From 4bf693f24f81ff12e4ecc7bd564e643c5683fd4e Mon Sep 17 00:00:00 2001 From: Roger Date: Thu, 24 Aug 2023 11:07:34 +0200 Subject: [PATCH 1/5] address #44 --- DESCRIPTION | 4 ++-- R/classInt.R | 2 +- inst/tinytest/test_largeN.R | 8 ++++++++ man/classIntervals.Rd | 2 +- 4 files changed, 12 insertions(+), 4 deletions(-) create mode 100644 inst/tinytest/test_largeN.R diff --git a/DESCRIPTION b/DESCRIPTION index 8affe86..2ef30b6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: classInt -Version: 0.4-9 -Date: 2023-02-21 +Version: 0.4-10 +Date: 2023-08-24 Title: Choose Univariate Class Intervals Authors@R: c( person("Roger", "Bivand", role=c("aut", "cre"), email="Roger.Bivand@nhh.no", comment=c(ORCID="0000-0003-2392-6140")), diff --git a/R/classInt.R b/R/classInt.R index d32ae28..bc72a39 100644 --- a/R/classInt.R +++ b/R/classInt.R @@ -136,7 +136,7 @@ classIntervals <- function(var, n, style="quantile", rtimes=3, ..., intervalClos if (nobs > largeN) { warning("N is large, and some styles will run very slowly; sampling imposed") sampling <- TRUE - nsamp <- ifelse(samp_prop*nobs > 3000, as.integer(ceiling(samp_prop*nobs)), 3000L) + nsamp <- ifelse(samp_prop*nobs < largeN, as.integer(ceiling(samp_prop*nobs)), as.integer(largeN)) } } if (style =="fixed") { diff --git a/inst/tinytest/test_largeN.R b/inst/tinytest/test_largeN.R new file mode 100644 index 0000000..0d531f6 --- /dev/null +++ b/inst/tinytest/test_largeN.R @@ -0,0 +1,8 @@ +library(classInt) +# issue 44 +set.seed(101) +large_n <- 1000 +x <- 1:(large_n + 1) +expect_warning(classInt::classIntervals(x, n = 10, style = "fisher", + largeN = large_n, samp_prop = 0.05)) + diff --git a/man/classIntervals.Rd b/man/classIntervals.Rd index dccefff..0d88dcc 100644 --- a/man/classIntervals.Rd +++ b/man/classIntervals.Rd @@ -30,7 +30,7 @@ classIntervals2shingle(x) \item{dataPrecision}{default NULL, permits rounding of the interval endpoints (added by Richard Dunlap). The data precision used for printing interval values in the legend returned by \code{findColours}, and in the \code{print} method for classIntervals objects. If intervalClosure is \dQuote{left}, the value returned is \code{ceiling} of the data value multiplied by 10 to the dataPrecision power, divided by 10 to the dataPrecision power. The argument does not round \code{var}, the input variable.} \item{warnSmallN}{default TRUE, if FALSE, quietens warning for n >= nobs} \item{warnLargeN}{default TRUE, if FALSE large data handling not used} - \item{largeN}{default 3000L, the QGIS sampling threshold; over 3000, the observations presented to "fisher" and "jenks" are either a \code{samp_prop=} sample or a sample of 3000, whichever is larger} + \item{largeN}{default 3000L, the QGIS sampling threshold; over \code{largeN}, the observations presented to "fisher" and "jenks" are either a \code{samp_prop=} sample or a sample of \code{largeN}, whichever is smaller} \item{samp_prop}{default 0.1, QGIS 10\% sampling proportion} \item{gr}{default \code{c("[", "]")}, if the \pkg{units} package is available, \code{units::units_options("group")} may be used directly to give the enclosing bracket style} \item{\dots}{arguments to be passed to the functions called in each style} From 3652101b2c2b4a193bf44bed0251c99db1d20925 Mon Sep 17 00:00:00 2001 From: Roger Date: Thu, 24 Aug 2023 11:50:55 +0200 Subject: [PATCH 2/5] address #44 --- R/classInt.