An all-in-one missingness report
na_summary(
df,
grouping_cols = NULL,
sort_by = NULL,
descending = FALSE,
exclude_cols = NULL,
pattern = NULL,
pattern_type = NULL,
regex_kind = "exclusion",
round_to = NULL,
reset_rownames = FALSE
)A valid R `object` for which the percentage of missing values is required.
A character vector. If supplied, one can provide the columns by which to group the data.
One of counts or percents. This determines whether the results are sorted by counts or percentages.
Logical. Should missing values be sorted in decreasing order ie largest to smallest? Defaults to FALSE.
A character vector indicating columns to exclude when returning results.
Pattern to use for exclusion or inclusion. column inclusion criteria.
A regular expression type. One of "starts_with", "contains", or "regex". Defaults to NULL. Only use for selective inclusion.
One of inclusion or exclusion. Defaults to exclusion to exclude columns using regular expressions.
Number of places to round 2. Defaults to user digits option.
Should the rownames be reset in the output? defaults to FALSE
na_summary(airquality)
#> variable missing complete percent_complete percent_missing
#> 1 Day 0 153 100.00000 0.000000
#> 2 Month 0 153 100.00000 0.000000
#> 3 Ozone 37 116 75.81699 24.183007
#> 4 Solar.R 7 146 95.42484 4.575163
#> 5 Temp 0 153 100.00000 0.000000
#> 6 Wind 0 153 100.00000 0.000000
# grouping
test2 <- data.frame(ID= c("A","A","B","A","B"),Vals = c(rep(NA,4),"No"),
ID2 = c("E","E","D","E","D"))
df <- data.frame(A=1:5,B=c(NA,NA,25,24,53), C=c(NA,1,2,3,4))
na_summary(test2,grouping_cols = c("ID","ID2"))
#> # A tibble: 2 x 7
#> ID ID2 variable missing complete percent_complete percent_missing
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 B D Vals 1 1 50 50
#> 2 A E Vals 3 0 0 100
# sort summary
na_summary(airquality,sort_by = "percent_missing",descending = TRUE)
#> variable missing complete percent_complete percent_missing
#> 3 Ozone 37 116 75.81699 24.183007
#> 4 Solar.R 7 146 95.42484 4.575163
#> 1 Day 0 153 100.00000 0.000000
#> 2 Month 0 153 100.00000 0.000000
#> 5 Temp 0 153 100.00000 0.000000
#> 6 Wind 0 153 100.00000 0.000000
na_summary(airquality,sort_by = "percent_complete")
#> variable missing complete percent_complete percent_missing
#> 3 Ozone 37 116 75.81699 24.183007
#> 4 Solar.R 7 146 95.42484 4.575163
#> 1 Day 0 153 100.00000 0.000000
#> 2 Month 0 153 100.00000 0.000000
#> 5 Temp 0 153 100.00000 0.000000
#> 6 Wind 0 153 100.00000 0.000000
# Include only via a regular expression
na_summary(mtcars, pattern_type = "contains",
pattern = "mpg|disp|wt", regex_kind = "inclusion")
#> variable missing complete percent_complete percent_missing
#> 1 disp 0 32 100 0
#> 2 mpg 0 32 100 0
#> 3 wt 0 32 100 0
na_summary(airquality, pattern_type = "starts_with",
pattern = "ozone", regex_kind = "inclusion")
#> variable missing complete percent_complete percent_missing
#> 1 Ozone 37 116 75.81699 24.18301
# exclusion via a regex
na_summary(airquality, pattern_type = "starts_with",
pattern = "oz|Sol", regex_kind = "exclusion")
#> variable missing complete percent_complete percent_missing
#> 1 Day 0 153 100 0
#> 2 Month 0 153 100 0
#> 3 Temp 0 153 100 0
#> 4 Wind 0 153 100 0
# reset rownames when sorting by variable
na_summary(df,sort_by="variable",descending=TRUE, reset_rownames = TRUE)
#> variable missing complete percent_complete percent_missing
#> 1 C 1 4 80 20
#> 2 B 2 3 60 40
#> 3 A 0 5 100 0