From 69415b25e687f7a5c7b443929f780fe322b0f103 Mon Sep 17 00:00:00 2001 From: kauedesousa Date: Thu, 9 Nov 2023 20:55:27 +0100 Subject: [PATCH] v1.0 --- DESCRIPTION | 8 +- NAMESPACE | 2 + NEWS.md | 8 + R/AAA-getDataCM.R | 22 +- R/getProjectProgress.R | 11 +- R/getProjectsCM.R | 4 +- R/randomise.R | 633 ------------------ R/randomize.R | 592 ++++++++++++++++ R/rankTricot.R | 340 +++++----- README.md | 4 +- codemeta.json | 122 +++- docs/404.html | 2 +- docs/CODE_OF_CONDUCT.html | 2 +- docs/CONTRIBUTING.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/Overview.html | 96 ++- .../figure-html/plrankings-1.png | Bin 0 -> 63903 bytes docs/articles/index.html | 2 +- docs/authors.html | 23 +- docs/index.html | 6 +- docs/news/index.html | 15 +- docs/pkgdown.yml | 4 +- docs/reference/ClimMobTools.html | 2 +- docs/reference/getDataCM.html | 38 +- docs/reference/getProjectProgress.html | 30 +- docs/reference/getProjectsCM.html | 21 +- docs/reference/getTraitList.html | 19 +- docs/reference/index.html | 6 +- docs/reference/randomise.html | 2 +- docs/reference/randomize.html | 203 ++++++ docs/reference/rankTricot.html | 78 ++- docs/reference/rmGeoIdentity.html | 41 +- docs/sitemap.xml | 3 + inst/CITATION | 20 + man/getDataCM.Rd | 17 +- man/getProjectProgress.Rd | 11 +- man/getProjectsCM.Rd | 4 +- man/{randomise.Rd => randomize.Rd} | 43 +- man/rankTricot.Rd | 19 +- vignettes/Overview.R | 96 +-- vignettes/Overview.Rmd | 25 +- 41 files changed, 1504 insertions(+), 1074 deletions(-) delete mode 100644 R/randomise.R create mode 100644 R/randomize.R create mode 100644 docs/articles/Overview_files/figure-html/plrankings-1.png create mode 100644 docs/reference/randomize.html create mode 100644 inst/CITATION rename man/{randomise.Rd => randomize.Rd} (61%) diff --git a/DESCRIPTION b/DESCRIPTION index e0771f4..bcff510 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: ClimMobTools Type: Package Title: API Client for the 'ClimMob' Platform -Version: 0.5.003 +Version: 1.0 Authors@R: c(person("Kauê", "de Sousa", email = "desousa.kaue@gmail.com", role = c("aut", "cre"), @@ -26,17 +26,21 @@ Depends: R (>= 3.5.0) Imports: httr, jsonlite, + lpSolve, Matrix, methods, RSpectra, + stats, utils Suggests: + climatrends, gosset, knitr, + nasapower, rmarkdown, sf, PlackettLuce, testthat (>= 2.1.0) -Language: en-GB +Language: en-US RoxygenNote: 7.2.1 VignetteBuilder: knitr diff --git a/NAMESPACE b/NAMESPACE index 9c874e0..6faa194 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,5 +17,7 @@ importFrom(httr,RETRY) importFrom(httr,accept_json) importFrom(httr,content) importFrom(jsonlite,fromJSON) +importFrom(lpSolve,lp) importFrom(methods,as) +importFrom(stats,runif) importFrom(utils,tail) diff --git a/NEWS.md b/NEWS.md index a6af162..6dbb09f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,11 @@ +ClimMobTools 1.0 (2023-11-10) +========================= + +### BUG FIXES + +* Fix an issue in `randomize()` to allocate blocks + + ClimMobTools 0.4.6 (2022-08-11) ========================= diff --git a/R/AAA-getDataCM.R b/R/AAA-getDataCM.R index 845220a..f63a3fb 100644 --- a/R/AAA-getDataCM.R +++ b/R/AAA-getDataCM.R @@ -29,13 +29,22 @@ #' # This function only works with an API key #' # the API key can be obtained once a free ClimMob account #' # is created via https://climmob.net/ +#' +#' library("ClimMobTools") +#' my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6" #' -#' my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c" +#' getDataCM(key = my_key, +#' project = "beanaru23", +#' userowner = "student", +#' server = "1000farms") +#' +#' # get in the wide format #' #' getDataCM(key = my_key, -#' project = "testmark", -#' userowner = "kauedesousa", -#' server = "testing") +#' project = "beanaru23", +#' userowner = "student", +#' server = "1000farms", +#' pivot.wider = TRUE) #' #' @seealso ClimMob website \url{https://climmob.net/} #' @importFrom httr accept_json content RETRY @@ -87,3 +96,8 @@ getDataCM <- function(key, return(cmdata) } + + + + + diff --git a/R/getProjectProgress.R b/R/getProjectProgress.R index 070e2f3..da34f4c 100644 --- a/R/getProjectProgress.R +++ b/R/getProjectProgress.R @@ -17,13 +17,14 @@ #' # This function only works with an API key #' # the API key can be obtained once a free ClimMob account #' # is created via https://climmob.net/ -#' -#' my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c" #' +#' library("ClimMobTools") +#' my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6" +#' #' getProjectProgress(key = my_key, -#' project = "testmark", -#' userowner = "kauedesousa", -#' server = "testing") +#' project = "beanaru23", +#' userowner = "student", +#' server = "1000FARMS") #' #' #' @seealso ClimMob website \url{https://climmob.net/} diff --git a/R/getProjectsCM.R b/R/getProjectsCM.R index cb4c5fb..f5a224a 100644 --- a/R/getProjectsCM.R +++ b/R/getProjectsCM.R @@ -69,9 +69,9 @@ #' # the API key can be obtained once a free ClimMob account #' # is created via https://climmob.net/ #' -#' my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c" +#' my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6" #' -#' getProjectsCM(key = my_key, server = "testing") +#' getProjectsCM(key = my_key, server = "1000FARMS") #' #' @seealso ClimMob website \url{https://climmob.net/} #' @export diff --git a/R/randomise.R b/R/randomise.R deleted file mode 100644 index c04f807..0000000 --- a/R/randomise.R +++ /dev/null @@ -1,633 +0,0 @@ -#' Randomised group of items -#' -#' Set a randomised group of items for crowdsourcing citizen science. -#' Generate designs for ranking of options. It is designed for tricot trials -#' specifically (comparing 3 options), but it will also work with comparisons -#' of any other number of options. -#' The design strives for approximate A optimality, this means that it is robust -#' to missing observations. It also strives for balance for positions of each option. -#' Options are equally divided between first, second, third, etc. position. -#' The strategy is to create a "pool" of combinations that does not repeat -#' combinations and is A-optimal. Then this pool is ordered to make subsets of -#' consecutive combinations also relatively balanced and A-optimal -#' -#' @author Jacob van Etten -#' @param ncomp an integer for the number of items to be assigned to each package -#' @param npackages an integer for the number of trial packages to be produced -#' @param itemnames a character for the name of items tested in the project -#' @param availability optional, a vector with integers indicating the -#' number of packages available for each \var{itemnames} -#' @param proportions optional, a numeric vector with the desired proportions -#' for each \var{itemnames} -#' @param ... additional arguments passed to methods -#' @references -#' Bailey and Cameron (2004). Combinations of optimal designs. -#' \url{https://webspace.maths.qmul.ac.uk/l.h.soicher/designtheory.org/library/preprints/optimal.pdf} -#' @return A dataframe with the randomised design -#' @examples -#' ncomp <- 3 -#' npackages <- 20 -#' itemnames <- c("apple","banana","grape","mango", "orange") -#' availability <- c(5, 8, 50, 50, 50) -#' -#' randomise(ncomp = ncomp, -#' npackages = npackages, -#' itemnames = itemnames) -#' -#' randomise(ncomp = ncomp, -#' npackages = npackages, -#' itemnames = itemnames, -#' availability = availability) -#' -#' @aliases randomize -#' @importFrom Matrix Diagonal -#' @importFrom methods as -#' @importFrom RSpectra eigs -#' @importFrom utils tail -#' @export -randomise <- function(npackages, - itemnames, - ncomp = 3, - availability = NULL, - proportions = NULL, - ...) { - - dots <- list(...) - - comp <- dots[["comp"]] - - if (is.null(comp)) { - comp <- 10 - } - - nitems <- length(itemnames) - - # check inputs - if (!is.null(availability) & length(availability) != nitems) { - stop("nitems is different than length of vector with availability") - } - - if (nitems < 3) { - stop("nitems must be larger than 2") - } - - nneeded <- npackages * ncomp - - if (!is.null(availability)) { - - maxav <- which.max(availability)[1] - availability[maxav] <- availability[maxav] + 1 - - if (sum(availability) < nneeded) { - stop("availability is not sufficient: smaller than npackages * ncomp \n") - } - if (length(availability) != nitems) { - stop("length of vector availability should be nitems \n") - } - } - - if (!is.null(proportions)) { - - if (length(proportions) != nitems) { - stop("length of vector proportions should be nitems") - - } - - if (sum(proportions) != 1) { - - proportions <- proportions / sum(proportions) - warning("sum of proportions is not 1; values have been rescaled \n") - - } - } - - # depth1 is the number of rows after the procedure starts to compare options with the Kirchoff index - depth1 <- floor(nitems / ncomp * 2) - - # in the second round, it is also how far it 'looks back' in the sequential balancing - depth2 <- min(20, floor(nitems / ncomp * 4)) - - # Varieties indicated by integers - varieties <- seq_len(nitems) - - # Full set of all combinations - varcombinations <- t((.combn(varieties, ncomp))) - - if (is.null(availability) & is.null(proportions)) { - - # if the full set of combinations is small and can be covered at least once - # the set will include each combination at least once - ncomb <- dim(varcombinations)[1] - n <- floor(npackages / ncomb) - nfixed <- ncomb * n - vars1 <- varcombinations[c(rep(1:(dim(varcombinations)[1]), times = n)), ] - - # the remaining combinations are sampled randomly but in a balanced way - # this means that no combination enters more than once - nremain <- npackages - nfixed - - # create set to get to full number of observers - vars2 <- matrix(nrow = nremain, ncol = ncomp) - - # set up array with set of combinations - varcomb <- matrix(0, nrow = nitems, ncol = nitems) - - if (dim(vars2)[1] > 0.5) { - - # select combinations for the vars2 set that optimize design - for (i in 1:nremain) { - - # calculate frequency of each variety - sumcomb <- (rowSums(varcomb) + colSums(varcomb)) / 2 - - # priority of each combination is equal to Shannon index of varieties - # in each combination - prioritycomb <- apply(varcombinations, 1, function(x){ - .getShannonVector(x, sumcomb, nitems) - }) - - # highest priority to be selected is the combination which has the lowest - # Shannon index - selected <- which(prioritycomb == min(prioritycomb)) - - # if there are ties, find out which combination reduces Kirchhoff index most - - if (length(selected) > 1 & i > depth1) { - - reduce <- max(2, min(comp, round(5000/npackages), round(200/nitems))) - - # randomly subsample from selected if there are too many combinations to check - if (length(selected) > reduce) { - selected <- sample(selected, reduce) - } - - # get a nitems x nitems matrix with number of connections - - # calculate Kirchhoff index and select smallest value - khi <- vector(length = length(selected)) - for (k in 1:length(selected)) { - - evalgraph <- varcomb - index <- t(.combn(varcombinations[selected[k],], 2)) - evalgraph[index] <- evalgraph[index] + 1 - khi[k] <- .KirchhoffIndex(evalgraph) - - } - - selected <- selected[which(khi == min(khi))] - - } - - # if there are still ties between ranks of combinations, selected randomly - # from the ties - if (length(selected) > 1) { - selected <- sample(selected, 1) - } - - # assign the selected combination - vars2[i,] <- varcombinations[selected,] - - varcomb[t(.combn(varcombinations[selected,],2))] <- - varcomb[t(.combn(varcombinations[selected,],2))] + 1 - - # remove used combination - varcombinations <- varcombinations[-selected,] - - } - } - - # merge vars1 and vars2 to create the full set of combinations - vars <- rbind(vars1, vars2) - - # calculate allocations available to each item - allocations <- as.integer(table(vars)) - - } - - if (!is.null(availability) | !is.null(proportions)) { - - #create the objects available or proportions is they are not available - if (is.null(availability)) { - available <- rep(npackages, times = nitems) - availability <- rep(npackages, times = nitems) - } else { - available <- availability - } - if (is.null(proportions)) { - proportions <- rep(1/nitems, times = nitems) #available / sum(available) - } - - #order vector from low availability to high as .smart.round will favour right size of vector - #to resolve dilemmas, it will add more of the items that are more abundant - names(available) <- varieties - available <- sort(available) - proportions <- proportions[as.integer(names(available))] - - # calculate the packages that are needed - this will round later numbers to higher values - # if needed to fill the quota - needed <- .smart.round((nneeded * proportions) / sum(proportions)) - - # prepare inputs into loop - allocations <- rep(0, times = nitems) - names(allocations) <- names(available) #just to check, can be removed - tremain <- 1 - - while(tremain > 0) { - - allocate <- pmin(available, needed) - available <- available - allocate - - allocations <- allocations + allocate - tremaining <- nneeded - sum(allocations) - - needed <- available > 0 - needed <- needed * (tremaining / sum(needed)) - needed <- .smart.round(needed) - - tremain <- tremaining - - } - - #reorder the vector with allocations back to original order - allocations <- allocations[match(1:nitems, as.integer(names(available)))] #should be in ascending order - - # prepare variables - ncomb <- dim(varcombinations)[1] - n <- floor(npackages / ncomb) - nfixed <- ncomb * n - - # create set to get to full number of observers - vars <- matrix(nrow = npackages, ncol = ncomp) - - # set up array with set of combinations - varcomb <- matrix(0, nrow = nitems, ncol = nitems) - - if (dim(vars)[1] > 0.5) { - - # select combinations for the vars set that optimize design - for (i in 1:npackages) { - - # calculate frequency of each variety and define input for Shannon function, which prefers - # low and even values - varfreq <- (rowSums(varcomb) + colSums(varcomb)) / 2 - sumcomb <- (varfreq / allocations) * mean(allocations) #adjust for unequal required allocations - - # priority of each combination is equal to Shannon index of varieties - # in each combination - prioritycomb <- apply(varcombinations, 1, function(x){ - .getShannonVector(x, sumcomb, nitems) - }) - - # highest priority to be selected is the combination which has the lowest - # Shannon index - selected <- which(prioritycomb == min(prioritycomb)) - - # if there are ties, find out which combination reduces Kirchhoff index most - - if (length(selected) > 1 & i > depth1) { - - reduce <- max(2, min(length(selected), comp, round(5000/npackages), round(200/nitems))) - - # randomly subsample from selected if there are too many combinations to check - if (length(selected) > reduce) { - selected <- sample(selected, reduce) - } - - # get a nitems x nitems matrix with number of connections - - # calculate Kirchhoff index and select smallest value - khi <- vector(length = length(selected)) - for (k in 1:length(selected)) { - - evalgraph <- varcomb - index <- t(.combn(varcombinations[selected[k],], 2)) - evalgraph[index] <- evalgraph[index] + 1 - khi[k] <- .KirchhoffIndex(evalgraph) - - } - - selected <- selected[which(khi == min(khi))] - - } - - # if there are still ties between ranks of combinations, selected randomly - # from the ties - if (length(selected) > 1) { - selected <- sample(selected, 1) - } - - # assign the selected combination - vars[i,] <- varcombinations[selected,] - - varcomb[t(.combn(varcombinations[selected,],2))] <- - varcomb[t(.combn(varcombinations[selected,],2))] + 1 - - } - } - - } - - # create empty object to contain ordered combinations of vars - varOrdered <- matrix(NA, nrow = npackages, ncol = ncomp) - - # set up array with set of combinations - varcomb <- matrix(0, nrow = nitems, ncol = nitems) - - # fill first row - selected <- sample(1:npackages, 1) - varcomb[t(.combn(vars[selected,],2))] <- 1 - varOrdered[1,] <- vars[selected,] - vars <- vars[-selected,] - - # optimize the order of overall design by repeating a similar procedure to the above - for (i in 2:(npackages-1)) { - - # calculate frequency of each variety - sumcomb <- (rowSums(varcomb) + colSums(varcomb)) / 2 - sumcomb <- (sumcomb / allocations) * mean(allocations) - - # priority of each combination is equal to Shannon index of varieties - # in each combination - prioritycomb <- apply(vars, 1, function(x){ - .getShannonVector(x, sumcomb, nitems) - }) - - # highest priority to be selected is the combination which has the - # lowest Shannon index - selected <- which(prioritycomb == min(prioritycomb)) - - # if there are ties, find out which combination reduces Kirchhoff index most - if (length(selected) > 1 & i > depth1) { - - reduce <- max(2, min(comp, round(5000/npackages), round(200/nitems))) - - # randomly subsample from selected if there are too many combinations to check - if (length(selected) > reduce) { - selected <- sample(selected, reduce) - } - - # get a nitems x nitems matrix with number of connections - sumcombMatrix <- varcomb * 0 - - # in this case, get matrix to calculate Kirchhoff index only for - # last depth2 observers - for (j in max(1,i-depth2):(i-1)) { - index <- t(.combn(varOrdered[j,],2)) - sumcombMatrix[index] <- sumcombMatrix[index] + 1 - - } - - # calculate Kirchhoff indices for the candidate matrix corresponding - # to each row in selected - khi <- vector(length = length(selected)) - for (k in 1:length(selected)) { - - evalgraph <- sumcombMatrix - index <- t(.combn(vars[selected[k],], 2)) - evalgraph[index] <- evalgraph[index] + 1 - khi[k] <- .KirchhoffIndex(evalgraph) - - } - - # select combination that produces the lowest Kirchhoff index - selected <- selected[which(khi == min(khi, na.rm=TRUE))] - - } - - # if there are still ties between ranks of combinations, select one randomly - if (length(selected) > 1) { - selected <- sample(selected, 1) - } - - # assign the selected combination - varOrdered[i,] <- vars[selected,] - varcomb[t(.combn(vars[selected,], 2))] <- varcomb[t(.combn(vars[selected,], 2))] + 1 - - # remove used combination - vars <- vars[-selected, ] - - } - - # assign last one - varOrdered[npackages,] <- vars - - # Equally distribute positions to achieve order balance - # First create matrix with frequency of position of each of nitems - position <- matrix(0, ncol = ncomp, nrow = nitems) - - # Sequentially reorder sets to achieve evenness in positions - # Shannon is good here, because evenness values are proportional - # the H denominator in the Shannon formula is the same - - for (i in 1:npackages) { - - varOrdered_all <- .getPerms(varOrdered[i,]) - varOrdered_Shannon <- apply(varOrdered_all, 1, function(x) { - .getShannonMatrix(x, position) - }) - varOrdered_i <- varOrdered_all[which(varOrdered_Shannon == - min(varOrdered_Shannon))[1],] - varOrdered[i,] <- varOrdered_i - pp <- position * 0 - pp[cbind(varOrdered_i,1:ncomp)] <- 1 - position <- position + pp - - } - - # The varOrdered matrix has the indices of the elements - # Create the final matrix - finalresults <- matrix(NA, ncol = ncomp, nrow = npackages) - - # loop over the rows and columns of the final matrix and put - # the elements randomized - # with the indexes in varOrdered - for (i in seq_len(npackages)){ - for (j in seq_len(ncomp)){ - finalresults[i,j] <- itemnames[varOrdered[i,j]] - } - } - - dimnames(finalresults) <- list(seq_len(npackages), - paste0("item_", LETTERS[1:ncomp])) - - finalresults <- as.data.frame(finalresults, stringsAsFactors = FALSE) - - r <- table(unlist(finalresults))[itemnames] - - if (!all(r <= availability)) { - - few <- itemnames[!r <= availability] - nfew <- availability[!r <= availability] - nmin <- r[!r <= availability] - - stop("You indicated the availability of ", paste(nfew, collapse = ", "), " packages for ", - paste(few, collapse = ", "), " but you require a minimum of ", - paste(nmin, collapse = ", "), " for the given items \n" ) - - } - - class(finalresults) <- union("CM_df", class(finalresults)) - - return(finalresults) - -} - -#' @inheritParams randomise -#' @export -randomize <- function(...){ - - randomise(...) - -} - -# Define function for Kirchhoff index -# This index determines which graph is connected in the most balanced way -# In this context, lower values (lower resistance) is better -.KirchhoffIndex <- function(x) { - # The input matrix only has one triangle filled - # First we make it symmetric - x <- x + t(x) - - # Then some maths to get the Kirchhoff index - - # Using rARPACK:eigs, setting k to n-1 because we don't need the - # last eigen value - Laplacian <- methods::as(Matrix::Diagonal(x = colSums(x)) - x, - "dsyMatrix") - lambda <- - try(RSpectra::eigs(Laplacian, - k = (dim(Laplacian)[1] - 1), - tol = 0.01, - retvec = FALSE)$values, silent = TRUE) - if (inherits(lambda, "try-error")) - lambda <- Inf - # RSpectra:eigs is faster than base:eigen - # lambda <- eigen(Laplacian)$values - # lambda <- lambda[-length(lambda)] - - return(sum(1 / lambda)) - -} - -# get all permutations -.getPerms <- function(x) { - if (length(x) == 1) { - return(x) - } - else { - res <- matrix(nrow = 0, ncol = length(x)) - for (i in seq_along(x)) { - res <- rbind(res, cbind(x[i], Recall(x[-i]))) - } - return(res) - } -} - -# Shannon (as evenness measure) -.shannon <- function(x){ - sum(ifelse(x == 0, 0, x * log(x))) -} - -# Get Shannon index for order positions -.getShannonMatrix <- function(x, position) { - pp <- position * 0 - pp[cbind(x, 1:length(x))] <- 1 - pp <- position + pp - return(.shannon(as.vector(pp))) - -} - -.getShannonVector <- function(x, sumcomb, nitems) { - xi <- rep(0, times = nitems) - xi[x] <- 1 - return(.shannon(sumcomb + xi)) - -} - -.combn <- function (x, m, FUN = NULL, simplify = TRUE, ...) -{ - stopifnot(length(m) == 1L, is.numeric(m)) - if (m < 0) - stop("m < 0", domain = NA) - if (is.numeric(x) && length(x) == 1L && x > 0 && trunc(x) == - x) - x <- seq_len(x) - n <- length(x) - if (n < m) - stop("n < m", domain = NA) - x0 <- x - if (simplify) { - if (is.factor(x)) - x <- as.integer(x) - } - m <- as.integer(m) - e <- 0 - h <- m - a <- seq_len(m) - nofun <- is.null(FUN) - if (!nofun && !is.function(FUN)) - stop("'FUN' must be a function or NULL") - len.r <- length(r <- if (nofun) x[a] else FUN(x[a], ...)) - count <- as.integer(round(choose(n, m))) - if (simplify) { - dim.use <- if (nofun) - c(m, count) - else { - d <- dim(r) - if (length(d) > 1L) - c(d, count) - else if (len.r > 1L) - c(len.r, count) - else c(d, count) - } - } - if (simplify) - out <- matrix(r, nrow = len.r, ncol = count) - else { - out <- vector("list", count) - out[[1L]] <- r - } - if (m > 0) { - i <- 2L - nmmp1 <- n - m + 1L - while (a[1L] != nmmp1) { - if (e < n - h) { - h <- 1L - e <- a[m] - j <- 1L - } - else { - e <- a[m - h] - h <- h + 1L - j <- 1L:h - } - a[m - h + j] <- e + j - r <- if (nofun) - x[a] - else FUN(x[a], ...) - if (simplify) - out[, i] <- r - else out[[i]] <- r - i <- i + 1L - } - } - if (simplify) { - if (is.factor(x0)) { - levels(out) <- levels(x0) - class(out) <- class(x0) - } - dim(out) <- dim.use - } - out -} - -# Rounding values to closest integer while retaining the same sum -# From https://stackoverflow.com/questions/32544646/round-vector-of-numerics-to-integer-while-preserving-their-sum -.smart.round <- function(x) { - y <- floor(x) - indices <- utils::tail(order(x-y), round(sum(x)) - sum(y)) - y[indices] <- y[indices] + 1 - y -} - - diff --git a/R/randomize.R b/R/randomize.R new file mode 100644 index 0000000..cda8ce8 --- /dev/null +++ b/R/randomize.R @@ -0,0 +1,592 @@ +#' Set an experimental incomplete block design +#' +#' Generate an incomplete block A-optional design. The function is optimized for +#' incomplete blocks of three, but it will also work with comparisons of any +#' other number of options. +#' The design strives for approximate A optimality, this means that it is robust +#' to missing observations. It also strives for balance for positions of each option. +#' Options are equally divided between first, second, third, etc. position. +#' The strategy is to create a "pool" of combinations that does not repeat +#' combinations and is A-optimal. Then this pool is ordered to make subsets of +#' consecutive combinations also relatively balanced and A-optimal +#' +#' @author Jacob van Etten +#' @param ncomp an integer for the number of items to be assigned to each incomplete block +#' @param npackages an integer for the number of incomplete blocks to be generated +#' @param itemnames a character for the name of items tested in the experiment +#' @param availability optional, a vector with integers indicating the +#' number of plots available for each \var{itemnames} +#' @param props optional, a numeric vector with the desired proportions +#' for each \var{itemnames} +#' @param ... additional arguments passed to methods +#' @references +#' Bailey and Cameron (2004). Combinations of optimal designs. +#' \url{https://webspace.maths.qmul.ac.uk/l.h.soicher/designtheory.org/library/preprints/optimal.pdf} +#' @return A dataframe with the randomized design +#' @examples +#' ncomp = 3 +#' npackages = 20 +#' itemnames = c("apple","banana","grape","mango", "orange") +#' availability = c(5, 8, 50, 50, 50) +#' +#' randomize(ncomp = ncomp, +#' npackages = npackages, +#' itemnames = itemnames) +#' +#' randomize(ncomp = ncomp, +#' npackages = npackages, +#' itemnames = itemnames, +#' availability = availability) +#' +#' @aliases randomise +#' @importFrom Matrix Diagonal +#' @importFrom methods as +#' @importFrom RSpectra eigs +#' @importFrom utils tail +#' @importFrom stats runif +#' @importFrom lpSolve lp +#' @export +randomize <- function(npackages, + itemnames, + ncomp = 3, + availability = NULL, + props = NULL, + ...) { + + dots <- list(...) + + comp <- dots[["comp"]] + + if (is.null(comp)) { + comp <- 10 + } + + nitems <- length(itemnames) + + # depth1 is the number of rows after the procedure starts to compare options with the Kirchoff index + depth1 <- floor(nitems / ncomp ) + + # Varieties indicated by integers + varieties <- seq_len(nitems) + + # Full set of all combinations + varcombinations <- .combn(varieties, ncomp) + + # check inputs + if (!is.null(availability) & (length(availability) != nitems)) { + stop("nitems is different than length of vector with availability") + } + + if (nitems < 3) { + stop("nitems must be larger than 2") + } + + nneeded <- npackages * ncomp + + if (!is.null(availability)) { + + if (sum(availability) < nneeded) { + stop("availability is not sufficient: smaller than npackages * ncomp \n") + } + if (length(availability) != nitems) { + stop("length of vector availability should be nitems \n") + } + } + + if (!is.null(props)) { + + if (length(props) != nitems) { + stop("length of vector props should be nitems") + + } + + if (sum(props) != 1) { + + props <- props / sum(props) + warning("sum of props is not 1; values have been rescaled \n") + + } + } + + #create the objects available or props if they are not available + if (is.null(availability)) { + availability <- rep(npackages, times = nitems) + } + + available <- availability + + if (is.null(props)) { + props <- rep(1/nitems, times = nitems) #available / sum(available) + } + + #order vector from low availability to high as .smart.round will favour right size of vector + #to resolve dilemmas, it will add more of the items that are more abundant + names(available) <- varieties + available <- sort(available) + props <- props[as.integer(names(available))] + + # calculate the packages that are needed - this will round later numbers to higher values + # if needed to fill the quota + needed <- .smart.round((nneeded * props) / sum(props)) + + # prepare inputs into loop + allocations <- rep(0, times = nitems) + names(allocations) <- names(available) #just to check, can be removed + tremain <- 1 + + while(tremain > 0) { + + allocate <- pmin(available, needed) + available <- available - allocate + + allocations <- allocations + allocate + tremaining <- nneeded - sum(allocations) + + needed <- available > 0 + needed <- needed * (tremaining / sum(needed)) + needed <- .smart.round(needed) + + tremain <- tremaining + + } + + #reorder the vector with allocations back to original order + allocations <- allocations[match(1:nitems, as.integer(names(available)))] #should be in ascending order + allocationsMatrix <- makeAllocationsMatrix(allocations) + + # prepare variables + ncomb <- dim(varcombinations)[1] + n <- floor(npackages / ncomb) + nfixed <- ncomb * n + n <- ceiling(npackages / ncomb) + + # make a set of variety combinations + # repeating the combinations if the unique combinations are not sufficient + vars <- varcombinations[c(rep(1:(dim(varcombinations)[1]), times = n)), ] + + # create matrix that will hold the blocks + blocks <- matrix(nrow = npackages, ncol = ncomp) + + # set up array with set of combinations + varcomb <- matrix(0, nrow = nitems, ncol = nitems) + + if (dim(vars)[1] > 0.5) { + + # select combinations for the vars set that optimize design + for (i in 1:npackages) { + + varcombScore <- apply(varcombinations, 1, function(x){ + .getScoreBlocks(x, varcomb, allocationsMatrix) + }) + # highest priority to be selected is the combination which has the highest score + selected <- which(varcombScore >= (max(varcombScore))) + + # if there are ties, find out which combination reduces Kirchhoff index most + + if (length(selected) > 1 & i > depth1) { + + reduce <- max(2, min(length(selected), comp)) + + # randomly subsample from selected if there are too many combinations to check + if (length(selected) > reduce) { + selected <- sample(selected, reduce) + } + + # calculate Kirchhoff index and select smallest value + khi <- vector(length = length(selected)) + for (k in 1:length(selected)) { + + evalgraph <- varcomb + index <- .combn(varcombinations[selected[k],], 2) + evalgraph[index] <- evalgraph[index] + 1 + evalgraph[cbind(index[,2], index[,1])] <- evalgraph[cbind(index[,2], index[,1])] + 1 + khi[k] <- .KirchhoffIndex(evalgraph / (allocationsMatrix+diag(nrow(allocationsMatrix)))) + + } + + selected <- selected[which(khi == min(khi))] + + } + + # if there are still ties between ranks of combinations, selected randomly + # from the ties + if (length(selected) > 1) { + selected <- sample(selected, 1) + } + + # assign the selected combination + blocks[i,] <- varcombinations[selected,] + + index <- .combn(varcombinations[selected,],2) + varcomb[index] <- varcomb[index] + 1 + varcomb[cbind(index[,2], index[,1])] <- varcomb[cbind(index[,2], index[,1])] + 1 + + } + } + + varOrdered <- blocks + + # Equally distribute positions to achieve order balance + # First create matrix with frequency of position of each of nitems + position <- matrix(0, ncol = ncomp, nrow = nitems) + + # Sequentially reorder sets to achieve evenness in positions + # Shannon represents evenness here + # the H denominator in the Shannon formula is the same + + for (i in 1:npackages) { + + varOrdered_all <- .getPerms(varOrdered[i,]) + varOrdered_Shannon <- apply(varOrdered_all, 1, function(x) { + .getShannonMatrix(x, position) + }) + varOrdered_i <- varOrdered_all[which(varOrdered_Shannon == + min(varOrdered_Shannon))[1],] + varOrdered[i,] <- varOrdered_i + pp <- position * 0 + pp[cbind(varOrdered_i,1:ncomp)] <- 1 + position <- position + pp + + } + + # The varOrdered matrix has the indices of the elements + # Create the final matrix + finalresults <- matrix(NA, ncol = ncomp, nrow = npackages) + + # loop over the rows and columns of the final matrix and put + # the elements randomized + # with the indexes in varOrdered + for (i in seq_len(npackages)){ + for (j in seq_len(ncomp)){ + finalresults[i,j] <- itemnames[varOrdered[i,j]] + } + } + + dimnames(finalresults) <- list(seq_len(npackages), + paste0("item_", LETTERS[1:ncomp])) + + finalresults <- as.data.frame(finalresults, stringsAsFactors = FALSE) + + r <- table(unlist(finalresults))[itemnames] + + if (!is.null(availability)){ + if (!all(r <= availability)) { + + few <- itemnames[!r <= availability] + nfew <- availability[!r <= availability] + nmin <- r[!r <= availability] + + warning("You indicated the availability of ", paste(nfew, collapse = ", "), " packages for ", + paste(few, collapse = ", "), " but you require a minimum of ", + paste(nmin, collapse = ", "), " for the given items. \nYou could try to run the randomization again to solve this issue." ) + + } + } + + class(finalresults) <- union("CM_df", class(finalresults)) + + return(finalresults) + +} + +#' @inheritParams randomize +#' @export +randomise <- function(...){ + + randomize(...) + +} +# Define function for Kirchhoff index +# This index determines which graph is connected in the most balanced way +# In this context, lower values (lower resistance) is better +#' @noRd +.KirchhoffIndex <- function(x) { + + # Add a tiny bit of noise to avoid zeros + noise <- x * 0 + stats::runif(length(x))/length(x)^3 + noise <- noise + t(noise) + x <- x + noise + + # Then some maths to get the Kirchhoff index + + # Using rARPACK:eigs, setting k to n-1 because we don't need the + # last eigen value + Laplacian <- methods::as(Matrix::Diagonal(x = colSums(x)) - x, + "dsyMatrix") + + # RSpectra:eigs is faster than base:eigen + # The following would also work if we want to reduce a dependency + # lambda <- eigen(Laplacian)$values + # lambda <- lambda[-length(lambda)] + lambda <- + try(RSpectra::eigs(Laplacian, + k = (dim(Laplacian)[1] - 1), + tol = 0.01, + retvec = FALSE)$values, silent = TRUE) + if(inherits(lambda, "try-error")){lambda <- Inf} + + return(sum(1 / lambda)) + +} + +# get all permutations +#' @noRd +.getPerms <- function(x) { + if (length(x) == 1) { + return(x) + } + else { + res <- matrix(nrow = 0, ncol = length(x)) + for (i in seq_along(x)) { + res <- rbind(res, cbind(x[i], Recall(x[-i]))) + } + return(res) + } +} + +# Shannon (as evenness measure) +#' @noRd +.shannon <- function(x){ + sum(ifelse(x == 0, 0, x * log(x))) +} + +# Get Shannon index for order positions +#TODO check this function!!! +#' @noRd +.getShannonMatrix <- function(x, position) { + + pp <- position * 0 + pp[cbind(x, 1:length(x))] <- 1 + pp <- position + pp + return(.shannon(as.vector(pp))) + +} + +#' @noRd +.getScoreBlocks <- function(x, varcomb, allocationsMatrix) { + + #get combinations from vector of varieties x + cb <- .combn(x,2) + + #get progress + progress <- sum(varcomb) / sum(allocationsMatrix) + + #get score pairwise + score1 <- varcomb[cb]/allocationsMatrix[cb] < progress + + #get score sums + score2 <- colSums(varcomb)[x]/colSums(allocationsMatrix)[x] < progress + + #the smallest available amount should be avoided, so this check penalizes it + if(any(score2 == FALSE) & min(colSums(varcomb)[x]) == min(colSums(varcomb))){score2[1] <- score2[1]-1} + + #calculate total score + score <- sum(score1+score2) + + return(score) + +} + +#' @noRd +makeAllocationsMatrix <- function(allocations){ + + #prepare basic parameters and empty matrix + n1 <- length(allocations) + combs <- .combn(1:n1, 2) + n2 <- nrow(combs) + a <- matrix(0,nrow=n1, ncol=n2) + + #fill matrix with constraints on row/column sums + for(i in 1:n1){ + + a[i,] <- (combs[,1] == i | combs[,2] == i) + + } + + #include auxiliary variable z to the matrix + #this variable will be maximized, pushing all values up equally + minShare <- pmin(allocations[combs[,1]], allocations[combs[,2]]) / ((n1-1)/2) + f.con <- rbind(a, -diag(n2)) + f.con <- cbind(f.con, c(rep(0, times=n1), minShare)) + + # set up vector with allocations (column sums) and zeros for z constraint + f.rhs <- c(allocations, rep(0, times=n2)) + + #objective function emphasizes raising the z value, which increases an equal spread + f.obj <- c(rep(1, times=n2), max(allocations)^2) + f.dir <- rep("<=", times=n1+n2) + + sol <- lpSolve::lp("max", f.obj, f.con, f.dir, f.rhs) + x <- sol$solution[1:n2] + result <- matrix(0, nrow=n1, ncol=n1) + result[combs] <- x + result <- result + t(result) + return(result) + +} + +#' @noRd +.combn <- function (x, m, FUN = NULL, simplify = TRUE, ...) +{ + stopifnot(length(m) == 1L, is.numeric(m)) + if (m < 0) + stop("m < 0", domain = NA) + if (is.numeric(x) && length(x) == 1L && x > 0 && trunc(x) == + x) + x <- seq_len(x) + n <- length(x) + if (n < m) + stop("n < m", domain = NA) + x0 <- x + if (simplify) { + if (is.factor(x)) + x <- as.integer(x) + } + m <- as.integer(m) + e <- 0 + h <- m + a <- seq_len(m) + nofun <- is.null(FUN) + if (!nofun && !is.function(FUN)) + stop("'FUN' must be a function or NULL") + len.r <- length(r <- if (nofun) x[a] else FUN(x[a], ...)) + count <- as.integer(round(choose(n, m))) + if (simplify) { + dim.use <- if (nofun) + c(m, count) + else { + d <- dim(r) + if (length(d) > 1L) + c(d, count) + else if (len.r > 1L) + c(len.r, count) + else c(d, count) + } + } + if (simplify) + out <- matrix(r, nrow = len.r, ncol = count) + else { + out <- vector("list", count) + out[[1L]] <- r + } + if (m > 0) { + i <- 2L + nmmp1 <- n - m + 1L + while (a[1L] != nmmp1) { + if (e < n - h) { + h <- 1L + e <- a[m] + j <- 1L + } + else { + e <- a[m - h] + h <- h + 1L + j <- 1L:h + } + a[m - h + j] <- e + j + r <- if (nofun) + x[a] + else FUN(x[a], ...) + if (simplify) + out[, i] <- r + else out[[i]] <- r + i <- i + 1L + } + } + if (simplify) { + if (is.factor(x0)) { + levels(out) <- levels(x0) + class(out) <- class(x0) + } + dim(out) <- dim.use + } + return(t(out)) +} + +# Rounding values to closest integer while retaining the same sum +# From https://stackoverflow.com/questions/32544646/round-vector-of-numerics-to-integer-while-preserving-their-sum +#' @noRd +.smart.round <- function(x) { + y <- floor(x) + indices <- utils::tail(order(x-y), round(sum(x)) - sum(y)) + y[indices] <- y[indices] + 1 + y +} + +# #-----------------Run an example------------------- +# +# ncomp = 3 +# npackages = 238 +# vars = 15 +# proportions = rep(1, vars)/vars +# itemnames = c(LETTERS[1:vars]) +# availability = rep(ceiling(238*3/vars), times=vars) +# availability[2] = availability[2]*2 +# availability[5] = availability[5]/2 +# +# a = randomise(ncomp = ncomp, +# npackages = npackages, +# itemnames = itemnames, +# availability = availability) +# +# +# a +# +# #----------Diagnostics------------- +# #the only input assumed here is table a +# #get item names from the table +# ua = sort(unique(c(t(a)))) +# ncomp = ncol(a) +# +# comb_balance = matrix(0, nrow=length(ua), ncol=length(ua)) +# +# for(i in 1:nrow(a)){ +# +# j = t(combn(match(a[i,], ua),2)) +# comb_balance[j] = comb_balance[j] + 1 +# +# } +# +# cb = comb_balance + t(comb_balance) +# cb +# #show total number of times options are included in packages +# cb = rbind(cb, rowSums(cb)/2) +# +# #show result nicely +# rownames(cb) = c(ua, "Total") +# colnames(cb) = ua +# cb +# #check the distances between packages that contain the same option +# #as a measure of sequential balance +# d = matrix(NA, nrow = length(itemnames), ncol=ncomp) +# +# for(i in 1:length(ua)) { +# s = apply(a, 1, function(x){sum(x==ua[i])}) +# di = diff(which(s==1)) +# hist(di) +# d[i,] = c(mean(di), sd(di), max(di)) +# } +# colnames(d) = c("mean", "sd", "max") +# rownames(d) = ua +# +# # print d nicely +# format(as.data.frame(d), digits=3) +# +# #check if options are equally distributed across columns +# f = matrix(NA, nrow=length(ua), ncol=ncomp) +# rownames(f) = ua +# f +# for(i in 1:ncomp){ +# +# tai = table(a[,i]) +# f[names(tai),i] = tai +# +# } +# +# f +# comb_balance +# # connection graph +# g = graph_from_adjacency_matrix(comb_balance+t(comb_balance), mode = "lower", weighted = "weight") +# g +# plot(g, edge.width = E(g)$weight, edge.label = E(g)$weight) + diff --git a/R/rankTricot.R b/R/rankTricot.R index 8fe32b8..9ceb0a1 100644 --- a/R/rankTricot.R +++ b/R/rankTricot.R @@ -1,6 +1,6 @@ #' Build Plackett-Luce rankings from tricot dataset #' -#' Create an object of class "rankings" from tricot data. +#' Create an object of class "rankings" from tricot data #' #' @author Kauê de Sousa and Jacob van Etten, with ideas from Heather Turner #' @param data a data.frame with columns specified by items and input values @@ -9,6 +9,8 @@ #' @param input a character or numerical vector for indexing the column(s) #' containing the values in \code{data} to be ranked #' @param group logical, if \code{TRUE} return an object of class "grouped_rankings" +#' @param validate.rankings logical, if \code{TRUE} implements a check on ranking consistency +#' looking for possible ties, NA or letters other than A, B, C. These entries are set to 0 #' @param additional.rank optional, a data frame for the comparisons between #' tricot items and the local item #' @param ... additional arguments passed to methods. See details @@ -31,7 +33,7 @@ #' #' # first build rankings with only tricot items #' # and return an object of class 'rankings' -#' R <- rankTricot(data = beans, +#' R = rankTricot(data = beans, #' items = c(1:3), #' input = c(4:5)) #' head(R) @@ -41,7 +43,7 @@ #' # pass the comparison with local item as an additional rankings, then #' # each of the 3 varieties are compared separately with the local item #' # and return an object of class grouped_rankings -#' G <- rankTricot(data = beans, +#' G = rankTricot(data = beans, #' items = c(1:3), #' input = c(4:5), #' group = TRUE, @@ -51,219 +53,194 @@ #' } #' #' @export -rankTricot <- function(data, items, input, - group = FALSE, - additional.rank = NULL, - ...) { +rankTricot = function(data, + items, + input, + group = FALSE, + validate.rankings = FALSE, + additional.rank = NULL, + ...) { # if tibble coerce into a data.frame if (.is_tibble(data)) { - data <- as.data.frame(data, stringsAsFactors = FALSE) + data = as.data.frame(data, stringsAsFactors = FALSE) } - items <- data[, items] + items = data[, items] - input <- data[, input] + input = data[, input] # get nrow - n <- nrow(data) + n = nrow(data) # get extra arguments - dots <- list(...) + dots = list(...) # if all data is required - full.output <- dots[["full.output"]] - - # check number of comparisons to decide which way data will - # be handled, if type is tricot or if it contains more comparisons - ncomp <- ncol(items) + full.output = dots[["full.output"]] - # with 3 comparisons - if (ncomp == 3) { - - n <- nrow(items) - - # check for more than two missing labels in items - mi <- rowSums(apply(items, 2, is.na)) - if (any(mi > 1)) { - stop("Cannot handle more than 2 NAs per row in 'items', + n = nrow(items) + + # check for more than two missing labels in items + mi = rowSums(apply(items, 2, is.na)) + if (any(mi > 1)) { + stop("Cannot handle more than 2 NAs per row in 'items', more than 2 NAs where found in rows ", - paste(which(mi > 1), collapse = ", "), "\n") - } - - # if there is one NA per row in items and observations - # with only two items add a pseudo-item which will be removed later - if (any(mi == 1)) { - items[is.na(items)] <- "pseudoitem" - } - - # data frame with items as matrix - im <- as.matrix(items) - - # get the names of items - itemnames <- unique(as.vector(im)) - - # a Sparse matrix where rows are the observations - # and columns the item names - r <- matrix(0, nrow = n, ncol = length(itemnames)) - colnames(r) <- itemnames + paste(which(mi > 1), collapse = ", "), "\n") + } + + # if there is one NA per row in items and observations + # with only two items add a pseudo-item which will be removed later + if (any(mi == 1)) { + items[is.na(items)] = "pseudoitem" + } + + # validate rankings, and set to 0 if required + keep = .validate_rankings(input) + + out = which(keep == FALSE) + + # data frame with items as matrix + im = as.matrix(items) + + # get the names of items + itemnames = unique(as.vector(im)) + + # a Sparse matrix where rows are the observations + # and columns the item names + r = matrix(0, nrow = n, ncol = length(itemnames)) + colnames(r) = itemnames + + # run over the rows filling the rankings that were observed + for(j in seq_len(n)){ - # run over the rows filling the rankings that were observed - for(j in seq_len(n)){ - - r[j, im[j,]] <- .setorder(as.vector(unlist(input[j,]))) + r[j, im[j,]] = .setorder(as.vector(unlist(input[j,]))) - } + } + + R = PlackettLuce::as.rankings(r) + + # if ranking validation was required, rankings that did not passed the + # validation are set to 0, this does not affect the final length + # of the rankings + if (isTRUE(validate.rankings)) { - a <- list(x = r) + R[!keep] = 0 - R <- do.call("as.rankings", args = a) - - # if full output is required, for internal use - # put r into the ordering format - if (isTRUE(full.output)) { - r2 <- matrix("", nrow = n, ncol = 3) - colnames(r2) <- c("best", "middle", "worst") - r[r==0] <- NA - for(j in seq_len(n)) { - jr <- sort(r[j, !is.na(r[j, ])]) - if (sum(jr == 2) > 1) { - names(jr)[jr == 2] <- paste(names(jr[jr == 2]), collapse = ", ") - } - r2[j, ] <- names(jr) - } - r <- r2 + } + + if (length(out) > 0) { + messag = paste0("Ties, NA's or letters different than A, B, C, were identified in rows ", + paste(out, collapse = ", "), "\n") + if (isFALSE(validate.rankings)) { + messag = paste(messag, "Use validate.rankings = TRUE to ignore these entries\n") } - + warning(messag) } - # with 4 or more comparisons - if (ncomp >= 4) { - - r <- .pivot_tetra(i = items, r = input) - - # get item names - itemnames <- sort(unique(as.vector(r))) - - # make a PlackettLuce rankings - a <- list(x = r, - input = "ordering", - items = itemnames) - R <- do.call("as.rankings", args = a) - + # if full output is required, for internal use + # put r into the ordering format + if (isTRUE(full.output)) { + r2 = matrix("", nrow = n, ncol = 3) + colnames(r2) = c("best", "middle", "worst") + r[r==0] = NA + for(j in seq_len(n)) { + jr = sort(r[j, !is.na(r[j, ])]) + if (sum(jr == 2) > 1) { + names(jr)[jr == 2] = paste(names(jr[jr == 2]), collapse = ", ") + } + r2[j, ] = names(jr) + } + r = r2 } # if pseudo-item were added, it is removed - pseudo <- grepl("pseudoitem", itemnames) + pseudo = grepl("pseudoitem", itemnames) if (any(pseudo)) { - R <- R[, !pseudo] + R = R[, !pseudo] } # check if additional rankings are required if (!is.null(additional.rank)) { # add comparisons with local rankings - R <- .additional_rankings(i = items, R = R, add = additional.rank) + R = .additional_rankings(i = items, R = R, add = additional.rank) } # and into a grouped_rankings - gi <- rep(seq_len(n), (nrow(R) / n)) - - a <- list(x = R, - index = gi) - - G <- do.call("group", args = a) + gi = rep(seq_len(n), (nrow(R) / n)) + G = PlackettLuce::group(R, index = gi) # check if all data is required if (isTRUE(full.output)) { - R <- list(PLranking = R, PLgrouped = G, myrank = r) + R = list(PLranking = R, PLgrouped = G, myrank = r) } # return a grouped_rankings if required if (group) { - R <- G + R = G } return(R) } + +#' Validate rankings +#' +#' This check ranking consistency making sure that +#' no NAs or ties are mantained in the final PlackettLuce ranking +#' +#' @param x data.frame with two columns indicating the tricot rankings +#' @noRd +.validate_rankings = function(x) { + + ABC = apply(x, 1, function(y) { + all(y %in% LETTERS[1:3]) + }) + + noNA = apply(x, 1, function(y) { + all(!is.na(y)) + }) + + noDups = apply(x, 1, function(y) { + all(!duplicated(y)) + }) + + keep = as.vector(ABC & noNA & noDups) + + return(keep) + +} + + #' Set the order of tricot rankings -#' @param x a vector of length 3 with the tricot letters A, B, C, -#' a Tie will be assigned the value 2 +#' +#' This function set the indices to place the order of best worst +#' technologies indicates in the tricot approach +#' +#' @param x a vector of length 2 with the LETTERS A, B or C, or Tie +#' first element in the vector indicates the best technology, +#' second element indicates the worst technology #' @examples -#' x <- c("C", "Tie") +#' x = c("C", "Tie") #' gosset:::.setorder(x) #' -#' x <- c("A", "B") +#' x = c("A", "B") #' gosset:::.setorder(x) #' @noRd -.setorder <- function(x){ - s <- rep(2, times = 3) #default value is 2 - L <- LETTERS[1:3] +.setorder = function(x){ + # default value is 2 + s = rep(2, times = 3) + L = LETTERS[1:3] # works backwards from C to A to give most importance # to item(s) listed as better - s[L %in% strsplit(x[2], split="")] <- 3 - s[L %in% strsplit(x[1], split="")] <- 1 + s[L %in% strsplit(x[2], split = "")] = 3 + s[L %in% strsplit(x[1], split = "")] = 1 - #avoid skipped numbers - # s <- order(s) return(s) -} - -#' Tetra rankings -#' This function deals with object in the tetra approach -#' in ClimMob when four or more items are tested by each participant -#' @param i is a dataframe with items -#' @param r is a dataframe with rankings -#' @noRd -.pivot_tetra <- function(i, r){ - - # fix names in r data - names(r) <- paste0("PosItem", 1:ncol(r)) - - # get the number of possible rankings - nrank <- ncol(r) - # number of rows - n <- nrow(r) - - # handle NAs - r[r==0] <- NA - - for (z in seq_len(nrank)) { - rm <- is.na(i[, z]) | is.na(r[, z]) - i[rm , z] <- NA - r[rm , z] <- NA - } - # check for more than two missing labels in items - mi <- rowSums(t(apply(i, 1, is.na))) - mi <- nrank - mi - if( any(mi <= 1) ) { - stop("Cannot handle more than 2 NAs per row in 'items' \n") - } - - # if there is accepted NAs in items - # put a label as pseudoitem which will be removed later - if ( any(mi < nrank) ) { - i <- t(apply(i, 1, function(p) { - pi <- length(p[is.na(p)]) - p[is.na(p)] <- paste0("pseudoitem", 1:pi) - p - })) - } - - # add a very high value if there is accepted NAs in rankings - if (any(mi < nrank)) { - r[is.na(r)] <- 999999 - } - - decoded <- .decode_ranking(i, r) - - return(decoded) - -} - +} #' this function adds additional ranks, generally when a local item #' is tested against the tricot items @@ -273,18 +250,18 @@ rankTricot <- function(data, items, input, #' indication whether the tricot items performed "Better" or "Worse" #' compared to the local item #' @noRd -.additional_rankings <- function(i, R, add){ +.additional_rankings = function(i, R, add){ - n <- nrow(add) + n = nrow(add) - ncomp <- ncol(i) + ncomp = ncol(i) # convert it into characters - add[1:ncol(add)] <- lapply(add[1:ncol(add)], as.character) + add[1:ncol(add)] = lapply(add[1:ncol(add)], as.character) - add <- as.matrix(add) + add = as.matrix(add) - i <- as.matrix(i) + i = as.matrix(i) # treat these comparisons as additional rankings. # first we convert the orderings of the items to @@ -296,9 +273,9 @@ rankTricot <- function(data, items, input, # make sure that values in add are integers # where 1 means Better and 2 means Worse - add <- apply(add, 2, function(x) { - x <- ifelse(x == "Better" | x == 1, 1, - ifelse(x == "Worse" | x == 2, 2, NA)) + add = apply(add, 2, function(x) { + x = ifelse(x == "Better" | x == 1, 1, + ifelse(x == "Worse" | x == 2, 2, NA)) x }) @@ -308,36 +285,31 @@ rankTricot <- function(data, items, input, } # add local to itemnames - itemnames <- dimnames(R)[[2]] - itemnames <- unique(c("Local Check", itemnames)) + itemnames = dimnames(R)[[2]] + itemnames = unique(c("Local", itemnames)) - paired <- list() + paired = list() for (p in seq_len(ncomp)) { - ordering <- matrix("Local Check", nrow = n, ncol = 2) - worse <- add[, p] == 2 + ordering = matrix("Local", nrow = n, ncol = 2) + worse = add[, p] == 2 # name of winner - ordering[!worse, 1] <- i[, p][!worse] + ordering[!worse, 1] = i[, p][!worse] # name of loser - ordering[worse, 2] <- i[, p][worse] - paired[[p]] <- ordering + ordering[worse, 2] = i[, p][worse] + paired[[p]] = ordering } # we then convert these orderings to sub-rankings of the full set of items # and combine them with the rankings - paired <- lapply(paired, function(x) { - a <- list(x = x, - input = "ordering", - items = itemnames) - x <- do.call("as.rankings", args = a) + paired = lapply(paired, function(x) { + x = PlackettLuce::as.rankings(x, input = "ordering", items = itemnames) }) - paired <- do.call("rbind", paired) + paired = do.call("rbind", paired) - R <- rbind(R, paired) + R = rbind(R, paired) return(R) } - - diff --git a/README.md b/README.md index 778116d..330683b 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,7 @@ ClimMobTools [![CRAN](https://www.r-pkg.org/badges/version/ClimMobTools)](https://cran.r-project.org/package=ClimMobTools) -[![CRANchecks](https://cranchecks.info/badges/worst/ClimMobTools)](https://cran.r-project.org/web/checks/check_results_ClimMobTools.html) -[![codecov](https://codecov.io/gh/agrdatasci/ClimMobTools/master.svg)](https://codecov.io/github/agrdatasci/ClimMobTools?branch=master) -[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) +[![CRANchecks](https://badges.cranchecks.info/worst/ClimMobTools.svg)](https://cran.r-project.org/web/checks/check_results_ClimMobTools.html) [![Downloads](https://cranlogs.r-pkg.org/badges/ClimMobTools)](https://cran.r-project.org/package=ClimMobTools) diff --git a/codemeta.json b/codemeta.json index a870025..70506d2 100644 --- a/codemeta.json +++ b/codemeta.json @@ -7,7 +7,7 @@ "codeRepository": "https://agrdatasci.github.io/ClimMobTools/", "issueTracker": "https://github.com/agrdatasci/ClimMobTools/issues", "license": "https://spdx.org/licenses/MIT", - "version": "0.5.3", + "version": "1.0", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", @@ -50,6 +50,18 @@ } ], "softwareSuggestions": [ + { + "@type": "SoftwareApplication", + "identifier": "climatrends", + "name": "climatrends", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=climatrends" + }, { "@type": "SoftwareApplication", "identifier": "gosset", @@ -74,6 +86,18 @@ }, "sameAs": "https://CRAN.R-project.org/package=knitr" }, + { + "@type": "SoftwareApplication", + "identifier": "nasapower", + "name": "nasapower", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=nasapower" + }, { "@type": "SoftwareApplication", "identifier": "rmarkdown", @@ -156,6 +180,18 @@ "sameAs": "https://CRAN.R-project.org/package=jsonlite" }, "4": { + "@type": "SoftwareApplication", + "identifier": "lpSolve", + "name": "lpSolve", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=lpSolve" + }, + "5": { "@type": "SoftwareApplication", "identifier": "Matrix", "name": "Matrix", @@ -167,12 +203,12 @@ }, "sameAs": "https://CRAN.R-project.org/package=Matrix" }, - "5": { + "6": { "@type": "SoftwareApplication", "identifier": "methods", "name": "methods" }, - "6": { + "7": { "@type": "SoftwareApplication", "identifier": "RSpectra", "name": "RSpectra", @@ -184,12 +220,88 @@ }, "sameAs": "https://CRAN.R-project.org/package=RSpectra" }, - "7": { + "8": { + "@type": "SoftwareApplication", + "identifier": "stats", + "name": "stats" + }, + "9": { "@type": "SoftwareApplication", "identifier": "utils", "name": "utils" }, "SystemRequirements": null }, - "fileSize": "1217.505KB" + "fileSize": "1248.669KB", + "citation": [ + { + "@type": "ScholarlyArticle", + "datePublished": "2023", + "author": [ + { + "@type": "Person", + "givenName": "Carlos", + "familyName": "Quirós" + }, + { + "@type": "Person", + "givenName": "Kauê", + "familyName": "de Sousa" + }, + { + "@type": "Person", + "givenName": "Jonathan", + "familyName": "Steinke" + }, + { + "@type": "Person", + "givenName": "Brandon", + "familyName": "Madriz" + }, + { + "@type": "Person", + "givenName": "Marie-Angélique", + "familyName": "Laporte" + }, + { + "@type": "Person", + "givenName": "Elizabeth", + "familyName": "Arnaud" + }, + { + "@type": "Person", + "givenName": "Rhys", + "familyName": "Manners" + }, + { + "@type": "Person", + "givenName": "Berta", + "familyName": "Ortiz-Crespo" + }, + { + "@type": "Person", + "givenName": "Anna", + "familyName": "Müller" + }, + { + "@type": "Person", + "givenName": "Jacob", + "familyName": "van Etten" + } + ], + "name": "{ClimMob: Software to Support Experimental Citizen Science in Agriculture}", + "identifier": "10.2139/ssrn.4463406", + "url": "http://dx.doi.org/10.2139/ssrn.4463406", + "@id": "https://doi.org/10.2139/ssrn.4463406", + "sameAs": "https://doi.org/10.2139/ssrn.4463406", + "isPartOf": { + "@type": "PublicationIssue", + "datePublished": "2023", + "isPartOf": { + "@type": ["PublicationVolume", "Periodical"], + "name": "{SSRN} Electronic Journal" + } + } + } + ] } diff --git a/docs/404.html b/docs/404.html index 1e38c7a..df50c42 100644 --- a/docs/404.html +++ b/docs/404.html @@ -39,7 +39,7 @@ ClimMobTools - 0.5 + 1.0 diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index 79921c7..089567b 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -17,7 +17,7 @@ ClimMobTools - 0.5 + 1.0 diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index ffb6f1f..ecbacd0 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -17,7 +17,7 @@ ClimMobTools - 0.5 + 1.0 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 5fc9910..67edb31 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -17,7 +17,7 @@ ClimMobTools - 0.5 + 1.0 diff --git a/docs/articles/Overview.html b/docs/articles/Overview.html index d2fe252..670b3b9 100644 --- a/docs/articles/Overview.html +++ b/docs/articles/Overview.html @@ -40,7 +40,7 @@ ClimMobTools - 0.5 + 1.0 @@ -90,10 +90,10 @@

