Question

我有一个像下面这样的数据集，上面有公司的独特观察。但是由于之前的合并，变量IndustryCode具有一些NA。

stkcd date industrycode
10    2002   .
10    2003   .
10    2004   E22
10    2005   E22
10    2006   E22
10    2007   E22
10    2008   G45
10    2009   G45
10    2010   .
10    2011   .
11    2001   .
11    2002   .
11    2003   D23
11    2004   D23
....

我想用同一家公司最近一年的价值估算NA。例如，对于公司10（stkcd = 10），2004年之前的IndustryCode更改为E22（2004年的值），2009年之后的NA替换为G45（2009年的值）。

如何在R中实现这一目标？

Answer 1

如何应用na.locf中两倍的zoo函数？

基本上，它将NA替换为最后一个值，您也需要向后移动。

一些数据：

dat <- data.frame(
  stkcd = rep(10, 10),
  year = 2002:2011,
  type = c(NA,NA, "E22", "E22","E22", "E22", "G45", "G45", NA, NA)
)


library(zoo)
dat$type <- na.locf(dat$type, na.rm = F) # computes NA with the last value found
dat$type <- na.locf(dat$type, na.rm = F, fromLast = T) # this because you start with NAs, so you need to go backwards too

# output:

# stkcd year type
# 1     10 2002  E22
# 2     10 2003  E22
# 3     10 2004  E22
# 4     10 2005  E22
# 5     10 2006  E22
# 6     10 2007  E22
# 7     10 2008  G45
# 8     10 2009  G45
# 9     10 2010  G45
# 10    10 2011  G45

如果您有多家公司，则需要先从group_by起dplyr：

library(dplyr)
library(zoo)

dat %>%
    group_by(stkcd) %>% # the variable used for the company name
    mutate(type = na.locf(type, na.rm = F),
           type = na.locf(type, na.rm = F, fromLast = T))

例如有两家公司：

dat <- data.frame(
  stkcd = c(rep(10, 10), rep(20,10)),
  year = rep(2002:2011, 2),
  type = c(NA,NA, "E22", "E22","E22", "E22", "G45", "G45", NA, NA,
           NA,NA, "E22", "E22","E22", "E22", "G45", "G45", NA, NA)
)

dat %>%
  group_by(stkcd) %>% # the variable used for the company name
  mutate(type = na.locf(type, na.rm = F),
         type = na.locf(type, na.rm = F, fromLast = T)) 

# A tibble: 20 x 3
# Groups:   stkcd [2]
# stkcd  year type 
# <dbl> <int> <fct>
#   1    10  2002 E22  
# 2    10  2003 E22  
# 3    10  2004 E22  
# 4    10  2005 E22  
# 5    10  2006 E22  
# 6    10  2007 E22  
# 7    10  2008 G45  
# 8    10  2009 G45  
# 9    10  2010 G45  
# 10    10  2011 G45  
# 11    20  2002 E22  
# 12    20  2003 E22  
# 13    20  2004 E22  
# 14    20  2005 E22  
# 15    20  2006 E22  
# 16    20  2007 E22  
# 17    20  2008 G45  
# 18    20  2009 G45  
# 19    20  2010 G45  
# 20    20  2011 G45

Answer 2

您当然需要按公司分组！

对于给定的公司，此方法有效：

inds = is.na(df1$industrycode)
resInds = sapply(df1$date[inds], function(x) { which.min( abs( x - df1$date[!inds] ) ) })

df1$industrycode[inds] <- df1$industrycode[!inds][resInds]

#   stkcd date industrycode
#1     10 2002          E22
#2     10 2003          E22
#3     10 2004          E22
#4     10 2005          E22
#5     10 2006          E22
#6     10 2007          E22
#7     10 2008          G45
#8     10 2009          G45
#9     10 2010          G45
#10    10 2011          G45

使用data.table

df1<-
    fread("stkcd date industrycode
10    2002   NA
10    2003   NA
10    2004   E22
10    2005   E22
10    2006   E22
10    2007   E22
10    2008   G45
10    2009   G45
10    2010   NA
10    2011   NA
11    2002   NA
11    2003   NA
11    2004   sE22
11    2005   sE22
11    2006   NA
11    2007   sE22
11    2008   sG45
11    2009   sG45
11    2010   NA
11    2011   NA") %>% setDF

na_fill <- function(data,variable) {
    inds = is.na(data[[variable]])
    resInds = sapply(data$date[inds], function(x) { which.min( abs( x - data$date[!inds] ) ) })
    data[[variable]][inds] <- data[[variable]][!inds][resInds]
    return(data[[variable]])
}

setDT(df1)[,na_fill(.SD,"industrycode"),by="stkcd"][]

#    stkcd   V1
# 1:    10  E22
# 2:    10  E22
# 3:    10  E22
# 4:    10  E22
# 5:    10  E22
# 6:    10  E22
# 7:    10  G45
# 8:    10  G45
# 9:    10  G45
#10:    10  G45
#11:    11 sE22
#12:    11 sE22
#13:    11 sE22
#14:    11 sE22
#15:    11 sE22
#16:    11 sE22
#17:    11 sG45
#18:    11 sG45
#19:    11 sG45
#20:    11 sG45

基于出现时间的归类分类变量的NA

2 个答案: