Help for package tbrf

Type:

Package

Title:

Time-Based Rolling Functions

Version:

0.1.7

Description:

Provides rolling statistical functions based on date and time windows instead of n-lagged observations.

URL:

https://mps9506.github.io/tbrf/

BugReports:

https://github.com/mps9506/tbrf/issues

License:

GPL-3 | file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.1

Depends:

R (≥ 2.10), ggplot2 (≥ 2.2.1)

Imports:

boot, dplyr, lubridate, purrr, rlang, stats, tibble, tidyr

Suggests:

spelling, covr, testthat, knitr, rmarkdown

VignetteBuilder:

knitr

Language:

en-US

Config/Needs/website:

mps9506/mpsTemplates

NeedsCompilation:

Packaged:

2025-08-19 13:46:51 UTC; michael.schramm

Author:

Michael Schramm

[aut, cre, cph], Frank Harrell [ctb], Bob Rudis [ctb]

Maintainer:

Michael Schramm <mpschramm@gmail.com>

Repository:

CRAN

Date/Publication:

2025-08-19 14:10:02 UTC

Time-Based Rolling Functions

Description

Provides rolling statistical functions based on date and time windows instead of n-lagged observations.

Author(s)

Michael Schramm

Dissolved oxygen measurements from the Tres Palacios River

Description

Data from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Information System. The 'AverageDO“ field is the mean of dissolved oxygen concentrations (mg/L) measured at a field site at that day. The MinDO is the minimum dissolved oxygen concentration measured at that site on that day.

Usage

data(Dissolved_Oxygen)

Format

A data frame with 236 rows and 6 variables:

Station_ID: unique water quality monitoring station identifier
Date: sampling date in yyyy-mm-dd format
Param_Code: unique parameter code
Param_Desc: parameter description with units
Average_DO: mean of dissolved oxygen measurement, in mg/L
Min_DO: minimum of dissolved oxygen measurement, in mg/L

Source

https://www80.tceq.texas.gov/SwqmisPublic/public/default.htm

Enterococci bacteria measurements from the Tres Palacios River

Description

Data from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Information System. The 'Value“ field is the lab measured value of Enterococci bacteria (MPN/100 mL) from grab samples collected at 'Station ID' on the Tres Palacios River on 'Date'.

Usage

data(Entero)

Format

A data frame with 212 rows and 5 variables:

Station_ID: unique water quality monitoring station identifier
Date: sampling date in yyyy-mm-dd format
Param_Code: unique parameter code
Param_Desc: parameter description with units
Value: Enterococci concentration, in MPN/L

Source

https://www80.tceq.texas.gov/SwqmisPublic/public/default.htm

Confidence Intervals for Binomial Probabilities

Description

An implementation of the binconf function in Frank Harrell's Hmisc package. Produces 1-alpha confidence intervals for binomial probabilities.

Usage

binom_ci(
  x,
  n,
  alpha = 0.05,
  method = c("wilson", "exact", "asymptotic"),
  return.df = FALSE
)

Arguments

x

vector containing the number of "successes" for binomial variates.

n

vector containing the numbers of corresponding observations.

alpha

probability of a type I error, so confidence coefficient = 1-alpha.

method

character string specifying which method to use. The "exact" method uses the F distribution to compute exact (based on the binomial cdf) intervals; the "wilson" interval is score-test-based; and the "asymptotic" is the text-book, asymptotic normal interval. Following Agresti and Coull, the Wilson interval is to be preferred and so is the default.

return.df

logical flag to indicate that a data frame rather than a matrix be returned.

Author(s)

Frank Harrell, modified by Michael Schramm

References

A. Agresti and B.A. Coull, Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119–126, 1998.

R.G. Newcombe, Logit confidence intervals and the inverse sinh transformation, American Statistician, 55:200–202, 2001.

L.D. Brown, T.T. Cai and A. DasGupta, Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101–133, 2001.

