troubleshooting and fixing

DESCRIPTION

@@ -2,16 +2,12 @@ Package: daDoctoR
 Type: Package
 Title: FUNCTIONS FOR HEALTH RESEARCH
 Version: 0.1.0.9023
-Author@R: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
+Author: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Description: I am a Danish medical doctor involved in neuropsychiatric research. 
    Here I have collected functions I use for my data analysis. You are very 
    welcome to get inspired or to use my work.
-Imports: broom,
-   dplyr,
-   epiR,
-   ggplot2,
-   MASS
+Imports: broom, dplyr, epiR, ggplot2, MASS
 Suggest: shiny
 License: GPL (>= 2)
 Encoding: UTF-8

R/col_fact.R

@@ -7,9 +7,6 @@
 #' @param labels labels for all selected columns
 #' @keywords factor
 #' @export
-#' @examples
-#' col_fact()
-
 
 col_fact<-function(string,data,levels=NULL,labels=NULL){
 ## Defining factors for columns containing string (can be vector of multiple strings), based on dplyr.

R/col_num.R

@@ -5,8 +5,6 @@
 #' @param data Dataframe
 #' @keywords numeric
 #' @export
-#' @examples
-#' col_num()
 
 col_num<-function(string,data){
 ## Defining factors for columns containing string (can be vector of multiple strings), based on dplyr

R/comb_olr.R

@@ -3,12 +3,9 @@
 #' Should be combined with "rep_olr()". The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 #' @param meas primary outcome (factor with >2 levels).
 #' @param vars variables in model. Input as c() of columnnames, use dput().
-#' @param dta data frame to pull variables from.
+#' @param data data frame to pull variables from.
 #' @keywords olr
 #' @export
-#' @examples
-#' comb_olr()
-
 
 comb_olr<-function(meas,vars,data){
   require(MASS)

R/date_convert_function.R

@@ -4,8 +4,6 @@
 #' @param x data as as dd/mm/yyyy.
 #' @keywords date
 #' @export
-#' @examples
-#' date_convert()
 
 date_convert<-function(x)
   ## Input format as dd/mm/yyyy, output is standard yyyy-mm-dd

R/hwe_allele.R

@@ -5,13 +5,11 @@
 #' @param y Allele 2.
 #' @keywords hardy-weinberg-equllibrium
 #' @export
-#' @examples
-#' hwe_allele()
 
 hwe_allele<-function(x,y)
 {
 ## Witten by Andreas Gammelgaard Damsbo, agdamsbo@pm.me, based on a non-working
-## applet at from http://www.husdyr.kvl.dk/htm/kc/popgen/genetik/applets/kitest.htm
+## applet at http://www.husdyr.kvl.dk/htm/kc/popgen/genetik/applets/kitest.htm
 
   all<-pmax(length(levels(factor(x))),length(levels(factor(y))))
 

R/hwe_app.R

@@ -3,6 +3,7 @@
 #' App to easily calculate and visualize the HWE.
 #'
 #' @export
+
 hwe_app <- function() {
   appDir <- system.file("shiny-examples", "hwe_calc", package = "daDoctoR")
   if (appDir == "") {

R/hwe_geno.R

@@ -4,8 +4,6 @@
 #' @param mm First count of genotype.
 #' @keywords hardy-weinberg-equllibrium
 #' @export
-#' @examples
-#' hwe_geno()
 
 hwe_geno<-function(mm,mn,nn,mo,no,oo,alleles=2)
 {

R/hwe_sum.R

@@ -6,8 +6,6 @@
 #' @param f factor for grouping.
 #' @keywords hardy-weinberg-equllibrium
 #' @export
-#' @examples
-#' hwe_sum()
 
 hwe_sum<-function (a1, a2, f) {
   require(daDoctoR)

R/plot_ord_odds.R

@@ -11,8 +11,6 @@
 #' @param input can be either "model", which is a olr model (polr()), or "df", which is a dataframe whith three columns for OR, lower CI and upper CI-
 #' @keywords forestplot
 #' @export
-#' @examples
-#' plot_ord_odds()
 
 plot_ord_odds<-function(x, title = NULL,dec=3,lbls=NULL,hori="OR (95 % CI)",vert="Variables",short=FALSE,input="model"){
 

R/rep_biv.R

@@ -9,8 +9,6 @@
 #' @param logistic flag for logistic binomial regression or not (linear is then selected).
 #' @keywords logistic regression
 #' @export
-#' @examples
-#' rep_biv()
 
 rep_biv<-function(y,v1,string,data,method="pval",logistic=FALSE,ci=FALSE,cut=0.1,v2=NULL,v3=NULL){
 

