsSee a list of available time units
.time_units
#> [1] "picoseconds" "nanoseconds" "microseconds" "milliseconds" "seconds"
#> [6] "minutes" "hours" "days" "weeks" "months"
#> [11] "years" "fortnights" "quarters" "semesters" "olympiads"
#> [16] "lustrums" "decades" "indictions" "scores" "centuries"
#> [21] "milleniums"age_years()Calculate ages (years) accurately
time_seq()A lubridate version of seq() for dates and datetimes
start <- dmy(31012020)
end <- start + years(1)
seq(start, end, by = "month") # Base R version
#> [1] "2020-01-31" "2020-03-02" "2020-03-31" "2020-05-01" "2020-05-31"
#> [6] "2020-07-01" "2020-07-31" "2020-08-31" "2020-10-01" "2020-10-31"
#> [11] "2020-12-01" "2020-12-31" "2021-01-31"
time_seq(start, end, time_by = "month") # lubridate version
#> [1] "2020-01-31" "2020-02-29" "2020-03-31" "2020-04-30" "2020-05-31"
#> [6] "2020-06-30" "2020-07-31" "2020-08-31" "2020-09-30" "2020-10-31"
#> [11] "2020-11-30" "2020-12-31" "2021-01-31"time_seq() doesn’t mind mixing dates and datetimes
time_seq(start, as_datetime(end), time_by = "2 weeks")
#> [1] "2020-01-31 UTC" "2020-02-14 UTC" "2020-02-28 UTC" "2020-03-13 UTC"
#> [5] "2020-03-27 UTC" "2020-04-10 UTC" "2020-04-24 UTC" "2020-05-08 UTC"
#> [9] "2020-05-22 UTC" "2020-06-05 UTC" "2020-06-19 UTC" "2020-07-03 UTC"
#> [13] "2020-07-17 UTC" "2020-07-31 UTC" "2020-08-14 UTC" "2020-08-28 UTC"
#> [17] "2020-09-11 UTC" "2020-09-25 UTC" "2020-10-09 UTC" "2020-10-23 UTC"
#> [21] "2020-11-06 UTC" "2020-11-20 UTC" "2020-12-04 UTC" "2020-12-18 UTC"
#> [25] "2021-01-01 UTC" "2021-01-15 UTC" "2021-01-29 UTC"time_seq_v()A vectorised version of time_seq() Currently it is vectorised over from, to and by
# 3 sequences
time_seq_v(from = start,
to = end,
time_by = list("months" = 1:3))
#> [1] "2020-01-31" "2020-02-29" "2020-03-31" "2020-04-30" "2020-05-31"
#> [6] "2020-06-30" "2020-07-31" "2020-08-31" "2020-09-30" "2020-10-31"
#> [11] "2020-11-30" "2020-12-31" "2021-01-31" "2020-01-31" "2020-03-31"
#> [16] "2020-05-31" "2020-07-31" "2020-09-30" "2020-11-30" "2021-01-31"
#> [21] "2020-01-31" "2020-04-30" "2020-07-31" "2020-10-31" "2021-01-31"
# Equivalent to
c(time_seq(start, end, time_by = "month"),
time_seq(start, end, time_by = "2 months"),
time_seq(start, end, time_by = "3 months"))
#> [1] "2020-01-31" "2020-02-29" "2020-03-31" "2020-04-30" "2020-05-31"
#> [6] "2020-06-30" "2020-07-31" "2020-08-31" "2020-09-30" "2020-10-31"
#> [11] "2020-11-30" "2020-12-31" "2021-01-31" "2020-01-31" "2020-03-31"
#> [16] "2020-05-31" "2020-07-31" "2020-09-30" "2020-11-30" "2021-01-31"
#> [21] "2020-01-31" "2020-04-30" "2020-07-31" "2020-10-31" "2021-01-31"time_seq_sizes()Vectorised function that calculates time sequence lengths
seq_lengths <- time_seq_sizes(start, start + days(c(1, 10, 20)),
time_by = list("days" = c(1, 5, 10)))
seq_lengths
#> [1] 2 3 3
# Use time_seq_v2() if you know the sequence lengths
seqs <- time_seq_v2(seq_lengths, start, time_by = list("days" = c(1, 5, 10)))
seqs
#> [1] "2020-01-31" "2020-02-01" "2020-01-31" "2020-02-05" "2020-02-10"
