From 2485f938961569cd375de067056eab2822b7f911 Mon Sep 17 00:00:00 2001
From: assignUser <github@krushak.me>
Date: Sat, 3 Oct 2020 16:09:20 +0200
Subject: [PATCH 1/4] add environment managment for ..vars Fixes #59

---
 R/add_correlated_data.R             |  32 +++-
 R/add_data.R                        |  27 +++-
 R/define_data.R                     |   2 +-
 R/generate_data.R                   |  15 +-
 R/generate_dist.R                   | 222 ++++++++++++++++++++--------
 R/internal_utility.R                |   4 +-
 R/simstudy-package.R                |   2 +-
 tests/testthat/test-generate_dist.R |  32 ++--
 8 files changed, 243 insertions(+), 93 deletions(-)

diff --git a/R/add_correlated_data.R b/R/add_correlated_data.R
index 0437d419..c2535ee9 100644
--- a/R/add_correlated_data.R
+++ b/R/add_correlated_data.R
@@ -95,6 +95,8 @@ addCorData <- function(dtOld, idname, mu, sigma, corMatrix = NULL,
 #' symmetrical and positive semi-definite. It is not a required field; if a
 #' matrix is not provided, then a structure and correlation coefficient rho must
 #' be specified.
+#' @param envir Environment the data definitions are evaluated in.
+#'  Defaults to [base::parent.frame].
 #' @return data.table with added column(s) of correlated data
 #' @examples
 #' defC <- defData(
@@ -143,9 +145,10 @@ addCorData <- function(dtOld, idname, mu, sigma, corMatrix = NULL,
 #' # Check global correlations - should not be as correlated
 #' cor(di[, list(A, B, C, D)])
 #' @concept correlated
+#' @md
 #' @export
 addCorFlex <- function(dt, defs, rho = 0, tau = NULL, corstr = "cs",
-                       corMatrix = NULL) {
+                       corMatrix = NULL, envir = parent.frame()) {
 
   # "Declare" vars to avoid R CMD warning
 
@@ -203,15 +206,30 @@ addCorFlex <- function(dt, defs, rho = 0, tau = NULL, corstr = "cs",
     iLink <- corDefs[i, link]
 
     if (iDist == "binary") {
-      params <- .getBinaryMean(dTemp, formula = iFormula, Size = 1, link = iLink)
+      params <- .getBinaryMean(dTemp,
+        formula = iFormula,
+        size = 1,
+        link = iLink,
+        envir = envir
+      )
 
       V <- dTemp[, stats::qbinom(Unew, 1, params[[1]])]
     } else if (iDist == "poisson") {
-      param1 <- .getPoissonMean(dTemp, formula = iFormula, link = iLink)
+      param1 <- .getPoissonMean(
+        dtSim = dTemp,
+        formula = iFormula,
+        link = iLink,
+        envir = envir
+      )
 
       V <- dTemp[, stats::qpois(Unew, param1)]
     } else if (iDist == "gamma") {
-      mn <- .getGammaMean(dTemp, formula = iFormula, link = iLink)
+      mn <- .getGammaMean(
+        dtSim = dTemp,
+        formula = iFormula,
+        link = iLink,
+        envir = envir
+      )
 
       ### Gamma parameters need to be transformed
 
@@ -221,7 +239,7 @@ addCorFlex <- function(dt, defs, rho = 0, tau = NULL, corstr = "cs",
 
       V <- dTemp[, stats::qgamma(Unew, param1, param2)]
     } else if (iDist == "negBinomial") {
-      mn <- .getNBmean(dTemp, formula = iFormula, link = iLink)
+      mn <- .getNBmean(dTemp, formula = iFormula, link = iLink, envir = envir)
 
       ### NB parameters need to be transformed
 
@@ -231,7 +249,7 @@ addCorFlex <- function(dt, defs, rho = 0, tau = NULL, corstr = "cs",
 
       V <- dTemp[, stats::qnbinom(Unew, param1, param2)]
     } else if (iDist == "normal") {
-      param1 <- .getNormalMean(dTemp, formula = iFormula)
+      param1 <- .getNormalMean(dtSim = dTemp, formula = iFormula, envir = envir)
       param2 <- sqrt(corDefs[i, variance])
 