R/rep_epi_tests.R

@@ -7,9 +7,6 @@
 #' @param data dataframe to draw variables from.
 #' @keywords ppv npv sensitivity specificity
 #' @export
-#' @examples
-#' rep_epi_tests()
-
 
 rep_epi_tests<-function(gold,test,data){
   require(epiR)

R/rep_glm.R

@@ -9,16 +9,15 @@
 #' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed.
 #' @keywords logistic
 #' @export
-#' @examples
-#' rep_glm()
-
-rep_glm<-function(meas,vars=NULL,string,ci=FALSE,data,fixed.var=FALSE){
 
+rep_glm<-function(meas,vars=NULL,string,ci=FALSE,data,fixed.var=FALSE)
+  {
+  ## Intro and definitions
   require(broom)
   y<-data[,c(meas)]
-
+  ## Factor check
   if(!is.factor(y)){stop("y is not a factor")}
-
+  ## Running "true" bivariate analysis
   if (fixed.var==FALSE){
     d<-data
     x<-data.frame(d[,c(vars,string)])
@@ -85,7 +84,7 @@ rep_glm<-function(meas,vars=NULL,string,ci=FALSE,data,fixed.var=FALSE){
     r<-data.frame(df[,1:2],pa,t)[-1,]
   }
 
-
+  ## Running multivariate analyses (eg "bivariate" analyses with fixed variables)
   if (fixed.var==TRUE){
     d<-data
     x<-data.frame(d[,c(string)])

R/rep_lm.R

@@ -5,12 +5,10 @@
 #' @param vars variables in model. Input as c() of columnnames, use dput().
 #' @param string variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
-#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed.
 #' @param data data frame to pull variables from.
-#' @keywords linear
+#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed.
+#' @keywords linear regression
 #' @export
-#' @examples
-#' rep_lm()
 
 rep_lm<-function(meas,vars=NULL,string,ci=FALSE,data,fixed.var=FALSE){
 

R/rep_olr.R

@@ -5,12 +5,9 @@
 #' @param vars variables in model. Input as c() of columnnames, use dput().
 #' @param string variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
-#' @param dta data frame to pull variables from.
+#' @param data data frame to pull variables from.
 #' @keywords olr
 #' @export
-#' @examples
-#' rep_olr()
-
 
 rep_olr<-function(meas,vars,string,ci=FALSE,data){
 

R/rep_olr_sngl.R

@@ -6,9 +6,6 @@
 #' @param dta data frame to pull variables from.
 #' @keywords olr
 #' @export
-#' @examples
-#' rep_olr_sngl()
-
 
 rep_olr_sngl<-function(meas,vars,data){
   require(MASS)

R/rep_reg_cie.R

@@ -9,8 +9,6 @@
 #' @param cut cut value for gating if including or dropping the tested variable. As suggested bu S. Greenland (1989).
 #' @keywords estimate-in-estimate
 #' @export
-#' @examples
-#' rep_reg_cie()
 
 rep_reg_cie<-function(meas,vars,string,data,cut=0.1){
 

R/strobe_diff_bygroup.R

@@ -9,8 +9,6 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.
 #' @keywords strobe
 #' @export
-#' @examples
-#' strobe_diff_bygroup()
 
 strobe_diff_bygroup<-function(meas,var,group,adj,data,dec=2){
 

R/strobe_diff_byvar.R

@@ -9,8 +9,6 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @keywords strobe
 #' @export
-#' @examples
-#' strobe_diff_byvar()
 
 strobe_diff_byvar<-function(meas,var,group,adj,data,dec=2){
 

R/strobe_diff_twodim.R

@@ -8,8 +8,6 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @keywords strobe
 #' @export
-#' @examples
-#' strobe_diff_twodim()
 
 strobe_diff_twodim<-function(meas,group,adj,data,dec=2){
   ## meas: sdmt

R/strobe_log.R

@@ -8,8 +8,6 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @keywords logistic
 #' @export
-#' @examples
-#' strobe_log()
 
 strobe_log<-function(meas,var,adj,data,dec=2){
   ## Ønskeliste:

