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mirror of https://github.com/msberends/AMR.git synced 2024-12-25 20:06:12 +01:00

kurtosis, skewness, start with ML

This commit is contained in:
dr. M.S. (Matthijs) Berends 2018-07-08 22:14:55 +02:00
parent c768ba0d9c
commit 14b990d769
18 changed files with 401 additions and 15 deletions

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@ -1,6 +1,6 @@
Package: AMR Package: AMR
Version: 0.2.0.9008 Version: 0.2.0.9009
Date: 2018-07-04 Date: 2018-07-06
Title: Antimicrobial Resistance Analysis Title: Antimicrobial Resistance Analysis
Authors@R: c( Authors@R: c(
person( person(
@ -28,6 +28,7 @@ Depends:
R (>= 3.0.0) R (>= 3.0.0)
Imports: Imports:
backports, backports,
broom,
clipr, clipr,
curl, curl,
dplyr (>= 0.7.0), dplyr (>= 0.7.0),

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@ -6,6 +6,11 @@ S3method(as.integer,mic)
S3method(as.numeric,mic) S3method(as.numeric,mic)
S3method(barplot,mic) S3method(barplot,mic)
S3method(barplot,rsi) S3method(barplot,rsi)
S3method(hist,frequency_tbl)
S3method(kurtosis,data.frame)
S3method(kurtosis,default)
S3method(kurtosis,matrix)
S3method(plot,frequency_tbl)
S3method(plot,mic) S3method(plot,mic)
S3method(plot,rsi) S3method(plot,rsi)
S3method(print,data.table) S3method(print,data.table)
@ -14,6 +19,9 @@ S3method(print,mic)
S3method(print,rsi) S3method(print,rsi)
S3method(print,tbl) S3method(print,tbl)
S3method(print,tbl_df) S3method(print,tbl_df)
S3method(skewness,data.frame)
S3method(skewness,default)
S3method(skewness,matrix)
S3method(summary,mic) S3method(summary,mic)
S3method(summary,rsi) S3method(summary,rsi)
export("%like%") export("%like%")
@ -43,6 +51,7 @@ export(interpretive_reading)
export(is.mic) export(is.mic)
export(is.rsi) export(is.rsi)
export(key_antibiotics) export(key_antibiotics)
export(kurtosis)
export(left_join_microorganisms) export(left_join_microorganisms)
export(like) export(like)
export(mo_property) export(mo_property)
@ -54,6 +63,7 @@ export(rsi)
export(rsi_df) export(rsi_df)
export(rsi_predict) export(rsi_predict)
export(semi_join_microorganisms) export(semi_join_microorganisms)
export(skewness)
export(top_freq) export(top_freq)
exportMethods(as.data.frame.frequency_tbl) exportMethods(as.data.frame.frequency_tbl)
exportMethods(as.double.mic) exportMethods(as.double.mic)
@ -61,6 +71,12 @@ exportMethods(as.integer.mic)
exportMethods(as.numeric.mic) exportMethods(as.numeric.mic)
exportMethods(barplot.mic) exportMethods(barplot.mic)
exportMethods(barplot.rsi) exportMethods(barplot.rsi)
exportMethods(hist.frequency_tbl)
exportMethods(kurtosis)
exportMethods(kurtosis.data.frame)
exportMethods(kurtosis.default)
exportMethods(kurtosis.matrix)
exportMethods(plot.frequency_tbl)
exportMethods(plot.mic) exportMethods(plot.mic)
exportMethods(plot.rsi) exportMethods(plot.rsi)
exportMethods(print.data.table) exportMethods(print.data.table)
@ -69,8 +85,13 @@ exportMethods(print.mic)
exportMethods(print.rsi) exportMethods(print.rsi)
exportMethods(print.tbl) exportMethods(print.tbl)
exportMethods(print.tbl_df) exportMethods(print.tbl_df)
exportMethods(skewness)
exportMethods(skewness.data.frame)
exportMethods(skewness.default)
exportMethods(skewness.matrix)
exportMethods(summary.mic) exportMethods(summary.mic)
exportMethods(summary.rsi) exportMethods(summary.rsi)
importFrom(broom,tidy)
importFrom(clipr,read_clip_tbl) importFrom(clipr,read_clip_tbl)
importFrom(clipr,write_clip) importFrom(clipr,write_clip)
importFrom(curl,nslookup) importFrom(curl,nslookup)
@ -107,6 +128,7 @@ importFrom(dplyr,vars)
importFrom(grDevices,boxplot.stats) importFrom(grDevices,boxplot.stats)
importFrom(graphics,axis) importFrom(graphics,axis)
importFrom(graphics,barplot) importFrom(graphics,barplot)
importFrom(graphics,hist)
importFrom(graphics,plot) importFrom(graphics,plot)
importFrom(graphics,text) importFrom(graphics,text)
importFrom(knitr,kable) importFrom(knitr,kable)
@ -119,6 +141,7 @@ importFrom(rvest,html_nodes)
importFrom(rvest,html_table) importFrom(rvest,html_table)
importFrom(stats,fivenum) importFrom(stats,fivenum)
importFrom(stats,mad) importFrom(stats,mad)
importFrom(stats,na.omit)
importFrom(stats,pchisq) importFrom(stats,pchisq)
importFrom(stats,sd) importFrom(stats,sd)
importFrom(tibble,tibble) importFrom(tibble,tibble)

