diff --git a/.travis.yml b/.travis.yml index 83dbc8b..8d139ac 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,5 +3,3 @@ language: R sudo: false cache: packages -after_success: - - Rscript -e 'covr::codecov()' diff --git a/DESCRIPTION b/DESCRIPTION index c61b0c8..29f9ab1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -22,7 +22,6 @@ Imports: Suggests: knitr, rmarkdown, - survival, - covr + survival RoxygenNote: 6.0.1 VignetteBuilder: knitr diff --git a/NEWS b/NEWS index b587b54..d41063c 100644 --- a/NEWS +++ b/NEWS @@ -2,7 +2,6 @@ Version 1.3.0 - Added travis CI to repo - Added CRAN badge to repo -- Added code coverage to repo - Corrected a typo in the README and vignette - Bugfix for when a statistical function returns a warning AND an error - Added NEWS file for future releases diff --git a/README.Rmd b/README.Rmd index 9d22b25..7ddc9cc 100644 --- a/README.Rmd +++ b/README.Rmd @@ -7,7 +7,7 @@ output: github_document knitr::opts_chunk$set(message = F, warning = F) ``` -[![Travis-CI Build Status](https://travis-ci.org/MaximeWack/desctable.svg?branch=dev)](https://travis-ci.org/MaximeWack/desctable) [![Coverage Status](https://img.shields.io/codecov/c/github/MaximeWack/desctable/dev.svg)](https://codecov.io/github/MaximeWack/desctable?branch=dev) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/desctable)](https://cran.r-project.org/package=desctable) [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/desctable)](http://www.r-pkg.org/pkg/desctable) +[![Travis-CI Build Status](https://travis-ci.org/MaximeWack/desctable.svg?branch=master)](https://travis-ci.org/MaximeWack/desctable) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/desctable)](https://cran.r-project.org/package=desctable) [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/desctable)](http://www.r-pkg.org/pkg/desctable) # Introduction diff --git a/README.md b/README.md index d1c6eb2..35c7318 100644 --- a/README.md +++ b/README.md @@ -1,30 +1,39 @@ Desctable ================ -[![Travis-CI Build Status](https://travis-ci.org/MaximeWack/desctable.svg?branch=dev)](https://travis-ci.org/MaximeWack/desctable) [![Coverage Status](https://img.shields.io/codecov/c/github/MaximeWack/desctable/dev.svg)](https://codecov.io/github/MaximeWack/desctable?branch=dev) [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/desctable)](https://cran.r-project.org/package=desctable) [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/desctable)](http://www.r-pkg.org/pkg/desctable) +[![Travis-CI Build +Status](https://travis-ci.org/MaximeWack/desctable.svg?branch=master)](https://travis-ci.org/MaximeWack/desctable) +[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/desctable)](https://cran.r-project.org/package=desctable) +[![CRAN RStudio mirror +downloads](http://cranlogs.r-pkg.org/badges/desctable)](http://www.r-pkg.org/pkg/desctable) -Introduction -============ +# Introduction -Desctable is a comprehensive descriptive and comparative tables generator for R. +Desctable is a comprehensive descriptive and comparative tables +generator for R. -Every person doing data analysis has to create tables for descriptive summaries of data (a.k.a. Table.1), or comparative tables. +Every person doing data analysis has to create tables for descriptive +summaries of data (a.k.a. Table.1), or comparative tables. -Many packages, such as the aptly named **tableone**, address this issue. However, they often include hard-coded behaviors, have outputs not easily manipulable with standard R tools, or their syntax are out-of-style (e.g. the argument order makes them difficult to use with the pipe (`%>%`)). +Many packages, such as the aptly named **tableone**, address this issue. +However, they often include hard-coded behaviors, have outputs not +easily manipulable with standard R tools, or their syntax are +out-of-style (e.g. the argument order makes them difficult to use with +the pipe (`%>%`)). -Enter **desctable**, a package built with the following objectives in mind: +Enter **desctable**, a package built with the following objectives in +mind: -- generate descriptive and comparative statistics tables with nesting -- keep the syntax as simple as possible -- have good reasonable defaults -- be entirely customizable, using standard R tools and functions -- produce the simplest (as a data structure) output possible -- provide helpers for different outputs -- integrate with "modern" R usage, and the **tidyverse** set of tools -- apply functional paradigms + - generate descriptive and comparative statistics tables with nesting + - keep the syntax as simple as possible + - have good reasonable defaults + - be entirely customizable, using standard R tools and functions + - produce the simplest (as a data structure) output possible + - provide helpers for different outputs + - integrate with “modern” R usage, and the **tidyverse** set of tools + - apply functional paradigms -Installation -============ +# Installation Install from CRAN with @@ -34,8 +43,7 @@ or install the development version from github with devtools::install_github("maximewack/desctable") -Loading -======= +# Loading ``` r # If you were to use DT, load it first @@ -51,17 +59,17 @@ It is recommended to read this manual through its vignette: vignette("desctable") ``` ------------------------------------------------------------------------- +----- -Descriptive tables -================== +# Descriptive tables -Simple usage ------------- +## Simple usage -**desctable** uses and exports the pipe (`%>%`) operator (from packages **magrittr** and **dplyr** fame), though it is not mandatory to use it. +**desctable** uses and exports the pipe (`%>%`) operator (from packages +**magrittr** and **dplyr** fame), though it is not mandatory to use it. -The single interface to the package is its eponymous `desctable` function. +The single interface to the package is its eponymous `desctable` +function. When used on a data.frame, it returns a descriptive table: @@ -70,40 +78,46 @@ iris %>% desctable ``` - ##   N % Mean sd Med IQR - ## 1 Sepal.Length 150 NA NA NA 5.80 1.3 - ## 2 Sepal.Width 150 NA 3.057333 0.4358663 3.00 0.5 - ## 3 Petal.Length 150 NA NA NA 4.35 3.5 - ## 4 Petal.Width 150 NA NA NA 1.30 1.5 - ## 5 Species 150 NA NA NA NA NA - ## 6 Species: setosa 50 33.33333 NA NA NA NA - ## 7 Species: versicolor 50 33.33333 NA NA NA NA - ## 8 Species: virginica 50 33.33333 NA NA NA NA + ## N Mean/% sd Med IQR + ## 1 Sepal.Length 150 NA NA 5.80 1.3 + ## 2 Sepal.Width 150 3.057333 0.4358663 NA NA + ## 3 Petal.Length 150 NA NA 4.35 3.5 + ## 4 Petal.Width 150 NA NA 1.30 1.5 + ## 5 Species 150 NA NA NA NA + ## 6 Species: setosa 50 33.333333 NA NA NA + ## 7 Species: versicolor 50 33.333333 NA NA NA + ## 8 Species: virginica 50 33.333333 NA NA NA ``` r desctable(mtcars) ``` - ##   N Mean sd Med IQR - ## 1 mpg 32 20.090625 6.0269481 19.200 7.37500 + ## N Mean sd Med IQR + ## 1 mpg 32 20.090625 6.0269481 NA NA ## 2 cyl 32 NA NA 6.000 4.00000 ## 3 disp 32 NA NA 196.300 205.17500 ## 4 hp 32 NA NA 123.000 83.50000 - ## 5 drat 32 3.596563 0.5346787 3.695 0.84000 + ## 5 drat 32 3.596563 0.5346787 NA NA ## 6 wt 32 NA NA 3.325 1.02875 - ## 7 qsec 32 17.848750 1.7869432 17.710 2.00750 + ## 7 qsec 32 17.848750 1.7869432 NA NA ## 8 vs 32 NA NA 0.000 1.00000 ## 9 am 32 NA NA 0.000 1.00000 ## 10 gear 32 NA NA 4.000 1.00000 ## 11 carb 32 NA NA 2.000 2.00000 -As you can see with these two examples, `desctable` describes every variable, with individual levels for factors. It picks statistical functions depending on the type and distribution of the variables