       V <- dTemp[, stats::qnorm(Unew, param1, param2)]
@@ -645,4 +663,4 @@ addCorGen <- function(dtOld, nvars, idvar = "id", rho, corstr, corMatrix = NULL,
 
 
   return(dtTemp[])
-}
+}
\ No newline at end of file
diff --git a/R/add_data.R b/R/add_data.R
index eadcbc7d..04d4fc88 100644
--- a/R/add_data.R
+++ b/R/add_data.R
@@ -2,6 +2,8 @@
 #'
 #' @param dtDefs name of definitions for added columns
 #' @param dtOld name of data table that is to be updated
+#' @param envir Environment the data definitions are evaluated in.
+#'  Defaults to [base::parent.frame].
 #' @return an updated data.table that contains the added simulated data
 #' @examples
 #' # New data set
@@ -20,8 +22,9 @@
 #' dt <- addColumns(def2, dt)
 #' dt
 #' @concept generate_data
+#' @md
 #' @export
-addColumns <- function(dtDefs, dtOld) {
+addColumns <- function(dtDefs, dtOld, envir = parent.frame()) {
 
   # "declares" varname to avoid global NOTE
   varname <- NULL
@@ -52,7 +55,13 @@ addColumns <- function(dtDefs, dtOld) {
   iter <- nrow(dtDefs)
   n <- nrow(dtOld)
   for (i in (1:iter)) {
-    dtOld <- .generate(dtDefs[i, ], n, dtOld, oldkey)
+    dtOld <- .generate(
+      args = dtDefs[i, ],
+      n = n,
+      dfSim = dtOld,
+      idname = oldkey,
+      envir = envir
+    )
   }
 
   dtOld <- data.table::data.table(dtOld)
@@ -66,7 +75,8 @@ addColumns <- function(dtDefs, dtOld) {
 #' @param condDefs Name of definitions for added column
 #' @param dtOld Name of data table that is to be updated
 #' @param newvar Name of new column to add
-#'
+#' @param envir Environment the data definitions are evaluated in.
+#'  Defaults to [base::parent.frame].
 #' @return An updated data.table that contains the added simulated data
 #' @examples
 #'
@@ -103,9 +113,10 @@ addColumns <- function(dtDefs, dtOld) {
 #' ggplot(data = dt, aes(x = y, y = NewVar, group = x)) +
 #'   geom_point(aes(color = factor(x)))
 #' @export
+#' @md
 #' @concept generate_data
 #' @concept condition
-addCondition <- function(condDefs, dtOld, newvar) {
+addCondition <- function(condDefs, dtOld, newvar, envir = parent.frame()) {
 
   # 'declare' vars
   varname <- NULL
@@ -163,7 +174,13 @@ addCondition <- function(condDefs, dtOld, newvar) {
     n <- nrow(dtTemp)
 
     if (n > 0) {
-      dtTemp <- .generate(cDefs[i, ], n, dtTemp, oldkey)
+      dtTemp <- .generate(
+        args = cDefs[i, ],
+        n = n,
+        dfSim = dtTemp,
+        idname =  oldkey,
+        envir = envir
+      )
 
       dtTemp <- data.table::data.table(dtTemp)
       dtTemp <- dtTemp[, list(get(oldkey), get(newvar))]
diff --git a/R/define_data.R b/R/define_data.R
index ac332858..fed3d029 100644
--- a/R/define_data.R
+++ b/R/define_data.R
@@ -89,7 +89,7 @@ defCondition <- function(dtDefs = NULL,
 #' @details The possible data distributions are: `r paste0(.getDists(),collapse = ", ")`.
 #'
 #' @examples
-#' extVar <<- 2.3
+#' extVar <- 2.3
 #' def <- defData(varname = "xNr", dist = "nonrandom", formula = 7, id = "idnum")
 #' def <- defData(def, varname = "xUni", dist = "uniform", formula = "10;20")
 #' def <- defData(def,
diff --git a/R/generate_data.R b/R/generate_data.R
index b6d26f8d..f07e4ac5 100644
--- a/R/generate_data.R
+++ b/R/generate_data.R
@@ -5,8 +5,11 @@
 #'  are provided
 #' a data set with ids only is generated.
 #' @param id The string defining the id of the record
+#' @param envir Environment the data definitions are evaluated in.
+#'  Defaults to [base::parent.frame].
 #' @return A data.table that contains the simulated data.
 #' @export
+#' @md
 #' @concept generate_data
 #' @examples
 #' genData(5)
@@ -43,7 +46,7 @@
 #' def
 #'
 #' genData(5, def)
-genData <- function(n, dtDefs = NULL, id = "id") {
+genData <- function(n, dtDefs = NULL, id = "id", envir = parent.frame()) {
   assertNotMissing(n = missing(n))
   assertValue(n = n, id = id)
   assertType(id = id, type = "character")
@@ -63,7 +66,13 @@ genData <- function(n, dtDefs = NULL, id = "id") {
     iter <- nrow(dtDefs) # generate a column of data for each row of dtDefs
 
