# Runner examples

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]
          }
        )
      )