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/classInt.R b/R/classInt.R index bc72a39..227f06a 100644 --- a/R/classInt.R +++ b/R/classInt.R @@ -136,7 +136,9 @@ classIntervals <- function(var, n, style="quantile", rtimes=3, ..., intervalClos if (nobs > largeN) { warning("N is large, and some styles will run very slowly; sampling imposed") sampling <- TRUE - nsamp <- ifelse(samp_prop*nobs < largeN, as.integer(ceiling(samp_prop*nobs)), as.integer(largeN)) +# issue 44 + nsamp <- as.integer(ceiling(samp_prop*nobs)) + if (nsamp > largeN) nsamp <- as.integer(largeN) } } if (style =="fixed") { From f7b2e038ab882517013bb6dbc6f3967ab5e60cc6 Mon Sep 17 00:00:00 2001 From: Roger Date: Sun, 27 Aug 2023 13:27:57 +0200 Subject: [PATCH 3/5] address #44 --- R/classInt.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/classInt.R b/R/classInt.R index 227f06a..27eb3e6 100644 --- a/R/classInt.R +++ b/R/classInt.R @@ -132,7 +132,7 @@ classIntervals <- function(var, n, style="quantile", rtimes=3, ..., intervalClos # introduced related to https://github.com/r-spatial/classInt/issues/7 sampling <- FALSE if (warnLargeN && - (style %in% c("kmeans", "hclust", "bclust", "fisher", "jenks"))) { + (style %in% c("fisher", "jenks"))) { if (nobs > largeN) { warning("N is large, and some styles will run very slowly; sampling imposed") sampling <- TRUE From d858db87437bc7608f4d5a7daf26856a6b8f711c Mon Sep 17 00:00:00 2001 From: Roger Date: Tue, 29 Aug 2023 11:16:14 +0200 Subject: [PATCH 4/5] address #44 --- man/classIntervals.Rd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/man/classIntervals.Rd b/man/classIntervals.Rd index 0d88dcc..dffe528 100644 --- a/man/classIntervals.Rd +++ b/man/classIntervals.Rd @@ -30,7 +30,7 @@ classIntervals2shingle(x) \item{dataPrecision}{default NULL, permits rounding of the interval endpoints (added by Richard Dunlap). The data precision used for printing interval values in the legend returned by \code{findColours}, and in the \code{print} method for classIntervals objects. If intervalClosure is \dQuote{left}, the value returned is \code{ceiling} of the data value multiplied by 10 to the dataPrecision power, divided by 10 to the dataPrecision power. The argument does not round \code{var}, the input variable.} \item{warnSmallN}{default TRUE, if FALSE, quietens warning for n >= nobs} \item{warnLargeN}{default TRUE, if FALSE large data handling not used} - \item{largeN}{default 3000L, the QGIS sampling threshold; over \code{largeN}, the observations presented to "fisher" and "jenks" are either a \code{samp_prop=} sample or a sample of \code{largeN}, whichever is smaller} + \item{largeN}{default 3000L, the QGIS sampling threshold; if \code{warnLargeN}is TRUE and the number of observations is greater than \code{largeN}, the observations presented to "fisher" and "jenks" are either a \code{samp_prop=} sample of the observations, or a sample of \code{largeN}, whichever is smaller} \item{samp_prop}{default 0.1, QGIS 10\% sampling proportion} \item{gr}{default \code{c("[", "]")}, if the \pkg{units} package is available, \code{units::units_options("group")} may be used directly to give the enclosing bracket style} \item{\dots}{arguments to be passed to the functions called in each style} From 5122560d69e9c817b51be7db929282f4a8928efc Mon Sep 17 00:00:00 2001 From: Roger Date: Tue, 29 Aug 2023 11:28:27 +0200 Subject: [PATCH 5/5] update docs --- docs/404.html | 4 +- docs/articles/headtailsR.html | 252 ++++++++++++----- .../figure-html/benchmarkbreaks-1.png | Bin 69256 -> 70413 bytes .../figure-html/breaksample-1.png | Bin 25461 -> 26149 bytes .../figure-html/breaksample-2.png | Bin 24888 -> 25494 bytes .../figure-html/breaksample-3.png | Bin 25311 -> 26049 bytes .../figure-html/charheavytail-1.png | Bin 29033 -> 30072 bytes .../figure-html/charheavytail-2.png | Bin 16508 -> 17180 bytes .../figure-html/examplesimp-1.png | Bin 21839 -> 22516 bytes .../figure-html/examplesimp-2.png | Bin 22454 -> 23078 bytes .../figure-html/examplesimp-3.png | Bin 23333 -> 24003 bytes .../figure-html/examplesimp-4.png | Bin 22568 -> 23181 bytes .../figure-html/finalplot -1.png | Bin 40951 -> 41425 bytes .../figure-html/finalplot -2.png | Bin 49195 -> 49945 bytes .../figure-html/finalplot -3.png | Bin 56044 -> 56602 bytes .../figure-html/finalplot -4.png | Bin 64123 -> 64745 bytes .../figure-html/stepbystep-1.png | Bin 20323 -> 21366 bytes .../figure-html/stepbystep-2.png | Bin 20574 -> 21430 bytes .../figure-html/stepbystep-3.png | Bin 18710 -> 19518 bytes .../figure-html/stepbystep-4.png | Bin 16490 -> 17176 bytes .../figure-html/summspdata-1.png | Bin 16336 -> 17033 bytes .../figure-html/summspdata-2.png | Bin 34966 -> 36141 bytes .../figure-html/testresults-1.png | Bin 43304 -> 43892 bytes .../figure-html/testresults-10.png | Bin 52153 -> 52992 bytes .../figure-html/testresults-11.png | Bin 36127 -> 37058 bytes .../figure-html/testresults-12.png | Bin 47295 -> 48156 bytes .../figure-html/testresults-13.png | Bin 50715 -> 51494 bytes .../figure-html/testresults-14.png | Bin 50372 -> 51118 bytes .../figure-html/testresults-2.png | Bin 43353 -> 43914 bytes .../figure-html/testresults-3.png | Bin 54526 -> 55499 bytes .../figure-html/testresults-4.png | Bin 47801 -> 48778 bytes .../figure-html/testresults-5.png | Bin 46172 -> 46940 bytes .../figure-html/testresults-6.png | Bin 74362 -> 75047 bytes .../figure-html/testresults-7.png | Bin 45867 -> 46529 bytes .../figure-html/testresults-8.png | Bin 27415 -> 28818 bytes .../figure-html/testresults-9.png | Bin 36383 -> 37215 bytes docs/articles/index.html | 4 +- docs/authors.html | 8 +- docs/index.html | 14 +- docs/news/index.html | 13 +- docs/pkgdown.yml | 6 +- docs/reference/Rplot002.png | Bin 17027 -> 18157 bytes docs/reference/Rplot003.png | Bin 15745 -> 16760 bytes docs/reference/Rplot004.png | Bin 13715 -> 14154 bytes docs/reference/classIntervals-1.png | Bin 57184 -> 58489 bytes docs/reference/classIntervals-2.png | Bin 57311 -> 58614 bytes docs/reference/classIntervals-3.png | Bin 24176 -> 24574 bytes docs/reference/classIntervals-4.png | Bin 37837 -> 38466 bytes docs/reference/classIntervals.html | 256 +++--------------- docs/reference/classify_intervals.html | 4 +- docs/reference/findColours-1.png | Bin 50157 -> 52001 bytes docs/reference/findColours-2.png | Bin 55089 -> 57061 bytes docs/reference/findColours.html | 4 +- docs/reference/findCols.html | 4 +- docs/reference/getBclustClassIntervals-1.png | Bin 61804 -> 64606 bytes docs/reference/getBclustClassIntervals-2.png | Bin 45515 -> 47132 bytes docs/reference/getBclustClassIntervals-3.png | Bin 35648 -> 36712 bytes docs/reference/getBclustClassIntervals.html | 4 +- docs/reference/index.html | 4 +- docs/reference/jenks.tests.html | 4 +- docs/reference/logLik.classIntervals.html | 4 +- 61 files changed, 257 insertions(+), 328 deletions(-) diff --git a/docs/404.html b/docs/404.html index dda3227..7afb842 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ classInt - 0.4-8 + 0.4-10 @@ -100,7 +100,7 @@

