# Introduction

This is the 1st part of a series of posts I intend to write on some common Machine Learning Algorithms in R and Python. In this first part I cover the following Machine Learning Algorithms

• Univariate Regression
• Multivariate Regression
• Polynomial Regression
• K Nearest Neighbors Regression

The code includes the implementation in both R and Python. This series of posts are based on the following 2 MOOC courses I did at Stanford Online and at Coursera

1. Statistical Learning, Prof Trevor Hastie & Prof Robert Tibesherani, Online Stanford
2. Applied Machine Learning in Python Prof Kevyn-Collin Thomson, University Of Michigan, Coursera

I have used the data sets from UCI Machine Learning repository(Communities and Crime and Auto MPG). I also use the Boston data set from MASS package

1. Machine Learning in plain English-Part 1
2. Machine Learning in plain English-Part 2
3. Machine Learning in plain English-Part 3

Check out my compact and minimal book  “Practical Machine Learning with R and Python:Third edition- Machine Learning in stereo”  available in Amazon in paperback($12.99) and kindle($8.99) versions. My book includes implementations of key ML algorithms and associated measures and metrics. The book is ideal for anybody who is familiar with the concepts and would like a quick reference to the different ML algorithms that can be applied to problems and how to select the best model. Pick your copy today!! While coding in R and Python I found that there were some aspects that were more convenient in one language and some in the other. For example, plotting the fit in R is straightforward in R, while computing the R squared, splitting as Train & Test sets etc. are already available in Python. In any case, these minor inconveniences can be easily be implemented in either language.

R squared computation in R is computed as follows $RSS=\sum (y-yhat)^{2}$ $TSS= \sum(y-mean(y))^{2}$ $Rsquared- 1-\frac{RSS}{TSS}$

Note: You can download this R Markdown file and the associated data sets from Github at MachineLearning-RandPython
Note 1: This post was created as an R Markdown file in RStudio which has a cool feature of including R and Python snippets. The plot of matplotlib needs a workaround but otherwise this is a real cool feature of RStudio!

## 1.1a Univariate Regression – R code

Here a simple linear regression line is fitted between a single input feature and the target variable

# Source in the R function library
source("RFunctions.R")
# Read the Boston data file
df=read.csv("Boston.csv",stringsAsFactors = FALSE) # Data from MASS - Statistical Learning

# Split the data into training and test sets (75:25)
train_idx <- trainTestSplit(df,trainPercent=75,seed=5)
train <- df[train_idx, ]
test <- df[-train_idx, ]

