About the swiss dataset

The swiss dataset is comprised of 47 observations, and 6 variables. According to the R Documentation Manual published by ETH Zurich [R Manual] (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/swiss.html) the dataset is officially titled:

“Swiss Fertility and Socioeconomic Indicators (1888)”

This dataset contains fertility, employment, educational, health and religious information for 47 French-speaking provinces in Switzerland around the year 1888.

The 6 variables from the dataset are listed, and defined below:

  • Fertility: Ig, ‘common standardized fertility measure’
  • Agriculture: % of males involved in agriculture as occupation
  • Examination : & of draftees receiving highest mark on army examination
  • Education: % of draftees having education beyond primary school
  • Catholic: % of the population that is Catholic (as opposed to Protestant)
  • Infant Mortality: % of live births who live less than 1 year.

Interactive swiss Datatable

Using this interactive datatable, the swiss data can be manipulated, and explored in more detail, the 47 provinces are displayed in the first column, and the variables are displayed from left to right. The columns can be sorted from largest to smallest (or vice versa), and the whole datatable is searchable as well.


Interactive datatable of Swiss Data

Analysis of swiss data

Below are two plots with the common variable of Fertility, these plots are an attempt to determine if there is any visible correlation between Infant Mortality & Fertility as well as Catholicism & Fertility.

From thes plots we can see that there does appear to be some correlation between Fertility and Infant Mortality, with a greater percentage of children living past the 1st year corresponding to a higher fertility measure.

The second plot shows a strong correlation between the provinces with the highest Fertility rate and the provinces with a greater proportion of Catholic residents. Additionally, there is a large grouping of provinces were Protestants and Catholics are more evenly divided among the population that show a lower level of fertility.

Additionally, here are two smiliar plots with the common variable of Education, these plots are an attempt to determine if there is any visible correlation between Education & Agricultural Employent and Education & Exam Success.

Interestingly, these plots appear to be near mirror images of one another. The first plot shows that Provinces with a greater percentage of residents who have attended secondary school are much less likely to have a large share of the male workforce engaged in Agriculture. We can see from the second plot, that there is a positive correlation between education and the percentage of draftees who score highest in their examinations.