Introduction

You will use this code as a template for your Visualization 3 assignment. The first step is to call a set of packages that you might use in this assignment. The final choices belong to you.

Note that each code chunk is set off with special tags.

library(ggplot2)
library(dplyr)
library(ggvis)
library(WDI)
library(plotly)
library(maps)
library(ggmap)

world <- map_data("world") # set up world map

Call package `WDI` to retrieve most updated figures available.

In this assignent, we need to update 8 series:

Tableau Name	WDI Series
Birth Rate	SP.DYN.CBRT.IN
Health Exp % GDP	SH.XPD.TOTL.ZS
Health Exp/Capita	SH.XPD.PCAP
Infant Mortality Rate	SP.DYN.IMRT.IN
Internet Usage	IT.NET.USER.ZS
Life Expectancy (Total)	SP.DYN.LE00.IN
Mobile Phone Usage	IT.CEL.SETS.P2
Population Total	SP.POP.TOTL

The next code chunk will call the WDI API and fetch the years 2000 through 2016, as available. It will then remove the country regional and other aggregates.

birth <- "SP.DYN.CBRT.IN"
hxpgdp <- "SH.XPD.TOTL.ZS"
hxpcap <- "SH.XPD.PCAP"
infmort <- "SP.DYN.IMRT.IN"
net <-"IT.NET.USER.ZS"
lifeexp <- "SP.DYN.LE00.IN"
mobile <- "IT.CEL.SETS.P2"
pop <- "SP.POP.TOTL"

# create a vector of the desired indicator series
indicators <- c(birth, hxpgdp, hxpcap, infmort, net, lifeexp, mobile, pop)

newdata <- WDI(country="all", indicator = indicators, 
     start = 2000, end = 2016, extra = TRUE)

# remove country groupings
newdata$longitude[newdata$longitude==""] <- NA
countries <- filter(newdata, !is.na(longitude))  # drop aggregate groups

## Warning: package 'bindrcpp' was built under R version 3.4.2

## rename columns for each of reference
countries <- rename(countries, birth = SP.DYN.CBRT.IN, 
       hxpgdp = SH.XPD.TOTL.ZS, hxpcap = SH.XPD.PCAP, 
       infmort = SP.DYN.IMRT.IN, net  = IT.NET.USER.ZS,
       lifeexp = SP.DYN.LE00.IN, mobile = IT.CEL.SETS.P2, 
       pop = SP.POP.TOTL)

glimpse(countries) ## data frame column names appear here

## Observations: 3,485
## Variables: 18
## $ iso2c     <chr> "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD", "AD"...
## $ country   <chr> "Andorra", "Andorra", "Andorra", "Andorra", "Andorra...
## $ year      <dbl> 2013, 2006, 2015, 2004, 2005, 2007, 2003, 2008, 2009...
## $ birth     <dbl> NA, 10.600, NA, 10.900, 10.700, 10.100, 10.300, 10.4...
## $ hxpgdp    <dbl> 11.478046, 5.313893, NA, 5.703882, 5.223353, 6.33545...
## $ hxpcap    <dbl> 4914.3912, 2256.1030, NA, 2127.7367, 2089.6678, 2997...
## $ infmort   <dbl> 2.7, 3.3, 2.5, 3.5, 3.3, 3.2, 3.6, 3.1, 3.0, 2.9, 2....
## $ net       <dbl> 94.00000, 48.93685, 96.91000, 26.83795, 37.60577, 70...
## $ lifeexp   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ mobile    <dbl> 80.70262, 84.27764, 88.12353, 73.82494, 79.48487, 78...
## $ pop       <dbl> 80788, 80991, 78014, 76244, 78867, 82683, 73182, 838...
## $ iso3c     <fctr> AND, AND, AND, AND, AND, AND, AND, AND, AND, AND, A...
## $ region    <fctr> Europe & Central Asia (all income levels), Europe &...
## $ capital   <fctr> Andorra la Vella, Andorra la Vella, Andorra la Vell...
## $ longitude <fctr> 1.5218, 1.5218, 1.5218, 1.5218, 1.5218, 1.5218, 1.5...
## $ latitude  <fctr> 42.5075, 42.5075, 42.5075, 42.5075, 42.5075, 42.507...
## $ income    <fctr> High income: nonOECD, High income: nonOECD, High in...
## $ lending   <fctr> Not classified, Not classified, Not classified, Not...