13
NEWS.md
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@ -1,10 +1,14 @@
# 0.2.0.90xx (development version) # 0.2.0.90xx (development version)
#### New #### New
* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package * Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
* Function `top_freq` function to get the top/below *n* items of frequency tables * For convience, descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
* Vignette about frequency tables * New for frequency tables (function `freq`):
* Header of frequency tables now also show MAD and IQR * A vignette to explain its usage
* Possibility to globally set the default for the amount of items to print in frequency tables (`freq` function), with `options(max.print.freq = n)` * Support for existing functions `hist` and `plot` to use a frequency table as input: `hist(freq(df$age))`
* Support for quasiquotation: `freq(mydata, mycolumn)` is the same as `mydata %>% freq(mycolumn)`
* Function `top_freq` function to return the top/below *n* items as vector
* Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
* Possibility to globally set the default for the amount of items to print, with `options(max.print.freq = n)` where *n* is your preset value
* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS * Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS
* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test) * Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test)
* Function `ratio` to transform a vector of values to a preset ratio (convenient to use with `g.test`). For example: * Function `ratio` to transform a vector of values to a preset ratio (convenient to use with `g.test`). For example:
@ -16,7 +20,6 @@ ratio(c(772, 1611, 737), ratio = "1:2:1")
#### Changed #### Changed
* `%like%` now supports multiple patterns * `%like%` now supports multiple patterns
* Frequency tables (function `freq`) now supports quasiquotation: `freq(mydata, mycolumn)`, or `mydata %>% freq(mycolumn)`
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed. * Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
* Small translational improvements to the `septic_patients` dataset * Small translational improvements to the `septic_patients` dataset
* Small improvements to the `microorganisms` dataset, especially for *Salmonella* * Small improvements to the `microorganisms` dataset, especially for *Salmonella*

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@ -46,8 +46,8 @@
#' #'
#' For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header: #' For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header:
#' \itemize{ #' \itemize{
#' \item{Oldest, using \code{\link[base]{min}}} #' \item{Oldest, using \code{\link{min}}}
#' \item{Newest, using \code{\link[base]{max}}, with difference between newest and oldest} #' \item{Newest, using \code{\link{max}}, with difference between newest and oldest}
#' \item{Median, using \code{\link[stats]{median}}, with percentage since oldest} #' \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
#' } #' }
#' #'
@ -522,3 +522,42 @@ as.data.frame.frequency_tbl <- function(x, ...) {
attr(x, 'opt') <- NULL attr(x, 'opt') <- NULL
as.data.frame.data.frame(x, ...) as.data.frame.data.frame(x, ...)
} }
#' @noRd
#' @exportMethod hist.frequency_tbl
#' @export
#' @importFrom dplyr %>% pull
#' @importFrom graphics hist
hist.frequency_tbl <- function(x, ...) {
opt <- attr(x, 'opt')
if (!is.null(opt$vars)) {
title <- opt$vars
} else {
title <- ""
}
items <- x %>% pull(item)
counts <- x %>% pull(count)
vect <- rep(items, counts)
hist(vect, main = paste("Histogram of", title), xlab = title, ...)
}
#' @noRd
#' @exportMethod plot.frequency_tbl
#' @export
#' @importFrom dplyr %>% pull
plot.frequency_tbl <- function(x, y, ...) {
opt <- attr(x, 'opt')
if (!is.null(opt$vars)) {
title <- opt$vars
} else {
title <- ""
}
items <- x %>% pull(item)
counts <- x %>% pull(count)
plot(x = items, y = counts, ylab = "Count", xlab = title, ...)
}