     for (i in (1:iter)) {
-      dfSimulate <- .generate(dtDefs[i, ], n, dfSimulate, idname)
+      dfSimulate <- .generate(
+        args = dtDefs[i, ],
+        n = n,
+        dfSim = dfSimulate,
+        idname = idname,
+        envir = envir
+      )
     }
 
     dt <- data.table::data.table(dfSimulate)
@@ -824,4 +833,4 @@ genSurv <- function(dtName, survDefs, digits = 3) {
   }
 
   return(dtSurv[])
-}
+}
\ No newline at end of file
diff --git a/R/generate_dist.R b/R/generate_dist.R
index 9f26e3c1..88425793 100644
--- a/R/generate_dist.R
+++ b/R/generate_dist.R
@@ -6,27 +6,116 @@
 #' @param args One row from data definitions data.table
 #' @param n The number of observations required in the data set
 #' @param dt Incomplete simulated data.table
+#' @param envir Environment the data definitions are evaluated in.
+#'  Defaults to [base::parent.frame].
 #' @return A data.frame with the updated simulated data
 #' @noRd
-.generate <- function(args, n, dfSim, idname) {
+.generate <- function(args, n, dfSim, idname, envir = parent.frame()) {
   newColumn <- switch(args$dist,
-    beta = .genbeta(n, args$formula, args$variance, args$link, dfSim),
+    beta = .genbeta(
+      n = n,
+      formula = args$formula,
+      precision = args$variance,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
     binary = {
       args$variance <- 1
-      .genbinom(n, args$formula, args$variance, args$link, dfSim)
+      .genbinom(
+        n = n,
+        formula = args$formula,
+        size = args$variance,
+        link = args$link,
+        dtSim = dfSim,
+        envir = envir
+      )
     },
-    binomial = .genbinom(n, args$formula, args$variance, args$link, dfSim),
-    categorical = .gencat(n, args$formula, args$link, dfSim),
-    exponential = .genexp(n, args$formula, args$link, dfSim),
-    gamma = .gengamma(n, args$formula, args$variance, args$link, dfSim),
-    mixture = .genmixture(n, args$formula, dfSim),
-    negBinomial = .gennegbinom(n, args$formula, args$variance, args$link, dfSim),
-    nonrandom = .gendeterm(n, args$formula, args$link, dfSim),
-    normal = .gennorm(n, args$formula, args$variance, args$link, dfSim),
-    noZeroPoisson = .genpoisTrunc(n, args$formula, args$link, dfSim),
-    poisson = .genpois(n, args$formula, args$link, dfSim),
-    uniform = .genunif(n, args$formula, dfSim),
-    uniformInt = .genUnifInt(n, args$formula, dfSim),
+    binomial = .genbinom(
+      n = n,
+      formula = args$formula,
+      size = args$variance,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    categorical = .gencat(
+      n = n,
+      formula = args$formula,
+      link = args$link,
+      dfSim = dfSim,
+      envir = envir
+    ),
+    exponential = .genexp(
+      n = n,
+      formula = args$formula,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    gamma = .gengamma(
+      n = n,
+      formula = args$formula,
+      dispersion = args$variance,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    mixture = .genmixture(
+      n = n,
+      formula = args$formula,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    negBinomial = .gennegbinom(
+      n = n,
+      formula = args$formula,
+      dispersion = args$variance,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    nonrandom = .gendeterm(
+      n = n,
+      formula = args$formula,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    normal = .gennorm(
+      n = n,
+      formula = args$formula,
+      variance = args$variance,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    noZeroPoisson = .genpoisTrunc(
+      n = n,
+      formula = args$formula,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    poisson = .genpois(
+      n = n,
+      formula = args$formula,
+      link = args$link,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    uniform = .genunif(
+      n = n,
+      formula = args$formula,
+      dtSim = dfSim,
+      envir = envir
+    ),
+    uniformInt = .genUnifInt(
+      n = n,
+      formula = args$formula,
+      dtSim = dfSim,
+      envir = envir
+    ),
     default = stop(
       paste(args$dist, "not a valid distribution. Please check spelling."),
       call. = FALSE
@@ -107,10 +196,11 @@
   return(mean)
 }
 
-.genbeta <- function(n, formula, precision, link = "identity", dtSim) {
+# TODO document internal functions
+.genbeta <- function(n, formula, precision, link = "identity", dtSim, envir) {
   mean <- .getBetaMean(dtSim, formula, link, n)
 
-  d <- .evalWith(precision, .parseDotVars(precision), dtSim, n)
+  d <- .evalWith(precision, .parseDotVars(precision, envir), dtSim, n)
 
   sr <- betaGetShapes(mean = mean, precision = d)
   new <- stats::rbeta(n, shape = sr$shape1, shape2 = sr$shape2)
@@ -126,9 +216,14 @@
 # @param dtSim Incomplete simulated data.table
 # @return A data.frame column  with the updated simulated data
 