Population Health Indicators graph (Graph 1)

Replace this text chunk with an explanation of your approach to the second graph, which averages the measures over all available years. How did you achieve the colored rectangles, or did you need to take an alternative approach?*

## Your plotting code goes here
library(ggplot2)
ggplot(countries, aes(as.factor(year), lifeexp, group = region, col = region)) + geom_bin2d() + theme_classic() + theme(axis.line=element_blank(),
      axis.ticks=element_blank(),
      axis.title.x=element_text(),
      axis.title.y=element_text(),
      legend.position="none",
      panel.background=element_blank(),
      panel.border=element_blank(),
      panel.grid.major=element_blank(),
      panel.grid.minor=element_blank(),
      plot.background=element_blank()) +
  xlab("Years(2000 - 2016)") +
  ylab("Life Expectency Frequency") + 
  ggtitle("Time Series of Average Life Expectancy, Colored by Region")

The original Tableau version of Health Indicators offered a deep analysis of multiple variables. However, I decided to take a slightly different approach, because it is very difficult to visualize trends in the Tableau version . This is a frequency plot (graphed with geom_bin2d) of life expectancy. It is colored and grouped by region, and filled by frequency (by default). We can see an upward trend of life expectancy throughout the years, indicating infant mortality is declining. It appears as if the regions with lower life expectancies are accelerating in this regard. These lower leveled regions include African countries.

Care Spending 2015 (Graph 2)

Replace this text chunk with an explanation of your approach to this graph, commenting on how you included a slider to allow users to choose a year.*

library(ggplot2)
ggplot(countries, aes(as.factor(year), hxpcap, col = region, alpha = hxpgdp)) + geom_point() + geom_jitter() + theme_classic() + coord_flip() + xlab("Health Exp as % of GDP Per Capita") + ylab("Years") + labs(title = "Health Exp. of the World", subtitle = "Colored by Region, Opacity by GDP")

## Warning: Removed 682 rows containing missing values (geom_point).

## Warning: Removed 682 rows containing missing values (geom_point).

Replace this text chunk with a sentence comparing this graph to the original Tableau version. Are the same countries still at the top of the list? o For the Care Spend graph, add a slider to select one year, and initially show the world in 2014.

Technology graph (Graph 3)

This is Health Expenditure of the world as a percent of GDP. In order to fit all of the countries I decided to use a scatter plot. Although it is not interactive, we can still still see the increase of health care expenditure over the years. It is interesting to see how the different countries in the same region behave alike. It appears that countries in Europe and North America spend the most percent of their income on health expenditure.

## Your plotting code goes here
library(ggplot2)
ggplot(countries, aes(as.factor(year), net)) + geom_boxplot(col = "red", outlier.colour = "orange", outlier.shape = 5, outlier.alpha = .8, fill = "green") + geom_smooth() + xlab("Years(2000-2016)") + ylab("Internet Usage of All Countries") +theme_classic() +ggtitle("Boxplot of Internet Usage by Year")

## `geom_smooth()` using method = 'loess'

It is clear that the world has continued its trend of hyperconnectivety. The percent of mobile users has steadied out, and the percent of internet users has increased. There are some outliers depicted as orange diamonds, yet they disapear as the world becomes better connected to the internet. # Conclusion

Overall, in every regard, (Life Expectancy, Mobile/Net usage, percent of Health Expenditure by GDP) the world has been improving and is continuing to improve. Although my graphs are not duplicates of the Tableau’s World Indicator graphs, they show how the world has improved since 2012.

Viz3

Introduction

Call package WDI to retrieve most updated figures available.

Population Health Indicators graph (Graph 1)

Care Spending 2015 (Graph 2)

Technology graph (Graph 3)

Call package `WDI` to retrieve most updated figures available.