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@ -35,12 +35,14 @@ globalVariables(c('abname',
'key_ab', 'key_ab',
'key_ab_lag', 'key_ab_lag',
'key_ab_other', 'key_ab_other',
'labs',
'median', 'median',
'mic', 'mic',
'microorganisms', 'microorganisms',
'mocode', 'mocode',
'molis', 'molis',
'n', 'n',
'na.omit',
'other_pat_or_mo', 'other_pat_or_mo',
'patient_id', 'patient_id',
'quantile', 'quantile',

40
R/kurtosis.R Normal file
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@ -0,0 +1,40 @@
#' Kurtosis of the sample
#'
#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
#'
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
#' @exportMethod kurtosis
#' @seealso \code{\link{skewness}}
#' @rdname kurtosis
#' @export
kurtosis <- function(x, na.rm = FALSE) {
UseMethod("kurtosis")
}
#' @exportMethod kurtosis.default
#' @rdname kurtosis
#' @export
kurtosis.default <- function (x, na.rm = FALSE) {
x <- as.vector(x)
if (na.rm == TRUE) {
x <- x[!is.na(x)]
}
n <- length(x)
n * base::sum((x - base::mean(x, na.rm = na.rm))^4, na.rm = na.rm) /
(base::sum((x - base::mean(x, na.rm = na.rm))^2, na.rm = na.rm)^2)
}
#' @exportMethod kurtosis.matrix
#' @rdname kurtosis
#' @export
kurtosis.matrix <- function (x, na.rm = FALSE) {
base::apply(x, 2, kurtosis.default, na.rm = na.rm)
}
#' @exportMethod kurtosis.data.frame
#' @rdname kurtosis
#' @export
kurtosis.data.frame <- function (x, na.rm = FALSE) {
base::sapply(x, kurtosis.default, na.rm = na.rm)
}

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@ -18,7 +18,7 @@
#' Pattern Matching #' Pattern Matching
#' #'
#' Convenient wrapper around \code{\link[base]{grepl}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors. #' Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors.
#' @inheritParams base::grepl #' @inheritParams base::grepl
#' @return A \code{logical} vector #' @return A \code{logical} vector
#' @name like #' @name like

40
R/skewness.R Normal file
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@ -0,0 +1,40 @@
#' Skewness of the sample
#'
#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
#'
#' When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
#' @exportMethod skewness
#' @seealso \code{\link{kurtosis}}
#' @rdname skewness
#' @export
skewness <- function(x, na.rm = FALSE) {
UseMethod("skewness")
}
#' @exportMethod skewness.default
#' @rdname skewness
#' @export
skewness.default <- function (x, na.rm = FALSE) {
x <- as.vector(x)
if (na.rm == TRUE) {
x <- x[!is.na(x)]
}
n <- length(x)
(base::sum((x - base::mean(x))^3) / n) / (base::sum((x - base::mean(x))^2) / n)^(3/2)
}
#' @exportMethod skewness.matrix
#' @rdname skewness
#' @export
skewness.matrix <- function (x, na.rm = FALSE) {
base::apply(x, 2, skewness.default, na.rm = na.rm)
}
#' @exportMethod skewness.data.frame
#' @rdname skewness
#' @export
skewness.data.frame <- function (x, na.rm = FALSE) {
base::sapply(x, skewness.default, na.rm = na.rm)
}

