---
title: "Introduction to Crosstable"
author: "Dan Chaltiel"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette: 
    keep_md: yes
vignette: >
  %\VignetteIndexEntry{Introduction to Crosstable}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r options, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning=FALSE, 
  message=FALSE
)
old = options(width = 100, 
              crosstable_verbosity_autotesting="quiet")
library(flextable)
```

```{r css, echo=FALSE, results="asis"}
cat("
<style>
kbd {
    background-color: #eee;
    border-radius: 3px;
    border: 1px solid #b4b4b4;
    box-shadow: 0 1px 1px rgba(0, 0, 0, .2), 0 2px 0 0 rgba(255, 255, 255, .7) inset;
    color: #333;
    display: inline-block;
    font-size: .85em;
    font-weight: 700;
    line-height: 1;
    padding: 2px 4px;
    white-space: nowrap;
}
</style>
")
```


## Crosstable

Crosstable is a package centered on a single function, `crosstable()`, which easily computes descriptive statistics on datasets.

Before starting this vignette, if you are not familiar with `dplyr` and pipes (`%>%`, <kbd>Ctrl</kbd>+<kbd>Shift</kbd>+<kbd>M</kbd> in RStudio), I warmly recommend you to read the [vignette](https://cran.r-project.org/package=dplyr/vignettes/dplyr.html), or, if you can read French, Julien Barnier's awesome [tidyverse tutorial](https://juba.github.io/tidyverse/10-dplyr.html#pipe). Nevertheless, this vignette should still be easy to understand otherwise, as `crosstable` is perfectly usable with base `R`.


## Example dataset: modified `mtcars`

First, since `crosstable()` uses the power of the `label` attribute, let's start by building a labelled dataset. 

In this vignette, we will use a modified version of the `mtcars` famous dataset, which comprises 11 aspects of design and performance for 32 automobiles. Let's modify it to add textual categories, keep row names as a column, make some numeric variables factors, and add labels from a table using `import_labels()`. 

For convenience, this dataset is already packed into {`crosstable`} as `?mtcars2` so don't bother re-creating it for your own tests.

```{r dataset}
library(crosstable)
library(dplyr)
mtcars_labels = read.table(header=TRUE, text="
  name  label
  model 'Model'
  mpg   'Miles/(US) gallon'
  cyl   'Number of cylinders'
  disp  'Displacement (cu.in.)'
  hp    'Gross horsepower'
  drat  'Rear axle ratio'
  wt    'Weight (1000 lbs)'
  qsec  '1/4 mile time'
  vs    'Engine'
  am    'Transmission'
  gear  'Number of forward gears'
  carb  'Number of carburetors'
")
mtcars2 = mtcars %>% 
  mutate(model=rownames(mtcars), 
         vs=ifelse(vs==0, "vshaped", "straight"),
         am=ifelse(am==0, "auto", "manual"), 
         across(c("cyl", "gear"), factor),
         .before=1) %>% 
  import_labels(mtcars_labels, name_from="name", label_from="label") %>% 
  as_tibble()
#I also could have used `labelled::set_variable_labels()` to add labels
```


## First overview

As a first example, let's describe the columns `mpg` and `cyl`, grouping by the column `am` (the transmission).

As tables are not very readable in the console, let's also use `as_flextable()` to turn the resulting `crosstable` into a beautiful, ready-to-print HTML table. This table will be automatically displayed in the Viewer pane if your are using RStudio. 


```{r crosstable-flextable}
crosstable(mtcars2, c(mpg, cyl), by=am) %>%
  as_flextable(keep_id=TRUE)
```


By default, numeric variables (like `mpg` and `disp`) are described with `min/max`, `median/IQR`, `mean/sd` and `number of observations/missing`, while categorical (factor/character) variables (like `cyl`) are described with levels counts and fractions. All of this is fully customizable, as you will see hereafter.

There are many ways to select variables: with names, character vector, `tidyselect` helpers, formula... This is described in details in `vignette("crosstable-selection")`.

The `by` column is usually a factor, character or logical vector. If it is a numeric vector, then only numeric vectors can be described and correlation coefficients will be displayed. While it is possible to apply several variables (`by=c(am, vs)`), I will use only one variable here for clarity.

In this vignette, I will often set `keep_id=TRUE` so you can see the variable name, but in practice you usually omit it. See `vignette("crosstable-report")` for more about `as_flextable()` and on how to integrate crosstables in MS Word document (using `{officer}`) and `Rmarkdown`. On the other hand, you can set `label=FALSE` if you don't want them to appear.


## Totals

To display totals, use the `total` argument as one of `c("none", "row", "column", "both")`.

```{r crosstable-total-both}
#of course, the total of a "column" in only meaningful for categorical variables.
crosstable(mtcars2, c(am, mpg), by=vs, total="both") %>% 
  as_flextable(keep_id=TRUE)
```

Note that totals always take missing values into account. Therefore, be aware that if `showNA="no"`, totals may be higher than the sum of the values inside the table.


## Controlling the description

The `crosstable()` function comes with a lot of arguments to add control on how it will describe your dataset. Some arguments are tied to a type of variable and will only apply for the descriptions of those.

Note that most of the parameters can be controlled using global options. This comes handy when you want to use the same parameterization on all your crosstables. See `?crosstable_options` for more details about this.


### Categorical variables

Categorical variables are described using counts and percentages. A numeric variable is considered categorical if its number of levels is lesser than the `unique_numeric` argument (default=3). 

If `by` is set, you can use the `margin` argument to control whether percentages should be calculated by row (default), column, or cell. You can also use `percent_digits` (default=2) to control how many decimals should be displayed.

Use `showNA` (one of `c("ifany", "always", "no")`) to control when missing values should be displayed.