Page not found (404)

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

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@@ -72,7 +72,7 @@

All vignettes

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/authors.html b/docs/authors.html index 8b7b4d0..3b0292c 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ classInt - 0.4-8 + 0.4-10 @@ -95,14 +95,14 @@

Citation

-

Bivand R (2022). +

Bivand R (2023). classInt: Choose Univariate Class Intervals. https://r-spatial.github.io/classInt/, https://github.com/r-spatial/classInt/.

@Manual{,
   title = {classInt: Choose Univariate Class Intervals},
   author = {Roger Bivand},
-  year = {2022},
+  year = {2023},
   note = {https://r-spatial.github.io/classInt/, https://github.com/r-spatial/classInt/},
 }
@@ -117,7 +117,7 @@

Citation

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/index.html b/docs/index.html index c62bc47..a90bf1f 100644 --- a/docs/index.html +++ b/docs/index.html @@ -33,7 +33,7 @@ classInt - 0.4-8 + 0.4-10 @@ -80,7 +80,7 @@
- +

Actions Status CRAN

Choose Univariate Class Intervals

Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes. From 0.4-3, an implementation of the “headtails” style contributed by Diego Hernangómez - Jiang, B. 2013 “Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution”, The Professional Geographer, 65 (3), 482 – 494. https://arxiv.org/abs/1209.2801v1. From 0.4-7, an implementation of the “maximum” style contributed by Josiah Parry. From 0.4-8, an implementation of the “box” style contributed by Angela Li and Diego Hernangómez.

@@ -125,13 +125,7 @@

Developers

-
-

Dev status

-
    -
  • Actions Status
  • -
  • CRAN
  • -
-
+
@@ -144,7 +138,7 @@

Dev status

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/news/index.html b/docs/news/index.html index cc68d76..cf5cdb7 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ classInt - 0.4-8 + 0.4-10 @@ -58,8 +58,13 @@

Changelog

- -
  • #18 and #38: classIntervals() has a new style "box", where a box map is an augmented quartile map, with an additional lower and upper category. When there are lower outliers, then the starting point for the breaks is the minimum value, and the second break is the lower fence. When there are no lower outliers, then the starting point for the breaks will be the lower fence, and the second break is the minimum value (there will be no observations that fall in the interval between the lower fence and the minimum value) (@angela-li, @dieghernan).
+ +
  • #41 issues. The maximum and minimum breaks are set to and to avoid errors induced in the earlier version where breaks could cease to be strictly ascending. The argument with value may be used to revert to the previous behaviour.
  • +
+
+ +
  • #18 and #38: classIntervals() has a new style "box", where a box map is an augmented quartile map, with an additional lower and upper category. When there are lower outliers, then the starting point for the breaks is the minimum value, and the second break is the lower fence. When there are no lower outliers, then the starting point for the breaks will be the lower fence, and the second break is the minimum value (there will be no observations that fall in the interval between the lower fence and the minimum value) (@angela-li, @dieghernan).
  • +
  • A new helper function classify_intervals() is introduced to return a vector of class intervals of same length as input (@JosiahParry)
  • @@ -88,7 +93,7 @@
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Choose univariate class intervals

plot(x, pal, ...) # S3 method for classIntervals print(x, digits = getOption("digits"), ..., - under="under", over="over", between="-", cutlabels=TRUE, unique=FALSE) + under="under", over="over", between="-", cutlabels=TRUE, unique=FALSE,big.mark=NULL) nPartitions(x) classIntervals2shingle(x)
@@ -85,7 +85,7 @@

Arguments

number of classes required, if missing, nclass.Sturges is used; see also the "dpih" and "headtails" styles for automatic choice of the number of classes

style
-

chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "headtails", or "maximum"

+

chosen style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "headtails", "maximum", or "box"

rtimes

number of replications of var to catenate and jitter; may be used with styles "kmeans" or "bclust" in case they have difficulties reaching a classification

@@ -103,7 +103,7 @@

Arguments

default TRUE, if FALSE large data handling not used

largeN
-

default 3000L, the QGIS sampling threshold; over 3000, the observations presented to "fisher" and "jenks" are either a samp_prop= sample or a sample of 3000, whichever is larger

+

default 3000L, the QGIS sampling threshold; if warnLargeNis TRUE and the number of observations is greater than largeN, the observations presented to "fisher" and "jenks" are either a samp_prop= sample of the observations, or a sample of largeN, whichever is smaller

samp_prop

default 0.1, QGIS 10% sampling proportion

@@ -135,6 +135,9 @@

Arguments

unique

default FALSE; if TRUE, collapse labels of single-value classes

+
big.mark
+

default NULL; an object of class character to specify to 'thousands' separator

+
pal

a character vector of at least two colour names for colour coding the class intervals in an ECDF plot; colorRampPalette is used internally to create the correct number of colours

@@ -154,7 +157,7 @@

Details

The "dpih" style uses the dpih() function from KernSmooth (Wand, 1995) implementing direct plug-in methodology to select the bin width of a histogram.