-.getBinaryMean <- function(dtSim, formula, Size, link, n = nrow(dtSim)) {
-  size <- .evalWith(Size, .parseDotVars(Size), dtSim, n)
-  p <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.getBinaryMean <- function(dtSim,
+                           formula,
+                           size,
+                           link,
+                           n = nrow(dtSim),
+                           envir = parent.frame()) {
+  size <- .evalWith(size, .parseDotVars(size, envir), dtSim, n)
+  p <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
 
   if (link == "logit") {
     p <- 1 / (1 + exp(-p))
@@ -137,8 +232,15 @@
   return(list(p, size))
 }
 
-.genbinom <- function(n, formula, Size, link, dtSim) {
-  params <- .getBinaryMean(dtSim, formula, Size, link, n)
+.genbinom <- function(n, formula, size, link, dtSim, envir) {
+  params <- .getBinaryMean(
+    dtSim = dtSim,
+    formula = formula,
+    size = size,
+    link = link,
+    n = n,
+    envir = envir
+  )
 
   return(stats::rbinom(n, params[[2]], params[[1]]))
 }
@@ -149,8 +251,10 @@
 # @param formula String that specifies the probabilities, each separated by ";"
 # @param dfSim Incomplete simulated data set
 # @param idkey Key of incomplete data set
+# @param envir Environment the data definitions are evaluated in.
+#  Defaults to [base::parent.frame].
 # @return A data.frame column with the updated simulated data
-.gencat <- function(n, formula, link, dfSim) {
+.gencat <- function(n, formula, link, dfSim, envir) {
   formulas <- .splitFormula(formula)
 
   if (length(formulas) < 2) {
@@ -161,7 +265,7 @@
   }
 
   parsedProbs <-
-    .evalWith(formulas, .parseDotVars(formulas), dfSim, n)
+    .evalWith(formulas, .parseDotVars(formulas, envir), dfSim, n)
 
   if (link == "logit") {
     parsedProbs <- exp(parsedProbs)
@@ -182,8 +286,8 @@
 # @param dtSim Incomplete simulated data.table
 # @return A data.frame column  with the updated simulated data
 
-.gendeterm <- function(n, formula, link, dtSim) {
-  new <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.gendeterm <- function(n, formula, link, dtSim, envir) {
+  new <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
 
   if (link == "log") {
     new <- exp(new)
@@ -197,9 +301,8 @@
 # @param n The number of observations required in the data set
 # @param formula String that specifies the mean (lambda)
 # @return A data.frame column with the updated simulated data
-
-.genexp <- function(n, formula, link = "identity", dtSim) {
-  mean <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.genexp <- function(n, formula, link = "identity", dtSim, envir) {
+  mean <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
   if (link == "log") {
     mean <- exp(mean)
   }
@@ -215,8 +318,8 @@
 # @param formula String that specifies the probabilities
 # @return A data.frame column with the updated simulated data
 
-.getGammaMean <- function(dtSim, formula, link, n = nrow(dtSim)) {
-  mean <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.getGammaMean <- function(dtSim, formula, link, n = nrow(dtSim), envir) {
+  mean <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
   if (link == "log") {
     mean <- exp(mean)
   }
@@ -224,9 +327,9 @@
   return(mean)
 }
 
-.gengamma <- function(n, formula, dispersion, link = "identity", dtSim) {
-  mean <- .getGammaMean(dtSim, formula, link, n)
-  d <- .evalWith(dispersion, .parseDotVars(dispersion), dtSim, n)
+.gengamma <- function(n, formula, dispersion, link = "identity", dtSim, envir) {
+  mean <- .getGammaMean(dtSim, formula, link, n, envir)
+  d <- .evalWith(dispersion, .parseDotVars(dispersion, envir), dtSim, n)
 
   sr <- gammaGetShapeRate(mean = mean, dispersion = d)
   new <- stats::rgamma(n, shape = sr$shape, rate = sr$rate)
@@ -234,14 +337,14 @@
   return(new)
 }
 
-.genmixture <- function(n, formula, dtSim) {
+.genmixture <- function(n, formula, dtSim, envir) {
   origFormula <- formula
   formula <- .rmWS(formula)
   var_pr <- strsplit(formula, "+", fixed = T)
   var_dt <- strsplit(var_pr[[1]], "|", fixed = T)
   formDT <- as.data.table(do.call(rbind, var_dt))
   ps <-
-    cumsum(.evalWith(unlist(formDT[, 2]), .parseDotVars(formDT[, 2])))
+    cumsum(.evalWith(unlist(formDT[, 2]), .parseDotVars(formDT[, 2], envir)))
 
   if (!isTRUE(all.equal(max(ps), 1))) {
     valueError(origFormula,
@@ -271,8 +374,8 @@
 # @param formula String that specifies the mean
 # @return A data.frame column with the updated simulated data
 
