R等价于.first或.last sas运算符

时间:2012-12-07 15:03:38

标签: r sas

有人知道什么是SAS的最佳替代品。或者持续。运营商?我没找到。

SAS拥有第一名。最后。自动变量,用于识别具有特定变量的相同值的组中的第一个和最后一个记录;所以在以下数据集中定义了FIRST.model和LAST.model:

Model,SaleID,First.Model,Last.Model
Explorer,1,1,0
Explorer,2,0,0
Explorer,3,0,0
Explorer,4,0,1
Civic,5,1,0
Civic,6,0,0
Civic,7,0,1

5 个答案:

答案 0 :(得分:9)

听起来您正在寻找!duplicatedfromLast参数为FALSETRUE

d <- datasets::Puromycin

d$state
# [1] treated   treated   treated   treated   treated   treated   treated  
# [8] treated   treated   treated   treated   treated   untreated untreated
#[15] untreated untreated untreated untreated untreated untreated untreated
#[22] untreated untreated
#Levels: treated untreated
!duplicated(d$state)
# [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#[13]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
!duplicated(d$state,fromLast=TRUE)
# [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

此功能有一些警告和边缘情况行为,您可以通过帮助文件(?duplicated)找到这些行为。

答案 1 :(得分:4)

更新(首先阅读)

如果您真的只对行索引感兴趣,可能会使用splitrange的直接使用。以下假设数据集中的rownames按顺序编号,但也可能进行调整。

irisFirstLast <- sapply(split(iris, iris$Species), 
                        function(x) range(as.numeric(rownames(x))))
irisFirstLast              ## Just the indices
#      setosa versicolor virginica
# [1,]      1         51       101
# [2,]     50        100       150
iris[irisFirstLast[1, ], ] ## `1` would represent "first"
#     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
# 1            5.1         3.5          1.4         0.2     setosa
# 51           7.0         3.2          4.7         1.4 versicolor
# 101          6.3         3.3          6.0         2.5  virginica
iris[irisFirstLast, ]      ## nothing would represent both first and last
#     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
# 1            5.1         3.5          1.4         0.2     setosa
# 50           5.0         3.3          1.4         0.2     setosa
# 51           7.0         3.2          4.7         1.4 versicolor
# 100          5.7         2.8          4.1         1.3 versicolor
# 101          6.3         3.3          6.0         2.5  virginica
# 150          5.9         3.0          5.1         1.8  virginica

d <- datasets::Puromycin   
dFirstLast <- sapply(split(d, d$state), 
                     function(x) range(as.numeric(rownames(x))))
dFirstLast
#      treated untreated
# [1,]       1        13
# [2,]      12        23
d[dFirstLast[2, ], ]       ## `2` would represent `last`
#    conc rate     state
# 12  1.1  200   treated
# 23  1.1  160 untreated

如果使用命名行,一般方法是相同的,但您必须自己指定范围。这是一般模式:

datasetFirstLast <- sapply(split(dataset, dataset$groupingvariable), 
                           function(x) c(rownames(x)[1], 
                                         rownames(x)[length(rownames(x))]))

初步答复(已编辑)

如果您有兴趣提取行而不是将行号用于其他目的,您还可以浏览data.table。以下是一些例子:

library(data.table)
DT <- data.table(iris, key="Species")
DT[J(unique(Species)), mult = "first"]
#       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1:     setosa          5.1         3.5          1.4         0.2
# 2: versicolor          7.0         3.2          4.7         1.4
# 3:  virginica          6.3         3.3          6.0         2.5
DT[J(unique(Species)), mult = "last"]
#       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1:     setosa          5.0         3.3          1.4         0.2
# 2: versicolor          5.7         2.8          4.1         1.3
# 3:  virginica          5.9         3.0          5.1         1.8
DT[, .SD[c(1,.N)], by=Species]
#       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1:     setosa          5.1         3.5          1.4         0.2
# 2:     setosa          5.0         3.3          1.4         0.2
# 3: versicolor          7.0         3.2          4.7         1.4
# 4: versicolor          5.7         2.8          4.1         1.3
# 5:  virginica          6.3         3.3          6.0         2.5
# 6:  virginica          5.9         3.0          5.1         1.8

