rsortingdplyrna

dplyr arrange() function sort by missing values

I am attempting to work through Hadley Wickham's R for Data Science and have gotten tripped up on the following question: "How could you use arrange() to sort all missing values to the start? (Hint: use is.na())" I am using the flights dataset included in the nycflights13 package. Given that arrange() sorts all unknown values to the bottom of the dataframe, I am not sure how one would do the opposite across the missing values of all variables. I realize that this question can be answered with base R code, but I am specifically interested in how this would be done using dplyr and a call to the arrange() and is.na() functions. Thanks.

Solution

  • We can wrap it with desc to get the missing values at the start

    flights %>% 
    arrange(desc(is.na(dep_time)),
           desc(is.na(dep_delay)),
           desc(is.na(arr_time)), 
           desc(is.na(arr_delay)),
           desc(is.na(tailnum)),
           desc(is.na(air_time)))

    The NA values were only found in those variables based on 

    names(flights)[colSums(is.na(flights)) >0]
#[1] "dep_time"  "dep_delay" "arr_time"  "arr_delay" "tailnum"   "air_time"

    Instead of passing each variable name at a time, we can also use NSE arrange_

    nm1 <- paste0("desc(is.na(", names(flights)[colSums(is.na(flights)) >0], "))")

r1 <- flights %>%
        arrange_(.dots = nm1) 

r1 %>%
   head()
#year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum
#  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>     <dbl>   <chr>  <int>   <chr>
#1  2013     1     2       NA           1545        NA       NA           1910        NA      AA    133    <NA>
#2  2013     1     2       NA           1601        NA       NA           1735        NA      UA    623    <NA>
#3  2013     1     3       NA            857        NA       NA           1209        NA      UA    714    <NA>
#4  2013     1     3       NA            645        NA       NA            952        NA      UA    719    <NA>
#5  2013     1     4       NA            845        NA       NA           1015        NA      9E   3405    <NA>
#6  2013     1     4       NA           1830        NA       NA           2044        NA      9E   3716    <NA>
#Variables not shown: origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#  time_hour <time>.

    Update

    With the newer versions of tidyverse (dplyr_0.7.3, rlang_0.1.2) , we can also make use of arrange_at, arrange_all, arrange_if

    nm1 <- names(flights)[colSums(is.na(flights)) >0]
r2 <- flights %>% 
          arrange_at(vars(nm1), funs(desc(is.na(.))))

    Or use arrange_if

    f <- rlang::as_function(~ any(is.na(.)))
r3 <- flights %>% 
          arrange_if(f, funs(desc(is.na(.))))


identical(r1, r2)
#[1] TRUE

identical(r1, r3)
#[1] TRUE