-.getNBmean <- function(dtSim, formula, link, n = nrow(dtSim)) {
-  mean <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.getNBmean <- function(dtSim, formula, link, n = nrow(dtSim), envir = parent.frame()) {
+  mean <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
   if (link == "log") {
     mean <- exp(mean)
   }
@@ -280,8 +383,8 @@
   return(mean)
 }
 
-.gennegbinom <- function(n, formula, dispersion, link = "identity", dtSim) {
-  mean <- .getNBmean(dtSim, formula, link, n)
+.gennegbinom <- function(n, formula, dispersion, link = "identity", dtSim, envir) {
+  mean <- .getNBmean(dtSim, formula, link, n, envir)
   d <- as.numeric(as.character(dispersion))
 
   sp <- negbinomGetSizeProb(mean = mean, dispersion = d)
@@ -299,13 +402,16 @@
 # @param dtSim Incomplete simulated data.table
 # @return A data.frame column  with the updated simulated data
 
-.getNormalMean <- function(dtSim, formula, n = nrow(dtSim)) {
-  .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.getNormalMean <- function(dtSim,
+                           formula,
+                           n = nrow(dtSim),
+                           envir = parent.frame()) {
+  .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
 }
 
-.gennorm <- function(n, formula, variance, link, dtSim) {
-  mean <- .getNormalMean(dtSim, formula, n)
-  v <- .evalWith(variance, .parseDotVars(variance), dtSim, n)
+.gennorm <- function(n, formula, variance, link, dtSim, envir) {
+  mean <- .getNormalMean(dtSim, formula, n, envir)
+  v <- .evalWith(variance, .parseDotVars(variance, envir), dtSim, n)
 
   return(stats::rnorm(n, mean, sqrt(v)))
 }
@@ -318,8 +424,8 @@
 # @param dtSim Incomplete simulated data.table
 # @return A data.frame column with the updated simulated data
 
-.getPoissonMean <- function(dtSim, formula, link, n = nrow(dtSim)) {
-  mean <- .evalWith(formula, .parseDotVars(formula), dtSim, n)
+.getPoissonMean <- function(dtSim, formula, link, n = nrow(dtSim), envir = parent.frame()) {
+  mean <- .evalWith(formula, .parseDotVars(formula, envir), dtSim, n)
 
   if (link == "log") {
     mean <- exp(mean)
@@ -328,8 +434,8 @@
   return(mean)
 }
 
-.genpois <- function(n, formula, link, dtSim) {
-  mean <- .getPoissonMean(dtSim, formula, link, n)
+.genpois <- function(n, formula, link, dtSim, envir) {
+  mean <- .getPoissonMean(dtSim, formula, link, n, envir)
 
   return(stats::rpois(n, mean))
 }
@@ -343,8 +449,8 @@
 # @param dtSim Incomplete simulated data.table
 # @return A data.frame column with the updated simulated data
 
-.genpoisTrunc <- function(n, formula, link, dtSim) {
-  mean <- .getPoissonMean(dtSim, formula, link, n)
+.genpoisTrunc <- function(n, formula, link, dtSim, envir) {
+  mean <- .getPoissonMean(dtSim, formula, link, n, envir)
 
   u <- stats::runif(n, min = 0, max = 1)
 
@@ -362,17 +468,17 @@
 # @param dtSim Incomplete simulated data set
 # @return A data.frame column  with the updated simulated data
 
-.genunif <- function(n, formula, dtSim) {
+.genunif <- function(n, formula, dtSim, envir) {
   if (!is.null(dtSim) && n != nrow(dtSim)) {
     stop("Length mismatch between 'n' and 'dtSim'")
   }
 
-  range <- .parseUnifFormula(formula, dtSim, n)
+  range <- .parseUnifFormula(formula, dtSim, n, envir)
 
   return(stats::runif(n, range$min, range$max))
 }
 
-.parseUnifFormula <- function(formula, dtSim, n) {
+.parseUnifFormula <- function(formula, dtSim, n, envir) {
   range <- .splitFormula(formula)
 
   if (length(range) != 2) {
@@ -384,7 +490,7 @@
     )
   }
 
-  parsedRange <- .evalWith(range, .parseDotVars(range), dtSim, n)
+  parsedRange <- .evalWith(range, .parseDotVars(range, envir), dtSim, n)
 
   r_min <- parsedRange[, 1]
   r_max <- parsedRange[, 2]
@@ -416,8 +522,8 @@
 # @param dtSim Incomplete simulated data set
 # @return A data.frame column  with the updated simulated data
 
