map_interval_dfc: Apply a function to each group and iterated interval in a...

In kaneplusplus/checkin: Process Check-in Data for the Analysis of Human Mobility

Apply a function to each group and iterated interval in a data.frame

Description

For checking data represented as a data frame, a column contatining timestamps, and other information associated with the checkin, this functions applies a specified function to the subsets of the data falling within the time interval. This can be used to get check in counts for each 15 minutes, for example. In addition, grouped data is supported (using dplyr::group_by) to further specify subsets of specific groups and time intervals on which the function should be applied. The map_interval_dfr() function appends the results by row and map_interval_dfc() function appends results by column.

Usage

map_interval_dfc(.x, .gen, .f, time, end = NULL, ...)

Arguments

.x	a data.frame to apply a function to.
.gen	a time interval iterator generator.
.f	the function to apply to the group and hourly interval.
time	the column denoting the checkin times.
end	the end time. By default this is the the end of the hour corresponding to the largest time.
...	other arguments to pass to the function .f.

Examples

library(lubridate)
library(dplyr)
library(purrr)

data(checkins)

# Create a data set for the first hour of the checkins data.
x <- checkins %>%
  filter(timestamp < min(timestamp) + hours(1))

# Create a generator for that hour with 15 minute durations.

gen_15 <- partial(time_interval_gen,
                  start = min(x$timestamp),
                  end = max(x$timestamp),
                  duration = minutes(15))

checkin_count <- function(it) {
  tibble(unique_ids = length(unique(it$id)), count = nrow(it))
}

# Count the number of checkins for each interval.
map_interval_dfr(x, gen_15, checkin_count, "timestamp")

kaneplusplus/checkin documentation built on Aug. 1, 2022, 1:11 p.m.