如何循环和使用 if else 在此示例中使用 R 的逻辑表达式
How to loop and use if else on this example with logical expressions using R
我有两个包含多个列和不同长度的冗长数据集，对于此示例，我们将子集设置为几行和 3 列：
Temp <- c(12.9423 ,12.9446 ,12.9412 ,12.9617 ,12.9742 ,12.9652 ,12.9463, 12.9847 ,12.9778,
12.9589, 12.9305, 12.9275 ,12.8569 ,12.8531 ,12.9092, 12.9471, 12.9298, 12.9266,
12.9374 ,12.9385, 12.9505, 12.9510, 12.9632 ,12.9621 ,12.9571, 12.9492 ,12.8988,
12.8895 ,12.8777, 12.8956, 12.8748 ,12.7850 ,12.7323, 12.7546 ,12.7375 ,12.7020,
12.7172, 12.7015, 12.6960, 12.6944, 12.6963, 12.6928, 12.6930 ,12.6883 ,12.6913)
Density <- c(26.38635 ,26.38531 ,26.38429, 26.38336, 26.38268 ,26.38242, 26.38265, 26.38343,
26.38486, 26.38697 ,26.38945, 26.39188, 26.39365, 26.39424 ,26.39376 ,26.39250,
26.39084 ,26.38912 ,26.38744 ,26.38587, 26.38456 ,26.38367, 26.38341 ,26.38398,
26.38547 ,26.38793 ,26.39120 ,26.39509, 26.39955 ,26.40455, 26.41002, 26.41578,
26.42126, 26.42593 ,26.42968, 26.43255 ,26.43463, 26.43603 ,26.43693 ,26.43750,
26.43787, 26.43815, 26.43841 ,26.43871 ,26.43904)
po4 <- c(0.4239840 ,0.4351156, 0.4456128, 0.4542392, 0.4608510, 0.4656445, 0.4690847,
0.4717291, 0.4742391 ,0.4774904 ,0.4831152, 0.4922122, 0.5029904, 0.5128720,
0.5190209, 0.5191368 ,0.5133212, 0.5027542 ,0.4905301 ,0.4796467 ,0.4708035,
0.4638879, 0.4578364 ,0.4519745, 0.4481336, 0.4483697, 0.4531310, 0.4622930,
0.4750474 ,0.4905152 ,0.5082183 ,0.5278212 ,0.5491580 ,0.5720519, 0.5961127,
0.6207716 ,0.6449603, 0.6675704 ,0.6878331 ,0.7051851,0.7195461, 0.7305200,
0.7359634 ,0.7343541, 0.7283988)
PP14 <- data.frame(Temp,Density,po4) ##df1
temp <- c(13.13875, 13.13477 ,13.12337 ,13.10662 ,13.09798 ,13.09542 ,13.08734 ,13.07616,
13.06671 ,13.05899, 13.05890 ,13.05293 ,13.03322, 13.01515, 13.02552 ,13.01668,
12.99829, 12.97075 ,12.95572 ,12.95045 ,12.94541 ,12.94365 ,12.94609 ,12.94256,
12.93565 ,12.93258 ,12.93489 ,12.93209 ,12.92219 ,12.90730 ,12.90416 ,12.89974,
12.89749 ,12.89626 ,12.89395, 12.89315 ,12.89274, 12.89276 ,12.89293 ,12.89302)
density <- c( 26.35897, 26.36274 ,26.36173 ,26.36401 ,26.36507 ,26.36662 ,26.36838,
26.36996,
26.37286 ,26.37452 ,26.37402, 26.37571 ,26.37776, 26.38008 ,26.37959 ,26.38178,
26.38642 ,26.39158 ,26.39350, 26.39467, 26.39601, 26.39601, 26.39596 ,26.39517,
26.39728 ,26.39766, 26.39774, 26.39699 ,26.40081 ,26.40328 ,26.40416, 26.40486,
26.40513 ,26.40474 ,26.40552 ,26.40584, 26.40613, 26.40602 ,26.40595 ,26.40498)
krho <- c( -9.999999e+06, -1.786843e+00, -9.142976e-01, -9.650734e-01, -2.532397e+00,
-3.760537e+00, -2.622484e+00, -1.776506e+00, -2.028391e+00, -2.225910e+00,
-3.486826e+00, -2.062341e-01, -3.010643e+00, -3.878437e+00, -3.796426e+00,
-3.227138e+00, -3.335446e+00, -3.738037e+00, -4.577778e+00, -3.818099e+00,
-3.891467e+00, -4.585045e+00 ,-3.150283e+00 ,-4.371089e+00 ,-3.902601e+00,
-4.546019e+00, -3.932538e+00, -4.331247e+00, -4.508137e+00, -4.789201e+00,
-4.383820e+00, -4.423486e+00, -4.334641e+00, -4.330544e+00, -4.838604e+00,
-4.729123e+00, -4.381797e+00, -4.207365e+00, -4.276804e+00, -4.001305e+00)
MS14 <- data.frame(temp,density,krho) ##df2
所以现在我想遍历两个数据集并检查 MS14$density=PP14$Density 是否为真那么我想使用该行中的列 krho 将其乘以对应的 delta po4到相同的密度，所以 diff(po4) 在该行或范围内。像
#MS14$krho[i] * diff(PP14$po4)[i]
但是当我 运行
PP14$Density == MS14$density
当然总是FALSE，因为大十进制数，none是完全一样的。我通过将数字四舍五入到小数点后第三位来解决这个问题，但这应该是一种将其包含在代码中的方法，例如密度 +- 0.005。好吧，或者只是将它四舍五入到小数点后第三位，例如：
PP14$Density_round2 <- round(PP14$Density ,digit=2)
无论如何，我不确定是否应该使用嵌套循环来检查两列并相应地进行操作，或者是否最好创建一个新的 data.frame 与每个 data.frame:
common <- intersect(PP14$Density, MS14$density)
然后进行计算....(??)
