Function that uses nonstandard indirect reference-CodePudding

I need a function f(B,A) that, given a dataset with the following structure,

T1 T2 T3 T4 T5  ... P1 P2 P3 P4 P5 ...
 1  2  5  8  9  ...  A  C  B  B  A ... 
 1  3  4  6  6  ...  C  A  C  A  B ...

finds the first time B and A appear in Pj columns (starting with j=1) and returns the value difference in the corresponding Ti columns. For instance:

in line 1: B appears in P3 first, A appears in P1 first. Then:

f(B, A) = T3 - T1 = 5-1 = 4

in line 2: B appears in P5 first, A appears in P2 first. Then:

f(B, A) = T5 - T2 = 6-3 = 3

I can find in which Pj columns B and A appear using str_detect() , but I don't know how to "move" from P_j1, P_j2 to T_j1, T_j2.

Using datatable syntax (or base R) will be appreciated

CodePudding user response：

Here is a data.table approach.

library(data.table)
DT <- fread("T1 T2 T3 T4 T5  P1 P2 P3 P4 P5
            1  2  5  8  9   A  C  B  B  A 
            1  3  4  6  6   C  A  C  A  B")
# Add row ID's
DT[, id := .I]
#melt to a long format
DT.melt <- data.table::melt(DT, 
                 id.vars = "id",
                 measure.vars = patterns(T = "^T", P = "^P"))
# Find first B for each id
val1 <- DT.melt[P == "B", T[1], by = .(id)]$V1
# [1] 5 6

# Find first A for each id
val2 <- DT.melt[P == "A", T[1], by = .(id)]$V1
# [1] 1 3

val1 - val2
# [1] 4 3

CodePudding user response：

base R

f <- function(l1, l2){
  apply(df, 1, function(x){
    dfP <- x[grepl("P", names(x))]
    dfT <- x[grepl("T", names(x))]
    
    as.numeric(dfT[which(dfP == l1)[1]]) - as.numeric(dfT[which(dfP == l2)[1]])
  })
}

f("B", "A")
[1] 4 3

Tidyverse

With this type of data, it's usually best to pivot to long and then back to wide: here is a tidyverse solution, with diff being the desired output.

library(tidyverse)

df %>% 
  mutate(id = row_number()) %>% 
  pivot_longer(-id, names_pattern = "(\\D)(\\d)", 
               names_to = c(".value", "group")) %>% 
  group_by(id) %>% 
  mutate(diff = first(T[P == "B"]) - first(T[P == "A"])) %>% 
  pivot_wider(c(id, diff), names_from = group, values_from = c(T, P), names_sep = "")

output

     id  diff    T1    T2    T3    T4    T5 P1    P2    P3    P4    P5   
  <int> <int> <int> <int> <int> <int> <int> <chr> <chr> <chr> <chr> <chr>
1     1     4     1     2     5     8     9 A     C     B     B     A    
2     2     3     1     3     4     6     6 C     A     C     A     B