Closed sa-lee closed 3 years ago
Hey Stuart,
Yes! naniar
has miss_var_run
and gg_miss_span
, does that sort of do what you're looking for?
library(naniar)
miss_var_run(pedestrian, hourly_counts)
#> # A tibble: 35 x 2
#> run_length is_na
#> <int> <chr>
#> 1 6628 complete
#> 2 1 missing
#> 3 5250 complete
#> 4 624 missing
#> 5 3652 complete
#> 6 1 missing
#> 7 1290 complete
#> 8 744 missing
#> 9 7420 complete
#> 10 1 missing
#> # … with 25 more rows
library(ggplot2)
# explore the number of missings in a given run
miss_var_run(pedestrian, hourly_counts) %>%
filter(is_na == "missing") %>%
count(run_length) %>%
ggplot(aes(x = run_length,
y = n)) +
geom_col()
#> Error in is_na == "missing": comparison (1) is possible only for atomic and list types
# look at the number of missing values and the run length of these.
miss_var_run(pedestrian, hourly_counts) %>%
ggplot(aes(x = is_na,
y = run_length)) +
geom_boxplot()
gg_miss_span(pedestrian, hourly_counts, span_every = 3000)
Created on 2020-11-16 by the reprex package (v0.3.0)
yes, thanks!
Hi Nick,
Not sure if this is the right place - so feel free to close if not.
Something that has come up in last communicating with dat class - when students are using
vis_dat()
for a time series data they often find there's no missing values over observations but aren't aware that there could be missings over time / an index variable.Is there any plans to incorporate a vis that looks at gaps or runs? I know you had some stuff going in with Earo but not sure where that is at.