R/strobe_olr.R

@@ -7,8 +7,6 @@
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @keywords olr
 #' @export
-#' @examples
-#' strobe_olr()
 
 strobe_olr<-function(meas,vars,data,dec=2){
 

R/strobe_pred.R

@@ -8,8 +8,6 @@
 #' @param n.by.adj flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.
 #' @keywords logistic
 #' @export
-#' @examples
-#' strobe_pred()
 
 strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
   ## Ønskeliste:

daDoctoR.Rproj

@@ -19,3 +19,4 @@ BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageCheckArgs: --as-cran
+PackageRoxygenize: rd,collate,namespace,vignette

man/col_fact.Rd

@@ -18,7 +18,4 @@ col_fact(string, data, levels = NULL, labels = NULL)
 \description{
 Depending on dply's contains()-function.
 }
-\examples{
-col_fact()
-}
 \keyword{factor}

man/col_num.Rd

@@ -14,7 +14,4 @@ col_num(string, data)
 \description{
 Depending on dply's contains()-function.
 }
-\examples{
-col_num()
-}
 \keyword{numeric}

man/comb_olr.Rd

@@ -11,12 +11,9 @@ comb_olr(meas, vars, data)
 
 \item{vars}{variables in model. Input as c() of columnnames, use dput().}
 
-\item{dta}{data frame to pull variables from.}
+\item{data}{data frame to pull variables from.}
 }
 \description{
 Should be combined with "rep_olr()". The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
-\examples{
-comb_olr()
-}
 \keyword{olr}

man/date_convert.Rd

@@ -12,7 +12,4 @@ date_convert(x)
 \description{
 Input format as dd/mm/yyyy, output is standard yyyy-mm-dd
 }
-\examples{
-date_convert()
-}
 \keyword{date}

man/hwe_allele.Rd

@@ -14,7 +14,4 @@ hwe_allele(x, y)
 \description{
 For easy calculation.
 }
-\examples{
-hwe_allele()
-}
 \keyword{hardy-weinberg-equllibrium}

man/hwe_geno.Rd

@@ -12,7 +12,4 @@ hwe_geno(mm, mn, nn, mo, no, oo, alleles = 2)
 \description{
 For easy calculation.
 }
-\examples{
-hwe_geno()
-}
 \keyword{hardy-weinberg-equllibrium}

man/hwe_sum.Rd

@@ -16,7 +16,4 @@ hwe_sum(a1, a2, f)
 \description{
 For easy printing.
 }
-\examples{
-hwe_sum()
-}
 \keyword{hardy-weinberg-equllibrium}

man/plot_ord_odds.Rd

@@ -28,7 +28,4 @@ plot_ord_odds(x, title = NULL, dec = 3, lbls = NULL,
 \description{
 Heavily inspired by https://www.r-bloggers.com/plotting-odds-ratios-aka-a-forrestplot-with-ggplot2/
 }
-\examples{
-plot_ord_odds()
-}
 \keyword{forestplot}

man/rep_biv.Rd

@@ -23,8 +23,5 @@ rep_biv(y, v1, string, data, method = "pval", logistic = FALSE,
 \description{
 For bivariate analyses, for gating by p-value or change-in-estimate.
 }
-\examples{
-rep_biv()
-}
 \keyword{logistic}
 \keyword{regression}

man/rep_epi_tests.Rd

@@ -19,9 +19,6 @@ For bivariate analyses. The confint() function is rather slow, causing the whole
 \details{
 Repeats the epi.tests from the epiR package. Either gs or test should be of length 1.
 }
-\examples{
-rep_epi_tests()
-}
 \keyword{npv}
 \keyword{ppv}
 \keyword{sensitivity}

man/rep_glm.Rd

@@ -23,7 +23,4 @@ rep_glm(meas, vars = NULL, string, ci = FALSE, data,
 \description{
 For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
-\examples{
-rep_glm()
-}
 \keyword{logistic}

man/rep_lm.Rd

@@ -23,7 +23,5 @@ rep_lm(meas, vars = NULL, string, ci = FALSE, data,
 \description{
 For bivariate analyses, to determine which variables to include in adjusted model.
 }
-\examples{
-rep_lm()
-}
 \keyword{linear}
+\keyword{regression}

man/rep_olr.Rd

@@ -15,12 +15,9 @@ rep_olr(meas, vars, string, ci = FALSE, data)
 