in the data, and applies those statistical functions only on the relevant variables. +As you can see with these two examples, `desctable` describes every +variable, with individual levels for factors. It picks statistical +functions depending on the type and distribution of the variables in the +data, and applies those statistical functions only on the relevant +variables. -Output ------- +## Output -The object produced by `desctable` is in fact a list of data.frames, with a "desctable" class. -Methods for reduction to a simple dataframe (`as.data.frame`, automatically used for printing), conversion to markdown (`pander`), and interactive html output with **DT** (`datatable`) are provided: +The object produced by `desctable` is in fact a list of data.frames, +with a “desctable” class. +Methods for reduction to a simple dataframe (`as.data.frame`, +automatically used for printing), conversion to markdown (`pander`), and +interactive html output with **DT** (`datatable`) are provided: ``` r iris %>% @@ -111,140 +125,73 @@ iris %>% pander ``` - --------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 N%MeansdMedIQR
Sepal.Length1505.81.3
Sepal.Width1503.10.4430.5
Petal.Length1504.33.5
Petal.Width1501.31.5
Species150
    setosa5033
    versicolor5033
    virginica5033
- -
You need to load these two packages first (and prior to **desctable** for **DT**) if you want to use them. - -Calls to `pander` and `datatable` with "regular" dataframes will not be affected by the defaults used in the package, and you can modify these defaults for **desctable** objects. - -The `datatable` wrapper function for desctable objects comes with some default options and formatting such as freezing the row names and table header, export buttons, and rounding of values. Both `pander` and `datatable` wrapper take a *digits* argument to set the number of decimals to show. (`pander` uses the *digits*, *justify* and *missing* arguments of `pandoc.table`, whereas `datatable` calls `prettyNum` with the `digits` parameter, and removes `NA` values. You can set `digits = NULL` if you want the full table and format it yourself) - -Advanced usage --------------- +| | N | Mean/% | sd | Med | IQR | +| :----------- | :-- | :----- | :--- | :-- | :-- | +| Sepal.Length | 150 | | | 5.8 | 1.3 | +| Sepal.Width | 150 | 3.1 | 0.44 | | | +| Petal.Length | 150 | | | 4.3 | 3.5 | +| Petal.Width | 150 | | | 1.3 | 1.5 | +| **Species** | 150 | | | | | +| setosa | 50 | 33 | | | | +| versicolor | 50 | 33 | | | | +| virginica | 50 | 33 | | | | + +
You need to load these two packages first (and prior to +**desctable** for **DT**) if you want to use them. + +Calls to `pander` and `datatable` with “regular” dataframes will not be +affected by the defaults used in the package, and you can modify these +defaults for **desctable** objects. + +The `datatable` wrapper function for desctable objects comes with some +default options and formatting such as freezing the row names and table +header, export buttons, and rounding of values. Both `pander` and +`datatable` wrapper take a *digits* argument to set the number of +decimals to show. (`pander` uses the *digits*, *justify* and *missing* +arguments of `pandoc.table`, whereas `datatable` calls `prettyNum` with +the `digits` parameter, and removes `NA` values. You can set `digits = +NULL` if you want the full table and format it yourself) + +## Advanced usage `desctable` chooses statistical functions for you using this algorithm: -- always show N -- if there are factors, show % -- if there are normally distributed variables, show Mean and SD -- if there are non-normally distributed variables, show Median and IQR + - always show N + - if there are factors, show % + - if there are normally distributed variables, show Mean and SD + - if there are non-normally distributed variables, show Median and IQR -For each variable in the table, compute the relevant statistical functions in that list (non-applicable functions will safely return `NA`). +For each variable in the table, compute the relevant statistical +functions in that list (non-applicable functions will safely return +`NA`). How does it work, and how can you adapt this behavior to your needs? -`desctable` takes an optional *stats* argument. This argument can either be: +`desctable` takes an optional *stats* argument. This argument can either +be: -- an automatic function to select appropriate statistical functions -- or a named list of - - statistical functions - - formulas describing conditions to use a statistical function. + - an automatic function to select appropriate statistical functions + - or a named list of + - statistical functions + - formulas describing conditions to use a statistical function. ### Automatic function -This is the default, using the `stats_auto` function provided in the package. +This is the default, using the `stats_auto` function provided in the +package. -Several other "automatic statistical functions" are defined in this package: `stats_auto`, `stats_default`, `stats_normal`, `stats_nonnormal`. +Several other “automatic statistical functions” are defined in this +package: `stats_auto`, `stats_default`, `stats_normal`, +`stats_nonnormal`. You can also provide your own automatic function, which needs to -- accept a dataframe as its argument (whether to use this dataframe or not in the function is your choice), and -- return a named list of statistical functions to use, as defined in the subsequent paragraphs. + - accept a dataframe as its argument (whether to use this dataframe or + not in the function is your choice), and + - return a named list of statistical functions to use, as defined in + the subsequent paragraphs. + + ``` r # Strictly equivalent to iris %>% desctable %>% pander @@ -253,108 +200,25 @@ iris %>% pander ``` - --------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 N%MeansdMedIQR
Sepal.Length1505.81.3
Sepal.Width1503.10.4430.5
Petal.Length1504.33.5
Petal.Width1501.31.5
Species150
    setosa5033
    versicolor5033
    virginica5033
+| | N | Mean/% | sd | Med | IQR | +| :----------- | :-- | :----- | :--- | :-- | :-- | +| Sepal.Length | 150 | | | 5.8 | 1.3 | +| Sepal.Width | 150 | 3.1 | 0.44 | | | +| Petal.Length | 150 | | | 4.3 | 3.5 | +| Petal.Width | 150 | | | 1.3 | 1.5 | +| **Species** | 150 | | | | | +| setosa | 50 | 33 | | | | +| versicolor | 50 | 33 | | | | +| virginica | 50 | 33 | | | | ### Statistical functions -Statistical functions can be any function defined in R that you want to use, such as `length` or `mean`. +Statistical functions can be any function defined in R that you want to +use, such as `length` or `mean`. -The only condition is that they return a single numerical value. One exception is when they return a vector of length `1 + nlevels(x)` when applied to factors, as is needed for the `percent` function. +The only condition is that they return a single numerical value. One +exception is when they return a vector of length `1 + nlevels(x)` when +applied to factors, as is needed for the `percent` function. As mentioned above, they need to be used inside a named list, such as @@ -364,99 +228,36 @@ mtcars %>% pander ``` - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 NMeanSD
mpg32206
cyl326.21.8
disp32231124
hp3214769
drat323.60.53
wt323.20.98
qsec32181.8
vs320.440.5
am320.410.5
gear323.70.74
carb322.81.6
+| | N | Mean | SD | +| :--- | :- | :--- | :--- | +| mpg | 32 | 20 | 6 | +| cyl | 32 | 6.2 | 1.8 | +| disp | 32 | 231 | 124 | +| hp | 32 | 147 | 69 | +| drat | 32 | 3.6 | 0.53 | +| wt | 32 | 3.2 | 0.98 | +| qsec | 32 | 18 | 1.8 | +| vs | 32 | 0.44 | 0.5 | +| am | 32 | 0.41 | 0.5 | +| gear | 32 | 3.7 | 0.74 | +| carb | 32 | 2.8 | 1.6 |
