| Title: | Relational Operators for Intervals |
|---|---|
| Description: | Evaluating if values of vectors are within different open/closed intervals (`x %[]% c(a, b)`), or if two closed intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`). Operators for negation and directional relations also implemented. |
| Authors: | Peter Solymos [cre, aut]
|
| Maintainer: | Peter Solymos <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0-0 |
| Built: | 2024-12-13 05:43:02 UTC |
| Source: | https://github.com/psolymos/intrval |
Evaluating if values of vectors are within different open/closed intervals ('x %[]% c(a, b)'), or if two closed intervals overlap ('c(a1, b1) %[]o[]% c(a2, b2)'). Operators for negation and directional relations also implemented.
The DESCRIPTION file:
| Package: | intrval |
| Type: | Package |
| Title: | Relational Operators for Intervals |
| Version: | 1.0-0 |
| Date: | 2024-05-19 |
| Author: | Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>) |
| Maintainer: | Peter Solymos <[email protected]> |
| Description: | Evaluating if values of vectors are within different open/closed intervals (`x %[]% c(a, b)`), or if two closed intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`). Operators for negation and directional relations also implemented. |
| License: | GPL-2 |
| Imports: | fpCompare |
| URL: | https://github.com/psolymos/intrval |
| BugReports: | https://github.com/psolymos/intrval/issues |
| LazyLoad: | yes |
| LazyData: | true |
| Repository: | https://psolymos.r-universe.dev |
| RemoteUrl: | https://github.com/psolymos/intrval |
| RemoteRef: | HEAD |
| RemoteSha: | cebd526354d2402896e31be2cd5085115a501927 |
Index of help topics:
intrval Relational Operators Comparing Values to
Intervals
intrval-options Global options for the intrval package
intrval-package Relational Operators for Intervals
ovrlap Relational Operators Comparing Two Intervals
Relational operators for value-to-interval comparisons:
%[]% and alikes.
Relational operators for interval-to-interval comparisons:
%[o]% and alikes.
Negated value matching: %ni%.
Peter Solymos [cre, aut] (<https://orcid.org/0000-0001-7337-1740>)
Maintainer: Peter Solymos <[email protected]>
Functions for evaluating if values of vectors are within intervals,
or less than or higher than interval endpoints.
The c within the brackets refer to cut,
a similar function.
x %[c]% interval x %[c)% interval x %(c]% interval x %(c)% intervalx %[c]% interval x %[c)% interval x %(c]% interval x %(c)% interval
x |
vector or |
interval |
vector, 2-column matrix, list, or |
Values of x are compared to interval endpoints
a and b (a <= b) (see %[]% for details).
The functions return an integer vector taking values
-1L (value of x is less than or equal to a,
depending on the interval type),
0L (value of x is inside the interval), or
1L (value of x is greater than or equal to b,
depending on the interval type).
Peter Solymos <[email protected]>
Similar functions (but not quite): sign,
cut, .bincode, findInterval.
See relational operators for intervals: %[]%.
See Syntax for operator precedence.
x <- 1:5 x %[c]% c(2,4) x %[c)% c(2,4) x %(c]% c(2,4) x %(c)% c(2,4)x <- 1:5 x %[c]% c(2,4) x %[c)% c(2,4) x %(c]% c(2,4) x %(c)% c(2,4)
%ni% is the negation of %in%,
which returns a logical vector indicating if there is a non-match or not
for its left operand. %nin% and %notin% are aliases for
better code readability (%in% can look very much like %ni%).
x %ni% table x %nin% table x %notin% tablex %ni% table x %nin% table x %notin% table
x |
vector or |
table |
vector or |
A logical vector, indicating if a non-match was located for each element of
x: thus the values are TRUE or FALSE and never NA.
Peter Solymos <[email protected]>
All the opposite of what is written for %in%.
See relational operators for intervals: %[]%.
See Syntax for operator precedence.