ClimMobTools: API Client for the ‘ClimMob’

Kauê de Sousa

- Department of Agricultural Sciences, Inland Norway University of -Applied Sciences, Hamar, Norway
Digital Inclusion, Bioversity -International, Montpelier, -France

Jacob + Digital Inclusion, Bioversity International, Montpelier, France +
Department of Agricultural Sciences, Inland Norway University of +Applied Sciences, Hamar, +Norway

Jacob van Etten

Digital Inclusion, Bioversity International, Montpelier, @@ -135,12 +135,16 @@

Usage an API key from the ClimMob user’s account.

 library("ClimMobTools")
+library("PlackettLuce")
+library("climatrends")
+library("nasapower")
 
 # the API key
 key <- "d39a3c66-5822-4930-a9d4-50e7da041e77"
 
 dat <- getDataCM(key = key,
                  project = "breadwheat",
+                 userowner = "gosset",
                  pivot.wider = TRUE)
 
 
@@ -154,10 +158,7 @@ 
Tricot data with environmenta
 Here we use function temperature() to compute the
 temperature indices for the first 80 days after planting.

 
-library("climatrends")
-library("nasapower")
-
-dat$plantingdate <- as.Date(dat$plantingdate, format = "%Y-%m-%d")
+dat$plantingdate <- as.Date(dat$plantingdate, format = "%Y-%m-%d")
 dat$lon <- as.numeric(dat$farm_geo_longitude)
 dat$lat <- as.numeric(dat$farm_geo_latitude)
 
@@ -165,7 +166,20 @@ 
Tricot data with environmenta
                     day.one = dat[, "plantingdate"],
                     span = 80)
 
-temp

+temp
+#>      maxDT minDT maxNT minNT   DTR    SU    TR   CFD  WSDI  CSDI  T10p  T90p
+#>      <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <dbl> <dbl>
+#> 1:   30.75 20.65 17.67  4.63    15     2     0     0     3     2  5.50 27.74
+#> 2:   29.15 20.65 16.52  4.63    15     0     0     0     4     2  5.50 27.46
+#> 3:   32.76 20.65 18.49  4.63    15     7     0     0     8     2  5.50 29.16
+#> 4:   29.15 20.65 16.52  4.63    15     0     0     0     3     2  5.50 27.27
+#> 5:   29.15 20.65 16.52  4.63    15     0     0     0     4     2  5.50 26.22
+#> ---                                                                         
+#> 489: 29.15 20.65 16.52  4.63    15     0     0     0     3     2  5.50 27.27
+#> 490: 29.15 20.65 16.52  4.63    15     0     0     0     3     2  5.50 27.27
+#> 491: 32.76 20.65 17.67  4.63    15     5     0     0     6     2  5.50 28.41
+#> 492: 29.15 20.65 16.52  4.63    15     0     0     0     3     2  5.50 27.27
+#> 493: 29.15 20.65 16.52  4.63    15     0     0     0     3     2  5.50 27.27

Tricot data into rankings @@ -179,9 +193,7 @@

Tricot data into rankingsWe build the rankings using the function rankTricot().