-The names will be used as column headers in the resulting table, and the functions will be applied safely on the variables (errors return `NA`, and for factors the function will be used on individual levels). +The names will be used as column headers in the resulting table, and the +functions will be applied safely on the variables (errors return `NA`, +and for factors the function will be used on individual levels). -Several convenience functions are included in this package. For statistical function we have: `percent`, which prints percentages of levels in a factor, and `IQR` which re-implements `stats::IQR` but works better with `NA` values. +Several convenience functions are included in this package. For +statistical function we have: `percent`, which prints percentages of +levels in a factor, and `IQR` which re-implements `stats::IQR` but works +better with `NA` values. -Be aware that **all functions will be used on variables stripped of their `NA` values!** -This is necessary for most statistical functions to be useful, and makes **N** (`length`) show only the number of observations in the dataset for each variable. +Be aware that **all functions will be used on variables stripped of +their `NA` values\!** +This is necessary for most statistical functions to be useful, and makes +**N** (`length`) show only the number of observations in the dataset for +each variable. ### Conditional formulas @@ -466,19 +267,26 @@ The general form of these formulas is predicate_function ~ stat_function_if_TRUE | stat_function_if_FALSE ``` -A predicate function is any function returning either `TRUE` or `FALSE` when applied on a vector, such as `is.factor`, `is.numeric`, and `is.logical`. -**desctable** provides the `is.normal` function to test for normality (it is equivalent to `length(na.omit(x)) > 30 & shapiro.test(x)$p.value > .1`). +A predicate function is any function returning either `TRUE` or `FALSE` +when applied on a vector, such as `is.factor`, `is.numeric`, and +`is.logical`. +**desctable** provides the `is.normal` function to test for normality +(it is equivalent to `length(na.omit(x)) > 30 & shapiro.test(x)$p.value +> .1`). -The *FALSE* option can be omitted and `NA` will be produced if the condition in the predicate is not met. +The *FALSE* option can be omitted and `NA` will be produced if the +condition in the predicate is not met. -These statements can be nested using parentheses. +These statements can be nested using parentheses. For example: `is.factor ~ percent | (is.normal ~ mean)` -will either use `percent` if the variable is a factor, or `mean` if and only if the variable is normally distributed. +will either use `percent` if the variable is a factor, or `mean` if and +only if the variable is normally distributed. -You can mix "bare" statistical functions and formulas in the list defining the statistics you want to use in your table. +You can mix “bare” statistical functions and formulas in the list +defining the statistics you want to use in your table. ``` r iris %>% @@ -488,76 +296,21 @@ iris %>% pander ``` - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 N%/MeanMedian
Sepal.Length1505.8
Sepal.Width1503.1
Petal.Length1504.3
Petal.Width1501.3
Species150
    setosa5033
    versicolor5033
    virginica5033
+| | N | %/Mean | Median | +| :----------- | :-- | :----- | :----- | +| Sepal.Length | 150 | | 5.8 | +| Sepal.Width | 150 | 3.1 | | +| Petal.Length | 150 | | 4.3 | +| Petal.Width | 150 | | 1.3 | +| **Species** | 150 | | | +| setosa | 50 | 33 | | +| versicolor | 50 | 33 | | +| virginica | 50 | 33 | |
-For reference, here is the body of the `stats_auto` function in the package: +For reference, here is the body of the `stats_auto` function in the +package: ## function (data) ## { @@ -580,22 +333,30 @@ For reference, here is the body of the `stats_auto` function in the package: ## list(N = length, `%` = percent) ## else if (!fact & normal & nonnormal) ## list(N = length, Mean = is.normal ~ mean, sd = is.normal ~ - ## sd, Med = stats::median, IQR = is.factor ~ NA | IQR) + ## sd, Med = is.normal ~ NA | median, IQR = is.normal ~ + ## NA | IQR) ## else if (!fact & normal & !nonnormal) ## list(N = length, Mean = mean, sd = stats::sd) ## else if (!fact & !normal & nonnormal) ## list(N = length, Med = stats::median, IQR = IQR) ## else stats_default(data) ## } + ## ## ### Labels -It is often the case that variable names are not "pretty" enough to be used as-is in a table. -Although you could still edit the variable labels in the table afterwards using subsetting or string replacement functions, it is possible to mention a **labels** argument. +It is often the case that variable names are not “pretty” enough to be +used as-is in a table. +Although you could still edit the variable labels in the table +afterwards using subsetting or string replacement functions, it is +possible to mention a **labels** argument. -The **labels** argument is a named character vector associating variable names and labels. -You don't need to provide labels for all the variables, and extra labels will be silently discarded. This allows you to define a "global" labels vector and use it for every table even after variable selections. +The **labels** argument is a named character vector associating variable +names and labels. +You don’t need to provide labels for all the variables, and extra labels +will be silently discarded. This allows you to define a “global” labels +vector and use it for every table even after variable selections. ``` r mtlabels <- c(mpg = "Miles/(US) gallon", @@ -616,159 +377,32 @@ mtcars %>% pander ``` - --------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 N%MeansdMedIQR
Miles/(US) gallon32206197.4
Number of cylinders3264
Displacement (cu.in.)32196205
Gross horsepower3212384
Rear axle ratio323.60.533.70.84
Weight (1000 lbs)323.31
¼ mile time32181.8182
V/S3201
Transmission32
    Automatic1959
    Manual1341
Number of forward gears3241
Number of carburetors3222
+| | N | Mean/% | sd | Med | IQR | +| :---------------------- | :- | :----- | :--- | :-- | :-- | +| Miles/(US) gallon | 32 | 20 | 6 | | | +| Number of cylinders | 32 | | | 6 | 4 | +| Displacement (cu.in.) | 32 | | | 196 | 205 | +| Gross horsepower | 32 | | | 123 | 84 | +| Rear axle ratio | 32 | 3.6 | 0.53 | | | +| Weight (1000 lbs) | 32 | | | 3.3 | 1 | +| ¼ mile time | 32 | 18 | 1.8 | | | +| V/S | 32 | | | 0 | 1 | +| **Transmission** | 32 | | | | | +| Automatic | 19 | 59 | | | | +| Manual | 13 | 41 | | | | +| Number of forward gears | 32 | | | 4 | 1 | +| Number of carburetors | 32 | | | 2 | 2 |