1:10 %ni% c(1,3,5,9) 1:10 %nin% c(1,3,5,9) 1:10 %notin% c(1,3,5,9) sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%") sstr[sstr %ni% c(letters, LETTERS)]1:10 %ni% c(1,3,5,9) 1:10 %nin% c(1,3,5,9) 1:10 %notin% c(1,3,5,9) sstr <- c("c","ab","B","bba","c",NA,"@","bla","a","Ba","%") sstr[sstr %ni% c(letters, LETTERS)]
Functions for evaluating if values of vectors are within intervals.
x %[]% interval x %)(% interval x %[<]% interval x %[>]% interval x %[)% interval x %)[% interval x %[<)% interval x %[>)% interval x %(]% interval x %](% interval x %(<]% interval x %(>]% interval x %()% interval x %][% interval x %(<)% interval x %(>)% interval intrval_types(type = NULL, plot = FALSE)x %[]% interval x %)(% interval x %[<]% interval x %[>]% interval x %[)% interval x %)[% interval x %[<)% interval x %[>)% interval x %(]% interval x %](% interval x %(<]% interval x %(>]% interval x %()% interval x %][% interval x %(<)% interval x %(>)% interval intrval_types(type = NULL, plot = FALSE)
x |
vector or |
interval |
vector, 2-column matrix, list, or |
type |
character, type of operator for subsetting the results. The default |
plot |
logical, whether to plot the results, or print a table to the console instead. |
Values of x are compared to interval endpoints
a and b (a <= b).
Endpoints can be defined as a vector with two values
(c(a, b)): these values will be compared as a single
interval with each value in x.
If endpoints are stored in a matrix-like object or a list,
comparisons are made element-wise. If lengths do not match,
shorter objects are recycled.
These value-to-interval operators work for numeric (integer, real)
and ordered vectors, and object types which are measured at
least on ordinal scale (e.g. dates), see Examples.
Note: interval endpoints are sorted internally thus ensuring the condition
a <= b is not necessary.
The type argument or the specification of the special function
determines the open (( and )) or
closed ([ and ]) endpoints and relations.
There are four types of intervals ([], [), (], ()),
their negation ()(, )[, ](, ][, respectively),
less than ([<], [<), (<], (<)),
and greater than ([>], [>), (>], (>)) relations.
Note that some operators return identical results but
are syntactically different:
%[<]% and %[<)% both evaluate x < a;
%[>]% and %(>]% both evaluate x > b;
%(<]% and %(<)% evaluate x <= a;
%[>)% and %(>)% both evaluate x >= b.
This is so because we evaluate only one end of the interval
but still conceptually referring to the relationship
defined by the right-hand-side interval object
and given that a <= b.
This implies 2 conditional logical evaluations
instead of treating it as a single 3-level ordered factor.
A logical vector, indicating if x is in the specified interval.
Values are TRUE, FALSE, or NA
(when any of the 3 values (x or endpoints in interval)
are NA).
The helper function intrval_types
can be used to understand and visualize the operators' effects.
It returns a matrix explaining the properties of the operators.
Peter Solymos <[email protected]>
See help page for relational operators: Comparison.
See %[o]% for relational operators for
interval-to-interval comparisons.
See factor for the behavior with factor arguments.
See also %in% for value matching
and %ni% for negated value matching
for factors.
See Syntax for operator precedence.
