Question

I am wondering whether a proper framework for interval manipulation and comparison does exist in R.

After some search, I was only able to find the following:
– function findInterval in base Package. (but I hardly understand it)
– some answers here and there about union and intersection (notably: http://r.789695.n4.nabble.com/Union-Intersect-two-continuous-sets-td4224545.html)

Would you know of an initiative to implement a comprehensive set of tools to easily handles frequent tasks in interval manipulation, like inclusion/setdiff/union/intersection/etc. (eg see here for a list of functionalities)?
or would you have advice in developing such an approach?

below are some drafts on my side for doing so. it is surely awkward and still has some bugs but it might illustrate what I am looking for.

---

preliminary aspects about the options taken
– should deal seamlessly with intervals or intervals set
– intervals are represented as 2 columns data.frames (lower boundary, higher boundary), on one row
– intervals sets are represented as 2 columns with several rows
– a third column might be needed for identification of intervals sets

---

UNION

    interval_union <- function(df){   # for data frame

    df <- interval_clean(df)
    if(is.empty(df)){
        return(as.data.frame(NULL))
    } else {

        if(is.POSIXct(df[,1])) {
            dated <- TRUE
            df <- colwise(as.numeric)(df)
        } else {
            dated <- FALSE
        }
        M <- as.matrix(df)

        o <- order(c(M[, 1], M[, 2])) 
        n <- cumsum( rep(c(1, -1), each=nrow(M))[o]) 
        startPos <- c(TRUE, n[-1]==1 & n[-length(n)]==0) 
        endPos <- c(FALSE, n[-1]==0 & n[-length(n)]==1) 

        M <- M[o] 

        if(dated == TRUE) {
            df2 <- colwise(mkDateTime)(as.data.frame(cbind(M[startPos], M[endPos])), from.s = TRUE)
        } else {
            df2 <- as.data.frame(cbind(M[startPos], M[endPos]))
        }
        colnames(df2) <- colnames(df)

        # print(df2)
        return(df2)

    }


}


union_1_1 <- function(test, ref){
    names(ref) <- names(test)
    tmp <- interval_union(as.data.frame(rbind(test, ref)))
    return(tmp)
}


union_1_n <- function(test, ref){
    return(union_1_1(test, ref))
}


union_n_n <- function(test, ref){
    testnn <- adply(.data = test, 1, union_1_n, ref, .expand = FALSE)
    return(testnn)
}

ref_interval_union <- function(df, ref){

    tmp0 <- adply(df, 1, union_1_1, ref, .expand = FALSE) # set to FALSE to keep ID
    return(tmp0)                
}

INTERSECTION

interval_intersect <- function(df){
    # adapted from : http://r.789695.n4.nabble.com/Union-Intersect-two-continuous-sets-td4224545.html
    M <- as.matrix(df)

    L <- max(M[, 1])
    R <- min(M[, 2]) 

    Inew <- if (L <= R) c(L, R) else c() 

    if (!is.empty(Inew)){
        df2 <- t(as.data.frame(Inew)) 
        colnames(df2) <- colnames(df)
        rownames(df2) <- NULL
    } else {
        df2 <- NULL
    }

    return(as.data.frame(df2))

}



ref_interval_intersect <- function(df, ref){

    tmpfun <- function(a, b){

        names(b) <- names(a)
        tmp <- interval_intersect(as.data.frame(rbind(a, b)))
        return(tmp)
    }

    tmp0 <- adply(df, 1, tmpfun, ref, .expand = FALSE) # [,3:4]
    #if(!is.empty(tmp0)) colnames(tmp0) <- colnames(df)
    return(tmp0)                
}


int_1_1 <- function(test, ref){

    te <- as.vector(test)
    re <- as.vector(ref)
    names(re) <- names(te)
    tmp0 <- c(max(te[1, 1], re[1, 1]), min(te[1, 2], re[1, 2]))

    if(tmp0[1]>tmp0[2]) tmp0 <- NULL   # inverse of a correct interval --> VOID

    if(!is.empty(tmp0)){
        tmp1 <- colwise(mkDateTime)(as.data.frame(t(as.data.frame(tmp0))))
        colnames(tmp1) <- colnames(test)
    } else {
        tmp1 <- data.frame(NULL)
    }

    return(tmp1)

