R/simulater.R
In radiant-rstats/radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny
Defines functions
simulater
.as_num
.as_int
Documented in
.as_int
.as_num
simulater
#' Convenience function used in "simulater"
#' @param x Character vector to be converted to integer
#' @param dataset Data list
#
#' @return An integer vector
#'
#' @export
.as_int <- function(x, dataset = list()) {
if (is.character(x)) x <- strsplit(x, "/") %>% unlist()
asInt <- function(x) ifelse(length(x) > 1, as.integer(as.integer(x[1]) / as.integer(x[2])), as.integer(x))
ret <- sshhr(asInt(x))
if (is.na(ret)) {
if (x %in% names(dataset)) {
dataset[[x]]
} else if (is.na(x)) {
x
} else {
ret <- try(eval(parse(text = paste0("with(dataset, ", x, ")"))), silent = TRUE)
if (inherits(ret, "try-error")) {
cat(glue('"{x}" not (yet) defined when called. Note that simulation\nvariables of type "Constant" are always evaluated first\n\n\n'))
NA
} else {
ret
}
}
} else {
ret
}
}
#' Convenience function used in "simulater"
#'
#' @param x Character vector to be converted to an numeric value
#' @param dataset Data list
#
#' @return An numeric vector
#'
#' @export
.as_num <- function(x, dataset = list()) {
if (is.character(x)) x <- strsplit(x, "/") %>% unlist()
asNum <- function(x) ifelse(length(x) > 1, as.numeric(x[1]) / as.numeric(x[2]), as.numeric(x))
ret <- sshhr(asNum(x))
if (is.na(ret)) {
if (x %in% names(dataset)) {
dataset[[x]]
} else if (is.na(x)) {
x
} else {
ret <- try(eval(parse(text = paste0("with(dataset, ", x, ")"))), silent = TRUE)
if (inherits(ret, "try-error")) {
cat(glue('"{x}" not (yet) defined when called. Note that simulation\nvariables of type "Constant" are always evaluated first\n\n\n'))
NA
} else {
ret
}
}
} else {
ret
}
}
#' Simulate data for decision analysis
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater.html} for an example in Radiant
#'
#' @param const A character vector listing the constants to include in the analysis (e.g., c("cost = 3", "size = 4"))
#' @param lnorm A character vector listing the log-normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the log-mean and the second is the log-standard deviation)
#' @param norm A character vector listing the normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the mean and the second is the standard deviation)
#' @param unif A character vector listing the uniformly distributed random variables to include in the analysis (e.g., "demand 0 1" where the first number is the minimum value and the second is the maximum value)
#' @param discrete A character vector listing the random variables with a discrete distribution to include in the analysis (e.g., "price 5 8 .3 .7" where the first set of numbers are the values and the second set the probabilities
#' @param binom A character vector listing the random variables with a binomial distribution to include in the analysis (e.g., "crash 100 .01") where the first number is the number of trials and the second is the probability of success)
#' @param pois A character vector listing the random variables with a poisson distribution to include in the analysis (e.g., "demand 10") where the number is the lambda value (i.e., the average number of events or the event rate)
#' @param sequ A character vector listing the start and end for a sequence to include in the analysis (e.g., "trend 1 100 1"). The number of 'steps' is determined by the number of simulations
#' @param grid A character vector listing the start, end, and step for a set of sequences to include in the analysis (e.g., "trend 1 100 1"). The number of rows in the expanded will over ride the number of simulations
#' @param data Dataset to be used in the calculations
#' @param form A character vector with the formula to evaluate (e.g., "profit = demand * (price - cost)")
#' @param funcs A named list of user defined functions to apply to variables generated as part of the simulation
#' @param seed Optional seed used in simulation
#' @param nexact Logical to indicate if normally distributed random variables should be simulated to the exact specified values
#' @param ncorr A string of correlations used for normally distributed random variables. The number of values should be equal to one or to the number of combinations of variables simulated
#' @param name Deprecated argument
#' @param nr Number of simulations
#' @param dataset Data list from previous simulation. Used by repeater function
#' @param envir Environment to extract data from
#'
#' @importFrom dplyr near
#'
#' @return A data.frame with the simulated data
#'
#' @examples
#' simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = "profit = demand * (price - cost)",
#' seed = 1234
#' ) %>% str()
#'
#' @seealso \code{\link{summary.simulater}} to summarize results
#' @seealso \code{\link{plot.simulater}} to plot results
#'
#' @export
simulater <- function(const = "", lnorm = "", norm = "", unif = "", discrete = "",
binom = "", pois = "", sequ = "", grid = "", data = NULL,
form = "", funcs = "", seed = NULL, nexact = FALSE, ncorr = NULL,
name = "", nr = 1000, dataset = NULL, envir = parent.frame()) {
if (!is.empty(seed)) set.seed(as.numeric(seed))
if (is.null(dataset)) {
dataset <- list()
} else {
## needed because number may be NA and missing if grid used in Simulate
nr <- attr(dataset, "radiant_sim_call")$nr
data <- attr(dataset, "radiant_sim_call")$data
}
## needed to be exported functions
if (!exists(".as_num") || !exists(".as_int")) {
.as_num <- radiant.model::.as_num
.as_int <- radiant.model::.as_int
}
grid <- sim_cleaner(grid)
if (grid != "" && length(dataset) == 0) {
s <- grid %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
if (is.empty(si[4])) si[4] <- 1
dataset[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), .as_num(si[4], dataset))
}
dataset <- as.list(expand.grid(dataset) %>% as.data.frame(stringsAsFactors = FALSE))
nr <- length(dataset[[1]])
}
if (is.empty(nr)) {
mess <- c("error", paste0("Please specify the number of simulations in '# sims'"))
return(add_class(mess, "simulater"))
}
## fetching data if needed
if (!is.empty(data, "none") && is_string(data)) {
if (exists(data, envir = envir)) {
data <- get_data(data, envir = envir)
} else {
stop(paste0("Data set ", data, " cannot be found", call. = FALSE))
}
}
## adding data to dataset list
if (is.data.frame(data)) {
for (i in colnames(data)) {
dataset[[i]] <- data[[i]]
}
}
## parsing constant
const <- sim_cleaner(const)
if (const != "") {
s <- const %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- .as_num(si[2], dataset)
}
}
## parsing uniform
unif <- sim_cleaner(unif)
if (unif != "") {
s <- unif %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- runif(nr, .as_num(si[2], dataset), .as_num(si[3], dataset))
}
}
## parsing log normal
lnorm <- sim_cleaner(lnorm)
if (lnorm != "") {
s <- lnorm %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
sdev <- .as_num(si[3], dataset)
if (is.na(sdev) || !sdev > 0) {
mess <- c("error", paste0("All log-normal variables should have a standard deviation larger than 0.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
dataset[[si[1]]] <- rlnorm(nr, .as_num(si[2], dataset), sdev)
}
}
## parsing normal
norm <- sim_cleaner(norm)
if (norm != "") {
s <- norm %>% sim_splitter()
means <- sds <- nms <- c()
for (i in seq_along(s)) {
si <- s[[i]]
sdev <- .as_num(si[3], dataset)
if (is.na(sdev) || !sdev > 0) {
mess <- c("error", paste0("All normal variables should have a standard deviation larger than 0.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
if (is.empty(ncorr) || length(s) == 1) {
if (nexact) {
dataset[[si[1]]] <- scale(rnorm(nr, 0, 1)) * sdev + .as_num(si[2], dataset)
} else {
dataset[[si[1]]] <- rnorm(nr, .as_num(si[2], dataset), sdev)
}
} else {
nms <- c(nms, si[1])
means <- c(means, .as_num(si[2], dataset))
sds <- c(sds, sdev)
}
}
if (!is.empty(ncorr) && length(nms) > 1) {
ncorr <- gsub(",", " ", ncorr) %>%
strsplit("\\s+") %>%
unlist() %>%
.as_num(dataset)
ncorr_nms <- combn(nms, 2) %>% apply(2, paste, collapse = "-")
if (length(ncorr) == 1 && length(ncorr_nms) > 2) {
ncorr <- rep(ncorr, length(ncorr_nms))
}
if (length(ncorr) != length(ncorr_nms)) {
mess <- c("error", paste0("The number of correlations specified is not equal to\nthe number of pairs of variables to be simulated.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
names(ncorr) <- ncorr_nms
df <- try(sim_cor(nr, ncorr, means, sds, exact = nexact), silent = TRUE)
if (inherits(df, "try-error")) {
mess <- c("error", paste0("Data with the specified correlation structure could not be generated.\nPlease review the input and try again"))
return(add_class(mess, "simulater"))
}
colnames(df) <- nms
for (i in nms) {
dataset[[i]] <- df[[i]]
}
}
}
## parsing binomial
binom <- sim_cleaner(binom)
if (binom != "") {
s <- binom %>% sim_splitter()
for (i in 1:length(s)) {
si <- s[[i]]
dataset[[si[1]]] <- rbinom(nr, .as_int(si[2], dataset), .as_num(si[3], dataset))
}
}
## parsing poisson
pois <- sim_cleaner(pois)
if (pois != "") {
s <- pois %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- rpois(nr, .as_num(si[2], dataset))
}
}
## parsing sequence
sequ <- sim_cleaner(sequ)
if (sequ != "") {
s <- sequ %>% sim_splitter()
for (i in 1:length(s)) {
si <- s[[i]]
dataset[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), length.out = .as_num(nr, dataset))
}
}
## parsing discrete
discrete <- sim_cleaner(discrete)
if (discrete != "") {
s <- discrete %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dpar <- si[-1] %>%
gsub(",", " ", .) %>%
strsplit("\\s+") %>%
unlist() %>%
strsplit("/")
asNum <- function(x) ifelse(length(x) > 1, .as_num(x[1], dataset) / .as_num(x[2], dataset), .as_num(x, dataset))
dpar <- sshhr(try(sapply(dpar, asNum) %>% matrix(ncol = 2), silent = TRUE))
if (inherits(dpar, "try-error") || any(is.na(dpar))) {
mess <- c("error", paste0("Input for discrete variable # ", i, " contains an error. Please review the input carefully"))
return(add_class(mess, "simulater"))
} else if (!near(sum(dpar[, 2]), 1)) {
mess <- c("error", glue("Probabilities for discrete variable # {i} do not sum to 1 ({sum(dpar[, 2])})"))
return(add_class(mess, "simulater"))
}
dataset[[si[1]]] <- sample(dpar[, 1], nr, replace = TRUE, prob = dpar[, 2])
}
}
## convert named list of functions to a string to evaluate
if (is.list(funcs)) {
funcs <- sapply(
names(funcs),
function(f) {
paste0(f, " = ", paste0(deparse(funcs[[f]], control = getOption("dctrl"), width.cutoff = 500L), collapse = "\n"))
}
) %>% paste0(collapse = ";")
}
if (!is.expression(funcs)) {
pfuncs <- parse(text = funcs, keep.source = TRUE)
} else {
pfuncs <- funcs
}
if (!is.empty(form)) {
form <- form %>%
gsub("[ ]{2,}", " ", .) %>%
gsub("<-", "=", .)
