使用R中的逻辑函数，将循环替换为apply系列函数（或dplyr）

我创建了这个具有代表性的数据框，它使用for循环指定条件类别

df <- data.frame(Date=c("08/29/2011", "08/29/2011", "08/30/2011", "08/30/2011", "08/30/2011", "08/29/2012", "08/29/2012", "01/15/2012", "08/29/2012"),
             Time=c("09:45", "10:00", "13:00", "13:30", "10:14", "9:09", "11:23", "17:06", "12:20"),
             Diff = c(0.2,4.3,6.5,15.0, 16.5, 31, 30.2, 21.9, 1.9))

df1<- df %>%
  mutate(Accuracy=ifelse(Diff<=3, "Excellent", "TBD"))

for(i in 1:nrow(df1)){
  if(df1$Diff[i]>3&&df1$Diff[i]<=10){
    df1$Accuracy[i]<-"Good"} 
  if(df1$Diff[i]>10&&df1$Diff[i]<=15){
    df1$Accuracy[i]<-"Fair"} 
  if(df1$Diff[i]>15&&df1$Diff[i]<=30){
    df1$Accuracy[i]<-"Poor"}
  if(df1$Diff[i]>30){
    df1$Accuracy[i]<-"Unacceptable"}
}

---

df您可以在
dplyr
中使用
case\u：

df1<- df %>%
mutate(Accuracy= case_when(
  .$Diff <=  3 ~ "Excellent",
  .$Diff <=  10  ~ "Good",
  .$Diff <=  15  ~ "Fair",
  .$Diff <=  30  ~ "Poor",
  .$Diff >   30  ~ "Unpublishable",
  TRUE  ~"TBD")
)

 df1
        Date  Time Diff      Accuracy
1 08/29/2011 09:45  0.2     Excellent
2 08/29/2011 10:00  4.3          Good
3 08/30/2011 13:00  6.5          Good
4 08/30/2011 13:30 15.0          Fair
5 08/30/2011 10:14 16.5          Poor
6 08/29/2012  9:09 31.0 Unpublishable
7 08/29/2012 11:23 30.2 Unpublishable
8 01/15/2012 17:06 21.9          Poor
9 08/29/2012 12:20  1.9     Excellent

df1%
变异（准确度=情况）(
.$Diff您的量表有一个错误，它从
“一般”
到
“良好”
再到
“不可发布”
。我将
“良好”
-值替换为
“差”
。
mutate(Accuracy=ifelse(pDiff<=3, "Excellent", 
                         ifelse(pDiff>3&pDiff<=10, "Good",
                                ifelse(pDiff>10&pDiff<=15, "Fair",
                                       ifelse(pDiff>15&pDiff<30, "Poor",
                                              ifelse(Diff>30, "Unpublishable", "TBD"))))))  

df1<- df %>%
mutate(Accuracy= case_when(
  .$Diff <=  3 ~ "Excellent",
  .$Diff <=  10  ~ "Good",
  .$Diff <=  15  ~ "Fair",
  .$Diff <=  30  ~ "Poor",
  .$Diff >   30  ~ "Unpublishable",
  TRUE  ~"TBD")
)

 df1
        Date  Time Diff      Accuracy
1 08/29/2011 09:45  0.2     Excellent
2 08/29/2011 10:00  4.3          Good
3 08/30/2011 13:00  6.5          Good
4 08/30/2011 13:30 15.0          Fair
5 08/30/2011 10:14 16.5          Poor
6 08/29/2012  9:09 31.0 Unpublishable
7 08/29/2012 11:23 30.2 Unpublishable
8 01/15/2012 17:06 21.9          Poor
9 08/29/2012 12:20  1.9     Excellent