The "headtails" style uses the algorithm proposed by Bin Jiang (2013), in order to find groupings or hierarchy for data with a heavy-tailed distribution. This classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. By default the algorithm uses thr = 0.4, meaning that when the head represents more than 40% of the observations the distribution is not considered heavy-tailed. The threshold argument thr may be modified through ... (see Examples).

The "maximum" style uses the Maximum Breaks method of classification finding the k - 1 largest differences in var. The mean of the values that generated the largest splits is used as the interval boundary.

-

The "box" style generates 7 breaks (therefore 6 categories) based on a box-and-whisker plot. First and last categories include the data values considered as outliers, and the four remaining categories are defined by the percentiles 25, 50 and 75 of the data distribution. By default, the identification of outliers is based on the interquantile range (IQR), so values lower than percentile 25 - 1.5 * IQR or higher than percentile 75 + 1.5 * IQR are considered as outliers. The multiplier applied to the IQR iqr_mult = 1.5 may be modified through .... As in the "quantile" style, the type= argument may be used to choose the quantile algoritm (default 7).

+

The "box" style generates 7 breaks (therefore 6 categories) based on a box-and-whisker plot. First and last categories include the data values considered as outliers, and the four remaining categories are defined by the percentiles 25, 50 and 75 of the data distribution. By default, the identification of outliers is based on the interquantile range (IQR), so values lower than percentile 25 - 1.5 * IQR or higher than percentile 75 + 1.5 * IQR are considered as outliers. The multiplier applied to the IQR iqr_mult = 1.5 may be modified through ...; the value must not be negative. As in the "quantile" style, the type= argument may be used to choose the quantile algoritm (default 7, standard boxplots use 5 or 2). From 0.4-9 and #41, the maximum and minimum are set to +Inf and -Inf to avoid errors induced in the earlier version where breaks could cease to be strictly ascending. The legacy= argument with value TRUE may be used to revert to the previous behaviour.

Value

@@ -218,6 +221,12 @@

Examples

} else { run <- TRUE } +#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package, +#> which was just load ed, were retired in October 2023. +#> Please refer to R-spatial evolution reports for details, especially +#> https://r-spatial.org/r/2023/05/15/evolution4.html. +#> It may be desirable to make the sf package available; +#> package maintainers should consider adding sf to Suggests:. if (run) { data(jenks71, package="spData") pal1 <- c("wheat1", "red3") @@ -416,10 +425,10 @@

Examples

} #> style: box #> one of 75,287,520 possible partitions of this variable into 6 classes -#> [-8.02125,15) [15,37.7325) [37.7325,53.33) [53.33,68.235) -#> 0 26 25 25 -#> [68.235,113.9887) [113.9887,155.3] -#> 22 4 +#> [-Inf,-8.02125) [-8.02125,37.7325) [37.7325,53.33) [53.33,68.235) +#> 0 26 25 25 +#> [68.235,113.9887) [113.9887,155.3] +#> 22 4 if (run) { print(classIntervals(jenks71$jenks71, style="box", iqr_mult = 0.25)) } @@ -461,82 +470,30 @@

