Calculating distance to minimum of similar cases (observations) in R -

i have dataset describes results of applying 3 algorithms number of cases.for each combination of algorithm , case, there result.

df = data.frame(   c("case1", "case1", "case1", "case2", "case2", "case2"),   c("algo1", "algo2", "algo3", "algo1", "algo2", "algo3"),   c(10, 11, 12, 22, 23, 20)   ); names(df) <- c("case", "algorithm", "result"); df 

these algorithms aim minimize result value. each algorithm , case want calculate gap lowest achieved result, achieved algorithm same case.

gap <- function(caseid, result) {   filtered = subset(df, case==caseid)   return (result - min(filtered[,'result'])); } 

when apply function manually, expected results.

gap("case1", 10)  # prints 0, since 10 best value case1 gap("case1", 11)  # prints 1, since 11-10=1 gap("case1", 12)  # prints 2, since 12-10=1  gap("case2", 22)  # prints 2, since 22-20=2 gap("case2", 23)  # prints 3, since 23-20=3 gap("case2", 20)  # prints 0, since 20 best value case2 

however, when want calculate new column across whole dataset, bogus results case2.

df$gap <- gap(df$case, df$result) df 

this produces

   case algorithm result gap 1 case1     algo1     10   0 2 case1     algo2     11   1 3 case1     algo3     12   2 4 case2     algo1     22  12 5 case2     algo2     23  13 6 case2     algo3     20  10 

it seems gap function working against overall result minimum of whole dataframe, whereas should consider rows same case. maybe subset filtering in gap function not working properly?

we can use dplyr

library(dplyr) df %>%   group_by(case) %>%    mutate(result = result - min(result)) # tibble: 6 x 3 # groups:   case [2] #    case algorithm result #   <fctr>    <fctr>  <dbl> #1  case1     algo1      0 #2  case1     algo2      1 #3  case1     algo3      2 #4  case2     algo1      2 #5  case2     algo2      3 #6  case2     algo3      0