 \item{ci}{flag to get results as OR with 95% confidence interval.}
 
-\item{dta}{data frame to pull variables from.}
+\item{data}{data frame to pull variables from.}
 }
 \description{
 For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
-\examples{
-rep_olr()
-}
 \keyword{olr}

man/rep_olr_sngl.Rd

@@ -16,7 +16,4 @@ rep_olr_sngl(meas, vars, data)
 \description{
 Should be combined with "rep_olr()". For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
-\examples{
-rep_olr_sngl()
-}
 \keyword{olr}

man/rep_reg_cie.Rd

@@ -22,7 +22,4 @@ rep_reg_cie(meas, vars, string, data, cut = 0.1)
 \description{
 For bivariate analyses, binary logistic or linear regression. From "Modeling and variable selection in epidemiologic analysis." - S. Greenland, 1989.
 }
-\examples{
-rep_reg_cie()
-}
 \keyword{estimate-in-estimate}

man/strobe_diff_bygroup.Rd

@@ -22,7 +22,4 @@ strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 \description{
 Printable table of three dimensional regression analysis of group vs var for meas. By group.
 }
-\examples{
-strobe_diff_bygroup()
-}
 \keyword{strobe}

man/strobe_diff_byvar.Rd

@@ -22,7 +22,4 @@ strobe_diff_byvar(meas, var, group, adj, data, dec = 2)
 \description{
 Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
 }
-\examples{
-strobe_diff_byvar()
-}
 \keyword{strobe}

man/strobe_diff_twodim.Rd

@@ -20,7 +20,4 @@ strobe_diff_twodim(meas, group, adj, data, dec = 2)
 \description{
 Printable table of regression analysis by group for meas. Detects wether to perform logistic or linear regression.
 }
-\examples{
-strobe_diff_twodim()
-}
 \keyword{strobe}

man/strobe_log.Rd

@@ -20,7 +20,4 @@ strobe_log(meas, var, adj, data, dec = 2)
 \description{
 Printable table of logistic regression analysis according to STROBE.
 }
-\examples{
-strobe_log()
-}
 \keyword{logistic}

man/strobe_olr.Rd

@@ -18,7 +18,4 @@ strobe_olr(meas, vars, data, dec = 2)
 \description{
 Printable table of logistic regression analysis oaccording to STROBE.
 }
-\examples{
-strobe_olr()
-}
 \keyword{olr}

man/strobe_pred.Rd

@@ -20,7 +20,4 @@ strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE)
 \description{
 Printable table of logistic regression analysis according to STROBE.
 }
-\examples{
-strobe_pred()
-}
 \keyword{logistic}

packing.R

@@ -1,13 +0,0 @@
-library(roxygen2)
-library(devtools)
-
-source("/Users/andreas/Documents/GitHub/daDoctoR/updatePackageVersion.R")
-
-setwd("/Users/andreas/Documents/GitHub/daDoctoR")
-
-updatePackageVersion()
-
-document()
-
-
-# Inspiration: "https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/"

torubleshooting.R

@@ -1,3 +0,0 @@
-## Looking for errors in code
-
-for (f in list.files("/Users/andreas/Documents/GitHub/daDoctoR/R", full.names=TRUE)) parse(f)

update local version.R

@@ -1,13 +0,0 @@
-# Install new version
-
-# Remove
-
-remove.packages("daDoctoR")
-.rs.restartR()
-
-
-setwd("/")
-devtools::install_github('agdamsbo/daDoctoR')
-
-library(daDoctoR)
-

updatePackageVersion.R

@@ -1,35 +0,0 @@
-updatePackageVersion <- function(packageLocation ="."){
-  ## Seen at: https://www.mango-solutions.com/blog/how-to-auto-update-a-package-version-number
-
-  ## Read DESCRIPTION file
-  desc <- readLines(file.path(packageLocation, "DESCRIPTION"))
-
-  ## Find the line where the version is defined
-  vLine <- grep("^Version\\:", desc)
-
-  ## Extract version number
-  vNumber <- gsub("^Version\\:\\s*", "", desc[vLine])
-
-    ## Split the version number into two; a piece to keep, a piece to increment
-    versionNumber <- strsplit(vNumber, "\\.")[[1]]
-  versionParts <- length(versionNumber)
-  vNumberKeep <- paste(versionNumber[1:(versionParts-1)], sep= "", collapse= ".")
-  vNumberUpdate <- versionNumber[versionParts]
-
-  ## Replace old version number with new one (increment by 1)
-  oldVersion <- as.numeric(vNumberUpdate)
-  newVersion <- oldVersion + 1
-
-  ## Build final version number
-  vFinal <- paste(vNumberKeep, newVersion, sep = ".")
-
-  ## Update DESCRIPTION file (in R)
-  desc[vLine] <- paste0("Version: ", vFinal )
-
-  ## Update the actual DESCRIPTION file
-  writeLines(desc, file.path(packageLocation, "DESCRIPTION"))
-
-  ## Return the updated version number to screen
-  return(vFinal)
-}
-