title: "Import des données" author: "Nom de l'auteur" date: "30 juin 2014" output: html_document: number_sections: yes toc: yes pdf_document: toc: yes
# Warning: encoding = UTF-8
# Load ggplot2 package to plot library(ggplot2) # If your files are not in root dir # library(knitr) # opts_knit$set(root.dir = '../.') # Load DescTools for description tools library(DescTools) # Create a label function to access easly to labels # without using label function from packages # like those from Hmisc label <- function(object) attr(x = object, which = "label")
Tableau de données au format Excel.
library(openxlsx) rep_rawdata <- read.xlsx( "rep_path_to_database")
Changer les noms de colonnes avec ceux définis dans le cahier de variable
colnames(rep_rawdata) <- rep_columns_names
Créer une copie du tableau de variable pour le nettoyage
rep_cleandata <- rep_rawdata
head(rep_rawdata$rep_rname, 10)
Variable non utilisée dans l'analyse. A supprimer
rep_cleandata$rep_rname <- NULL
rep_cleandata$rep_rname <- as.numeric(rep_rawdata$rep_rname)
rep_cleandata$rep_rname <- as.integer(rep_rawdata$rep_rname)
rep_cleandata$rep_rname <- factor( x = rep_rawdata$rep_rname, levels = rep_levels, labels = rep_names )
rep_cleandata$rep_rname <- factor( x = rep_rawdata$rep_rname, levels = rep_levels, labels = rep_names, ordered = TRUE )
rep_cleandata$rep_rname <- as.Date( x = rep_rawdata$rep_rname, format = "rep_unit" )
<--- Check import --->
Ajouter un label (étiquette).
attr(rep_cleandata$rep_rname, "label") <- "rep_varlabel"
# Premières données head(rep_cleandata$rep_rname, 10) # Description Desc(rep_cleandata$rep_rname, plotit = TRUE, main = label(rep_cleandata$rep_rname) )
Recherche de données manquantes graphiquement
library(dfexplore) dfplot(rep_cleandata)
donnees <- rep_cleandata save(donnees, file = "donnees.RData")
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