# 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]
}
)
)