R: ifelse statement test involving multiple dataframes - r

Question

I am trying to create a new variable using ifelse by combining data from two data.frames (similar to this question but without factors).
My problem is that df1 features yearly data, whereas vars in df2 are temporally aggregated: e.g. df1 has multiple obs (1997,1998,...,2005) and df2 only has a range (1900-2001).
For illustration, a 2x2 example would look like
df1$id <- c("2","20")
df1$year <- c("1960","1870")
df2$id <- df1$id
df2$styear <- c("1800","1900")
df2$endyear <- c("2001","1950")
I want to combine both in such a way that the id (same variable exists in both) is matched, and further, the year in df1 is within the range of df2. I tried the following
df1$new.var <- ifelse(df1$id==df2$id & df1$year>=df2$styear &
df1$year<df2$endyear,1,0)
Which ideally should return 1 and 0, respectively.
But instead I get warning messages:
1: In df1$id == df2$id : longer object length is not a multiple of
shorter object length
2: In df1$year >= df2$styear : longer object length is not a
multiple of shorter object length
3: In df1$year < df2$endyear : longer object length is not a
multiple of shorter object length
For the record, the 'real' df1 has 500 obs and df2 has 14. How can I make this work?
Edit: I realised some obs in df2 are repeated, with multiple periods e.g.
id styear endyear
1 1800 1915
1 1950 2002
2 1912 1988
3 1817 2000
So, I believe what I need is something like a double-ifelse:
df1$new.var <- ifelse(df1$id==df2$id & df1$year>=df2$styear &
df1$year<df2$endyear | df1$year>=df2$styear &
df1$year<df2$endyear,1,0)
Obviously, this wouldn't work, but it is a way to get out of the duplicates-problem.
For example, if id=1 in df1$year=1801, it will pass the first year-range test (1801 is between 1800-1915), but fail the second one (1801 is not between 1950-2002), so it is only coded once and no extra rows are added (currently the duplicates add extra rows).

Answer 1

df1$id <- c("2","20")
df1$year <- c("1960","1870")
df2$id <- df1$id
df2$styear <- c("1800","1900")
df2$endyear <- c("2001","1950")
library(dplyr)
df3 <- left_join(df1,df2,by = "id") %>% filter(year <= endyear,year >= startyear)
I highly recommend the dplyr package for data manipulation.

Answer 2

With base R:
df1 <- data.frame(id=c(2,20,22), year=c(1960,1870, 2016))
df2 <- data.frame(id=c(2,20,21), styear=c(1800,1900,2000), endyear=c(2001,1950,2016))
df1
id year
1 2 1960
2 20 1870
3 22 2016
df2
id styear endyear
1 2 1800 2001
2 20 1900 1950
3 21 2000 2016
df1 <- merge(df1, df2, by='id', all.x = TRUE)
df1$new.var <- !is.na(df1$styear) & df1$year>=df1$styear & df1$year< df1$endyear
df1 <- df1[c('id', 'year', 'new.var')]
df1
id year new.var
1 2 1960 TRUE
2 20 1870 FALSE
3 22 2016 FALSE

Answer 3

Alright, I made it work for myself. Beware, it is quite convoluted and probably contain some redundancies. After a brief look at the data wrangling cheatsheet, assuming you have df1 and df2 with an identical var and df2 contains new.var, one can do the following:
library(dplyr)
#Join everything, all values and rows
df3 <- full_join(df1,df2,by="id")
#filter out obs those year is greater than endyear
df3 <- filter(df3,df3$year<=df3$endyear)
#same, the other way around
df3 <- filter(df3,df3$year>=df3$styear)
df3 <- distinct(df3) #remove duplicate rows (at least I had some)
As far as I can tell by looking at the end result, this method only extracts information from the correct time period while dropping all other time periods in df2. Then, it is a matter of merging with the original data.frame (df1) and filling in the NAs:
df1 <- merge(df1,df3,by=(id),all.x=TRUE)
df1 <- distinct(df1) #just to make sure, I still had three
df1$new.var <- ifelse(is.na(df1$new.var),0,df1$new.var)
which is what I wanted.

Answer 4

This can be solved easily and efficiently using non-equi joins in data.table devel version (1.9.7+):
library(data.table)
setDT(df1); setDT(df2) # converting to data.table in place
df1[, new.var := df2[df1, on = .(id, styear <= year, endyear >= year),
.N > 0, by = .EACHI]$V1]
df1
# id year new.var
#1: 2 1960 TRUE
#2: 20 1870 FALSE
The above join looks for matches in df2 for each row of df1 (by = .EACHI), and checks the number of matching rows (.N).