-.genUnifInt <- function(n, formula, dtSim) {
-  range <- .parseUnifFormula(formula, dtSim, n)
+.genUnifInt <- function(n, formula, dtSim, envir) {
+  range <- .parseUnifFormula(formula, dtSim, n, envir)
 
   if (any(!sapply(range, function(x) floor(x) == x))) {
     stop(paste(
@@ -429,4 +535,4 @@
   unifCont <- stats::runif(n, range$min, range$max + 1)
 
   return(as.integer(floor(unifCont)))
-}
+}
\ No newline at end of file
diff --git a/R/internal_utility.R b/R/internal_utility.R
index 1bb58803..c6976cb5 100644
--- a/R/internal_utility.R
+++ b/R/internal_utility.R
@@ -13,13 +13,13 @@
 #' .parseDotVars(c("a + ..extVar1", "b + ..extVar2"))
 #' .parseDotVars(data.frame("a + ..extVar1", "b + ..extVar2"))
 #' @noRd
-.parseDotVars <- function(formula) {
+.parseDotVars <- function(formula, envir = parent.frame()) {
   vars <- all.vars(parse(text = formula))
   dotVars <- startsWith(vars, "..")
   # TODO clarify inheritance in case of non globalEnvs in documentation
   varValues <- mget(
     sub("..", "", vars[dotVars]),
-    envir = parent.frame(),
+    envir = envir,
     inherits = TRUE,
     ifnotfound = NA
   )
diff --git a/R/simstudy-package.R b/R/simstudy-package.R
index b184d178..36397309 100644
--- a/R/simstudy-package.R
+++ b/R/simstudy-package.R
@@ -51,7 +51,7 @@ NULL
 #'  `p_1,...,p_n`. For more information see
 #'  [rdatagen.net](https://www.rdatagen.net/post/adding-mixture-distributions-to-simstudy/).
 #' @examples
-#' ext_var <<- 2.9
+#' ext_var <- 2.9
 #' def <- defData(varname = "external", formula = "3 + log(..ext_var)", variance = .5)
 #' def
 #' genData(5, def)
diff --git a/tests/testthat/test-generate_dist.R b/tests/testthat/test-generate_dist.R
index 3b45ce8b..77fcd08c 100644
--- a/tests/testthat/test-generate_dist.R
+++ b/tests/testthat/test-generate_dist.R
@@ -1,7 +1,7 @@
 # .gencat ----
 test_that(".gencat throws errors", {
-  expect_error(.gencat(n, "1;", "identity", NULL), "two probabilities")
-  expect_error(.gencat(n, "1; ", "identity", NULL), "two probabilities")
+  expect_error(.gencat(n, "1;", "identity", NULL, environment()), "two probabilities")
+  expect_error(.gencat(n, "1; ", "identity", NULL, environment()), "two probabilities")
 })
 
 # .genunif ----
@@ -12,10 +12,10 @@ test_that("unif data is generated as expected.", {
 
   dt <- genData(n, def)
   dterr <- genData(n - 5, def)
-  expect_error(.genunif(n, "test;test2", dterr), "Length mismatch")
-  expect_length(.genunif(n, "test;test2", dt), n)
-  expect_true(all(!is.na(.genunif(n, "test;test2", dt))))
-  expect_length(.genunif(n, "1.3;100.2", dt), n)
+  expect_error(.genunif(n, "test;test2", dterr, environment()), "Length mismatch")
+  expect_length(.genunif(n, "test;test2", dt, environment()), n)
+  expect_true(all(!is.na(.genunif(n, "test;test2", dt, environment()))))
+  expect_length(.genunif(n, "1.3;100.2", dt, environment()), n)
 })
 
 test_that("'uniform' formula checked correctly", {
@@ -28,14 +28,14 @@ test_that("'uniform' formula checked correctly", {
       x
     }),
     function(x) {
-      expect_silent(.genunif(x$n, x$range, NULL))
+      expect_silent(.genunif(x$n, x$range, NULL, environment()))
     }
   )
 
-  expect_warning(.genunif(3, "1;1", NULL), "are equal")
-  expect_error(.genunif(3, "", NULL), "format")
-  expect_error(.genunif(3, "1;2;3", NULL), "format")
-  expect_error(.genunif(3, "2;1", NULL), "'max' < 'min'")
+  expect_warning(.genunif(3, "1;1", NULL, environment()), "are equal")
+  expect_error(.genunif(3, "", NULL, environment()), "format")
+  expect_error(.genunif(3, "1;2;3", NULL, environment()), "format")
+  expect_error(.genunif(3, "2;1", NULL, environment()), "'max' < 'min'")
 })
 
