Economy in the World
Yiğit Alp Yalçın
20 06 2021
title: “project_interim_Migrainee” author: “Migraine” date: “17 05 2021” output: html_document: df_print: paged spacing: double fontsize: 12pt —
Team Member
-Yiğit Alp Yalçın
Topic
The topic is Economy in the world with every aspects of it. Economy is been a issue by a long time. It can change every year,month or even in second dramatically.That effects human life standard. In order to see these affects on human life it will be examined to understand which attributes of economy are significant for the humanity.
About the Plan
First step is understanding the problem and the data and try to find a proper route for it.Than it will be extract into R. After the extraction it will be examined by the class of columns and the missing datas in the set. After cleaning complete visualization will start. Line,histogram and many more will be used in the data for visualization. After the visualization it will be more clear to see every aspect. Finally with whole data we have, It will be merged with a geometry map data set to get a world map at the end of it.
Final Product
As final product, I here tried to show the difference between whole countries in the world by economically with other attributes also.In each graph there have been used different attributes comparison. Finally it will be mapped in order to see clearly difference by region or by countries.
View(Alldata)colnames(Alldata)## [1] "Name" "Index Year" "Overall Score"
## [4] "Property Rights" "Government Integrity" "Judicial Effectiveness"
## [7] "Tax Burden" "Government Spending" "Fiscal Health"
## [10] "Business Freedom" "Labor Freedom" "Monetary Freedom"
## [13] "Trade Freedom" "Investment Freedom" "Financial Freedom"dim(Alldata)## [1] 4053 15names(Alldata)[1]<-paste("Country")
names(Alldata)[2]<-paste("Year")
names(Alldata)[3]<-paste("Overall.score")
names(Alldata)[4]<-paste("Property.rights")
names(Alldata)[5]<-paste("Goverment.integrity")
names(Alldata)[6]<-paste("Judicial.effect")
names(Alldata)[7]<-paste("Tax.burden")
names(Alldata)[8]<-paste("Goverment.spending")
names(Alldata)[9]<-paste("Fiscal.health")
names(Alldata)[10]<-paste("Business.freedom")
names(Alldata)[11]<-paste("Labor.freedom")
names(Alldata)[12]<-paste("Monetary.freedom")
names(Alldata)[13]<-paste("Trade.freedom")
names(Alldata)[14]<-paste("Investment.freedom")
names(Alldata)[15]<-paste("Financial.freedom")
View(Alldata)str(Alldata)## spec_tbl_df [4,053 x 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Country : chr [1:4053] "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Year : num [1:4053] 2021 2021 2021 2021 2021 ...
