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padr

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padr is an R package that assists with preparing time series data. It provides two main functions that will quickly get the data in the format you want. When data is observed on too low a level, thicken will add a column of a higher interval to the data frame, after which the user can apply the appropriate aggregation. When there are missing records for time points where observations were absent, pad will automatically insert these records. A number of fill_ functions help to subsequently fill the missing values.

Usage

library(padr)
library(tidyverse)
coffee <- data.frame(
  time_stamp =  as.POSIXct(c(
    '2016-07-07 09:11:21', '2016-07-07 09:46:48',
    
    '2016-07-09 13:25:17',
    '2016-07-10 10:45:11'
  )),
  amount = c(3.14, 2.98, 4.11, 3.14)
)

coffee |>
  thicken('day') |>
  dplyr::group_by(time_stamp_day) |>
  dplyr::summarise(day_amount = sum(amount)) |>
  pad() |>
  fill_by_value(day_amount, value = 0)
## # A tibble: 4 × 2
##   time_stamp_day day_amount
##   <date>              <dbl>
## 1 2016-07-07           6.12
## 2 2016-07-08           0   
## 3 2016-07-09           4.11
## 4 2016-07-10           3.14

More information

See the the general introduction Vignette for more examples. The implementation details Vignette describes how padr handles different time zones and daylight savings time.

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Version

Install

install.packages('padr')

Monthly Downloads

11,684

Version

0.7.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Edwin Thoen

Last Published

July 16th, 2026

Functions in padr (0.7.0)

span_around

Span an equally spaced vector around a datetime variable
pad_int

Pad the integer column of a data frame
span_date

Wrapper around seq.Date.
closest_weekday

Retrieve the closest given weekday
fill_by_function

Fill missing values by a function of the nonmissings
center_interval

Shift to the middle of each interval
get_interval

Get the interval of a datetime variable
coffee

Coffee Data Set
format_interval

Make a period character vector
pad

Pad the datetime column of a data frame
fill_by_prevalent

Fill missing values by the most prevalent nonmissing value
fill_by_value

Fill missing values by a single value
subset_span

Subset a spanned datetime vector
thicken_cust

Thicken with a custom spanning
span_time

Wrapper around seq.POSIXct.
pad_cust

Pad with a custom spanning
thicken

Add a variable of a higher interval to a data frame
emergency

Emergency Calls for Montgomery County, PA