如何在R中使用dplyr进行条件选择？

Question

I have the following situation. Given the table

df <- data.frame(ID = c(1, 2, 2, 3, 3, 4),
             type = c("MC", "MC", "MK", "MC", "MK", "MC"),
             value1 = c(512, 261, 4523, 1004, 1221, 2556),
             value2 = c(726, 4000, 280, 998, 113, 6789))

I am trying to find a way to implement the following logic: If for an ID, both types (MC and MK) occur, use value1 from MK and value2 from MC. Otherwise (only the type MC occurs), use MC.

Hence, the final result is supposed to be:

data.frame(ID = c(1, 2, 3, 4),
             type = c("MC", "MC", "MC", "MC"),
             value1 = c(512, 4523, 1221, 2556),
             value2 = c(726, 4000, 998, 6789))

Assuming the type MK is dropped after extracting the value1.

Answer 1

Another version with dplyr

library(dplyr)

df %>%
  group_by(ID) %>%
  mutate(value1 = ifelse(any(type == "MK"), value1[type=="MK"],value1[type=="MC"]), 
         value2 = value2[type == "MC"]) %>%
  filter(type == "MC")

#     ID type  value1 value2
#  <dbl> <fct>  <dbl>  <dbl>
#1     1 MC       512    726
#2     2 MC      4523   4000
#3     3 MC      1221    998
#4     4 MC      2556   6789

Here, for value1 we check value in "MK" if it is present or take corresponding "MC" value instead and for value2 by default we take "MC" value and keep only rows with type "MC". This is assuming every group ( ID ) would have a "MC" type row.

Answer 2

data.table solution

setDT(df1)[,{x=.SD;if(all(c("MC","MK") %in% type)){x$value1[] = last(value1)};first(x)},by=ID]

result:

#  ID type value1 value2
#1  1   MC    512    726
#2  2   MC   4523   4000
#3  3   MC   1221    998
#4  4   MC   2556   6789

---

dplyr :

df1 %>% group_by(ID) %>% do(.,(function(x){if(all(c("MC","MK") %in% x$type)){x$value1[] = x$value1[x$type=="MK"]};x[1,]})(.))

# A tibble: 4 x 4
# Groups:   ID [4]
#     ID type  value1 value2
#  <dbl> <fct>  <dbl>  <dbl>
#1     1 MC       512    726
#2     2 MC      4523   4000
#3     3 MC      1221    998
#4     4 MC      2556   6789

Answer 3

For efficiency I would definitely prefer @Andre Elrico' answer but here is a dplyr option. Try:

df <- data.frame(ID = c(1, 2, 2, 3, 3, 4),
                 type = c("MC", "MC", "MK", "MC", "MK", "MC"),
                 value1 = c(512, 261, 4523, 1004, 1221, 2556),
                 value2 = c(726, 4000, 280, 998, 113, 6789)) 
library(dplyr)
df %>%
  reshape(., idvar = "ID", timevar = "type", direction = "wide") %>%
  group_by(ID) %>%
  mutate(value1 = ifelse(is.na(value1.MK), value1.MC, value1.MK),
         value2 = ifelse(is.na(value2.MC), value2.MK, value2.MC),
         type = "MC") %>%
  select(ID, type, value1, value2)
# output
# A tibble: 4 x 4
# Groups:   ID [4]
     ID  type value1 value2
  <dbl> <chr>  <dbl>  <dbl>
1     1    MC    512    726
2     2    MC   4523   4000
3     3    MC   1221    998
4     4    MC   2556   6789