Synopsis

This R markdown file is the fourth assignment in the course Data Wrangling with R.In this markdown several different packages are installed. Different data visualizations are created to explain the questions asked on the gapminder data. Data transformation is done using the dplyr package in the R. The dplyr package is a core package in the tidyverse meta-package. The various functionalities offered by dplyr is experimented in this assignment.

Packages Required

Tidyverse-Tidyverse is a set of packages that works in harmony because they share common data representations and API design. It makes loading and installing other core packages easier from the tidyverse in a single command.

ggplot2- the ggplot packages is mainly used for data visualization. It is a plotting system for R.

gapminder- The gapminder package loads the dataset. This data set contains gdpPer capita of several continents and countries.The gapminder_unfiltered data set is used in this assignment.

Source Code

The following are the variables that are present in the gapminder_unfiltered data set.

library(gapminder)
library(dplyr)
library(ggplot2)
library(knitr)
head(gapminder_unfiltered)

## # A tibble: 6 × 6
##       country continent  year lifeExp      pop gdpPercap
##        <fctr>    <fctr> <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan      Asia  1952  28.801  8425333  779.4453
## 2 Afghanistan      Asia  1957  30.332  9240934  820.8530
## 3 Afghanistan      Asia  1962  31.997 10267083  853.1007
## 4 Afghanistan      Asia  1967  34.020 11537966  836.1971
## 5 Afghanistan      Asia  1972  36.088 13079460  739.9811
## 6 Afghanistan      Asia  1977  38.438 14880372  786.1134

names(gapminder_unfiltered)

## [1] "country"   "continent" "year"      "lifeExp"   "pop"       "gdpPercap"

From the command ?gapminder we obtain information about the variables in the gapminder_unfiltered data set.

Gapminder data-Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country(as quoted by R documentation) The gapminder data has 6 variables.

The following are the variables-

Country- factor with 142 levels
Continent- factor with 5 levels
Year- Ranges from 1952 to 2007 (increments of 5 years)
lifeExp- life expectance at birth,in years
pop- population per country
gdpPercap-GDP per capita

Data Description

The total number of rows in the data set is given by nrow. The is.na command tests for null values in the data set.The summary statistics are given by the summary command.The names of various continents and countries, quantiles, mean and medians of the year, life expectancy,population and gdpPer capita is obtained.

NROW(gapminder_unfiltered)

## [1] 3313

sum(is.na(gapminder_unfiltered))

## [1] 0

summary(gapminder_unfiltered)

##            country        continent         year         lifeExp     
##  Czech Republic:  58   Africa  : 637   Min.   :1950   Min.   :23.60  
##  Denmark       :  58   Americas: 470   1st Qu.:1967   1st Qu.:58.33  
##  Finland       :  58   Asia    : 578   Median :1982   Median :69.61  
##  Iceland       :  58   Europe  :1302   Mean   :1980   Mean   :65.24  
##  Japan         :  58   FSU     : 139   3rd Qu.:1996   3rd Qu.:73.66  
##  Netherlands   :  58   Oceania : 187   Max.   :2007   Max.   :82.67  
##  (Other)       :2965                                                 
##       pop              gdpPercap       
##  Min.   :5.941e+04   Min.   :   241.2  
##  1st Qu.:2.680e+06   1st Qu.:  2505.3  
##  Median :7.560e+06   Median :  7825.8  
##  Mean   :3.177e+07   Mean   : 11313.8  
##  3rd Qu.:1.961e+07   3rd Qu.: 17355.8  
##  Max.   :1.319e+09   Max.   :113523.1  
## 

Exploratory Data Analysis

Problem 1 -

hist(gapminder_unfiltered$gdpPercap,breaks=100,main="Distribution of GDP by countries", border="darkgreen")

Problem 2-

forcontinent<-filter(gapminder_unfiltered,year==2007)
ggplot(data = forcontinent, mapping = aes(x = continent, y = gdpPercap),color="darkgreen") +
  geom_boxplot()

Problem 3-

forcontinent<-filter(gapminder_unfiltered,year==2007)
top10<- arrange(forcontinent, desc(gdpPercap))
top10<-top10%>% head(10)
kable(top10)

Problem 4-

indianGdp<-filter(gapminder_unfiltered,country=="India")
  kable(indianGdp)

ggplot(data = indianGdp, mapping = aes(x = year, y = gdpPercap)) + 
  geom_point(color="Blue")+
  geom_line(color="darkgreen")

Problem 5-

additional<- mutate(indianGdp,lag=lag(gdpPercap),percentDiff=((gdpPercap-lag)/lag)*100)
percentGrowth<-filter(additional,year==2007)
kable(percentGrowth)

Problem 6-

additional<- mutate(indianGdp,lag=lag(gdpPercap),difference=((gdpPercap-lag)/lag)*100)
kable(additional)

ggplot(data = additional, mapping = aes(x = year, y = difference, color="darkGreen")) + geom_point(color="blue")+
  geom_line(color="darkgreen")