#> [6] "2020-01-31" "2020-02-10" "2020-02-20"Dealing with impossible dates and datetimes is very simple
time_seq(start, end, time_by = "month", roll_month = "postday") # roll impossible months forward
#> [1] "2020-01-31" "2020-03-01" "2020-03-31" "2020-05-01" "2020-05-31"
#> [6] "2020-07-01" "2020-07-31" "2020-08-31" "2020-10-01" "2020-10-31"
#> [11] "2020-12-01" "2020-12-31" "2021-01-31"
time_seq(start, end, time_by = "month", roll_month = "NA") # no roll
#> [1] "2020-01-31" NA "2020-03-31" NA "2020-05-31"
#> [6] NA "2020-07-31" "2020-08-31" NA "2020-10-31"
#> [11] NA "2020-12-31" "2021-01-31"
time_seq(start, end, time_by = dmonths(1)) # lubridate version with durations
#> [1] "2020-01-31 00:00:00 UTC" "2020-03-01 10:30:00 UTC"
#> [3] "2020-03-31 21:00:00 UTC" "2020-05-01 07:30:00 UTC"
#> [5] "2020-05-31 18:00:00 UTC" "2020-07-01 04:30:00 UTC"
#> [7] "2020-07-31 15:00:00 UTC" "2020-08-31 01:30:00 UTC"
#> [9] "2020-09-30 12:00:00 UTC" "2020-10-30 22:30:00 UTC"
#> [11] "2020-11-30 09:00:00 UTC" "2020-12-30 19:30:00 UTC"
#> [13] "2021-01-30 06:00:00 UTC"iso_week()Simple function to get formatted ISO weeks.
iso_week(today())
#> [1] "2023-W50"
iso_week(today(), day = TRUE)
#> [1] "2023-W50-2"
iso_week(today(), year = FALSE)
#> [1] "W50"time_cut()Create pretty time axes using time_cut() and time_breaks()
times <- flights$time_hour
dates <- flights$date
levels(time_cut(dates, n = 10))
#> [1] "[2013-01-01, 2013-03-01)" "[2013-03-01, 2013-05-01)"
#> [3] "[2013-05-01, 2013-07-01)" "[2013-07-01, 2013-09-01)"
#> [5] "[2013-09-01, 2013-11-01)" "[2013-11-01, 2013-12-31]"
date_breaks <- time_breaks(dates, n = 12)
time_breaks <- time_breaks(times, n = 12, time_floor = TRUE)
weekly_data <- flights %>%
time_count(time = date, time_by = "week",
to = max(time_span(date, time_by = "week")),
include_interval = TRUE) %>%
# Filter full weeks
mutate(n_days = interval/days(1)) %>%
filter(n_days == 7)
weekly_data %>%
ggplot(aes(x = date, y = n)) +
geom_bar(stat = "identity", fill = "#0072B2") +
scale_x_date(breaks = date_breaks, labels = scales::label_date_short())
flights %>%
ggplot(aes(x = time_hour)) +
geom_bar(fill = "#0072B2") +
scale_x_datetime(breaks = time_breaks, labels = scales::label_date_short())
group_collapse()Collapse your data into unique groups with key information
flights %>%
group_collapse(origin, dest)
#> # A tibble: 224 × 7
#> origin dest .group .loc .start .end .size
#> <chr> <chr> <int> <list<int>> <int> <int> <int>
#> 1 EWR IAH 35 [3,973] 1 336738 3973
#> 2 LGA IAH 188 [2,951] 2 336619 2951
#> 3 JFK MIA 123 [3,314] 3 336567 3314
#> 4 JFK BQN 94 [599] 4 335788 599
#> 5 LGA ATL 157 [10,263] 5 336671 10263
#> 6 EWR ORD 56 [6,100] 6 336676 6100
#> 7 EWR FLL 27 [3,793] 7 336707 3793
#> 8 LGA IAD 187 [1,803] 8 336642 1803
#> 9 JFK MCO 121 [5,464] 9 336764 5464
#> 10 LGA ORD 205 [8,857] 10 336710 8857
#> # ℹ 214 more rows
# Sorted (like dplyr::group_data())
flights %>%
group_collapse(origin, dest, sort = TRUE)
#> # A tibble: 224 × 7
#> origin dest .group .loc .start .end .size
#> <chr> <chr> <int> <list<int>> <int> <int> <int>
