根据 r 中的不同值读取值

Question

好的，所以我有一点困境，我知道它必须有一个解决方案。 我有一个包含 13 列的数据表，但是我们只关心两个（Fare 和 pClass）。有 1309 行，1308 行有票价值，我想通过基于不同类别（pClass）的平均值来找到缺失值。所以我想要的是告诉R找到Fare = NA的行，读取pClass（1,2或3）中的值，然后找到该指定类的平均值，然后替换Fare中的缺失值以这个平均值

所以我想总结一下你的任务，无论谁勇敢和善良地帮助我。我想找到一个缺失值，找出它是什么类，专门平均那个缺失值类，然后用正确的平均值替换那个缺失值

当我在 R 中有多个缺失值时，我可以用正确的平均值替换而不管决定列如何，使用它而不是仅仅找到缺失的行并读取它是一个更好的途径。

感谢您的宝贵时间，

-迪伦

好的，因为这太具体了，无法回答最初的问题，所以新计划男孩（和女孩以及其他任何你想成为 idrc 的人，只要你知道你在说什么）。所以！新计划是使 3 个变量对应于三个不同的 pClass（1、2 和 3）。这些 pClass 平均值中的每一个（将调用 'em pClassAVG.(x) 其中 x = 1、2 或 3）然后让 R 找到票价的缺失值并将它们替换为相应 pClass 的 pClass 变量（平均值） R 的思考过程应该是这样的“好吧，缺失值。pClass 是什么？好吧它是 2，所以我们应该用 pClassAVG.2 替换缺失值”

上次我因为不包含我的代码而得到 -1，所以在这里

    setwd("C:/Users/Maker/Desktop/Data Science/Data/Dylan T/Titanic data")
titanic.train <- read.csv(file = "train.csv", stringsAsFactors = FALSE, header = TRUE)
titanic.test <- read.csv(file = "test.csv", stringsAsFactors = FALSE, header = TRUE)
# line one tells it where to look for data. line 2 & 3 tell it that hey we wanna manipulate this stuff
#the string as factors does makes the factors line up bc we are gonna clean the data sheets togeather
#the headers = true makes the computer understand that there are headers and to not count or read the 
#first line as data but as a title
#currently reads incorrectly

titanic.train$IsTrainSet <- TRUE
titanic.test$IsTrainSet <- FALSE
#makes a new column to tell us if it is the train set or test set

titanic.test$Survived <- NA
#makes a new column and fills it with NA to make the columns line up and have the same names

titanic.full <- rbind(titanic.train, titanic.test)
titanic.full[titanic.full$Embarked=='', "Embarked"] <- 'S'
#ended day 1 at 12 minutes

age.median <- median(titanic.full$Age, na.rm = TRUE)
#creates a variable called age.median and assigns it the median of the age column excluding the missing values (if we included missing
#values it would break bc its adding an undefined number)
#this method is better for replacing data that can change for example real time data that changes over the course of the day and your 
#data gets its info updated every so often thus eliminating the problem of missing values and an incorrect median.

titanic.full[is.na(titanic.full$Age), "Age"] <- age.median
#table(is.na(titanic.full$Age) counts the missing values in the column age of titanic.full and returns true if there are missing value

pClassAVG.1 <- median(titanic.full$Fare, na.rm = TRUE, titanic.full$Pclass == 1 )
pClassAVG.2 <- median(titanic.full$Fare, na.rm = TRUE, titanic.full$Pclass == 2 )

最后两行是我试图告诉它生成上述 pClassAVG.1 和 pClassAVG.2

Answer 1

df <- data_frame(Fare=c(10,20,30,40,50,60,NA,70,80), pClass=c(1,2,3,1,2,3,1,2,3))

a <- df$pClass[which(is.na(df$Fare))] # find the pClass where Fare is missing

df$Fare[which(is.na(df$Fare))] <-   mean(df$Fare[df$pClass==a], na.rm=T) # replace the missinf Fare with mean of corresponding pClass

这仅在缺少一个 Fare 值时才有效

Answer 2

这一定行得通……如果不行，请告诉我

apply 可能有更优雅的解决方案......但这也有效

#Creating a data frame named df
fare<- c(6,8,3,NA,5,1,0,7,NA,4,1,8,6,NA,2)
pclass<- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
df<-as.data.frame(cbind(fare,pclass))

#Creating a loop to look at each row
for(i in 1:length(df$fare)){

#And if the value for fare is missing
if(is.na(df$fare[i])){

#then, replace with the mean according to the group defined in pclass
df$fare[i]<- mean(df$fare[df$pclass==df$pclass[i]],na.rm = TRUE)

 }
}