------------------------------------------------------------------------- +----- -Comparative tables -================== +# Comparative tables -Simple usage ------------- +## Simple usage -Creating a comparative table (between groups defined by a factor) using `desctable` is as easy as creating a descriptive table. +Creating a comparative table (between groups defined by a factor) using +`desctable` is as easy as creating a descriptive table. It uses the well known `group_by` function from **dplyr**: @@ -780,57 +414,63 @@ iris %>% iris_by_Species ``` - ##   Species: setosa (n=50) / N Species: setosa (n=50) / Mean + ## Species: setosa (n=50) / N Species: setosa (n=50) / Mean ## 1 Sepal.Length 50 5.006 ## 2 Sepal.Width 50 3.428 ## 3 Petal.Length 50 NA ## 4 Petal.Width 50 NA ## Species: setosa (n=50) / sd Species: setosa (n=50) / Med - ## 1 0.3524897 5.0 - ## 2 0.3790644 3.4 + ## 1 0.3524897 NA + ## 2 0.3790644 NA ## 3 NA 1.5 ## 4 NA 0.2 - ## Species: setosa (n=50) / IQR Species: versicolor (n=50) / N - ## 1 0.400 50 - ## 2 0.475 50 - ## 3 0.175 50 - ## 4 0.100 50 - ## Species: versicolor (n=50) / Mean Species: versicolor (n=50) / sd - ## 1 5.936 0.5161711 - ## 2 2.770 0.3137983 - ## 3 4.260 0.4699110 - ## 4 NA NA - ## Species: versicolor (n=50) / Med Species: versicolor (n=50) / IQR - ## 1 5.90 0.700 - ## 2 2.80 0.475 - ## 3 4.35 0.600 - ## 4 1.30 0.300 - ## Species: virginica (n=50) / N Species: virginica (n=50) / Mean - ## 1 50 6.588 - ## 2 50 2.974 - ## 3 50 5.552 - ## 4 50 NA - ## Species: virginica (n=50) / sd Species: virginica (n=50) / Med - ## 1 0.6358796 6.50 - ## 2 0.3224966 3.00 - ## 3 0.5518947 5.55 - ## 4 NA 2.00 - ## Species: virginica (n=50) / IQR tests / p - ## 1 0.675 1.505059e-28 - ## 2 0.375 4.492017e-17 - ## 3 0.775 4.803974e-29 - ## 4 0.500 3.261796e-29 + ## Species: setosa (n=50) / IQR Species: versicolor (n=50) / N1 + ## 1 NA 50 + ## 2 NA 50 + ## 3 0.175 50 + ## 4 0.100 50 + ## Species: versicolor (n=50) / Mean1 Species: versicolor (n=50) / sd1 + ## 1 5.936 0.5161711 + ## 2 2.770 0.3137983 + ## 3 4.260 0.4699110 + ## 4 NA NA + ## Species: versicolor (n=50) / Med1 Species: versicolor (n=50) / IQR1 + ## 1 NA NA + ## 2 NA NA + ## 3 NA NA + ## 4 1.3 0.3 + ## Species: virginica (n=50) / N2 Species: virginica (n=50) / Mean2 + ## 1 50 6.588 + ## 2 50 2.974 + ## 3 50 5.552 + ## 4 50 NA + ## Species: virginica (n=50) / sd2 Species: virginica (n=50) / Med2 + ## 1 0.6358796 NA + ## 2 0.3224966 NA + ## 3 0.5518947 NA + ## 4 NA 2 + ## Species: virginica (n=50) / IQR2 tests / p + ## 1 NA 1.505059e-28 + ## 2 NA 4.492017e-17 + ## 3 NA 4.803974e-29 + ## 4 0.5 3.261796e-29 ## tests / test ## 1 . %>% oneway.test(var.equal = F) ## 2 . %>% oneway.test(var.equal = T) ## 3 kruskal.test ## 4 kruskal.test -The result is a table containing a descriptive subtable for each level of the grouping factor (the statistical functions rules are applied to each subtable independently), with the statistical tests performed, and their p values. +The result is a table containing a descriptive subtable for each level +of the grouping factor (the statistical functions rules are applied to +each subtable independently), with the statistical tests performed, and +their p values. -When displayed as a flat dataframe, the grouping header appears in each variable. +When displayed as a flat dataframe, the grouping header appears in each +variable. -You can also see the grouping headers by inspecting the resulting object, which is a deep list of dataframes, each dataframe named after the grouping factor and its levels (with sample size for each). +You can also see the grouping headers by inspecting the resulting +object, which is a deep list of dataframes, each dataframe named after +the grouping factor and its levels (with sample size for each). ``` r str(iris_by_Species) @@ -843,20 +483,20 @@ str(iris_by_Species) ## ..$ N : num [1:4] 50 50 50 50 ## ..$ Mean: num [1:4] 5.01 3.43 NA NA ## ..$ sd : num [1:4] 0.352 0.379 NA NA - ## ..$ Med : num [1:4] 5 3.4 1.5 0.2 - ## ..$ IQR : num [1:4] 0.4 0.475 0.175 0.1 + ## ..$ Med : num [1:4] NA NA 1.5 0.2 + ## ..$ IQR : num [1:4] NA NA 0.175 0.1 ## $ Species: versicolor (n=50):'data.frame': 4 obs. of 5 variables: ## ..$ N : num [1:4] 50 50 50 50 ## ..$ Mean: num [1:4] 5.94 2.77 4.26 NA ## ..$ sd : num [1:4] 0.516 0.314 0.47 NA - ## ..$ Med : num [1:4] 5.9 2.8 4.35 1.3 - ## ..$ IQR : num [1:4] 0.7 0.475 0.6 0.3 + ## ..$ Med : num [1:4] NA NA NA 1.3 + ## ..$ IQR : num [1:4] NA NA NA 0.3 ## $ Species: virginica (n=50) :'data.frame': 4 obs. of 5 variables: ## ..$ N : num [1:4] 50 50 50 50 ## ..$ Mean: num [1:4] 6.59 2.97 5.55 NA ## ..$ sd : num [1:4] 0.636 0.322 0.552 NA - ## ..$ Med : num [1:4] 6.5 3 5.55 2 - ## ..$ IQR : num [1:4] 0.675 0.375 0.775 0.5 + ## ..$ Med : num [1:4] NA NA NA 2 + ## ..$ IQR : num [1:4] NA NA NA 0.5 ## $ tests :'data.frame': 4 obs. of 2 variables: ## ..$ p : num [1:4] 1.51e-28 4.49e-17 4.80e-29 3.26e-29 ## ..$ test: chr [1:4] ". %>% oneway.test(var.equal = F)" ". %>% oneway.test(var.equal = T)" "kruskal.test" "kruskal.test" @@ -872,180 +512,18 @@ mtcars %>% pander ``` - -------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 cyl: 4 (n=11)
N
Med
IQR		cyl: 6 (n=7)
N
Med
IQR		cyl: 8 (n=14)
N
Med
IQR		tests
p
test
mpg11267.67202.414151.82.6e-06kruskal.test
disp111084271683614350881.6e-06kruskal.test
hp11913071101314192653.3e-06kruskal.test
drat114.10.3573.90.56143.10.150.00075kruskal.test
wt112.20.7473.20.62143.80.481.1e-05kruskal.test
qsec11191.47182.414171.50.0062kruskal.test
vs111071114003.2e-05kruskal.test
am1110.570114000.014kruskal.test
gear1140740.514300.0062kruskal.test
carb1121741.5143.51.80.0017kruskal.test
+| | cyl: 4 (n=11)
N |
Med |
IQR | cyl: 6 (n=7)
N1 |
Med1 |
IQR1 | cyl: 8 (n=14)
N2 |
Med2 |
IQR2 | tests
p |
test | +| :--- | :------------------ | :------- | :------- | :------------------ | :-------- | :-------- | :------------------- | :-------- | :-------- | :---------- | :----------- | +| mpg | 11 | 26 | 7.6 | 7 | 20 | 2.4 | 14 | 15 | 1.8 | 2.6e-06 | kruskal.test | +| disp | 11 | 108 | 42 | 7 | 168 | 36 | 14 | 350 | 88 | 1.6e-06 | kruskal.test | +| hp | 11 | 91 | 30 | 7 | 110 | 13 | 14 | 192 | 65 | 3.3e-06 | kruskal.test | +| drat | 11 | 4.1 | 0.35 | 7 | 3.9 | 0.56 | 14 | 3.1 | 0.15 | 0.00075 | kruskal.test | +| wt | 11 | 2.2 | 0.74 | 7 | 3.2 | 0.62 | 14 | 3.8 | 0.48 | 1.1e-05 | kruskal.test | +| qsec | 11 | 19 | 1.4 | 7 | 18 | 2.4 | 14 | 17 | 1.5 | 0.0062 | kruskal.test | +| vs | 11 | 1 | 0 | 7 | 1 | 1 | 14 | 0 | 0 | 3.2e-05 | kruskal.test | +| am | 11 | 1 | 0.5 | 7 | 0 | 1 | 14 | 0 | 0 | 0.014 | kruskal.test | +| gear | 11 | 4 | 0 | 7 | 4 | 0.5 | 14 | 3 | 0 | 0.0062 | kruskal.test | +| carb | 11 | 2 | 1 | 7 | 4 | 1.5 | 14 | 3.5 | 1.8 | 0.0017 | kruskal.test | Also with conditions: @@ -1056,182 +534,16 @@ iris %>% pander ``` - ----------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 Petal.Length > 5: FALSE (n=108)
N
%
Mean
sd
Med
IQR		Petal.Length > 5: TRUE (n=42)
N
%
Mean
sd
Med
IQR		tests
p
test
Sepal.Length1085.51426.70.851.6e-15wilcox.test
Sepal.Width1083.10.4830.64230.40.69wilcox.test
Petal.Length1083.53425.60.672.1e-21wilcox.test
Petal.Width10811.2422.10.282.10.471.6e-19wilcox.test
Species108422.5e-26fisher.test
    setosa504600
    versicolor494512.4
    virginica98.34198
+| | Petal.Length \> 5: FALSE (n=108)
N |
Mean/% |
sd |
Med |
IQR | Petal.Length \> 5: TRUE (n=42)
N1 |
Mean/%1 |
sd1 |
Med1 |
IQR1 | tests
p |
test | +| :----------- | :------------------------------------- | :---------- | :------ | :------- | :------- | :------------------------------------ | :----------- | :------- | :-------- | :-------- | :---------- | :---------- | +| Sepal.Length | 108 | | | 5.5 | 1 | 42 | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test | +| Sepal.Width | 108 | 3.1 | 0.48 | | | 42 | | | 3 | 0.4 | 0.69 | wilcox.test | +| Petal.Length | 108 | | | 3.5 | 3 | 42 | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test | +| Petal.Width | 108 | | | 1 | 1.2 | 42 | 2.1 | 0.28 | | | 1.6e-19 | wilcox.test | +| **Species** | 108 | | | | | 42 | | | | | 2.5e-26 | fisher.test | +| setosa | 50 | 46 | | | | 0 | 0 | | | | | | +| versicolor | 49 | 45 | | | | 1 | 2.4 | | | | | | +| virginica | 9 | 8.3 | | | | 41 | 98 | | | | | |
@@ -1245,372 +557,73 @@ mtcars %>% pander ``` - -------------------------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 vs: 0 (n=18)
am: Automatic (n=12)
cyl: 8 (n=12)
N