form_no_comments <- remove_comments(form)
out <- try(do.call(within, list(dataset, c(pfuncs, parse(text = form_no_comments)))), silent = TRUE)
if (!inherits(out, "try-error")) {
dataset <- out
} else {
mess <- c(
"error", paste0("Formula was not successfully evaluated:\n\n", form) %>%
paste0(collapse = "\n"), "\n\nMessage: ", attr(out, "condition")$message
)
return(add_class(mess, "simulater"))
}
}
## removing data from dataset list
if (is.data.frame(data)) {
dataset[colnames(data)] <- NULL
}
## remove functions
ind <- radiant.data::get_class(dataset) == "function"
dataset[ind] <- NULL
## convert list to a data.frame
dataset <- as.data.frame(dataset, stringsAsFactors = FALSE) %>% na.omit()
## capturing the function call for use in repeat
sc <- formals()
smc <- lapply(match.call()[-1], eval, envir = envir)
smc$envir <- NULL
sc[names(smc)] <- smc
sc$nr <- nr
sc$ncorr <- ncorr
sc$nexact <- nexact
sc$funcs <- pfuncs
if (is.empty(sc$data, "none")) {
attr(dataset, "sim_data_name") <- NULL
} else if (is_string(sc$data)) {
attr(dataset, "sim_data_name") <- sc$data
sc$data <- data
} else {
attr(dataset, "sim_data_name") <- deparse(substitute(data))
}
attr(dataset, "radiant_sim_call") <- sc
if (nrow(dataset) == 0) {
mess <- c("error", paste0("The simulated data set has 0 rows"))
return(add_class(mess, "simulater"))
}
form <- gsub("*", "\\*", form, fixed = TRUE) %>%
gsub("^\\s*?\\#+[^\\#]", "##### # ", .) %>%
gsub("[;\n]\\s*?\\#+[^\\#]", "; ##### # ", .) %>%
gsub(";\\s*", "\n\n", .)
mess <- paste0("\n### Simulated data\n\nFormulas:\n\n", form, "\n\nDate: ", lubridate::now())
add_class(set_attr(dataset, "description", mess), "simulater")
}
#' Summary method for the simulater function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater.html} for an example in Radiant
#'
#' @param object Return value from \code{\link{simulater}}
#' @param dec Number of decimals to show
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' simdat <- simulater(norm = "demand 2000 1000", seed = 1234)
#' summary(simdat)
#'
#' @seealso \code{\link{simulater}} to generate the results
#' @seealso \code{\link{plot.simulater}} to plot results
#'
#' @export
summary.simulater <- function(object, dec = 4, ...) {
if (is.character(object)) {
if (length(object) == 2 && object[1] == "error") {
return(cat(object[2]))
}
stop("To generate summary statistics please provide a simulated dataset as input", call. = FALSE)
}
sc <- attr(object, "radiant_sim_call")
clean <- function(x) {
paste0(x, collapse = ";") %>%
gsub(";", "; ", .) %>%
gsub("\\n", "", .) %>%
paste0(., "\n")
}
cat("Simulation\n")
cat("Simulations:", format_nr(nrow(object), dec = 0), "\n")
cat("Random seed:", sc$seed, "\n")
if (is.empty(sc$name)) {
cat("Sim data :", deparse(substitute(object)), "\n")
} else {
cat("Sim data :", sc$name, "\n")
}
if (!is.empty(sc$binom)) cat("Binomial :", clean(sc$binom))
if (!is.empty(sc$discrete)) cat("Discrete :", clean(sc$discrete))
if (!is.empty(sc$lnorm)) cat("Log normal :", clean(sc$lnorm))
if (!is.empty(sc$norm)) cat("Normal :", clean(ifelse(sc$nexact, paste0(sc$norm, "(exact)"), sc$norm)))
if (!is.empty(sc$unif)) cat("Uniform :", clean(sc$unif))
if (!is.empty(sc$pois)) cat("Poisson :", clean(sc$pois))
if (!is.empty(sc$const)) cat("Constant :", clean(sc$const))
if (is.data.frame(sc$data)) cat("Data :", attr(object, "sim_data_name"), "\n")
if (!is.empty(sc$grid)) cat("Grid search:", clean(sc$grid))
if (!is.empty(sc$sequ)) cat("Sequence :", clean(sc$sequ))
funcs <- attr(object, "radiant_funcs")
if (!is.empty(funcs)) {
funcs <- parse(text = funcs)
lfuncs <- list()
for (i in seq_len(length(funcs))) {
tmp <- strsplit(as.character(funcs[i]), "(\\s*=|\\s*<-)")[[1]][1]
lfuncs[[tmp]] <- as.symbol(tmp)
}
cat("Functions :", paste0(names(lfuncs), collapse = ", "), "\n")
}
if (!is.empty(sc$form)) {
cat(paste0("Formulas :\n\t", paste0(sc$form, collapse = ";") %>% gsub(";", "\n", .) %>% gsub("\n", "\n\t", .), "\n"))
}
cat("\n")
if (!is.empty(sc$ncorr) && is.numeric(sc$ncorr)) {
cat("Correlations:\n")
print(sc$ncorr)
cat("\n")
}
sim_summary(object, dec = ifelse(is.empty(dec), 4, round(dec, 0)))
}
#' Plot method for the simulater function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater} for an example in Radiant
#'
#' @param x Return value from \code{\link{simulater}}
#' @param bins Number of bins used for histograms (1 - 50)
#' @param shiny Did the function call originate inside a shiny app
#' @param custom Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and \url{https://ggplot2.tidyverse.org} for options.
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' simdat <- simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = "profit = demand * (price - cost)",
#' seed = 1234
#' )
#' plot(simdat, bins = 25)
#'
#' @seealso \code{\link{simulater}} to generate the result
#' @seealso \code{\link{summary.simulater}} to summarize results
#'
#' @export
plot.simulater <- function(x, bins = 20, shiny = FALSE, custom = FALSE, ...) {
if (is.character(x)) {
return(invisible())
}
if (nrow(x) == 0) {
return(invisible())
}
plot_list <- list()
for (i in colnames(x)) {
dat <- select_at(x, .vars = i)
if (!does_vary(x[[i]])) next
plot_list[[i]] <- select_at(x, .vars = i) %>%
visualize(xvar = i, bins = bins, custom = TRUE)
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = min(length(plot_list), 2)) %>%
(function(x) if (shiny) x else print(x))
}
}
}
#' Repeated simulation
#'
#' @param dataset Return value from the simulater function
#' @param nr Number times to repeat the simulation
#' @param vars Variables to use in repeated simulation
#' @param grid Character vector of expressions to use in grid search for constants
#' @param sum_vars (Numeric) variables to summaries
#' @param byvar Variable(s) to group data by before summarizing
#' @param fun Functions to use for summarizing
#' @param form A character vector with the formula to apply to the summarized data
#' @param seed Seed for the repeated simulation
#' @param name Deprecated argument
#' @param envir Environment to extract data from
#'
#' @importFrom shiny getDefaultReactiveDomain
#'
#' @examples
#' simdat <- simulater(
#' const = c("var_cost 5", "fixed_cost 1000"),
#' norm = "E 0 100;",
#' discrete = "price 6 8 .3 .7;",
#' form = c(
#' "demand = 1000 - 50*price + E",
#' "profit = demand*(price-var_cost) - fixed_cost",
#' "profit_small = profit < 100"
#' ),
#' seed = 1234
#' )
#'
#' repdat <- repeater(
#' simdat,
#' nr = 12,
#' vars = c("E", "price"),
#' sum_vars = "profit",
#' byvar = ".sim",
#' form = "profit_365 = profit_sum < 36500",
#' seed = 1234,
#' )
#'
#' head(repdat)
#' summary(repdat)
#' plot(repdat)
#'
#' @seealso \code{\link{summary.repeater}} to summarize results from repeated simulation
#' @seealso \code{\link{plot.repeater}} to plot results from repeated simulation
#'
#' @export
repeater <- function(dataset, nr = 12, vars = "", grid = "", sum_vars = "",
byvar = ".sim", fun = "sum", form = "", seed = NULL,
name = "", envir = parent.frame()) {
if (byvar %in% c(".sim", "sim")) grid <- ""
if (is.empty(nr)) {
if (is.empty(grid)) {
mess <- c("error", paste0("Please specify the number of repetitions in '# reps'"))
return(add_class(mess, "repeater"))
} else {
nr <- 1
}
}
## needed to be exported functions
if (!exists(".as_num") || !exists(".as_int")) {
.as_num <- radiant.model::.as_num
.as_int <- radiant.model::.as_int
}
if (is_string(dataset)) {
sim_df_name <- dataset
dataset <- get_data(dataset, envir = envir)
} else {
sim_df_name <- deparse(substitute(dataset))
}
if (!is.empty(seed)) set.seed(as.numeric(seed))
if (identical(vars, "") && identical(grid, "")) {
mess <- c("error", paste0("Select variables to re-simulate and/or a specify a constant\nto change using 'Grid search' when Group by is set to Repeat"))
return(add_class(mess, "repeater"))
}
if (identical(vars, "")) vars <- character(0)
grid_list <- list()
if (!identical(grid, "")) {
grid <- sim_cleaner(grid)
if (grid != "") {
s <- grid %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
if (is.empty(s[[i]][4])) s[[i]][4] <- 1
grid_list[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), .as_num(si[4], dataset))
}
}
## expanding list of variables but removing ""
vars <- c(vars, names(grid_list)) %>% unique()
}
