Field trial in rural northern Ghana on the effect of Vitamin A given in large doses to children under 5 . The data in this exercise were taken from the baseline survey. They include interview data provided by the mother or her substitute, clinical and laboratory data. You will find the da-ta in VASTCHS.csv . Please note that in this csv file semi colons (;) have been used as field separators and comma’s as decimal-point characters. Take this into account when you import the dataset in R!
1. Preparation
Keyboard shortcuts:
- `ctrl + shift + m` -\> `%>%` (operador pipe)
- `ctrl + alt + i` -\> Crea un chunk de código
Set working directory
- setwd("C:/Users/xxx/yyy")
- getwd()
Load (basic) packages
To import Excel files library(readxl) To calculate OR or RR library(EpiStats)
library(readxl) # To import Excel fileslibrary(tidyverse) # Varias funciones de tidyverse (dplyr, ggplot2, etc.)
library(dslabs) # Paquete que contiene la base de datos de Gapminderlibrary(EpiStats) # To calculate OR or RR
Warning: package 'EpiStats' was built under R version 4.2.2
Loading required package: epiR
Warning: package 'epiR' was built under R version 4.2.2
Loading required package: survival
Package epiR 2.0.53 is loaded
Type help(epi.about) for summary information
Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses
Import data
From Text (.csv)-example
data <- read.csv(“H:/Teaching/My_Courses/IIH-epistat/R/Sharm_Exercise/mysharm.csv”)
From Excel file (.xls, .xlsx)-example
library(readxl)
data <- read_excel(“H:/Teaching/My_Courses/IIH-epistat/R/Sharm_Exercise/mysharm.xlsx”)
from STATA file (.dta)-example
library(haven)
data <- read_dta(“H:/Teaching/My_Courses/IIH-epistat/R/Sharm_Exercise/mysharm.dta”)
Importing a CSV under the name of vastchs, with “;” as separator, and with “,” as decimal:
vastchs <-read.csv("C:/Users/pined/OneDrive - Universidad Nacional Mayor de San Marcos/Javier 2022/Belgica/AC2/Linear Regresion Exercise Database/Datasets/VASTCHS.csv", sep=";", dec=",")
2. Descriptive Statistics
Summary
Numeric Variable
summary(vastchs$AGE) #summary
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 10.00 21.00 22.49 34.00 55.00
vastchs$wasted<-ifelse(vastchs$WHZ<-2, 1,0) #wasted si es menos a -2 poner 1 sino 0vastchs$stunded<-ifelse(vastchs$HAZ<-2, 1,0) #stunded si es menos a -2 poner 1 sino 0vastchs$underweight<-ifelse(vastchs$WAZ<-2, 1,0) #undweig si es menos a -2 poner 1 sino 0vastchs$anemia<-ifelse(vastchs$HB<8, 1,0) #anemia si es menos que 8 poner 1 sino 0vastchs$vitadef<-ifelse(vastchs$RETINOL<0.7, 1,0) #Def of VitA si es menos de 0.7 poner 1 sino 0vastchs$HANDPUMP2<-ifelse(vastchs$HANDPUMP==1, 1,0)
table(vastchs$vitadef) #Counting
0 1
302 835
prop.table(table(vastchs$vitadef)) #Percentages
0 1
0.2656113 0.7343887
table(vastchs$HANDPUMP, vastchs$HANDPUMP2)
0 1
1 0 903
2 65 0
3 169 0
Breast feeding as a potential protective factor agains vit A deficiency
table (vastchs$CURRBF) #To see what values have CURRBF
1 2
793 344
vastchs$CURRBF2<-ifelse(vastchs$CURRBF==1, 1,0) #To change values to 0 to 1table (vastchs$CURRBF, vastchs$CURRBF2) #To see if has already changed
0 1
1 0 793
2 344 0
Calculate de OR
table (vastchs$vitadef, vastchs$CURRBF2)
0 1
0 74 228
1 270 565
prop.table(table(vastchs$vitadef, vastchs$CURRBF2)) #Proportion above total sum
$df1
Cases Controls Total
Exposed 565 228 793
Unexposed 270 74 344
Total 835 302 1137
Proportion exposed 0.68 0.75 0.70
$df2
Point estimate 95%CI-ll 95%CI-ul
Odds ratio 0.68 0.50 0.92
Prev. frac. ex. 0.32 0.08 0.50
Prev. frac. pop 0.24 NA NA
chi2(1) 6.45 NA NA
Pr>chi2 0.011 NA NA
Agruping Variable
# Regrouping (e.g. age in months to agegroup) vastchs$agegrp <-NA vastchs$agegrp[vastchs$AGE<12] <-"1" vastchs$agegrp[vastchs$AGE>11& vastchs$AGE<24] <-"2" vastchs$agegrp[vastchs$AGE>23& vastchs$AGE<36] <-"3" vastchs$agegrp[vastchs$AGE>35& vastchs$AGE<48] <-"4" vastchs$agegrp[vastchs$AGE>47& vastchs$AGE<60] <-"5"# don't forget to convert agegrp to a factor variable vastchs$agegrp <-factor(vastchs$agegrp)