 # .genUnifInt ----
@@ -47,9 +47,9 @@ test_that("unifInt data is generated as expected.", {
   dt <- genData(n, def)
   dt$test2 <- ceiling(dt$test2)
 
-  expect_length(.genUnifInt(n, "test;test2", dt), n)
-  expect_true(all(!is.na(.genUnifInt(n, "test;test2", dt))))
-  expect_length(.genUnifInt(n, "1;100", dt), n)
+  expect_length(.genUnifInt(n, "test;test2", dt, environment()), n)
+  expect_true(all(!is.na(.genUnifInt(n, "test;test2", dt, environment()))))
+  expect_length(.genUnifInt(n, "1;100", dt, environment()), n)
 })
 
 test_that("'uniformInt' formula checked correctly", {
@@ -62,9 +62,9 @@ test_that("'uniformInt' formula checked correctly", {
       x
     }),
     function(x) {
-      expect_silent(.genUnifInt(x$n, x$range, NULL))
+      expect_silent(.genUnifInt(x$n, x$range, NULL, environment()))
     }
   )
 
-  expect_error(.genUnifInt(3, "1.1;2.4", NULL), "must be integer")
+  expect_error(.genUnifInt(3, "1.1;2.4", NULL, environment()), "must be integer")
 })

From cf8b89e555ce8f3cd6605cdbd5971d0a56057855 Mon Sep 17 00:00:00 2001
From: assignUser <github@krushak.me>
Date: Sat, 3 Oct 2020 16:09:34 +0200
Subject: [PATCH 2/4] Document

---
 NEWS.md              |  2 +-
 man/addColumns.Rd    |  5 ++++-
 man/addCondition.Rd  |  5 ++++-
 man/addCorFlex.Rd    | 15 +++++++++++++--
 man/defData.Rd       |  2 +-
 man/distributions.Rd |  2 +-
 man/genData.Rd       |  7 +++++--
 7 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 4247db22..cbb17a9b 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,7 +4,7 @@
 * Moved genCorOrdCat's functionality into genOrdCat. genCorOrdCat is now
   deprecated.
 * Introduced a new system for formula definitions and completely reworked the
-  underlying code. See vignette "NAME".
+  underlying code. See vignette "double-dot".
 * Added new vignettes.
 * Created documentation page: https://kgoldfeld.github.io/simstudy/
 * Fixed bug in trtAssign related new ratio argument.
diff --git a/man/addColumns.Rd b/man/addColumns.Rd
index 982dac8f..b1d41825 100644
--- a/man/addColumns.Rd
+++ b/man/addColumns.Rd
@@ -4,12 +4,15 @@
 \alias{addColumns}
 \title{Add columns to existing data set}
 \usage{
-addColumns(dtDefs, dtOld)
+addColumns(dtDefs, dtOld, envir = parent.frame())
 }
 \arguments{
 \item{dtDefs}{name of definitions for added columns}
 
 \item{dtOld}{name of data table that is to be updated}
+
+\item{envir}{Environment the data definitions are evaluated in.
+Defaults to \link[base:sys.parent]{base::parent.frame}.}
 }
 \value{
 an updated data.table that contains the added simulated data
diff --git a/man/addCondition.Rd b/man/addCondition.Rd
index 124fe2af..97fb63c6 100644
--- a/man/addCondition.Rd
+++ b/man/addCondition.Rd
@@ -4,7 +4,7 @@
 \alias{addCondition}
 \title{Add a single column to existing data set based on a condition}
 \usage{
-addCondition(condDefs, dtOld, newvar)
+addCondition(condDefs, dtOld, newvar, envir = parent.frame())
 }
 \arguments{
 \item{condDefs}{Name of definitions for added column}
@@ -12,6 +12,9 @@ addCondition(condDefs, dtOld, newvar)
 \item{dtOld}{Name of data table that is to be updated}
 
 \item{newvar}{Name of new column to add}
+
+\item{envir}{Environment the data definitions are evaluated in.
+Defaults to \link[base:sys.parent]{base::parent.frame}.}
 }
 \value{
 An updated data.table that contains the added simulated data
diff --git a/man/addCorFlex.Rd b/man/addCorFlex.Rd
index c08a3f87..81af26ef 100644
--- a/man/addCorFlex.Rd
+++ b/man/addCorFlex.Rd
@@ -4,12 +4,20 @@
 \alias{addCorFlex}
 \title{Create multivariate (correlated) data - for general distributions}
 \usage{
-addCorFlex(dt, defs, rho = 0, tau = NULL, corstr = "cs", corMatrix = NULL)
+addCorFlex(
+  dt,
+  defs,
+  rho = 0,
+  tau = NULL,
+  corstr = "cs",
+  corMatrix = NULL,
+  envir = parent.frame()
+)
 }
 \arguments{
 \item{dt}{Data table that will be updated.}
 