## $ Overall.score : num [1:4053] 53 65.2 49.7 54.2 52.7 71.9 82.4 73.9 70.1 69.9 ...
## $ Property.rights : num [1:4053] 30.3 46.1 34 30.3 46.1 57.3 81.5 86.8 67.9 71.5 ...
## $ Goverment.integrity: num [1:4053] 29.1 40.6 32.7 20.4 54 45 89.8 84.8 46.8 64.4 ...
## $ Judicial.effect : num [1:4053] 25.7 22.8 41.6 22.8 45.7 55.3 90 83.5 55.8 65.8 ...
## $ Tax.burden : num [1:4053] 91.1 89 67.2 87.3 70.4 87.1 62.6 45.7 88.1 100 ...
## $ Goverment.spending : num [1:4053] 76.1 74.6 55.4 86.9 52.8 81.3 58.1 29.1 65.2 67.1 ...
## $ Fiscal.health : num [1:4053] 99.9 86.6 49.1 77.9 38.4 84.3 88.7 90 99.4 0 ...
## $ Business.freedom : num [1:4053] 53.9 66.1 63.5 56.9 59.5 81.9 87.4 72.6 80.5 76.7 ...
## $ Labor.freedom : num [1:4053] 59.9 51.6 51.3 59.6 46.3 74.5 84.1 68.4 65.9 71.4 ...
## $ Monetary.freedom : num [1:4053] 80.8 82 84.3 67.5 41.9 76.9 86.7 81.7 73.2 82.8 ...
## $ Trade.freedom : num [1:4053] 68.6 82.8 57.4 70.2 62.6 73.8 89.8 84 68 83.6 ...
## $ Investment.freedom : num [1:4053] 10 70 30 30 55 75 80 90 70 75 ...
## $ Financial.freedom : num [1:4053] 10 70 30 40 60 70 90 70 60 80 ...
## - attr(*, "spec")=
## .. cols(
## .. Name = col_character(),
## .. `Index Year` = col_double(),
## .. `Overall Score` = col_character(),
## .. `Property Rights` = col_character(),
## .. `Government Integrity` = col_character(),
## .. `Judicial Effectiveness` = col_character(),
## .. `Tax Burden` = col_character(),
## .. `Government Spending` = col_character(),
## .. `Fiscal Health` = col_character(),
## .. `Business Freedom` = col_character(),
## .. `Labor Freedom` = col_character(),
## .. `Monetary Freedom` = col_character(),
## .. `Trade Freedom` = col_character(),
## .. `Investment Freedom` = col_character(),
## .. `Financial Freedom` = col_character()
## .. )summary(Alldata)## Country Year Overall.score Property.rights
## Length:4053 Min. :2000 Min. : 1.00 Min. : 0.00
## Class :character 1st Qu.:2005 1st Qu.:53.30 1st Qu.:30.00
## Mode :character Median :2011 Median :59.80 Median :45.35
## Mean :2011 Mean :60.04 Mean :47.49
## 3rd Qu.:2016 3rd Qu.:67.60 3rd Qu.:65.90
## Max. :2021 Max. :90.20 Max. :98.40
## NA's :318 NA's :281
## Goverment.integrity Judicial.effect Tax.burden Goverment.spending
## Min. : 0.00 Min. : 5.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 26.00 1st Qu.:30.60 1st Qu.: 67.50 1st Qu.:51.50
## Median : 34.50 Median :43.25 Median : 76.90 Median :70.60
## Mean : 41.06 Mean :45.44 Mean : 74.91 Mean :64.81
## 3rd Qu.: 52.00 3rd Qu.:58.40 3rd Qu.: 84.10 3rd Qu.:83.70
## Max. :100.00 Max. :93.80 Max. :100.00 Max. :99.30
## NA's :265 NA's :3131 NA's :302 NA's :286
## Fiscal.health Business.freedom Labor.freedom Monetary.freedom
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 51.80 1st Qu.: 55.00 1st Qu.: 49.80 1st Qu.:71.00
## Median : 79.90 Median : 64.30 Median : 60.40 Median :76.90
## Mean : 67.96 Mean : 63.76 Mean : 60.51 Mean :74.68
## 3rd Qu.: 92.80 3rd Qu.: 73.70 3rd Qu.: 71.90 3rd Qu.:81.40
## Max. :100.00 Max. :100.00 Max. :100.00 Max. :95.40
## NA's :3138 NA's :269 NA's :1057 NA's :280
## Trade.freedom Investment.freedom Financial.freedom
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:63.60 1st Qu.:35.00 1st Qu.:30.00
## Median :73.60 Median :50.00 Median :50.00
## Mean :71.43 Mean :53.25 Mean :49.65
## 3rd Qu.:82.10 3rd Qu.:70.00 3rd Qu.:70.00
## Max. :95.00 Max. :95.00 Max. :90.00
## NA's :295 NA's :280 NA's :302alldata2<-AlldataAlldatafrom2018<- Alldata %>%
filter(Year>2017)
View(Alldatafrom2018)summary(Alldatafrom2018)## Country Year Overall.score Property.rights