#> 1 EWR ALB 1 [439] 361 331200 439
#> 2 EWR ANC 2 [8] 255456 302527 8
#> 3 EWR ATL 3 [5,022] 30 336725 5022
#> 4 EWR AUS 4 [968] 440 336715 968
#> 5 EWR AVL 5 [265] 212 336461 265
#> 6 EWR BDL 6 [443] 364 334280 443
#> 7 EWR BNA 7 [2,336] 512 336618 2336
#> 8 EWR BOS 8 [5,327] 108 336756 5327
#> 9 EWR BQN 9 [297] 720 335753 297
#> 10 EWR BTV 10 [931] 307 302836 931
#> # ℹ 214 more rows
# By order of first appearance
flights %>%
group_collapse(origin, dest, order = FALSE)
#> # A tibble: 224 × 7
#> origin dest .group .loc .start .end .size
#> <chr> <chr> <int> <list<int>> <int> <int> <int>
#> 1 EWR IAH 1 [3,973] 1 336738 3973
#> 2 LGA IAH 2 [2,951] 2 336619 2951
#> 3 JFK MIA 3 [3,314] 3 336567 3314
#> 4 JFK BQN 4 [599] 4 335788 599
#> 5 LGA ATL 5 [10,263] 5 336671 10263
#> 6 EWR ORD 6 [6,100] 6 336676 6100
#> 7 EWR FLL 7 [3,793] 7 336707 3793
#> 8 LGA IAD 8 [1,803] 8 336642 1803
#> 9 JFK MCO 9 [5,464] 9 336764 5464
#> 10 LGA ORD 10 [8,857] 10 336710 8857
#> # ℹ 214 more rowsfcount()/fadd_count()flights %>%
fgroup_by(origin, dest, tailnum) %>%
fcount(flight, carrier)
#> # A tibble: 186,870 × 6
#> # Groups: origin, dest, tailnum [52,783]
#> origin dest tailnum flight carrier n
#> <chr> <chr> <chr> <int> <chr> <int>
#> 1 EWR ALB N10575 4117 EV 2
#> 2 EWR ALB N10575 4162 EV 2
#> 3 EWR ALB N10575 4309 EV 1
#> 4 EWR ALB N10575 4566 EV 1
#> 5 EWR ALB N10575 6043 EV 2
#> 6 EWR ALB N11113 4264 EV 1
#> 7 EWR ALB N11119 4093 EV 1
#> 8 EWR ALB N11119 4271 EV 2
#> 9 EWR ALB N11150 5675 EV 1
#> 10 EWR ALB N11164 4088 EV 1
#> # ℹ 186,860 more rows
flights %>%
fselect(origin, dest, tailnum, flight, carrier) %>%
fadd_count(across(all_of(c("flight", "carrier"))),
.by = c(origin, dest, tailnum))
#> # A tibble: 336,776 × 6
#> origin dest tailnum flight carrier n
#> <chr> <chr> <chr> <int> <chr> <int>
#> 1 EWR IAH N14228 1545 UA 1
#> 2 LGA IAH N24211 1714 UA 1
#> 3 JFK MIA N619AA 1141 AA 2
#> 4 JFK BQN N804JB 725 B6 1
#> 5 LGA ATL N668DN 461 DL 2
#> 6 EWR ORD N39463 1696 UA 1
#> 7 EWR FLL N516JB 507 B6 3
#> 8 LGA IAD N829AS 5708 EV 2
#> 9 JFK MCO N593JB 79 B6 3
#> 10 LGA ORD N3ALAA 301 AA 5
#> # ℹ 336,766 more rowsgroup_id()/add_group_id()This calculates sorted and non-sorted group IDs
flights %>%
fgroup_by(origin, dest) %>%
group_id(order = FALSE) %>%
unique()
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224
flights %>%
fselect(origin, dest) %>%
add_group_id(.by = everything()) %>%
fdistinct(origin, dest, group_id)
#> # A tibble: 224 × 3
#> origin dest group_id
#> <chr> <chr> <int>
#> 1 EWR IAH 35
#> 2 LGA IAH 188
#> 3 JFK MIA 123
#> 4 JFK BQN 94
#> 5 LGA ATL 157
#> 6 EWR ORD 56
#> 7 EWR FLL 27
#> 8 LGA IAD 187
#> 9 JFK MCO 121
#> 10 LGA ORD 205
#> # ℹ 214 more rowsfslice()Fast row index slicing with lots of groups
flights %>%
fgroup_by(origin, dest, tailnum) %>%
fslice(1:5)
#> # A tibble: 172,983 × 20
#> # Groups: origin, dest, tailnum [52,783]
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 30 2224 2000 144 2316 2101
#> 2 2013 2 17 2012 2010 2 2120 2114
#> 3 2013 2 26 2356 2000 236 41 2104
#> 4 2013 3 13 1958 2005 -7 2056 2109
