#' DF dummy data
#'
#' @description Antikörper SARS-Cov2-Impfung
#'
#' @format A data frame with 441 rows and 34 variables:
#' \describe{
#' \item{patid}{Study patid}
#' \item{alter}{Alter}
#' \item{grunderkrankung }{Grunderkrankungen}
#' }
#' @docType data
#' @keywords datasets
#' @name DFdummy
#' @examples
#'
#'
#'
#' data("DFdummy")
#' #' DF1 <- DFdummy %>% filter2(study.agreement)
#' attr(DF1, "filter")
#' DF2 <- DF1 %>% filter2(
#' st.p.sars.cov2 == "nein",
#' !is.na(spike.igg.3.impfung),
#' !is.na(MPN)
#'
#' )
#'
#' DF3 <- DF2 %>% filter2(
#' study.agreement,
#' sero.negativ.after.dose.2,
#' !is.na(spike.igg.3.impfung),
#' !is.na(spike.igg.4.impfung),
#' spike.igg.3.impfung == "<7.1 BAU/ml"
#' )
#' dat <- prepare_consort(DF1, DF2, DF3)
#'
#' require(consort_plot )
#' out <- consort_plot(
#' data = dat,
#' orders = c(
#' Trial.Nr = "Population",
#' Condition.1 = "Excluded",
#' Trial.Nr = "Allocated",
#' Condition.2 = "Lost of Follow-up",
#' Trial.Nr = "Finished Followup",
#' Condition.3 = "Not evaluable for the final analysis",
#' Trial.Nr = "Final Analysis"
#' ),
#' side_box = c("Condition.1", "Condition.2", "Condition.3"),
#' cex = 0.9
#' )
#'
#'
#'
#'
#' plot(out)
"DFdummy"
#
#
#
#
#
#
#
#
#
#
# get_dummy_data <- function(){
# require(tidyverse)
# library(stp25tools)
# require(stp25stat2)
# require(stp25output2)
#
# load(
# "C:/Users/wpete/Dropbox/1_Projekte/838_Julia_Berger/Processed data/JuliaBerger.Rdata"
# )
#
#
# new_levels<- function(x, levels) {
# levels(x) <- levels
# x
# }
#
# lvs <- c("--", "-", "o", "+", "++")
#
#
#
# n <- nrow(DF)
#
# DF <- DF %>%
# mutate(
# spike.3.positiv = spike.igg.3.impfung == "positiv",
# spike.igg.3.impfung = new_levels( spike.igg.3.impfung, c(">7.1 BAU/ml", "<7.1 BAU/ml")),
# spike.3 = spike.igg.3.impfung,
# sero.after.dose.2 = spike.igg.pos.neg,
# sero.negativ.after.dose.2 = sero.after.dose.2 == levels(sero.after.dose.2)[2],
#
# MMF = factor(MMF , rev(levels(MMF))),
# Tac = factor(Tac , rev(levels(Tac))),
# MPN = factor(MPN , rev(levels(MPN))),
# ntx.vintage = ntx.vintage / 12,
#
# Gesundheit = gl(length(lvs), 1, n, lvs),
# Sport = gl(length(lvs), 2, n, lvs),
# Freizeit = gl(length(lvs), 3, n, lvs),
# Essen = gl(length(lvs), 5, n, lvs),
# alter = round(alter),
#
# study.agreement= TRUE
#
#
# ) %>% select(
# patid,
# geschlecht,
# alter,
# gewicht,
# bmi,
# grunderkrankung,
# spendertyp,
# ntx.vintage,
# zahlntx,
#
# Gesundheit,
# Sport,
# Freizeit,
# Essen,
# haemoglobin,
# leukozyten,
# albumin,
# herzinsuffizienz,
# khk,
# pavk,
# cavk,
# diabetes,
# hba1c,
# x1.impfstoff,
# x3.impfstoff,
#
# Tac,
# MPA,
# MMF,
# MMF,
# MPN,
#
# sero.negativ.after.dose.2,
# spike.3.positiv,
# spike.igg.3.impfung,
# spike.igg.4.impfung,
# st.p.sars.cov2,
# study.agreement
# )
#
#
# DF_in <- DF[ seq_len( 441 - n ), ] %>%
# mutate( study.agreement = FALSE,
# patid = patid+1000,
# geschlecht = sample(geschlecht),
# alter = sample(alter))
#
#
#
# DFdummy <- rbind(DF, DF_in)
#
#
#
# save(DFdummy, file ="data/DFdummy.Rdata")
#
#
#
#
#
#
#
# # Analyse -----------------------------------------------------------------
#
# DF %>% Tbll_desc(
# geschlecht,
# alter[median, 0],
# gewicht,
# bmi,
# grunderkrankung,
# spendertyp,
# ntx.vintage[median, 0],
# zahlntx[median, 0],
# spike.igg.3.impfung,
# include.total = TRUE
# ) %>% Output("Demografische Merkmale der Teilnehmer aufgeteilt in Spike IgG 3.Impfung")
#
#
# }
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