问题
I am trying to create a column in a data.frame or data.table with two conditions. The difference to the posts I have seen and which I have tried to modify below is that I do not have 'value' for the conditions but the conditions depend on other variables in the data.frame.
Let's assume this is my data frame:
mydf <- data.frame (Year = c(2000, 2001, 2002, 2004, 2005,
2007, 2000, 2001, 2002, 2003,
2003, 2004, 2005, 2006, 2006, 2007),
Name = c("Tom", "Tom", "Tom", "Fred", "Gill",
"Fred", "Gill", "Gill", "Tom", "Tom",
"Fred", "Fred", "Gill", "Fred", "Gill", "Gill"))
I want to find out how many times the 3 subjects have experienced an event in the last 5 years. However, if the event dates go back more than 5 years, I do not want to include it. I thought I could do a sum of an indicator variable (set to 1 if the subject experienced the event in the year) while specifying something along the lines of Year < Year & Year >= Year-5. So basically sum the experiences for the year smaller than the focal year and larger than or equal to 5 years before the focal year.
I have create an indicator for summing and a variable for focal year - 5
mydf$Ind <- 1
mydf$Yearm5 <- mydf$Year-5
Then I convert to data table for speed (the original df has +60k obs)
library(data.table)
mydf <- data.table(mydf)
The issue now is that I cannot get the two conditions to work. The post I have seen seem to all know a specific value by which to subset (e.g. R data.table subsetting on multiple conditions.), but in my case the value changes from observation to observation (not sure if this means I need to do some looping?).
I thought I need something along the lines of:
mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year < Year & Year >= Yearm5]
gives:
Empty data.table (0 rows) of 5 cols: Year,Name,Ind,Yearm5,Exp
Using just one condition
mydf1 <- mydf[, c("Exp"):= sum(Ind), by = c("Name")][Year >= Yearm5]
gives the total experience so I am assuming that something is wrong with the Year < Year condition.
I am not quite sure what though. I have also tried to modify the suggestions in: how to cumulatively add values in one vector in R with not luck again something seems to be wrong with the way I specify the conditions.
library(dplyr)
mytest1 <- mydf %>%
group_by(Name, Year) %>%
filter(Year < Year & Year >= Yearm5) %>%
mutate(Exp = sum(Ind))
The result should look as follows:
myresult <- data.frame (Year = c(2003, 2004, 2004, 2006,
2007, 2000, 2001, 2005,
2005, 2006, 2007, 2000,
2001, 2002, 2002, 2003),
Name = c("Fred", "Fred", "Fred", "Fred",
"Fred", "Gill", "Gill", "Gill",
"Gill", "Gill", "Gill", "Tom",
"Tom", "Tom", "Tom", "Tom"),
Ind = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
Exp = c(0, 1, 1, 3, 4, 0, 1, 1, 1, 2, 3, 0, 1, 2, 2, 4),
Yearm5 = c(1998, 1999, 1999, 2001, 2002,
1995, 1996, 2000, 2000, 2001,
2002, 1995, 1996, 1996, 1997, 1998))
Any help or pointers would be appreciated!
回答1:
Here is an approach using rollapply and data.table
library(zoo)
setDT(mydf)
setkey(mydf, Name,Year)
# create a data.table that has all Years and incidences including the 5 year window
# and sum up the number of incidences per year for each subject
m <- mydf[CJ(unique(Name),seq(min(Year)-5, max(Year))),allow.cartesian=TRUE][,
list(Ind = unique(Ind), I2 = sum(Ind,na.rm=TRUE)),
keyby=list(Name,Year)]
# use rollapply over this larger data.table to get the number of
# incidences in the previous 5 years (not including this year (hence head(x,-1))
m[,Exp := rollapply(I2, 5, function(x) sum(head(x,-1)),
align = 'right', fill=0),by=Name]
# join with the original to create your required data
m[mydf,!c('I2'),with=FALSE]
Name Year Ind Exp
# 1: Fred 2003 1 0
# 2: Fred 2004 1 1
# 3: Fred 2004 1 1
# 4: Fred 2006 1 3
# 5: Fred 2007 1 4
# 6: Gill 2000 1 0
# 7: Gill 2001 1 1
# 8: Gill 2005 1 1
# 9: Gill 2005 1 1
# 10: Gill 2006 1 2
# 11: Gill 2007 1 3
# 12: Tom 2000 1 0
# 13: Tom 2001 1 1
# 14: Tom 2002 1 2
# 15: Tom 2002 1 2
# 16: Tom 2003 1 4
回答2:
Here's a more data.table approach, using roll.
setDT(mydf)
# this is our desired end point
boundary = mydf[, list(Name, year.end = Year + 4)]
# set the key for the following merges
setkey(mydf, Name, Year)
setkey(boundary, Name, year.end)
# add indices that will keep track of the positions to compute deltas
mydf[, idx := .I]
boundary[, idx := .I]
# merge, rolling to match the end correctly, and then subtract the indices
# to get the desired delta.
# Note that we need to unique data because of duplicates.
# Depending on data you may also need to add `allow.cartesian = TRUE`.
# Final note - in data.table <= 1.9.2 you should omit the `by = .EACHI` part.
mydf[unique(boundary)[unique(mydf), list(Exp = i.idx - idx),
roll = -Inf, by = .EACHI]]
# Year Name idx Exp
# 1: 2003 Fred 1 0
# 2: 2004 Fred 2 1
# 3: 2004 Fred 3 1
# 4: 2006 Fred 4 3
# 5: 2007 Fred 5 4
# 6: 2000 Gill 6 0
# 7: 2001 Gill 7 1
# 8: 2005 Gill 8 1
# 9: 2005 Gill 9 1
#10: 2006 Gill 10 2
#11: 2007 Gill 11 3
#12: 2000 Tom 12 0
#13: 2001 Tom 13 1
#14: 2002 Tom 14 2
#15: 2002 Tom 15 2
#16: 2003 Tom 16 4
回答3:
With data.table, I think the syntax you are looking for should be like this:
setDT(mydf)
mydf[ , Exp := rank(x=Year,ties.method="min")-1, by=Name]
来源：https://stackoverflow.com/questions/25172732/sum-over-rows-with-multiple-changing-conditions-r-data-table