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timeplyr

Fast Tidy Tools for Date and Datetime Manipulation

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.

Installation

# CRAN version
install.packages("timeplyr")

# Development version
remotes::install_github("NicChr/timeplyr")

library(timeplyr)

Basic examples

Convert ts, mts, xts, zooand timeSeries objects using ts_as_tibble

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::desc()   masks timeplyr::desc()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> 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 rows

Easily plot time series using time_ggplot

eu_stock %>%
  time_ggplot(time, value, group)

library(nycflights13)
library(lubridate)
flights <- flights %>%
  mutate(date = as_date(time_hour))

time_by

Group your time variable by any time unit

flights_monthly <- flights %>%
  select(date, arr_delay) %>%
  time_by(date, "month")

flights_monthly
#> # A tibble: 336,776 x 3
#> # Time:     time_intv_month [12]
#> # By:       month
#> # Span:     2013-01-01 - 2013-12-31
#>    date       arr_delay          time_intv_month
#>    <date>         <dbl>                <tm_intv>
#>  1 2013-01-01        11 [2013-01-01, 2013-02-01)
#>  2 2013-01-01        20 [2013-01-01, 2013-02-01)
#>  3 2013-01-01        33 [2013-01-01, 2013-02-01)
#>  4 2013-01-01       -18 [2013-01-01, 2013-02-01)
#>  5 2013-01-01       -25 [2013-01-01, 2013-02-01)
#>  6 2013-01-01        12 [2013-01-01, 2013-02-01)
#>  7 2013-01-01        19 [2013-01-01, 2013-02-01)
#>  8 2013-01-01       -14 [2013-01-01, 2013-02-01)
#>  9 2013-01-01        -8 [2013-01-01, 2013-02-01)
#> 10 2013-01-01         8 [2013-01-01, 2013-02-01)
#> # ℹ 336,766 more rows

We 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
#>             time_intv_month     n mean_arr_delay
#>                   <tm_intv> <int>          <dbl>
#>  1 [2013-01-01, 2013-02-01) 27004          6.13 
#>  2 [2013-02-01, 2013-03-01) 24951          5.61 
#>  3 [2013-03-01, 2013-04-01) 28834          5.81 
#>  4 [2013-04-01, 2013-05-01) 28330         11.2  
#>  5 [2013-05-01, 2013-06-01) 28796          3.52 
#>  6 [2013-06-01, 2013-07-01) 28243         16.5  
#>  7 [2013-07-01, 2013-08-01) 29425         16.7  
#>  8 [2013-08-01, 2013-09-01) 29327          6.04 
#>  9 [2013-09-01, 2013-10-01) 27574         -4.02 
#> 10 [2013-10-01, 2013-11-01) 28889         -0.167
#> 11 [2013-11-01, 2013-12-01) 27268          0.461
#> 12 [2013-12-01, 2014-01-01) 28135         14.9

If 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 21
#> # Time:     time_intv_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 13 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_intv_hour <tm_intv>

time_complete()

Complete missing gaps in time

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 rows

We can also make use of timeplyr time intervals

quarters <- time_aggregate(flights$date, time_by = "quarter", as_interval = TRUE)
interval_count(quarters)
#> # A tibble: 4 × 2
#>                   interval     n
#>                  <tm_intv> <int>
#> 1 [2013-01-01, 2013-04-01) 80789
#> 2 [2013-04-01, 2013-07-01) 85369
#> 3 [2013-07-01, 2013-10-01) 86326
#> 4 [2013-10-01, 2014-01-01) 84292


# Or simply
flights %>%
  time_by(date, time_by = "quarter", as_interval = TRUE) %>%
  count()
#> # A tibble: 4 x 2
#> # Time:     time_intv_3_months [4]
#> # By:       3 months
#> # Span:     2013-01-01 - 2013-12-31
#>         time_intv_3_months     n
#>                  <tm_intv> <int>
#> 1 [2013-01-01, 2013-04-01) 80789
#> 2 [2013-04-01, 2013-07-01) 85369
#> 3 [2013-07-01, 2013-10-01) 86326
#> 4 [2013-10-01, 2014-01-01) 84292

Ensure full weeks by setting from to the start of the week

start <- dmy("17-Jan-2013")
flights %>%
  time_by(date, "week", 
          from = floor_date(start, unit = "week")) %>%
  count()
#> # A tibble: 52 x 2
#> # Time:     time_intv_week [52]
#> # By:       week
#> # Span:     2013-01-13 - 2013-12-31
#>              time_intv_week     n
#>                   <tm_intv> <int>
#>  1 [2013-01-13, 2013-01-20)  6076
#>  2 [2013-01-20, 2013-01-27)  6012
#>  3 [2013-01-27, 2013-02-03)  6072
#>  4 [2013-02-03, 2013-02-10)  6089
#>  5 [2013-02-10, 2013-02-17)  6217
#>  6 [2013-02-17, 2013-02-24)  6349
#>  7 [2013-02-24, 2013-03-03)  6411
#>  8 [2013-03-03, 2013-03-10)  6551
#>  9 [2013-03-10, 2013-03-17)  6556
#> 10 [2013-03-17, 2013-03-24)  6549
#> # ℹ 42 more rows

Check for missing gaps in time

missing_dates(flights$date) # No missing dates
#> Date of length 0

time_num_gaps(flights$time_hour, time_by = "hours") # Missing hours
#> [1] 1819

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 FALSE

time_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 rows

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 rows

The ability to create time sequences by group is one of the most powerful features of timeplyr.