这最后一种方法非常方便。例如,如果您想要每组的前三行和最后三行,您可以使用:DT[, .SD[c(1:3, (.N-2):.N)], by=Species](仅供参考:.N表示每组的案例数。

其他有用的方法包括:

DT[, tail(.SD, 2), by = Species] ## last two rows of each group
DT[, head(.SD, 4), by = Species] ## first four rows of each group

答案 2 :(得分:4)

头部和尾部功能与n = 1选项结合使用是一个很好的方法。 参见 R for SAS和SPss用户**(Robert Muenchen)根据感兴趣的变量制作数据框 即最后一次。

dfby<- data.frame(df$var1, df$var2)
mylastList<-by(df,dfby,tail, n=1)
#turn into a dataframe
mylastDF<-do.call(rbind,mylastList)

答案 3 :(得分:2)

这是一个dplyr解决方案:

# input
dataset <- structure(list(Model = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 1L
), .Label = c("Civic", "Explorer"), class = "factor"), SaleID = 1:7), .Names = c("Model", 
"SaleID"), class = "data.frame", row.names = c(NA, -7L))


# code 
library(dplyr)

dataset %>% 

  group_by(Model) %>%

  mutate(
          "First"        = row_number() == min( row_number() ),
          "Last"         = row_number() == max( row_number() )
  )

# output:

     Model SaleID First  Last
    <fctr>  <int> <lgl> <lgl>
1 Explorer      1  TRUE FALSE
2 Explorer      2 FALSE FALSE
3 Explorer      3 FALSE FALSE
4 Explorer      4 FALSE  TRUE
5    Civic      5  TRUE FALSE
6    Civic      6 FALSE FALSE
7    Civic      7 FALSE  TRUE

PS:如果你没有安装dplyr:

install.packages("dplyr")

答案 4 :(得分:1)

以下功能基于@ Joe对First / Last的描述。
该函数返回向量列表。

每个列表条目对应于数据帧的列(即数据集的特征或变量)
  然后,在给定的列表条目中,存在属于的索引   每个观察类别的第一个(或最后一个)元素。

示例用法:

# Pass in your data frame, and indicate whether or not you want to find Last or find First. 
# Assign to the appropriate variable
first <- findFirstLast(myDF)
last  <- findFirstLast(myDF, findFirst=FALSE)

使用data(iris)

的示例
data(iris)
first <- findFirstLast(iris)
last  <- findFirstLast(iris, findFirst=FALSE)

每种物种的观察结果:

 first$Species
 #    setosa versicolor  virginica 
 #        1         51        101 

 last$Species
 #    setosa versicolor  virginica 
 #        50        100        150 

每次首次观察一个分支

时抓住整行
iris[first$Species, ]
#      Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#  1            5.1         3.5          1.4         0.2     setosa
#  51           7.0         3.2          4.7         1.4 versicolor
#  101          6.3         3.3          6.0         2.5  virginica


<小时/>

功能代码findFirstLast():

  findFirstLast <- function(myDF, findFirst=TRUE) {
  # myDF should be a data frame or matrix 

    # By default, this function finds the first occurence of each unique value in a column
    # If instead we want to find last, set findFirst to FALSE.  This will give `maxOrMin` a value of -1
    #    finding the min of the negative indecies is the same as finding the max of the positive indecies. 
    maxOrMin <- ifelse(findFirst, 1, -1) 


    # For each column in myDF, make a list of all unique values (`levs`) and iterate over that list, 
    #   finding the min (or max) of all the indicies of where that given value appears within the column  
    apply(myDF, 2, function(colm) {
        levs <- unique(colm)
        sapply(levs, function(lev) {
          inds <- which(colm==lev)
          ifelse(length(inds)==0, NA, maxOrMin*min(inds*maxOrMin) ) 
        })   
      })
  }