tests/testthat/test-sparse-array.R
In kuwisdelu/matter: Scientific computing for out-of-memory signals and images
require(testthat)
require(matter)
context("sparse-array")
test_that("sparse vector subsetting", {
set.seed(1, kind="default")
x <- rbinom(100, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
y <- sparse_vec(x)
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
expect_equal(x[c(NA,1:10,NA)], y[c(NA,1:10,NA)])
expect_error(y[-1])
expect_error(y[length(y) + 1])
z <- y[1:50,drop=NULL]
expect_is(z, "sparse_vec")
expect_equal(x[1:50], z[])
domain(y) <- seq_along(y) - 1L
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
atomindex(y) <- matter_vec(atomindex(y))
atomdata(y) <- matter_vec(atomdata(y))
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
z <- sparse_vec(1:5, index=1:5)
domain(z) <- seq(from=2, to=3, by=0.25)
tolerance(z) <- 0.3
expect_equal(c(2, 2, 0, 3, 3), z[])
})
test_that("sparse vector subsetting w/ interpolation", {
z <- sparse_vec(index=c(1.0, 1.01, 1.11,
2.0, 2.22,
3.0, 3.33, 3.333,
4.0),
data=c(1.0, 1.01, 1.11,
2.0, 2.22,
3.0, 3.33, 3.333,
4.0),
domain=seq(from=0, to=4, by=0.5))
test1 <- c(0, 0, 1, 0, 2, 0, 3, 0, 4)
expect_equal(test1, z[])
tolerance(z) <- 0.25
test2 <- c(0, 0, 1, 0, 2, 0, 3, 3.333, 4)
expect_equal(test2, z[])
sampler(z) <- "sum"
test3 <- c(0, 0, 1.0+1.01+1.11, 0, 2.0+2.22, 0, 3, 3.33+3.333, 4)
expect_equal(test3, z[])
sampler(z) <- "mean"
test4 <- c(0, 0, (1.0+1.01+1.11)/3, 0, (2.0+2.22)/2, 0, 3, (3.33+3.333)/2, 4)
expect_equal(test4, z[])
sampler(z) <- "linear"
tolerance(z) <- 1.0
test5 <- c(1, 1, 1, 1.5, 2, 2.5, 3, 3.5, 4)
expect_equal(test5, z[])
z2 <- sparse_vec(index=rev(aindex(z)),
data=rev(adata(z)),
domain=domain(z))
expect_equal(test1, z2[])
z3 <- sparse_vec(index=seq_len(11),
data=c(rep_len(1, 5), 10,
rep_len(1, 5)),
domain=seq_len(11))
tolerance(z3) <- 2
sampler(z3) <- "sum"
test6 <- c(3, 4, 5, 14, 14, 14, 14, 14, 5, 4, 3)
expect_equal(test6, z3[])
sampler(z3) <- "mean"
wts <- rep_len(1, 5)
test7 <- filter(adata(z3), wts / sum(wts), circular=TRUE)
expect_equal(as.vector(test7), z3[])
sampler(z3) <- "gaussian"
wts <- dnorm((-2):2)
test8 <- filter(adata(z3), wts / sum(wts), circular=TRUE)
expect_equal(as.vector(test8), z3[])
})
test_that("sparse matrix subsetting (csc)", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_equal(x[1,,drop=FALSE], as.matrix(y[1,,drop=NULL]))
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
y <- sparse_mat(x, pointers=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
})
test_that("sparse matrix subsetting (csr)", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_equal(x[1,,drop=FALSE], as.matrix(y[1,,drop=NULL]))
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
y <- sparse_mat(x, pointers=TRUE, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
})
test_that("sparse matrix subsetting w/ interpolation", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
d <- seq_len(nrow(x)) - 1 + round(runif(nrow(x))/4, 2)
y <- sparse_mat(x, domain=d, tolerance=0.5)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
d <- seq_len(ncol(x)) - 1 + round(runif(ncol(x))/4, 2)
