Tallest Buildings in the World

Data retrieved from http://skyscrapercenter.com/buildings on 9/26/2015

Make country into different column - separate from city

suppressMessages(library(ggplot2))
suppressMessages(library(tidyr))
suppressMessages(library(dplyr))
suppressMessages(library(countrycode))  
suppressMessages(library(printr))


bldg=read.csv("tallestbuildings.csv")
head(bldg)

Rank	Building.Name	City	HeightMeter	HeightFeet	Floors	Completed	Material	Use
1	Burj Khalifa	Dubai (AE)	828.0	2,717	163	2010	steel/concrete	office / residential / hotel
2	Makkah Royal Clock Tower Hotel	Mecca (SA)	601.0	1,972	120	2012	steel/concrete	other / hotel
3	One World Trade Center	New York City (US)	541.3	1,776	94	2014	composite	office
4	TAIPEI 101	Taipei (TW)	508.0	1,667	101	2004	composite	office
5	Shanghai World Financial Center	Shanghai (CN)	492.0	1,614	101	2008	composite	hotel / office
6	International Commerce Centre	Hong Kong (CN)	484.0	1,588	108	2010	composite	hotel / office

str(bldg)

## 'data.frame':    100 obs. of  9 variables:
##  $ Rank         : int  1 2 3 4 5 6 7 7 9 10 ...
##  $ Building.Name: Factor w/ 100 levels "23 Marina","4 World Trade Center",..: 15 53 65 79 73 40 68 69 100 96 ...
##  $ City         : Factor w/ 40 levels "Abu Dhabi (AE)",..: 9 23 28 35 32 13 19 19 27 6 ...
##  $ HeightMeter  : num  828 601 541 508 492 ...
##  $ HeightFeet   : Factor w/ 77 levels "1,001","1,002",..: 65 64 63 62 61 60 59 59 58 57 ...
##  $ Floors       : int  163 120 94 101 101 108 88 88 66 108 ...
##  $ Completed    : int  2010 2012 2014 2004 2008 2010 1998 1998 2010 1974 ...
##  $ Material     : Factor w/ 4 levels "composite","concrete",..: 4 4 1 1 1 1 1 1 1 3 ...
##  $ Use          : Factor w/ 13 levels "hotel","hotel / office",..: 7 8 6 6 2 2 6 6 2 6 ...

# Make HeightFeet numeric
bldg$HeightFeet=as.character(bldg$HeightFeet)
bldg$HeightFeet=gsub(",","",bldg$HeightFeet)
bldg$HeightFeet=as.numeric(bldg$HeightFeet)

# same as: bldg$HeightFeet=as.numeric(gsub(",","",as.character(bldg$HeightFeet)))

# http://astrostatistics.psu.edu/su07/R/html/base/html/regex.html

newbldg=bldg %>% separate(City, c("City","Country"),sep="[:punct:]") 

# Number of buildings in different countries

table(newbldg$Country)

AE	AU	CA	CL	CN	GB	JP	KR	KW	MY	QA	RU	SA	TH	TW	US	VN
21	2	1	1	36	1	1	2	2	4	1	4	3	1	2	17	1

# change country codes to country names

newbldg$Country=countrycode(newbldg$Country,"iso2c","country.name")

#table(newbldg$Country)

## dplyr used here

newbldg %>% group_by(Country)%>% 
  summarise(Count=length(Country))%>%
  ggplot(aes(x= reorder(Country,Count),y=Count)) +
    geom_bar(stat="identity") +xlab("")+coord_flip() +geom_text(aes(Country,Count+1, label=Count))

newbldg %>% group_by(Country)%>% 
  summarise(Mean.Height=round(mean(HeightFeet),2))%>%
    ggplot(aes(x= reorder(Country,Mean.Height),y=Mean.Height)) +
    geom_bar(stat="identity") +xlab("")+coord_flip() +geom_text(aes(Country,Mean.Height-100, label=Mean.Height),color="white")

newbldg %>% group_by(Country)%>% 
  summarise(Mean.Height=round(mean(HeightFeet),2),
            Count=length(Country))%>%
    ggplot(aes(x= reorder(Country,Mean.Height),y=Mean.Height,fill=as.factor(Count))) +
    geom_bar(stat="identity") +xlab("")+coord_flip() +geom_text(aes(Country,Mean.Height+75, label=Mean.Height))+ scale_fill_discrete(name="Number of \nBuildings")

newbldg %>% group_by(City)%>% 
  summarise(Count=length(City))%>%
  ggplot(aes(x= reorder(City,Count),y=Count)) +
    geom_bar(stat="identity") +xlab("")+coord_flip() +scale_y_continuous(breaks=c(1,2,3,4,6,7,18))

newbldg %>% group_by(City)%>% 
  summarise(Mean.Height=round(mean(HeightFeet),2))%>%
    ggplot(aes(x= reorder(City,Mean.Height),y=Mean.Height)) +
    geom_point() +xlab("")+coord_flip() 

utilbldg=newbldg %>% separate(Use, c("Use1","Use2","Use3"),sep="/")%>%
  gather(numuses,Use,Use1:Use3)%>%
  filter(Use!="")
head(utilbldg)

Rank	Building.Name	City	Country	HeightMeter	HeightFeet	Floors	Completed	Material	numuses	Use
1	Burj Khalifa	Dubai	United Arab Emirates	828.0	2717	163	2010	steel/concrete	Use1	office
2	Makkah Royal Clock Tower Hotel	Mecca	Saudi Arabia	601.0	1972	120	2012	steel/concrete	Use1	other
3	One World Trade Center	New York City	United States	541.3	1776	94	2014	composite	Use1	office
4	TAIPEI 101	Taipei	Taiwan, Province of China	508.0	1667	101	2004	composite	Use1	office
5	Shanghai World Financial Center	Shanghai	China	492.0	1614	101	2008	composite	Use1	hotel
6	International Commerce Centre	Hong Kong	China	484.0	1588	108	2010	composite	Use1	hotel

#http://stackoverflow.com/questions/2261079/how-to-trim-leading-and-trailing-whitespace-in-r

trim <- function (x) gsub("^\\s+|\\s+$", "", x)
utilbldg$Use <- trim(utilbldg$Use)

utilbldg%>%group_by(Use)%>% summarise(MeanHeight=mean(HeightFeet))

Use	MeanHeight
hotel	1190.100
office	1166.222
other	1972.000
residential	1133.387
retail	1072.667
serviced apartments	1118.000

utilbldg %>% group_by(Use)%>%summarise(Count=length(Use))

Use	Count
hotel	40
office	72
other	1
residential	31
retail	3
serviced apartments	2

utilbldg%>%group_by(Use)%>% summarise(MeanHeight=mean(HeightFeet),Count=length(Use))%>%
  ggplot(.,aes(x=Use,y=MeanHeight,fill=as.factor(Count)))+geom_bar(stat="identity")+geom_text(aes(Use,MeanHeight+40, label=round(MeanHeight,2)))+ scale_fill_discrete(name="Number of \nBuildings")+ylab("Mean Height, in feet")