我必须编写一个函数,它接受数据文件目录和完整案例的阈值,并计算每个文件中硫酸盐和硝酸盐(两列)之间的相关性,其中完全观察到的案例数(在所有变量上)是大于门槛。该函数应返回满足阈值要求的监视器的相关向量。如果没有文件满足阈值要求,则该函数应返回长度为0的数字向量。此函数的原型如下
我的代码看起来像这样
corr <- function(directory,threshold=0){
a<-list.files("specdata")
for (i in a) {
data <- read.csv(paste(directory, "/", i, sep =""))
x<-complete.cases(data)
j<-sum(as.numeric(x))
sulfate<-data[,2]
nitrate<-data[,3]
b<-cor(sulfate,nitrate)
}
if (j>threshold)
return(b)
else
numeric()
}
没有错误信息
如果我输入
z,其中; -corr( “specdata”)
头(z)的 [1] NA
我不知道问题是什么。我不知道列中的NA值是否与它有关。我觉得我的代码中缺少一些东西。我认为read.csv在每个文件需要一个数据帧时会创建一个唯一的数据帧,但我不明白为什么在这种情况下返回为NA(当没有阈值时)。
但是,如果我引入更大的阈值(1000):
z<-corr("specdata",1000)
head(z)
numeric(0)
我需要的预期输出是
cr <- corr("specdata", 150)
head(cr)
[1] -0.01895754 -0.14051254 -0.04389737 -0.06815956 -0.12350667 -0.07588814
答案 0 :(得分:2)
this is the correct and running solution you can refer to this
corr <- function(directory, threshold = 0) {
## 'directory' is a character vector of length 1 indicating the location of
## the CSV files
## 'threshold' is a numeric vector of length 1 indicating the number of
## completely observed observations (on all variables) required to compute
## the correlation between nitrate and sulfate; the default is 0
## Return a numeric vector of correlations
df = complete(directory)
ids = df[df["nobs"] > threshold, ]$id
corrr = numeric()
for (i in ids) {
newRead = read.csv(paste(directory, "/", formatC(i, width = 3, flag = "0"),
".csv", sep = ""))
dff = newRead[complete.cases(newRead), ]
corrr = c(corrr, cor(dff$sulfate, dff$nitrate))
}
return(corrr)
}
complete <- function(directory, id = 1:332) {
f <- function(i) {
data = read.csv(paste(directory, "/", formatC(i, width = 3, flag = "0"),
".csv", sep = ""))
sum(complete.cases(data))
}
nobs = sapply(id, f)
return(data.frame(id, nobs))
}
cr <- corr("specdata", 150)
head(cr)
答案 1 :(得分:0)
这个问题可能最好分为两个步骤 - 计算每个文件的值并收集所有文件的结果。
corr.file <- function(filename) {
data <- read.csv(paste(directory, "/", i, sep =""))
x <- complete.cases(data)
sulfate <- data[,2]
nitrate <- data[,3]
b <- cor(sulfate,nitrate)
if (j>threshold) return(b) else return(numeric())
}
a <- list.files("specdata")
correlations <- sapply(a, corr.file)