# Fit a linear regression line between 'Median value of owner occupied homes' vs 'lower status of
# population'
fit=lm(medv~lstat,data=df)
# Display details of fir
summary(fit)
##
## Call:
## lm(formula = medv ~ lstat, data = df)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -15.168  -3.990  -1.318   2.034  24.500
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.55384    0.56263   61.41   <2e-16 ***
## lstat       -0.95005    0.03873  -24.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.216 on 504 degrees of freedom
## Multiple R-squared:  0.5441, Adjusted R-squared:  0.5432
## F-statistic: 601.6 on 1 and 504 DF,  p-value: < 2.2e-16
# Display the confidence intervals
confint(fit)
##                 2.5 %     97.5 %
## (Intercept) 33.448457 35.6592247
## lstat       -1.026148 -0.8739505
plot(df$lstat,df$medv, xlab="Lower status (%)",ylab="Median value of owned homes ($1000)", main="Median value of homes ($1000) vs Lowe status (%)")
abline(fit)
abline(fit,lwd=3)
abline(fit,lwd=3,col="red") rsquared=Rsquared(fit,test,test$medv) sprintf("R-squared for uni-variate regression (Boston.csv) is : %f", rsquared) ##  "R-squared for uni-variate regression (Boston.csv) is : 0.556964" ## 1.1b Univariate Regression – Python code import numpy as np import pandas as pd import os import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression #os.chdir("C:\\software\\machine-learning\\RandPython") # Read the CSV file df = pd.read_csv("Boston.csv",encoding = "ISO-8859-1") # Select the feature variable X=df['lstat'] # Select the target y=df['medv'] # Split into train and test sets (75:25) X_train, X_test, y_train, y_test = train_test_split(X, y,random_state = 0) X_train=X_train.values.reshape(-1,1) X_test=X_test.values.reshape(-1,1) # Fit a linear model linreg = LinearRegression().fit(X_train, y_train) # Print the training and test R squared score print('R-squared score (training): {:.3f}'.format(linreg.score(X_train, y_train))) print('R-squared score (test): {:.3f}'.format(linreg.score(X_test, y_test))) # Plot the linear regression line fig=plt.scatter(X_train,y_train) # Create a range of points. Compute yhat=coeff1*x + intercept and plot x=np.linspace(0,40,20) fig1=plt.plot(x, linreg.coef_ * x + linreg.intercept_, color='red') fig1=plt.title("Median value of homes ($1000) vs Lowe status (%)")
fig1=plt.xlabel("Lower status (%)")
fig1=plt.ylabel("Median value of owned homes ($1000)") fig.figure.savefig('foo.png', bbox_inches='tight') fig1.figure.savefig('foo1.png', bbox_inches='tight') print "Finished"  ## R-squared score (training): 0.571 ## R-squared score (test): 0.458 ## Finished ## 1.2a Multivariate Regression – R code # Read crimes data crimesDF <- read.csv("crimes.csv",stringsAsFactors = FALSE) # Remove the 1st 7 columns which do not impact output crimesDF1 <- crimesDF[,7:length(crimesDF)] # Convert all to numeric crimesDF2 <- sapply(crimesDF1,as.numeric) # Check for NAs a <- is.na(crimesDF2) # Set to 0 as an imputation crimesDF2[a] <-0 #Create as a dataframe crimesDF2 <- as.data.frame(crimesDF2) #Create a train/test split train_idx <- trainTestSplit(crimesDF2,trainPercent=75,seed=5) train <- crimesDF2[train_idx, ] test <- crimesDF2[-train_idx, ] # Fit a multivariate regression model between crimesPerPop and all other features fit <- lm(ViolentCrimesPerPop~.,data=train) # Compute and print R Squared rsquared=Rsquared(fit,test,test$ViolentCrimesPerPop)
sprintf("R-squared for multi-variate regression (crimes.csv)  is : %f", rsquared)
##  "R-squared for multi-variate regression (crimes.csv)  is : 0.653940"

## 1.2b Multivariate Regression – Python code

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
#Remove the 1st 7 columns
crimesDF1=crimesDF.iloc[:,7:crimesDF.shape]
# Convert to numeric
crimesDF2 = crimesDF1.apply(pd.to_numeric, errors='coerce')
# Impute NA to 0s
crimesDF2.fillna(0, inplace=True)

# Select the X (feature vatiables - all)
X=crimesDF2.iloc[:,0:120]

# Set the target
y=crimesDF2.iloc[:,121]

X_train, X_test, y_train, y_test = train_test_split(X, y,random_state = 0)
# Fit a multivariate regression model
linreg = LinearRegression().fit(X_train, y_train)

# compute and print the R Square
print('R-squared score (training): {:.3f}'.format(linreg.score(X_train, y_train)))
print('R-squared score (test): {:.3f}'.format(linreg.score(X_test, y_test)))
## R-squared score (training): 0.699
## R-squared score (test): 0.677

## 1.3a Polynomial Regression – R

For Polynomial regression , polynomials of degree 1,2 & 3 are used and R squared is computed. It can be seen that the quadaratic model provides the best R squared score and hence the best fit

 # Polynomial degree 1
df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI
df1 <- as.data.frame(sapply(df,as.numeric))

# Select key columns
df2 <- df1 %>% select(cylinder,displacement, horsepower,weight, acceleration, year,mpg)
df3 <- df2[complete.cases(df2),]

# Split as train and test sets
train_idx <- trainTestSplit(df3,trainPercent=75,seed=5)
train <- df3[train_idx, ]
test <- df3[-train_idx, ]

