week-4
Ishant Nayer
11/04/2016
Gapminder_Unfiltered Analysis
Synopsis
This HTML document is associated with the R Markdown file that is used to complete the week 4 assignment.This R markdown file made by me summarizes the packages I have used to complete my homework assignment. RMD file is actually a very helpful tool that summarizes the whole task. Our code along with their results get documented neatly, as summarized below. Initial findings: 1. GDP per capita for year 2007 is highest for European nations and lowest for African nations. 2. Qatar has the highest GDP per capita for the year 2007 among all the countries. 3. India’s GDP per capita over the years has been positive and increased by 348.7%.
Packages Required
Following packages were installed and used:
library(ggplot2) ##Package to produce complex multi-layered graphs in R
library(gapminder) ##Provides our data in a data frame and tab delimited form
library(tidyverse) ##set of packages that work in harmony because they share common data representations and 'API' design## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statsSource Code
Our Gapminder data is on life expectancy, GDP per capita, and population by country. The supplemental data frame gapminder_unfiltered, our analysis data, was not filtered on year or for complete data and has 3313 rows and 6 columns. Each variable is described below: 1. Country: Tells the name of the country and has 187 distinct values 2. Continent: Tells the continent where the country lies and has 6 disctinct values 3. Year: Ranges from 1952 to 2007 in increments of 5 years 4. lifeExp: Life expectancy at birth, in years 5. pop: Gives the total population of the country 6. gdpPercap: GDP per capita
Data Description
#Understanding the data
?gapminder_unfiltered
#Checking the structure
str(gapminder_unfiltered)## Classes 'tbl_df', 'tbl' and 'data.frame': 3313 obs. of 6 variables:
## $ country : Factor w/ 187 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 6 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...#Converting year into factor variable
gapminder_unfiltered$year<-factor(gapminder_unfiltered$year)
#Number of rows and variables
dim(gapminder_unfiltered)## [1] 3313 6#Names of variables
names(gapminder_unfiltered)## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"#Checking top and bottom values
head(gapminder_unfiltered)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <fctr> <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 786.1134tail(gapminder_unfiltered)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <fctr> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.363 7636524 788.8550
## 2 Zimbabwe Africa 1987 62.351 9216418 706.1573
## 3 Zimbabwe Africa 1992 60.377 10704340 693.4208
## 4 Zimbabwe Africa 1997 46.809 11404948 792.4500
## 5 Zimbabwe Africa 2002 39.989 11926563 672.0386
## 6 Zimbabwe Africa 2007 43.487 12311143 469.7093#Replace the missing values with NA
gapminder_unfiltered$gdpPercap[gapminder_unfiltered$gdpPercap==-99] <-NA
gapminder_unfiltered$pop[gapminder_unfiltered$pop==-99] <-NA
gapminder_unfiltered$lifeExp[gapminder_unfiltered$lifeExp==-99] <-NA
#Counting the missing values
sum(is.na(gapminder_unfiltered$gdpPercap==TRUE))## [1] 0sum(is.na(gapminder_unfiltered$pop==TRUE))## [1] 0sum(is.na(gapminder_unfiltered$lifeExp==TRUE))## [1] 0#Displaying summary statistics of final dataset
summary(gapminder_unfiltered)## country continent year lifeExp
## Czech Republic: 58 Africa : 637 2002 : 187 Min. :23.60
## Denmark : 58 Americas: 470 1997 : 184 1st Qu.:58.33
## Finland : 58 Asia : 578 1992 : 183 Median :69.61
## Iceland : 58 Europe :1302 2007 : 183 Mean :65.24
## Japan : 58 FSU : 139 1977 : 171 3rd Qu.:73.66
## Netherlands : 58 Oceania : 187 1982 : 171 Max. :82.67
## (Other) :2965 (Other):2234
## 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 - 1
gdppercap2007<-select(filter(gapminder_unfiltered, year==2007), country, continent, gdpPercap)
ggplot(data = gdppercap2007) +
geom_point(mapping = aes(x = country, y = gdpPercap)) +
ggtitle("2007 Country wise gdpPercap distribution") +
ylab("gdpPercap")+
xlab("Country")
# For 2007, 0-20000 level of GDP per capita has the most dense distribution. As the level goes up, density decreases.Exploratory Data Analysis - 2
ggplot(data = gdppercap2007, mapping = aes(x = reorder(continent, gdpPercap, FUN = median), y = gdpPercap, color = continent)) +
geom_boxplot()+
ggtitle("2007 Lowest -> Highest Continent wise gdpPercap distribution")
#Africa has the lowest GDP per capita where as Europe has the highest for 2007.Exploratory Data Analysis - 3
top10_gdppercap2007<-top_n(select(filter(gapminder_unfiltered, year==2007), country, gdpPercap), 10, gdpPercap)
ggplot(top10_gdppercap2007, aes(x = reorder(factor(country), desc(gdpPercap)), y = gdpPercap , fill = factor(country))) + geom_bar(stat = "identity")+
ggtitle("2007 Top10 Countries with largest gdpPercap") +
ylab("gdpPercap")+
xlab("Top10_Countries")
#Qatar has the highest GDP per capita for 2007.Exploratory Data Analysis - 4
India_gdppercap<-select(filter(gapminder_unfiltered, country == 'India'), year, gdpPercap)
ggplot(India_gdppercap, aes(x = reorder(factor(year), gdpPercap), y = gdpPercap, fill=year)) + geom_bar(stat = "identity")+
ggtitle("India's gdpPercap over the years") +
ylab("gdpPercap")+
xlab("Year")
Exploratory Data Analysis - 5
India_GDP_2002<-select(filter(gapminder_unfiltered, country == 'India' & (year ==2002)),gdpPercap)
India_GDP_2007<-select(filter(gapminder_unfiltered, country == 'India' & (year ==2007)),gdpPercap)
Growth_GDP_2007<-(India_GDP_2007-India_GDP_2002)*100/India_GDP_2002
Growth_GDP_2007## gdpPercap
## 1 40.38546Exploratory Data Analysis - 6
India_GDP_1952<-select(filter(gapminder_unfiltered, country == 'India' & (year ==1952)),gdpPercap)
Growth_GDP_Overall<-(India_GDP_2007-India_GDP_1952)*100/India_GDP_1952
Growth_GDP_Overall## gdpPercap
## 1 348.6579ggplot(India_gdppercap, aes(x = reorder(factor(year), gdpPercap), y = gdpPercap, fill=year)) + geom_bar(stat = "identity")+
geom_smooth(stat="smooth",position = "identity")+
ggtitle("India's gdpPercap over the years") +
ylab("gdpPercap")+
xlab("Year")
#India's GDP per capita over the years has been positive and increased by 348.7%.