Med


IQR

tests
p


test
am: Manual (n=6)
cyl: 4 (n=1)
N


Med


IQR

cyl: 6 (n=3)
N


Med


IQR

cyl: 8 (n=2)
N


Med


IQR

tests
p


test		vs: 1 (n=14)
am: Automatic (n=7)
cyl: 4 (n=3)
N


Med


IQR

cyl: 6 (n=4)
N


Med


IQR

tests
p


test
am: Manual (n=7)
cyl: 4 (n=7)
N


Med


IQR

tests
p


test
mpg12152.6no.test12603210.652150.40.11kruskal.test3231.54191.70.057wilcox.test7306.3no.test
disp12355113no.test1120031607.52326250.11kruskal.test3141134196660.05wilcox.test77924no.test
hp1218044no.test19103110322300360.11kruskal.test395184116140.05wilcox.test76636no.test
drat123.10.11no.test14.4033.90.1423.90.340.33kruskal.test33.70.1143.50.920.85wilcox.test74.10.2no.test
wt123.80.81no.test12.1032.80.1323.40.20.12kruskal.test33.10.3643.40.0610.05wilcox.test71.90.53no.test
qsec12170.67no.test11703160.762150.050.17kruskal.test3201.44190.890.23wilcox.test7190.62no.test
gear1230no.test150340.52500.29kruskal.test340.543.510.84wilcox.test740no.test
carb1232no.test1203412620.26kruskal.test320.542.530.85wilcox.test711no.test
+| | vs: 0 (n=18)
am: Automatic (n=12)
cyl: 8 (n=12)
N |