Examples

#> 0 (0,19.5] (19.5,20.63] (20.63,22.6] 26 #> 10 33 39 28 10 -# \dontrun{ +if (FALSE) { set.seed(1) n <- 1e+05 x <- runif(n) classIntervals(x, n=5, style="sd") -#> style: sd -#> [-0.07925682,0.06546355) [0.06546355,0.2101839) [0.2101839,0.3549043) -#> 6597 14579 14403 -#> [0.3549043,0.4996247) [0.4996247,0.644345) [0.644345,0.7890654) -#> 14395 14446 14439 -#> [0.7890654,0.9337858) [0.9337858,1.078506] -#> 14463 6678 classIntervals(x, n=5, style="pretty") -#> style: pretty -#> [0,0.2) [0.2,0.4) [0.4,0.6) [0.6,0.8) [0.8,1] -#> 20142 19936 19888 19993 20041 classIntervals(x, n=5, style="equal") -#> style: equal -#> [3.895489e-06,0.1999925) [0.1999925,0.3999812) [0.3999812,0.5999698) -#> 20141 19935 19888 -#> [0.5999698,0.7999584) [0.7999584,0.9999471] -#> 19991 20045 classIntervals(x, n=5, style="quantile") -#> style: quantile -#> [3.895489e-06,0.1984408) [0.1984408,0.3993007) [0.3993007,0.6003913) -#> 20000 20000 20000 -#> [0.6003913,0.8003984) [0.8003984,0.9999471] -#> 20000 20000 # the class intervals found vary a little because of sampling classIntervals(x, n=5, style="kmeans") -#> Warning: N is large, and some styles will run very slowly; sampling imposed -#> style: kmeans -#> [3.895489e-06,0.1992885) [0.1992885,0.4002208) [0.4002208,0.600954) -#> 20075 20034 19946 -#> [0.600954,0.800892) [0.800892,0.9999471] -#> 20007 19938 classIntervals(x, n=5, style="fisher") -#> Warning: N is large, and some styles will run very slowly; sampling imposed -#> style: fisher -#> [3.895489e-06,0.1969869) [0.1969869,0.394688) [0.394688,0.595018) -#> 19860 19690 19893 -#> [0.595018,0.7967174) [0.7967174,0.9999471] -#> 20194 20363 classIntervals(x, n=5, style="fisher") -#> Warning: N is large, and some styles will run very slowly; sampling imposed -#> style: fisher -#> [3.895489e-06,0.1887506) [0.1887506,0.3856013) [0.3856013,0.5868507) -#> 19036 19653 19966 -#> [0.5868507,0.7925222) [0.7925222,0.9999471] -#> 20553 20792 classIntervals(x, n=5, style="fisher") -#> Warning: N is large, and some styles will run very slowly; sampling imposed -#> style: fisher -#> [3.895489e-06,0.1974303) [0.1974303,0.3969872) [0.3969872,0.5978375) -#> 19912 19832 19987 -#> [0.5978375,0.7994717) [0.7994717,0.9999471] -#> 20166 20103 -# } +} have_units <- FALSE -if (require(units, quietly=TRUE)) have_units <- TRUE +if (require(units, quietly=TRUE)) have_units <- TRUE #> udunits database from /usr/share/udunits/udunits2.xml if (have_units) { set.seed(1) -x_units <- set_units(sample(seq(1, 100, 0.25), 100), km/h) -# \dontrun{ +x_units <- set_units(sample(seq(1, 100, 0.25), 100), km/h) +if (FALSE) { classIntervals(x_units, n=5, style="sd") -# } } -#> style: sd -#> one of 14,887,031,544 possible partitions of this [km/h] variable into 8 classes -#> [-6.990949,7.637413) [7.637413,22.26578) [22.26578,36.89414) -#> 6 15 16 -#> [36.89414,51.5225) [51.5225,66.15086) [66.15086,80.77922) -#> 12 12 16 -#> [80.77922,95.40759) [95.40759,110.0359] -#> 18 5 +} if (have_units) { classIntervals(x_units, n=5, style="pretty") } @@ -545,14 +502,10 @@

Examples

#> [0,20) [20,40) [40,60) [60,80) [80,100] #> 18 21 19 19 23 if (have_units) { -# \dontrun{ +if (FALSE) { classIntervals(x_units, n=5, style="equal") -# } } -#> style: equal -#> one of 3,764,376 possible partitions of this [km/h] variable into 5 classes -#> [4,23.1) [23.1,42.2) [42.2,61.3) [61.3,80.4) [80.4,99.5] -#> 22 19 17 19 23 +} if (have_units) { classIntervals(x_units, n=5, style="quantile") } @@ -561,14 +514,10 @@

Examples

#> [4,21.95) [21.95,41.5) [41.5,64.05) [64.05,82.8) [82.8,99.5] #> 20 20 20 20 20 if (have_units) { -# \dontrun{ +if (FALSE) { classIntervals(x_units, n=5, style="kmeans") -# } } -#> style: kmeans -#> one of 3,764,376 possible partitions of this [km/h] variable into 5 classes -#> [4,19.375) [19.375,39.375) [39.375,60.5) [60.5,79.625) [79.625,99.5] -#> 18 21 19 19 23 +} if (have_units) { classIntervals(x_units, n=5, style="fisher") } @@ -588,155 +537,27 @@