# Fit a model of degree 1
fit <- lm(mpg~. ,data=train)
rsquared1 <-Rsquared(fit,test,test$mpg) sprintf("R-squared for Polynomial regression of degree 1 (auto_mpg.csv) is : %f", rsquared1) ##  "R-squared for Polynomial regression of degree 1 (auto_mpg.csv) is : 0.763607" # Polynomial degree 2 - Quadratic x = as.matrix(df3[1:6]) # Make a polynomial of degree 2 for feature variables before split df4=as.data.frame(poly(x,2,raw=TRUE)) df5 <- cbind(df4,df3) # Split into train and test set train_idx <- trainTestSplit(df5,trainPercent=75,seed=5) train <- df5[train_idx, ] test <- df5[-train_idx, ] # Fit the quadratic model fit <- lm(mpg~. ,data=train) # Compute R squared rsquared2=Rsquared(fit,test,test$mpg)
sprintf("R-squared for Polynomial regression of degree 2 (auto_mpg.csv)  is : %f", rsquared2)
##  "R-squared for Polynomial regression of degree 2 (auto_mpg.csv)  is : 0.831372"
#Polynomial degree 3
x = as.matrix(df3[1:6])
# Make polynomial of degree 4  of feature variables before split
df4=as.data.frame(poly(x,3,raw=TRUE))
df5 <- cbind(df4,df3)
train_idx <- trainTestSplit(df5,trainPercent=75,seed=5)

train <- df5[train_idx, ]
test <- df5[-train_idx, ]
# Fit a model of degree 3
fit <- lm(mpg~. ,data=train)
# Compute R squared
rsquared3=Rsquared(fit,test,test$mpg) sprintf("R-squared for Polynomial regression of degree 2 (auto_mpg.csv) is : %f", rsquared3) ##  "R-squared for Polynomial regression of degree 2 (auto_mpg.csv) is : 0.773225" df=data.frame(degree=c(1,2,3),Rsquared=c(rsquared1,rsquared2,rsquared3)) # Make a plot of Rsquared and degree ggplot(df,aes(x=degree,y=Rsquared)) +geom_point() + geom_line(color="blue") + ggtitle("Polynomial regression - R squared vs Degree of polynomial") + xlab("Degree") + ylab("R squared") ## 1.3a Polynomial Regression – Python For Polynomial regression , polynomials of degree 1,2 & 3 are used and R squared is computed. It can be seen that the quadaratic model provides the best R squared score and hence the best fit import numpy as np import pandas as pd import os import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures autoDF =pd.read_csv("auto_mpg.csv",encoding="ISO-8859-1") autoDF.shape autoDF.columns # Select key columns autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']] # Convert columns to numeric autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce') # Drop NAs autoDF3=autoDF2.dropna() autoDF3.shape X=autoDF3[['cylinder','displacement','horsepower','weight','acceleration','year']] y=autoDF3['mpg'] # Polynomial degree 1 X_train, X_test, y_train, y_test = train_test_split(X, y,random_state = 0) linreg = LinearRegression().fit(X_train, y_train) print('R-squared score - Polynomial degree 1 (training): {:.3f}'.format(linreg.score(X_train, y_train))) # Compute R squared rsquared1 =linreg.score(X_test, y_test) print('R-squared score - Polynomial degree 1 (test): {:.3f}'.format(linreg.score(X_test, y_test))) # Polynomial degree 2 poly = PolynomialFeatures(degree=2) X_poly = poly.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X_poly, y,random_state = 0) linreg = LinearRegression().fit(X_train, y_train) # Compute R squared print('R-squared score - Polynomial degree 2 (training): {:.3f}'.format(linreg.score(X_train, y_train))) rsquared2 =linreg.score(X_test, y_test) print('R-squared score - Polynomial degree 2 (test): {:.3f}\n'.format(linreg.score(X_test, y_test))) #Polynomial degree 3 poly = PolynomialFeatures(degree=3) X_poly = poly.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X_poly, y,random_state = 0) linreg = LinearRegression().fit(X_train, y_train) print('(R-squared score -Polynomial degree 3 (training): {:.3f}' .format(linreg.score(X_train, y_train))) # Compute R squared rsquared3 =linreg.score(X_test, y_test) print('R-squared score Polynomial degree 3 (test): {:.3f}\n'.format(linreg.score(X_test, y_test))) degree=[1,2,3] rsquared =[rsquared1,rsquared2,rsquared3] fig2=plt.plot(degree,rsquared) fig2=plt.title("Polynomial regression - R squared vs Degree of polynomial") fig2=plt.xlabel("Degree") fig2=plt.ylabel("R squared") fig2.figure.savefig('foo2.png', bbox_inches='tight') print "Finished plotting and saving"  ## R-squared score - Polynomial degree 1 (training): 0.811 ## R-squared score - Polynomial degree 1 (test): 0.799 ## R-squared score - Polynomial degree 2 (training): 0.861 ## R-squared score - Polynomial degree 2 (test): 0.847 ## ## (R-squared score -Polynomial degree 3 (training): 0.933 ## R-squared score Polynomial degree 3 (test): 0.710 ## ## Finished plotting and saving ## 1.4 K Nearest Neighbors The code below implements KNN Regression both for R and Python. This is done for different neighbors. The R squared is computed in each case. This is repeated after performing feature scaling. It can be seen the model fit is much better after feature scaling. Normalization refers to $X_{normalized} = \frac{X-min(X)}{max(X-min(X))}$ Another technique that is used is Standardization which is $X_{standardized} = \frac{X-mean(X)}{sd(X)}$ ## 1.4a K Nearest Neighbors Regression – R( Unnormalized) The R code below does not use feature scaling # KNN regression requires the FNN package df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI df1 <- as.data.frame(sapply(df,as.numeric)) df2 <- df1 %>% select(cylinder,displacement, horsepower,weight, acceleration, year,mpg) df3 <- df2[complete.cases(df2),] # Split train and test train_idx <- trainTestSplit(df3,trainPercent=75,seed=5) train <- df3[train_idx, ] test <- df3[-train_idx, ] # Select the feature variables train.X=train[,1:6] # Set the target for training train.Y=train[,7] # Do the same for test set test.X=test[,1:6] test.Y=test[,7] rsquared <- NULL # Create a list of neighbors neighbors <-c(1,2,4,8,10,14) for(i in seq_along(neighbors)){ # Perform a KNN regression fit knn=knn.reg(train.X,test.X,train.Y,k=neighbors[i]) # Compute R sqaured rsquared[i]=knnRSquared(knn$pred,test.Y)
}