## motivating example from example(lm) ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D9 <- lm(weight ~ group) ## compare 95% confidence intervals with 0 (CI.D9 <- confint(lm.D9)) 0 %[]% CI.D9 ## comparing dates DATE <- as.Date(c("2000-01-01","2000-02-01", "2000-03-31")) DATE %[<]% as.Date(c("2000-01-151", "2000-03-15")) DATE %[]% as.Date(c("2000-01-151", "2000-03-15")) DATE %[>]% as.Date(c("2000-01-151", "2000-03-15")) ## interval formats x <- rep(4, 5) a <- 1:5 b <- 3:7 cbind(x=x, a=a, b=b) x %[]% cbind(a, b) # matrix x %[]% data.frame(a=a, b=b) # data.frame x %[]% list(a, b) # list ## helper functions intrval_types() # print intrval_types(plot = TRUE) # plot ## graphical examples ## bounding box set.seed(1) n <- 10^4 x <- runif(n, -2, 2) y <- runif(n, -2, 2) iv1 <- x %[]% c(-1, 1) & y %[]% c(-1, 1) plot(x, y, pch = 19, cex = 0.25, col = iv1 + 1, main = "Bounding box") ## time series filtering x <- seq(0, 4*24*60*60, 60*60) dt <- as.POSIXct(x, origin="2000-01-01 00:00:00") f <- as.POSIXlt(dt)$hour %[]% c(0, 11) plot(sin(x) ~ dt, type="l", col="grey", main = "Filtering date/time objects") points(sin(x) ~ dt, pch = 19, col = f + 1) ## watch precedence (2 * 1:5) %[]% (c(2, 3) * 2) 2 * 1:5 %[]% (c(2, 3) * 2) (2 * 1:5) %[]% c(2, 3) * 2 2 * 1:5 %[]% c(2, 3) * 2## motivating example from example(lm) ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D9 <- lm(weight ~ group) ## compare 95% confidence intervals with 0 (CI.D9 <- confint(lm.D9)) 0 %[]% CI.D9 ## comparing dates DATE <- as.Date(c("2000-01-01","2000-02-01", "2000-03-31")) DATE %[<]% as.Date(c("2000-01-151", "2000-03-15")) DATE %[]% as.Date(c("2000-01-151", "2000-03-15")) DATE %[>]% as.Date(c("2000-01-151", "2000-03-15")) ## interval formats x <- rep(4, 5) a <- 1:5 b <- 3:7 cbind(x=x, a=a, b=b) x %[]% cbind(a, b) # matrix x %[]% data.frame(a=a, b=b) # data.frame x %[]% list(a, b) # list ## helper functions intrval_types() # print intrval_types(plot = TRUE) # plot ## graphical examples ## bounding box set.seed(1) n <- 10^4 x <- runif(n, -2, 2) y <- runif(n, -2, 2) iv1 <- x %[]% c(-1, 1) & y %[]% c(-1, 1) plot(x, y, pch = 19, cex = 0.25, col = iv1 + 1, main = "Bounding box") ## time series filtering x <- seq(0, 4*24*60*60, 60*60) dt <- as.POSIXct(x, origin="2000-01-01 00:00:00") f <- as.POSIXlt(dt)$hour %[]% c(0, 11) plot(sin(x) ~ dt, type="l", col="grey", main = "Filtering date/time objects") points(sin(x) ~ dt, pch = 19, col = f + 1) ## watch precedence (2 * 1:5) %[]% (c(2, 3) * 2) 2 * 1:5 %[]% (c(2, 3) * 2) (2 * 1:5) %[]% c(2, 3) * 2 2 * 1:5 %[]% c(2, 3) * 2
Options store and allow to set global values for the intrval functions.
intrval_options(...)intrval_options(...)
... |
Options to set. |
When parameters are set by intrval_options, their former values are
returned in an invisible named list. Such a list can be passed as an
argument to intrval_options to restore the parameter values.
Tags are the following:
use_fpCompare: use the fpCompare package for the reliable comparison of floating point numbers.
str(intrval_options()) x1 <- 0.5 - 0.3 x2 <- 0.3 - 0.1 # save old values and set the new one op <- intrval_options(use_fpCompare = FALSE) # this is the base R behavior x1 x2 # reset defaults intrval_options(op) # using fpCompare x1 x2str(intrval_options()) x1 <- 0.5 - 0.3 x2 <- 0.3 - 0.1 # save old values and set the new one op <- intrval_options(use_fpCompare = FALSE) # this is the base R behavior x1 x2 # reset defaults intrval_options(op) # using fpCompare x1 x2
Functions for evaluating if two intervals overlap or not.