## from http://stackoverflow.com/a/7664655/1974918
## keep those list elements that, e.g., q is in
nr_sim <- nrow(dataset)
sc <- attr(dataset, "radiant_sim_call")
if (is.data.frame(sc$data)) {
data <- sc$data
} else {
data <- NULL
}
## reset dataset to list with vectors of the correct length
dataset <- as.list(dataset)
if ("const" %in% names(sc)) {
s <- sc$const
if (length(s) < 2) {
s <- strsplit(gsub("\n", "", s), ";\\s*")[[1]] %>% strsplit("\\s+")
} else {
s <- strsplit(s, "\\s+")
}
for (const in seq_len(length(s))) {
nm <- s[[const]][1]
dataset[[nm]] <- dataset[[nm]][1]
}
}
## needed if inputs are provided as vectors
sc[1:(which(names(sc) == "seed") - 1)] %<>% lapply(paste, collapse = ";")
sc$name <- sc$seed <- "" ## cleaning up the sim call
## using \\b based on https://stackoverflow.com/a/34074458/1974918
sc_keep <- grep(paste(paste0("\\b", vars, "\\b"), collapse = "|"), sc, value = TRUE)
sc_keep["funcs"] <- sc$funcs
## ensure that only the selected variables of a specific type are resimulated
## e.g., if A, B, and C are normal and A should be re-sim'd, don't also re-sim B and C
for (i in names(sc_keep)) {
if (i %in% c("form", "funcs")) next
sc_check <- sim_cleaner(sc_keep[[i]]) %>%
sim_splitter(";")
if (length(sc_check) < 2) {
next
} else {
sc_keep[[i]] <- grep(paste(paste0("\\b", vars, "\\b"), collapse = "|"), sc_check, value = TRUE) %>%
paste0(collapse = ";\n")
}
}
## needed in case there is no 'form' in simulate
sc[1:(which(names(sc) == "seed") - 1)] <- ""
sc[names(sc_keep)] <- sc_keep
sc$dataset <- dataset
if (!is.empty(sc$data, "none") && is_string(sc$data)) {
if (exists(sc$data, envir = envir)) {
sc$data <- get(sc$data, envir = envir)
} else {
stop(paste0("Data set ", sc$data, " cannot be found", call. = FALSE))
}
}
summarize_sim <- function(object) {
if (is.empty(fun) || any(fun == "none")) {
object <- select_at(object, .vars = c(".rep", ".sim", sum_vars))
} else {
cn <- unlist(sapply(fun, function(f) paste0(sum_vars, "_", f), simplify = FALSE))
first <- function(x, ...) dplyr::first(x)
last <- function(x, ...) dplyr::last(x)
object <- group_by_at(object, byvar) %>%
summarise_at(.vars = sum_vars, .funs = fun, na.rm = TRUE) %>%
set_colnames(c(byvar, cn))
}
object
}
rep_sim <- function(rep_nr, nr, sfun = function(x) x) {
bind_cols(
data.frame(.rep = rep(rep_nr, nr_sim), .sim = 1:nr_sim, stringsAsFactors = FALSE),
do.call(simulater, sc)
) %>%
na.omit() %>%
sfun() %T>%
(function(x) incProgress(rep_nr / nr, detail = paste("\nCompleted run", rep_nr, "out of", nr)))
}
rep_grid_sim <- function(gval, rep_nr, nr, sfun = function(x) x) {
gvars <- names(gval)
## removing form and funcs ...
sc_grid <- grep(paste(gvars, collapse = "|"), sc_keep, value = TRUE) %>%
(function(x) x[which(!names(x) %in% c("form", "funcs"))]) %>%
gsub("[ ]{2,}", " ", .)
for (i in 1:length(gvars)) {
sc_grid %<>% sub(paste0("[;\n]", gvars[i], " [.0-9]+"), paste0("\n", gvars[i], " ", gval[gvars[i]]), .) %>%
sub(paste0("^", gvars[i], " [.0-9]+"), paste0(gvars[i], " ", gval[gvars[i]]), .)
}
sc[names(sc_grid)] <- sc_grid
bind_cols(
data.frame(.rep = rep(paste(gval, collapse = "|"), nr_sim), .sim = 1:nr_sim, stringsAsFactors = FALSE),
do.call(simulater, sc)
) %>%
na.omit() %>%
sfun() %>%
{
incProgress(rep_nr / nr, detail = paste("\nCompleted run", rep_nr, "out of", nr))
.
}
}
if (length(shiny::getDefaultReactiveDomain()) > 0) {
trace <- FALSE
incProgress <- shiny::incProgress
withProgress <- shiny::withProgress
} else {
incProgress <- function(...) {}
withProgress <- function(...) list(...)[["expr"]]
}
withProgress(message = "Running repeated simulation", value = 0, {
if (length(grid_list) == 0) {
if (byvar == ".sim") {
ret <- bind_rows(lapply(1:nr, rep_sim, nr)) %>%
summarize_sim() %>%
add_class("repeater")
} else {
ret <- bind_rows(lapply(1:nr, function(x) rep_sim(x, nr, summarize_sim))) %>%
add_class("repeater")
}
} else {
grid <- expand.grid(grid_list)
nr <- nrow(grid)
if (byvar == ".sim") {
ret <- bind_rows(lapply(1:nr, function(x) rep_grid_sim(grid[x, , drop = FALSE], x, nr))) %>%
summarize_sim() %>%
add_class("repeater")
} else {
ret <- bind_rows(lapply(1:nr, function(x) rep_grid_sim(grid[x, , drop = FALSE], x, nr, summarize_sim))) %>%
add_class("repeater")
}
}
})
if (is.data.frame(data)) {
ret <- as.list(ret)
for (i in colnames(data)) {
ret[[i]] <- data[[i]]
}
sim_data_name <- attr(dataset, "sim_data_name")
} else {
sim_data_name <- NULL
}
if (!is.empty(form)) {
form <- form %>%
gsub("[ ]{2,}", " ", .) %>%
gsub("<-", "=", .)
form_no_comments <- remove_comments(form)
out <- try(do.call(within, list(ret, parse(text = form_no_comments))), silent = TRUE)
if (!inherits(out, "try-error")) {
ret <- out
} else {
mess <- c("error", paste0("Formula was not successfully evaluated:\n\n", form) %>% unlist() %>% paste0(collapse = "\n"), "\n\nMessage: ", attr(out, "condition")$message, "\n\nNote that repeated simulation formulas can only be applied to\n(summarized) 'Output variables'")
if (!is.empty(fun)) {
cn <- unlist(sapply(fun, function(f) paste0(sum_vars, "_", f), simplify = FALSE))
mess[2] <- paste0(mess[2], "\n\nAvailable (summarized) output variables:\n* ", paste0(cn, collapse = "\n* "))
}
return(add_class(mess, "repeater"))
}
}
## removing data from dataset list
if (is.data.frame(data)) {
ret[colnames(data)] <- NULL
}
## tbl_df remove attributes so use as.data.frame for now
ret <- as.data.frame(ret, stringsAsFactors = FALSE)
## capturing the function call for use in summary and plot
rc <- formals()
rmc <- lapply(match.call()[-1], eval, envir = envir)
rmc$envir <- NULL
rc[names(rmc)] <- rmc
rc$sc <- sc[base::setdiff(names(sc), "dat")]
attr(ret, "radiant_rep_call") <- rc
attr(ret, "sim_df_name") <- sim_df_name
attr(ret, "sim_data_name") <- sim_data_name
mess <- paste0(
"\n### Repeated simulation data\n\nFormula:\n\n",
gsub("*", "\\*", sc$form, fixed = TRUE) %>%
gsub("[;\n]\\s*?\\#+[^\\#]", "; ##### # ", .) %>%
gsub(";", "\n\n", .),
"\n\nDate: ",
lubridate::now()
)
add_class(set_attr(ret, "description", mess), "repeater")
}
#' Summarize repeated simulation
#'
#' @param object Return value from \code{\link{repeater}}
#' @param dec Number of decimals to show
#' @param ... further arguments passed to or from other methods
#'
#' @seealso \code{\link{repeater}} to run a repeated simulation
#' @seealso \code{\link{plot.repeater}} to plot results from repeated simulation
#'
#' @export
summary.repeater <- function(object, dec = 4, ...) {
if (is.character(object)) {
if (length(object) == 2 && object[1] == "error") {
return(cat(object[2]))
}
stop("To generate summary statistics please provide a simulated dataset as input", call. = FALSE)
}
## getting the repeater call
rc <- attr(object, "radiant_rep_call")
clean <- function(x) {
paste0(x, collapse = ";") %>%
gsub(";", "; ", .) %>%
gsub("\\n", "", .) %>%
paste0(., "\n")
}
## show results
cat("Repeated simulation\n")
cat("Simulations :", ifelse(is.empty(rc$sc$nr), "", format_nr(rc$sc$nr, dec = 0)), "\n")
cat("Repetitions :", format_nr(ifelse(is.empty(rc$nr), nrow(object), rc$nr), dec = 0), "\n")
if (!is.empty(rc$vars)) {
cat("Re-simulated :", paste0(rc$vars, collapse = ", "), "\n")
}
cat("Group by :", ifelse(rc$byvar == ".rep", "Repeat", "Simulation"), "\n")
cat("Function :", rc$fun, "\n")
cat("Random seed :", rc$seed, "\n")
if (is.data.frame(rc$sim)) {
rc$sim <- attr(rc$sim, "radiant_sim_call")$name
}
cat("Simulated data:", attr(object, "sim_df_name"), "\n")
attr(object, "sim_data_name") %>%
{
if (!is.empty(.)) cat("Data :", ., "\n")
}
if (is.empty(rc$name)) {
cat("Repeat data :", deparse(substitute(object)), "\n")
} else {
cat("Repeat data :", rc$name, "\n")
}
if (isTRUE(rc$byvar == "rep") && !is.empty(rc$grid)) {
cat("Grid search. :", clean(rc$grid))
}
if (!is.empty(rc$form)) {
rc$form %<>% sim_cleaner()
paste0(
"Formulas :\n\t",
paste0(rc$form, collapse = ";") %>%
gsub(";", "\n", .) %>%
gsub("\n", "\n\t", .),
"\n"
) %>% cat()
}
cat("\n")
sim_summary(select(object, -1), fun = rc$fun, dec = ifelse(is.na(dec), 4, dec))
}
#' Plot repeated simulation
#'
#' @param x Return value from \code{\link{repeater}}
#' @param bins Number of bins used for histograms (1 - 50)
#' @param shiny Did the function call originate inside a shiny app
#' @param custom Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and \url{https://ggplot2.tidyverse.org} for options.
