R：如何从列中添加额外的行？

Question

I have a data-set of human hands, where currently a single person is defined as a single observation. I want to reshape dataframe to have hands as individual observations. I tried something with "dplyr" package and "gather" function but had no success at all.

So from this, where each person is on one row :

id Gender Age   Present_R    Present_L    Dominant
1    F     2      TRUE         TRUE          R
2    F     5      TRUE         FALSE         L
3    M     8      FALSE        FALSE         R

to this, where each hand is on one row:

id Gender Age   Hand    Present  Dominant
1    F     2     R       TRUE     TRUE
2    F     2     L       TRUE     FALSE
3    F     5     R       TRUE     FALSE
4    F     5     L       FALSE    TRUE
5    M     8     R       FALSE    TRUE
6    M     8     L       FALSE    FALSE

Note that hand dominance becomes logical.

Answer 1

We can gather into 'long' format, arrange by 'id', then create the 'Dominant' by unlist ing the 'Present' columns, 'Hand' by removing the substring of the 'Hand' column

library(tidyverse)
gather(df1, Hand, Present, Present_R:Present_L) %>%
   arrange(id) %>%
   mutate(Dominant = unlist(df1[c("Present_L", "Present_R")]),
          id = row_number(),
          Hand = str_remove(Hand, ".*_"))
#   id Gender Age Dominant Hand Present
#1  1      F   2     TRUE    R    TRUE
#2  2      F   2    FALSE    L    TRUE
#3  3      F   5    FALSE    R    TRUE
#4  4      F   5     TRUE    L   FALSE
#5  5      M   8     TRUE    R   FALSE
#6  6      M   8    FALSE    L   FALSE

Based on the OP' comments, it seems like we need to compare the 'Dominant' with the 'Hand'

gather(df1, Hand, Present, Present_R:Present_L) %>%
    arrange(id) %>% 
    mutate(id = row_number(),
           Hand = str_remove(Hand, ".*_"),
           Dominant = Dominant == Hand)
#   id Gender Age Dominant Hand Present
#1  1      F   2     TRUE    R    TRUE
#2  2      F   2    FALSE    L    TRUE
#3  3      F   5    FALSE    R    TRUE
#4  4      F   5     TRUE    L   FALSE
#5  5      M   8     TRUE    R   FALSE
#6  6      M   8    FALSE    L   FALSE

Answer 2

With a small data frame (ie, few variables, regardless of the number of cases), "hand-coding" may be the easiest approach:

with(df, data.frame(id = c(id,id), Gender=c(Gender,Gender), Age=c(Age, Age), 
                    Hand = c(rep("R", nrow(df)), rep("L", nrow(df))),
                    Present = c(Present_R, Present_L),
                    Dominant = c(Dominant=="R", Dominant=="L")
                    ))