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 vignette("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 visdat
.
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
gg_miss_upset
function:
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 or rather
intersections
is controlled by nintersects
.
You could, for example look at all of the number of missing variables
using n_var_miss
:
## [1] 24
If there are 40 intersections, there will be up to 40 combinations of
variables explored. The number of 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
intersections.
Setting nintersects
to NA
it will plot all
sets and all intersections.
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 geom_miss_point()
.
geom_miss_point
library(ggplot2)
# using regular geom_point()
ggplot(airquality,
aes(x = Ozone,
y = Solar.R)) +
geom_point()
## Warning: Removed 42 rows containing missing values or values outside the scale range
## (`geom_point()`).
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.
gg_miss_var
This plot shows the number of missing values in each variable in a
dataset. It is powered by the miss_var_summary()
function.
If you wish, you can also change whether to show the % of missing
instead with show_pct = TRUE
.
You can also plot the number of missings in a variable grouped by
another variable using the facet
argument.
gg_miss_case
This plot shows the number of missing values in each case. It is
powered by the miss_case_summary()
function.
You can also order by the number of cases using
order_cases = TRUE
You can also explore the missingness in cases over some variable
using facet = Month
gg_miss_fct
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
miss_var_summary()
.
library(ggplot2)
gg_miss_fct(x = riskfactors, fct = marital) + labs(title = "NA in Risk Factors and Marital status")
## # A tibble: 231 × 4
## # Groups: marital [7]
## marital variable n_miss pct_miss
## <fct> <chr> <int> <num>
## 1 Married smoke_stop 120 91.6
## 2 Married pregnant 117 89.3
## 3 Married smoke_last 84 64.1
## 4 Married smoke_days 73 55.7
## 5 Married drink_average 68 51.9
## 6 Married health_poor 67 51.1
## 7 Married drink_days 67 51.1
## 8 Married weight_lbs 6 4.58
## 9 Married bmi 6 4.58
## 10 Married diet_fruit 4 3.05
## # ℹ 221 more rows
gg_miss_fct
can also be used to explore missingness
along time, like so:
(Thanks to Maria Paula Caldas for inspiration for this visualisation, discussed here)
gg_miss_span
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 miss_var_span
function
## # A tibble: 13 × 6
## span_counter n_miss n_complete prop_miss prop_complete n_in_span
## <int> <int> <int> <dbl> <dbl> <int>
## 1 1 0 3000 0 1 3000
## 2 2 0 3000 0 1 3000
## 3 3 1 2999 0.000333 1.00 3000
## 4 4 121 2879 0.0403 0.960 3000
## 5 5 503 2497 0.168 0.832 3000
## 6 6 555 2445 0.185 0.815 3000
## 7 7 190 2810 0.0633 0.937 3000
## 8 8 0 3000 0 1 3000
## 9 9 1 2999 0.000333 1.00 3000
## 10 10 0 3000 0 1 3000
## 11 11 0 3000 0 1 3000
## 12 12 745 2255 0.248 0.752 3000
## 13 13 432 1268 0.254 0.746 1700
# works with the rest of ggplot
gg_miss_span(pedestrian, hourly_counts, span_every = 3000) + labs(x = "custom")
You can also explore miss_var_span
by group with the
facet
argument.
gg_miss_case_cumsum
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
miss_case_cumsum()
function.
gg_miss_var_cumsum
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
miss_var_cumsum()
function.