Examples

} #> style: box #> one of 71,523,144 possible partitions of this [km/h] variable into 6 classes -#> [-46.375,4) [4,26.9375) [26.9375,52) [52,75.8125) [75.8125,100) -#> 0 25 25 25 25 -#> [100,149.125] -#> 0 -# \dontrun{ +#> [-Inf,-46.375) [-46.375,26.9375) [26.9375,52) [52,75.8125) +#> 0 25 25 25 +#> [75.8125,149.125) [149.125,Inf] +#> 25 0 +if (FALSE) { st <- Sys.time() x_POSIXt <- sample(st+((0:500)*3600), 100) fx <- st+((0:5)*3600)*100 classIntervals(x_POSIXt, style="fixed", fixedBreaks=fx) -#> style: fixed -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-23 10:44:52.686004,2022-09-27 14:44:52.686004) -#> 21 -#> [2022-09-27 14:44:52.686004,2022-10-01 18:44:52.686004) -#> 18 -#> [2022-10-01 18:44:52.686004,2022-10-05 22:44:52.686004) -#> 19 -#> [2022-10-05 22:44:52.686004,2022-10-10 02:44:52.686004) -#> 21 -#> [2022-10-10 02:44:52.686004,2022-10-14 06:44:52.686004] -#> 21 classIntervals(x_POSIXt, n=5, style="sd") -#> style: sd -#> one of 14,887,031,544 possible partitions of this variable into 8 classes -#> [2022-09-21 13:44:20.740213,2022-09-24 14:50:28.726661) -#> 5 -#> [2022-09-24 14:50:28.726661,2022-09-27 15:56:36.713108) -#> 16 -#> [2022-09-27 15:56:36.713108,2022-09-30 17:02:44.699556) -#> 17 -#> [2022-09-30 17:02:44.699556,2022-10-03 18:08:52.686004) -#> 12 -#> [2022-10-03 18:08:52.686004,2022-10-06 19:15:00.672451) -#> 14 -#> [2022-10-06 19:15:00.672451,2022-10-09 20:21:08.6588) -#> 14 -#> [2022-10-09 20:21:08.6588,2022-10-12 21:27:16.645347) -#> 16 -#> [2022-10-12 21:27:16.645347,2022-10-15 22:33:24.631794] -#> 6 classIntervals(x_POSIXt, n=5, style="pretty") -#> style: pretty -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-18 13:20:00,2022-09-24 08:13:20) -#> 4 -#> [2022-09-24 08:13:20,2022-09-30 03:06:40) -#> 33 -#> [2022-09-30 03:06:40,2022-10-05 22:00:00) -#> 21 -#> [2022-10-05 22:00:00,2022-10-11 16:53:20) -#> 30 -#> [2022-10-11 16:53:20,2022-10-17 11:46:40] -#> 12 classIntervals(x_POSIXt, n=5, style="equal") -#> style: equal -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-23 23:44:52.686004,2022-09-28 00:08:52.686004) -#> 23 -#> [2022-09-28 00:08:52.686004,2022-10-02 00:32:52.686004) -#> 16 -#> [2022-10-02 00:32:52.686004,2022-10-06 00:56:52.686004) -#> 19 -#> [2022-10-06 00:56:52.686004,2022-10-10 01:20:52.686004) -#> 20 -#> [2022-10-10 01:20:52.686004,2022-10-14 01:44:52.686004] -#> 22 classIntervals(x_POSIXt, n=5, style="quantile") -#> style: quantile -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-23 23:44:52.686004,2022-09-27 10:32:52.686004) -#> 20 -#> [2022-09-27 10:32:52.686004,2022-10-02 03:44:52.686004) -#> 20 -#> [2022-10-02 03:44:52.686004,2022-10-06 03:08:52.686004) -#> 20 -#> [2022-10-06 03:08:52.686004,2022-10-10 05:20:52.686004) -#> 20 -#> [2022-10-10 05:20:52.686004,2022-10-14 01:44:52.686004] -#> 20 classIntervals(x_POSIXt, n=5, style="kmeans") -#> style: kmeans -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-23 23:44:52.686004,2022-09-27 02:44:52.686004) -#> 19 -#> [2022-09-27 02:44:52.686004,2022-10-01 01:44:52.686004) -#> 19 -#> [2022-10-01 01:44:52.686004,2022-10-05 10:14:52.686004) -#> 17 -#> [2022-10-05 10:14:52.686004,2022-10-09 14:14:52.686004) -#> 23 -#> [2022-10-09 14:14:52.686004,2022-10-14 01:44:52.686004] -#> 22 classIntervals(x_POSIXt, n=5, style="fisher") -#> style: fisher -#> one of 3,764,376 possible partitions of this variable into 5 classes -#> [2022-09-23 