Package 'tbrf'

Title: Time-Based Rolling Functions
Description: Provides rolling statistical functions based on date and time windows instead of n-lagged observations.
Authors: Michael Schramm [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-1876-6592>), Frank Harrell [ctb], Bob Rudis [ctb]
Maintainer: Michael Schramm <[email protected]>
License: GPL-3 | file LICENSE
Version: 0.1.7
Built: 2026-05-29 10:38:36 UTC
Source: https://github.com/mps9506/tbrf

Help Index

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

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.

See Also

binom_ci

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)

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.

See Also

gm_mean_ci

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)

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.

See Also

mean_ci

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)

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.

See Also

median_ci

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)

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.

See Also

sd

Examples

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

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.

See Also

sum

Examples

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

Time-Based Rolling Functions

Description

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

Author(s)

Michael Schramm

See Also

Useful links:

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