growth {timeplyr}

Rolling basic growth

Description

Calculate basic growth calculations on a rolling basis. growth() calculates the percent change between the totals of two numeric vectors when they're of equal length, otherwise the percent change between the means. rolling_growth() does the same calculation on 1 numeric vector, on a rolling basis. Pairs of windows of length n, lagged by the value specified by lag are compared in a similar manner. When lag = n then data.table::frollsum() is used, otherwise data.table::frollmean() is used.

Usage

growth(x, y, na.rm = FALSE, log = FALSE, inf_fill = NULL)

rolling_growth(
  x,
  n = 1,
  lag = n,
  na.rm = FALSE,
  partial = TRUE,
  offset = NULL,
  weights = NULL,
  inf_fill = NULL,
  log = FALSE,
  ...
)

Arguments

x

Numeric vector.

y

numeric vector

na.rm

Should missing values be removed when calculating window? Defaults to FALSE.

log

If TRUE Growth (relative change) in total and mean events will be calculated on the log-scale.

inf_fill

Numeric value to replace Inf values with. Default behaviour is to keep Inf values.

n

Rolling window size, default is 1.

lag

Lag of basic growth comparison, default is the rolling window size.

partial

Should rates be calculated outwith the window using partial windows? If TRUE (the default), (n - 1) pairs of equally-sized rolling windows are compared, their size increasing by 1 up to size n, at which point the rest of the window pairs are all of size n. If FALSE all window-pairs will be of size n.

offset

Numeric vector of values to use as offset, e.g. population sizes or exposure times.

weights

Importance weights. These can either be length 1 or the same length as x. Currently, no normalisation of weights occurs.

...

Further arguments to be passed on to frollmean.

Value

growth returns a numeric(1) and rolling_growth returns a numeric(length(x)).

Examples

library(timeplyr)

set.seed(42)
# Growth rate is 6% per day
x <- 10 * (1.06)^(0:25)

# Simple growth from one day to the next
rolling_growth(x, n = 1)

# Growth comparing rolling 3 day cumulative
rolling_growth(x, n = 3)

# Growth comparing rolling 3 day cumulative, lagged by 1 day
rolling_growth(x, n = 3, lag = 1)

# Growth comparing windows of equal size
rolling_growth(x, n = 3, partial = FALSE)

# Seven day moving average growth
roll_mean(rolling_growth(x), window = 7, partial = FALSE)