# Make a dataframe for plotting
df <- data.frame(neighbors,Rsquared=rsquared)
# Plot the number of neighors vs the R squared
ggplot(df,aes(x=neighbors,y=Rsquared)) + geom_point() +geom_line(color="blue") +
xlab("Number of neighbors") + ylab("R squared") +
ggtitle("KNN regression - R squared vs Number of Neighors (Unnormalized)") ## 1.4b K Nearest Neighbors Regression – Python( Unnormalized)

The Python code below does not use feature scaling

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.neighbors import KNeighborsRegressor
autoDF.shape
autoDF.columns
autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']]
autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce')
autoDF3=autoDF2.dropna()
autoDF3.shape
X=autoDF3[['cylinder','displacement','horsepower','weight','acceleration','year']]
y=autoDF3['mpg']

# Perform a train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
# Create a list of neighbors
rsquared=[]
neighbors=[1,2,4,8,10,14]
for i in neighbors:
# Fit a KNN model
knnreg = KNeighborsRegressor(n_neighbors = i).fit(X_train, y_train)
# Compute R squared
rsquared.append(knnreg.score(X_test, y_test))
print('R-squared test score: {:.3f}'
.format(knnreg.score(X_test, y_test)))
# Plot the number of neighors vs the R squared
fig3=plt.plot(neighbors,rsquared)
fig3=plt.title("KNN regression - R squared vs Number of neighbors(Unnormalized)")
fig3=plt.xlabel("Neighbors")
fig3=plt.ylabel("R squared")
fig3.figure.savefig('foo3.png', bbox_inches='tight')
print "Finished plotting and saving"
## R-squared test score: 0.527
## R-squared test score: 0.678
## R-squared test score: 0.707
## R-squared test score: 0.684
## R-squared test score: 0.683
## R-squared test score: 0.670
## Finished plotting and saving

## 1.4c K Nearest Neighbors Regression – R( Normalized)

It can be seen that R squared improves when the features are normalized.

df=read.csv("auto_mpg.csv",stringsAsFactors = FALSE) # Data from UCI
df1 <- as.data.frame(sapply(df,as.numeric))
df2 <- df1 %>% select(cylinder,displacement, horsepower,weight, acceleration, year,mpg)
df3 <- df2[complete.cases(df2),]

