Merge branch 'rc0.2.0' into develop

kaz-yos · Feb 14, 2014 · c3e49d8 · c3e49d8
2 parents 1858995 + d64aaaf
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,15 +2,16 @@ Package: tableone
 Type: Package
 Title: Create the "Table 1" to describe baseline characteristics
 Version: 0.2.0
-Date: 2014-02-12
+Date: 2014-02-13
 Author: Kazuki Yoshida, Justin Bohn
 Maintainer: Kazuki Yoshida <[email protected]>
-Description: This package creates create the "Table1", i.e.,
-    description of baseline characteristics in R although it is
-    essential in every medical research. The package was insipired by
-    and based on descriptive statistics functions in Deducer by Ian Fellows,
-    a Java-based GUI package. This package does not require GUI or Java,
-    and intended for CUI users.
+Description: This package creates "Table 1", i.e., description of baseline
+    patient characteristics, which is essential every medical research. This
+    package provides functions to create such summaries for continuous and
+    categorical variables, optionally with subgroups and groupwise comparison.
+    The package was insipired by and based on descriptive statistics functions
+    in Deducer, a Java-based GUI package by Ian Fellows. This package does not
+    require GUI or Java, and intended for CUI users.
 License: GPL-2
 Depends:
     e1071,

diff --git a/NEWS b/NEWS
@@ -1,4 +1,14 @@
-tableone 0.1.3 (not released yet)
+tableone 0.2.0
+----------------------------------------------------------------
+NEW FEATURES
+
+* CreateTableOne and related print/summary methods were added.
+
+* CreateTableOne can crate a table with both categorical and
+   continuous variables.
+
+
+tableone 0.1.3
 ----------------------------------------------------------------
 NEW FEATURES
 
@@ -17,7 +27,8 @@ BUG FIXES
 
 * Fixed incorrect specification of S3 method export.
 
-* To make an object an S3 method and export it both at_S3method and at_export tags are needed in the Roxygen part of the code.
+* To make an object an S3 method and export it both at_S3method and at_export
+  tags are needed in the Roxygen part of the code.
 
 * Passed all the default tests by R CMD check file.tar.gz
 

diff --git a/R/CreateTableOne.R b/R/CreateTableOne.R
@@ -1,11 +1,9 @@
-### 2014-02-09 Unifying function suggested by Justin Bohn
+##' Create an object summarizing both categorical and continuous variables
 ##'
-##' Create an object|output summarizing both categorical and continuous variables
-##'
-##' Create an object|output summarizing categorical variables optionally stratifying
+##' Create an object summarizing categorical variables optionally stratifying
 ##' by one or more startifying variables and performing statistical tests. The
-##' object gives a table that is easy to use in medical research papers.
-##'
+##' object gives a table that is easy to use in medical research papers. See also \code{\link{print.TableOne}} and \code{\link{summary.TableOne}}.
+##' 
 ##' @param vars Variables to be summarized given as a character vector. Factors are
 ##' handled as categorical variables, whereas numeric variables are handled as continuous variables.
 ##' @param strata Stratifying (grouping) variable name(s) given as a character
@@ -31,8 +29,7 @@
 ##' memory limitation. In this situation, the large sample approximation based
 ##' should suffice.
 ##' @param argsExact A named list of arguments passed to the function specified in testExact. The default is \code{list(workspace = 2*10^5)}, which specifies the memory space allocated for \code{\link{fisher.test}}.
-##' @return An object of class \code{TableOne}, which really is a list of two \code{\link{by}} objects with
-##' additional attributes. These correspond to structures holding results for
+##' @return An object of class \code{TableOne}, which really is a list of three objects. The first object named \code{object$TableOne} is the categorical-continuous mixture table formatted and printed by the \code{\link{print.TableOne}} method. The second object named \code{object$ContTable} is the table object containing continuous variables only. The third object named \code{object$CatTable} is the table object containing categorical variables only. The second and third objects can be then be examined with the \code{print} and \code{summary} method, for example, \code{summary(object$CatTable)} to examine the categorical variables in detail.
 ##' @author Justin Bohn, Kazuki Yoshida
 ##' @seealso
 ##' \code{\link{CreateTableOne}}, \code{\link{print.TableOne}}, \code{\link{summary.TableOne}},
@@ -53,12 +50,15 @@
 ##' varsToFactor <- c("status","trt","ascites","hepato","spiders","edema","stage")
 ##' pbc[varsToFactor] <- lapply(pbc[varsToFactor], factor)
 ##'
-##' ## Create Table 1 stratified by sex and trt
-##' tableOne <- CreateTableOne(vars = c("time","status","age","ascites","hepato",
-##'                                     "spiders","edema","bili","chol","albumin",
-##'                                     "copper","alk.phos","ast","trig","platelet",
-##'                                     "protime","stage"),
-##'                            strata = c("sex","trt"), data = pbc)
+##' ## Create a variable list
+##' dput(names(pbc))
+##' vars <- c("time","status","age","sex","ascites","hepato",
+##'           "spiders","edema","bili","chol","albumin",
+##'           "copper","alk.phos","ast","trig","platelet",
+##'           "protime","stage")
+##'
+##' ## Create Table 1 stratified by trt
+##' tableOne <- CreateTableOne(vars = vars, strata = c("trt"), data = pbc)
 ##'
 ##' ## Just typing the object name will invoke the print.TableOne method
 ##' tableOne
@@ -68,12 +68,20 @@
 ##' ## argument to obtain the exact test p-values.
 ##' print(tableOne, nonnormal = c("time"), exact = c("ascites"))
 ##'
-##' ## Use the summary.TableOne method for depth summary
+##' ## Use the summary.TableOne method for detailed summary
 ##' summary(tableOne)
 ##'
+##' ## See the categorical part only using $ operator
+##' tableOne$CatTable
+##'
+##' ## See the continuous part only using $ operator
+##' tableOne$ContTable
+##'
 ##' @export
 CreateTableOne <-
-    function(vars, strata, data,
+    function(vars,                                      # character vector of variable names
+             strata,                                    # character vector of variable names
+             data,                                      # data frame
              test          = TRUE,                      # whether to put p-values
              ## Test configuration for categorical data
              testApprox    = chisq.test,                # function for approximation test
@@ -97,6 +105,9 @@ CreateTableOne <-
         ## Abort if no variables exist at this point
         ModuleStopIfNoVarsLeft(vars)
 