}


int_1_n <- function(test, ref){

    test1 <- adply(.data = ref, 1, int_1_1, test = test, .expand = FALSE)

    if(is.empty(test1)){
        return(data.frame(NULL))
    } else {

        testn <- interval_union(test1[,2:3])    
        return(testn)
    }

}


int_n_n <- function(test, ref){

    testnn <- adply(.data = test, 1, int_1_n, ref, .expand = FALSE)
    # return(testnn[,2:3])  # return interval set without index (1st column)
    return(testnn)          # return interval set with index (1st column) --> usefull to go with merge to keep metadata going alon g with interval description
}


int_intersect <- function(df, ref){

    mycols <- colnames(df)
    df$X1 <- 1:nrow(df)
    test <- df[, 1:2]
    tmp <- int_n_n(test, ref)

    intersection <- merge(tmp, df, by = "X1", suffixes = c("", "init"))
    return(intersection[,mycols])   

}

EXCLUSION

excl_1_1 <- function(test, ref){
    te <- as.vector(test)
    re <- as.vector(ref)
    names(re) <- names(te)


    if(te[1] < re[1]){          # Lower Bound
        if(te[2] > re[1]){          # overlap
            x <- unlist(c(te[1], re[1]))
        } else {                    # no overlap
            x <- unlist(c(te[1], te[2]))
        }
    } else {                    # test > ref on lower bound side
        x <- NULL
    }

    if(te[2] > re[2]){          # Upper Bound
        if(te[1] < re[2]){          # overlap
            y <- unlist(c(re[2], te[2]))    
        } else {                    # no overlap
            y <- unlist(c(te[1], te[2]))
        }
    } else {                    # test < ref on upper bound side
        y <- NULL
    }

    if(is.empty(x) & is.empty(y)){
        tmp0 <- NULL
        tmp1 <- tmp0
    } else {

        tmp0 <- as.data.frame(rbind(x, y))
        colnames(tmp0) <- colnames(test)
        tmp1 <- interval_union(tmp0)    

    }

    return(tmp1)    

}



excl_1_n <- function(test, ref){


    testn0 <- adply(.data = ref, 1, excl_1_1, test = test, .expand=FALSE)

    # boucle pour intersecter successivement les intervalles sets, pour gérer les intervalles disjoints (identifiés par X1, col1)

    tmp <- range(testn0)
    names(tmp) <- colnames(testn0)[2:3]
    tmp <- as.data.frame(t(tmp))

    for(i in unique(testn0[,1])){
        tmp <- int_n_n(tmp, testn0[testn0[,1]==i, 2:3])
    }
    return(tmp)

}

INCLUSION

incl_1_1 <- function(test, ref){
    te <- as.vector(test)
    re <- as.vector(ref)
    if(te[1] >= re[1] & te[2] <= re[2]){ return(TRUE) } else { return(FALSE) }
}


incl_1_n <- function(test, ref){
    testn <- adply(.data = ref, 1, incl_1_1, test = test)
    return(any(testn[,ncol(testn)]))
}

incl_n_n <- function(test, ref){

    testnn <- aaply(.data = test, 1, incl_1_n, ref, .expand = FALSE)
    names(testnn) <- NULL
    return(testnn)
}

flat_incl_n_n <- function(test, ref){

    ref <- interval_union(ref)
    return(incl_n_n(test, ref))

}


# testing for a vector, instead of an interval set
incl_x_1 <- function(x, ref){

    test <- (x>=ref[1,1] & x<ref[1,2])
    return(test)

}

incl_x_n <- function(x, ref){

    test <- any(x>=ref[,1] & x<ref[,2])
    return(test)

}

Answer 1

I think you might be able to make good use of the many interval-related functions in the sets package.

Here’s a small example illustrating the package’s support for interval construction, intersection, set difference, union, and complementation, as well as its test for inclusion in an interval. These and many other related functions are documented on the help page for ?interval.

library(sets)
i1 <- interval(1,6)
i2 <- interval(5,10)
i3 <- interval(200,400)
i4 <- interval(202,402)
i5 <- interval_union(interval_intersection(i1,i2), 
                     interval_symdiff(i3,i4))

i5
# [5, 6] U [200, 202) U (400, 402]
interval_complement(i5)
# [-Inf, 5) U (6, 200) U [202, 400] U (402, Inf]

interval_contains_element(i5, 5.5)
# [1] TRUE
interval_contains_element(i5, 201)
# [1] TRUE

If your intervals are currently encoded in a two-column data.frame, you could use something like mapply() to convert them to intervals of the type used by the sets package:

df   <- data.frame(lBound = c(1,5,100), uBound = c(10, 6, 200))
Ints <- with(df, mapply("interval", l=lBound, r=uBound, SIMPLIFY=FALSE))
Ints
# [[1]]
# [1, 10]

# [[2]]
# [5, 6]

# [[3]]
# [100, 200]