123
R/trends.R Normal file
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@ -0,0 +1,123 @@
#' Detect trends using Machine Learning
#'
#' Test text
#' @param data a \code{data.frame}
#' @param threshold_unique do not analyse more unique \code{threshold_unique} items per variable
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
#' @param info print relevant combinations to console
#' @return A \code{list} with class \code{"trends"}
#' @importFrom stats na.omit
#' @importFrom broom tidy
# @export
trends <- function(data, threshold_unique = 30, na.rm = TRUE, info = TRUE) {
cols <- colnames(data)
relevant <- list()
count <- 0
for (x in 1:length(cols)) {
for (y in 1:length(cols)) {
if (x == y) {
next
}
if (n_distinct(data[, x]) > threshold_unique | n_distinct(data[, y]) > threshold_unique) {
next
}
count <- count + 1
df <- data %>%
group_by_at(c(cols[x], cols[y])) %>%
summarise(n = n())
n <- df %>% pull(n)
# linear regression model
lin <- stats::lm(1:length(n) ~ n, na.action = ifelse(na.rm == TRUE, na.omit, NULL))
res <- list(
df = df,
x = cols[x],
y = cols[y],
m = base::mean(n, na.rm = na.rm),
sd = stats::sd(n, na.rm = na.rm),
cv = cv(n, na.rm = na.rm),
cqv = cqv(n, na.rm = na.rm),
kurtosis = kurtosis(n, na.rm = na.rm),
skewness = skewness(n, na.rm = na.rm),
lin.p = broom::tidy(lin)[2, 'p.value']
#binom.p <- broom::tidy(binom)[2, 'p.value']
)
include <- TRUE
# ML part
if (res$cv > 0.25) {
res$reason <- "cv > 0.25"
} else if (res$cqv > 0.75) {
res$reason <- "cqv > 0.75"
} else {
include <- FALSE
}
if (include == TRUE) {
relevant <- c(relevant, list(res))
if (info == TRUE) {
# minus one because the whole data will be added later
cat(paste0("[", length(relevant), "]"), "Relevant:", cols[x], "vs.", cols[y], "\n")
}
}
}
}
cat("Total of", count, "combinations analysed;", length(relevant), "seem relevant.\n")
class(relevant) <- 'trends'
relevant <- c(relevant, list(data = data))
relevant
}
# @exportMethod print.trends
# @export
#' @noRd
print.trends <- function(x, ...) {
cat(length(x) - 1, "relevant trends, out of", length(x$data)^2, "\n")
}
# @exportMethod plot.trends
# @export
#' @noRd
# plot.trends <- function(x, n = NULL, ...) {
# if (is.null(n)) {
# oask <- devAskNewPage(TRUE)
# on.exit(devAskNewPage(oask))
# n <- c(1:(length(x) - 1))
# } else {
# if (n > length(x) - 1) {
# stop('trend unavailable, max is ', length(x) - 1, call. = FALSE)
# }
# oask <- NULL
# }
# for (i in n) {
# data <- x[[i]]$df
# if (as.character(i) %like% '1$') {
# suffix <- "st"
# } else if (as.character(i) %like% '2$') {
# suffix <- "nd"
# } else if (as.character(i) %like% '3$') {
# suffix <- "rd"
# } else {
# suffix <- "th"
# }
# if (!is.null(oask)) {
# cat(paste("Coming up:", colnames(data)[1], "vs.", colnames(data)[2]), "\n")
# }
# print(
# ggplot(
# data,
# aes_string(x = colnames(data)[1],
# y = colnames(data)[3],
# group = colnames(data)[2],
# fill = colnames(data)[2])) +
# geom_col(position = "dodge") +
# theme_minimal() +
# labs(title = paste(colnames(data)[1], "vs.", colnames(data)[2]),
# subtitle = paste0(i, suffix, " trend"))
# )
# }
# }

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@ -32,7 +32,7 @@ With `AMR` you can also:
* Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology ([WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/)), to be able to: * Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology ([WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/)), to be able to:
* Translate antibiotic codes (like *AMOX*), official names (like *amoxicillin*) and even trade names (like *Amoxil* or *Trimox*) to an [ATC code](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like *J01CA04*) and vice versa with the `abname` function * Translate antibiotic codes (like *AMOX*), official names (like *amoxicillin*) and even trade names (like *Amoxil* or *Trimox*) to an [ATC code](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like *J01CA04*) and vice versa with the `abname` function
* Get the latest antibiotic properties like hierarchic groups and [defined daily dose](https://en.wikipedia.org/wiki/Defined_daily_dose) (DDD) with units and administration form from the WHOCC website with the `atc_property` function * Get the latest antibiotic properties like hierarchic groups and [defined daily dose](https://en.wikipedia.org/wiki/Defined_daily_dose) (DDD) with units and administration form from the WHOCC website with the `atc_property` function
* Create frequency tables with the `freq` function * Conduct descriptive statistics: calculate kurtosis, skewness and create frequency tables
And it contains: And it contains:
* A recent data set with ~2500 human pathogenic microorganisms, including family, genus, species, gram stain and aerobic/anaerobic * A recent data set with ~2500 human pathogenic microorganisms, including family, genus, species, gram stain and aerobic/anaerobic
@ -41,13 +41,17 @@ And it contains:
With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E). With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
#### Read all changes and new functions in [NEWS.md](NEWS.md).
## How to get it? ## How to get it?
This package is available on CRAN and also here on GitHub. This package is available on CRAN and also here on GitHub.
### From CRAN (recommended) ### From CRAN (recommended)
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR) [![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
Downloads via RStudio CRAN server (downloads by all other CRAN mirrors not measured):
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](https://cranlogs.r-pkg.org/downloads/daily/last-month/AMR)
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended): - <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended):
- Click on `Tools` and then `Install Packages...` - Click on `Tools` and then `Install Packages...`