## Length:742 Min. :2018 Min. : 4.20 Min. : 5.20
## Class :character 1st Qu.:2018 1st Qu.:54.52 1st Qu.:38.73
## Mode :character Median :2019 Median :61.40 Median :52.40
## Mean :2019 Mean :61.25 Mean :53.52
## 3rd Qu.:2020 3rd Qu.:68.58 3rd Qu.:67.45
## Max. :2021 Max. :90.20 Max. :98.40
## NA's :24 NA's :4
## Goverment.integrity Judicial.effect Tax.burden Goverment.spending
## Min. : 7.50 Min. : 5.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:28.32 1st Qu.:31.07 1st Qu.: 70.65 1st Qu.:53.35
## Median :37.90 Median :43.85 Median : 78.60 Median :70.30
## Mean :43.15 Mean :45.69 Mean : 77.24 Mean :65.21
## 3rd Qu.:51.17 3rd Qu.:58.08 3rd Qu.: 85.80 3rd Qu.:83.85
## Max. :97.20 Max. :93.80 Max. :100.00 Max. :96.60
## NA's :4 NA's :4 NA's :23 NA's :12
## Fiscal.health Business.freedom Labor.freedom Monetary.freedom
## Min. : 0.00 Min. : 5.00 Min. : 5.00 Min. : 0.00
## 1st Qu.: 53.02 1st Qu.:54.70 1st Qu.:50.30 1st Qu.:71.80
## Median : 80.00 Median :64.00 Median :59.90 Median :77.40
## Mean : 68.39 Mean :63.69 Mean :59.30 Mean :75.11
## 3rd Qu.: 92.90 3rd Qu.:75.00 3rd Qu.:68.78 3rd Qu.:81.70
## Max. :100.00 Max. :96.40 Max. :92.60 Max. :91.60
## NA's :12 NA's :5 NA's :8 NA's :8
## Trade.freedom Investment.freedom Financial.freedom
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:66.00 1st Qu.:45.00 1st Qu.:30.00
## Median :75.60 Median :60.00 Median :50.00
## Mean :73.68 Mean :57.04 Mean :48.75
## 3rd Qu.:84.00 3rd Qu.:75.00 3rd Qu.:60.00
## Max. :95.00 Max. :95.00 Max. :90.00
## NA's :15 NA's :9 NA's :20Alldatafrom2018<-drop_na(Alldatafrom2018)
View(Alldatafrom2018)
summary(Alldatafrom2018)## Country Year Overall.score Property.rights
## Length:718 Min. :2018 Min. : 4.20 Min. : 5.20
## Class :character 1st Qu.:2018 1st Qu.:54.52 1st Qu.:39.70
## Mode :character Median :2019 Median :61.40 Median :53.30
## Mean :2019 Mean :61.25 Mean :54.21
## 3rd Qu.:2020 3rd Qu.:68.58 3rd Qu.:67.90
## Max. :2021 Max. :90.20 Max. :98.40
## Goverment.integrity Judicial.effect Tax.burden Goverment.spending
## Min. : 7.50 Min. : 5.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:29.43 1st Qu.:32.02 1st Qu.: 70.62 1st Qu.:53.62
## Median :38.20 Median :44.65 Median : 78.55 Median :70.45
## Mean :43.82 Mean :46.40 Mean : 77.22 Mean :65.49
## 3rd Qu.:51.50 3rd Qu.:58.85 3rd Qu.: 85.78 3rd Qu.:83.90
## Max. :97.20 Max. :93.80 Max. :100.00 Max. :96.60
## Fiscal.health Business.freedom Labor.freedom Monetary.freedom
## Min. : 0.00 Min. : 5.00 Min. : 5.00 Min. : 0.00
## 1st Qu.: 53.92 1st Qu.:55.12 1st Qu.:50.30 1st Qu.:72.20
## Median : 80.45 Median :65.15 Median :60.05 Median :77.50
## Mean : 68.99 Mean :64.13 Mean :59.43 Mean :75.42
## 3rd Qu.: 92.90 3rd Qu.:75.20 3rd Qu.:69.20 3rd Qu.:81.90
## Max. :100.00 Max. :96.40 Max. :92.60 Max. :91.60
## Trade.freedom Investment.freedom Financial.freedom
## Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.:66.2 1st Qu.:45.00 1st Qu.:30.00
## Median :75.8 Median :60.00 Median :50.00
## Mean :73.8 Mean :57.52 Mean :48.58
## 3rd Qu.:84.0 3rd Qu.:75.00 3rd Qu.:60.00
## Max. :95.0 Max. :95.00 Max. :90.00Alldata<-Alldata[-c(6,9,11)]
View(Alldata)Alldata=na.omit(Alldata)
View(Alldata)
summary(Alldata)## Country Year Overall.score Property.rights
## Length:3735 Min. :2000 Min. : 1.00 Min. : 0.00
## Class :character 1st Qu.:2005 1st Qu.:53.30 1st Qu.:30.00
## Mode :character Median :2011 Median :59.80 Median :46.40
## Mean :2011 Mean :60.04 Mean :47.72
## 3rd Qu.:2016 3rd Qu.:67.60 3rd Qu.:66.70
## Max. :2021 Max. :90.20 Max. :98.40
## Goverment.integrity Tax.burden Goverment.spending Business.freedom
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 26.00 1st Qu.: 67.50 1st Qu.:51.80 1st Qu.: 55.00