-\item{defs}{Field definition table created by function `defDataAdd`.}
+\item{defs}{Field definition table created by function \code{defDataAdd}.}
 
 \item{rho}{Correlation coefficient, -1 <= rho <= 1. Use if corMatrix is not
 provided.}
@@ -26,6 +34,9 @@ and "ar1" for an autoregressive structure. Defaults to "cs".}
 symmetrical and positive semi-definite. It is not a required field; if a
 matrix is not provided, then a structure and correlation coefficient rho must
 be specified.}
+
+\item{envir}{Environment the data definitions are evaluated in.
+Defaults to \link[base:sys.parent]{base::parent.frame}.}
 }
 \value{
 data.table with added column(s) of correlated data
diff --git a/man/defData.Rd b/man/defData.Rd
index c184cc45..b01ee4ab 100644
--- a/man/defData.Rd
+++ b/man/defData.Rd
@@ -39,7 +39,7 @@ Add single row to definitions table
 The possible data distributions are: normal, binary, binomial, poisson, noZeroPoisson, uniform, categorical, gamma, beta, nonrandom, uniformInt, negBinomial, exponential, mixture.
 }
 \examples{
-extVar <<- 2.3
+extVar <- 2.3
 def <- defData(varname = "xNr", dist = "nonrandom", formula = 7, id = "idnum")
 def <- defData(def, varname = "xUni", dist = "uniform", formula = "10;20")
 def <- defData(def,
diff --git a/man/distributions.Rd b/man/distributions.Rd
index c1918181..3630b8cd 100644
--- a/man/distributions.Rd
+++ b/man/distributions.Rd
@@ -62,7 +62,7 @@ part of the new distribution \verb{x_1,...,X_n} is assigned a probability
 }
 
 \examples{
-ext_var <<- 2.9
+ext_var <- 2.9
 def <- defData(varname = "external", formula = "3 + log(..ext_var)", variance = .5)
 def
 genData(5, def)
diff --git a/man/genData.Rd b/man/genData.Rd
index 7620efa9..f5dbd422 100644
--- a/man/genData.Rd
+++ b/man/genData.Rd
@@ -4,16 +4,19 @@
 \alias{genData}
 \title{Calling function to simulate data}
 \usage{
-genData(n, dtDefs = NULL, id = "id")
+genData(n, dtDefs = NULL, id = "id", envir = parent.frame())
 }
 \arguments{
 \item{n}{the number of observations required in the data set.}
 
 \item{dtDefs}{name of definitions data.table/data.frame. If no definitions
- are provided
+are provided
 a data set with ids only is generated.}
 
 \item{id}{The string defining the id of the record}
+
+\item{envir}{Environment the data definitions are evaluated in.
+Defaults to \link[base:sys.parent]{base::parent.frame}.}
 }
 \value{
 A data.table that contains the simulated data.

From d474356f9966bc22ef75569330984e25df509a57 Mon Sep 17 00:00:00 2001
From: assignUser <github@krushak.me>
Date: Sat, 3 Oct 2020 16:33:44 +0200
Subject: [PATCH 3/4] test pkgdown dev version

---
 DESCRIPTION  | 2 +-
 _pkgdown.yml | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index dde5ef48..a2983e80 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: simstudy
 Title: Simulation of Study Data
-Version: 0.2.0
+Version: 0.1.16.9000
 Date: 2020-09-25
 Authors@R: 
     c(person(given = "Keith",
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 10633a71..4b3d7ee5 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -3,6 +3,8 @@ useurl: https://kgoldfeld.github.io/simstudy
 authors:
   Keith Goldfeld:
     href: "https://www.rdatagen.net/"
+development: 
+  mode: auto
 reference:
 - title: Define Data
 - contents:

From 2005fafa47da1f4d2c67639210c8f1b70ce912b9 Mon Sep 17 00:00:00 2001
From: assignUser <github@krushak.me>
Date: Sat, 3 Oct 2020 16:40:47 +0200
Subject: [PATCH 4/4] adjust action

---
 .github/workflows/pkgdown.yaml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 1f83b70e..a1a65781 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -1,6 +1,8 @@
 on:
   push:
-    branches: release
+    branches: 
+      - main
+      - release
 
 name: pkgdown