循環遍歷數據幀的連續列

Question

我想循環遍歷一個數據幀的列，並將計算結果存入矩陣。

該場景可以與下面的示例數據被複制：

df = data.frame(replicate(10,sample(0:20,10,rep=TRUE))) # the columns to be calculated on 

M1 = as.data.frame(matrix(0, nrow = 10, ncol = 10)) # a matrix to hold the results. 
rownames(M1) = colnames(df) 
colnames(M1) = colnames(df) 

並且顯示如下：

> df # Frame with columns of data, X1 to X10 

    X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 
1 1 19 2 6 6 5 0 2 5 10 
2 16 7 14 16 16 18 11 2 18 11 
3 7 6 11 4 4 1 18 11 10 16 
4 20 2 4 20 4 6 10 5 16 7 
5 9 8 16 19 11 2 14 7 13 7 
6 5 16 6 8 20 15 5 11 4 0 
7 11 16 12 8 18 20 20 20 10 14 
8 17 14 10 4 3 10 13 11 5 1 
9 9 20 10 5 1 7 12 10 5 6 
10 8 14 3 14 20 10 17 20 9 14 

> M1 # Output frame to hold results 

    X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 
X1 0 0 0 0 0 0 0 0 0 0 
X2 0 0 0 0 0 0 0 0 0 0 
X3 0 0 0 0 0 0 0 0 0 0 
X4 0 0 0 0 0 0 0 0 0 0 
X5 0 0 0 0 0 0 0 0 0 0 
X6 0 0 0 0 0 0 0 0 0 0 
X7 0 0 0 0 0 0 0 0 0 0 
X8 0 0 0 0 0 0 0 0 0 0 
X9 0 0 0 0 0 0 0 0 0 0 
X10 0 0 0 0 0 0 0 0 0 0 

在df列X1和X2是在投入到計算，然後X1和X3，然後X1和X4等，然後循環將循環X2和X3，然後循環X2和X4等。

列n和m被輸入到計算/循環中，並且結果應該被放置在對應於列的矩陣中的適當位置上，其中 x m。計算本身簡單地將Xn和Xm之間的區域確定爲繪製線。我不知道如何正確地構建循環來做到這一點：

# The first iteration of the calculation, column X1 and X2 (X1 and X1 would = 0) 

    y = seq(1,10,1) 
    f1 = approxfun(y, df[,1] - df[,2]) # takes two columns as inputs 
    f2 = function(x) abs(f1(x)) 

    area1 = integrate(f2, 1, 10, subdivisions = 500) 
    M1[2,1] = area1$value 

結果框架會產生一個「半矩陣」（即所有需要的鏡像一半是相同的）：

> M1 
    X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 
X1 0 0 0 0 0 0 0 0 0 0 
X2 A 0 0 0 0 0 0 0 0 0 
X3 A A 0 0 0 0 0 0 0 0 
X4 A A A 0 0 0 0 0 0 0 
X5 A A A A 0 0 0 0 0 0 
X6 A A A A A 0 0 0 0 0 
X7 A A A A A A 0 0 0 0 
X8 A A A A A A A 0 0 0 
X9 A A A A A A A A 0 0 
X10 A A A A A A A A A 0 

我開始構建一個for循環，但我使用i和j保持在X1，直到它已通過X2-X10循環，然後移動到X2等

感謝絆倒了！

Answer 1

我無法讓您的功能正常工作。因此，與使用隨機替換功能，這個循環對我的作品：

area=list() # because the actual function doesn't work 
for(i in 1:ncol(df)){ 
    for(j in 1:ncol(df)){ 
    if(i==j){M[i,i]=0;next} 
    selection=df[,c(i,j)] 
    #area=integrate(f2, 1, 200, subdivisions = 500) 
    area$value=mean(colSums(selection)) # something random to check 
    M[i,j]=area$value 
    M[j,i]=area$value 
    } 
} 

但循環一般不做事的最有效的方式。因此，您可能更喜歡此選項：

df = data.frame(replicate(10,sample(0:20,10,rep=TRUE))) # the columns to be calculated on 
my.f = function(x) abs(x[,1]-x[,2]) 

#y = t(as.matrix(combn(ncol(df), 2L, function(y) integrate(my.f(df[y]), 1, 200, subdivisions = 500),simplify=F))) # This doesn't work, but should be close to what you want to do 

y = t(as.matrix(combn(ncol(df), 2L, function(y) mean(f(df[y]),simplify=F)))) # this works, but is just an example 

N = seq_len(ncol(y)) 
nams = colnames(df) 
M = matrix(ncol = length(nams), nrow = length(nams)) 
M[lower.tri(M)] = y 
M = t(out) 
M[lower.tri(M)] = y 
M = t(M) 
diag(M) = 0 
rownames(M) = colnames(out) = colnames(df) 
M 

    X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 
X1 0.0 8.6 6.4 8.8 7.1 6.6 7.0 4.0 7.0 3.7 
X2 8.6 0.0 5.0 4.4 5.5 5.4 4.4 9.2 8.0 7.7 
X3 6.4 5.0 0.0 7.2 5.9 5.8 7.6 7.0 10.4 6.5 
X4 8.8 4.4 7.2 0.0 5.9 4.4 5.4 9.6 8.4 7.3 
X5 7.1 5.5 5.9 5.9 0.0 7.3 5.3 9.1 8.5 8.0 
X6 6.6 5.4 5.8 4.4 7.3 0.0 6.0 8.4 5.6 3.7 
X7 7.0 4.4 7.6 5.4 5.3 6.0 0.0 8.8 4.4 5.7 
X8 4.0 9.2 7.0 9.6 9.1 8.4 8.8 0.0 9.6 6.9 
X9 7.0 8.0 10.4 8.4 8.5 5.6 4.4 9.6 0.0 5.5 
X10 3.7 7.7 6.5 7.3 8.0 3.7 5.7 6.9 5.5 0.0