```{r crosstable-categorical}
mtcars3 = mtcars2
mtcars3$cyl[1:5] = NA
crosstable(mtcars3, c(am, cyl), by=vs, showNA="always", 
           percent_digits=0, percent_pattern="{n} ({p_col}/{p_row})") %>% 
  as_flextable(keep_id=TRUE)
```

You can see that missing values are never taken into account when calculating percentage calculation in `R`. You can change this behaviour by using `tidyr::replace_na()` or `forcats::fct_na_value_to_level()` on your dataset before applying `crosstable()`. 


### Numeric variables

Numeric variables are described with summary functions. By default, it outputs `min/max`, `median/IQR`, `mean/sd` and `number of observations/missing`.

However, this might not be the information you need, so you can use the `funs` argument to apply the set of functions of your choice:

```{r crosstable-functions}
crosstable(mtcars2, c(mpg, wt), funs=c("mean"=mean, "std dev"=sd, q1=~quantile(.x, prob=0.25))) %>% 
  as_flextable(keep_id=TRUE)
```

To this end, you might want to use `crosstable`'s convenience functions such as `meansd()`, `meanCI()`, `mediqr()`, `minmax()`, or `nna()`.

For more advanced use cases, you can also use anonymous and lambda functions, for instance `crosstable(mtcars2, c(mpg, wt), funs=c("mean square"=function(xx) mean(xx^2), "mean cube"= ~mean(.x^3)))`.


If some functions need additional arguments, you can use the `funs_arg` argument. For instance, you could write `crosstable(mtcars2, c(disp, hp), funs=c(quantile), funs_arg=list(probs=c(0.25,0.75)))`. 

Numbers are formatted to have the same number of decimal places. You can use `funs_arg=list(dig=3)` to customize the number of decimals. You might  want to take a look at `?summaryFunctions` and `?format_fixed`.

On the other hand, if `by` refers to a numeric variable, correlation coefficients will be calculated. 

```{r crosstable-correlation}
library(survival)
crosstable(mtcars2, where(is.numeric), cor_method="pearson", by=mpg) %>% 
  as_flextable(keep_id=TRUE)
```

You can use the `cor_method` argument to choose which coefficient to calculate (`"pearson"`, `"kendall"`, or `"spearman"`).


### Other variables


#### Survival data 

Crosstable is also able to describe survival data. 

Use `times` to set the specific times of interest and `followup` to compute the median followup. For each time, you will get the survival, the number of events before this time, and the number of patients at risk, as per `survival:::summary.survfit()`.

```{r crosstable-survival}
library(survival)
aml$surv = Surv(aml$time, aml$status)
crosstable(aml, surv, by=x, times=c(0,50,150), followup=TRUE) %>% 
  as_flextable(keep_id=TRUE)
```

Note that, using the formula interface, you could declare the `Surv` object directly inside the `crosstable` function: `crosstable(aml, Surv(time, status) ~ x)`.



#### Dates 

Although less usual, you can describe variables of call `Date` or `POSIXt` with `crosstable()`. Use `date_format` to apply a specific format (see `?strptime` for formats). Beside this, they are considered as numeric variables.

```{r crosstable-dates}
mtcars2$x_date = as.Date(mtcars2$hp , origin="2010-01-01") %>% set_label("Date")
mtcars2$x_posix = as.POSIXct(mtcars2$qsec*3600*24 , origin="2010-01-01") %>% set_label("Date+time")
crosstable(mtcars2, c(x_date, x_posix), date_format="%d/%m/%Y") %>% 
  as_flextable(keep_id=TRUE)
```

For the standard deviation to be readable, date unit is chosen automatically among `["seconds", "minutes", "hours", "days", "months", "years"]`. If you don't want two groups to have different date unit, you can set it globally using the `date_unit` key in `funs_arg`:

```{r crosstable-dates2}
crosstable(mtcars2, c(x_date, x_posix), funs=meansd, funs_arg=list(date_unit="days")) %>% 
  as_flextable(keep_id=TRUE)
```


## Effects

If there is only one `by` variable with only 2 levels, it is possible to automatically compute an effect-size, most often along with its confidence interval. 

```{r crosstable-effect}
crosstable(mtcars2, c(vs, qsec), by=am, funs=mean, effect=TRUE) %>% 
  as_flextable(keep_id=TRUE)
```

Type of effect (method, bootstrap, ...) are also chosen depending on the characteristics of the crossed variables (class, size, distribution, ...). 

See `crosstable_effect_args()` for more details on the effect choice algorithm and how to customize it.


## Tests

It is also possible to perform statistical tests automatically. 

```{r crosstable-tests}
library(flextable)
crosstable(mtcars2, c(vs, qsec), by=am, funs=mean, test=TRUE) %>% 
  as_flextable(keep_id=TRUE)
```

Of course, this should only be done in an exploratory context, as it would cause extensive [alpha inflation](https://en.wikipedia.org/wiki/Multiple_comparisons_problem) otherwise.

Tests are chosen depending on the characteristics of the crossed variables (class, size, distribution, ...). 

See `crosstable_test_args()` for more details on the test choice algorithm.

```{r, include = FALSE}
options(old)
```