Med |


IQR |

tests
p |


test |
am: Manual (n=6)
cyl: 4 (n=1)
N3 |


Med3 |


IQR3 |

cyl: 6 (n=3)
N1 |


Med1 |


IQR1 |

cyl: 8 (n=2)
N2 |


Med2 |


IQR2 |

tests
p1 |


test1 | vs: 1 (n=14)
am: Automatic (n=7)
cyl: 4 (n=3)
N4 |


Med4 |


IQR4 |

cyl: 6 (n=4)
N11 |


Med11 |


IQR11 |

tests
p2 |


test2 |
am: Manual (n=7)
cyl: 4 (n=7)
N21 |


Med21 |


IQR21 |

tests
p11 |


test11 | +| :--- | :------------------------------------------------------------ | :----------------- | :----------------- | :-------------------- | :------------------ | :-------------------------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :--------------------- | :------------------- | :----------------------------------------------------------- | :------------------ | :------------------ | :----------------------------- | :------------------- | :------------------- | :--------------------- | :------------------- | :--------------------------------------------- | :------------------- | :------------------- | :---------------------- | :-------------------- | +| mpg | 12 | 15 | 2.6 | | no.test | 1 | 26 | 0 | 3 | 21 | 0.65 | 2 | 15 | 0.4 | 0.11 | kruskal.test | 3 | 23 | 1.5 | 4 | 19 | 1.7 | 0.057 | wilcox.test | 7 | 30 | 6.3 | | no.test | +| disp | 12 | 355 | 113 | | no.test | 1 | 120 | 0 | 3 | 160 | 7.5 | 2 | 326 | 25 | 0.11 | kruskal.test | 3 | 141 | 13 | 4 | 196 | 66 | 0.05 | wilcox.test | 7 | 79 | 24 | | no.test | +| hp | 12 | 180 | 44 | | no.test | 1 | 91 | 0 | 3 | 110 | 32 | 2 | 300 | 36 | 0.11 | kruskal.test | 3 | 95 | 18 | 4 | 116 | 14 | 0.05 | wilcox.test | 7 | 66 | 36 | | no.test | +| drat | 12 | 3.1 | 0.11 | | no.test | 1 | 4.4 | 0 | 3 | 3.9 | 0.14 | 2 | 3.9 | 0.34 | 0.33 | kruskal.test | 3 | 3.7 | 0.11 | 4 | 3.5 | 0.92 | 0.85 | wilcox.test | 7 | 4.1 | 0.2 | | no.test | +| wt | 12 | 3.8 | 0.81 | | no.test | 1 | 2.1 | 0 | 3 | 2.8 | 0.13 | 2 | 3.4 | 0.2 | 0.12 | kruskal.test | 3 | 3.1 | 0.36 | 4 | 3.4 | 0.061 | 0.05 | wilcox.test | 7 | 1.9 | 0.53 | | no.test | +| qsec | 12 | 17 | 0.67 | | no.test | 1 | 17 | 0 | 3 | 16 | 0.76 | 2 | 15 | 0.05 | 0.17 | kruskal.test | 3 | 20 | 1.4 | 4 | 19 | 0.89 | 0.23 | wilcox.test | 7 | 19 | 0.62 | | no.test | +| gear | 12 | 3 | 0 | | no.test | 1 | 5 | 0 | 3 | 4 | 0.5 | 2 | 5 | 0 | 0.29 | kruskal.test | 3 | 4 | 0.5 | 4 | 3.5 | 1 | 0.84 | wilcox.test | 7 | 4 | 0 | | no.test | +| carb | 12 | 3 | 2 | | no.test | 1 | 2 | 0 | 3 | 4 | 1 | 2 | 6 | 2 | 0.26 | kruskal.test | 3 | 2 | 0.5 | 4 | 2.5 | 3 | 0.85 | wilcox.test | 7 | 1 | 1 | | no.test |
-In the case of nested groups (a.k.a. sub-group analysis), statistical tests are performed only between the groups of the deepest grouping level. +In the case of nested groups (a.k.a. sub-group analysis), statistical +tests are performed only between the groups of the deepest grouping +level. -Statistical tests are automatically selected depending on the data and the grouping factor. +Statistical tests are automatically selected depending on the data and +the grouping factor. -Advanced usage --------------- +## Advanced usage `desctable` choses the statistical tests using the following algorithm: -- if the variable is a factor, use `fisher.test` -- if the grouping factor has only one level, use the provided `no.test` (which does nothing) -- if the grouping factor has two levels - - and the variable presents homoskedasticity (p value for `var.test` > .1) and normality of distribution in both groups, use `t.test(var.equal = T)` - - and the variable does not present homoskedasticity (p value for `var.test` < .1) but normality of distribution in both groups, use `t.test(var.equal = F)` - - else use `wilcox.test` -- if the grouping factor has more than two levels - - and the variable presents homoskedasticity (p value for `bartlett.test` > .1) and normality of distribution in all groups, use `oneway.test(var.equal = T)` - - and the variable does not present homoskedasticity (p value for `bartlett.test` < .1) but normality of distribution in all groups, use `oneway.test(var.equal = F)` - - else use `kruskal.test` - -But what if you want to pick a specific test for a specific variable, or change all the tests altogether? - -`desctable` takes an optional *tests* argument. This argument can either be - -- an automatic function to select appropriate statistical test functions -- or a named list of statistical test functions + - if the variable is a factor, use `fisher.test` + - if the grouping factor has only one level, use the provided + `no.test` (which does nothing) + - if the grouping factor has two levels + - and the variable presents homoskedasticity (p value for + `var.test` \> .1) and normality of distribution in both groups, + use `t.test(var.equal = T)` + - and the variable does not present homoskedasticity (p value for + `var.test` \< .1) but normality of distribution in both groups, + use `t.test(var.equal = F)` + - else use `wilcox.test` + - if the grouping factor has more than two levels + - and the variable presents homoskedasticity (p value for + `bartlett.test` \> .1) and normality of distribution in all + groups, use `oneway.test(var.equal = T)` + - and the variable does not present homoskedasticity (p value for + `bartlett.test` \< .1) but normality of distribution in all + groups, use `oneway.test(var.equal = F)` + - else use `kruskal.test` + +But what if you want to pick a specific test for a specific variable, or +change all the tests altogether? + +`desctable` takes an optional *tests* argument. This argument can either +be + + - an automatic function to select appropriate statistical test + functions + - or a named list of statistical test functions ### Automatic function -This is the default, using the `tests_auto` function provided in the package. +This is the default, using the `tests_auto` function provided in the +package. You can also provide your own automatic function, which needs to -- accept a variable and a grouping factor as its arguments, and -- return a single-term formula containing a statistical test function. + - accept a variable and a grouping factor as its arguments, and + - return a single-term formula containing a statistical test function. -This function will be used on every variable and every grouping factor to determine the appropriate test. +This function will be used on every variable and every grouping factor +to determine the appropriate +test. ``` r # Strictly equivalent to iris %>% group_by(Species) %>% desctable %>% pander @@ -1620,146 +633,31 @@ iris %>% pander ``` - -------------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 Species: setosa (n=50)