#> 5 2013 5 16 2214 2000 134 2307 2112
#> 6 2013 9 8 2156 2159 -3 2250 2303
#> 7 2013 1 26 1614 1620 -6 1706 1724
#> 8 2013 2 11 NA 1619 NA NA 1723
#> 9 2013 2 17 1604 1609 -5 1715 1713
#> 10 2013 11 8 2203 2159 4 2250 2301
#> # ℹ 172,973 more rows
#> # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> # hour <dbl>, minute <dbl>, time_hour <dttm>, date <date>
flights %>%
fgroup_by(origin, dest, tailnum) %>%
fslice_head(n = 5)
#> # A tibble: 172,983 × 20
#> # Groups: origin, dest, tailnum [52,783]
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 30 2224 2000 144 2316 2101
#> 2 2013 2 17 2012 2010 2 2120 2114
#> 3 2013 2 26 2356 2000 236 41 2104
#> 4 2013 3 13 1958 2005 -7 2056 2109
#> 5 2013 5 16 2214 2000 134 2307 2112
#> 6 2013 9 8 2156 2159 -3 2250 2303
#> 7 2013 1 26 1614 1620 -6 1706 1724
#> 8 2013 2 11 NA 1619 NA NA 1723
#> 9 2013 2 17 1604 1609 -5 1715 1713
#> 10 2013 11 8 2203 2159 4 2250 2301
#> # ℹ 172,973 more rows
#> # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> # hour <dbl>, minute <dbl>, time_hour <dttm>, date <date>
# Use keep_order to retain the data input order
flights %>%
fgroup_by(origin, dest, tailnum) %>%
fslice_tail(prop = 0.5, keep_order = TRUE)
#> # A tibble: 153,350 × 20
#> # Groups: origin, dest, tailnum [40,633]
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 2 919 830 49 1135 1038
#> 2 2013 1 2 1126 1125 1 1333 1325
#> 3 2013 1 3 603 605 -2 709 705
#> 4 2013 1 3 1336 1340 -4 1641 1626
#> 5 2013 1 3 1348 1350 -2 1631 1640
#> 6 2013 1 4 628 630 -2 1124 1140
#> 7 2013 1 4 712 715 -3 1021 1035
#> 8 2013 1 4 716 720 -4 855 840
#> 9 2013 1 4 1101 1106 -5 1349 1404
#> 10 2013 1 4 1249 1235 14 1434 1415
#> # ℹ 153,340 more rows
#> # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#> # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#> # hour <dbl>, minute <dbl>, time_hour <dttm>, date <date>
# Stratified sampling
flights %>%
fselect(origin, dest) %>%
fgroup_by(origin, dest) %>%
add_row_id() %>%
fslice_sample(seed = 91239)
#> # A tibble: 336,776 × 3
#> # Groups: origin, dest [224]
#> origin dest row_id
#> <chr> <chr> <int>
#> 1 EWR ALB 347
#> 2 EWR ALB 211
#> 3 EWR ALB 298
#> 4 EWR ALB 316
#> 5 EWR ALB 267
#> 6 EWR ALB 286
#> 7 EWR ALB 418
#> 8 EWR ALB 270
#> 9 EWR ALB 306
#> 10 EWR ALB 78
#> # ℹ 336,766 more rowsfdistinct()Distinct rows
flights %>%
fgroup_by(origin, dest, tailnum) %>%
fdistinct(year, month, day)
#> # A tibble: 316,477 × 6
#> # Groups: origin, dest, tailnum [52,783]
#> origin dest tailnum year month day
#> <chr> <chr> <chr> <int> <int> <int>
#> 1 EWR IAH N14228 2013 1 1
#> 2 LGA IAH N24211 2013 1 1
#> 3 JFK MIA N619AA 2013 1 1
#> 4 JFK BQN N804JB 2013 1 1
#> 5 LGA ATL N668DN 2013 1 1
#> 6 EWR ORD N39463 2013 1 1
#> 7 EWR FLL N516JB 2013 1 1
#> 8 LGA IAD N829AS 2013 1 1
#> 9 JFK MCO N593JB 2013 1 1
#> 10 LGA ORD N3ALAA 2013 1 1
#> # ℹ 316,467 more rowsfduplicates()Duplicate rows
flights %>%
fgroup_by(origin, dest, tailnum) %>%
fduplicates(year, month, day)
#> # A tibble: 20,299 × 6
#> # Groups: origin, dest, tailnum [6,122]
#> origin dest tailnum year month day
