1 / 10

Epidemiological Data Analysis and Visualization with R and Epicalc by Brugnaro Luca and Boscaro Gianni

Learn how to load Excel data into R, perform descriptive analyses, create variables, visualize data, and analyze relationships using R and Epicalc. This guide covers importing data, generating descriptive statistics, creating new variables, plotting graphs, calculating risks, and assessing statistical significance.

baruch
Download Presentation

Epidemiological Data Analysis and Visualization with R and Epicalc by Brugnaro Luca and Boscaro Gianni

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. EpidemiologiaR and Epicalc Brugnaro Luca Boscaro Gianni

  2. Caricare dei dati in R da excel Importare un file di excel in R: read.csv2(file.choose()) Come invocare l’help: ?read.csv()

  3. Prime analisi descrittive Come descrivere le variabili: des() Prime analisi descrittive: summ() , summary(dataset)

  4. Come creare una variabili Come creare un’unica variabile «malato» ovvero una persona con uno dei seguenti segni o sintomi: nausa, vomito, dolori addominali o diarrea: casi=(nausea==1|vomiting==1|abdpain==1|diarrhea==1) label.var(casi,"malati") Modificare i 90 di Eclair in dati mancanti (NA): recode(Eclair,90,NA)

  5. Primi grafici (1) tabpct(Eclair,case) e… se volessi modificare il titolo? tabpct(Eclair,casi,main="Distribuzione dei mali per Eclair") Cosa dice il grafico ? Come si può migliorarlo ? …

  6. Primi grafici (2) … Pastine=cut(Eclair,breaks=c(0,0.4,1,2,79), labels=c("0, "1", "2", ">2")) …e se non ci piace «true,false» ? malati=factor(casi, labels=c("Falso", "Vero")) tabpct(Pastine,Malati1,main="Distribuzione dei malati per nr pasterelle mangiate")

  7. Secondi grafici summ(age) #ripartizione empirica dotplot(age) summ(age,by=sex) dotplot(age,by=sex) ?dotplot(age,by=sex) dotplot(age,by=sex,dot.col=c("pink","blue")) pyramid(age,sex, printTable=TRUE,percent="each")

  8. Relazioni (1) paste=Eclair>0 label.var(paste,"paste mangiate") cs(malati,pastine) NoPaste=!paste cs(malati,NoPaste)

  9. Relazioni (2) cs(malati,Pastine) Un po’ di odds… cc(malati,paste)

  10. Ora tocca a voi • Calcolare rischio attribuibile e RR di Beefcurry e Saltegg • Sono RA e RR statisticamente significativi?

More Related