N
Mean
sd
Med
IQR		Species: versicolor (n=50)
N
Mean
sd
Med
IQR		Species: virginica (n=50)
N
Mean
sd
Med
IQR		tests
p
test
Sepal.Length5050.3550.4505.90.525.90.7506.60.646.50.671.5e-28. %>% oneway.test(var.equal = F)
Sepal.Width503.40.383.40.48502.80.312.80.485030.3230.384.5e-17. %>% oneway.test(var.equal = T)
Petal.Length501.50.18504.30.474.30.6505.60.555.50.784.8e-29kruskal.test
Petal.Width500.20.1501.30.35020.53.3e-29kruskal.test
+| | Species: setosa (n=50)
N |
Mean |
sd |
Med |
IQR | Species: versicolor (n=50)
N1 |
Mean1 |
sd1 |
Med1 |
IQR1 | Species: virginica (n=50)
N2 |
Mean2 |
sd2 |
Med2 |
IQR2 | tests
p |
test | +| :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :-------------------------------- | +| Sepal.Length | 50 | 5 | 0.35 | | | 50 | 5.9 | 0.52 | | | 50 | 6.6 | 0.64 | | | 1.5e-28 | . %\>% oneway.test(var.equal = F) | +| Sepal.Width | 50 | 3.4 | 0.38 | | | 50 | 2.8 | 0.31 | | | 50 | 3 | 0.32 | | | 4.5e-17 | . %\>% oneway.test(var.equal = T) | +| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | | | 50 | 5.6 | 0.55 | | | 4.8e-29 | kruskal.test | +| Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test |
### List of statistical test functions -You can provide a named list of statistical functions, but here the mechanism is a bit different from the *stats* argument. +You can provide a named list of statistical functions, but here the +mechanism is a bit different from the *stats* argument. The list must contain either `.auto` or `.default`. -- `.auto` needs to be an automatic function, such as `tests_auto`. It will be used by default on all variables to select a test -- `.default` needs to be a single-term formula containing a statistical test function that will be used on all variables + - `.auto` needs to be an automatic function, such as `tests_auto`. It + will be used by default on all variables to select a test + - `.default` needs to be a single-term formula containing a + statistical test function that will be used on all variables -You can also provide overrides to use specific tests for specific variables. -This is done using list items named as the variable and containing a single-term formula function. +You can also provide overrides to use specific tests for specific +variables. +This is done using list items named as the variable and containing a +single-term formula function. ``` r iris %>% @@ -1769,182 +667,16 @@ iris %>% pander ``` - ----------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 Petal.Length > 5: FALSE (n=108)
N
%
Mean
sd
Med
IQR		Petal.Length > 5: TRUE (n=42)
N
%
Mean
sd
Med
IQR		tests
p
test
Sepal.Length1085.51426.70.851.6e-15wilcox.test
Sepal.Width1083.10.4830.64230.40.69wilcox.test
Petal.Length1083.53425.60.672.1e-21wilcox.test
Petal.Width10811.2422.10.282.10.471.6e-19wilcox.test
Species108422.7e-24chisq.test
    setosa504600
    versicolor494512.4
    virginica98.34198
+| | Petal.Length \> 5: FALSE (n=108)
N |
Mean/% |
sd |
Med |
IQR | Petal.Length \> 5: TRUE (n=42)
N1 |
Mean/%1 |
sd1 |
Med1 |
IQR1 | tests
p |
test | +| :----------- | :------------------------------------- | :---------- | :------ | :------- | :------- | :------------------------------------ | :----------- | :------- | :-------- | :-------- | :---------- | :---------- | +| Sepal.Length | 108 | | | 5.5 | 1 | 42 | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test | +| Sepal.Width | 108 | 3.1 | 0.48 | | | 42 | | | 3 | 0.4 | 0.69 | wilcox.test | +| Petal.Length | 108 | | | 3.5 | 3 | 42 | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test | +| Petal.Width | 108 | | | 1 | 1.2 | 42 | 2.1 | 0.28 | | | 1.6e-19 | wilcox.test | +| **Species** | 108 | | | | | 42 | | | | | 2.7e-24 | chisq.test | +| setosa | 50 | 46 | | | | 0 | 0 | | | | | | +| versicolor | 49 | 45 | | | | 1 | 2.4 | | | | | | +| virginica | 9 | 8.3 | | | | 41 | 98 | | | | | |
@@ -1957,162 +689,46 @@ mtcars %>% pander ``` - ----------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 am: Automatic (n=19)
N
Med
IQR		am: Manual (n=13)
N
Med
IQR		tests
p
test
mpg19174.213239.40.0014t.test
cyl198213420.0039wilcox.test
disp1927616413120810.00055wilcox.test
hp191757613109470.046wilcox.test
drat193.10.63134.10.370.00014wilcox.test
wt193.50.41132.30.844.3e-05wilcox.test
qsec1918213172.10.27wilcox.test
vs190113110.36wilcox.test
gear193013417.6e-06wilcox.test
carb193213230.74wilcox.test
+| | am: Automatic (n=19)
N |
Med |
IQR | am: Manual (n=13)
N1 |
Med1 |
IQR1 | tests
p |
test | +| :--- | :------------------------- | :------- | :------- | :----------------------- | :-------- | :-------- | :---------- | :---------- | +| mpg | 19 | 17 | 4.2 | 13 | 23 | 9.4 | 0.0014 | t.test | +| cyl | 19 | 8 | 2 | 13 | 4 | 2 | 0.0039 | wilcox.test | +| disp | 19 | 276 | 164 | 13 | 120 | 81 | 0.00055 | wilcox.test | +| hp | 19 | 175 | 76 | 13 | 109 | 47 | 0.046 | wilcox.test | +| drat | 19 | 3.1 | 0.63 | 13 | 4.1 | 0.37 | 0.00014 | wilcox.test | +| wt | 19 | 3.5 | 0.41 | 13 | 2.3 | 0.84 | 4.3e-05 | wilcox.test | +| qsec | 19 | 18 | 2 | 13 | 17 | 2.1 | 0.27 | wilcox.test | +| vs | 19 | 0 | 1 | 13 | 1 | 1 | 0.36 | wilcox.test | +| gear | 19 | 3 | 0 | 13 | 4 | 1 | 7.6e-06 | wilcox.test | +| carb | 19 | 3 | 2 | 13 | 2 | 3 | 0.74 | wilcox.test |
