The most fundamental function in runner package is runner. With runner::runner one can apply any R function on running windows. This tutorial presents set of examples explaining how to tackle some tasks. Some of the examples are referenced to original topic on stack-overflow.

Number of unique elements in 7 days window

library(runner)

x <- sample(letters, 20, replace = TRUE)
date <- Sys.Date() + cumsum(sample(1:5, 20, replace = TRUE)) # unequally spaced time series

runner(
  x, 
  k = "7 days",
  idx = date, 
  f = function(x) length(unique(x))
)

weekly trimmed mean

library(runner)

x <- cumsum(rnorm(20))
date <- Sys.Date() + cumsum(sample(1:5, 20, replace = TRUE)) # unequaly spaced time series

runner(
  x, 
  k = "week", 
  idx = date, 
  f = function(x) mean(x, trim = 0.05)
)

Prediction on current day based on preceding 2-weeks regression

library(runner)

# sample data
x <- cumsum(rnorm(20))
data <- data.frame(
  date = Sys.Date() + cumsum(sample(1:3, 20, replace = TRUE)), # unequally spaced time series, 
  y = 3 * x + rnorm(20), 
  x = cumsum(rnorm(20))
)

# solution
data$pred <- runner(
  data,
  lag = "1 days",
  k = "2 weeks",
  idx = data$date,
  f = function(data) {
    predict(
      lm(y ~ x, data = data)
    )[nrow(data)]
  }
)


plot(data$date, data$y, type = "l", col = "red")
lines(data$date, data$pred, col = "blue")

Rolling sums for groups with uneven time gaps

SO discussion

library(runner)
library(dplyr)

set.seed(3737)
df <- data.frame(
  user_id = c(rep(27, 7), rep(11, 7)),
  date = as.Date(rep(c('2016-01-01', '2016-01-03', '2016-01-05', '2016-01-07', 
                       '2016-01-10', '2016-01-14', '2016-01-16'), 2)),
  value = round(rnorm(14, 15, 5), 1))

df %>%
  group_by(user_id) %>%
  mutate(
    v_minus7  = sum_run(value, 7, idx = date),
    v_minus14 = sum_run(value, 14, idx = date)
  )

runner with `dplyr`

Unique for specified time frame

SO discussion

library(runner)
library(dplyr)

df <- read.table(text = "  user_id       date category
       27 2016-01-01    apple
       27 2016-01-03    apple
       27 2016-01-05     pear
       27 2016-01-07     plum
       27 2016-01-10    apple
       27 2016-01-14     pear
       27 2016-01-16     plum
       11 2016-01-01    apple
       11 2016-01-03     pear
       11 2016-01-05     pear
       11 2016-01-07     pear
       11 2016-01-10    apple
       11 2016-01-14    apple
       11 2016-01-16    apple", header = TRUE)

df %>%
  group_by(user_id) %>%
  mutate(
    distinct_7  = runner(category, 
                         k = "7 days", 
                         idx = as.Date(date), 
                         f = function(x) length(unique(x))),
    distinct_14 = runner(category, 
                         k = "14 days", 
                         idx = as.Date(date), 
                         f = function(x) length(unique(x)))
  )

runner with group_by mutate

library(dplyr)

x <- cumsum(rnorm(20))
y <- 3 * x + rnorm(20)
date <- Sys.Date() + cumsum(sample(1:3, 20, replace = TRUE)) # unequaly spaced time series
group <-  rep(c("a", "b"), each = 10)


data.frame(date, group, y, x) %>%
  group_by(group) %>%
  run_by(idx = "date", k = "5 days") %>%
  mutate(
    alpha_5 = runner(
      x = ., 
      f = function(x) {
        coefficients(lm(x ~ y, x))[1]
      }
    ),
    beta_5 = runner(
      x = ., 
      f = function(x) {
        coefficients(lm(x ~ y, x))[1]
      }
    )
  )

Aggregating values from another data.frame in `grouped_df`

SO Discussion

library(runner)
library(dplyr)

Date <- seq(from = as.Date("2014-01-01"), 
            to = as.Date("2019-12-31"), 
            by = 'day')
market_return <- c(rnorm(2191))

AAPL <- data.frame(
  Company.name = "AAPL", 
  Date =  Date,
  market_return = market_return
)

MSFT <- data.frame(
  Company.name = "MSFT", 
  Date = Date,
  market_return = market_return
)

df <- rbind(AAPL, MSFT)
df$stock_return <- c(rnorm(4382))
df <- df[order(df$Date),]

df2 <- data.frame(
  Company.name2 = c(replicate(450, "AAPL"), replicate(450, "MSFT")), 
  Event_date = sample(
    seq(as.Date('2015/01/01'), 
        as.Date('2019/12/31'), 
        by = "day"),
    size =  900)
)


df2 %>%
  group_by(Company.name2) %>%
  mutate(
    intercept = runner(
      x = df[df$Company.name ==  Company.name2[1], ], 
      k = "180 days", 
      lag = "5 days",
      idx = df$Date[df$Company.name == Company.name2[1]],
      at = Event_date,
      f = function(x) {
        coef(
          lm(stock_return ~ market_return, data = x)
        )[1]
      }
    ),
    slope = runner(
      x = df[df$Company.name == Company.name2[1], ],  
      k = "180 days", 
      lag = "5 days",
      idx = df$Date[df$Company.name == Company.name2[1]],
      at = Event_date,
      f = function(x) {
        coef(
          lm(stock_return ~ market_return, data = x)
        )[2]
      }
    )
  )

Runner examples