Data Extraction from World Bank
In this research light has been shed on the health and development conditions of New Zealand. A subset of the original dataset collected from World Bank has been used for the analysis. The subset has been chosen for the simplicity of the analysis. New Zealand has been chosen as it is a moderately populated country and it was of interest to understand the health conditions of a moderately populated country. The factors that has been considered for analysis are total unemployment, gross national income and life expectancy at birth. It is known that unemployment is factor to directly affect the gross national income of a country. It might also be affecting the life expectancy of birth of an infant. The relationships between these variables have been considered in this research.
2. Data Setup
The data has been extracted from World Bank. The format of the data was comma delimited (.csv). As the whole analysis has been performed in R Studio, the data has been imported to R from excel. There are 26 attributes in the data over 15 years from 2001 to 2015 and on the countries of East Asia and the Pacific. The necessity of the analysis has been kept in mind and thus the data was extracted as per the needs. 2015 has been eliminated from the data as there were a lot of missing information for that particular year. Table 2.1 shows the R-Codes for data Extraction.
Various libraries have been used in R to run the analysis. These libraries are listed as follows:
- dplyr: This library is used for filtering the data
- ggplot2: This is a library that is used to plot the data
- reshape2: With the help of this library, the data can be reshaped
- cluster: This library is used for the k-mean clustering analysis
Table 2.1: R-Codes for the Libraries Used and the Extraction of the Data
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# Libraries necessary for analysis library(dplyr) library(ggplot2) library(reshape2) library(cluster) # Extracting the data data <- read.csv(file.choose(), sep = ”,”, header = TRUE, na.strings = “..”) attach(data) data <- filter(data, Country.Code == “NZL”) data <- subset(data, select=-c(Country.Name, Country.Code, ï..Series.Name, X2015..YR2015.)) data <- na.omit(data) data <- melt(data, Series.Code = “Country.Code”) |
3. Exploratory Data Analysis
Descriptive analysis has to be conducted at first for the chosen variables or attributes. At first, analysis has been conducted on the gross national income of New Zealand. It has been obtained from the analysis that the standard deviation of the GNI for New Zealand is 8741.171, which is quite high. Thus, it indicates that the gross national income of the country is not close to the average GNI and are quite scattered. The distribution of the income is shown with the help of a boxplot in figure 3.1. Negative skewness is observed from the figure. Thus, it can be said that the GNI is higher when the population is high.
Table 3.1: R-Codes to Obtain Summary Statistics for Gross National Income
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# summary statistics summary(NY.GNP.PCAP.CD) sd(NY.GNP.PCAP.CD) var(NY.GNP.PCAP.CD) # boxplot boxplot(NY.GNP.PCAP.CD, main = “Boxplot for Gross National Income”, xlab = “Gross National Income”, col = 5) |
Exploratory Data Analysis on Gross National Income and Total Unemployment
Table 3.2: Results of the Summary Statistics for Gross National Income
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Min.: 13800 1st Qu.: 22470 Median : 28370 Mean : 27500 3rd Qu. : 31610 Max. : 41670 St. Dev. : 8741.171 Variance : 76408069 |
Figure 3.1: Boxplot showing the distribution of Gross National Income
The second variable on which analysis has been conducted is the total unemployment of New Zealand. It has been obtained from the analysis that the standard deviation of the total unemployment for New Zealand is 1.136, which is quite less. Thus, it indicates that the total unemployment of the country is close to the average total unemployment and are not scattered. The distribution of the total unemployment is shown with the help of a histogram in figure 3.2. Symmetricity is observed from the figure.
Table 3.3: R-Codes to Obtain Summary Statistics for Total Unemployment
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# Summary summary(SL.UEM.TOTL.ZS) sd(SL.UEM.TOTL.ZS) var(SL.UEM.TOTL.ZS) # histogram hist(SL.UEM.TOTL.ZS, data=data, main = “Histogram for Total Unemployment”, xlab = “Total Unemployment”, col = 5) |
Table 3.4: Results of the Summary Statistics for Total Unemployment
| Min.: 3.700 1st Qu. : 4.050 Median : 5.350 Mean : 5.207 3rd Qu. : 6.175 Max. : 6.900 St. dev. : 1.136435 Variance : 1.291483 |
The third variable on which analysis has been conducted is the Life Expectancy at Birth of an Infant in New Zealand. It has been obtained from the analysis that the standard deviation of the Life Expectancy at Birth of an Infant in New Zealand is 0.9044, which is quite less. Thus, it indicates that the Life Expectancy at Birth of an Infant in the country is close to the average Life Expectancy at Birth of an Infant and are not scattered. The distribution of the Life Expectancy at Birth of an Infant is shown with the help of a boxplot in figure 3.3. Symmetricity is observed from the figure.
