library(gtsummary)
library(dplyr)

## 
## 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

library(ggplot2)
library(magrittr)
library(broom)
library(dataset)

Create dataset maternal_child_health

mch_data <- data.frame(
  age_mother = c(22, 25, 19, 30, 27, 24, 33, 20, 29, 26),
  education_level = c("Primary", "Secondary", "None", "Higher", "Secondary",
                      "Primary", "Higher", "None", "Secondary", "Primary"),
  antenatal_visits = c(4, 5, 2, 6, 3, 4, 7, 1, 5, 4),
  birth_weight = c(2.8, 3.1, 2.5, 3.4, 2.9, 3.0, 3.6, 2.3, 3.2, 3.0),
  delivery_place = c("Hospital", "Hospital", "Home", "Hospital", "Home",
                     "Hospital", "Hospital", "Home", "Hospital", "Home"),
  child_survival = c("Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes")
)

View first few rows

head(mch_data)

#Structure of dataset

str(mch_data)

## 'data.frame':    10 obs. of  6 variables:
##  $ age_mother      : num  22 25 19 30 27 24 33 20 29 26
##  $ education_level : chr  "Primary" "Secondary" "None" "Higher" ...
##  $ antenatal_visits: num  4 5 2 6 3 4 7 1 5 4
##  $ birth_weight    : num  2.8 3.1 2.5 3.4 2.9 3 3.6 2.3 3.2 3
##  $ delivery_place  : chr  "Hospital" "Hospital" "Home" "Hospital" ...
##  $ child_survival  : chr  "Yes" "Yes" "No" "Yes" ...

#Summary statistics

summary(mch_data)

##    age_mother   education_level    antenatal_visits  birth_weight  
##  Min.   :19.0   Length:10          Min.   :1.00     Min.   :2.300  
##  1st Qu.:22.5   Class :character   1st Qu.:3.25     1st Qu.:2.825  
##  Median :25.5   Mode  :character   Median :4.00     Median :3.000  
##  Mean   :25.5                      Mean   :4.10     Mean   :2.980  
##  3rd Qu.:28.5                      3rd Qu.:5.00     3rd Qu.:3.175  
##  Max.   :33.0                      Max.   :7.00     Max.   :3.600  
##  delivery_place     child_survival    
##  Length:10          Length:10         
##  Class :character   Class :character  
##  Mode  :character   Mode  :character  
##                                       
##                                       
##

#univariate analysis # Distribution of mother’s age

hist(mch_data$age_mother,
     main = "Distribution of Mother's Age",
     xlab = "Age of Mother (years)",
     col = "lightblue", border = "black")

Boxplot of birth weight

boxplot(mch_data$birth_weight,
        main = "Boxplot of Birth Weight",
        ylab = "Birth Weight (kg)",
        col = "lightgreen")

Frequency of delivery place

barplot(table(mch_data$delivery_place),
        main = "Delivery Place Distribution",
        col = c("orange", "pink"),
        xlab = "Delivery Place",
        ylab = "Count")

#bivariate Analysis # Scatter plot: Mother’s age vs Birth weight

plot(mch_data$age_mother, mch_data$birth_weight,
     main = "Mother's Age vs Birth Weight",
     xlab = "Age of Mother",
     ylab = "Birth Weight (kg)",
     pch = 19, col = "purple")

Boxplot: Birth weight by delivery place

boxplot(birth_weight ~ delivery_place, data = mch_data,
        main = "Birth Weight by Delivery Place",
        xlab = "Delivery Place",
        ylab = "Birth Weight (kg)",
        col = c("lightcoral", "lightcyan"))

Relationship: Antenatal visits vs Child survival

table(mch_data$antenatal_visits, mch_data$child_survival)

##    
##     No Yes
##   1  1   0
##   2  1   0
##   3  0   1
##   4  0   3
##   5  0   2
##   6  0   1
##   7  0   1

#multivariate visualization

ggplot(mch_data, aes(x = age_mother, y = birth_weight,
                     color = delivery_place, size = antenatal_visits)) +
  geom_point() +
  labs(title = "Multivariate Analysis: Age, Birth Weight, Delivery Place, and Antenatal Visits")

#correlation analysis

numeric_data <- mch_data[, c("age_mother", "antenatal_visits", "birth_weight")]
cor(numeric_data)

##                  age_mother antenatal_visits birth_weight
## age_mother        1.0000000        0.8701858    0.9384344
## antenatal_visits  0.8701858        1.0000000    0.9776258
## birth_weight      0.9384344        0.9776258    1.0000000

#summary report

summary(mch_data)