#' @param ... further arguments passed to or from other methods
#'
#' @seealso \code{\link{repeater}} to run a repeated simulation
#' @seealso \code{\link{summary.repeater}} to summarize results from repeated simulation
#'
#' @export
plot.repeater <- function(x, bins = 20, shiny = FALSE, custom = FALSE, ...) {
if (is.character(x)) {
return(invisible())
}
if (nrow(x) == 0) {
return(invisible())
}
## getting the repeater call
rc <- attr(x, "radiant_rep_call")
plot_list <- list()
for (i in colnames(x)[-1]) {
dat <- select_at(x, .vars = i)
if (!does_vary(x[[i]])) next
plot_list[[i]] <- select_at(x, .vars = i) %>%
visualize(xvar = i, bins = bins, custom = TRUE)
if (i %in% rc$sum_vars && !is.empty(rc$fun, "none")) {
plot_list[[i]] <- plot_list[[i]] + labs(x = paste0(rc$fun, " of ", i))
}
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = min(length(plot_list), 2)) %>%
(function(x) if (shiny) x else print(x))
}
}
}
#' Print simulation summary
#'
#' @param dataset Simulated data
#' @param dc Variable classes
#' @param fun Summary function to apply
#' @param dec Number of decimals to show
#'
#' @seealso \code{\link{simulater}} to run a simulation
#' @seealso \code{\link{repeater}} to run a repeated simulation
#'
#' @examples
#' simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = c("profit = demand * (price - cost)", "profit5K = profit > 5000"),
#' seed = 1234
#' ) %>% sim_summary()
#'
#' @export
sim_summary <- function(dataset, dc = get_class(dataset), fun = "", dec = 4) {
isFct <- "factor" == dc
isNum <- dc %in% c("numeric", "integer", "Duration")
isChar <- "character" == dc
isLogic <- "logical" == dc
dec <- ifelse(is.na(dec), 4, as.integer(dec))
if (sum(isNum) > 0) {
isConst <- !sapply(dataset, does_vary) & isNum
if (sum(isConst) > 0) {
cn <- names(dc)[isConst]
cat("Constants:\n")
select(dataset, which(isConst)) %>%
na.omit() %>%
.[1, ] %>%
as.data.frame(stringsAsFactors = FALSE) %>%
round(dec) %>%
mutate_all(~ formatC(., big.mark = ",", digits = dec, format = "f")) %>%
set_rownames("") %>%
set_colnames(cn) %>%
print()
cat("\n")
}
isRnd <- isNum & !isConst
if (sum(isRnd) > 0) {
cn <- names(dc)[isRnd]
cat("Variables:\n")
select(dataset, which(isNum & !isConst)) %>%
gather("variable", "values", !!cn) %>%
group_by_at(.vars = "variable") %>%
summarise_all(
list(
n_obs = n_obs, mean = mean, sd = sd, min = min,
p25 = p25, median = median, p75 = p75, max = max
),
na.rm = TRUE
) %>%
mutate(variable = format(variable, justify = "left")) %>%
data.frame(check.names = FALSE, stringsAsFactors = FALSE) %>%
format_df(dec = dec, mark = ",") %>%
rename(` ` = "variable") %>%
print(row.names = FALSE)
cat("\n")
}
}
if (sum(isLogic) > 0) {
cat("Logicals:\n")
select(dataset, which(isLogic)) %>%
summarise_all(list(sum, mean), na.rm = TRUE) %>%
round(dec) %>%
matrix(ncol = 2) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
set_colnames(c("TRUE (nr) ", "TRUE (prop)")) %>%
set_rownames(names(dataset)[isLogic]) %>%
format(big.mark = ",", scientific = FALSE) %>%
print()
cat("\n")
}
if (sum(isFct) > 0 || sum(isChar) > 0) {
cat("Factors:\n")
df <- select(dataset, which(isFct | isChar)) %>%
mutate(across(where(is.character), as_factor)) %>%
as.data.frame()
tab <- summary(df)
pt <- lapply(df, function(x) prop.table(table(x)))
for (i in seq_len(ncol(tab))) {
tab[, i] <- paste0(tab[, i], "(", 100 * round(pt[[i]], dec), "%)")
}
tab[tab == "NA(100%)"] <- ""
print(tab)
cat("\n")
}
}
#' Clean input command string
#'
#' @param x Input string
#'
#' @return Cleaned string
#'
#' @export
sim_cleaner <- function(x) {
gsub("[ ]{2,}", " ", paste(x, collapse = ";")) %>%
gsub("[ ]*[\n;]+[ ]*", ";", .) %>%
gsub("[;]{2,}", ";", .) %>%
gsub(";$", "", .) %>%
gsub("^;", "", .)
}
#' Remove comments from formula before it is evaluated
#'
#' @param x Input string
#'
#' @return Cleaned string
#'
#' @export
remove_comments <- function(x) {
gsub("[ ]*\\#{1,}[^\n;]*[\n]", "\n", x) %>%
gsub("[ ]*\\#{1,}[^\n;]*[;]", ";", .) %>%
gsub("^[ ]*;{1,}", "", .) %>%
gsub(";{2,}", ";", .) %>%
gsub("^[ ]*\n{1,}", "", .) %>%
gsub("\n{2,}", "\n", .) %>%
gsub("^[ ]{1,}", "", .)
}
#' Split input command string
#'
#' @param x Input string
#' @param symbol Symbol used to split the command string
#'
#' @return Split input command string
#'
#' @export
sim_splitter <- function(x, symbol = " ") {
strsplit(x, "(;\\s*|\n)") %>%
extract2(1) %>%
# from https://stackoverflow.com/a/16644618/1974918
gsub("\\s+(?=[^(\\)]*\\))", "", ., perl = TRUE) %>%
strsplit(symbol)
}
#' Find maximum value of a vector
#'
#' @details Find the value of y at the maximum value of x
#' @param x Variable to find the maximum for
#' @param y Variable to find the value for at the maximum of var
#'
#' @return Value of val at the maximum of var
#'
#' @examples
#' find_max(1:10, 21:30)
#'
#' @export
find_max <- function(x, y) {
if (missing(y)) {
stop("Error in find_max (2 inputs required)\nSpecify the variable to evaluate at the maximum of the first input")
}
y[which.max(x)]
}
#' Find minimum value of a vector
#'
#' @details Find the value of y at the minimum value of x
#' @param x Variable to find the minimum for
#' @param y Variable to find the value for at the maximum of var
#'
#' @return Value of val at the minimum of var
#'
#' @examples
#' find_min(1:10, 21:30)
#'
#' @export
find_min <- function(x, y) {
if (missing(y)) {
stop("Error in find_min (2 inputs required)\nSpecify the variable to evaluate at the minimum of the first input")
}
y[which.min(x)]
}
#' Standard deviation of weighted sum of variables
#'
#' @param ... A matched number of weights and stocks
#'
#' @return A vector of standard deviation estimates
#'
#' @export
sdw <- function(...) {
dl <- list(...)
nr <- length(dl) / 2
w <- data.frame(dl[1:nr], stringsAsFactors = FALSE)
d <- data.frame(dl[(nr + 1):length(dl)], stringsAsFactors = FALSE)
apply(w, 1, function(w) sd(rowSums(sweep(d, 2, w, "*"))))
}
#' Simulate correlated normally distributed data
#'
#' @param n The number of values to simulate (i.e., the number of rows in the simulated data)
#' @param rho A vector of correlations to apply to the columns of the simulated data. The number of values should be equal to one or to the number of combinations of variables to be simulated
#' @param means A vector of means. The number of values should be equal to the number of variables to simulate
#' @param sds A vector of standard deviations. The number of values should be equal to the number of variables to simulate
#' @param exact A logical that indicates if the inputs should be interpreted as population of sample characteristics
#'
#' @return A data.frame with the simulated data
#'
#' @examples
#' sim <- sim_cor(100, .74, c(0, 10), c(1, 5), exact = TRUE)
#' cor(sim)
#' sim_summary(sim)
#'
#' @export
sim_cor <- function(n, rho, means, sds, exact = FALSE) {
nrx <- length(means)
C <- matrix(1, nrow = nrx, ncol = nrx)
C[lower.tri(C)] <- C[upper.tri(C)] <- rho
X <- matrix(rnorm(n * nrx, 0, 1), ncol = nrx)
if (exact) {
X <- psych::principal(X, nfactors = nrx, scores = TRUE)$scores
}
X <- X %*% chol(C)
X <- sweep(X, 2, sds, "*")
X <- sweep(X, 2, means, "+")
as.data.frame(X, stringsAsFactors = FALSE)
}
radiant-rstats/radiant.model documentation built on Nov. 29, 2023, 5:59 a.m.