y <- sparse_mat(x, domain=d, tolerance=0.5, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
z <- sparse_mat(list(as.double(1:5)), list(1:5), nrow=5, ncol=1)
domain(z) <- seq(from=2, to=3, by=0.25)
tolerance(z) <- 0.3
expect_equal(c(2, 2, 0, 3, 3), as.double(z[]))
})
test_that("sparse matrix combining", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x)
expect_equal(cbind(x, x), cbind(y, y)[])
y <- sparse_mat(x, rowMaj=TRUE)
expect_equal(rbind(x, x), rbind(y, y)[])
})
test_that("sparse matrix multiplication", {
set.seed(1, kind="default")
x <- rbinom(35, 1, 0.4)
y <- rbinom(35, 1, 0.4)
x[x != 0] <- seq_len(sum(x != 0))
y[y != 0] <- seq_len(sum(y != 0))
dim(x) <- c(5, 7)
dim(y) <- c(7, 5)
options(matter.matmul.bpparam=NULL)
xx <- sparse_mat(x)
yy <- sparse_mat(y)
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=SerialParam())
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=NULL)
xx <- matter_mat(x, rowMaj=TRUE)
yy <- matter_mat(y, rowMaj=TRUE)
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=SerialParam())
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
})
kuwisdelu/matter documentation built on July 16, 2024, 1:28 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
require(testthat)
require(matter)
context("sparse-array")
test_that("sparse vector subsetting", {
set.seed(1, kind="default")
x <- rbinom(100, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
y <- sparse_vec(x)
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
expect_equal(x[c(NA,1:10,NA)], y[c(NA,1:10,NA)])
expect_error(y[-1])
expect_error(y[length(y) + 1])
z <- y[1:50,drop=NULL]
expect_is(z, "sparse_vec")
expect_equal(x[1:50], z[])
domain(y) <- seq_along(y) - 1L
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
atomindex(y) <- matter_vec(atomindex(y))
atomdata(y) <- matter_vec(atomdata(y))
expect_equal(x, y[])
expect_equal(x[1:10], y[1:10])
expect_equal(x[10:1], y[10:1])
z <- sparse_vec(1:5, index=1:5)
domain(z) <- seq(from=2, to=3, by=0.25)
tolerance(z) <- 0.3
expect_equal(c(2, 2, 0, 3, 3), z[])
})
test_that("sparse vector subsetting w/ interpolation", {
z <- sparse_vec(index=c(1.0, 1.01, 1.11,
2.0, 2.22,
3.0, 3.33, 3.333,
4.0),
data=c(1.0, 1.01, 1.11,
2.0, 2.22,
3.0, 3.33, 3.333,
4.0),
domain=seq(from=0, to=4, by=0.5))
test1 <- c(0, 0, 1, 0, 2, 0, 3, 0, 4)
expect_equal(test1, z[])
tolerance(z) <- 0.25
test2 <- c(0, 0, 1, 0, 2, 0, 3, 3.333, 4)
expect_equal(test2, z[])
sampler(z) <- "sum"
test3 <- c(0, 0, 1.0+1.01+1.11, 0, 2.0+2.22, 0, 3, 3.33+3.333, 4)
expect_equal(test3, z[])
sampler(z) <- "mean"
test4 <- c(0, 0, (1.0+1.01+1.11)/3, 0, (2.0+2.22)/2, 0, 3, (3.33+3.333)/2, 4)
expect_equal(test4, z[])
sampler(z) <- "linear"
tolerance(z) <- 1.0
test5 <- c(1, 1, 1, 1.5, 2, 2.5, 3, 3.5, 4)
expect_equal(test5, z[])
z2 <- sparse_vec(index=rev(aindex(z)),
data=rev(adata(z)),
domain=domain(z))
expect_equal(test1, z2[])
z3 <- sparse_vec(index=seq_len(11),
data=c(rep_len(1, 5), 10,
rep_len(1, 5)),
domain=seq_len(11))
tolerance(z3) <- 2
sampler(z3) <- "sum"
test6 <- c(3, 4, 5, 14, 14, 14, 14, 14, 5, 4, 3)
expect_equal(test6, z3[])
sampler(z3) <- "mean"
wts <- rep_len(1, 5)
