Homework-1

Introduction

In this analysis, we explore the Swiss fertility data collected in 1888. The dataset contains information on fertility rates and various socioeconomic indicators for different regions or cantons within Switzerland during that period.

Swiss Data Overview

Inspecting the structure and contents of the Swiss data.

Generate Summary Table for the Swiss Data

summary (swiss)

   Fertility      Agriculture     Examination      Education    
 Min.   :35.00   Min.   : 1.20   Min.   : 3.00   Min.   : 1.00  
 1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00   1st Qu.: 6.00  
 Median :70.40   Median :54.10   Median :16.00   Median : 8.00  
 Mean   :70.14   Mean   :50.66   Mean   :16.49   Mean   :10.98  
 3rd Qu.:78.45   3rd Qu.:67.65   3rd Qu.:22.00   3rd Qu.:12.00  
 Max.   :92.50   Max.   :89.70   Max.   :37.00   Max.   :53.00  
    Catholic       Infant.Mortality
 Min.   :  2.150   Min.   :10.80   
 1st Qu.:  5.195   1st Qu.:18.15   
 Median : 15.140   Median :20.00   
 Mean   : 41.144   Mean   :19.94   
 3rd Qu.: 93.125   3rd Qu.:21.70   
 Max.   :100.000   Max.   :26.60   

Load and view Swiss Data

data(swiss)

head(swiss)

             Fertility Agriculture Examination Education Catholic
Courtelary        80.2        17.0          15        12     9.96
Delemont          83.1        45.1           6         9    84.84
Franches-Mnt      92.5        39.7           5         5    93.40
Moutier           85.8        36.5          12         7    33.77
Neuveville        76.9        43.5          17        15     5.16
Porrentruy        76.1        35.3           9         7    90.57
             Infant.Mortality
Courtelary               22.2
Delemont                 22.2
Franches-Mnt             20.2
Moutier                  20.3
Neuveville               20.6
Porrentruy               26.6

library(gt) # loads gt, do once in your session
gt(as.data.frame.matrix(summary(swiss)))

Fertility	Agriculture	Examination	Education	Catholic	Infant.Mortality
Min. :35.00	Min. : 1.20	Min. : 3.00	Min. : 1.00	Min. : 2.150	Min. :10.80
1st Qu.:64.70	1st Qu.:35.90	1st Qu.:12.00	1st Qu.: 6.00	1st Qu.: 5.195	1st Qu.:18.15
Median :70.40	Median :54.10	Median :16.00	Median : 8.00	Median : 15.140	Median :20.00
Mean :70.14	Mean :50.66	Mean :16.49	Mean :10.98	Mean : 41.144	Mean :19.94
3rd Qu.:78.45	3rd Qu.:67.65	3rd Qu.:22.00	3rd Qu.:12.00	3rd Qu.: 93.125	3rd Qu.:21.70
Max. :92.50	Max. :89.70	Max. :37.00	Max. :53.00	Max. :100.000	Max. :26.60

Table Showing Counties and Variables

knitr::kable(swiss[1:5, ], )

	Fertility	Agriculture	Examination	Education	Catholic	Infant.Mortality
Courtelary	80.2	17.0	15	12	9.96	22.2
Delemont	83.1	45.1	6	9	84.84	22.2
Franches-Mnt	92.5	39.7	5	5	93.40	20.2
Moutier	85.8	36.5	12	7	33.77	20.3
Neuveville	76.9	43.5	17	15	5.16	20.6

Plot Swiss Data Comparing Fertility with Education

plot (Infant.Mortality~Fertility, data = swiss,
     xlab = "Fertility",
     ylab = "Infant.Mortality",
     main = "Infant Mortality and Fertility",
     pch = 16) # Point shape

Bar Graph of education Levels

barplot(table(swiss$Education), 
        main = "Bar Graph of Education Levels",
        xlab = "Education Level",
        ylab = "Frequency",
        col = "skyblue",
        ylim = c(0, max(table(swiss$Education)) + 2)  # Adjust ylim to ensure all bars are visible
)

Use Histograms to Visualize Swiss Data

hist(swiss$Fertility)

hist(swiss$Agriculture)

hist(swiss$Infant.Mortality)