Presentation-

title: "Swiss Fertility and Socioeconomic Indicators Data (1888) Exploration" author: "Amber Wang" date: "3/7/2018" output: ioslides_presentation smaller: true

knitr::opts_chunk$set(echo = FALSE)

Introduction

This is the reproducible pitch presentation for the final course porject of Coursera Data Science Specialization Course 9: Developing Data Science Tools. This document will go over the basics of developing the Shiny app. For more information, please see the following links:

The Swiss Fertility and Socioeconomic Indicators Data (swiss data) can be accessed with data(swiss) in R
The GitHub repository containing the R codes required to build the Shiny App (server.R and ui.R)can be accessed here
The Shiny app can be accessed here, which contains

Exploring the distribution of each variable in a histogram
Exploring the relationship of up to three variables in a scatter plot

The Swiss Data {.smaller}

Except for fertility, all variables are expressed in the proportions (%) of the population
Use ?swiss to read more about the study

data(swiss)
summary(swiss)

Codes for Histogram {.smaller}

inputVar <- 'Fertility'; inputBin <- 10; histVal <- swiss[, inputVar]
hist(histVal, breaks = seq(min(histVal), max(histVal), length.out = inputBin+1),
     xlab = inputVar, main = paste('Distribution of', inputVar),
     col = 'darkgray', border = 'white')

Codes for Scatter Plot {.smaller}

library(ggplot2)
scatX <- 'Fertility'; scatY <- 'Education'; scatC <- 'Examination'
ggplot(data = swiss, aes(x = Fertility, y = Education, color = Examination)) + 
      geom_point() + xlab(scatX) + ylab(scatY) + labs(colour = scatC) +
      ggtitle(paste('Scatter plot of', scatX, 'vs', scatY)) +
      theme(plot.title = element_text(hjust = 0.5))