easiest way to add missing rows in R

Question

I have the following table that I generate via a table/cumsum command.

> temp
   numCars
18       1
17       2
16       8
15      18
14      25
13      29
12      42
11      55
10      70
9      134
8      160
7      172
6      177
5      180
3      181
2      181
1      181
0      181

 temp <- structure(c(1L, 2L, 8L, 18L, 25L, 29L, 42L, 55L, 70L, 134L, 160L,                                             
 172L, 177L, 180L, 181L, 181L, 181L, 181L), .Dim = c(18L, 1L), .Dimnames = list(                               
     c("18", "17", "16", "15", "14", "13", "12", "11", "10", "9",                                              
     "8", "7", "6", "5", "3", "2", "1", "0"), "numCars"))  

As you can see the row with name 4 is missing. What's the easiest R way to fill it in where the value should be the value of the number lower (in this case 181).

I understand I can do this with a messy for loop where I can go in, size it, create a new DF, then put in any blank values. I'm just wondering if there's a better way?

Here's the table code:

cohortSizeByMileage <- data.matrix(cumsum(rev(table(cleanMileage$OdometerBucket))))
colnames(cohortSizeByMileage) <- "numCars"

Answer 1

We create the row names as column from the original dataset 'temp', based on the minimum and maximum value of row number in temp, another dataset ('df2') was created, merge or left_join the datasets, and fill the NA elements using na.locf from library(zoo).

df1 <- data.frame(numCars=temp[[1]], rn1=as.numeric(row.names(temp)))
df2 <- data.frame(rn1= max(df1$rn1):min(df1$rn1))
library(dplyr)
library(zoo)
left_join(df2, df1) %>%
        mutate(numCars= na.locf(numCars,fromLast=TRUE ))
#   rn1 numCars
#1   18       1
#2   17       2
#3   16       8
#4   15      18
#5   14      25
#6   13      29
#7   12      42
#8   11      55
#9   10      70
#10   9     134
#11   8     160
#12   7     172
#13   6     177
#14   5     180
#15   4     181
#16   3     181
#17   2     181
#18   1     181
#19   0     181