+        ## Toggle test FALSE if no strata is given
+        test <- ModuleReturnFalseIfNoStrata(strata, test)
+
         ## Get the classes of the variables
         varClasses  <- sapply(data[vars], class)
         varFactors  <- names(varClasses[varClasses == "factor"])
@@ -106,20 +117,26 @@ CreateTableOne <-
         logiFactors <- sapply(data[vars], is.factor)
 
         ## Create lists of arguments
-        argsCreateContTable <- list(strata = strata, data = data,
-                                    test       = test,
-                                    testNormal = testNormal,
-                                    argsNormal = argsNormal,
+        argsCreateContTable <- list(data          = data,
+                                    test          = test,
+                                    testNormal    = testNormal,
+                                    argsNormal    = argsNormal,
                                     testNonNormal = testNonNormal,
                                     argsNonNormal = argsNonNormal
                                     )
-        argsCreateCatTable <- list(strata = strata, data = data,
-                                   test       = test,
-                                   testApprox = testApprox,
-                                   argsApprox = argsApprox,
-                                   testExact  = testExact,
-                                   argsExact  = argsExact
+        argsCreateCatTable <- list(data           = data,
+                                   test           = test,
+                                   testApprox     = testApprox,
+                                   argsApprox     = argsApprox,
+                                   testExact      = testExact,
+                                   argsExact      = argsExact
                                    )
+        ## Add strata = strata for argument only if strata is given
+        if(!missing(strata)) {
+
+            argsCreateContTable <- c(list(strata = strata), argsCreateContTable)
+            argsCreateCatTable  <- c(list(strata = strata), argsCreateCatTable)
+        }
 
 
         ## Condition on the absence of factor/numeric
@@ -172,8 +189,8 @@ CreateTableOne <-
             ContTable <- do.call(CreateContTable,
                                  args = c(list(vars = varNumerics), argsCreateContTable))
             ## Aggregated CatTable
-            CatTable <- do.call(CreateCatTable,
-                                args = c(list(vars = varFactors), argsCreateCatTable))
+            CatTable  <- do.call(CreateCatTable,
+                                 args = c(list(vars = varFactors),  argsCreateCatTable))
 
             ## Create a list
             listOfTables <- list(TableOne  = TableOne,

diff --git a/R/modules.R b/R/modules.R
@@ -35,7 +35,7 @@ ModuleStopIfNoVarsLeft <- function(vars) {
 ##
 ModuleReturnFalseIfNoStrata <- function(strata, test) { # Give strata variable names
 
