diff --git a/.gitignore b/.gitignore index e38ad3b..3f6d1a5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,2 @@ inst/doc README.html -README.md -man/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..d1c6eb2 --- /dev/null +++ b/README.md @@ -0,0 +1,2486 @@ +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) + +Introduction +============ + +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. + +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: + +- 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 +============ + +Install from CRAN with + + install.packages("desctable") + +or install the development version from github with + + devtools::install_github("maximewack/desctable") + +Loading +======= + +``` r +# If you were to use DT, load it first +library(DT) + +library(desctable) +library(pander) # pander can be loaded at any time +``` + +It is recommended to read this manual through its vignette: + +``` r +vignette("desctable") +``` + +------------------------------------------------------------------------ + +Descriptive tables +================== + +Simple usage +------------ + +**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. + +When used on a data.frame, it returns a descriptive table: + +``` r +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 + +``` r +desctable(mtcars) +``` + + ##   N Mean sd Med IQR + ## 1 mpg 32 20.090625 6.0269481 19.200 7.37500 + ## 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 + ## 6 wt 32 NA NA 3.325 1.02875 + ## 7 qsec 32 17.848750 1.7869432 17.710 2.00750 + ## 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. + +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: + +``` r +iris %>% + desctable %>% + 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 +-------------- + +`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 + +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: + +- 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. + +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. + +``` r +# Strictly equivalent to iris %>% desctable %>% pander +iris %>% + desctable(stats = stats_auto) %>% + pander +``` + + +++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 N%MeansdMedIQR
Sepal.Length1505.81.3
Sepal.Width1503.10.4430.5
Petal.Length1504.33.5
Petal.Width1501.31.5
Species150
    setosa5033
    versicolor5033
    virginica5033
+ +### Statistical functions + +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. + +As mentioned above, they need to be used inside a named list, such as + +``` r +mtcars %>% + desctable(stats = list("N" = length, "Mean" = mean, "SD" = sd)) %>% + 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
+ +
+ +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. + +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 + +The general form of these formulas is + +``` r +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`). + +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. +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. + +You can mix "bare" statistical functions and formulas in the list defining the statistics you want to use in your table. + +``` r +iris %>% + desctable(stats = list("N" = length, + "%/Mean" = is.factor ~ percent | (is.normal ~ mean), + "Median" = is.normal ~ NA | median)) %>% + pander +``` + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 N%/MeanMedian
Sepal.Length1505.8
Sepal.Width1503.1
Petal.Length1504.3
Petal.Width1501.3
Species150
    setosa5033
    versicolor5033
    virginica5033
+ +
+ +For reference, here is the body of the `stats_auto` function in the package: + + ## function (data) + ## { + ## shapiro <- data %>% Filter(f = is.numeric) %>% lapply(is.normal) %>% + ## unlist + ## if (length(shapiro) == 0) { + ## normal <- F + ## nonnormal <- F + ## } + ## else { + ## normal <- any(shapiro) + ## nonnormal <- any(!shapiro) + ## } + ## fact <- any(data %>% lapply(is.factor) %>% unlist) + ## if (fact & normal & !nonnormal) + ## stats_normal(data) + ## else if (fact & !normal & nonnormal) + ## stats_nonnormal(data) + ## else if (fact & !normal & !nonnormal) + ## 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) + ## 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. + +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", + cyl = "Number of cylinders", + disp = "Displacement (cu.in.)", + hp = "Gross horsepower", + drat = "Rear axle ratio", + wt = "Weight (1000 lbs)", + qsec = "¼ mile time", + vs = "V/S", + am = "Transmission", + gear = "Number of forward gears", + carb = "Number of carburetors") + +mtcars %>% + dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>% + desctable(labels = mtlabels) %>% + 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
+ +
+ +------------------------------------------------------------------------ + +Comparative tables +================== + +Simple usage +------------ + +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**: + +``` r +iris %>% + group_by(Species) %>% + desctable -> iris_by_Species + +iris_by_Species +``` + + ##   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 + ## 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 + ## 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. + +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). + +``` r +str(iris_by_Species) +``` + + ## List of 5 + ## $ Variables :'data.frame': 4 obs. of 1 variable: + ## ..$ Variables: chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" + ## $ Species: setosa (n=50) :'data.frame': 4 obs. of 5 variables: + ## ..$ 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 + ## $ 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 + ## $ 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 + ## $ 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" + ## - attr(*, "class")= chr "desctable" + +You can specify groups based on any variable, not only factors: + +``` r +# With pander output +mtcars %>% + group_by(cyl) %>% + desctable %>% + 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
+ +Also with conditions: + +``` r +iris %>% + group_by(Petal.Length > 5) %>% + desctable %>% + 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
+ +
+ +And even on multiple nested groups: + +``` r +mtcars %>% + dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>% + group_by(vs, am, cyl) %>% + desctable %>% + 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
+ +
+ +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. + +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 + +### Automatic function + +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. + +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 +iris %>% + group_by(Species) %>% + desctable(tests = tests_auto) %>% + 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
+ +
+ +### 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. + +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 + +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 %>% + group_by(Petal.Length > 5) %>% + desctable(tests = list(.auto = tests_auto, + Species = ~chisq.test)) %>% + 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
+ +
+ +``` r +mtcars %>% + dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>% + group_by(am) %>% + desctable(tests = list(.default = ~wilcox.test, + mpg = ~t.test)) %>% + 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
+ +
+ +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. + +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. + +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 +=============== + +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 %>% + desctable(stats = list("N" = length, + "Sum of squares" = function(x) sum(x^2), + "Q1" = . %>% quantile(prob = .25), + "Q3" = purrr::partial(quantile, probs = .75))) %>% + pander +``` + + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 NSum of squaresQ1Q3
mpg32140421523
cyl32132448
disp322179627121326
hp3283427896180
drat324233.13.9
wt323612.63.6
qsec32102931719
vs321401
am321301
gear3245234
carb3233424
+ +
+ +In the *tests* arguments, you can also provide function definitions, functional sequences, and partial applications in the formulas: + +``` r +iris %>% + group_by(Species) %>% + desctable(tests = list(.auto = tests_auto, + Sepal.Width = ~function(f) oneway.test(f, var.equal = F), + Petal.Length = ~. %>% oneway.test(var.equal = T), + Sepal.Length = ~purrr::partial(oneway.test, var.equal = T))) %>% + 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
+ +
+ +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 +library(survival) + +bladder$surv <- Surv(bladder$stop, bladder$event) + +bladder %>% + group_by(rx) %>% + desctable(tests = list(.default = ~wilcox.test, + surv = ~. %>% survdiff %>% .$chisq %>% pchisq(1, lower.tail = F) %>% list(p.value = .))) %>% + 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 = .)
diff --git a/inst/doc/desctable.html b/inst/doc/desctable.html new file mode 100644 index 0000000..2ee6084 --- /dev/null +++ b/inst/doc/desctable.html @@ -0,0 +1,780 @@ + + + + + + + + + + + + + + +desctable usage vignette + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