所以我可能需要一个嵌套循环，例如：
{for i:PP14
for j:MS14
new-> PP14$Density[i] == MS14$density[j]
#if new is true then PP14$krho[i]* MS14$diff(po4)[j]#[for that particular row]
#and print it into a new data.frame df3
#}
所以，请随时提出最好的继续进行的方法..可能有几种方法..
非常感谢您！！
Ps: 也欢迎使用 Matlab 的建议
是这样的吗？
compareDec <- function(x, y, digits = NULL, tol = .Machine$double.eps^0.5){
if(is.null(digits)){
abs(x - y) < tol
} else {
round(x, digits = digits) == round(y, digits = digits)
}
}
icomp <- outer(MS14$density, PP14$Density, compareDec, digits = 2)
m <- outer(MS14$krho, c(0, diff(PP14$po4)))
new <- which(icomp, arr.ind = TRUE)
df3 <- cbind.data.frame(new, Prod = m[new])
head(df3)
# row col Prod
#1 17 1 0.00000000
#2 18 1 0.00000000
#3 19 1 0.00000000
#4 20 1 0.00000000
#5 17 2 -0.03712885
#6 18 2 -0.04161033
我有两个包含多个列和不同长度的冗长数据集，对于此示例，我们将子集设置为几行和 3 列：
Temp <- c(12.9423 ,12.9446 ,12.9412 ,12.9617 ,12.9742 ,12.9652 ,12.9463, 12.9847 ,12.9778,
12.9589, 12.9305, 12.9275 ,12.8569 ,12.8531 ,12.9092, 12.9471, 12.9298, 12.9266,
12.9374 ,12.9385, 12.9505, 12.9510, 12.9632 ,12.9621 ,12.9571, 12.9492 ,12.8988,
12.8895 ,12.8777, 12.8956, 12.8748 ,12.7850 ,12.7323, 12.7546 ,12.7375 ,12.7020,
12.7172, 12.7015, 12.6960, 12.6944, 12.6963, 12.6928, 12.6930 ,12.6883 ,12.6913)
Density <- c(26.38635 ,26.38531 ,26.38429, 26.38336, 26.38268 ,26.38242, 26.38265, 26.38343,
26.38486, 26.38697 ,26.38945, 26.39188, 26.39365, 26.39424 ,26.39376 ,26.39250,
26.39084 ,26.38912 ,26.38744 ,26.38587, 26.38456 ,26.38367, 26.38341 ,26.38398,
26.38547 ,26.38793 ,26.39120 ,26.39509, 26.39955 ,26.40455, 26.41002, 26.41578,
26.42126, 26.42593 ,26.42968, 26.43255 ,26.43463, 26.43603 ,26.43693 ,26.43750,
26.43787, 26.43815, 26.43841 ,26.43871 ,26.43904)
po4 <- c(0.4239840 ,0.4351156, 0.4456128, 0.4542392, 0.4608510, 0.4656445, 0.4690847,
0.4717291, 0.4742391 ,0.4774904 ,0.4831152, 0.4922122, 0.5029904, 0.5128720,
0.5190209, 0.5191368 ,0.5133212, 0.5027542 ,0.4905301 ,0.4796467 ,0.4708035,
0.4638879, 0.4578364 ,0.4519745, 0.4481336, 0.4483697, 0.4531310, 0.4622930,
0.4750474 ,0.4905152 ,0.5082183 ,0.5278212 ,0.5491580 ,0.5720519, 0.5961127,
0.6207716 ,0.6449603, 0.6675704 ,0.6878331 ,0.7051851,0.7195461, 0.7305200,
0.7359634 ,0.7343541, 0.7283988)
PP14 <- data.frame(Temp,Density,po4) ##df1
temp <- c(13.13875, 13.13477 ,13.12337 ,13.10662 ,13.09798 ,13.09542 ,13.08734 ,13.07616,
13.06671 ,13.05899, 13.05890 ,13.05293 ,13.03322, 13.01515, 13.02552 ,13.01668,
12.99829, 12.97075 ,12.95572 ,12.95045 ,12.94541 ,12.94365 ,12.94609 ,12.94256,