-    if(missing(strata)){
+    if(missing(strata)) {
         ## test cannot be performed if no strata
         test <- FALSE
     }
@@ -44,7 +44,7 @@ ModuleReturnFalseIfNoStrata <- function(strata, test) { # Give strata variable n
 ## Check statra variables and conditionally create
 ModuleReturnStrata <- function(strata, data, dat) {     # Give strata variable names
 
-    if(missing(strata)){
+    if(missing(strata)) {
         ## If there is no strata, give "Overall" to every subject
         strata <- rep("Overall", dim(dat)[1])                           # Check if dim(dat)[[1]] is correct.
     } else {
@@ -107,7 +107,7 @@ ModuleCreateStrataVarName <- function(obj) {
 ## Used to define non-failing functions, that return NA when there is an error
 tryCatch.W.E <- function(expr) { 
     W <- NULL
-    w.handler <- function(w){ # warning handler
+    w.handler <- function(w) { # warning handler
         W <<- w
         invokeRestart("muffleWarning")
     }

diff --git a/R/print.CatTable.R b/R/print.CatTable.R
@@ -456,11 +456,15 @@ print.CatTable <- function(x, missing = FALSE,
 
         ## Name the row dimension with it. 1st dimension name should be empty.
         names(dimnames(out)) <- c("", strataString)
+    } else {
+
+        names(dimnames(out)) <- c("", "")
     }
 
 
     ## Modular version of quote/print toggle.
-    out <- ModuleQuoteAndPrintMat(matObj = out, quote = quote, printToggle = printToggle)
+    out <- ModuleQuoteAndPrintMat(matObj = out,
+                                  quote = quote, printToggle = printToggle)
 
     ## Print CrossTable() if requested
     if (CrossTable) {

diff --git a/R/print.ContTable.R b/R/print.ContTable.R
@@ -349,11 +349,15 @@ print.ContTable <- function(x, missing = FALSE,
 
         ## Name the row dimension with it. 1st dimension name should be empty.
         names(dimnames(out)) <- c("", strataString)
+    }  else {
+
+        names(dimnames(out)) <- c("", "")
     }
 
 
     ## (module) Takes an matrix object format, print if requested
-    out <- ModuleQuoteAndPrintMat(matObj = out, quote = quote, printToggle = printToggle)
+    out <- ModuleQuoteAndPrintMat(matObj = out,
+                                  quote = quote, printToggle = printToggle)
 
     ## Add attributes for column widths in characters
     attributes(out) <- c(attributes(out),

diff --git a/R/print.TableOne.R b/R/print.TableOne.R
@@ -67,7 +67,7 @@
 ##' ## argument to obtain the exact test p-values.
 ##' print(tableOne, nonnormal = c("time"), exact = c("ascites"))
 ##'
-##' ## Use the summary.TableOne method for depth summary
+##' ## Use the summary.TableOne method for detailed summary
 ##' summary(tableOne)
 ##'
 ##' @export
@@ -115,7 +115,8 @@ print.TableOne <- function(x, missing = FALSE,
 
                                   attributes(matObj)$vecColWidths
                               },
-                              simplify = TRUE)
+                              simplify = FALSE)
+    columnWidthInfo <- do.call(cbind, columnWidthInfo)
 
     ## Get the max values for each stratum
     vecMaxValues <- apply(columnWidthInfo, MARGIN = 1, FUN = max, na.rm = TRUE)
@@ -163,28 +164,25 @@ print.TableOne <- function(x, missing = FALSE,
     ## Row-combin n and all variables
     out <- do.call(rbind, c(list(stratumSizesRow), spaceFormattedTables))
 
+    ## Add stratification information to the column header (This is also in the constructor)
+    if (length(TableOne[[1]]) > 1 ) {
+        ## Combine variable names with : in between
+        strataVarName <- attributes(TableOne[[1]])$strataVarName
+
+        ## Create strata string
+        strataString <- paste0("Stratified by ", strataVarName)
+
+        ## Name the row dimension with it. 1st dimension name should be empty.
+        names(dimnames(out)) <- c("", strataString)
+    } else {
+
+        names(dimnames(out)) <- c("", "")
+    }
+
     ## Modular version of quote/print toggle.
     out <- ModuleQuoteAndPrintMat(matObj = out,
                                   quote = quote, printToggle = printToggle)
 