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Defines functions simulater .as_num .as_int
Documented in .as_int .as_num simulater
#' Convenience function used in "simulater"
#' @param x Character vector to be converted to integer
#' @param dataset Data list
#
#' @return An integer vector
#'
#' @export
.as_int <- function(x, dataset = list()) {
if (is.character(x)) x <- strsplit(x, "/") %>% unlist()
asInt <- function(x) ifelse(length(x) > 1, as.integer(as.integer(x[1]) / as.integer(x[2])), as.integer(x))
ret <- sshhr(asInt(x))
if (is.na(ret)) {
if (x %in% names(dataset)) {
dataset[[x]]
} else if (is.na(x)) {
x
} else {
ret <- try(eval(parse(text = paste0("with(dataset, ", x, ")"))), silent = TRUE)
if (inherits(ret, "try-error")) {
cat(glue('"{x}" not (yet) defined when called. Note that simulation\nvariables of type "Constant" are always evaluated first\n\n\n'))
NA
} else {
ret
}
}
} else {
ret
}
}
#' Convenience function used in "simulater"
#'
#' @param x Character vector to be converted to an numeric value
#' @param dataset Data list
#
#' @return An numeric vector
#'
#' @export
.as_num <- function(x, dataset = list()) {
if (is.character(x)) x <- strsplit(x, "/") %>% unlist()
asNum <- function(x) ifelse(length(x) > 1, as.numeric(x[1]) / as.numeric(x[2]), as.numeric(x))
ret <- sshhr(asNum(x))
if (is.na(ret)) {
if (x %in% names(dataset)) {
dataset[[x]]
} else if (is.na(x)) {
x
} else {
ret <- try(eval(parse(text = paste0("with(dataset, ", x, ")"))), silent = TRUE)
if (inherits(ret, "try-error")) {
cat(glue('"{x}" not (yet) defined when called. Note that simulation\nvariables of type "Constant" are always evaluated first\n\n\n'))
NA
} else {
ret
}
}
} else {
ret
}
}
#' Simulate data for decision analysis
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater.html} for an example in Radiant
#'
#' @param const A character vector listing the constants to include in the analysis (e.g., c("cost = 3", "size = 4"))
#' @param lnorm A character vector listing the log-normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the log-mean and the second is the log-standard deviation)
#' @param norm A character vector listing the normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the mean and the second is the standard deviation)
#' @param unif A character vector listing the uniformly distributed random variables to include in the analysis (e.g., "demand 0 1" where the first number is the minimum value and the second is the maximum value)
#' @param discrete A character vector listing the random variables with a discrete distribution to include in the analysis (e.g., "price 5 8 .3 .7" where the first set of numbers are the values and the second set the probabilities
#' @param binom A character vector listing the random variables with a binomial distribution to include in the analysis (e.g., "crash 100 .01") where the first number is the number of trials and the second is the probability of success)
#' @param pois A character vector listing the random variables with a poisson distribution to include in the analysis (e.g., "demand 10") where the number is the lambda value (i.e., the average number of events or the event rate)
#' @param sequ A character vector listing the start and end for a sequence to include in the analysis (e.g., "trend 1 100 1"). The number of 'steps' is determined by the number of simulations
#' @param grid A character vector listing the start, end, and step for a set of sequences to include in the analysis (e.g., "trend 1 100 1"). The number of rows in the expanded will over ride the number of simulations
#' @param data Dataset to be used in the calculations
#' @param form A character vector with the formula to evaluate (e.g., "profit = demand * (price - cost)")
#' @param funcs A named list of user defined functions to apply to variables generated as part of the simulation
#' @param seed Optional seed used in simulation
#' @param nexact Logical to indicate if normally distributed random variables should be simulated to the exact specified values
#' @param ncorr A string of correlations used for normally distributed random variables. The number of values should be equal to one or to the number of combinations of variables simulated
#' @param name Deprecated argument
#' @param nr Number of simulations
#' @param dataset Data list from previous simulation. Used by repeater function
#' @param envir Environment to extract data from
#'
#' @importFrom dplyr near
#'
#' @return A data.frame with the simulated data
#'
#' @examples
#' simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = "profit = demand * (price - cost)",
#' seed = 1234
#' ) %>% str()
#'
#' @seealso \code{\link{summary.simulater}} to summarize results
#' @seealso \code{\link{plot.simulater}} to plot results
#'
#' @export
simulater <- function(const = "", lnorm = "", norm = "", unif = "", discrete = "",
binom = "", pois = "", sequ = "", grid = "", data = NULL,
form = "", funcs = "", seed = NULL, nexact = FALSE, ncorr = NULL,
name = "", nr = 1000, dataset = NULL, envir = parent.frame()) {
if (!is.empty(seed)) set.seed(as.numeric(seed))
if (is.null(dataset)) {
dataset <- list()
} else {
## needed because number may be NA and missing if grid used in Simulate
nr <- attr(dataset, "radiant_sim_call")$nr
data <- attr(dataset, "radiant_sim_call")$data
}
## needed to be exported functions
if (!exists(".as_num") || !exists(".as_int")) {
.as_num <- radiant.model::.as_num
.as_int <- radiant.model::.as_int
}
grid <- sim_cleaner(grid)
if (grid != "" && length(dataset) == 0) {
s <- grid %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
if (is.empty(si[4])) si[4] <- 1
dataset[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), .as_num(si[4], dataset))
}
dataset <- as.list(expand.grid(dataset) %>% as.data.frame(stringsAsFactors = FALSE))
nr <- length(dataset[[1]])
}
if (is.empty(nr)) {
mess <- c("error", paste0("Please specify the number of simulations in '# sims'"))
return(add_class(mess, "simulater"))
}
## fetching data if needed
if (!is.empty(data, "none") && is_string(data)) {
if (exists(data, envir = envir)) {
data <- get_data(data, envir = envir)
} else {
stop(paste0("Data set ", data, " cannot be found", call. = FALSE))
}
}
## adding data to dataset list
if (is.data.frame(data)) {
for (i in colnames(data)) {
dataset[[i]] <- data[[i]]
}
}
## parsing constant
const <- sim_cleaner(const)
if (const != "") {
s <- const %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- .as_num(si[2], dataset)
}
}
## parsing uniform
unif <- sim_cleaner(unif)
if (unif != "") {
s <- unif %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- runif(nr, .as_num(si[2], dataset), .as_num(si[3], dataset))
}
}
## parsing log normal
lnorm <- sim_cleaner(lnorm)
if (lnorm != "") {
s <- lnorm %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
sdev <- .as_num(si[3], dataset)
if (is.na(sdev) || !sdev > 0) {
mess <- c("error", paste0("All log-normal variables should have a standard deviation larger than 0.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
dataset[[si[1]]] <- rlnorm(nr, .as_num(si[2], dataset), sdev)
}
}
## parsing normal
norm <- sim_cleaner(norm)
if (norm != "") {
s <- norm %>% sim_splitter()
means <- sds <- nms <- c()
for (i in seq_along(s)) {
si <- s[[i]]
sdev <- .as_num(si[3], dataset)
if (is.na(sdev) || !sdev > 0) {
mess <- c("error", paste0("All normal variables should have a standard deviation larger than 0.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
if (is.empty(ncorr) || length(s) == 1) {
if (nexact) {
dataset[[si[1]]] <- scale(rnorm(nr, 0, 1)) * sdev + .as_num(si[2], dataset)
} else {
dataset[[si[1]]] <- rnorm(nr, .as_num(si[2], dataset), sdev)
}
} else {
nms <- c(nms, si[1])
means <- c(means, .as_num(si[2], dataset))
sds <- c(sds, sdev)
}
}
if (!is.empty(ncorr) && length(nms) > 1) {
ncorr <- gsub(",", " ", ncorr) %>%
strsplit("\\s+") %>%
unlist() %>%
.as_num(dataset)
ncorr_nms <- combn(nms, 2) %>% apply(2, paste, collapse = "-")
if (length(ncorr) == 1 && length(ncorr_nms) > 2) {
ncorr <- rep(ncorr, length(ncorr_nms))
}
if (length(ncorr) != length(ncorr_nms)) {
mess <- c("error", paste0("The number of correlations specified is not equal to\nthe number of pairs of variables to be simulated.\nPlease review the input carefully"))
return(add_class(mess, "simulater"))
}
names(ncorr) <- ncorr_nms
df <- try(sim_cor(nr, ncorr, means, sds, exact = nexact), silent = TRUE)
if (inherits(df, "try-error")) {
mess <- c("error", paste0("Data with the specified correlation structure could not be generated.\nPlease review the input and try again"))
return(add_class(mess, "simulater"))
}
colnames(df) <- nms
for (i in nms) {
dataset[[i]] <- df[[i]]
}
}
}
## parsing binomial
binom <- sim_cleaner(binom)
if (binom != "") {
s <- binom %>% sim_splitter()
for (i in 1:length(s)) {
si <- s[[i]]
dataset[[si[1]]] <- rbinom(nr, .as_int(si[2], dataset), .as_num(si[3], dataset))
}
}
## parsing poisson
pois <- sim_cleaner(pois)
if (pois != "") {
s <- pois %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dataset[[si[1]]] <- rpois(nr, .as_num(si[2], dataset))
}
}
## parsing sequence
sequ <- sim_cleaner(sequ)
if (sequ != "") {
s <- sequ %>% sim_splitter()
for (i in 1:length(s)) {
si <- s[[i]]
dataset[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), length.out = .as_num(nr, dataset))
}
}
## parsing discrete
discrete <- sim_cleaner(discrete)
if (discrete != "") {
s <- discrete %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
dpar <- si[-1] %>%
gsub(",", " ", .) %>%
strsplit("\\s+") %>%
unlist() %>%
strsplit("/")
asNum <- function(x) ifelse(length(x) > 1, .as_num(x[1], dataset) / .as_num(x[2], dataset), .as_num(x, dataset))
dpar <- sshhr(try(sapply(dpar, asNum) %>% matrix(ncol = 2), silent = TRUE))
if (inherits(dpar, "try-error") || any(is.na(dpar))) {
mess <- c("error", paste0("Input for discrete variable # ", i, " contains an error. Please review the input carefully"))
return(add_class(mess, "simulater"))
} else if (!near(sum(dpar[, 2]), 1)) {
mess <- c("error", glue("Probabilities for discrete variable # {i} do not sum to 1 ({sum(dpar[, 2])})"))
return(add_class(mess, "simulater"))
}
dataset[[si[1]]] <- sample(dpar[, 1], nr, replace = TRUE, prob = dpar[, 2])
}
}
## convert named list of functions to a string to evaluate
if (is.list(funcs)) {
funcs <- sapply(
names(funcs),
function(f) {
paste0(f, " = ", paste0(deparse(funcs[[f]], control = getOption("dctrl"), width.cutoff = 500L), collapse = "\n"))
}
) %>% paste0(collapse = ";")
}
if (!is.expression(funcs)) {
pfuncs <- parse(text = funcs, keep.source = TRUE)
} else {
pfuncs <- funcs
}
if (!is.empty(form)) {
form <- form %>%
gsub("[ ]{2,}", " ", .) %>%
gsub("<-", "=", .)
