suppressWarnings(library(plotly))
economyRanking<-read.csv("Economy Rankings2.csv")
#economyRanking<-read.csv("Economy Rankings.csv", header= TRUE,sep=",",na.strings = "?",stringsAsFactors=FALSE)
#head(economyRanking,3)
economyRanking<-subset(economyRanking, select=c("code","Economy", "Ease.of.Doing.Business.Rank"))
colnames(economyRanking)[3] <- "Rank"
l <- list(color = toRGB("grey"), width = 0.5)
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
p <- plot_geo(economyRanking) %>%
add_trace(
z = ~Rank, color = ~Rank,
text = ~Economy, locations = ~code, marker = list(line = l)
) %>%
layout(
title = 'Ease of doing business Ranking',
geo = g
)
p
########Bubble#####
yearWiseRanking<-economyRanking<-read.csv("distance-to-frontier.csv",na.strings = "?",stringsAsFactors=FALSE)
head(yearWiseRanking)
## Economy DB.2010 DB.2011 DB.2012 DB.2013
## 1 United States - Los Angeles NA NA NA NA
## 2 Mexico - Monterrey NA NA NA NA
## 3 Japan - Osaka NA NA NA NA
## 4 Russian Federation - Saint Petersburg NA NA NA NA
## 5 Malta NA NA 62.1 62.05
## 6 San Marino NA NA NA 63.81
## DB.2014 DB.2015 DB.2016 DB.2017 X
## 1 78.56 80.68 80.96 80.96 #VALUE!
## 2 71.24 75.20 75.73 75.93 #VALUE!
## 3 77.13 75.06 75.27 75.44 #VALUE!
## 4 66.51 70.92 72.83 72.94 #VALUE!
## 5 62.34 63.84 62.52 65.01 #VALUE!
## 6 63.00 62.67 63.55 64.11 #VALUE!
#head<-head(yearWiseRanking)
head<-subset(yearWiseRanking, Economy=='India' | Economy=='Ukraine')
library(dplyr)
library(tidyr)
head<-subset(yearWiseRanking, Economy=='Russia' |Economy=='Ukraine'|Economy=='Yemen')
head1<-gather(head, "Year", "scoreOutOf100", 4:8)
p <- plot_ly(head1, x = ~Year, y = ~Economy, text = ~scoreOutOf100, type = 'scatter', mode = 'markers', color = ~scoreOutOf100,
marker = list(size = ~scoreOutOf100, opacity = 0.5)) %>%
layout(title = 'Significant changes of countries over time',
xaxis = list(showgrid = FALSE),
yaxis = list(showgrid = FALSE))
p