-library("PlackettLuce")
-
-R <- rankTricot(dat, 
+R <- rankTricot(dat, 
                 items = c("item_A","item_B","item_C"), 
                 input = c("overallperf_pos","overallperf_neg"),
                 group = TRUE)
@@ -192,8 +204,66 @@ 
Tricot data into rankings             data = pld)
 
 summary(pl)
+#> $`2`
+#> Call: PlackettLuce(rankings = y, weights = weights, na.action = NULL, 
+#>     start = start)
+#> 
+#> Coefficients:
+#>           Estimate Std. Error z value Pr(>|z|)    
+#> CSW18       0.0000         NA      NA       NA    
+#> WR544      -3.3246     0.4380  -7.590 3.20e-14 ***
+#> PBW343     -0.6595     0.4512  -1.462 0.143852    
+#> HP1633     -3.4306     0.4323  -7.936 2.09e-15 ***
+#> HW2045     -3.5339     0.4396  -8.039 9.03e-16 ***
+#> DBW17      -1.4614     0.4132  -3.537 0.000405 ***
+#> HD2985     -2.0511     0.4413  -4.648 3.35e-06 ***
+#> DPW621-50  -2.0924     0.4376  -4.782 1.74e-06 ***
+#> HD2824     -3.1032     0.4347  -7.138 9.46e-13 ***
+#> RAJ4120    -3.0018     0.4331  -6.932 4.16e-12 ***
+#> PBW550     -3.2180     0.4269  -7.537 4.79e-14 ***
+#> K0307      -3.4931     0.4458  -7.835 4.67e-15 ***
+#> HI1563     -3.4131     0.4425  -7.713 1.23e-14 ***
+#> PBW502     -3.1382     0.4539  -6.914 4.71e-12 ***
+#> HD2932     -2.7048     0.4320  -6.261 3.83e-10 ***
+#> K9107       0.2791     0.4794   0.582 0.560486    
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual deviance:  766.05 on 855 degrees of freedom
+#> AIC:  796.05 
+#> Number of iterations: 25
+#> 
+#> $`3`
+#> Call: PlackettLuce(rankings = y, weights = weights, na.action = NULL, 
+#>     start = start)
+#> 
+#> Coefficients:
+#>           Estimate Std. Error z value Pr(>|z|)    
+#> CSW18       0.0000         NA      NA       NA    
+#> WR544      -2.1327     0.4498  -4.741 2.13e-06 ***
+#> PBW343      0.2557     0.4366   0.586 0.558045    
+#> HP1633     -2.1386     0.4676  -4.574 4.79e-06 ***
+#> HW2045     -1.9125     0.4482  -4.267 1.98e-05 ***
+#> DBW17      -0.7026     0.4481  -1.568 0.116904    
+#> HD2985     -1.1698     0.4286  -2.729 0.006343 ** 
+#> DPW621-50  -1.5821     0.4475  -3.535 0.000408 ***
+#> HD2824     -2.5380     0.4852  -5.231 1.69e-07 ***
+#> RAJ4120    -2.5301     0.4974  -5.087 3.64e-07 ***
+#> PBW550     -2.1052     0.4693  -4.486 7.26e-06 ***
+#> K0307      -2.3455     0.4467  -5.251 1.51e-07 ***
+#> HI1563     -2.3205     0.4502  -5.154 2.54e-07 ***
+#> PBW502     -1.9471     0.4245  -4.586 4.51e-06 ***
+#> HD2932     -1.5077     0.4469  -3.374 0.000741 ***
+#> K9107       1.1110     0.5217   2.129 0.033215 *  
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual deviance:  559.08 on 594 degrees of freedom
+#> AIC:  589.08 
+#> Number of iterations: 15
 
 plot(pl)
+

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zLdEZp_qumS_N(jZ69VR4a6GwI^zx=t>-GU5jYQ|sF<0r_*gv(TXxS$fEI70!fgC2h#%KK zO=FMqMtM@*%RP$aJ~%URx_?DM-t?FIzJA*3=%`HsVS$EgJLP;Qqm|K#gCWEI0L$$f zjsBcq6jj};1X*nYMZtnYB;7xKx&(qaZ;U-dK%le zS>1RuMFYhww(xfV4W+$~;?b}~EdffXjB0fE#nF)^uJ(kM&3zHr8KDjIUw_u}9-GlY^6`oEJDQ+jT3|IjB==TgUt7+L=CH0K`bmoclZ`hm8-H*wt z!`J>mH$-H4F!N*D&Wt^4d;M8%MLVhOIPLNj-!7Q z*Pf3A)xT?bUD+iGm!$_ABhv+7oLlJ;O+4Wk3(lj@Gy7Df8t^p|<=1v27-sxhRv5WR@OZ-&!q?rTKX1=v>&cYM?ga-FRIfpbsAL z1AC=C%lrjg_{3Ax`6fjahX{pK4u>K;SAfb)yG>Zz`vzaEzd-lhG>JJ*SM#mRHhYa5 zg8q|yfpLHRG2g3^wm&F|3Qxb>?8XqF(8WepawW^|U}*fX2hGl8#*9u ClimMobTools - 0.5 + 1.0

diff --git a/docs/authors.html b/docs/authors.html index ab9c55d..3d71fa6 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ ClimMobTools - 0.5 + 1.0 @@ -74,21 +74,20 @@

Authors

Citation

- Source: DESCRIPTION + Source: inst/CITATION
-

de Sousa K, van Etten J, Madriz B (2022). -ClimMobTools: API Client for the 'ClimMob' Platform. -R package version 0.5, https://agrdatasci.github.io/ClimMobTools/. -

- 
@Manual{,
-  title = {ClimMobTools: API Client for the 'ClimMob' Platform},
-  author = {Kauê {de Sousa} and Jacob {van Etten} and Brandon Madriz},
-  year = {2022},
-  note = {R package version 0.5},
-  url = {https://agrdatasci.github.io/ClimMobTools/},
+    
Carlos Quirós, Kauê de Sousa, Jonathan Steinke, Brandon Madriz, Marie-Angélique Laporte, Elizabeth Arnaud, Rhys Manners, Berta Ortiz-Crespo, Anna Müller, Jacob van Etten. ClimMob: Software to Support Experimental Citizen Science in Agriculture. SSRN http://dx.doi.org/10.2139/ssrn.4463406

+    
@Article{,
+  title = {{ClimMob: Software to Support Experimental Citizen Science in Agriculture}},
+  doi = {10.2139/ssrn.4463406},
+  url = {http://dx.doi.org/10.2139/ssrn.4463406},
+  year = {2023},
+  publisher = {{Elsevier BV}},
+  author = {Carlos Quirós and Kauê {de Sousa} and Jonathan Steinke and Brandon Madriz and Marie-Angélique Laporte and Elizabeth Arnaud and Rhys Manners and Berta Ortiz-Crespo and Anna Müller and Jacob {van Etten}},
+  journal = {{SSRN} Electronic Journal},
 }

 
   
diff --git a/docs/index.html b/docs/index.html
index 210519d..452cef2 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -47,7 +47,7 @@
       
       
         ClimMobTools
-        0.5
+        1.0
       
     
 
@@ -192,9 +192,7 @@ 
Developers

 
Dev status

 
    
 
  • CRAN
    • 
-
  • CRANchecks
    • 
-
  • codecov
    • 
-
  • lifecycle
    • 
+
  • CRANchecks
    • 
 
  • Downloads
    • 
 

 
diff --git a/docs/news/index.html b/docs/news/index.html
index be198b7..caa23e6 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -17,7 +17,7 @@
       
       
         ClimMobTools
-        0.5
+        1.0
       
     
 
@@ -57,6 +57,13 @@ 
Changelog 

       Source: NEWS.md
     
 
+    

+
ClimMobTools 1.0 (2023-11-09)

+

+
BUG FIXES

+

+

     

 
ClimMobTools 0.4.6 (2022-08-11)2022-08-10

 

@@ -79,7 +86,7 @@ 
IMPROVEMENTSClimMobTools 0.4.3 (2021-11-20)

 

 
BUG FIXES

-
  • Fix an issue in randomise() to check for unbalanced designs
    • 
+
    • Fix an issue in randomise() to check for unbalanced designs
      • 
 
    • Remove dependency to “PlackettLuce” and “climatrends” to avoid errors in CRAN check for Windows
      • 
 

 

@@ -88,7 +95,7 @@ 
C
 

 
IMPROVEMENTS

 
  • 
-randomise() deals with unbalanced proportions of technologies, meaning that we can test a whole set of technologies even when not all have equal availability
    • 
+randomise() deals with unbalanced proportions of technologies, meaning that we can test a whole set of technologies even when not all have equal availability
 
  • 
 rmGeoIdentity() is added to offer an approach to remove geographical identity of participants
    • 
 
  • 
@@ -135,7 +142,7 @@ 
    C
 
    
 
    CHANGES IN BEHAVIOUR
    
 
    • Migrating functions from ClimMobTools to gosset and climatrends. ClimMobTools will keep only the functions exclusively related to the ‘ClimMob’ platform. Other functions are transferred to gosset and climatrends to provide a better environment for data handling, analysis and visualization.
      • 
-
    • Retain function getDataCM(), getProjectCM(), randomise() and seed_need()
+
    • Retain function getDataCM(), getProjectCM(), randomise() and seed_need()
 
      • 
 
    • Changes in package description
      • 
 
    • A S3 method as.data.frame() is provided to coerce CM_list into a data frame
      • 
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5b8759f..5db94b0 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,7 +1,7 @@
-pandoc: 2.17.1.1
+pandoc: 3.1.1
 pkgdown: 2.0.6
 pkgdown_sha: ~
 articles:
   Overview: Overview.html
-last_built: 2022-09-23T12:56Z
+last_built: 2023-11-09T19:32Z
 
diff --git a/docs/reference/ClimMobTools.html b/docs/reference/ClimMobTools.html
index ad2ce5a..4aa5a88 100644
--- a/docs/reference/ClimMobTools.html
+++ b/docs/reference/ClimMobTools.html
@@ -19,7 +19,7 @@
       
       
         ClimMobTools
-        0.5
+        1.0
       
     
    
 
diff --git a/docs/reference/getDataCM.html b/docs/reference/getDataCM.html
index b18617e..5310420 100644
--- a/docs/reference/getDataCM.html
+++ b/docs/reference/getDataCM.html
@@ -18,7 +18,7 @@
       
       
         ClimMobTools
-        0.5
+        1.0
       
     

 
@@ -163,19 +163,29 @@ 
Author

 
     

     
Examples

-    
if (FALSE) { # interactive()
-
-# This function only works with an API key
-# the API key can be obtained once a free ClimMob account
-# is created via https://climmob.net/
-
-my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c"
-
-getDataCM(key = my_key,
-          project = "testmark",
-          userowner = "kauedesousa",
-          server = "testing")
-}

+    
if (FALSE) { # interactive()
+
+# This function only works with an API key
+# the API key can be obtained once a free ClimMob account
+# is created via https://climmob.net/
+ 
+library("ClimMobTools")
+my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6"
+
+getDataCM(key = my_key,
+          project = "beanaru23",
+          userowner = "student",
+          server = "1000farms")
+          
+# get in the wide format
+
+getDataCM(key = my_key,
+          project = "beanaru23",
+          userowner = "student",
+          server = "1000farms",
+          pivot.wider = TRUE)
+}
+

     

