# plotNA.distributionBar

##### Visualize Distribution of Missing Values (Barplot)

Visualization of missing values in barplot form. Especially useful for time series with a lot of observations.

##### Usage

```
plotNA.distributionBar(x, breaks = nclass.Sturges(x), breaksize = NULL,
percentage = TRUE, legend = TRUE, axis = TRUE, space = 0,
col = c("indianred2", "green2"), main = "Distribution of NAs",
xlab = "Time Lapse", ylab = NULL, ...)
```

##### Arguments

- x
Numeric Vector (

`vector`

) or Time Series (`ts`

) object containing NAs- breaks
Defines the number of bins to be created. Default number of breaks is calculated by

`nclass.Sturges`

using Sturges' formula. If the breaksize parameter is set to a value different to NULL this parameter is ignored.- breaksize
Defines how many observations should be in one bin. The required number of overall bins is afterwards calculated automatically. This parameter if used overwrites the breaks parameter.

- percentage
Whether the NA / non-NA ration should be given as percent or absolute numbers

- legend
If TRUE a legend is shown at the bottom of the plot. A custom legend can be obtained by setting this parameter to FALSE and using

`legend`

function- axis
If TRUE a x-axis with labels is added. A custom axis can be obtained by setting this parameter to FALSE and using

`axis`

function- space
The amount of space (as a fraction of the average bar width) left before each bar.

- col
A vector of colors for the bars or bar components.

- main
Main title for the plot

- xlab
Label for x axis of the plot

- ylab
Label for y axis of plot

- ...
Additional graphical parameters that can be passed through to barplot

##### Details

This function visualizes the distribution of missing values within a time series.
In comparison to the `plotNA.distribution`

function this is not done by plotting
each observation of the time series separately Instead observations for time intervals are represented as bars.
For these intervals information about the amount of missing values are shown. This has the advantage, that also
for large time series a plot which is easy to overview can be created.

##### See Also

##### Examples

```
# NOT RUN {
#Example 1: Visualize the missing values in tsNH4 time series
plotNA.distributionBar(tsNH4)
#Example 2: Visualize the missing values in tsHeating time series
plotNA.distributionBar(tsHeating, breaks = 20)
#Example 3: Same as example 1, just written with pipe operator
tsNH4 %>% plotNA.distributionBar
# }
```

*Documentation reproduced from package imputeTS, version 2.7, License: GPL-3*