如何比较 R 中的多行？

Question

I would like to compare multiple values by USER.

Based on USER "A", If the values (A,B,C,D,and E) are same with USER "B", it should be written as B at the newly created variable EQUAL

Here is my data

Desired value

I am very new to R, I tried to look at the compare function but got a little overwhelmed. Would very much appreciate any help.

Answer 1

Here's an abridged version of the data you provided:

library(tidyverse)

df <- data.frame(
  id = c(1001, 1002, 1003, 1001, 1002, 1003),
  user = c('a', 'a', 'a', 'b', 'b', 'b'),
  point_a = c(1, 1, NA, 1, 1, NA),
  point_b = c(NA, NA, 2, NA, NA, NA),
  point_c = c(3, 2, 3, 3, 2, 3),
  point_d = c(2, 1, NA, 2, 1, NA),
  point_e = c(4, NA, 1, 4, NA, NA)
)

df

    id user point_a point_b point_c point_d point_e
1 1001    a       1      NA       3       2       4
2 1002    a       1      NA       2       1      NA
3 1003    a      NA       2       3      NA       1
4 1001    b       1      NA       3       2       4
5 1002    b       1      NA       2       1      NA
6 1003    b      NA      NA       3      NA      NA

If you inner_join on the columns you want to match, and then filter for rows where user.x is greater than user.y (ie first in alphabetical order, to get rid of duplicates and rows matching to themselves), you should be left with the matches you're looking for:

df %>%
  inner_join(df, by = c('point_a', 'point_b', 'point_c', 'point_d', 'point_e')) %>%
  filter(user.x < user.y) %>%
  rename(user = user.x,
         equal = user.y)

  id.x user point_a point_b point_c point_d point_e id.y equal
1 1001    a       1      NA       3       2       4 1001     b
2 1002    a       1      NA       2       1      NA 1002     b

Answer 2

We may split the data along users, and put the result in mapply and calculate the rowSums of TRUE s after comparison with `==` . From the resulting matrix we want to know which.max which allows us to subset the users (without "A" ). The result just needs to be subsetted by user "A" .

transform(dat, EQUAL=
            split(dat, dat$user) |>
            (\(.) mapply(\(x, y) rowSums(x == y, na.rm=TRUE), 
                         unname(.['A']), 
                         .[c('B', 'C')]))() |>
            (\(.) sort(unique(dat$user))[-1][apply(., 1, which.max)])()
) |>
  (\(.) .[.$user == 'A', ])()
#     id user point_a point_b point_c point_d point_e EQUAL
# 1 1001    A       1      NA       3       2       4     B
# 2 1002    A       1      NA       2       1      NA     B
# 3 1003    A      NA       2       3      NA       1     C

Note: R version 4.1.2 (2021-11-01)

---

Data:

dat <- structure(list(id = c(1001L, 1002L, 1003L, 1001L, 1002L, 1003L, 
1001L, 1002L, 1003L), user = c("A", "A", "A", "B", "B", "B", 
"C", "C", "C"), point_a = c(1, 1, NA, 1, 1, NA, 4, 1, NA), point_b = c(NA, 
NA, 2, NA, NA, NA, 3, NA, 2), point_c = c(3, 2, 3, 3, 2, 3, 3, 
2, 3), point_d = c(2, 1, NA, 2, 1, NA, 2, 1, NA), point_e = c(4, 
NA, 1, 4, NA, NA, 4, NA, 1)), class = "data.frame", row.names = c(NA, 
-9L))