# Perform MinMaxScaling of feature variables
train.X.scaled=MinMaxScaler(train.X)
test.X.scaled=MinMaxScaler(test.X)

# Create a list of neighbors
rsquared <- NULL
neighbors <-c(1,2,4,6,8,10,12,15,20,25,30)
for(i in seq_along(neighbors)){
# Fit a KNN model
knn=knn.reg(train.X.scaled,test.X.scaled,train.Y,k=i)
# Compute R ssquared
rsquared[i]=knnRSquared(knn$pred,test.Y) } df <- data.frame(neighbors,Rsquared=rsquared) # Plot the number of neighors vs the R squared ggplot(df,aes(x=neighbors,y=Rsquared)) + geom_point() +geom_line(color="blue") + xlab("Number of neighbors") + ylab("R squared") + ggtitle("KNN regression - R squared vs Number of Neighors(Normalized)") ## 1.4d K Nearest Neighbors Regression – Python( Normalized) R squared improves when the features are normalized with MinMaxScaling import numpy as np import pandas as pd import os import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.neighbors import KNeighborsRegressor from sklearn.preprocessing import MinMaxScaler autoDF =pd.read_csv("auto_mpg.csv",encoding="ISO-8859-1") autoDF.shape autoDF.columns autoDF1=autoDF[['mpg','cylinder','displacement','horsepower','weight','acceleration','year']] autoDF2 = autoDF1.apply(pd.to_numeric, errors='coerce') autoDF3=autoDF2.dropna() autoDF3.shape X=autoDF3[['cylinder','displacement','horsepower','weight','acceleration','year']] y=autoDF3['mpg'] # Perform a train/ test split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) # Use MinMaxScaling scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) # Apply scaling on test set X_test_scaled = scaler.transform(X_test) # Create a list of neighbors rsquared=[] neighbors=[1,2,4,6,8,10,12,15,20,25,30] for i in neighbors: # Fit a KNN model knnreg = KNeighborsRegressor(n_neighbors = i).fit(X_train_scaled, y_train) # Compute R squared rsquared.append(knnreg.score(X_test_scaled, y_test)) print('R-squared test score: {:.3f}' .format(knnreg.score(X_test_scaled, y_test))) # Plot the number of neighors vs the R squared fig4=plt.plot(neighbors,rsquared) fig4=plt.title("KNN regression - R squared vs Number of neighbors(Normalized)") fig4=plt.xlabel("Neighbors") fig4=plt.ylabel("R squared") fig4.figure.savefig('foo4.png', bbox_inches='tight') print "Finished plotting and saving" ## R-squared test score: 0.703 ## R-squared test score: 0.810 ## R-squared test score: 0.830 ## R-squared test score: 0.838 ## R-squared test score: 0.834 ## R-squared test score: 0.828 ## R-squared test score: 0.827 ## R-squared test score: 0.826 ## R-squared test score: 0.816 ## R-squared test score: 0.815 ## R-squared test score: 0.809 ## Finished plotting and saving # Conclusion In this initial post I cover the regression models when the output is continous. I intend to touch upon other Machine Learning algorithms. Comments, suggestions and corrections are welcome. Watch this this space! To be continued…. To see all posts see Index of posts # Analyzing World Bank data with WDI, googleVis Motion Charts Recently I was surfing the web, when I came across a real cool post New R package to access World Bank data, by Markus Gesmann on using googleVis and motion charts with World Bank Data. The post also introduced me to Hans Rosling, Professor of Sweden’s Karolinska Institute. Hans Rosling, the creator of the famous Gapminder chart, the “Heath and Wealth of Nations” displays global trends through animated charts (A must see!!!). As they say, in Hans Rosling’s hands, data dances and sings. Take a look at some of his Ted talks for e.g. Hans Rosling:New insights on poverty. Prof Rosling developed the breakthrough software behind the visualizations, in the Gapminder. The free software, which can be loaded with any data – was purchased by Google in March 2007. In this post, I recreate some of the Gapminder charts with the help of R packages WDI and googleVis. The WDI package of Vincent Arel-Bundock, provides a set of really useful functions to get to data based on the World Bank Data indicators. googleVis provides motion charts with which you can animate the data.. Incidentally Datacamp has a very nice, short course on googleVis “Having fun with googleVis You can clone/download the code from Github at worldBankAnalysis which is in the form of an Rmd file. library(WDI) library(ggplot2) library(googleVis) library(plyr) ## 1.Get the data from 1960 to 2016 for the following 1. Population – SP.POP.TOTL 2. GDP in US$ – NY.GDP.MKTP.CD
3. Life Expectancy at birth (Years) – SP.DYN.LE00.IN
4. GDP Per capita income – NY.GDP.PCAP.PP.CD
5. Fertility rate (Births per woman) – SP.DYN.TFRT.IN
6. Poverty headcount ratio – SI.POV.2DAY
# World population total
population = WDI(indicator='SP.POP.TOTL', country="all",start=1960, end=2016)
# GDP in US $gdp= WDI(indicator='NY.GDP.MKTP.CD', country="all",start=1960, end=2016) # Life expectancy at birth (Years) lifeExpectancy= WDI(indicator='SP.DYN.LE00.IN', country="all",start=1960, end=2016) # GDP Per capita income = WDI(indicator='NY.GDP.PCAP.PP.CD', country="all",start=1960, end=2016) # Fertility rate (births per woman) fertility = WDI(indicator='SP.DYN.TFRT.IN', country="all",start=1960, end=2016) # Poverty head count poverty= WDI(indicator='SI.POV.2DAY', country="all",start=1960, end=2016) ## 2.Rename the columns names(population)="Total population" names(lifeExpectancy)="Life Expectancy (Years)" names(gdp)="GDP (US$)"
names(income)="GDP per capita income"
names(fertility)="Fertility (Births per woman)"
names(poverty)="Poverty headcount ratio"

