replace all values in original df with updated values from new df-CodePudding

say I have:

T1 <- c("A","B","C","D","E", "M")
T2 <- c("F","G","H","I","J", "K")
score1 <- c(1,2,3,4,5, 6)
score3 <- c(7,8,9,10,11, 12)
score2 <- c(13, 14, 15, 16, 17, 18)
df1 <- data.frame(T2, T1, score1, score2, score3)

T1 <- c("A","D","C","B","K")
T2 <- c("f","g","h","i","j")
score1 <- c(-1,-2,-3,-4,-5)
score2 <- c(-13, -14, -15, -16, -17)
df2 <- data.frame(T1, T2, score1, score2)

> df1
  T2 T1 score1 score2 score3
1  F  A      1     13      7
2  G  B      2     14      8
3  H  C      3     15      9
4  I  D      4     16     10
5  J  E      5     17     11
6  K  M      6     18     12
> df2
  T1 T2 score1 score2
1  A  f     -1    -13
2  D  g     -2    -14
3  C  h     -3    -15
4  B  i     -4    -16
5  L  j     -5    -17

df1 represents original values but then i have an updated df2 that recorded only some of the same variables. But df2 is more up-to-date so I want to replace all the values (for all matching columns) when df1$T1 matches df2$T1.

I thought something like df1[match(df1$T1, df2$T1),] <- df2 but it doesn't work when they are different dimensions, also it won't match along the same colnames. Joining dfs is tedious because it doesn't actually replace the values (it adds a new column like T2.y, score1.y, etc.)

I want to get something like:

> df1
  T2 T1 score1 score2 score3
1  f  A     -1    -13      7
2  i  B     -4    -16      8
3  h  C     -3    -15      9
4  g  D     -2    -14     10
5  J  E      5     17     11
6  K  M      6     18     12

CodePudding user response：

The new dplyr rows_update makes this easy.

library(dplyr)

df1 %>% rows_update(df2, by = 'T1', unmatched = "ignore")

#  T2 T1 score1 score2 score3
#1  f  A     -1    -13      7
#2  i  B     -4    -16      8
#3  h  C     -3    -15      9
#4  g  D     -2    -14     10
#5  J  E      5     17     11
#6  K  M      6     18     12