R - 如何根据数据框中一行中的值创建列的子集

I have a matrix that I would like to subset and eventually use to make a plot. The data is a list of counts for specific blood markers for each patient in a population. It looks like this:

我有一个矩阵，我想分组并最终用于制作情节。该数据是群体中每个患者的特定血液标记的计数列表。它看起来像这样：

    df <- data.frame(MarkerID=c("Class","A123","A124"),
             MarkerName=c("","X","Y"),
             Patient.1=c(0,1,5),
             Patent.2=c(1,2,6),
             Patent.3=c(0,3,7),
             Patient.4=c(1,4,8))

I would like to make a data frame of all of the patients (columns 3-6) that have a class value of zero (1st row) and a second data frame of all of the patients with a class value of 1.

我想建立所有患者（第3-6列）的数据框，其类值为零（第1行），并且所有患者的第二个数据框的类值为1。

In the past I have used the subset function to select rows based on the values in a column, is it possible to select a subset of columns based on the values in a row?

在过去，我使用子集函数根据列中的值选择行，是否可以根据行中的值选择列的子集？

I've tried this:

我试过这个：

x <- subset(data, data[1,] == 0)

however, when I do dim(x) the number of columns is the same as dim(data) but the number of rows is different. Any ideas on how I can make this return just those columns whose value in row 1 is 0?

但是，当我做dim（x）时，列数与dim（数据）相同，但行数不同。有关如何使其返回的任何想法只返回第1行中值为0的列？

Roland, Yes. You're example df is what the data frame looks like. There are ~30,000 markers and >400 patients in the data frame so I didn't post the dput(head(data)). Thanks for the reshaping tip, I'll give that a try.

罗兰，是的。你的例子是df是数据框的样子。数据框中有大约30,000个标记和> 400个患者，因此我没有发布输入（头部（数据））。感谢重塑提示，我会尝试一下。

Your example code did work to subset the columns based on the rows

您的示例代码确实可以根据行对列进行子集化

data[,c(TRUE,TRUE,data[1,-(1:2)]==1)]

on the data I was then able to get a data frame with all of the rows and only the columns with the indicated class.

在数据上我然后能够获得包含所有行的数据框，并且只能获得具有指示类的列。

1 个解决方案

#1

Your data is nor arranged in a good way. It would be better to reshape it.

您的数据也没有很好的安排。重塑它会更好。

In absence of input data this is just a guess:

如果没有输入数据，这只是一个猜测：

df <- data.frame(MarkerID=c("Class","A123","A124"),
                 MarkerName=c("","X","Y"),
                 Patient.1=c(0,1,5),
                 Patent.2=c(1,2,6),
                 Patent.3=c(0,3,7),
                 Patient.4=c(1,4,8))

#  MarkerID MarkerName Patient.1 Patent.2 Patent.3 Patient.4
#1    Class                    0        1        0         1
#2     A123          X         1        2        3         4
#3     A124          Y         5        6        7         8

df[,c(TRUE,TRUE,df[1,-(1:2)]==0)]

#  MarkerID MarkerName Patient.1 Patent.3
#1    Class                    0        0
#2     A123          X         1        3
#3     A124          Y         5        7

Here c(TRUE,TRUE,df[1,-(1:2)]==0) creates a logical vector, which is TRUE for the first two columns and for those columns, which have a 0 in the first row. Then I subset the columns based on this vector.

这里c（TRUE，TRUE，df [1， - （1：2）] == 0）创建一个逻辑向量，对于前两列和那些在第一行中为0的列为TRUE。然后我根据这个向量对列进行子集化。

df[,c(TRUE,TRUE,df[1,-(1:2)]==1)]

#  MarkerID MarkerName Patent.2 Patient.4
#1    Class                   1         1
#2     A123          X        2         4
#3     A124          Y        6         8

This would reshape your data into a more common format (for statistical software):

这会将您的数据重塑为更常见的格式（对于统计软件）：

library(reshape2)  
df2 <- merge(melt(df[1,],variable.name="Patient",value.name="class")[-(1:2)],
             melt(df[-1,],variable.name="Patient"),all=TRUE)

#    Patient class MarkerID MarkerName value
#1  Patent.2     1     A123          X     2
#2  Patent.2     1     A124          Y     6
#3  Patent.3     0     A123          X     3
#4  Patent.3     0     A124          Y     7
#5 Patient.1     0     A123          X     1
#6 Patient.1     0     A124          Y     5
#7 Patient.4     1     A123          X     4
#8 Patient.4     1     A124          Y     8

You could then use subset:

然后你可以使用子集：

subset(df2,class==0)

#    Patient class MarkerID MarkerName value
#3  Patent.3     0     A123          X     3
#4  Patent.3     0     A124          Y     7
#5 Patient.1     0     A123          X     1
#6 Patient.1     0     A124          Y     5

#1