interval1 %[o]% interval2 interval1 %)o(% interval2 interval1 %[<o]% interval2 interval1 %[o>]% interval2 interval1 %(o)% interval2 interval1 %]o[% interval2 interval1 %(<o)% interval2 interval1 %(o>)% interval2 interval1 %[]o[]% interval2 interval1 %[]o[)% interval2 interval1 %[]o(]% interval2 interval1 %[]o()% interval2 interval1 %[)o[]% interval2 interval1 %[)o[)% interval2 interval1 %[)o(]% interval2 interval1 %[)o()% interval2 interval1 %(]o[]% interval2 interval1 %(]o[)% interval2 interval1 %(]o(]% interval2 interval1 %(]o()% interval2 interval1 %()o[]% interval2 interval1 %()o[)% interval2 interval1 %()o(]% interval2 interval1 %()o()% interval2interval1 %[o]% interval2 interval1 %)o(% interval2 interval1 %[<o]% interval2 interval1 %[o>]% interval2 interval1 %(o)% interval2 interval1 %]o[% interval2 interval1 %(<o)% interval2 interval1 %(o>)% interval2 interval1 %[]o[]% interval2 interval1 %[]o[)% interval2 interval1 %[]o(]% interval2 interval1 %[]o()% interval2 interval1 %[)o[]% interval2 interval1 %[)o[)% interval2 interval1 %[)o(]% interval2 interval1 %[)o()% interval2 interval1 %(]o[]% interval2 interval1 %(]o[)% interval2 interval1 %(]o(]% interval2 interval1 %(]o()% interval2 interval1 %()o[]% interval2 interval1 %()o[)% interval2 interval1 %()o(]% interval2 interval1 %()o()% interval2
interval1, interval2
|
vector, 2-column matrix, list, or |
The operators define the open/closed nature of the lower/upper
limits of the intervals on the left and right hand side of the o
in the middle.
The overlap of two closed intervals, [a1, b1] and [a2, b2],
is evaluated by the %[o]% (alias for %[]o[]%)
operator (a1 <= b1, a2 <= b2).
Endpoints can be defined as a vector with two values
(c(a1, b1))or can be stored in matrix-like objects or a lists
in which case comparisons are made element-wise.
If lengths do not match, shorter objects are recycled.
These value-to-interval operators work for numeric (integer, real)
and ordered vectors, and object types which are measured at
least on ordinal scale (e.g. dates), see Examples.
Note: interval endpoints
are sorted internally thus ensuring the conditions
a1 <= b1 and a2 <= b2 is not necessary.
%)o(% is used for the negation of two closed interval overlap,
directional evaluation is done via the operators
%[<o]% and %[o>]%.
The overlap of two open intervals
is evaluated by the %(o)% (alias for %()o()%).
%]o[% is used for the negation of two open interval overlap,
directional evaluation is done via the operators
%(<o)% and %(o>)%.
Overlap operators with mixed endpoint do not have negation and directional counterparts.
A logical vector, indicating if interval1 overlaps interval2.
Values are TRUE, FALSE, or NA.
Peter Solymos <[email protected]>
See help page for relational operators: Comparison.
See %[]% for relational operators for
value-to-interval comparisons.
See factor for the behavior with factor arguments.
See also %in% for value matching
and %ni% for negated value matching
for factors.
See Syntax for operator precedence.
