Here is one more option using the ave() function. Using Jim's data and naming convention

fkdf$X1_change <- ave(fkdf[,'X1'], fkdf$Country, FUN=function(x) c(0,diff(x))) fkdf$X2_change <- ave(fkdf[,'X2'], fkdf$Country, FUN=function(x) c(0,diff(x)))

hope this is helpful,

Dan

--
Daniel Nordlund
Port Townsend, WA  USA


On 7/20/2019 4:17 PM, Jim Lemon wrote:
Hi Faradj,
Rui's advice is correct, here's a way to do it. Note that I have
replaced the comma decimal points with full stops for my convenience:

fkdf<-read.csv(text="Year,Country,X1,X2
1990,United States,0,0.22
1991,United States,0,0.22
1992,United States,0,0.22
1993,United States,0,0.22
1994,United States,0,0.22
1995,United States,0,0.22
1996,United States,0,0.22
1997,United States,0,0.5
1998,United States,0,0.5
1999,United States,0,0.5
2000,United States,0,0.5
2001,United States,0,0.5
2002,United States,2,NA
2003,United States,2,0.5
2004,United States,2,1
2005,United States,1,1
2006,United States,1,1
2007,United States,1,1
2008,United States,1,1
2009,United States,1,1
2010,United States,1,0.5
2011,United States,0,0.5
1990,Canada,1,1.5
1991,Canada,1,1.5
1992,Canada,1,NA
1993,Canada,1,1.5
1994,Canada,1,1.5
1995,Canada,1,1.5
1996,Canada,1,1.5
1997,Canada,1,1.5
1998,Canada,1,2
1999,Canada,2,2
2000,Canada,2,2
2001,Canada,2,2
2002,Canada,2,2
2003,Canada,1,2
2004,Canada,2,0.5
2005,Canada,1,0.5
2006,Canada,0,0.5
2007,Canada,1,0.5
2008,Canada,0,0.5
2009,Canada,1,0.5
2010,Canada,1,0.5
2011,Canada,0,1",
header=TRUE,stringsAsFactors=FALSE)
diffX1<-aggregate(fkdf$X1,by=list(fkdf[,2]),FUN=diff)
diffX2<-aggregate(fkdf$X2,by=list(fkdf[,2]),FUN=diff)
diffX1<-data.frame(diffX1$Group.1,diffX1$x)
diffyears<-unique(fkdf$Year)[-1]
names(diffX1)<-c("Country",diffyears)
diffX2<-data.frame(diffX2$Group.1,diffX2$x)
names(diffX2)<-c("Country",diffyears)

Jim

On Sun, Jul 21, 2019 at 5:34 AM Faradj Koliev <farad...@gmail.com> wrote:
Dear R-users,

I have a country-year data for 180 countries from 1970 to 2010. I’m interested 
in capturing positive and negative changes in some of the variables. Some of 
these variables are continuous (0,25, 0,33, 1, 1,5 etc) others are ordered 
(0,1, 2).

To do this, I use this code data$X1_change<- +c(FALSE,diff(data$X1))

My data looks something like this (please see below).

There’re some problems with this code:  (1) I can’t capture the smaller 
changes, say from 0,25 to 0,33 ( I get weird numbers). I would love to get the 
exact difference ( for ex: +1, -0,22, +4, -2 etc).  (2) It can’t make 
difference between countries. That is, it takes the difference between 
countries while it should only do this for each country ( for ex: when the US 
ends in 2011, and Canada starts, it counts this a difference but it shouldn’t, 
see below). (3) NAs, missing values, is neither a positive or negative change, 
although it does think that what comes after the NA is a difference.

  So, I wonder if anyone here can help me to adjust this code. I appreciate all 
comments.



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