flights %>%
  time_by(date, "month", as_interval = TRUE) %>%
  summarise(across(c(arr_time, dep_time), ~ mean(.x, na.rm = TRUE)))
#> # A tibble: 12 × 3
#>             time_intv_month arr_time dep_time
#>                   <tm_intv>    <dbl>    <dbl>
#>  1 [2013-01-01, 2013-02-01)    1523.    1347.
#>  2 [2013-02-01, 2013-03-01)    1522.    1348.
#>  3 [2013-03-01, 2013-04-01)    1510.    1359.
#>  4 [2013-04-01, 2013-05-01)    1501.    1353.
#>  5 [2013-05-01, 2013-06-01)    1503.    1351.
#>  6 [2013-06-01, 2013-07-01)    1468.    1351.
#>  7 [2013-07-01, 2013-08-01)    1456.    1353.
#>  8 [2013-08-01, 2013-09-01)    1495.    1350.
#>  9 [2013-09-01, 2013-10-01)    1504.    1334.
#> 10 [2013-10-01, 2013-11-01)    1520.    1340.
#> 11 [2013-11-01, 2013-12-01)    1523.    1344.
#> 12 [2013-12-01, 2014-01-01)    1505.    1357.

Grouped rolling time functions

By-group rolling mean over the last 3 calendar months

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)

By-group rolling (locf) NA fill

# Prerequisite: Create Time series with missing values
x <- ts(c(NA, 3, 4, NA, 6, NA, NA, 8))
g <- cheapr::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  8

year_month and year_quarter

timeplyr has its own lightweight ‘yearmonth’ and `yearquarter’ classes inspired by the excellent ‘zoo’ and ‘tsibble’ packages.

today <- today()
year_month(today)
#> [1] "2024 Aug"

The underlying data for a year_month is the number of months since 1 January 1970 (epoch).

unclass(year_month("1970-01-01"))
#> [1] 0

unclass(year_month("1971-01-01"))
#> [1] 12

year_month(today) + 0:12
#>  [1] "2024 Aug" "2024 Sep" "2024 Oct" "2024 Nov" "2024 Dec" "2025 Jan"
#>  [7] "2025 Feb" "2025 Mar" "2025 Apr" "2025 May" "2025 Jun" "2025 Jul"
#> [13] "2025 Aug"

year_quarter(today) + 0:4
#> [1] "2024 Q3" "2024 Q4" "2025 Q1" "2025 Q2" "2025 Q3"

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)

tail(sort(table(time_elapsed(flight_201$time_hour, "hours"))))
#> 
#>  23  25  48   6  18  24 
#>   2   3   4  33  34 218

# 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          NaN

time_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   364

We can see that the gaps typically occur at 11pm and the sequence resumes at 5am.

Other convenience functions are included below

calendar()

Easily join common date information to your data

flights_calendar <- flights %>%
    select(time_hour) %>%
    reframe(calendar(time_hour))

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))
#> # 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_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()

age_years(dmy("28-02-2000"))
#> [1] 24

time_seq()

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(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()

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"

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()

iso_week(today())
#> [1] "2024-W33"

iso_week(today(), day = TRUE)
#> [1] "2024-W33-6"

iso_week(today(), year = FALSE)
#> [1] "W33"

time_cut()

times <- flights$time_hour
dates <- flights$date

date_breaks <- time_breaks(dates, n = 12)
time_breaks <- time_breaks(times, n = 12, time_floor = TRUE)

weekly_data <- flights %>%
    time_by(time = date, time_by = "week",
            to = max(time_span(date, time_by = "week")),
            .name = "date") %>%
    count()
weekly_data %>%
  ggplot(aes(x = interval_start(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())

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

timeplyr

Fast Tidy Tools for Date and Datetime Manipulation

Installation

Basic examples

Convert `ts`, `mts`, `xts`, `zoo`and `timeSeries` objects using `ts_as_tibble`

Easily plot time series using `time_ggplot`

`time_by`

Group your time variable by any time unit

`time_complete()`

Complete missing gaps in time

We can also make use of timeplyr time intervals

Ensure full weeks by setting from to the start of the week

Check for missing gaps in time

`time_expand()`

Grouped rolling time functions

By-group rolling mean over the last 3 calendar months

By-group rolling (locf) NA fill

`year_month` and `year_quarter`

`time_elapsed()`

Other convenience functions are included below

`calendar()`

Easily join common date information to your data

`.time_units`

`age_years()`

`time_seq()`

`time_seq_v()`

`time_seq_sizes()`

`iso_week()`

`time_cut()`

timeplyr

Fast Tidy Tools for Date and Datetime Manipulation

Installation

Basic examples

Convert ts, mts, xts, zooand timeSeries objects using ts_as_tibble

Easily plot time series using time_ggplot

time_by

Group your time variable by any time unit

time_complete()

Complete missing gaps in time

We can also make use of timeplyr time intervals

Ensure full weeks by setting from to the start of the week

Check for missing gaps in time

time_expand()

Grouped rolling time functions

By-group rolling mean over the last 3 calendar months

By-group rolling (locf) NA fill

year_month and year_quarter

time_elapsed()

Other convenience functions are included below

calendar()

Easily join common date information to your data

.time_units

age_years()

time_seq()

time_seq_v()

time_seq_sizes()

iso_week()

time_cut()

Convert `ts`, `mts`, `xts`, `zoo`and `timeSeries` objects using `ts_as_tibble`

Easily plot time series using `time_ggplot`

`time_by`

`time_complete()`

`time_expand()`

`year_month` and `year_quarter`

`time_elapsed()`

`calendar()`

`.time_units`

`age_years()`

`time_seq()`

`time_seq_v()`

`time_seq_sizes()`

`iso_week()`

`time_cut()`