## motivating examples from example(lm) ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D90 <- lm(weight ~ group - 1) # omitting intercept ## compare 95% confidence of the 2 groups to each other (CI.D90 <- confint(lm.D90)) CI.D90[1,] %[o]% CI.D90[2,] ## simple interval comparisons c(2:3) %[o]% c(0:1) ## vectorized comparisons c(2:3) %[o]% list(0:4, 1:5) c(2:3) %[o]% cbind(0:4, 1:5) c(2:3) %[o]% data.frame(a=0:4, b=1:5) list(0:4, 1:5) %[o]% c(2:3) cbind(0:4, 1:5) %[o]% c(2:3) data.frame(a=0:4, b=1:5) %[o]% c(2:3) list(0:4, 1:5) %[o]% cbind(rep(2,5), rep(3,5)) cbind(rep(2,5), rep(3,5)) %[o]% list(0:4, 1:5) cbind(rep(3,5),rep(4,5)) %)o(% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %[<o]% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %[o>]% cbind(1:5, 2:6) ## open intervals list(0:4, 1:5) %(o)% cbind(rep(2,5), rep(3,5)) cbind(rep(2,5), rep(3,5)) %(o)% list(0:4, 1:5) cbind(rep(3,5),rep(4,5)) %]o[% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %(<o)% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %(o>)% cbind(1:5, 2:6) dt1 <- as.Date(c("2000-01-01", "2000-03-15")) dt2 <- as.Date(c("2000-03-15", "2000-06-07")) dt1 %[]o[]% dt2 dt1 %[]o[)% dt2 dt1 %[]o(]% dt2 dt1 %[]o()% dt2 dt1 %[)o[]% dt2 dt1 %[)o[)% dt2 dt1 %[)o(]% dt2 dt1 %[)o()% dt2 dt1 %(]o[]% dt2 dt1 %(]o[)% dt2 dt1 %(]o(]% dt2 dt1 %(]o()% dt2 dt1 %()o[]% dt2 dt1 %()o[)% dt2 dt1 %()o(]% dt2 dt1 %()o()% dt2 ## watch precedence (2 * c(1, 3)) %[o]% (c(2, 4) * 2) (2 * c(1, 3)) %[o]% c(2, 4) * 2 2 * c(1, 3) %[o]% (c(2, 4) * 2) 2 * c(1, 3) %[o]% c(2, 4) * 2## motivating examples from example(lm) ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D90 <- lm(weight ~ group - 1) # omitting intercept ## compare 95% confidence of the 2 groups to each other (CI.D90 <- confint(lm.D90)) CI.D90[1,] %[o]% CI.D90[2,] ## simple interval comparisons c(2:3) %[o]% c(0:1) ## vectorized comparisons c(2:3) %[o]% list(0:4, 1:5) c(2:3) %[o]% cbind(0:4, 1:5) c(2:3) %[o]% data.frame(a=0:4, b=1:5) list(0:4, 1:5) %[o]% c(2:3) cbind(0:4, 1:5) %[o]% c(2:3) data.frame(a=0:4, b=1:5) %[o]% c(2:3) list(0:4, 1:5) %[o]% cbind(rep(2,5), rep(3,5)) cbind(rep(2,5), rep(3,5)) %[o]% list(0:4, 1:5) cbind(rep(3,5),rep(4,5)) %)o(% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %[<o]% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %[o>]% cbind(1:5, 2:6) ## open intervals list(0:4, 1:5) %(o)% cbind(rep(2,5), rep(3,5)) cbind(rep(2,5), rep(3,5)) %(o)% list(0:4, 1:5) cbind(rep(3,5),rep(4,5)) %]o[% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %(<o)% cbind(1:5, 2:6) cbind(rep(3,5),rep(4,5)) %(o>)% cbind(1:5, 2:6) dt1 <- as.Date(c("2000-01-01", "2000-03-15")) dt2 <- as.Date(c("2000-03-15", "2000-06-07")) dt1 %[]o[]% dt2 dt1 %[]o[)% dt2 dt1 %[]o(]% dt2 dt1 %[]o()% dt2 dt1 %[)o[]% dt2 dt1 %[)o[)% dt2 dt1 %[)o(]% dt2 dt1 %[)o()% dt2 dt1 %(]o[]% dt2 dt1 %(]o[)% dt2 dt1 %(]o(]% dt2 dt1 %(]o()% dt2 dt1 %()o[]% dt2 dt1 %()o[)% dt2 dt1 %()o(]% dt2 dt1 %()o()% dt2 ## watch precedence (2 * c(1, 3)) %[o]% (c(2, 4) * 2) (2 * c(1, 3)) %[o]% c(2, 4) * 2 2 * c(1, 3) %[o]% (c(2, 4) * 2) 2 * c(1, 3) %[o]% c(2, 4) * 2