desctable usage vignette

+ + + +

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.

+

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:

+ +
+
+

Descriptive tables

+
+

Simple usage

+

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.

+

When used on a data.frame, it returns a descriptive table:

+
iris %>%
+  desctable
+
##                         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
+
desctable(mtcars)
+
##          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      NA        NA
+## 6    wt 32        NA        NA   3.325   1.02875
+## 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.

+
+
+

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:

+
iris %>%
+  desctable %>%
+  pander
+ ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 NMean/%sdMedIQR
Sepal.Length1505.81.3
Sepal.Width1503.10.44
Petal.Length1504.33.5
Petal.Width1501.31.5
Species150
    setosa5033
    versicolor5033
    virginica5033
+
mtcars %>%
+  desctable %>%
+  datatable
+

+
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.

+

Subsequent outputs in this vignette section will use DT. 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
    • +
+

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:

+
    +
  • 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.

+

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.
    • +
+
# Strictly equivalent to iris %>% desctable %>% datatable
+iris %>%
+  desctable(stats = stats_auto) %>%
+  datatable
+
+ +
+
+

Statistical functions

+

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.

+

As mentioned above, they need to be used inside a named list, such as

+
mtcars %>%
+  desctable(stats = list("N" = length, "Mean" = mean, "SD" = sd)) %>%
+  datatable
+

+

+

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.

+

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

+

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).

+

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.
+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.

+

You can mix “bare” statistical functions and formulas in the list defining the statistics you want to use in your table.

+
iris %>%
+  desctable(stats = list("N"      = length,
+                         "%/Mean" = is.factor ~ percent | (is.normal ~ mean),
+                         "Median" = is.normal ~ NA | median)) %>%
+  datatable
+

+

+

For reference, here is the body of the stats_auto function in the package:

+
## function (data) 
+## {
+##     shapiro <- data %>% Filter(f = is.numeric) %>% lapply(is.normal) %>% 
+##         unlist
+##     if (length(shapiro) == 0) {
+##         normal <- F
+##         nonnormal <- F
+##     }
+##     else {
+##         normal <- any(shapiro)
+##         nonnormal <- any(!shapiro)
+##     }
+##     fact <- any(data %>% lapply(is.factor) %>% unlist)
+##     if (fact & normal & !nonnormal) 
+##         stats_normal(data)
+##     else if (fact & !normal & nonnormal) 
+##         stats_nonnormal(data)
+##     else if (fact & !normal & !nonnormal) 
+##         list(N = length, `%` = percent)
+##     else if (!fact & normal & nonnormal) 
+##         list(N = length, Mean = is.normal ~ mean, sd = is.normal ~ 
+##             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)
+## }
+## <environment: namespace:desctable>
+
+
+

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.