     ## Return the result
     return(invisible(out))
 }
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/R/summary.TableOne.R b/R/summary.TableOne.R
@@ -45,7 +45,7 @@
 ##' ## argument to obtain the exact test p-values.
 ##' print(tableOne, nonnormal = c("time"), exact = c("ascites"))
 ##'
-##' ## Use the summary.TableOne method for depth summary
+##' ## Use the summary.TableOne method for detailed summary
 ##' summary(tableOne)
 ##'
 ##' @export

diff --git a/R/tableone-package.R b/R/tableone-package.R
@@ -1,28 +1,18 @@
 ##' Create "Table 1" to describe baseline characteristics
 ##'
-##' Table 1, i.e., description of baseline patient characteristics is essential
-##' every medical research. This package provides functions to create such
-##' summaries for continuous and categorical variables, optionally with subgroups
-##' and groupwise comparison. The package was insipired by descriptive statistics
-##' functions in Deducer, Java-based GUI package. This package does not require
-##' GUI or Java, and intended for CUI users.
-##'
-##' \tabular{ll}{ Package: \tab tableone\cr Type: \tab Package\cr Version: \tab
-##' 0.1.3\cr Date: \tab 2014-02-08\cr License: \tab GPL-2\cr } Create an object
-##' summarizing continous and categorical variables optionally stratifying by
-##' one or more startifying variables and performing statistical tests. The
-##' object gives a table that is easy to use in medical research papers.
-##' Continuous variables are handled by CreateContTable, and categorical
-##' variables are handled by CreateCatTable.
+##' This package creates "Table 1", i.e., description of baseline patient characteristics, which is essential every medical research. This package provides functions to create such summaries for continuous and categorical variables, optionally with subgroups and groupwise comparison. The package was insipired by and based on descriptive statistics functions in Deducer, a Java-based GUI package by Ian Fellows. This package does not require GUI or Java, and intended for CUI users.
 ##'
 ##' @name tableone-package
 ##' @aliases tableone-package tableone
 ##' @docType package
 ##' @note Special Thanks:
+##'
 ##' Ian Fellows for developing the Deducer package, which this package is based on.
-##' Hadley Wickham for packaging advice and for creating tools this package was made with.
+##'
+##' Hadley Wickham for packaging advice and for creating tools this package was made with (roxygen2, devtools, testthat).
 ##'
 ##' Developmental repository is on github. Your contributions are appreciated.
+##'
 ##' https://github.com/kaz-yos/tableone
 ##'
 ##' @author Kazuki Yoshida, Justin Bohn
@@ -44,22 +34,35 @@
 ##' ## Check variables
 ##' head(pbc)
 ##'
-##' ## Create an overall table for categorical variables
-##' catVars <- c("status","ascites","hepato","spiders","edema","stage")
-##' catTableOverall <- CreateCatTable(vars = catVars, data = pbc)
+##' ## Make categorical variables factors
+##' varsToFactor <- c("status","trt","ascites","hepato","spiders","edema","stage")
+##' pbc[varsToFactor] <- lapply(pbc[varsToFactor], factor)
+##'
+##' ## Create a variable list
+##' dput(names(pbc))
+##' vars <- c("time","status","age","sex","ascites","hepato",
+##'           "spiders","edema","bili","chol","albumin",
+##'           "copper","alk.phos","ast","trig","platelet",
+##'           "protime","stage")
+##'
+##' ## Create Table 1 stratified by trt
+##' tableOne <- CreateTableOne(vars = vars, strata = c("trt"), data = pbc)
+##'
+##' ## Just typing the object name will invoke the print.TableOne method
+##' tableOne
 ##'
-##' ## Simply typing the object name will invoke the print.CatTable method,
-##' ## which will show the sample size, frequencies and percentages.
-##' ## For 2-level variables, only the higher level is shown for simplicity.
-##' catTableOverall
+##' ## Specifying nonnormal variables will show the variables appropriately,
+##' ## and show nonparametric test p-values. Specify variables in the exact
+##' ## argument to obtain the exact test p-values.
+##' print(tableOne, nonnormal = c("time"), exact = c("ascites"))
 ##'
-##' ## Create an overall table for continuous variables
-##' contVars <- c("time","age","bili","chol","albumin","copper","alk.phos","ast",
-##'               "trig","platelet","protime")
-##' contTableOverall <- CreateContTable(vars = contVars, data = pbc)
+##' ## Use the summary.TableOne method for detailed summary
+##' summary(tableOne)
+##' 
+##' ## See the categorical part only using $ operator
+##' tableOne$CatTable
 ##'
-##' ## Simply typing the object name will invoke the print.ContTable method,
-##' ## which will show the sample size, means and standard deviations.
-##' contTableOverall
+##' ## See the continuous part only using $ operator
+##' tableOne$ContTable
 ##'
 NULL