Add multiple columns lagged by one year - r

Question

I need to add a 1-year-lagged version of multiple columns from my dataframe. Here's my data:
data<-data.frame(Year=c("2011","2011","2011","2012","2012","2012","2013","2013","2013"),
Country=c("America","China","India","America","China","India","America","China","India"),
Value1=c(234,443,754,334,117,112,987,903,476),
Value2=c(2,4,5,6,7,8,1,2,2))
And I want to add two columns that contain Value1 and Value2 at t-1, so that my dataframe looks like this:
How can I do this? Would this be the correct way to lag my variables by year?
Thanks in advance!

Answer 1

Using data.table:
library(data.table)
setDT(data)
cols <- grep("^Value", colnames(data), value = TRUE)
data[, paste0(cols, "_lag") := lapply(.SD, shift), .SDcols = cols, by = Country]
# Year Country Value1 Value2 Value1_lag Value2_lag
# 1: 2011 America 234 2 NA NA
# 2: 2011 China 443 4 NA NA
# 3: 2011 India 754 5 NA NA
# 4: 2012 America 334 6 234 2
# 5: 2012 China 117 7 443 4
# 6: 2012 India 112 8 754 5
# 7: 2013 America 987 1 334 6
# 8: 2013 China 903 2 117 7
# 9: 2013 India 476 2 112 8

Answer 2

In dplyr, use lag by group:
library(dplyr) #1.1.0
data %>%
mutate(across(contains("Value"), lag, .names = "{col}_lagged"), .by = Country)
Year Country Value1 Value2 Value1_lagged Value2_lagged
1 2011 America 234 2 NA NA
2 2011 China 443 4 NA NA
3 2011 India 754 5 NA NA
4 2012 America 334 6 234 2
5 2012 China 117 7 443 4
6 2012 India 112 8 754 5
7 2013 America 987 1 334 6
8 2013 China 903 2 117 7
9 2013 India 476 2 112 8
Below 1.1.0:
data %>%
group_by(Country) %>%
mutate(across(c(GDP, Population), lag, .names = "{col}_lagged")) %>%
ungroup()

Answer 3

Another way using dplyr to ge tthe job done.
library(dplyr)
data_lagged <- data %>%
group_by(Country) %>%
mutate(Value1_Lagged = lag(Value1),
Value2_Lagged = lag(Value2),
Year = as.integer(as.character(Year)) + 1)
data_final <- cbind(data, data_lagged[, c("Value1_Lagged", "Value2_Lagged")])
data_final
Output:
Year Country Value1 Value2 Value1_Lagged Value2_Lagged
1 2011 America 234 2 NA NA
2 2011 China 443 4 NA NA
3 2011 India 754 5 NA NA
4 2012 America 334 6 234 2
5 2012 China 117 7 443 4
6 2012 India 112 8 754 5
7 2013 America 987 1 334 6
8 2013 China 903 2 117 7
9 2013 India 476 2 112 8