imputeTS (version 2.7)

plotNA.distributionBar: Visualize Distribution of Missing Values (Barplot)

Description

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

plotNA.distribution, plotNA.gapsize, plotNA.imputations

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

# }