   

   
 
@@ -113,19 +113,21 @@ 
Author

 
     

     
Examples

-    
if (FALSE) { # interactive()
-# This function only works with an API key
-# the API key can be obtained once a free ClimMob account
-# is created via https://climmob.net/
-
-my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c"
- 
-getProjectProgress(key = my_key,
-                   project = "testmark",
-                   userowner = "kauedesousa",
-                   server = "testing")
-
-}

+    
if (FALSE) { # interactive()
+# This function only works with an API key
+# the API key can be obtained once a free ClimMob account
+# is created via https://climmob.net/
+ 
+library("ClimMobTools")
+my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6"
+
+getProjectProgress(key = my_key,
+                   project = "beanaru23",
+                   userowner = "student",
+                   server = "1000FARMS")
+
+}
+

     

   
   
 
@@ -126,15 +126,16 @@ 
Author

 
     

     
Examples

-    
if (FALSE) { # interactive()
-# This function only works with an API key
-# the API key can be obtained once a free ClimMob account
-# is created via https://climmob.net/
-
-my_key <- "92cec84d-44f5-4858-9ef0-bd872496311c"
-
-getProjectsCM(key = my_key, server = "testing")
-}

+    
if (FALSE) { # interactive()
+# This function only works with an API key
+# the API key can be obtained once a free ClimMob account
+# is created via https://climmob.net/
+
+my_key <- "ff05a174-28d0-4a40-ab5a-35dc486133a6"
+
+getProjectsCM(key = my_key, server = "1000FARMS")
+}
+

     

   
   
 
@@ -101,14 +101,15 @@ 
Value

 
     

     
Examples

-    
if (FALSE) { # interactive()
-
-require("gosset")
-
-data("breadwheat", package = "gosset")
-
-getTraitList(breadwheat, c("_best", "_worst"))
-}

+    
if (FALSE) { # interactive()
+
+require("gosset")
+
+data("breadwheat", package = "gosset")
+
+getTraitList(breadwheat, c("_best", "_worst"))
+}
+

     

   
   
 
@@ -81,9 +81,9 @@ 
All functions 
         
Organise trait ranks in a ClimMob data

       
-          
randomise() 

+          
randomize() 

         
-        
Randomised group of items

+        
Set an experimental incomplete block design

       
           
rankTricot() 

         
diff --git a/docs/reference/randomise.html b/docs/reference/randomise.html
index d322ae4..5accec0 100644
--- a/docs/reference/randomise.html
+++ b/docs/reference/randomise.html
@@ -26,7 +26,7 @@
       
       
         ClimMobTools
-        0.5
+        0.6
       
     
 
diff --git a/docs/reference/randomize.html b/docs/reference/randomize.html
new file mode 100644
index 0000000..7ee4ce3
--- /dev/null
+++ b/docs/reference/randomize.html
@@ -0,0 +1,203 @@
+
+Set an experimental incomplete block design — randomize • ClimMobTools
+    
+
+    

+      

+
+      
+
+      

+  

+    

+    
Set an experimental incomplete block design

+    Source: R/randomize.R
+    
+    

+
+    

+    
Generate an incomplete block A-optional design. The function is optimized for 
+incomplete blocks of three, but it will also work with comparisons of any 
+other number of options. 
+The design strives for approximate A optimality, this means that it is robust 
+to missing observations. It also strives for balance for positions of each option.
+Options are equally divided between first, second, third, etc. position. 
+The strategy is to create a "pool" of combinations that does not repeat 
+combinations and is A-optimal. Then this pool is ordered to make subsets of 
+consecutive combinations also relatively balanced and A-optimal

+    

+
+    

+    
randomize(
+  npackages,
+  itemnames,
+  ncomp = 3,
+  availability = NULL,
+  props = NULL,
+  ...
+)

+    

+
+    

+    
Arguments

+    
npackages

+
an integer for the number of incomplete blocks to be generated

+
+
+
itemnames

+
a character for the name of items tested in the experiment

+
+
+
ncomp

+
an integer for the number of items to be assigned to each incomplete block

+
+
+
availability

+
optional, a vector with integers indicating the 
+number of plots available for each itemnames

+
+
+
props

+
optional, a numeric vector with the desired proportions
+for each itemnames

+
+
+
...

+
additional arguments passed to methods

+
+

+    

+    
Value

+    
+
+
A dataframe with the randomized design

+    

+    

+    
References

+    
Bailey and Cameron (2004). Combinations of optimal designs. 
+  https://webspace.maths.qmul.ac.uk/l.h.soicher/designtheory.org/library/preprints/optimal.pdf

+    

+    

+    
Author

+    
Jacob van Etten

+    

+
+    

+    
Examples

+    
ncomp = 3
+npackages = 20
+itemnames = c("apple","banana","grape","mango", "orange")
+availability = c(5, 8, 50, 50, 50)
+
+randomize(ncomp = ncomp,
+          npackages = npackages,
+          itemnames = itemnames)
+#>     item_A item_B item_C
+#>      <chr>  <chr>  <chr>
+#> 1:   apple banana  mango
+#> 2:   grape  apple orange
+#> 3:  banana  mango  grape
+#> 4:   mango orange banana
+#> 5:  orange  grape  apple
+#> ---                     
+#> 16: banana  mango orange
+#> 17:  apple  grape  mango
+#> 18:  grape  apple banana
+#> 19: orange banana  apple
+#> 20:  mango orange  grape
+
+randomize(ncomp = ncomp,
+          npackages = npackages,
+          itemnames = itemnames,
+          availability = availability)
+#>     item_A item_B item_C
+#>      <chr>  <chr>  <chr>
+#> 1:   grape  mango orange
+#> 2:   apple banana  grape
+#> 3:   mango orange  apple
+#> 4:  banana orange  mango
+#> 5:  orange  grape  mango
+#> ---                     
+#> 16:  mango orange  grape
+#> 17: banana  mango orange
+#> 18: orange  grape  mango
+#> 19:  apple banana  grape
+#> 20:  grape  mango orange
+          
+

+    

+  

+  
+

+
+
+      

+  
Developed by Kauê de Sousa, Jacob van Etten, Brandon Madriz.

+

+
+

+  
Site built with pkgdown 2.0.6.

+

+
+      

+
+  
+
+
+  
+
+  
+
diff --git a/docs/reference/rankTricot.html b/docs/reference/rankTricot.html
index 03a280e..9ef8583 100644
--- a/docs/reference/rankTricot.html
+++ b/docs/reference/rankTricot.html
@@ -1,5 +1,5 @@
 
-Build Plackett-Luce rankings from tricot dataset — rankTricot • ClimMobToolsBuild Plackett-Luce rankings from tricot dataset — rankTricot • ClimMobTools
@@ -17,7 +17,7 @@
       
       
         ClimMobTools
-        0.5
+        1.0
       
     
 
@@ -59,11 +59,19 @@ 
Build Plackett-Luce rankings from tricot dataset

     
 
     

-    
Create an object of class "rankings" from tricot data.

+    
Create an object of class "rankings" from tricot data

     

 
     

-    
rankTricot(data, items, input, group = FALSE, additional.rank = NULL, ...)

+    
rankTricot(
+  data,
+  items,
+  input,
+  group = FALSE,
+  validate.rankings = FALSE,
+  additional.rank = NULL,
+  ...
+)

     

 
     

@@ -86,6 +94,11 @@ 
Arguments

 
logical, if TRUE return an object of class "grouped_rankings"

 
 
+
validate.rankings

+
logical, if TRUE implements a check on ranking consistency 
+looking for possible ties, NA or letters other than A, B, C. These entries are set to 0

+
+
 
additional.rank

 
optional, a data frame for the comparisons between 
 tricot items and the local item

@@ -118,34 +131,35 @@ 
Author

 
     

     
Examples

-    
if (FALSE) { # interactive()
-# beans data where each observer compares 3 varieties randomly distributed
-# from a list of 11 and additionally compares these 3 varieties
-# with their local variety
-if (require("PlackettLuce")){
-  data("beans", package = "PlackettLuce")
-  
-  # first build rankings with only tricot items
-  # and return an object of class 'rankings'
-  R <- rankTricot(data = beans,
-                  items = c(1:3),
-                  input = c(4:5))
-  head(R)
-  
-  ############################################################
-  
-  # pass the comparison with local item as an additional rankings, then
-  # each of the 3 varieties are compared separately with the local item
-  # and return an object of class grouped_rankings
-  G <- rankTricot(data = beans,
-                  items = c(1:3),
-                  input = c(4:5),
-                  group = TRUE,
-                  additional.rank = beans[c(6:8)])
-  
-  head(G)
-}
-}

+    
if (FALSE) { # interactive()
+# beans data where each observer compares 3 varieties randomly distributed
+# from a list of 11 and additionally compares these 3 varieties
+# with their local variety
+if (require("PlackettLuce")){
+  data("beans", package = "PlackettLuce")
+  
+  # first build rankings with only tricot items
+  # and return an object of class 'rankings'
+  R = rankTricot(data = beans,
+                  items = c(1:3),
+                  input = c(4:5))
+  head(R)
+  
+  ############################################################
+  
+  # pass the comparison with local item as an additional rankings, then
+  # each of the 3 varieties are compared separately with the local item
+  # and return an object of class grouped_rankings
+  G = rankTricot(data = beans,
+                  items = c(1:3),
+                  input = c(4:5),
+                  group = TRUE,
+                  additional.rank = beans[c(6:8)])
+  
+  head(G)
+}
+}
+

     

   

   
 
@@ -99,25 +99,26 @@ 
Value

 
     

     
Examples

-    
if (FALSE) { # interactive()
-xy <- matrix(c(11.097799, 60.801090,
-               11.161298, 60.804199,
-               11.254428, 60.822457),
-             nrow = 3, ncol = 2, byrow = TRUE)
-
-rmGeoIdentity(xy)
-
-#' the function also handles NAs
-
-xy2 <- matrix(c(11.097799, 60.801090,
-                NA, NA,
-                11.161298, 60.804199,
-                11.254428, 60.822457,
-                11.254428, NA),
-              nrow = 5, ncol = 2, byrow = TRUE)
-
-rmGeoIdentity(xy2)
-}

+    
if (FALSE) { # interactive()
+xy <- matrix(c(11.097799, 60.801090,
+               11.161298, 60.804199,
+               11.254428, 60.822457),
+             nrow = 3, ncol = 2, byrow = TRUE)
+
+rmGeoIdentity(xy)
+
+#' the function also handles NAs
+
+xy2 <- matrix(c(11.097799, 60.801090,
+                NA, NA,
+                11.161298, 60.804199,
+                11.254428, 60.822457,
+                11.254428, NA),
+              nrow = 5, ncol = 2, byrow = TRUE)
+
+rmGeoIdentity(xy2)
+}
+