+

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.

+
mtlabels <- c(mpg  = "Miles/(US) gallon",
+              cyl  = "Number of cylinders",
+              disp = "Displacement (cu.in.)",
+              hp   = "Gross horsepower",
+              drat = "Rear axle ratio",
+              wt   = "Weight (1000 lbs)",
+              qsec = "¼ mile time",
+              vs   = "V/S",
+              am   = "Transmission",
+              gear = "Number of forward gears",
+              carb = "Number of carburetors")
+
+mtcars %>%
+  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
+  desctable(labels = mtlabels) %>%
+  datatable
+

+

+
+
+
+
+
+

Comparative tables

+
+

Simple usage

+

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:

+
iris %>%
+  group_by(Species) %>%
+  desctable -> iris_by_Species
+
+iris_by_Species
+
##                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                           NA
+## 2                   0.3790644                           NA
+## 3                          NA                          1.5
+## 4                          NA                          0.2
+##   Species: setosa (n=50) / IQR Species: versicolor (n=50) / N
+## 1                           NA                             50
+## 2                           NA                             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                               NA                               NA
+## 2                               NA                               NA
+## 3                               NA                               NA
+## 4                              1.3                              0.3
+##   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                              NA
+## 2                      0.3224966                              NA
+## 3                      0.5518947                              NA
+## 4                             NA                               2
+##   Species: virginica (n=50) / IQR    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.

+

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).

+
str(iris_by_Species)
+
## List of 5
+##  $ Variables                 :'data.frame':  4 obs. of  1 variable:
+##   ..$ Variables: chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
+##  $ Species: setosa (n=50)    :'data.frame':  4 obs. of  5 variables:
+##   ..$ 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] 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] 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] 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"
+##  - attr(*, "class")= chr "desctable"
+

You can specify groups based on any variable, not only factors:

+
# With pander output
+mtcars %>%
+  group_by(cyl) %>%
+  desctable %>%
+  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
+

Also with conditions:

+
# With datatable output
+iris %>%
+  group_by(Petal.Length > 5) %>%
+  desctable %>%
+  datatable
+

+

+

And even on multiple nested groups:

+
mtcars %>%
+  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
+  group_by(vs, am, cyl) %>%
+  desctable %>%
+  datatable
+

+

+

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.

+
+
+

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
    • +
+
+

Automatic function

+

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.
    • +
+

This function will be used on every variable and every grouping factor to determine the appropriate test.

+
# Strictly equivalent to iris %>% group_by(Species) %>% desctable %>% datatable
+iris %>%
+  group_by(Species) %>%
+  desctable(tests = tests_auto) %>%
+  datatable
+

+

+
+
+

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.

+

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
    • +
+

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.

+
iris %>%
+  group_by(Petal.Length > 5) %>%
+  desctable(tests = list(.auto   = tests_auto,
+                         Species = ~chisq.test)) %>%
+  datatable
+

+

+
mtcars %>%
+  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
+  group_by(am) %>%
+  desctable(tests = list(.default = ~wilcox.test,
+                         mpg      = ~t.test)) %>%
+  datatable
+

+

+

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.

+

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.
    • +
+

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

+

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):

+
mtcars %>%
+  desctable(stats = list("N"              = length,
+                         "Sum of squares" = function(x) sum(x^2),
+                         "Q1"             = . %>% quantile(prob = .25),
+                         "Q3"             = purrr::partial(quantile, probs = .75))) %>%
+  datatable
+

+

+

In the tests arguments, you can also provide function definitions, functional sequences, and partial applications in the formulas:

+
iris %>%
+  group_by(Species) %>%
+  desctable(tests = list(.auto = tests_auto,
+                         Sepal.Width = ~function(f) oneway.test(f, var.equal = F),
+                         Petal.Length = ~. %>% oneway.test(var.equal = T),
+                         Sepal.Length = ~purrr::partial(oneway.test, var.equal = T))) %>%
+  datatable
+

+

+

This allows you to modulate the behavior of desctable in every detail, such as using paired tests, or non htest tests.

+
# This is a contrived example, which would be better solved with a dedicated function
+library(survival)
+
+bladder$surv <- Surv(bladder$stop, bladder$event)
+
+bladder %>%
+  group_by(rx) %>%
+  desctable(tests = list(.default = ~wilcox.test,
+                         surv = ~. %>% survdiff %>% .$chisq %>% pchisq(1, lower.tail = F) %>% list(p.value = .))) %>%
+  datatable
+
+ +
+ + + + + + + +