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WHAT IS THE BEST CONCLUSION FOR THIS WORK? Birth data analysis offers new perspectives on trends and patterns impacting public health policy, healthcare planning, and medical research. Focusing on essential characteristics such birth weight, delivery techniques, Apgar ratings, and timing of births, this research investigates the 2006 birth data accessible in the Nutshell library using R programming (Bartz, et al., 2021; Machils,2020; Kephart,2012; Marin, et al., 2013; R Core Team,2021; Tollefson, 2021). Comprising 427,323 records across 13 variables, the dataset lets one investigate closely the elements influencing birth outcomes. However, I must say that this nutshell library did not work for me, hence my deployment of the ggpolt tool. Descriptive statistics, graphical approaches, and statistical summaries are used in this study to expose correlations and patterns (Bartz et al., 2021; Machils,2020; Kephart,2012; Marin et al., 2013; R Core Team,2021; Tollefson, 2021). The findings may guide clinical decisions and budget allocation in mother and newborn care. setwd(“~/Documents/JerryS/”) birth_data<-read.csv("births2006.csv") library(ggplot2) library(reshape2) First, list the data for the first five births. head(birth_data) ## X DOB_MM DOB_WK MAGER TBO_REC WTGAIN SEX APGAR5 DMEDUC ## 1 591430 9 1 25 2 NA F NA NULL ## 2 1827276 2 6 28 2 26 M 9 2 years of college ## 3 1705673 2 2 18 2 25 F 9 NULL ## 4 3368269 10 5 21 2 6 M 9 NULL ## 5 2990253 7 7 25 1 36 M 10 2 years of high school ## 6 966967 3 3 28 3 35 M 8 NULL ## UPREVIS ESTGEST DMETH_REC DPLURAL DBWT ## 1 10 99 Vaginal 1 Single 3800 ## 2 10 37 Vaginal 1 Single 3625 ## 3 14 38 Vaginal 1 Single 3650 ## 4 22 38 Vaginal 1 Single 3045 ## 5 15 40 Vaginal 1 Single 3827 ## 6 18 39 Vaginal 1 Single 3090 Next, a bar chart of the birth frequencies according to the day of the week of the birth will be shown. What do you conclude from the bar chart? ggplot(birth_data)+ geom_bar(aes(x=factor(DOB_WK)))+ labs(x='Day of Week',y='Frequencies of Births') Image transcription text 60000 - 40000 - Frequencies of Births II 20000 - 0 - N - 4 Cn - Day of Week Then, frequencies for two-way classifications of birth were obtained according to the day of the week and the delivery method. Show the results, and discuss your findings. two_way_freqs<-table(birth_data[c('DOB_WK','DMETH_REC')]) print(two_way_freqs) ## DMETH_REC ## DOB_WK C-section Unknown Vaginal ## 1 8836 90 31348 ## 2 20454 272 42031 ## 3 22921 247 46607 ## 4 23103 252 46935 ## 5 22825 258 47081 ## 6 23233 289 44858 ## 7 10696 109 34878 Now, use lattice (trellis) graphs (R package lattice) to condition density histograms on the values of a third variable. The 'facet_grid' function is handy here, as it allows us to create separate histograms for each combination of the two conditioning variables, the delivery method, and the birth plurality. This helps us to visualize the birth weight distribution according to these variables. Show your graphs and what you concluded. ggplot(birth_data)+ geom_histogram(aes(DBWT))+ facet_grid(DMETH_REC~DPLURAL) ## `stat_bin()` using `bins = 30`. Pick a better value with `binwidth`. ## Warning: Removed 434 rows containing non-finite outside the scale range ## (`stat_bin()`). Next, do a box plot of birth weight against the Apgar score and box plots of birth weight by day of week of delivery. Provide R commands and graphs, and discuss your results. ggplot(birth_data)+ geom_boxplot(aes(x=factor(APGAR5), y=DBWT))+ labs(x='Apgar score',y='Birth Weight')+ theme(axis.text.x=element_text(angle=45,hjust=1)) ## Warning: Removed 434 rows containing non-finite outside the scale range ## (`stat_boxplot()`). Image transcription text 8000 - 6000 - Birth Weight 4000 - 2000 - 0 - A Apgar score ggplot(birth_data)+ geom_boxplot(aes(x=factor(DOB_WK), y=DBWT))+ labs(x='Day of Week',y='Birth Weight')+ theme(axis.text.x=element_text(angle=45,hjust=1)) ## Warning: Removed 434 rows containing non-finite outside the scale range ## (`stat_boxplot()`). Calculate the average birth weight as a function of multiple births for males and females separately. In this case, we use the 'tapply' function, which applies a function to subsets of a vector, to calculate the average birth weight. For missing values, we use 'option nz.rm=TRUE.' categories<-as.list(birth_data[c('DPLURAL','SEX' )]) birth_avgs<-tapply(birth_data$DBWT,categories,mean,na.rm=T) birth_avgs<-data.frame(multiple=rownames(birth_avgs),birth_avgs) rownames(birth_avgs)<-NULL birth_avgs<-melt(birth_avgs,id.vars='multiple') # Make a barplot of the above ggplot(birth_avgs)+ geom_col(aes(x=multiple,y=value,fill=variable),position='dodge')+ labs(fill='Sex',x='Type of Multiple Birth',y='Average Birth Weight',title='Plurality of Birth Weights')+ theme(axis.text.x=element_text(angle=45,hjust=1)) Image transcription text Plurality of Birth Weights 3000 - 2000 - Sex Average Birth Weight F M 1000 - 0 - Twin 1 Single S Triplet 4 Quadruplet 5 Quintuplet or higher Type of Multiple Birth Interpretation of the Graphs and Code Bar Chart of Birth Frequencies by Day of the Week: Graph Description: This graph illustrates the frequency of births across each day of the week. Conclusion: Births are relatively fewer on Sundays (day 7), with a peak mid-week, particularly from Tuesday (day 2) to Friday (day 5). Two-Way Frequency Table: Table Description: The table shows the frequency of births categorized by the day of the week and method of delivery (C-section, Unknown, Vaginal). Conclusion: Vaginal deliveries are considerably more frequent than C-sections. There is also an indication of more scheduled C-sections early in the week, tapering off toward the weekend. Density Histograms Conditioned on Method and Plurality of Birth: Graph Description: Each grid cell represents the birth weight distribution conditioned on the delivery method (C-section, Unknown, Vaginal) and plurality (Single, Twin, etc.). Conclusion: The weight distribution is concentrated for single births, while higher-order multiples tend to have more variable and generally lower birth weights. Vaginal deliveries dominate single births, with a sparse distribution for higher multiples. Box Plot of Birth Weight Against Apgar Score: Graph Description: The box plot shows birth weight distribution across Apgar scores. Conclusion: Higher Apgar scores correlate with higher birth weights. Lower scores show a wider range of weights, possibly indicating varied health issues affecting both weight and score. Box Plot of Birth Weight by Day of Week: Graph Description: This box plot represents birth weights for each day of the week. Conclusion: Birth weights are consistent across the days, with some variability, but no single day shows a significant deviation in median or spread. Average Birth Weight by Type of Multiple Birth and Sex: Graph Description: A bar plot showing average birth weights for different types of births (single, twin, etc.) separated by sex. Conclusion: Single births have the highest average weight, decreasing with higher-order multiples. Females tend to have slightly higher average weights in single and twin births than males. Key Observations Birth Scheduling: There is evidence of scheduling, particularly in the day-of-week birth frequencies and C-section trends. Plurality Impact: Birth weight is inversely related to the number of births per pregnancy (multiples). Health Indicators: Higher Apgar scores tend to be associated with higher birth weights. Data Handling and Code Insights Handling Missing Values: na.rm = TRUE is used to handle missing data in calculations. Faceting and Conditioning: facet grid and facet wrap help visualize data conditioned on multiple variables. Complex Tables: The table function allows for easy creation of frequency tables to capture interactions between categorical variables.

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