-You might wonder why the formula expression. That is needed to capture the test name, and to provide it in the resulting table. +You might wonder why the formula expression. That is needed to capture +the test name, and to provide it in the resulting table. -As with statistical functions, any statistical test function defined in R can be used. +As with statistical functions, any statistical test function defined in +R can be used. The conditions are that the function -- accepts a formula (`variable ~ grouping_variable`) as a first positional argument (as is the case with most tests, like `t.test`), and -- returns an object with a `p.value` element. + - accepts a formula (`variable ~ grouping_variable`) as a first + positional argument (as is the case with most tests, like `t.test`), + and + - returns an object with a `p.value` element. -Several convenience function are provided: formula versions for `chisq.test` and `fisher.test` using generic S3 methods (thus the behavior of standard calls to `chisq.test` and `fisher.test` are not modified), and `ANOVA`, a partial application of `oneway.test` with parameter *var.equal* = T. +Several convenience function are provided: formula versions for +`chisq.test` and `fisher.test` using generic S3 methods (thus the +behavior of standard calls to `chisq.test` and `fisher.test` are not +modified), and `ANOVA`, a partial application of `oneway.test` with +parameter *var.equal* = T. -Tips and tricks -=============== +# Tips and tricks -In the *stats* argument, you can not only feed function names, but even arbitrary function definitions, functional sequences (a feature provided with the pipe (`%>%`)), or partial applications (with the **purrr** package): +In the *stats* argument, you can not only feed function names, but even +arbitrary function definitions, functional sequences (a feature provided +with the pipe (`%>%`)), or partial applications (with the **purrr** +package): ``` r mtcars %>% @@ -2123,107 +739,24 @@ mtcars %>% pander ``` - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 NSum of squaresQ1Q3
mpg32140421523
cyl32132448
disp322179627121326
hp3283427896180
drat324233.13.9
wt323612.63.6
qsec32102931719
vs321401
am321301
gear3245234
carb3233424
+| | N | Sum of squares | Q1 | Q3 | +| :--- | :- | :------------- | :-- | :-- | +| mpg | 32 | 14042 | 15 | 23 | +| cyl | 32 | 1324 | 4 | 8 | +| disp | 32 | 2179627 | 121 | 326 | +| hp | 32 | 834278 | 96 | 180 | +| drat | 32 | 423 | 3.1 | 3.9 | +| wt | 32 | 361 | 2.6 | 3.6 | +| qsec | 32 | 10293 | 17 | 19 | +| vs | 32 | 14 | 0 | 1 | +| am | 32 | 13 | 0 | 1 | +| gear | 32 | 452 | 3 | 4 | +| carb | 32 | 334 | 2 | 4 |
-In the *tests* arguments, you can also provide function definitions, functional sequences, and partial applications in the formulas: +In the *tests* arguments, you can also provide function definitions, +functional sequences, and partial applications in the formulas: ``` r iris %>% @@ -2235,136 +768,18 @@ iris %>% pander ``` - -------------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 Species: setosa (n=50)
N
Mean
sd
Med
IQR		Species: versicolor (n=50)
N
Mean
sd
Med
IQR		Species: virginica (n=50)
N
Mean
sd
Med
IQR		tests
p
test
Sepal.Length5050.3550.4505.90.525.90.7506.60.646.50.671.7e-31purrr::partial(oneway.test, var.equal = T)
Sepal.Width503.40.383.40.48502.80.312.80.485030.3230.381.4e-14function(f) oneway.test(f, var.equal = F)
Petal.Length501.50.18504.30.474.30.6505.60.555.50.782.9e-91. %>% oneway.test(var.equal = T)
Petal.Width500.20.1501.30.35020.53.3e-29kruskal.test
+| | Species: setosa (n=50)
N |
Mean |
sd |
Med |
IQR | Species: versicolor (n=50)
N1 |
Mean1 |
sd1 |
Med1 |
IQR1 | Species: virginica (n=50)
N2 |
Mean2 |
sd2 |
Med2 |
IQR2 | tests
p |
test | +| :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :----------------------------------------- | +| Sepal.Length | 50 | 5 | 0.35 | | | 50 | 5.9 | 0.52 | | | 50 | 6.6 | 0.64 | | | 1.7e-31 | purrr::partial(oneway.test, var.equal = T) | +| Sepal.Width | 50 | 3.4 | 0.38 | | | 50 | 2.8 | 0.31 | | | 50 | 3 | 0.32 | | | 1.4e-14 | function(f) oneway.test(f, var.equal = F) | +| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | | | 50 | 5.6 | 0.55 | | | 2.9e-91 | . %\>% oneway.test(var.equal = T) | +| Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test |
-This allows you to modulate the behavior of `desctable` in every detail, such as using paired tests, or non *htest* tests. +This allows you to modulate the behavior of `desctable` in every detail, +such as using paired tests, or non *htest* +tests. ``` r # This is a contrived example, which would be better solved with a dedicated function @@ -2379,108 +794,12 @@ bladder %>% pander ``` - ----------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 rx: 1 (n=188)
N
Med
IQR		rx: 2 (n=152)
N
Med
IQR		tests
p
test
id188242415266191.3e-56wilcox.test
number18812152120.62wilcox.test
size18812152120.32wilcox.test
stop188232015225280.17wilcox.test
event18801152010.02wilcox.test
enum1882.51.51522.51.51wilcox.test
surv3763040.023. %>% survdiff %>% .$chisq %>% pchisq(1, lower.tail = F) %>% list(p.value = .)
+| | rx: 1 (n=188)
N |
Med |
IQR | rx: 2 (n=152)
N1 |
Med1 |
IQR1 | tests
p |
test | +| :----- | :------------------ | :------- | :------- | :------------------- | :-------- | :-------- | :---------- | :--------------------------------------------------------------------------------- | +| id | 188 | 24 | 24 | 152 | 66 | 19 | 1.3e-56 | wilcox.test | +| number | 188 | 1 | 2 | 152 | 1 | 2 | 0.62 | wilcox.test | +| size | 188 | 1 | 2 | 152 | 1 | 2 | 0.32 | wilcox.test | +| stop | 188 | 23 | 20 | 152 | 25 | 28 | 0.17 | wilcox.test | +| event | 188 | 0 | 1 | 152 | 0 | 1 | 0.02 | wilcox.test | +| enum | 188 | 2.5 | 1.5 | 152 | 2.5 | 1.5 | 1 | wilcox.test | +| surv | 376 | | | 304 | | | 0.023 | . %\>% survdiff %\>% .$chisq %\>% pchisq(1, lower.tail = F) %\>% list(p.value = .) |