Examples

binom_ci(46,50,method="wilson")

Calculates the Geometric Mean

Description

Originally from Paul McMurdie, Ben Bolker, and Gregor on Stack Overflow: https://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in

Usage

gm_mean(x, na.rm = TRUE, zero.propagate = FALSE)

Arguments

x

vector of numeric values

na.rm

logical TRUE/FALSE remove NA values

zero.propagate

logical TRUE/FALSE. Allows the optional propagation of zeros.

Value

the geometric mean of the vector

Returns the Geomean and CI

Description

Generates Geometric mean and confidence intervals using bootstrap.

Usage

gm_mean_ci(
  window,
  conf = 0.95,
  na.rm = TRUE,
  type = "basic",
  R = 1000,
  parallel = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL,
  zero.propagate = FALSE
)

Arguments

window

vector of data values

conf

confidence level of the required interval. NA if skipping calculating the bootstrapped CI

na.rm

logical TRUE/FALSE. Remove NAs from the dataset. Defaults TRUE

type

character string, one of c("norm","basic", "stud", "perc", "bca"). "all" is not a valid value. See boot.ci

R

the number of bootstrap replicates. see boot

parallel

The type of parallel operation to be used (if any). see boot

ncpus

integer: number of process to be used in parallel operation. see boot

cl

optional parallel or snow cluster for use if parallel = "snow". see boot

zero.propagate

logical TRUE/FALSE Allows the optional propagation of zeros.

Value

named list with geometric mean and (optionally) specified confidence interval

List NA

Description

function to return tibble with NAs as specified

Usage

list_NA(x)

Arguments

x

named vector

Value

empty tibble

Returns the mean and CI

Description

Generates mean and confidence intervals using bootstrap.

Usage

mean_ci(
  window,
  conf = 0.95,
  na.rm = TRUE,
  type = "basic",
  R = 1000,
  parallel = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL
)

Arguments

window

vector of data values

conf

confidence level of the required interval. NA if skipping calculating the bootstrapped CI

na.rm

logical TRUE/FALSE. Remove NAs from the dataset. Defaults TRUE

type

character string, one of c("norm","basic", "stud", "perc", "bca"). "all" is not a valid value. See boot.ci

R

the number of bootstrap replicates. see boot

parallel

The type of parallel operation to be used (if any). see boot

ncpus

integer: number of process to be used in parallel operation. see boot

cl

optional parallel or snow cluster for use if parallel = "snow". see boot

Value

named list with mean and (optionally) specified confidence interval

Returns the median and CI

Description

Generates median and confidence intervals using bootstrap.

Usage

median_ci(
  window,
  conf = 0.95,
  na.rm = TRUE,
  type = "basic",
  R = 1000,
  parallel = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL
)

Arguments

window

vector of data values

conf

confidence level of the required interval. NA if skipping calculating the bootstrapped CI

na.rm

logical TRUE/FALSE. Remove NAs from the dataset. Defaults TRUE

type

character string, one of c("norm","basic", "stud", "perc", "bca"). "all" is not a valid value. See boot.ci

R

the number of bootstrap replicates. see boot

parallel

The type of parallel operation to be used (if any). see boot

ncpus

integer: number of process to be used in parallel operation. see boot

cl

optional parallel or snow cluster for use if parallel = "snow". see boot

Value

named list with mean and (optionally) specified confidence interval

Open Window

Description

calculates the period at each row from the row of interest

Usage

open_window(x, tcolumn, unit = "years", n, i, na.pad)

Arguments

x

dataframe

tcolumn

time column

unit

unit

n

desired n

i

row number

na.pad

logical if 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

Value

vector

Step ribbon statistic

Description

Provides stairstep values for ribbon plots. This was originally in Bob Rudis's ggalt package which is no longer on CRAN.

Usage

stat_stepribbon(
  mapping = NULL,
  data = NULL,
  geom = "ribbon",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  direction = "hv",
  ...
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

which geom to use; defaults to "ribbon"

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

direction

hv for horizontal-veritcal steps, vh for vertical-horizontal steps

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

Author(s)

Bob Rudis

References

https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs

Examples

x <- 1:10
df <- data.frame(x=x, y=x+10, ymin=x+7, ymax=x+12)

gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
                       stat="stepribbon", fill="#b2b2b2")
gg <- gg + geom_step(color="#2b2b2b")
gg

gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
                       stat="stepribbon", fill="#b2b2b2",
                       direction="hv")
gg <- gg + geom_step(color="#2b2b2b")
gg

Time-Based Rolling Binomial Probability

Description

Produces a a rolling time-window based vector of binomial probability and confidence intervals.