12.93565 ,12.93258 ,12.93489 ,12.93209 ,12.92219 ,12.90730 ,12.90416 ,12.89974,
12.89749 ,12.89626 ,12.89395, 12.89315 ,12.89274, 12.89276 ,12.89293 ,12.89302)
density <- c( 26.35897, 26.36274 ,26.36173 ,26.36401 ,26.36507 ,26.36662 ,26.36838,
26.36996,
26.37286 ,26.37452 ,26.37402, 26.37571 ,26.37776, 26.38008 ,26.37959 ,26.38178,
26.38642 ,26.39158 ,26.39350, 26.39467, 26.39601, 26.39601, 26.39596 ,26.39517,
26.39728 ,26.39766, 26.39774, 26.39699 ,26.40081 ,26.40328 ,26.40416, 26.40486,
26.40513 ,26.40474 ,26.40552 ,26.40584, 26.40613, 26.40602 ,26.40595 ,26.40498)
krho <- c( -9.999999e+06, -1.786843e+00, -9.142976e-01, -9.650734e-01, -2.532397e+00,
-3.760537e+00, -2.622484e+00, -1.776506e+00, -2.028391e+00, -2.225910e+00,
-3.486826e+00, -2.062341e-01, -3.010643e+00, -3.878437e+00, -3.796426e+00,
-3.227138e+00, -3.335446e+00, -3.738037e+00, -4.577778e+00, -3.818099e+00,
-3.891467e+00, -4.585045e+00 ,-3.150283e+00 ,-4.371089e+00 ,-3.902601e+00,
-4.546019e+00, -3.932538e+00, -4.331247e+00, -4.508137e+00, -4.789201e+00,
-4.383820e+00, -4.423486e+00, -4.334641e+00, -4.330544e+00, -4.838604e+00,
-4.729123e+00, -4.381797e+00, -4.207365e+00, -4.276804e+00, -4.001305e+00)
MS14 <- data.frame(temp,density,krho) ##df2
所以现在我想遍历两个数据集并检查 MS14$density=PP14$Density 是否为真那么我想使用该行中的列 krho 将其乘以对应的 delta po4到相同的密度，所以 diff(po4) 在该行或范围内。像
#MS14$krho[i] * diff(PP14$po4)[i]
但是当我 运行
PP14$Density == MS14$density
当然总是FALSE，因为大十进制数，none是完全一样的。我通过将数字四舍五入到小数点后第三位来解决这个问题，但这应该是一种将其包含在代码中的方法，例如密度 +- 0.005。好吧，或者只是将它四舍五入到小数点后第三位，例如：
PP14$Density_round2 <- round(PP14$Density ,digit=2)
无论如何，我不确定是否应该使用嵌套循环来检查两列并相应地进行操作，或者是否最好创建一个新的 data.frame 与每个 data.frame:
common <- intersect(PP14$Density, MS14$density)
然后进行计算....(??)
所以我可能需要一个嵌套循环，例如：
{for i:PP14
for j:MS14
new-> PP14$Density[i] == MS14$density[j]
#if new is true then PP14$krho[i]* MS14$diff(po4)[j]#[for that particular row]
#and print it into a new data.frame df3
#}
所以，请随时提出最好的继续进行的方法..可能有几种方法..
非常感谢您！！
Ps: 也欢迎使用 Matlab 的建议
是这样的吗？
compareDec <- function(x, y, digits = NULL, tol = .Machine$double.eps^0.5){
if(is.null(digits)){
abs(x - y) < tol
} else {
round(x, digits = digits) == round(y, digits = digits)
}
}
icomp <- outer(MS14$density, PP14$Density, compareDec, digits = 2)
m <- outer(MS14$krho, c(0, diff(PP14$po4)))
new <- which(icomp, arr.ind = TRUE)
df3 <- cbind.data.frame(new, Prod = m[new])
head(df3)
# row col Prod
#1 17 1 0.00000000
#2 18 1 0.00000000
#3 19 1 0.00000000
#4 20 1 0.00000000
#5 17 2 -0.03712885
#6 18 2 -0.04161033