knitr::opts_chunk$set(fig.align = "center", fig.width = 5, fig.height = 4, dpi = 100)
There are a variety of different plots to explore missing data available in the naniar package. This vignette simply showcases all of the visualisations. If you would like to know more about the philosophy of the
naniar package, you should read the vignette Getting Started with naniar.
A key point to remember with the visualisation tools in
naniar is that there is a way to get the data from the plot out from the visualisation.
One of the first plots that I recommend you start with when you are first exploring your missing data, is the
vis_miss() plot, which is re-exported from
This plot provides a specific visualiation of the amount of missing data, showing in black the location of missing values, and also providing information on the overall percentage of missing values overall (in the legend), and in each variable.
An upset plot from the
UpSetR package can be used to visualise the patterns of missingness, or rather the combinations of missingness across cases. To see combinations of missingness and intersections of missingness amongst variables, use the
This tells us:
We can explore this with more complex data, such as riskfactors:
The default option of
gg_miss_upset is taken from
UpSetR::upset - which is
to use up to 5 sets and up to 40 interactions. Here, setting
nsets = 5 means
to look at 5 variables and their combinations. The number of combinations
intersections is controlled by
nintersects. You could, for example
look at all of the number of missing variables using
# how many missings? n_var_miss(riskfactors) gg_miss_upset(riskfactors, nsets = n_var_miss(riskfactors))
If there are 40 intersections, there will be up to 40 combinations of variables explored. The numberof sets and intersections can be changed by passing arguments
nsets = 10
to look at 10 sets of variables, and
nintersects = 50 to look at 50
gg_miss_upset(riskfactors, nsets = 10, nintersects = 50)
NA it will plot all sets and all intersections.
gg_miss_upset(riskfactors, nsets = 10, nintersects = NA)
There are a few different ways to explore different missing data mechanisms and relationships. One way incorporates the method of shifting missing values so that they can be visualised on the same axes as the regular values, and then colours the missing and not missing points. This is implemented with
library(ggplot2) # using regular geom_point() ggplot(airquality, aes(x = Ozone, y = Solar.R)) + geom_point() library(naniar) # using geom_miss_point() ggplot(airquality, aes(x = Ozone, y = Solar.R)) + geom_miss_point() # Facets! ggplot(airquality, aes(x = Ozone, y = Solar.R)) + geom_miss_point() + facet_wrap(~Month) # Themes ggplot(airquality, aes(x = Ozone, y = Solar.R)) + geom_miss_point() + theme_dark()
Here are some function that provide quick summaries of missingness in your data, they all start with
gg_miss_ - so that they are easy to remember and tab-complete.
This plot shows the number of missing values in each variable in a dataset. It is powered by the
gg_miss_var(airquality) library(ggplot2) gg_miss_var(airquality) + labs(y = "Look at all the missing ones")
If you wish, you can also change whether to show the % of missing instead with
show_pct = TRUE.
gg_miss_var(airquality, show_pct = TRUE)
You can also plot the number of missings in a variable grouped by another variable using the
gg_miss_var(airquality, facet = Month)
This plot shows the number of missing values in each case. It is powered by the
gg_miss_case(airquality) gg_miss_case(airquality) + labs(x = "Number of Cases")
You can also order by the number of cases using
order_cases = TRUE
gg_miss_case(airquality, order_cases = TRUE)
You can also explore the misisngness in cases over some variable using
facet = Month
gg_miss_case(airquality, facet = Month)
This plot shows the number of missings in each column, broken down by a categorical variable from the dataset. It is powered by a
dplyr::group_by statement followed by
gg_miss_fct(x = riskfactors, fct = marital) library(ggplot2) gg_miss_fct(x = riskfactors, fct = marital) + labs(title = "NA in Risk Factors and Marital status") # using group_by library(dplyr) riskfactors %>% group_by(marital) %>% miss_var_summary()
This plot shows the number of missings in a given span, or breaksize, for a single selected variable. In this case we look at the span of
hourly_counts from the pedestrian dataset. It is powered by the
# data method miss_var_span(pedestrian, hourly_counts, span_every = 3000) gg_miss_span(pedestrian, hourly_counts, span_every = 3000) # works with the rest of ggplot gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + labs(x = "custom") gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + theme_dark()
You can also explore
miss_var_span by group with the
gg_miss_span(pedestrian, hourly_counts, span_every = 3000, facet = sensor_name)
This plot shows the cumulative sum of missing values, reading the rows of the dataset from the top to bottom. It is powered by the
gg_miss_case_cumsum(airquality) library(ggplot2) gg_miss_case_cumsum(riskfactors, breaks = 50) + theme_bw()
This plot shows the cumulative sum of missing values, reading columns from the left to the right of your dataframe. It is powered by the
This plot shows a set of rectangles that indicate whether there is a missing element in a column or not.
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