---
title: "Visualizing Incomplete Data"
date: "2019-08-29"
output:
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{Visualizing Incomplete Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.align = 'center',
  out.width = '100%'
)
library(JointAI)
options(width = 80)
```


When analyzing data with missing values it is important to be familiar
with the variables of interest, i.e., to know their measurement level and
distribution, as well as the amount of missing values per variable, and to
investigate the pattern of missing values.

Currently, the package **JointAI** has two function that help this fist
exploration of an unknown dataset.

In this vignette, we use the `NHANES` data and the `simLong` data, that are part of
the **JointAI** package.
For more info on these datasets, check the help files
[for the NHANES](https://nerler.github.io/JointAI/reference/NHANES.html) and
[for the simLong](https://nerler.github.io/JointAI/reference/simLong.html) data or
go to the [web page of the National Health and Nutrition Examination Survey (NHANES)](https://www.cdc.gov/nchs/nhanes/index.htm).


## Visualize the distribution of each variable
Using `plot_all()`, an array of histograms (for continuous variables and dates)
and barplots (for categorical variables) can be obtained.

```{r, fig.height = 8}
op <- par(mar = c(2.5, 3, 2.5, 1), mgp = c(2, 0.8, 0))
plot_all(NHANES)
par(op)
```

**Note:** Here, we are looking at the marginal distribution of each variable.
Since missing values in one variable are (usually) imputed conditionally on 
other variables, marginal plots can only be used as an indication of what 
type of imputation model (e.g., normal vs. non-normal) is appropriate.


### Plot titles: proportion of missing values
The argument `allNA` allows to select whether the proportion of missing values
is given only for incomplete variables or (by setting `allNA = TRUE`) for all
variables.

### Changing colors
The color of the bars and their border can be controlled with the arguments
`fill` and `border`.

### Layout
Depending on the number of variables in the data, the
number of rows and columns of the plot layout is determined automatically, 
but can be overwritten using `nrow` and/or `ncol`.

### Additional arguments
It is also possible to provide some of the other arguments of [`hist()`](https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/hist.html)
or [`barplot()`](https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/barplot.html).

For example:
```{r, fig.width = 8, fig.height = 10}
par(mar = c(2.5, 3, 2.5, 1), mgp = c(2, 0.8, 0))
plot_all(NHANES, allNA = TRUE, fill = '#e30f41', border = '#34111b', ncol = 3, nclass = 30)
```


### Multi-level data
When the data has a multi-level structure, e.g., subjects are nested within
hospitals, or subjects have been measured repeatedly as in the `simLong` data,
the level of each variable can be taken into account by specifying
`use_levels = TRUE` and providing the name of the id variable in the argument
`idvar`.


```{r, fig.width = 8, fig.height = 10}
par(mar = c(2.5, 3, 2.5, 1), mgp = c(2, 0.8, 0))
plot_all(simLong, allNA = TRUE, fill = '#e30f41', border = '#34111b',
         ncol = 3, nclass = 30, use_level = TRUE, idvar = "ID")
```
The information whether a variable is level-1 (i.e., varies within the groups
specified by `idvar`) or level-2 (i.e., constant within the groups) is added to
the title of each plot, and level-2 variables are plotted on the group-level
instead of the observation level.

## Missing Data Pattern
The pattern of the missing data can be visualized with the function `md_pattern()`:
```{r, fig.height = 6}
md_pattern(NHANES)
```

In the resulting plot, variables are given in the columns and each row corresponds
to one pattern of missingness across the variables.
The numbers on the right margin give the number of cases per pattern. Underneath
the plot, the number of missing values per variable is given.
Columns are automatically sorted by number of missing values, rows by number of
cases per pattern.

The plot is generated with the help of [`ggplot()`](https://ggplot2.tidyverse.org/),
and, hence,  the package [**ggplot2**](https://CRAN.R-project.org/package=ggplot2)
needs to be installed. By setting `plot = FALSE`, no plot will be generated (and
then **ggplot2** is not required).

#### Missing data pattern as a matrix
When the argument `pattern = TRUE` is set, the missing data pattern is returned 
as a matrix, where observed values are represented by `1` and missing values
by `0`:
```{r}
md_pattern(NHANES, pattern = T, plot = F)
```


### Changing colors
The arguments `color` and `border` can be used to change the color that is
used for observed and missing values (in that order, i.e., `color` is a vector
of length 2), and the border separating the rectangles, respectively. 

### Legend position
The position of the legend can be controlled with the argument `legend.position`,
which can be one of `"left"`, `"right"`, `"bottom"` or `"top"`. 
When `legend.position = 'none'`, no legend is printed.

### Changing the axes
To hide the x-axis, the argument `print_xaxis` can be set to `FALSE`.
With the corresponding argument `print_yaxis = FALSE` the y-axis can be omitted.

To change the title of the y-axis, you can change the argument `ylab`. Setting
`ylab = ''` will hide the axis title, but keep the number of observations per
missing data pattern.

```{r, fig.height = 5}
md_pattern(NHANES, color = c('#34111b', '#e30f41'), border = 'white',
           legend.position = 'none', print_xaxis = FALSE, 
           ylab = 'Alternative axis title')
```

<br>

For the simLong data, the missing data pattern is:
```{r, fig.height = 9}
md_pattern(simLong, color = c('#34111b', '#e30f41'))
```