form_no_comments <- remove_comments(form)
out <- try(do.call(within, list(dataset, c(pfuncs, parse(text = form_no_comments)))), silent = TRUE)
if (!inherits(out, "try-error")) {
dataset <- out
} else {
mess <- c(
"error", paste0("Formula was not successfully evaluated:\n\n", form) %>%
paste0(collapse = "\n"), "\n\nMessage: ", attr(out, "condition")$message
)
return(add_class(mess, "simulater"))
}
}
## removing data from dataset list
if (is.data.frame(data)) {
dataset[colnames(data)] <- NULL
}
## remove functions
ind <- radiant.data::get_class(dataset) == "function"
dataset[ind] <- NULL
## convert list to a data.frame
dataset <- as.data.frame(dataset, stringsAsFactors = FALSE) %>% na.omit()
## capturing the function call for use in repeat
sc <- formals()
smc <- lapply(match.call()[-1], eval, envir = envir)
smc$envir <- NULL
sc[names(smc)] <- smc
sc$nr <- nr
sc$ncorr <- ncorr
sc$nexact <- nexact
sc$funcs <- pfuncs
if (is.empty(sc$data, "none")) {
attr(dataset, "sim_data_name") <- NULL
} else if (is_string(sc$data)) {
attr(dataset, "sim_data_name") <- sc$data
sc$data <- data
} else {
attr(dataset, "sim_data_name") <- deparse(substitute(data))
}
attr(dataset, "radiant_sim_call") <- sc
if (nrow(dataset) == 0) {
mess <- c("error", paste0("The simulated data set has 0 rows"))
return(add_class(mess, "simulater"))
}
form <- gsub("*", "\\*", form, fixed = TRUE) %>%
gsub("^\\s*?\\#+[^\\#]", "##### # ", .) %>%
gsub("[;\n]\\s*?\\#+[^\\#]", "; ##### # ", .) %>%
gsub(";\\s*", "\n\n", .)
mess <- paste0("\n### Simulated data\n\nFormulas:\n\n", form, "\n\nDate: ", lubridate::now())
add_class(set_attr(dataset, "description", mess), "simulater")
}
#' Summary method for the simulater function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater.html} for an example in Radiant
#'
#' @param object Return value from \code{\link{simulater}}
#' @param dec Number of decimals to show
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' simdat <- simulater(norm = "demand 2000 1000", seed = 1234)
#' summary(simdat)
#'
#' @seealso \code{\link{simulater}} to generate the results
#' @seealso \code{\link{plot.simulater}} to plot results
#'
#' @export
summary.simulater <- function(object, dec = 4, ...) {
if (is.character(object)) {
if (length(object) == 2 && object[1] == "error") {
return(cat(object[2]))
}
stop("To generate summary statistics please provide a simulated dataset as input", call. = FALSE)
}
sc <- attr(object, "radiant_sim_call")
clean <- function(x) {
paste0(x, collapse = ";") %>%
gsub(";", "; ", .) %>%
gsub("\\n", "", .) %>%
paste0(., "\n")
}
cat("Simulation\n")
cat("Simulations:", format_nr(nrow(object), dec = 0), "\n")
cat("Random seed:", sc$seed, "\n")
if (is.empty(sc$name)) {
cat("Sim data :", deparse(substitute(object)), "\n")
} else {
cat("Sim data :", sc$name, "\n")
}
if (!is.empty(sc$binom)) cat("Binomial :", clean(sc$binom))
if (!is.empty(sc$discrete)) cat("Discrete :", clean(sc$discrete))
if (!is.empty(sc$lnorm)) cat("Log normal :", clean(sc$lnorm))
if (!is.empty(sc$norm)) cat("Normal :", clean(ifelse(sc$nexact, paste0(sc$norm, "(exact)"), sc$norm)))
if (!is.empty(sc$unif)) cat("Uniform :", clean(sc$unif))
if (!is.empty(sc$pois)) cat("Poisson :", clean(sc$pois))
if (!is.empty(sc$const)) cat("Constant :", clean(sc$const))
if (is.data.frame(sc$data)) cat("Data :", attr(object, "sim_data_name"), "\n")
if (!is.empty(sc$grid)) cat("Grid search:", clean(sc$grid))
if (!is.empty(sc$sequ)) cat("Sequence :", clean(sc$sequ))
funcs <- attr(object, "radiant_funcs")
if (!is.empty(funcs)) {
funcs <- parse(text = funcs)
lfuncs <- list()
for (i in seq_len(length(funcs))) {
tmp <- strsplit(as.character(funcs[i]), "(\\s*=|\\s*<-)")[[1]][1]
lfuncs[[tmp]] <- as.symbol(tmp)
}
cat("Functions :", paste0(names(lfuncs), collapse = ", "), "\n")
}
if (!is.empty(sc$form)) {
cat(paste0("Formulas :\n\t", paste0(sc$form, collapse = ";") %>% gsub(";", "\n", .) %>% gsub("\n", "\n\t", .), "\n"))
}
cat("\n")
if (!is.empty(sc$ncorr) && is.numeric(sc$ncorr)) {
cat("Correlations:\n")
print(sc$ncorr)
cat("\n")
}
sim_summary(object, dec = ifelse(is.empty(dec), 4, round(dec, 0)))
}
#' Plot method for the simulater function
#'
#' @details See \url{https://radiant-rstats.github.io/docs/model/simulater} for an example in Radiant
#'
#' @param x Return value from \code{\link{simulater}}
#' @param bins Number of bins used for histograms (1 - 50)
#' @param shiny Did the function call originate inside a shiny app
#' @param custom Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and \url{https://ggplot2.tidyverse.org} for options.
#' @param ... further arguments passed to or from other methods
#'
#' @examples
#' simdat <- simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = "profit = demand * (price - cost)",
#' seed = 1234
#' )
#' plot(simdat, bins = 25)
#'
#' @seealso \code{\link{simulater}} to generate the result
#' @seealso \code{\link{summary.simulater}} to summarize results
#'
#' @export
plot.simulater <- function(x, bins = 20, shiny = FALSE, custom = FALSE, ...) {
if (is.character(x)) {
return(invisible())
}
if (nrow(x) == 0) {
return(invisible())
}
plot_list <- list()
for (i in colnames(x)) {
dat <- select_at(x, .vars = i)
if (!does_vary(x[[i]])) next
plot_list[[i]] <- select_at(x, .vars = i) %>%
visualize(xvar = i, bins = bins, custom = TRUE)
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = min(length(plot_list), 2)) %>%
(function(x) if (shiny) x else print(x))
}
}
}
#' Repeated simulation
#'
#' @param dataset Return value from the simulater function
#' @param nr Number times to repeat the simulation
#' @param vars Variables to use in repeated simulation
#' @param grid Character vector of expressions to use in grid search for constants
#' @param sum_vars (Numeric) variables to summaries
#' @param byvar Variable(s) to group data by before summarizing
#' @param fun Functions to use for summarizing
#' @param form A character vector with the formula to apply to the summarized data
#' @param seed Seed for the repeated simulation
#' @param name Deprecated argument
#' @param envir Environment to extract data from
#'
#' @importFrom shiny getDefaultReactiveDomain
#'
#' @examples
#' simdat <- simulater(
#' const = c("var_cost 5", "fixed_cost 1000"),
#' norm = "E 0 100;",
#' discrete = "price 6 8 .3 .7;",
#' form = c(
#' "demand = 1000 - 50*price + E",
#' "profit = demand*(price-var_cost) - fixed_cost",
#' "profit_small = profit < 100"
#' ),
#' seed = 1234
#' )
#'
#' repdat <- repeater(
#' simdat,
#' nr = 12,
#' vars = c("E", "price"),
#' sum_vars = "profit",
#' byvar = ".sim",
#' form = "profit_365 = profit_sum < 36500",
#' seed = 1234,
#' )
#'
#' head(repdat)
#' summary(repdat)
#' plot(repdat)
#'
#' @seealso \code{\link{summary.repeater}} to summarize results from repeated simulation
#' @seealso \code{\link{plot.repeater}} to plot results from repeated simulation
#'
#' @export
repeater <- function(dataset, nr = 12, vars = "", grid = "", sum_vars = "",
byvar = ".sim", fun = "sum", form = "", seed = NULL,
name = "", envir = parent.frame()) {
if (byvar %in% c(".sim", "sim")) grid <- ""
if (is.empty(nr)) {
if (is.empty(grid)) {
mess <- c("error", paste0("Please specify the number of repetitions in '# reps'"))
return(add_class(mess, "repeater"))
} else {
nr <- 1
}
}
## needed to be exported functions
if (!exists(".as_num") || !exists(".as_int")) {
.as_num <- radiant.model::.as_num
.as_int <- radiant.model::.as_int
}
if (is_string(dataset)) {
sim_df_name <- dataset
dataset <- get_data(dataset, envir = envir)
} else {
sim_df_name <- deparse(substitute(dataset))
}
if (!is.empty(seed)) set.seed(as.numeric(seed))
if (identical(vars, "") && identical(grid, "")) {
mess <- c("error", paste0("Select variables to re-simulate and/or a specify a constant\nto change using 'Grid search' when Group by is set to Repeat"))
return(add_class(mess, "repeater"))
}
if (identical(vars, "")) vars <- character(0)
grid_list <- list()
if (!identical(grid, "")) {
grid <- sim_cleaner(grid)
if (grid != "") {
s <- grid %>% sim_splitter()
for (i in seq_along(s)) {
si <- s[[i]]
if (is.empty(s[[i]][4])) s[[i]][4] <- 1
grid_list[[si[1]]] <- seq(.as_num(si[2], dataset), .as_num(si[3], dataset), .as_num(si[4], dataset))
}
}
## expanding list of variables but removing ""
vars <- c(vars, names(grid_list)) %>% unique()
}