#> <chr> <chr> <chr> <int> <int> <int>
#> 1 EWR BOS N206JB 2013 1 1
#> 2 JFK DCA N846MQ 2013 1 1
#> 3 JFK RDU N828MQ 2013 1 1
#> 4 LGA CMH N739MQ 2013 1 1
#> 5 LGA MIA N3EMAA 2013 1 1
#> 6 EWR FLL N516JB 2013 1 1
#> 7 JFK MCO N5FMAA 2013 1 1
#> 8 LGA DCA N951UW 2013 1 1
#> 9 EWR PWM N11544 2013 1 1
#> 10 LGA DFW N3DUAA 2013 1 1
#> # ℹ 20,289 more rowsrow_id()/add_row_id()Fast grouped row IDs
iris <- as_tibble(iris)
range(row_id(iris))
#> [1] 1 150
iris %>%
add_row_id()
#> # A tibble: 150 × 6
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species row_id
#> <dbl> <dbl> <dbl> <dbl> <fct> <int>
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3 1.4 0.2 setosa 2
#> 3 4.7 3.2 1.3 0.2 setosa 3
#> 4 4.6 3.1 1.5 0.2 setosa 4
#> 5 5 3.6 1.4 0.2 setosa 5
#> 6 5.4 3.9 1.7 0.4 setosa 6
#> 7 4.6 3.4 1.4 0.3 setosa 7
#> 8 5 3.4 1.5 0.2 setosa 8
#> 9 4.4 2.9 1.4 0.2 setosa 9
#> 10 4.9 3.1 1.5 0.1 setosa 10
#> # ℹ 140 more rows
iris %>%
add_row_id(Species)
#> # A tibble: 150 × 6
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species row_id
#> <dbl> <dbl> <dbl> <dbl> <fct> <int>
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3 1.4 0.2 setosa 2
#> 3 4.7 3.2 1.3 0.2 setosa 3
#> 4 4.6 3.1 1.5 0.2 setosa 4
#> 5 5 3.6 1.4 0.2 setosa 5
#> 6 5.4 3.9 1.7 0.4 setosa 6
#> 7 4.6 3.4 1.4 0.3 setosa 7
#> 8 5 3.4 1.5 0.2 setosa 8
#> 9 4.4 2.9 1.4 0.2 setosa 9
#> 10 4.9 3.1 1.5 0.1 setosa 10
#> # ℹ 140 more rowsstat_summarise()Fast Grouped statistical functions
# This is extremely fast and efficient, especially with lots of groups
flights %>%
stat_summarise(arr_time, .by = origin, stat = c("n", "mean", "min", "max"))
#> The below stat functions are available for use in stat_summarise
#> n
#> nmiss
#> ndistinct
#> min
#> max
#> mean
#> median
#> sd
#> var
#> mode
#> first
#> last
#> sum
#> prop_complete
#> This message is displayed once per session.
#> origin n mean min max
#> <char> <int> <num> <int> <int>
#> 1: EWR 120835 1491.876 1 2400
#> 2: JFK 111279 1520.070 1 2400
#> 3: LGA 104662 1494.424 1 2400q_summarise()flights %>%
q_summarise(arr_time, .by = tailnum)
#> tailnum p0 p25 p50 p75 p100
#> <char> <num> <num> <num> <num> <num>
#> 1: D942DN 1142 1424.00 1578.0 1680.25 1807
#> 2: N0EGMQ 15 1128.50 1528.5 1915.25 2354
#> 3: N10156 3 1012.75 1412.5 1840.25 2352
#> 4: N102UW 701 812.50 1275.5 1507.25 2319
#> 5: N103US 633 801.75 1150.5 1355.75 1732
#> ---
#> 4040: N998AT 32 1250.00 1839.0 2035.00 2207
#> 4041: N998DL 5 1124.00 1525.0 1925.75 2349
#> 4042: N999DN 153 1058.00 1500.0 1913.00 2254
#> 4043: N9EAMQ 11 1148.00 1535.0 1921.25 2348
#> 4044: <NA> NA NA NA NA NA
# Pivot longer for data wrangling or plotting
flights %>%
q_summarise(arr_time, .by = origin,
pivot = "long")
#> origin .quantile arr_time
#> <char> <fctr> <num>
#> 1: EWR p0 1
#> 2: EWR p25 1102
#> 3: EWR p50 1522
#> 4: EWR p75 1928
#> 5: EWR p100 2400
#> 6: JFK p0 1
#> 7: JFK p25 1059
#> 8: JFK p50 1625
#> 9: JFK p75 2016
#> 10: JFK p100 2400
#> 11: LGA p0 1
#> 12: LGA p25 1112
#> 13: LGA p50 1509
#> 14: LGA p75 1913
#> 15: LGA p100 2400