如何创建一个变量(在某个阈值处捕获增量)R? [英] How to create a variable (that captures increases at a certain threshold) R?

问题描述

我有一个看起来像这样的数据集

I have a dataset that looks something like this

    Subject  Year   X   Y   
        A   1990    1   0   
        A   1991    1   0   
        A   1992    2   0   
        A   1993    3   1   
        A   1994    4   0   
        A   1995    4   0   
        B   1990    0   0   
        B   1991    1   0   
        B   1992    1   0   
        B   1993    2   1   
        C   1991    1   0   
        C   1992    2   0   
        C   1993    3   0   
        C   1994    3   0   
        D   1991    1   0   
        D   1992    2   0   
        D   1993    3   0   
        D   1994    4   0   
        D   1995    5   0   
        D   1996    5   1   
        D   1997    6   0   

如何在其中创建另外两个列

How can I create two additional columns where

• 如果X增加，则A1为1，并且该主题的最大值至少为4.否则为0.我尝试了data$A1 <- as.numeric(data$X >4)，但是，这并不是我想要的.
• A2的解释有点复杂，我不知道如何在R中执行它.但是，它基本上与A1具有相同的概念，这意味着它仍应捕获大于3的所有X.在接下来的5年中，如果Y = 0，则be = 1.我举一个例子，A2变量应该是什么样子.有可能在R中执行此操作吗?还是我需要手动执行此操作?

• A1 is 1 if X increased and the maximum for the subject is at least 4. Otherwise it is 0. I tried data$A1 <- as.numeric(data$X >4) However, it's not quite what I want.
• A2 is a bit more complicated to explain and I have no clue how to perform it in R. But it basically has the same idea as A1 meaning that it still should capture all X's that are more than 3. Only, it should be = 1 when Y = 0 for the following 5 years. I give an example what the A2 variable should look like. Is it possible do this in R? Or do I need to do this manually?

结果:

            Subject  Year   X   A1   Y   A2
                A   1990    1    1   0    0
                A   1991    1    0   0    0
                A   1992    2    1   0    0
                A   1993    3    1   1    0
                A   1994    4    1   0    0
                A   1995    4    0   0    0
                B   1990    0    0   0    0
                B   1991    1    0   0    0
                B   1992    1    0   0    0 
                B   1993    2    0   1    0
                C   1991    1    0   0    0
                C   1992    2    0   0    0 
                C   1993    3    0   0    0 
                C   1994    3    0   0    0
                D   1991    1    1   0    1
                D   1992    2    1   0    1
                D   1993    3    1   0    1
                D   1994    4    1   0    1 
                D   1995    5    1   0    1 
                D   1996    5    0   1    0
                D   1997    6    1   0    0

没有变量A1和A2的原始数据:

Rawdata without the variables A1 and A2:

> dput(data)
structure(list(Subject = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", 
"B", "C", "D"), class = "factor"), Year = c(1990L, 1991L, 1992L, 
1993L, 1994L, 1995L, 1990L, 1991L, 1992L, 1993L, 1991L, 1992L, 
1993L, 1994L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L
), X = c(1L, 1L, 2L, 3L, 4L, 4L, 0L, 1L, 1L, 2L, 1L, 2L, 3L, 
3L, 1L, 2L, 3L, 4L, 5L, 5L, 6L), Y = c(0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L)), .Names = c("Subject", 
"Year", "X", "Y"), class = "data.frame", row.names = c(NA, -21L
))

推荐答案

我们可以使用data.table

library(data.table)
setDT(data)[, A1 := if(any(X >=4)) c(1, diff(X)) else 0, by = Subject]
data[,  A2 := if(any(X >=3))  inverse.rle(within.list(rle(Y==0), 
              values[values][lengths[values] < 5] <- 0)) else 0, by = Subject]

data[, c("Subject", "Year", "X", "A1", "Y", "A2"), with = FALSE]
#    Subject Year X A1 Y A2
# 1:       A 1990 1  1 0  0
# 2:       A 1991 1  0 0  0
# 3:       A 1992 2  1 0  0
# 4:       A 1993 3  1 1  0
# 5:       A 1994 4  1 0  0
# 6:       A 1995 4  0 0  0
# 7:       B 1990 0  0 0  0
# 8:       B 1991 1  0 0  0
# 9:       B 1992 1  0 0  0
#10:       B 1993 2  0 1  0
#11:       C 1991 1  0 0  0
#12:       C 1992 2  0 0  0
#13:       C 1993 3  0 0  0
#14:       C 1994 3  0 0  0
#15:       D 1991 1  1 0  1
#16:       D 1992 2  1 0  1
#17:       D 1993 3  1 0  1
#18:       D 1994 4  1 0  1
#19:       D 1995 5  1 0  1
#20:       D 1996 5  0 1  0
#21:       D 1997 6  1 0  0

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