Table 3.5: R-Codes to Obtain Summary Statistics for Total Life expectancy at Birth
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# summary summary(SP.DYN.LE00.IN) sd(SP.DYN.LE00.IN) var(SP.DYN.LE00.IN) # boxplot boxplot(SP.DYN.LE00.IN, main = “Boxplot for Total Life Expectancy at Birth”, xlab = “Total Life Expectency at Birth”, col = 5) |
Table 3.6: Results of the Summary Statistics for Total Life expectancy at Birth
| Min.: 78.69 1st Qu. : 79.62 Median : 80.25 Mean : 80.21 3rd Qu. : 80.85 Max. : 81.41 St. dev. : 0.9043788 Variance : 0.8179011 |
Relationship between two variables are shown in the second part of this analysis. Three variables are involved in this study and the relationship between two at a time will be illustrated in this part. At first, relationship between GNI and unemployment are established. Correlation analysis is conducted for this. The correlation coefficient has been obtained as 0.421 which indicates that the relationship between the two variables are moderately positive. The scatterplot in figure 3.4 illustrates the relationship diagrammatically.
Table 3.7: R-Codes for Scatterplot and Correlation between GNI and Unemployment
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# scatterplot plot(SL.UEM.TOTL.ZS~NY.GNP.PCAP.CD, data=data, main=”Scatterplot of Gross National Income and Total Unemployment”,xlab=”Gross National Income (Per Capita)”, ylab=”Total Unemployment (% of total labor force)”, col=2, pch=19) # correlation coefficient cor(SL.UEM.TOTL.ZS, NY.GNP.PCAP.CD) |
Next, the relationship between Life expectancy at birth of an infant and unemployment are established. Correlation analysis is conducted for this. The correlation coefficient has been obtained as 0.985 which indicates that the relationship between the two variables are strongly positive. The scatterplot in figure 3.5 illustrates the relationship diagrammatically.
K-means Clustering Analysis
Table 3.7: R-Codes for Scatterplot and Correlation between Life expectancy at birth of an infant and Unemployment
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# scatterplot plot(NY.GNP.PCAP.CD ~ SP.DYN.LE00.IN, data=data, main=”Scatterplot of Gross National Income and Total Life Expectancy at Birth”,xlab=”Gross National Income (Per Capita)”, ylab=” Total Life Expectancy at Birth (years)”, col=2, pch=19) # correlation coefficient cor(NY.GNP.PCAP.CD, SP.DYN.LE00.IN) |
Advanced Analysis
After the exploratory analysis, an advanced analysis will be conducted on the variables. Thus, clustering analysis and regression analysis will be conducted further for the purpose of the study. Relationship between GNI and life expectancy has been very high and that with GNI and unemployment was moderate as seen from the analysis conducted so far.
4.1 Cluster Analysis
The relation obtained above is the main cause to run the clustering analysis. The stronger variables such as GNI and life expectancy has been chosen for clustering with k-means. The values of a data frame are grouped into different clusters according to the closeness to the cluster means (Guha and Mishra 2016).
Table 4.1 provides the R-Codes that has been used to conduct the k-means clustering analysis (Celebi, Kingravi and Vela 2013). The analysis is represented diagrammatically in figure 4.1.
Table 4.1: R-Codes for Clustering Analysis
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# Data extraction for k-means clustering data <- read.csv(file.choose(), sep = “,”, header = TRUE, na.strings = “..”) data3 <- filter(data, Series.Code %in% c(“NY.GNP.PCAP.CD” , “SP.DYN.LE00.IN”)) data3 <- subset(data3, select = -(X2015..YR2015.)) data3 <- melt(data3, Series.Code = c(“Series.Code”,”Country.Name”,”Country.Code”)) data4 <- dcast(data3, formula = Country.Code ~ Series.Code, mean) data4 <- na.omit(data4) View(data4) # Clustering grpdata <- kmeans(data4[,c(“NY.GNP.PCAP.CD” , “SP.DYN.LE00.IN”)],centers = 3, nstart = 10) grpdata o = order(grpdata$cluster) data.frame(data4$Country.Code[o], grpdata$cluster[o]) # plotting data plot(data4$NY.GNP.PCAP.CD, data4$SP.DYN.LE00.IN, type=”n”, xlim=c(0,50000), main=”k- means Clustering” ,xlab=”Gross National Income”, ylab=”Life Expectancy at Birth”) text(x=data4$NY.GNP.PCAP.CD,y=data4$SP.DYN.LE00.IN,labels=data4$Country.Code,col=grp data$cluster+1) |
2 Regression Analysis
To establish the nature of the strength of the relationship obtained in the correlation analysis, the regression analysis has been performed. The value of the dependent variable is predicted with the help of the independent variable with the help of this analysis (Montgomery, Peck and Vining 2015). The relationship is denoted with the help of the following formula:
y = β0 + β1x + ε
Here, x and y are the independent and the dependent variables respectively. The scale parameter β0 represents the value of y in the absence of x and β1 indicates the amount of increase or decrease in the value of y for increase in the value of x (Kabacoff 2015).