Usage

tbr_binom(.tbl, x, tcolumn, unit = "years", n, alpha = 0.05, na.pad = TRUE)

Arguments

.tbl

dataframe with two variables.

x

indicates the variable column containing "success" and "failure" observations coded as 1 or 0.

tcolumn

indicates the variable column containing Date or Date-Time values.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window in the selected units.

alpha

numeric, probability of a type 1 error, so confidence coefficient = 1-alpha

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defatuls to 'TRUE'

Value

tibble with binomial point estimate and confidence intervals.

Examples

## Generate Sample Data
df <- tibble::tibble(
date = sample(seq(as.Date('2000-01-01'), as.Date('2015/12/30'), by = "day"), 100),
value = rbinom(100, 1, 0.25)
)

## Run Function
tbr_binom(df, x = value,
tcolumn = date, unit = "years", n = 5,
alpha = 0.1, na.pad = FALSE)

Binomial test based on time window

Description

Binomial test based on time window

Usage

tbr_binom_window(x, tcolumn, unit = "years", n, i, alpha, na.pad)

Arguments

x

column containing "success" and "failure" observations as 0 or 1

tcolumn

formatted time column

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

rows

alpha

numeric, probability of a type 1 error, so confidence coefficient = 1-alpha

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

Value

list

Time-Based Rolling Geometric Mean

Description

Produces a a rolling time-window based vector of geometric means and confidence intervals.

Usage

tbr_gmean(.tbl, x, tcolumn, unit = "years", n, na.pad = TRUE, ...)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the values to calculate the geometric mean.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defatuls to 'TRUE'

...

additional arguments passed to gm_mean_ci

Value

tibble with columns for the rolling geometric mean and upper and lower confidence levels.

Examples


## Return a tibble with new rolling geometric mean column
tbr_gmean(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, na.pad = FALSE)

## Not run: 
## Return a tibble with rolling geometric mean and 95% CI
tbr_gmean(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, conf = .95)
## End(Not run)

Geometric mean based on a time-window

Description

Geometric mean based on a time-window

Usage

tbr_gmean_window(x, tcolumn, unit = "years", n, i, na.pad, ...)

Arguments

x

column containing the values to calculate the geometric mean.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

row

...

additional arguments passed to gmean_ci

Value

list

Time-Based Rolling Mean

Description

Produces a a rolling time-window based vector of means and confidence intervals.

Usage

tbr_mean(.tbl, x, tcolumn, unit = "years", n, na.pad = TRUE, ...)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the numeric values to calculate the mean.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defaults to 'TRUE'

...

additional arguments passed to mean_ci.

Value

tibble with columns for the rolling mean and upper and lower confidence intervals.

Examples

## Return a tibble with new rolling mean column
tbr_mean(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, na.pad = FALSE)

## Not run: 
## Return a tibble with rolling mean and 95% CI
tbr_mean(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, conf = .95)
## End(Not run)

Mean Based on a Time-Window

Description

Mean Based on a Time-Window

Usage

tbr_mean_window(x, tcolumn, unit = "years", n, i, na.pad, ...)

Arguments

x

column containing the values to calculate the mean.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

row

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

...

additional arguments passed to mean_ci

Value

list

Time-Based Rolling Median

Description

Produces a a rolling time-window based vector of medians and confidence intervals.

Usage

tbr_median(.tbl, x, tcolumn, unit = "years", n, na.pad = TRUE, ...)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the numeric values to calculate the mean.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defaults to 'TRUE'

...

additional arguments passed to median_ci

Value

tibble with columns for the rolling median and upper and lower confidence intervals.

Examples

## Return a tibble with new rolling median column
tbr_median(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years",
n = 5, na.pad = FALSE)

## Not run: 
## Return a tibble with rolling median and 95% CI 
tbr_median(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, conf = .95)
## End(Not run)

Median Based on a Time-Window

Description

Median Based on a Time-Window

Usage

tbr_median_window(x, tcolumn, unit = "years", n, i, na.pad, ...)

