Discussion12
Dominika Markowska-Desvallons
4/18/2021
happiness<-read.csv("https://raw.githubusercontent.com/hrensimin05/Cuny_DataScience/master/2019.csv")
#there are 156 observations and 9 variables
happy<-data.frame(happiness)
head(happy)## Overall.rank Country.or.region Score GDP.per.capita Social.support
## 1 1 Finland 7.769 1.340 1.587
## 2 2 Denmark 7.600 1.383 1.573
## 3 3 Norway 7.554 1.488 1.582
## 4 4 Iceland 7.494 1.380 1.624
## 5 5 Netherlands 7.488 1.396 1.522
## 6 6 Switzerland 7.480 1.452 1.526
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 0.986 0.596 0.153
## 2 0.996 0.592 0.252
## 3 1.028 0.603 0.271
## 4 1.026 0.591 0.354
## 5 0.999 0.557 0.322
## 6 1.052 0.572 0.263
## Perceptions.of.corruption
## 1 0.393
## 2 0.410
## 3 0.341
## 4 0.118
## 5 0.298
## 6 0.343summary(happy)## Overall.rank Country.or.region Score GDP.per.capita
## Min. : 1.00 Length:156 Min. :2.853 Min. :0.0000
## 1st Qu.: 39.75 Class :character 1st Qu.:4.545 1st Qu.:0.6028
## Median : 78.50 Mode :character Median :5.380 Median :0.9600
## Mean : 78.50 Mean :5.407 Mean :0.9051
## 3rd Qu.:117.25 3rd Qu.:6.184 3rd Qu.:1.2325
## Max. :156.00 Max. :7.769 Max. :1.6840
## Social.support Healthy.life.expectancy Freedom.to.make.life.choices
## Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.056 1st Qu.:0.5477 1st Qu.:0.3080
## Median :1.272 Median :0.7890 Median :0.4170
## Mean :1.209 Mean :0.7252 Mean :0.3926
## 3rd Qu.:1.452 3rd Qu.:0.8818 3rd Qu.:0.5072
## Max. :1.624 Max. :1.1410 Max. :0.6310
## Generosity Perceptions.of.corruption
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.1087 1st Qu.:0.0470
## Median :0.1775 Median :0.0855
## Mean :0.1848 Mean :0.1106
## 3rd Qu.:0.2482 3rd Qu.:0.1412
## Max. :0.5660 Max. :0.4530library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(corrplot)## Warning: package 'corrplot' was built under R version 4.0.3## corrplot 0.84 loadedlibrary(plotly)## Warning: package 'plotly' was built under R version 4.0.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutCorrelation
cor.test(happy$Score,happy$Generosity)##
## Pearson's product-moment correlation
##
## data: happy$Score and happy$Generosity
## t = 0.94366, df = 154, p-value = 0.3468
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08229763 0.23022136
## sample estimates:
## cor
## 0.07582369corrplot(cor(happy %>%
select(Score,Generosity)),
method="color",
sig.level = 0.01, insig = "blank",
addCoef.col = "black",
tl.srt=45,
type="upper"
)
Linear Regression Model
model <- lm( Generosity ~ Score , data = happy)
summary(model)$adj.r.squared ## [1] -0.0007069411summary(model)##
## Call:
## lm(formula = Generosity ~ Score, data = happy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18407 -0.07288 -0.00380 0.06494 0.38795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.149762 0.037953 3.946 0.00012 ***
## Score 0.006489 0.006876 0.944 0.34682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09529 on 154 degrees of freedom
## Multiple R-squared: 0.005749, Adjusted R-squared: -0.0007069
## F-statistic: 0.8905 on 1 and 154 DF, p-value: 0.3468Residual Analysis
par.orig <- par(mfrow=c(2,2))
plot(log(happy$Score), resid(model), main="Predictors vs Residuals")
abline(0,0)
plot(fitted(model), resid(model),main="Fitted vs Residuals", xlab="Fitted Values")
abline(0,0)
qqnorm(resid(model), main="QQ-Plot of Residuals")
qqline(resid(model))
hist(resid(model), main="Histogram of Residuals")
par(par.orig)plot_ly(data = happy,
x=~Generosity, y=~Score, color=~Generosity, type = "scatter",
text = ~paste("Country:", Country.or.region)) %>%
layout(title = "Happiness and Generocity ",
xaxis = list(title = "Generosity"),
yaxis = list(title = "Happiness Score"))## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.corrplot(cor(happy %>%
select(Score:Perceptions.of.corruption)),
method="color",
sig.level = 0.01, insig = "blank",
addCoef.col = "black",
tl.srt=45,
type="upper"
)
Conclusion
After analyzing data of Global Happiness Levels in the world, created by the United Nations Sustainable Development Solutions Network,I was able to discover no significat impact of the generosity factor in determining “happiness.” I decided to focus on creating graphs of their relationship to confirm more that there is no direct relation between these factors.
By looking at and analyzing the report, I was able to discover if being generous really makes countries and their citizens happier and if does have significant correlation with happiness. As I thought that generosity will make people happy but my analysis proved that it is not a factor. After running test on all variable in below graph we was able what have the biggest impact on the happiness score according to the World Happiness Report. It turned out that gross domestic product (GDP) per capital has the biggest impact, means the richer country happier people.