## 3.Join the data frames

Join the individual data frames to one large wide data frame with all the indicators for the countries  


j1 <- join(population, gdp)
j2 <- join(j1,lifeExpectancy)
j3 <- join(j2,income)
j4 <- join(j3,poverty)
wbData <- join(j4,fertility)


## 4.Use WDI_data

Use WDI_data to get the list of indicators and the countries. Join the countries and region

#This returns  list of 2 matrixes
wdi_data =WDI_data
# The 1st matrix is the list is the set of all World Bank Indicators
indicators=wdi_data[]
# The 2nd  matrix gives the set of countries and regions
countries=wdi_data[]
df = as.data.frame(countries)
aa <- df$region != "Aggregates" # Remove the aggregates countries_df <- df[aa,] # Subset from the development data only those corresponding to the countries bb = subset(wbData, country %in% countries_df$country)
cc = join(bb,countries_df)
dd = complete.cases(cc)
developmentDF = cc[dd,]

## 5.Create and display the motion chart

gg<- gvisMotionChart(cc,
idvar = "country",
timevar = "year",
xvar = "GDP",
yvar = "Life Expectancy",
sizevar ="Population",
colorvar = "region")
plot(gg)
cat(gg$html$chart, file="chart1.html")


Note: Unfortunately it is not possible to embed the motion chart in WordPress. It is has to hosted on a server as a Webpage. After exploring several possibilities I came up with the following process to display the animation graph. The plot is saved as a html file using ‘cat’ as shown above. The chart1.html page is then hosted as a Github page (gh-page) on Github.

Here is the ggvisMotionChart

Do give  World Bank Motion Chart1  a spin.  Here is how the Motion Chart has to be used You can select Life Expectancy, Population, Fertility etc by clicking the black arrows. The blue arrow shows the ‘play’ button to set animate the motion chart. You can also select the countries and change the size of the circles. Do give it a try. Here are some quick analysis by playing around with the motion charts with different parameters chosen

The set of charts below are screenshots captured by running the motion chart World Bank Motion Chart1

a. Life Expectancy vs Fertility chart

This chart is used by Hans Rosling in his Ted talk. The left chart shows low life expectancy and high fertility rate for several sub Saharan and East Asia Pacific countries in the early 1960’s. Today the fertility has dropped and the life expectancy has increased overall. However the sub Saharan countries still have a high fertility rate b. Population vs GDP

The chart below shows that GDP of India and China have the same GDP from 1973-1994 with US and Japan well ahead. From 1998- 2014 China really pulls away from India and Japan as seen below c. Per capita income vs Life Expectancy

In the 1990’s the per capita income and life expectancy of the sub -saharan countries are low (42-50). Japan and US have a good life expectancy in 1990’s. In 2014 the per capita income of the sub-saharan countries are still low though the life expectancy has marginally improved.  In the early 1990’s China had a higher poverty head count ratio than India. By 2004 China had this all figured out and the poverty head count ratio drops significantly. This can also be seen in the chart below. In the chart above China shows a drastic reduction in poverty headcount ratio vs India. Strangely Zambia shows an increase in the poverty head count ratio.