test7 <- filter(adata(z3), wts / sum(wts), circular=TRUE)
expect_equal(as.vector(test7), z3[])
sampler(z3) <- "gaussian"
wts <- dnorm((-2):2)
test8 <- filter(adata(z3), wts / sum(wts), circular=TRUE)
expect_equal(as.vector(test8), z3[])
})
test_that("sparse matrix subsetting (csc)", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_equal(x[1,,drop=FALSE], as.matrix(y[1,,drop=NULL]))
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
y <- sparse_mat(x, pointers=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
})
test_that("sparse matrix subsetting (csr)", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_equal(x[1,,drop=FALSE], as.matrix(y[1,,drop=NULL]))
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
y <- sparse_mat(x, pointers=TRUE, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
expect_equal(x[,c(1,NA,10)], y[,c(1,NA,10)])
expect_equal(x[c(1,NA,10),], y[c(1,NA,10),])
expect_error(y[,-1])
expect_error(y[-1,])
expect_error(y[,ncol(y) + 1])
expect_error(y[nrow(y) + 1,])
z <- y[1:5,1:5,drop=NULL]
expect_is(z, "sparse_mat")
expect_equal(x[1:5,1:5], z[])
})
test_that("sparse matrix subsetting w/ interpolation", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
d <- seq_len(nrow(x)) - 1 + round(runif(nrow(x))/4, 2)
y <- sparse_mat(x, domain=d, tolerance=0.5)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
d <- seq_len(ncol(x)) - 1 + round(runif(ncol(x))/4, 2)
y <- sparse_mat(x, domain=d, tolerance=0.5, rowMaj=TRUE)
expect_equal(x, y[])
expect_equal(x[,1], y[,1])
expect_equal(x[1,], y[1,])
expect_equal(x[,10], y[,10])
expect_equal(x[20,], y[20,])
expect_equal(x[1:10,1:5], y[1:10,1:5])
expect_equal(x[10:1,5:1], y[10:1,5:1])
expect_equal(x[11:20,6:10], y[11:20,6:10])
z <- sparse_mat(list(as.double(1:5)), list(1:5), nrow=5, ncol=1)
domain(z) <- seq(from=2, to=3, by=0.25)
tolerance(z) <- 0.3
expect_equal(c(2, 2, 0, 3, 3), as.double(z[]))
})
test_that("sparse matrix combining", {
set.seed(1, kind="default")
x <- rbinom(200, 1, 0.2)
x[x != 0] <- seq_len(sum(x != 0))
dim(x) <- c(20, 10)
y <- sparse_mat(x)
expect_equal(cbind(x, x), cbind(y, y)[])
y <- sparse_mat(x, rowMaj=TRUE)
expect_equal(rbind(x, x), rbind(y, y)[])
})
test_that("sparse matrix multiplication", {
set.seed(1, kind="default")
x <- rbinom(35, 1, 0.4)
y <- rbinom(35, 1, 0.4)
x[x != 0] <- seq_len(sum(x != 0))
y[y != 0] <- seq_len(sum(y != 0))
dim(x) <- c(5, 7)
dim(y) <- c(7, 5)
options(matter.matmul.bpparam=NULL)
xx <- sparse_mat(x)
yy <- sparse_mat(y)
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=SerialParam())
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=NULL)
xx <- matter_mat(x, rowMaj=TRUE)
yy <- matter_mat(y, rowMaj=TRUE)
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
options(matter.matmul.bpparam=SerialParam())
expect_equal(x %*% y, xx %*% y)
expect_equal(x %*% y, x %*% yy)
expect_equal(y %*% x, yy %*% x)
expect_equal(y %*% x, y %*% xx)
expect_equal(crossprod(x, x), crossprod(xx, x))
expect_equal(crossprod(x, x), crossprod(x, xx))
expect_equal(tcrossprod(x, x), tcrossprod(xx, x))
expect_equal(tcrossprod(x, x), tcrossprod(x, xx))
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.