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@ -66,8 +66,8 @@ For numeric values of any class, these additional values will all be calculated
For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header: For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header:
\itemize{ \itemize{
\item{Oldest, using \code{\link[base]{min}}} \item{Oldest, using \code{\link{min}}}
\item{Newest, using \code{\link[base]{max}}, with difference between newest and oldest} \item{Newest, using \code{\link{max}}, with difference between newest and oldest}
\item{Median, using \code{\link[stats]{median}}, with percentage since oldest} \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
} }

28
man/kurtosis.Rd Normal file
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@ -0,0 +1,28 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kurtosis.R
\name{kurtosis}
\alias{kurtosis}
\alias{kurtosis.default}
\alias{kurtosis.matrix}
\alias{kurtosis.data.frame}
\title{Kurtosis of the sample}
\usage{
kurtosis(x, na.rm = FALSE)
\method{kurtosis}{default}(x, na.rm = FALSE)
\method{kurtosis}{matrix}(x, na.rm = FALSE)
\method{kurtosis}{data.frame}(x, na.rm = FALSE)
}
\arguments{
\item{x}{a vector of values, a \code{matrix} or a \code{data frame}}
\item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.}
}
\description{
Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
}
\seealso{
\code{\link{skewness}}
}

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@ -29,7 +29,7 @@ x \%like\% pattern
A \code{logical} vector A \code{logical} vector
} }
\description{ \description{
Convenient wrapper around \code{\link[base]{grepl}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors. Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors.
} }
\details{ \details{
Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...). Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).

30
man/skewness.Rd Normal file
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@ -0,0 +1,30 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/skewness.R
\name{skewness}
\alias{skewness}
\alias{skewness.default}
\alias{skewness.matrix}
\alias{skewness.data.frame}
\title{Skewness of the sample}
\usage{
skewness(x, na.rm = FALSE)
\method{skewness}{default}(x, na.rm = FALSE)
\method{skewness}{matrix}(x, na.rm = FALSE)
\method{skewness}{data.frame}(x, na.rm = FALSE)
}
\arguments{
\item{x}{a vector of values, a \code{matrix} or a \code{data frame}}
\item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.}
}
\description{
Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
}
\seealso{
\code{\link{kurtosis}}
}

23
man/trends.Rd Normal file
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@ -0,0 +1,23 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trends.R
\name{trends}
\alias{trends}
\title{Detect trends using Machine Learning}
\usage{
trends(data, threshold_unique = 30, na.rm = TRUE, info = TRUE)
}
\arguments{
\item{data}{a \code{data.frame}}
\item{threshold_unique}{do not analyse more unique \code{threshold_unique} items per variable}
\item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.}
\item{info}{print relevant combinations to console}
}
\value{
A \code{list} with class \code{"trends"}
}
\description{
Test text
}

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@ -49,5 +49,9 @@ test_that("frequency table works", {
# input must be freq tbl # input must be freq tbl
expect_error(septic_patients %>% top_freq(1)) expect_error(septic_patients %>% top_freq(1))
# charts from plot and hist, should not raise errors
plot(freq(septic_patients, age))
hist(freq(septic_patients, age))
}) })

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@ -0,0 +1,13 @@
context("kurtosis.R")
test_that("kurtosis works", {
expect_equal(kurtosis(septic_patients$age),
6.423118,
tolerance = 0.00001)
expect_equal(unname(kurtosis(data.frame(septic_patients$age))),
6.423118,
tolerance = 0.00001)
expect_equal(kurtosis(matrix(septic_patients$age)),
6.423118,
tolerance = 0.00001)
})

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@ -0,0 +1,13 @@
context("skewness.R")
test_that("skewness works", {
expect_equal(skewness(septic_patients$age),
-1.637164,
tolerance = 0.00001)
expect_equal(unname(skewness(data.frame(septic_patients$age))),
-1.637164,
tolerance = 0.00001)
expect_equal(skewness(matrix(septic_patients$age)),
-1.637164,
tolerance = 0.00001)
})