## from http://stackoverflow.com/a/7664655/1974918
## keep those list elements that, e.g., q is in
nr_sim <- nrow(dataset)
sc <- attr(dataset, "radiant_sim_call")
if (is.data.frame(sc$data)) {
data <- sc$data
} else {
data <- NULL
}
## reset dataset to list with vectors of the correct length
dataset <- as.list(dataset)
if ("const" %in% names(sc)) {
s <- sc$const
if (length(s) < 2) {
s <- strsplit(gsub("\n", "", s), ";\\s*")[[1]] %>% strsplit("\\s+")
} else {
s <- strsplit(s, "\\s+")
}
for (const in seq_len(length(s))) {
nm <- s[[const]][1]
dataset[[nm]] <- dataset[[nm]][1]
}
}
## needed if inputs are provided as vectors
sc[1:(which(names(sc) == "seed") - 1)] %<>% lapply(paste, collapse = ";")
sc$name <- sc$seed <- "" ## cleaning up the sim call
## using \\b based on https://stackoverflow.com/a/34074458/1974918
sc_keep <- grep(paste(paste0("\\b", vars, "\\b"), collapse = "|"), sc, value = TRUE)
sc_keep["funcs"] <- sc$funcs
## ensure that only the selected variables of a specific type are resimulated
## e.g., if A, B, and C are normal and A should be re-sim'd, don't also re-sim B and C
for (i in names(sc_keep)) {
if (i %in% c("form", "funcs")) next
sc_check <- sim_cleaner(sc_keep[[i]]) %>%
sim_splitter(";")
if (length(sc_check) < 2) {
next
} else {
sc_keep[[i]] <- grep(paste(paste0("\\b", vars, "\\b"), collapse = "|"), sc_check, value = TRUE) %>%
paste0(collapse = ";\n")
}
}
## needed in case there is no 'form' in simulate
sc[1:(which(names(sc) == "seed") - 1)] <- ""
sc[names(sc_keep)] <- sc_keep
sc$dataset <- dataset
if (!is.empty(sc$data, "none") && is_string(sc$data)) {
if (exists(sc$data, envir = envir)) {
sc$data <- get(sc$data, envir = envir)
} else {
stop(paste0("Data set ", sc$data, " cannot be found", call. = FALSE))
}
}
summarize_sim <- function(object) {
if (is.empty(fun) || any(fun == "none")) {
object <- select_at(object, .vars = c(".rep", ".sim", sum_vars))
} else {
cn <- unlist(sapply(fun, function(f) paste0(sum_vars, "_", f), simplify = FALSE))
first <- function(x, ...) dplyr::first(x)
last <- function(x, ...) dplyr::last(x)
object <- group_by_at(object, byvar) %>%
summarise_at(.vars = sum_vars, .funs = fun, na.rm = TRUE) %>%
set_colnames(c(byvar, cn))
}
object
}
rep_sim <- function(rep_nr, nr, sfun = function(x) x) {
bind_cols(
data.frame(.rep = rep(rep_nr, nr_sim), .sim = 1:nr_sim, stringsAsFactors = FALSE),
do.call(simulater, sc)
) %>%
na.omit() %>%
sfun() %T>%
(function(x) incProgress(rep_nr / nr, detail = paste("\nCompleted run", rep_nr, "out of", nr)))
}
rep_grid_sim <- function(gval, rep_nr, nr, sfun = function(x) x) {
gvars <- names(gval)
## removing form and funcs ...
sc_grid <- grep(paste(gvars, collapse = "|"), sc_keep, value = TRUE) %>%
(function(x) x[which(!names(x) %in% c("form", "funcs"))]) %>%
gsub("[ ]{2,}", " ", .)
for (i in 1:length(gvars)) {
sc_grid %<>% sub(paste0("[;\n]", gvars[i], " [.0-9]+"), paste0("\n", gvars[i], " ", gval[gvars[i]]), .) %>%
sub(paste0("^", gvars[i], " [.0-9]+"), paste0(gvars[i], " ", gval[gvars[i]]), .)
}
sc[names(sc_grid)] <- sc_grid
bind_cols(
data.frame(.rep = rep(paste(gval, collapse = "|"), nr_sim), .sim = 1:nr_sim, stringsAsFactors = FALSE),
do.call(simulater, sc)
) %>%
na.omit() %>%
sfun() %>%
{
incProgress(rep_nr / nr, detail = paste("\nCompleted run", rep_nr, "out of", nr))
.
}
}
if (length(shiny::getDefaultReactiveDomain()) > 0) {
trace <- FALSE
incProgress <- shiny::incProgress
withProgress <- shiny::withProgress
} else {
incProgress <- function(...) {}
withProgress <- function(...) list(...)[["expr"]]
}
withProgress(message = "Running repeated simulation", value = 0, {
if (length(grid_list) == 0) {
if (byvar == ".sim") {
ret <- bind_rows(lapply(1:nr, rep_sim, nr)) %>%
summarize_sim() %>%
add_class("repeater")
} else {
ret <- bind_rows(lapply(1:nr, function(x) rep_sim(x, nr, summarize_sim))) %>%
add_class("repeater")
}
} else {
grid <- expand.grid(grid_list)
nr <- nrow(grid)
if (byvar == ".sim") {
ret <- bind_rows(lapply(1:nr, function(x) rep_grid_sim(grid[x, , drop = FALSE], x, nr))) %>%
summarize_sim() %>%
add_class("repeater")
} else {
ret <- bind_rows(lapply(1:nr, function(x) rep_grid_sim(grid[x, , drop = FALSE], x, nr, summarize_sim))) %>%
add_class("repeater")
}
}
})
if (is.data.frame(data)) {
ret <- as.list(ret)
for (i in colnames(data)) {
ret[[i]] <- data[[i]]
}
sim_data_name <- attr(dataset, "sim_data_name")
} else {
sim_data_name <- NULL
}
if (!is.empty(form)) {
form <- form %>%
gsub("[ ]{2,}", " ", .) %>%
gsub("<-", "=", .)
form_no_comments <- remove_comments(form)
out <- try(do.call(within, list(ret, parse(text = form_no_comments))), silent = TRUE)
if (!inherits(out, "try-error")) {
ret <- out
} else {
mess <- c("error", paste0("Formula was not successfully evaluated:\n\n", form) %>% unlist() %>% paste0(collapse = "\n"), "\n\nMessage: ", attr(out, "condition")$message, "\n\nNote that repeated simulation formulas can only be applied to\n(summarized) 'Output variables'")
if (!is.empty(fun)) {
cn <- unlist(sapply(fun, function(f) paste0(sum_vars, "_", f), simplify = FALSE))
mess[2] <- paste0(mess[2], "\n\nAvailable (summarized) output variables:\n* ", paste0(cn, collapse = "\n* "))
}
return(add_class(mess, "repeater"))
}
}
## removing data from dataset list
if (is.data.frame(data)) {
ret[colnames(data)] <- NULL
}
## tbl_df remove attributes so use as.data.frame for now
ret <- as.data.frame(ret, stringsAsFactors = FALSE)
## capturing the function call for use in summary and plot
rc <- formals()
rmc <- lapply(match.call()[-1], eval, envir = envir)
rmc$envir <- NULL
rc[names(rmc)] <- rmc
rc$sc <- sc[base::setdiff(names(sc), "dat")]
attr(ret, "radiant_rep_call") <- rc
attr(ret, "sim_df_name") <- sim_df_name
attr(ret, "sim_data_name") <- sim_data_name
mess <- paste0(
"\n### Repeated simulation data\n\nFormula:\n\n",
gsub("*", "\\*", sc$form, fixed = TRUE) %>%
gsub("[;\n]\\s*?\\#+[^\\#]", "; ##### # ", .) %>%
gsub(";", "\n\n", .),
"\n\nDate: ",
lubridate::now()
)
add_class(set_attr(ret, "description", mess), "repeater")
}
#' Summarize repeated simulation
#'
#' @param object Return value from \code{\link{repeater}}
#' @param dec Number of decimals to show
#' @param ... further arguments passed to or from other methods
#'
#' @seealso \code{\link{repeater}} to run a repeated simulation
#' @seealso \code{\link{plot.repeater}} to plot results from repeated simulation
#'
#' @export
summary.repeater <- function(object, dec = 4, ...) {
if (is.character(object)) {
if (length(object) == 2 && object[1] == "error") {
return(cat(object[2]))
}
stop("To generate summary statistics please provide a simulated dataset as input", call. = FALSE)
}
## getting the repeater call
rc <- attr(object, "radiant_rep_call")
clean <- function(x) {
paste0(x, collapse = ";") %>%
gsub(";", "; ", .) %>%
gsub("\\n", "", .) %>%
paste0(., "\n")
}
## show results
cat("Repeated simulation\n")
cat("Simulations :", ifelse(is.empty(rc$sc$nr), "", format_nr(rc$sc$nr, dec = 0)), "\n")
cat("Repetitions :", format_nr(ifelse(is.empty(rc$nr), nrow(object), rc$nr), dec = 0), "\n")
if (!is.empty(rc$vars)) {
cat("Re-simulated :", paste0(rc$vars, collapse = ", "), "\n")
}
cat("Group by :", ifelse(rc$byvar == ".rep", "Repeat", "Simulation"), "\n")
cat("Function :", rc$fun, "\n")
cat("Random seed :", rc$seed, "\n")
if (is.data.frame(rc$sim)) {
rc$sim <- attr(rc$sim, "radiant_sim_call")$name
}
cat("Simulated data:", attr(object, "sim_df_name"), "\n")
attr(object, "sim_data_name") %>%
{
if (!is.empty(.)) cat("Data :", ., "\n")
}
if (is.empty(rc$name)) {
cat("Repeat data :", deparse(substitute(object)), "\n")
} else {
cat("Repeat data :", rc$name, "\n")
}
if (isTRUE(rc$byvar == "rep") && !is.empty(rc$grid)) {
cat("Grid search. :", clean(rc$grid))
}
if (!is.empty(rc$form)) {
rc$form %<>% sim_cleaner()
paste0(
"Formulas :\n\t",
paste0(rc$form, collapse = ";") %>%
gsub(";", "\n", .) %>%
gsub("\n", "\n\t", .),
"\n"
) %>% cat()
}
cat("\n")
sim_summary(select(object, -1), fun = rc$fun, dec = ifelse(is.na(dec), 4, dec))
}
#' Plot repeated simulation
#'
#' @param x Return value from \code{\link{repeater}}
#' @param bins Number of bins used for histograms (1 - 50)
#' @param shiny Did the function call originate inside a shiny app
#' @param custom Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and \url{https://ggplot2.tidyverse.org} for options.
