如何使用共享相同级别的各个列创建虚拟变量

时间:2019-05-14 13:50:56

标签: r linear-regression dummy-variable

我正在尝试获取下表的虚拟变量:

df:

Value1       var1       var2      var3      var4
9.330154398  HomeATL    AwayHOU   HomeEast  AwayWest
32.43881489  AwaySDN    HomeATL   HomeWest  AwayWest
54.77178387  AwayLAN    HomeATL   AwayEast  HomeSame
54.77178387  AwayLAN    HomeATL   AwayWest  HomeEast

var1var2具有相同的级别。另一方面,列var3var4也包含它们的级别。因此,我需要在创建虚拟变量期间,创建的新列不应具有重复级别。我的意思是,在var3和var4的示例中,第一行和第三行都具有AwayWest,所以我只需要在名为AwayWest的1列中每行填充数字1。

我想要的输出是:

Value1  HomeEast    HomeWest    AwayEast    AwayWest    HomeSame    HomeATL AwayHOU AwaySDN AwayLAN
9.330154398 1   0   0   1   0   1   1   0   0
32.43881489 0   1   0   1   0   1   0   1   0
54.77178387 0   0   1   0   1   1   0   0   1
54.77178387 1   0   0   1   0   1   0   0   1

我尝试为要转换的每一列创建新的1列(col1

spread(df,var1, col1) %>%
spread(var2, col1)%>%
spread(var3, col1)%>%
spread(var1, col1)

但是它不起作用。

谢谢

3 个答案:

答案 0 :(得分:3)

R的基本选项是使用model.matrix

df <- cbind(df[, "Value1", drop = F], model.matrix(Value1 ~ . - 1, data = df))
df
#     Value1 var1AwayLAN var1AwaySDN var1HomeATL var2HomeATL var3AwayWest
#1  9.330154           0           0           1           0            0
#2 32.438815           0           1           0           1            0
#3 54.771784           1           0           0           1            0
#4 54.771784           1           0           0           1            1
#  var3HomeEast var3HomeWest var4HomeEast var4HomeSame
#1            1            0            0            0
#2            0            1            0            0
#3            0            0            0            1
#4            0            0            1            0

如有必要,我们可以使用

来固定列名
names(df) <- sub("var\\d", "", names(df))

再现您的预期输出。


样本数据

df <- read.table(text =
    "Value1       var1       var2      var3      var4
9.330154398  HomeATL    AwayHOU   HomeEast  AwayWest
32.43881489  AwaySDN    HomeATL   HomeWest  AwayWest
54.77178387  AwayLAN    HomeATL   AwayEast  HomeSame
54.77178387  AwayLAN    HomeATL   AwayWest  HomeEast", header = T)

答案 1 :(得分:2)

base R选项为table

tbl <- +(table(c(col(df1[-1])), unlist(df1[-1]) ) > 0)
cbind(df1[1], as.data.frame.matrix(tbl))

或使用tidyverse

library(tidyverse)
rownames_to_column(df1, 'rn') %>%
    gather(key, val, var1:var4) %>% 
    count(rn, val) %>%
    spread(val, n, fill = 0)  %>%
    select(-rn) %>%
    bind_cols(df1[1], .)

数据

df1 <- structure(list(Value1 = c(9.330154398, 32.43881489, 54.77178387, 
54.77178387), var1 = c("HomeATL", "AwaySDN", "AwayLAN", "AwayLAN"
), var2 = c("AwayHOU", "HomeATL", "HomeATL", "HomeATL"), var3 = c("HomeEast", 
"HomeWest", "AwayEast", "AwayWest"), var4 = c("AwayWest", "AwayWest", 
"HomeSame", "HomeEast")), class = "data.frame", row.names = c(NA, 
-4L))

答案 2 :(得分:0)

您也可以这样做-

   > data.table::setDT(df)[,id:=1:.N]
   > cbind(df[,.(Value1)],dcast(
      melt(setDT(df)[, c(.(id=id), lapply(c("var1","var2","var3","var4"), function(x) paste0(x, get(x))))], id.vars="id"),
      id ~ value,
      length))

输出-

     Value1  id var1AwayLAN var1AwaySDN var1HomeATL var2AwayHOU var2HomeATL var3AwayEast var3AwayWest var3HomeEast var3HomeWest
1:  9.330154  1           0           0           1           1           0            0            0            1            0
2: 32.438815  2           0           1           0           0           1            0            0            0            1
3: 54.771784  3           1           0           0           0           1            1            0            0            0
4: 54.771784  4           1           0           0           0           1            0            1            0            0