Arguments

x

column containing the values to calculate the median.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

row

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

...

additional arguments passed to median_ci

Value

list

Use Generic Functions with Time Windows

Description

Use Generic Functions with Time Windows

Usage

tbr_misc(.tbl, x, tcolumn, unit = "years", n, na.pad = TRUE, func, ...)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the values the function is applied to.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defaults to 'TRUE'

func

specified function

...

optional additional arguments passed to function func

Value

tibble

Examples

tbr_misc(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", 
n = 5, na.pad = FALSE, func = mean)

Time-Based Rolling Standard Deviation

Description

Time-Based Rolling Standard Deviation

Usage

tbr_sd(.tbl, x, tcolumn, unit = "years", n, na.rm = FALSE, na.pad = TRUE)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the values to calculate the standard deviation.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.rm

logical. Should missing values be removed?

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defaults to 'TRUE'

Value

tibble with column for the rolling sd.

Examples

tbr_sd(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n = 5, na.pad = FALSE)

Standard Deviation Based on a Time-Window

Description

Standard Deviation Based on a Time-Window

Usage

tbr_sd_window(x, tcolumn, unit = "years", n, i, na.pad, ...)

Arguments

x

column containing the values to calculate the standard deviation.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

row

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

...

additional arguments passed to base::sd()

Value

numeric value

Time-Based Rolling Sum

Description

Time-Based Rolling Sum

Usage

tbr_sum(.tbl, x, tcolumn, unit = "years", n, na.rm = FALSE, na.pad = TRUE)

Arguments

.tbl

a data frame with at least two variables; time column formatted as date, date/time and value column.

x

column containing the values to calculate the sum.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

na.rm

logical. Should missing values be removed?

na.pad

logical. If 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'. Defatuls to 'TRUE'

Value

dataframe with column for the rolling sum.

Examples

tbr_sum(Dissolved_Oxygen, x = Average_DO, tcolumn = Date, unit = "years", n =
5, na.pad = FALSE)

Sum Based on a Time-Window

Description

Sum Based on a Time-Window

Usage

tbr_sum_window(x, tcolumn, unit = "years", n, i, na.rm, na.pad)

Arguments

x

column containing the values to calculate the sum.

tcolumn

formatted time column.

unit

character, one of "years", "months", "weeks", "days", "hours", "minutes", "seconds"

n

numeric, describing the length of the time window.

i

row

na.rm

logical. Should missing values be removed?

na.pad

logical if 'na.pad = TRUE' incomplete windows (duration of the window < 'n') return 'NA'.

Value

numeric value

tbrf extensions to ggplot2

Description

tbrf makes use of the ggproto class system to extend the functionality of ggplot2. In general the actual classes should be of little interest to users as the standard ggplot2 api of using geom_* and stat_* functions for building up the plot is encouraged.

References

https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs

Time-Based Rolling Functions

Description

Author(s)

See Also

Dissolved oxygen measurements from the Tres Palacios River

Description

Usage

Format

Source

Enterococci bacteria measurements from the Tres Palacios River

Description

Usage

Format

Source

Confidence Intervals for Binomial Probabilities

Description

Usage

Arguments

Author(s)

References

Examples

Calculates the Geometric Mean

Description

Usage

Arguments

Value

Returns the Geomean and CI

Description

Usage

Arguments

Value

List NA

Description

Usage

Arguments

Value

Returns the mean and CI

Description

Usage

Arguments

Value

Returns the median and CI

Description

Usage

Arguments

Value

Open Window

Description

Usage

Arguments

Value

Step ribbon statistic

Description

Usage

Arguments

Author(s)

References

Examples

Time-Based Rolling Binomial Probability

Description

Usage

Arguments

Value

See Also

Examples

Binomial test based on time window

Description

Usage

Arguments

Value

Time-Based Rolling Geometric Mean

Description

Usage

Arguments

Value

See Also

Examples

Geometric mean based on a time-window

Description

Usage