## 6.Get the data for the 2nd set of indicators

1. Total population  – SP.POP.TOTL
2. GDP in US$– NY.GDP.MKTP.CD 3. Access to electricity (% population) – EG.ELC.ACCS.ZS 4. Electricity consumption KWh per capita -EG.USE.ELEC.KH.PC 5. CO2 emissions -EN.ATM.CO2E.KT 6. Sanitation Access – SH.STA.ACSN # World population population = WDI(indicator='SP.POP.TOTL', country="all",start=1960, end=2016) # GDP in US$
gdp= WDI(indicator='NY.GDP.MKTP.CD', country="all",start=1960, end=2016)
elecAccess= WDI(indicator='EG.ELC.ACCS.ZS', country="all",start=1960, end=2016)
# Electric power consumption Kwh per capita
elecConsumption= WDI(indicator='EG.USE.ELEC.KH.PC', country="all",start=1960, end=2016)
#CO2 emissions
co2Emissions= WDI(indicator='EN.ATM.CO2E.KT', country="all",start=1960, end=2016)
sanitationAccess= WDI(indicator='SH.STA.ACSN', country="all",start=1960, end=2016)

## 7.Rename the columns

names(population)="Total population"
names(gdp)="GDP US($)" names(elecAccess)="Access to Electricity (% popn)" names(elecConsumption)="Electric power consumption (KWH per capita)" names(co2Emissions)="CO2 emisions" names(sanitationAccess)="Access to sanitation(% popn)" ## 8.Join the individual data frames Join the individual data frames to one large wide data frame with all the indicators for the countries  j1 <- join(population, gdp) j2 <- join(j1,elecAccess) j3 <- join(j2,elecConsumption) j4 <- join(j3,co2Emissions) wbData1 <- join(j3,sanitationAccess)  ## 9.Use WDI_data Use WDI_data to get the list of indicators and the countries. Join the countries and region #This returns list of 2 matrixes wdi_data =WDI_data # The 1st matrix is the list is the set of all World Bank Indicators indicators=wdi_data[] # The 2nd matrix gives the set of countries and regions countries=wdi_data[] df = as.data.frame(countries) aa <- df$region != "Aggregates"
# Remove the aggregates
countries_df <- df[aa,]
# Subset from the development data only those corresponding to the countries
ee = subset(wbData1, country %in% countries_df$country) ff = join(ee,countries_df) ## Joining by: iso2c, country ## 10.Create and display the motion chart gg1<- gvisMotionChart(ff, idvar = "country", timevar = "year", xvar = "GDP", yvar = "Access to Electricity", sizevar ="Population", colorvar = "region") plot(gg1) cat(gg1$html\$chart, file="chart2.html")


This is World Bank Motion Chart2  which has a different set of parameters like Access to Energy, CO2 emissions etc

The set of charts below are screenshots of the motion chart World Bank Motion Chart 2 The above chart shows that in China 100% population have access to electricity. India has made decent progress from 50% in 1990 to 79% in 2012. However Pakistan seems to have been much better in providing access to electricity. Pakistan moved from 59% to close 98% access to electricity

b. Power consumption vs population The above chart shows the Power consumption vs Population. China and India have proportionally much lower consumption that Norway, US, Canada

c. CO2 emissions vs Population In 1963 the CO2 emissions were fairly low and about comparable for all countries. US, India have shown a steady increase while China shows a steep increase. Interestingly UK shows a drop in CO2 emissions

India shows an improvement but it has a long way to go with only 40% of population with access to sanitation. China has made much better strides with 80% having access to sanitation in 2015. Strangely Nigeria shows a drop in sanitation by almost about 20% of population.

The code is available at Github at worldBankAnalysys

Conclusion: So there you have it. I have shown some screenshots of some sample parameters of the World indicators. Please try to play around with World Bank Motion Chart1 & World Bank Motion Chart 2  with your own set of parameters and countries.  You can also create your own motion chart from the 100s of WDI indicators avaialable at  World Bank Data indicator.

Finally, I  would really like to thank Prof Hans Rosling, googleVis and  WDI (Vincent  Arel-Bundock) for making this visualization possible!

To see all posts Index of posts