#' @param ... further arguments passed to or from other methods
#'
#' @seealso \code{\link{repeater}} to run a repeated simulation
#' @seealso \code{\link{summary.repeater}} to summarize results from repeated simulation
#'
#' @export
plot.repeater <- function(x, bins = 20, shiny = FALSE, custom = FALSE, ...) {
if (is.character(x)) {
return(invisible())
}
if (nrow(x) == 0) {
return(invisible())
}
## getting the repeater call
rc <- attr(x, "radiant_rep_call")
plot_list <- list()
for (i in colnames(x)[-1]) {
dat <- select_at(x, .vars = i)
if (!does_vary(x[[i]])) next
plot_list[[i]] <- select_at(x, .vars = i) %>%
visualize(xvar = i, bins = bins, custom = TRUE)
if (i %in% rc$sum_vars && !is.empty(rc$fun, "none")) {
plot_list[[i]] <- plot_list[[i]] + labs(x = paste0(rc$fun, " of ", i))
}
}
if (length(plot_list) > 0) {
if (custom) {
if (length(plot_list) == 1) plot_list[[1]] else plot_list
} else {
patchwork::wrap_plots(plot_list, ncol = min(length(plot_list), 2)) %>%
(function(x) if (shiny) x else print(x))
}
}
}
#' Print simulation summary
#'
#' @param dataset Simulated data
#' @param dc Variable classes
#' @param fun Summary function to apply
#' @param dec Number of decimals to show
#'
#' @seealso \code{\link{simulater}} to run a simulation
#' @seealso \code{\link{repeater}} to run a repeated simulation
#'
#' @examples
#' simulater(
#' const = "cost 3",
#' norm = "demand 2000 1000",
#' discrete = "price 5 8 .3 .7",
#' form = c("profit = demand * (price - cost)", "profit5K = profit > 5000"),
#' seed = 1234
#' ) %>% sim_summary()
#'
#' @export
sim_summary <- function(dataset, dc = get_class(dataset), fun = "", dec = 4) {
isFct <- "factor" == dc
isNum <- dc %in% c("numeric", "integer", "Duration")
isChar <- "character" == dc
isLogic <- "logical" == dc
dec <- ifelse(is.na(dec), 4, as.integer(dec))
if (sum(isNum) > 0) {
isConst <- !sapply(dataset, does_vary) & isNum
if (sum(isConst) > 0) {
cn <- names(dc)[isConst]
cat("Constants:\n")
select(dataset, which(isConst)) %>%
na.omit() %>%
.[1, ] %>%
as.data.frame(stringsAsFactors = FALSE) %>%
round(dec) %>%
mutate_all(~ formatC(., big.mark = ",", digits = dec, format = "f")) %>%
set_rownames("") %>%
set_colnames(cn) %>%
print()
cat("\n")
}
isRnd <- isNum & !isConst
if (sum(isRnd) > 0) {
cn <- names(dc)[isRnd]
cat("Variables:\n")
select(dataset, which(isNum & !isConst)) %>%
gather("variable", "values", !!cn) %>%
group_by_at(.vars = "variable") %>%
summarise_all(
list(
n_obs = n_obs, mean = mean, sd = sd, min = min,
p25 = p25, median = median, p75 = p75, max = max
),
na.rm = TRUE
) %>%
mutate(variable = format(variable, justify = "left")) %>%
data.frame(check.names = FALSE, stringsAsFactors = FALSE) %>%
format_df(dec = dec, mark = ",") %>%
rename(` ` = "variable") %>%
print(row.names = FALSE)
cat("\n")
}
}
if (sum(isLogic) > 0) {
cat("Logicals:\n")
select(dataset, which(isLogic)) %>%
summarise_all(list(sum, mean), na.rm = TRUE) %>%
round(dec) %>%
matrix(ncol = 2) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
set_colnames(c("TRUE (nr) ", "TRUE (prop)")) %>%
set_rownames(names(dataset)[isLogic]) %>%
format(big.mark = ",", scientific = FALSE) %>%
print()
cat("\n")
}
if (sum(isFct) > 0 || sum(isChar) > 0) {
cat("Factors:\n")
df <- select(dataset, which(isFct | isChar)) %>%
mutate(across(where(is.character), as_factor)) %>%
as.data.frame()
tab <- summary(df)
pt <- lapply(df, function(x) prop.table(table(x)))
for (i in seq_len(ncol(tab))) {
tab[, i] <- paste0(tab[, i], "(", 100 * round(pt[[i]], dec), "%)")
}
tab[tab == "NA(100%)"] <- ""
print(tab)
cat("\n")
}
}
#' Clean input command string
#'
#' @param x Input string
#'
#' @return Cleaned string
#'
#' @export
sim_cleaner <- function(x) {
gsub("[ ]{2,}", " ", paste(x, collapse = ";")) %>%
gsub("[ ]*[\n;]+[ ]*", ";", .) %>%
gsub("[;]{2,}", ";", .) %>%
gsub(";$", "", .) %>%
gsub("^;", "", .)
}
#' Remove comments from formula before it is evaluated
#'
#' @param x Input string
#'
#' @return Cleaned string
#'
#' @export
remove_comments <- function(x) {
gsub("[ ]*\\#{1,}[^\n;]*[\n]", "\n", x) %>%
gsub("[ ]*\\#{1,}[^\n;]*[;]", ";", .) %>%
gsub("^[ ]*;{1,}", "", .) %>%
gsub(";{2,}", ";", .) %>%
gsub("^[ ]*\n{1,}", "", .) %>%
gsub("\n{2,}", "\n", .) %>%
gsub("^[ ]{1,}", "", .)
}
#' Split input command string
#'
#' @param x Input string
#' @param symbol Symbol used to split the command string
#'
#' @return Split input command string
#'
#' @export
sim_splitter <- function(x, symbol = " ") {
strsplit(x, "(;\\s*|\n)") %>%
extract2(1) %>%
# from https://stackoverflow.com/a/16644618/1974918
gsub("\\s+(?=[^(\\)]*\\))", "", ., perl = TRUE) %>%
strsplit(symbol)
}
#' Find maximum value of a vector
#'
#' @details Find the value of y at the maximum value of x
#' @param x Variable to find the maximum for
#' @param y Variable to find the value for at the maximum of var
#'
#' @return Value of val at the maximum of var
#'
#' @examples
#' find_max(1:10, 21:30)
#'
#' @export
find_max <- function(x, y) {
if (missing(y)) {
stop("Error in find_max (2 inputs required)\nSpecify the variable to evaluate at the maximum of the first input")
}
y[which.max(x)]
}
#' Find minimum value of a vector
#'
#' @details Find the value of y at the minimum value of x
#' @param x Variable to find the minimum for
#' @param y Variable to find the value for at the maximum of var
#'
#' @return Value of val at the minimum of var
#'
#' @examples
#' find_min(1:10, 21:30)
#'
#' @export
find_min <- function(x, y) {
if (missing(y)) {
stop("Error in find_min (2 inputs required)\nSpecify the variable to evaluate at the minimum of the first input")
}
y[which.min(x)]
}
#' Standard deviation of weighted sum of variables
#'
#' @param ... A matched number of weights and stocks
#'
#' @return A vector of standard deviation estimates
#'
#' @export
sdw <- function(...) {
dl <- list(...)
nr <- length(dl) / 2
w <- data.frame(dl[1:nr], stringsAsFactors = FALSE)
d <- data.frame(dl[(nr + 1):length(dl)], stringsAsFactors = FALSE)
apply(w, 1, function(w) sd(rowSums(sweep(d, 2, w, "*"))))
}
#' Simulate correlated normally distributed data
#'
#' @param n The number of values to simulate (i.e., the number of rows in the simulated data)
#' @param rho A vector of correlations to apply to the columns of the simulated data. The number of values should be equal to one or to the number of combinations of variables to be simulated
#' @param means A vector of means. The number of values should be equal to the number of variables to simulate
#' @param sds A vector of standard deviations. The number of values should be equal to the number of variables to simulate
#' @param exact A logical that indicates if the inputs should be interpreted as population of sample characteristics
#'
#' @return A data.frame with the simulated data
#'
#' @examples
#' sim <- sim_cor(100, .74, c(0, 10), c(1, 5), exact = TRUE)
#' cor(sim)
#' sim_summary(sim)
#'
#' @export
sim_cor <- function(n, rho, means, sds, exact = FALSE) {
nrx <- length(means)
C <- matrix(1, nrow = nrx, ncol = nrx)
C[lower.tri(C)] <- C[upper.tri(C)] <- rho
X <- matrix(rnorm(n * nrx, 0, 1), ncol = nrx)
if (exact) {
X <- psych::principal(X, nfactors = nrx, scores = TRUE)$scores
}
X <- X %*% chol(C)
X <- sweep(X, 2, sds, "*")
X <- sweep(X, 2, means, "+")
as.data.frame(X, stringsAsFactors = FALSE)
}
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