First update for some time. On road to major revision.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: daDoctoR
 Title: Functions For Health Research
-Version: 0.19.14
+Version: 0.21.1
-Year: 2019
+Year: 2021
 Author: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Description: R functions for convenient data management an danalysis in health research.
@@ -10,4 +10,4 @@ Suggest: shiny
 License: GPL (>= 2)
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.1

R/age_calc.R

@@ -6,9 +6,9 @@
 #' @keywords age
 #' @export
 #' @examples
-#'   ##Kim Larsen
+#'   ##Kim Larsen (cpr is known from album)
 #'   dob<-dob_extract_cpr("231045-0637")
-#'   date<-as.Date("2018-09-29")
+#'   date<-as.Date("2018-09-30")
 #'   trunc(age_calc(dob,date))
 
 age_calc<-function (dob, enddate = Sys.Date(), units = "years", precise = TRUE)

R/strobe_diff_bygroup.R

@@ -1,14 +1,20 @@
 #' Print regression results according to STROBE
 #'
-#' Printable table of three dimensional regression analysis of group vs var for meas. By group.
+#' Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
-#' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
+#' Group and variable has to be dichotomous factor.
+#' @param meas outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
 #' @param var binary exposure variable to compare against (active vs placebo). As string.
-#' @param group group to compare, as string.
+#' @param group binary group to compare, as string.
 #' @param adj variables to adjust for, as string.
-#' @param data dataframe of data.
+#' @param data dataframe to subset from.
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.
 #' @keywords strobe
 #' @export
+#' @examples
+#'   data('mtcars')
+#'   mtcars$vs<-factor(mtcars$vs)
+#'   mtcars$am<-factor(mtcars$am)
+#'   strobe_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
 
 strobe_diff_bygroup<-function(meas,var,group,adj,data,dec=2){
 

inst/shiny-examples/hwe_calc/server.R

@@ -15,6 +15,9 @@ library(shiny)
 library(ggplot2)
 source("https://raw.githubusercontent.com/agdamsbo/daDoctoR/master/R/hwe_geno.R")
 
+# source("https://raw.githubusercontent.com/agdamsbo/daDoctoR/master/inst/shiny-examples/hwe_calc/ui.R")
+# source(list.files(system.file("shiny-examples", "hwe_calc", package = "daDoctoR"), pattern="ui.R", full.names=TRUE))
+
 # Define server logic required to draw a histogram
 
 server <- function(input, output, session) {
@@ -88,5 +91,6 @@ server <- function(input, output, session) {
 }
 
 # Run the application
+
 shinyApp(ui = ui, server = server)
 

man/age_calc.Rd

@@ -15,9 +15,9 @@ age_calc(dob, enddate = Sys.Date(), units = "years", precise = TRUE)
 For age calculations.
 }
 \examples{
-  ##Kim Larsen
+  ##Kim Larsen (cpr is known from album)
   dob<-dob_extract_cpr("231045-0637")
-  date<-as.Date("2018-09-29")
+  date<-as.Date("2018-09-30")
   trunc(age_calc(dob,date))
 }
 \keyword{age}

man/euler_plot.Rd

@@ -4,8 +4,7 @@
 \alias{euler_plot}
 \title{Creates Euler model from list of identifier numbers.}
 \usage{
-euler_plot(x, total, dec = 1, label = as.character(c(1:5)),
+euler_plot(x, total, dec = 1, label = as.character(c(1:5)), shape = "ellipse")
-  shape = "ellipse")
 }
 \arguments{
 \item{x}{list of variables included. Has to be vectors of identifier numbers.}

man/plot_biv_olr.Rd

@@ -4,9 +4,18 @@
 \alias{plot_biv_olr}
 \title{Forrest plot from ordinal logistic regression, version2 of plot_ord_ords().}
 \usage{
-plot_biv_olr(meas, vars, data, title = NULL, dec = 3, lbls = NULL,
+plot_biv_olr(
-  hori = "OR (95 \% CI)", vert = "Variables", short = FALSE,
+  meas,
-  analysis = c("biv", "multi"))
+  vars,
+  data,
+  title = NULL,
+  dec = 3,
+  lbls = NULL,
+  hori = "OR (95 \% CI)",
+  vert = "Variables",
+  short = FALSE,
+  analysis = c("biv", "multi")
+)
 }
 \arguments{
 \item{meas}{outcome meassure variable name or response in data-data.frame as a string. Should be factor, preferably ordered.}

man/plot_ord_odds.Rd

@@ -4,9 +4,16 @@
 \alias{plot_ord_odds}
 \title{Forrest plot from ordinal logistic regression.}
 \usage{
-plot_ord_odds(x, title = NULL, dec = 3, lbls = NULL,
+plot_ord_odds(
-  hori = "OR (95 \% CI)", vert = "Variables", short = FALSE,
+  x,
-  input = c("model", "df"))
+  title = NULL,
+  dec = 3,
+  lbls = NULL,
+  hori = "OR (95 \% CI)",
+  vert = "Variables",
+  short = FALSE,
+  input = c("model", "df")
+)
 }
 \arguments{
 \item{x}{input data.}

man/rep_biv.Rd

@@ -4,8 +4,18 @@
 \alias{rep_biv}
 \title{A repeated function for bivariate analyses}
 \usage{
-rep_biv(y, v1, string, data, method = "pval", logistic = FALSE,
+rep_biv(
-  ci = FALSE, cut = 0.1, v2 = NULL, v3 = NULL)
+  y,
+  v1,
+  string,
+  data,
+  method = "pval",
+  logistic = FALSE,
+  ci = FALSE,
+  cut = 0.1,
+  v2 = NULL,
+  v3 = NULL
+)
 }
 \arguments{
 \item{y}{Effect meassure.}

man/rep_glm.Rd

@@ -4,8 +4,7 @@
 \alias{rep_glm}
 \title{A repeated logistic regression function}
 \usage{
-rep_glm(meas, vars = NULL, string, ci = FALSE, data,
+rep_glm(meas, vars = NULL, string, ci = FALSE, data, fixed.var = FALSE)
-  fixed.var = FALSE)
 }
 \arguments{
 \item{meas}{Effect meassure. Input as c() of columnnames, use dput().}

man/rep_lm.Rd

@@ -4,8 +4,7 @@
 \alias{rep_lm}
 \title{A repeated linear regression function}
 \usage{
-rep_lm(meas, vars = NULL, string, ci = FALSE, data,
+rep_lm(meas, vars = NULL, string, ci = FALSE, data, fixed.var = FALSE)
-  fixed.var = FALSE)
 }
 \arguments{
 \item{meas}{Effect meassure. Input as c() of columnnames, use dput().}

man/strobe_diff_bygroup.Rd

@@ -7,19 +7,26 @@
 strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 }
 \arguments{
-\item{meas}{outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
+\item{meas}{outcome measure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
 
 \item{var}{binary exposure variable to compare against (active vs placebo). As string.}
 
-\item{group}{group to compare, as string.}
+\item{group}{binary group to compare, as string.}
 
 \item{adj}{variables to adjust for, as string.}
 
-\item{data}{dataframe of data.}
+\item{data}{dataframe to subset from.}
 
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.}
 }
 \description{
-Printable table of three dimensional regression analysis of group vs var for meas. By group.
+Printable table of two dimensional regression analysis of group vs variable for outcome measure. By group. Includes p-value
+Group and variable has to be dichotomous factor.
+}
+\examples{
+  data('mtcars')
+  mtcars$vs<-factor(mtcars$vs)
+  mtcars$am<-factor(mtcars$am)
+  strobe_diff_bygroup(meas="mpg",var="vs",group = "am",adj=c("disp","wt"),data=mtcars)
 }
 \keyword{strobe}

man/strobe_pred.Rd

@@ -4,8 +4,7 @@
 \alias{strobe_pred}
 \title{Regression model of predictors according to STROBE, bi- and multivariate.}
 \usage{
-strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE,
+strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE, p.val = FALSE)
-  p.val = FALSE)
 }
 \arguments{
 \item{meas}{binary outcome meassure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}