# Use R Programming (RStudio Notebook) to analyze the following datas...

## Question

Use R Programming (RStudio Notebook) to analyze the following dataset - 'World Nations Dataset'. Do full exploratory data analysis (EDA), descriptive, and inferential analysis (with various plots) . Identify top nations for some key categories, such as populations, death rate, birth rate, life expectancy, percentage of various workers, GDP per capita, … Check correlation map (heatmap) among a few key variable such as GDP per capita and population data. Your code and plots should be embedded into your report, in a similar format as the projects. All your work needs to be exported into a readable document and uploaded here. Also, provide a brief concluding section at the end, interpreting the result of your analysis.

## Solution Preview

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Exploratory Data Analysis
library(dplyr)
library(ggplot2)
library(psych)
library(corrplot)
library(reshape2)
library(PerformanceAnalytics)
Reading dataset, it contains 153 observations of 32 variables. Nation is the only categorical variable, while all other are numberic variable. Each observation shows data about a particular nation.
dat <- read_xlsx("World-Nations-Dataset.xlsx", sheet = 1, range = "B5:AG158")
## Warning in strptime(x, format, tz = tz): unknown timezone 'zone/tz/2018i.
## 1.0/zoneinfo/Asia/Kolkata'
dim(dat)
## [1] 153 32
str(dat)
## Classes 'tbl_df', 'tbl' and 'data.frame':    153 obs. of 32 variables:
## \$ Nation   : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## \$ Arable% : num 12 21 3 2 9 18 7 17 19 61 ...
## \$ Coast    : num 0 362 998 1600 4989 ...
## \$ CoastCap : num 0 102 31 151 132 ...
## \$ Land    : num 648 29 2381 1247 2737 ...
## \$ LandCap : num 23.3 8.1 73.8 117.7 72.4 ...
## \$ BirthRate: num 41 18.6 22.3 46.2 18.2 ...
## \$ DeathRate: num 17.43 6.49 5.15 24.35 7.57 ...
## \$ Fertile : num 5.72 2.27 2.63 6.43 2.41 1.53 1.77 1.4 2.29 2.72 ...
## \$ LifeExp : num 46.6 72.1 70.2 38.9 75.5 66.6 80 78 63.1 60.9 ...
## \$ InfMort : num 144.8 38.6 39.2 191.7 17.2 ...
## \$ Pop      : num 27.76 3.54 32.28 10.59 37.81 ...
## \$ PopFem   : num 13.1 1.8 15.8 5.2 19.3 1.8 9.9 4.3 4.1 63.5 ...
## \$ PopMale : num 14.7 1.7 16.5 5.4 18.5 1.6 9.7 3.9 3.7 70 ...
## \$ Pop14%   : num 42 28.8 33.5 43.3 26.3 22.2 20.4 16.4 28.9 33.8 ...
## \$ Pop65%   : num 2.8 7.2 4.1 2.8 10.5 10.1 12.6 15.4 7.4 3.4 ...
## \$ PopGrow% : num 3.43 1.06 1.68 2.18 1.13 -0.15 0.96 0.23 0.38 1.59 ...
## \$ AgrLab% : num 80 50 25 85 7 44 5 4 32 63 ...
## \$ Literate : num 36 93 62 42 96 99 100 98 97 56 ...
## \$ Urban%   : num 21 41 60 34 90 70 85 65 57 24 ...
## \$ GDP      : num 21 13.2 177 13.3 453 ...
## \$ GDPCap   : num 800 3800 5600 1330 12000 3350 24000 27000 3100 1750 ...
## \$ Elec    : num 0.38 4.74 23.56 1.19 82.8 ...
## \$ ElecCap : num 14 1337 730 112 2190 ...
## \$ Rail    : num 25 447 4820 2771 33744 ...
## \$ RailCap : num 1 126 149 262 892 256 1730 746 273 21 ...
## \$ HwyTot   : num 21 18 104 76.6 215.4 ...
## \$ HwyPav   : num 2.79 5.4 71.66 19.16 63.55 ...
## \$ HwyPav% : num 13.3 30 68.9 25 29.5 92.9 38.7 100 86.6 9.5 ...
## \$ HwyCap   : num 0.76 5.08 3.22 7.23 5.7 ...
## \$ Phones   : num 29 370 2334 96 10500 ...
## \$ Phon1000 : num 1.2 16.2 44.4 7 125.2 ...
Variables are grouped in several categories such that variables are categorised into geography, demographic, social and economic.
Selecting variables based on their respected category.
dat.geo <- dat[, c(1,2:6)]
dat.demo <- dat[, c(1, 7:17)]
dat.social <- dat[, c(18, 19, 20)]
dat.eco <- dat[, c(1, 20:32)]...

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