Select value from previous group based on condition-CodePudding

I have the following df

df<-data.frame(value = c(1,1,1,2,1,1,2,2,1,2),
              group = c(5,5,5,6,7,7,8,8,9,10),
             no_rows = c(3,3,3,1,2,2,2,2,1,1))

where identical consecutive values form a group, i.e., values in rows 1:3 fall under group 5. Column "no_rows" tells us how many rows/entries each group has, i.e., group 5 has 3 rows/entries.

I am trying to substitute all values, where no_rows < 2, with the value from a previous group. I expect my end df to look like this:

df_end<-data.frame(value = c(1,1,1,1,1,1,2,2,2,2),
              group = c(5,5,5,6,7,7,8,8,9,10),
             no_rows = c(3,3,3,1,2,2,2,2,1,1))

I came up with this combination of if...else in a for loop, which gives me the desired output, however it is very slow and I am looking for a way to optimise it.

  for (i in 2:length(df$group)){
    if (df$no_rows[i] < 2){
      df$value[i] <- df$value[i-1]
    } 
 }

I have also tried with dplyr::mutate and lag() but it does not give me the desired output (it only removes the first value per group instead of taking the value of a previous group).

  df<-df%>%
    group_by(group) %>%
    mutate(value = ifelse(no_rows < 2, lag(value), value))

I looked for a solution now for a few days but I could not find anything that fit my problem completly. Any ideas?

CodePudding user response：

a data.table approach...

first, get the values of groups with length >=2, then fill in missing values (NA) by last-observation-carried-forward.

library(data.table)
# make it a data.table
setDT(df, key = "group")
# get values for groups of no_rows >= 2
df[no_rows >= 2, new_value := value][]
#    value group no_rows new_value
# 1:     1     5       3         1
# 2:     1     5       3         1
# 3:     1     5       3         1
# 4:     2     6       1        NA
# 5:     1     7       2         1
# 6:     1     7       2         1
# 7:     2     8       2         2
# 8:     2     8       2         2
# 9:     1     9       1        NA
#10:     2    10       1        NA

# fill down missing values in new_value
setnafill(df, "locf", cols = c("new_value"))
#    value group no_rows new_value
# 1:     1     5       3         1
# 2:     1     5       3         1
# 3:     1     5       3         1
# 4:     2     6       1         1
# 5:     1     7       2         1
# 6:     1     7       2         1
# 7:     2     8       2         2
# 8:     2     8       2         2
# 9:     1     9       1         2
#10:     2    10       1         2