How to create a column that allocates designated values among rows sharing the same characteristics-CodePudding

My question is best expressed with the below example. Let's start with the below dataframe:

> myData
  Name Group Code
1    R     0    0
2    R     0    2
3    T     0    2
4    R     0    0
5    N     1    3
6    N     1    0
7    T     0    4

myData <- 
  data.frame(
    Name = c("R","R","T","R","N","N","T"),
    Group = c(0,0,0,0,1,1,0),
    Code = c(0,2,2,0,3,0,4)
  )

Now, I'd like to add a column, CodeGrp, whereby if a row's Group is > 0, then allocate the max Code for that Group to all Group members with the same Group number so the results look this (note that only one Group member (where Group > 0) can have a Code > 0 and the rest of those Group members have code 0; maybe there's something easier than my proposed max) can only be :

  Name Group Code CodeGrp Explain
1    R     0    0       0 Copy over Code since Group = 0 
2    R     0    2       2 Copy over Code since Group = 0
3    T     0    2       2 Copy over Code since Group = 0
4    R     0    0       0 Copy over Code since Group = 0
5    N     1    3       3 Group is > 0 so insert in CodeGrp column the max Code in this Group
6    N     1    0       3 Group is > 0 so insert in CodeGrp column the max Code in this Group
7    T     0    4       4 Copy over Code since Group = 0

Any recommendations for how to do this, in a simple manner, using base R or dplyr?

CodePudding user response：

Here is one approach. You want to group your data by Group. Then, you want to assign values in CodeGrp using a conditional statement. For each group, if Group is 0, assign values in Code. If Group is not 0, assign the max value of the group in CodeGrp.

group_by(myData, Group) %>% 
mutate(CodeGrp = if_else(Group == 0, Code, max(Code)))

  Name  Group  Code CodeGrp
  <chr> <dbl> <dbl>   <dbl>
1 R         0     0       0
2 R         0     2       2
3 T         0     2       2
4 R         0     0       0
5 N         1     3       3
6 N         1     0       3
7 T         0     4       4