23:44:52.686004,2022-09-27 02:44:52.686004) -#> 19 -#> [2022-09-27 02:44:52.686004,2022-10-01 01:44:52.686004) -#> 19 -#> [2022-10-01 01:44:52.686004,2022-10-05 10:14:52.686004) -#> 17 -#> [2022-10-05 10:14:52.686004,2022-10-09 14:14:52.686004) -#> 23 -#> [2022-10-09 14:14:52.686004,2022-10-14 01:44:52.686004] -#> 22 classIntervals(x_POSIXt, style="headtails") -#> style: headtails -#> one of 99 possible partitions of this variable into 2 classes -#> [2022-09-23 23:44:52.686004,2022-10-03 18:08:52.686004) -#> 50 -#> [2022-10-03 18:08:52.686004,2022-10-14 01:44:52.686004] -#> 50 classIntervals(x_POSIXt, style="maximum") -#> style: maximum -#> one of 14,887,031,544 possible partitions of this variable into 8 classes -#> [2022-09-23 23:44:52.686004,2022-09-30 08:44:52.686004) -#> 37 -#> [2022-09-30 08:44:52.686004,2022-10-01 01:44:52.686004) -#> 1 -#> [2022-10-01 01:44:52.686004,2022-10-01 18:14:52.686004) -#> 1 -#> [2022-10-01 18:14:52.686004,2022-10-04 02:44:52.686004) -#> 12 -#> [2022-10-04 02:44:52.686004,2022-10-07 09:44:52.686004) -#> 16 -#> [2022-10-07 09:44:52.686004,2022-10-09 14:14:52.686004) -#> 11 -#> [2022-10-09 14:14:52.686004,2022-10-12 22:14:52.686004) -#> 16 -#> [2022-10-12 22:14:52.686004,2022-10-14 01:44:52.686004] -#> 6 classIntervals(x_POSIXt, style="box") -#> style: box -#> one of 71,523,144 possible partitions of this variable into 6 classes -#> [2022-09-13 03:07:22.686004,2022-09-23 23:44:52) -#> 0 -#> [2022-09-23 23:44:52,2022-09-28 10:14:52.686004) -#> 25 -#> [2022-09-28 10:14:52.686004,2022-10-03 17:44:52.686004) -#> 25 -#> [2022-10-03 17:44:52.686004,2022-10-08 14:59:52.686004) -#> 25 -#> [2022-10-08 14:59:52.686004,2022-10-14 01:44:53) -#> 25 -#> [2022-10-14 01:44:53,2022-10-23 22:07:22.686004] -#> 0 -# } +} # see vignette for further details -# \dontrun{ +if (FALSE) { # Head Tails method is suitable for right-sided heavy-tailed distributions set.seed(1234) # Heavy tails----- @@ -758,7 +579,6 @@

Examples

plot(classIntervals(weibulldist, style = "headtails"), pal = pal1, main = "HeadTails: Weibull Dist.") - plot(classIntervals(paretodist, n = 5, style = "fisher"), pal = pal1, main = "Fisher: Pareto Dist.") @@ -768,7 +588,6 @@

Examples

plot(classIntervals(weibulldist, n= 4, style = "fisher"), pal = pal1, main = "Fisher: Weibull Dist.") - par(opar) @@ -806,9 +625,8 @@

Examples

pal = pal1, main = "Uniform Distribution thr = .6" ) - par(opar) -# } +}
@@ -823,7 +641,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/classify_intervals.html b/docs/reference/classify_intervals.html index b3b5332..4863b12 100644 --- a/docs/reference/classify_intervals.html +++ b/docs/reference/classify_intervals.html @@ -17,7 +17,7 @@ classInt - 0.4-8 + 0.4-10 @@ -206,7 +206,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

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Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/findCols.html b/docs/reference/findCols.html index e593f45..b872309 100644 --- a/docs/reference/findCols.html +++ b/docs/reference/findCols.html @@ -17,7 +17,7 @@ classInt - 0.4-8 + 0.4-10 @@ -166,7 +166,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

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All functions
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Examples

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Examples

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