## Median : 35.00 Median : 76.80 Median :70.60 Median : 64.80
## Mean : 41.39 Mean : 74.84 Mean :64.89 Mean : 63.88
## 3rd Qu.: 52.00 3rd Qu.: 84.00 3rd Qu.:83.70 3rd Qu.: 73.80
## Max. :100.00 Max. :100.00 Max. :99.30 Max. :100.00
## Monetary.freedom Trade.freedom Investment.freedom Financial.freedom
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:71.10 1st Qu.:63.80 1st Qu.:40.00 1st Qu.:30.00
## Median :77.00 Median :73.70 Median :50.00 Median :50.00
## Mean :74.78 Mean :71.43 Mean :53.42 Mean :49.63
## 3rd Qu.:81.50 3rd Qu.:82.10 3rd Qu.:70.00 3rd Qu.:60.00
## Max. :95.40 Max. :95.00 Max. :95.00 Max. :90.00Alldata %>%
filter(Year==2021) %>%
ggplot(aes(continent, Tax.burden, color=continent)) + geom_boxplot() + geom_jitter()+
ggtitle("Distribution of Tax Burden by Region", subtitle = " by Region")+
theme(axis.text.x = element_text(angle = 90))
library(DT)
top_rankdt <- Alldata[c(1,2,5)] %>%
filter(Year==2021)
datatable(head(top_rankdt, 50), class = 'cell-border stripe', caption = 'Highest Goverment Integrity to Lowest at 2021', options = list(
order = list(list(3, 'desc'))))Alldata %>%
filter(Year==2021 & Goverment.spending <29.2 & Goverment.spending> 6 ) %>%
ggplot(aes(continent, fill=Country)) + geom_bar() +
ggtitle("Top 10 Lowest Goverment spending", subtitle = "by the Region")+
theme(axis.text.x = element_text(angle = 90))
Alldata%>%
filter(Year==2020 & Overall.score>75 ) %>%
ggplot(aes(x=Overall.score, y=Investment.freedom, color=Country)) +
geom_point(size=3) +
ggtitle("2020 Overall Score and Investmen Freedom comparison ",
subtitle = "By Country")
Alldata%>%
filter(Year==2010 & Property.rights>70 &Overall.score>75) %>%
ggplot(aes(x=Property.rights, y=Overall.score, color=Country)) +
geom_point(size=3) +
ggtitle("Property rights comparison by Overall Score at 2010 ",
subtitle = "Top Countries")
Alldata%>%
filter(Year==2015 & Monetary.freedom>5) %>%
ggplot(aes(continent,Monetary.freedom , color = continent)) + geom_boxplot() + geom_jitter() +
ggtitle("Monetary Freedom Distribution by Region at 2005",
subtitle = "By Region") +
theme(axis.text.x = element_text(angle = 90))
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(grid)
p1<-Alldata%>%
filter(Year==2005,continent=="Europe & Central Asia",Overall.score>76) %>%
ggplot(aes(Country, Overall.score, color = Country)) + geom_boxplot() + geom_jitter() +
ggtitle("2005 Europe Overall Score",
subtitle = "By Countries") +
theme(axis.text.x = element_text(angle = 90))
p2<-Alldata%>%
filter(Year==2010,continent=="Europe & Central Asia",Overall.score>76) %>%
ggplot(aes(Country, Overall.score, color = Country)) + geom_boxplot() + geom_jitter() +
ggtitle("2010 Europe Overall Score",
subtitle = "By Countries") +
theme(axis.text.x = element_text(angle = 90))
p3<-Alldata%>%
filter(Year==2015,continent=="Europe & Central Asia",Overall.score>76) %>%
ggplot(aes(Country, Overall.score, color = Country)) + geom_boxplot() + geom_jitter() +
ggtitle("2015 Europe Overall Score",
subtitle = "By Countries") +
theme(axis.text.x = element_text(angle = 90))
p4<-Alldata%>%
filter(Year==2020,continent=="Europe & Central Asia",Overall.score>76) %>%
ggplot(aes(Country, Overall.score, color = Country)) + geom_boxplot() + geom_jitter() +
ggtitle("2020 Europe Overall Score",
subtitle = "By Countries") +
theme(axis.text.x = element_text(angle = 90))
grid.arrange(p1,p2,p3,p4,ncol=2)
tax_burden <- Alldata[c(1,2,3,6,13)]
tax_burden%>%
filter(Year==2020) %>%
ggplot(aes(x=Tax.burden, y=Overall.score)) +
geom_point()+
geom_smooth() +
ggtitle("Tax burden and Economic Score")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
