Analysis of Swiss Dataset in R

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In this blog post I would like to provide an anylsis of the Swiss dataset which can be accessed in R. The exercises below are part of the course MAST90139: Statistical Modelling for Data Science at the University of Melbourne. Analysis of Swiss dataset in R (Swiss Fertility and Socioeconomic Indicators 1888)

First we clean up any variables that may be left in the existing R environment.

rm(list = ls())

Load data from Faraway.

library(faraway); require(graphics);
data(swiss)
?swiss
dim(swiss); 
## [1] 47  6
head(swiss)

1. Initial data analysis that explores the numerical and graphical characteristics of the data

Numerical characteristics

Print out numerical summary of variables

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
cor(swiss)
##                   Fertility Agriculture Examination   Education   Catholic
## Fertility         1.0000000  0.35307918  -0.6458827 -0.66378886  0.4636847
## Agriculture       0.3530792  1.00000000  -0.6865422 -0.63952252  0.4010951
## Examination      -0.6458827 -0.68654221   1.0000000  0.69841530 -0.5727418
## Education        -0.6637889 -0.63952252   0.6984153  1.00000000 -0.1538589
## Catholic          0.4636847  0.40109505  -0.5727418 -0.15385892  1.0000000
## Infant.Mortality  0.4165560 -0.06085861  -0.1140216 -0.09932185  0.1754959
##                  Infant.Mortality
## Fertility              0.41655603
## Agriculture           -0.06085861
## Examination           -0.11402160
## Education             -0.09932185
## Catholic               0.17549591
## Infant.Mortality       1.00000000

The numerical summary of the data shows that all the 6 variables are numerical with weak to moderate linear correlations among them.

Graphical characteristics

pairs(swiss, panel = panel.smooth, main = "swiss data", col = 3 + (swiss$Catholic > 50))