Regression between GNI and Unemployment are established at first with unemployment as the dependent variable. The results show that 17.74 percent of the variability can be explained by GNI (R-Square). The relationship is expressed with the following equation:
Total Unemployment = 3.701 + (0.00006 * GNI) + Error
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# Regression for unemployment on GNI Reg1 <- lm(formula = SL.UEM.TOTL.ZS ~NY.GNP.PCAP.CD, data = data) summary(Reg1) # plotting line plot1 <- ggplot(data, aes(x= NY.GNP.PCAP.CD, y= SL.UEM.TOTL.ZS)) + geom_point(shape=1) + scale_x_continuous(name = “Gross National Income”) + scale_y_continuous(name = “Total Unemployment”)+ geom_smooth(method=lm) +theme_bw()+ ggtitle(“Regression of Total Unemployment on Gross National Income”) plot1 |
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Residuals: Min 1Q Median 3Q Max -1.5421 -1.0199 0.2389 0.9129 1.1836 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.701e+00 9.790e-01 3.780 0.00262 ** NY.GNP.PCAP.CD 5.477e-05 3.404e-05 1.609 0.13360 — Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.073 on 12 degrees of freedom Multiple R-squared: 0.1774, Adjusted R-squared: 0.1089 F-statistic: 2.589 on 1 and 12 DF, p-value: 0.1336 |
Regression between Life Expectancy at Birth and Unemployment are established next with unemployment as the dependent variable. The results show that 96.97 percent of the variability can be explained by Life Expectancy at Birth (R-Square). The relationship is expressed with the following equation:
Life Expectancy at Birth = (7.741e+01) + (0.0001 * GNI) + Error
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# Regression of Life Expectancy at birth on GNI Reg2 <- lm(formula = SP.DYN.LE00.IN~SL.UEM.TOTL.ZS, data = data) summary(Reg2) # plotting line plot8 <- ggplot(data, aes(x=SL.UEM.TOTL.ZS, y=SP.DYN.LE00.IN)) + geom_point(shape=1) + scale_x_continuous(name = “Total Unemployment”) + scale_y_continuous(name = “Total Life Expectancy at Birth”)+ geom_smooth(method=lm) +theme_bw()+ ggtitle(“Regression of Life Expectancy at Birth on Total Unemployment”) plot8 |
| Residuals: Min 1Q Median 3Q Max -0.24688 -0.10782 -0.02588 0.02552 0.27936 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.741e+01 1.496e-01 517.33 < 2e-16 ***NY.GNP.PCAP.CD 1.019e-04 5.202e-06 19.58 1.78e-10 ***—Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.164 on 12 degrees of freedomMultiple R-squared: 0.9697, Adjusted R-squared: 0.9671 F-statistic: 383.5 on 1 and 12 DF, p-value: 1.783e-10 |
5. Conclusion
From the analysis, GNI and life Expectancy at birth has shown an extremely strong positive relationship. Thus, GNI has to be kept high in order to protect the infants and keep them healthy. The relationship between the other two variables were not significant.
6. Reflections
I faced a lot of problem in handling the large dataset with a lot of missing values for conducting the research. A lot of extraction had to be done to obtain a proper subset that was fit for the analysis.
References
Celebi, M.E., Kingravi, H.A. and Vela, P.A., 2013. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), pp.200-210.
Guha, S. and Mishra, N., 2016. Clustering data streams. In Data Stream Management (pp. 169-187). Springer Berlin Heidelberg.
Kabacoff, R., 2015. R in action: data analysis and graphics with R. Manning Publications Co..
Montgomery, D.C., Peck, E.A. and Vining, G.G., 2015. Introduction to linear regression analysis. John Wiley & Sons.
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