years<-c("2000","2001","2002")p6<-Alldata%>%
filter(Year==2005,Country=="Turkey",) %>%
ggplot(aes(x=Overall.score, y=Monetary.freedom)) +
geom_boxplot() +
geom_jitter() +
ggtitle("2005 Turkey")
p7<-Alldata%>%
filter(Year==2010,Country=="Turkey",) %>%
ggplot(aes(x=Overall.score, y=Monetary.freedom)) +
geom_boxplot() +
geom_jitter() +
ggtitle("2010 Turkey")
p8<-Alldata%>%
filter(Year==2015,Country=="Turkey",) %>%
ggplot(aes(x=Overall.score, y=Monetary.freedom)) +
geom_boxplot() +
geom_jitter() +
ggtitle("2015 Turkey")
p9<-Alldata%>%
filter(Year==2020,Country=="Turkey",) %>%
ggplot(aes(x=Overall.score, y=Monetary.freedom)) +
geom_boxplot() +
geom_jitter() +
ggtitle("2020 Turkey")
grid.arrange(p6,p7,p8,p9,ncol=4)
View(Alldata)p<-Alldata %>%
filter(Year==2020 &continent=="East Asia & Pacific" & Investment.freedom>60 &Business.freedom>60)%>%
ggplot(Alldata = pef, aes(x = Business.freedom, y = Investment.freedom))+
geom_point(mapping=aes(color = Country,size=3))
p
names(Alldatafrom2018)[1]<-paste("region")mapdata<-map_data("world")
mapdata<-left_join(mapdata,Alldatafrom2018,by="region")mapdata<- mapdata[-6]st_crs(mapdata)## Coordinate Reference System: NAmapdata1<-mapdata %>%
filter(!is.na(mapdata$Overall.score))map9<-mapdata1 %>%
ggplot(mapping=aes(x = long, y = lat, group=group))+
geom_polygon(mapping=aes(fill=Overall.score),color="black")
map10<-map9+scale_fill_gradient(name="Score",low="yellow",high="red",
na.value="grey50")+
theme(axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y = element_blank())+
theme_void()+
ggtitle("Overall score in the World")+
theme(plot.title = element_text(hjust= 0.5))+
theme(plot.title=element_text(face="bold",color="Black",size=19))
map10
newDF <- mapdata1 %>%
st_as_sf(coords = c("long","lat"), crs = 4326)class(newDF)## [1] "sf" "data.frame"st_crs(newDF)## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]colors2<-c("blue","yellow","orange","red")
pal3<-colorFactor(colors2,newDF$Overall.score)class(newDF)## [1] "sf" "data.frame"library(extrafont)## Registering fonts with Rggplot() +
geom_polygon(data = mapdata1, aes(x = long, y = lat, group = group, fill= Financial.freedom)) +
geom_point(data = mapdata1, aes(x = long, y = lat), color = "white") +
labs(title = 'Countries with highest ') +
theme(panel.background = element_rect(fill = "grey")
,plot.background = element_rect(fill = "#444444")
,panel.grid = element_blank()
,plot.title = element_text(size = 30)
,plot.subtitle = element_text(size = 10)
,axis.text = element_blank()
,axis.title = element_blank()
,axis.ticks = element_blank()
,legend.position = "none")
world[239, "region"] <- "United States"
world[234, "region"] <- "Tanzania"
world[122, "region"] <- "Kyrgyz Republic"
world[44, "region"] <- "Côte d'Ivoire"pal_col <- colorNumeric(palette="Greys", domain = worldmap$Overall.score) worldmap$Skor<- factor(worldmap$Skor, levels = c("Düşük", "Orta", "Yüksek", "Çok Yüksek"))colors2<-c("blue","yellow","orange","red")
pal3<-colorFactor(colors2,worldmap$Skor)str(worldmap$Skor)## Factor w/ 4 levels "Düşük","Orta",..: 3 3 3 3 3 3 3 3 2 2 ...#References
https://rstudio.github.io/leaflet/shapes.html
https://datahub.io/core/geo-countries
http://www.sthda.com/english/wiki/ggplot2-axis-ticks-a-guide-to-customize-tick-marks-and-labels
https://www.rdocumentation.org/packages/geojsonio/versions/0.7.0/topics/geojson_read
http://rstudio-pubs-static.s3.amazonaws.com/84577_d3eb8b4712b64dbdb810773578d3c726.html
https://www.youtube.com/watch?v=eIpiL6y1oQQ&ab_channel=RockEDUScienceOutreach
https://www.youtube.com/watch?v=dx3khWsUO1Y&t=119s&ab_channel=A%26GStatworks
https://davidgohel.github.io/ggiraph/