Home_w1

0.1 Loading data

load(url("http://www.ms.ut.ee/mart/andmeteadus/births.Rdata"))

data=births

### Available variables 
names(births)

## [1] "id"      "bweight" "lowbw"   "gestwks" "preterm" "matage"  "hyp"    
## [8] "sex"

### dataset looks like (6 first rows)

head(births)

##   id bweight lowbw gestwks preterm matage hyp sex
## 1  1    2974     0   38.52       0     34   0   2
## 2  2    3270     0      NA      NA     30   0   1
## 3  3    2620     0   38.15       0     35   0   2
## 4  4    3751     0   39.80       0     31   0   1
## 5  5    3200     0   38.89       0     33   1   1
## 6  6    3673     0   40.97       0     33   0   2

0.2 define for catergorical variable

data$lowbw=as.factor(data$lowbw) ## Indicator for birth weight less than 2500 g.
data$preterm=as.factor(data$preterm) ## Indicator for gestation period less than 37 weeks
data$hyp=as.factor(data$hyp) ## Indicator for maternal hypertension
data$sex=as.factor(data$sex) ##Sex of baby: 1:Male, 2:Female.

0.3 loading packages need for making combined table

library(table1)

## Warning: package 'table1' was built under R version 4.0.4

## 
## Attaching package: 'table1'

## The following objects are masked from 'package:base':
## 
##     units, units<-

library(compareGroups)

## Warning: package 'compareGroups' was built under R version 4.0.3

0.4 solving for each task

0.4.1 the first oneside table (solved the first question: 1. Table 1: descriptive statistics)

table1(~lowbw+preterm+hyp+sex+bweight+gestwks+matage, data=data)

	Overall (N=500)
lowbw
0	440 (88.0%)
1	60 (12.0%)
preterm
0	427 (85.4%)
1	63 (12.6%)
Missing	10 (2.0%)
hyp
0	428 (85.6%)
1	72 (14.4%)
sex
1	264 (52.8%)
2	236 (47.2%)
bweight
Mean (SD)	3140 (637)
Median [Min, Max]	3190 [628, 4550]
gestwks
Mean (SD)	38.7 (2.31)
Median [Min, Max]	39.1 [24.7, 43.2]
Missing	10 (2.0%)
matage
Mean (SD)	34.0 (3.90)
Median [Min, Max]	34.0 [23.0, 43.0]

0.4.2 2. Table 2: the results of the statistical analysis

0.4.2.1 part a+b

t1=compareGroups(lowbw~preterm+hyp+sex+bweight+gestwks+matage, data=data)

## Warning: glm.fit: algorithm did not converge

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

####### In this result, we will see the first, second, and the 4th collums are presented the for option a, b, d of required task.
createTable(t1)

## 
## --------Summary descriptives table by 'lowbw'---------
## 
## __________________________________________ 
##               0           1      p.overall 
##             N=440       N=60               
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
## preterm:                          <0.001   
##     0    406 (94.2%) 21 (35.6%)            
##     1    25 (5.80%)  38 (64.4%)            
## hyp:                              <0.001   
##     0    388 (88.2%) 40 (66.7%)            
##     1    52 (11.8%)  20 (33.3%)            
## sex:                               0.249   
##     1    237 (53.9%) 27 (45.0%)            
##     2    203 (46.1%) 33 (55.0%)            
## bweight  3308 (426)  1884 (522)   <0.001   
## gestwks  39.2 (1.45) 34.9 (3.53)  <0.001   
## matage   34.1 (3.90) 33.8 (3.87)   0.579   
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

0.4.2.2 results for this command will be reflected the actual test and type of data for each variables. The tests have been used including: chi-square test and independent t-test.

t1   

## 
## 
## -------- Summary of results by groups of 'lowbw'---------
## 
## 
##   var     N   p.value  method            selection
## 1 preterm 490 <0.001** categorical       ALL      
## 2 hyp     500 <0.001** categorical       ALL      
## 3 sex     500 0.249    categorical       ALL      
## 4 bweight 500 <0.001** continuous normal ALL      
## 5 gestwks 490 <0.001** continuous normal ALL      
## 6 matage  500 0.579    continuous normal ALL      
## -----
## Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1

0.4.3 3. figure

# Library
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.3

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.0.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

0.4.4 plot for this task reveals the mean value maternal age by term and preterm

p <- ggplot(data, aes(x=preterm , y=matage, fill=preterm )) + 
  geom_boxplot()+

labs(title="mean value maternal age by term (0) and preterm (1)", x="Indicator for birth weight less than 2500 g", y = "Maternal age (weeks) ")
p