Taking a 3 year average across in a panel data set with NAs

孤人 提交于 2020-01-03 05:03:12

问题


I have the following dataframe, called DF,

Country Year Var1 Var2
USA 2010 5 3
USA 2011 6 5
USA 2012 NA 8
USA 2013 4 NA
USA 2014 NA 6
USA 2015 6 9
CHN 2010 NA 5
CHN 2011 7 NA
CHN 2012 6 NA
CHN 2013 4 4
CHN 2014 NA 6
CHN 2015 NA 8
EGY 2010 3 NA
EGY 2011 3 5
EGY 2012 3 6
EGY 2013 NA 8
EGY 2014 NA NA
EGY 2015 NA 2

I want to take a 3 year average of the data. However, if there are only two years of available data within a particular three year interval, I want to ignore the NA and take a two year average. Similarly, if data is only available for one year within a particular three year interval, I want to keep that data point as the "average" for that three year interval. Basically, within each three year interval, I want to take the mean, and ignoring the NAs.

I have tried the following solution recommended in : R: Calculating 5 year averages in panel data

int<-cut(DF$Year,seq(2010,2016,by=3),right=F)
id<-c("Var1", "Var2")
ag<-aggregate(DF[id],list(DF$Country,int), mean)

It yielded the following:

Group.1 Group.2 Var1 Var2
CHN [2010,2013) NA NA
EGY [2010,2013) 3 NA
USA [2010,2013) NA 5.333333
CHN [2013,2016) NA 6.000000
EGY [2013,2016) NA NA 
USA [2013,2016) NA NA 

But the output I am interested in is:

Group.1 Group.2 Var1 Var2
CHN [2010,2013) 6.5 5
EGY [2010,2013) 3 5.5
USA [2010,2013) 5.5 5.3
CHN [2013,2016) 4 6
EGY [2013,2016) NA 5 
USA [2013,2016) 5 7.5

回答1:


Here's how you can do that with package dplyr. Basically, you first create a "year group" using mutate. I used ifelse but it you have more groups, you should consider looking at case_when although nested ifelse will work. Then, we summarise by country and Year_group.

df1 <- read.table(text="Country Year Var1 Var2
                  USA 2010 5 3
                  USA 2011 6 5
                  USA 2012 NA 8
                  USA 2013 4 NA
                  USA 2014 NA 6
                  USA 2015 6 9
                  CHN 2010 NA 5
                  CHN 2011 7 NA
                  CHN 2012 6 NA
                  CHN 2013 4 4
                  CHN 2014 NA 6
                  CHN 2015 NA 8
                  EGY 2010 3 NA
                  EGY 2011 3 5
                  EGY 2012 3 6
                  EGY 2013 NA 8
                  EGY 2014 NA NA
                  EGY 2015 NA 2",header=TRUE, stringsAsFactors=FALSE)
library(dplyr)
df1%>%
  group_by(Country)%>%
  mutate(Year_group=ifelse(Year<2013,"2010-2012","2013-2016"))%>%
  group_by(Country,Year_group)%>%
  summarise(Mean_var1=mean(Var1,na.rm=TRUE),Mean_var2=mean(Var2,na.rm=TRUE)

  Country Year_group Mean_var1 Mean_var2
    <chr>      <chr>     <dbl>     <dbl>
1     CHN  2010-2012       6.5  5.000000
2     CHN  2013-2016       4.0  6.000000
3     EGY  2010-2012       3.0  5.500000
4     EGY  2013-2016       NaN  5.000000
5     USA  2010-2012       5.5  5.333333
6     USA  2013-2016       5.0  7.500000



回答2:


You are almost there, only one addition to your code is required:

int <- cut(DF$Year, seq(2010, 2016, by = 3), right = FALSE)
id <- c("Var1", "Var2")
ag <- aggregate(DF[id], list(DF$Country, int), mean, na.rm = TRUE)
#                                                    |
#-----------------------------------------------------

ag
#  Group.1     Group.2 Var1     Var2
#1     CHN [2010,2013)  6.5 5.000000
#2     EGY [2010,2013)  3.0 5.500000
#3     USA [2010,2013)  5.5 5.333333
#4     CHN [2013,2016)  4.0 6.000000
#5     EGY [2013,2016)  NaN 5.000000
#6     USA [2013,2016)  5.0 7.500000

aggregate() accepts further arguments passed to or used by methods. This way, you can pass the na.rm = TRUE parameter to mean().



来源:https://stackoverflow.com/questions/44370180/taking-a-3-year-average-across-in-a-panel-data-set-with-nas

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