Value of a variable matching the first row of another variable by group
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就像标题中一样，我希望有一个过程可以让我将第一变量的一组唯一值分配给第二变量的最常见值，并与第三值的第一行匹配。示例：
1 2 3 4 5 6 | Name Year Job Alicia 1990 Butcher Alicia 1991 Baker George 1989 Scientist George 1990 Banker George 1991 Banker |
我想轻松地确定每个唯一名称所做的第一项工作：
1 2 3 4 5 6 | Name Year Job First Job Alicia 1990 Butcher Butcher Alicia 1991 Baker Butcher George 1989 Scientist Scientist George 1990 Banker Scientist George 1991 Banker Scientist |
我们可以为此使用
1 2 3 4 5 6 7 8 9 10 11 12 | library(data.table) setDT(df1)[order(Year),FirstJob:=Job[1],.(Name)][] ## or using which.min instead of ordering as akrun suggested: # setDT(df1)[,FirstJob:=Job[which.min(Year)], .(Name)][] #> Name Year Job FirstJob #> 1: Alicia 1990 Butcher Butcher #> 2: Alicia 1991 Baker Butcher #> 3: George 1989 Scientist Scientist #> 4: George 1990 Banker Scientist #> 5: George 1991 Banker Scientist |
数据：
1 2 3 4 5 6 7 | read.table(text="Name Year Job Alicia 1990 Butcher Alicia 1991 Baker George 1989 Scientist George 1990 Banker George 1991 Banker", header=T, stringsAsFactors=F) -> df1 |
我们可以通过\\'Name \\'分组并提取
1 2 3 4 5 6 7 8 9 10 11 12 13 | library(dplyr) df1 %>% group_by(Name) %>% mutate(FirstJob = first(Job)) # A tibble: 5 x 4 # Groups: Name [2] # Name Year Job FirstJob # <chr> <int> <chr> <chr> #1 Alicia 1990 Butcher Butcher #2 Alicia 1991 Baker Butcher #3 George 1989 Scientist Scientist #4 George 1990 Banker Scientist #5 George 1991 Banker Scientist |
如果未订购\\'Year \\'
1 2 3 | df1 %>% group_by(Name) %>% mutate(FirstJob = Job[which.min(Year)]) |
数据
1 2 3 4 5 | df1 <- structure(list(Name = c("Alicia","Alicia","George","George", "George"), Year = c(1990L, 1991L, 1989L, 1990L, 1991L), Job = c("Butcher", "Baker","Scientist","Banker","Banker")), class ="data.frame", row.names = c(NA, -5L)) |