My question is based upon this question.
I have a data as below. I want to fill cells by first looking down and then by looking up as long as the bom is same. In case of bom=A, I want to fill up rows as shown. But in case of bom=B, as the type_p column is different, I want to duplicate rows and feel the blanks
bom=c(rep("A",4),rep("B",3)) Part=c("","lambda","beta","","tim","tom","") type_p=c("","sub","sub","","sub","pan","") ww=c(1,2,3,4,1,2,3) df=data.frame(bom,Part,type_p,ww) > df bom Part type_p ww 1 A 1 2 A lambda sub 2 3 A beta sub 3 4 A 4 5 B tim sub 1 6 B tom pan 2 7 B 3 The final data that I want is as below
bom Part type_p ww 1 A lambda sub 1 2 A lambda sub 2 3 A beta sub 3 4 A beta sub 4 5 B tim sub 1 6 B tim sub 2 7 B tim sub 3 5 B tom pan 1 6 B tom pan 2 7 B tom pan 3 ________________________________________Update 1
The logic that I want is as below. Please remember that my data is very huge and I have thousands of values in each column.
bom and ww columns are always populated/filled in incoming data
- Check if an entry in column bom has more than 1 value in the column type_p
- If there is only 1 value then fill blanks in type_p and ww columns by first looking down and then looking up. In this case bom=A has only one value in type_p (sub)
- If an entry in column bom has more than 1 unique value in type_p column then create additional sets of the same rows of that bom such that total sets will be equal to distinct values in type_p column for that bom. In this case bom=B has two values in type_p (sub and pan)
- Fill blanks in type_p and ww columns by first looking down and then looking up (look at the source row to fill up values)
===========================================================Update 2
After step 3, the data frame would look like below
> df bom Part type_p ww 1 A lambda sub 1 2 A lambda sub 2 3 A beta sub 3 4 A beta sub 4 5 B tim sub 1 6 B 2 7 B 3 8 B 1 9 B tom pan 2 10 B 3 1 Answers
Answers 1
With tidyr and dplyr, you could manage to do something near what you aim
library(tidyr) library(dplyr) # replacing empty string with NA df <- df %>% mutate_each(funs(sub("^$", NA, .)), Part, type_p) # filling missing values df <- df %>% fill(Part, type_p,.direction = "down") %>% fill(Part, type_p,.direction = "up") df #> bom Part type_p ww #> 1 A lambda sub 1 #> 2 A lambda sub 2 #> 3 A beta sub 3 #> 4 A beta sub 4 #> 5 B tim sub 1 #> 6 B tom pan 2 #> 7 B tom pan 3 To obtain what you described (in question and comment), you could treat BOM A & B separately:
bind_rows( df %>% filter(bom == "A"), df %>% filter(bom == "B") %>% complete(nesting(bom, Part, type_p), ww) ) #> Source: local data frame [10 x 4] #> #> bom Part type_p ww #> (fctr) (chr) (chr) (dbl) #> 1 A lambda sub 1 #> 2 A lambda sub 2 #> 3 A beta sub 3 #> 4 A beta sub 4 #> 5 B tim sub 1 #> 6 B tim sub 2 #> 7 B tim sub 3 #> 8 B tom pan 1 #> 9 B tom pan 2 #> 10 B tom pan 3 
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