Boston Rent Data

library

library(acs) # access acs survey data
library(tidyverse) # data manupliation
library(tidycensus) # access census data
library(ggplot2)
library(ggmap)
library(maptools)
library(ggthemes)
library(rgeos)
library(broom)
library(dplyr)
library(plyr)
library(grid)
library(gridExtra)
library(reshape2)
library(scales)
library(tigris)
library(sf)

Linking to GEOS 3.8.1, GDAL 2.4.4, PROJ 7.0.0

library(rgdal)

rgdal: version: 1.4-8, (SVN revision 845)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
 Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
 GDAL binary built with GEOS: FALSE 
 Loaded PROJ.4 runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
 Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
 Linking to sp version: 1.3-2 

library(leaflet)
library(sp)
library(devtools)
library(censusapi)

Attaching package: ‘censusapi’

The following object is masked from ‘package:methods’:

    getFunction

library(XML)
library(RCurl)

Attaching package: ‘RCurl’

The following object is masked from ‘package:tidyr’:

    complete

register_google(key = "AIzaSyA2ssODSDKBAXe5MOgxz6rL_JfnN7JTX30")

# Next, we’re going to define two themes that will tell ggplot how to construct both maps and plots. Defining our themes up front ensures that we don’t have to repeat this code over and again for every plot we generate below

plotTheme <- function(base_size = 12) {
  theme(
    text = element_text( color = "black"),
    plot.title = element_text(size = 18,colour = "black"),
    plot.subtitle = element_text(face="italic"),
    plot.caption = element_text(hjust=0),
    axis.ticks = element_blank(),
    panel.background = element_blank(),
    panel.grid.major = element_line("grey80", size = 0.1),
    panel.grid.minor = element_blank(),
    strip.background = element_rect(fill = "grey80", color = "white"),
    strip.text = element_text(size=12),
    axis.title = element_text(size=8),
    axis.text = element_text(size=8),
    axis.title.x = element_text(hjust=1),
    axis.title.y = element_text(hjust=1),
    plot.background = element_blank(),
    legend.background = element_blank(),
    legend.title = element_text(colour = "black", face = "italic"),
    legend.text = element_text(colour = "black", face = "italic"))
}
 
# And another that we will use for maps
mapTheme <- function(base_size = 12) {
  theme(
    text = element_text( color = "black"),
    plot.title = element_text(size = 18,colour = "black"),
    plot.subtitle=element_text(face="italic"),
    plot.caption=element_text(hjust=0),
    axis.ticks = element_blank(),
    panel.background = element_blank(),
    panel.grid.major = element_line("grey80", size = 0.1),
    strip.text = element_text(size=12),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.minor = element_blank(),
    strip.background = element_rect(fill = "grey80", color = "white"),
    plot.background = element_blank(),
    legend.background = element_blank(),
    legend.title = element_text(colour = "black", face = "italic"),
    legend.text = element_text(colour = "black", face = "italic"))
}
 
# Define some palettes
palette_9_colors <- c("#0DA3A0","#2999A9","#458FB2","#6285BB","#7E7CC4","#9A72CD","#B768D6","#D35EDF","#F055E9")
palette_8_colors <- c("#0DA3A0","#2D97AA","#4D8CB4","#6E81BF","#8E76C9","#AF6BD4","#CF60DE","#F055E9")
palette_7_colors <- c("#2D97AA","#4D8CB4","#6E81BF","#8E76C9","#AF6BD4","#CF60DE","#F055E9")
palette_1_colors <- c("#0DA3A0")

Get Boston Rental Data as a table dataframe

BR_2_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1361188921.txt")
BR_3_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1363604521.txt")
BR_4_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1366282922.txt")
BR_5_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1368874922.txt")
BR_6_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1371553321.txt")
BR_7_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1374145322.txt")
BR_8_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1376823721.txt")
BR_9_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1379502122.txt")
BR_10_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1382094122.txt")
BR_11_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1384776122.txt")
BR_11_21_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1385024431.txt")
BR_12_18_2013 <-read.table("https://www.jefftk.com/apartment_prices/apts-1387368122.txt")
BR_1_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1390046522.txt")
BR_2_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1392724922.txt")
BR_3_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1395140522.txt")
BR_4_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1397818922.txt")
BR_5_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1400410922.txt")
BR_6_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1403089321.txt")
BR_7_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1405681321.txt")
BR_8_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1408359722.txt")
BR_9_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1411038121.txt")
BR_10_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1413597722.txt")
BR_11_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1416276121.txt")
BR_12_18_2014 <-read.table("https://www.jefftk.com/apartment_prices/apts-1418868122.txt")
BR_1_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1421546523.txt")
BR_2_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1424224922.txt")
BR_3_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1426644122.txt")
BR_4_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1429322522.txt")
BR_5_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1431914522.txt")
BR_6_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1434592921.txt")
BR_7_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1437184922.txt")
BR_8_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1439863322.txt")
BR_9_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1442541722.txt")
BR_10_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1445133723.txt")
BR_11_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1447812123.txt")
BR_12_18_2015 <-read.table("https://www.jefftk.com/apartment_prices/apts-1450404122.txt")
BR_1_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1453082522.txt")
BR_2_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1455760922.txt")
BR_3_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1458266521.txt")
BR_4_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1460944923.txt")
BR_5_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1463536922.txt")
BR_6_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1466215323.txt")
BR_7_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1468807322.txt")
BR_8_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1471485722.txt")
BR_9_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1474164121.txt")
BR_10_28_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1477662757.txt")
BR_11_18_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1479477370.txt")
BR_12_19_2016 <-read.table("https://www.jefftk.com/apartment_prices/apts-1482155325.txt")
BR_1_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1484743042.txt")
BR_2_19_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1487511252.txt")
BR_3_19_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1489951548.txt")
BR_4_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1492528051.txt")
BR_5_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1495128565.txt")
BR_6_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1497785436.txt")
BR_7_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1500389951.txt")
BR_8_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1503056501.txt")
BR_9_19_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1505830448.txt")
BR_10_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1508347437.txt")
BR_11_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1511025718.txt")
BR_12_18_2017 <-read.table("https://www.jefftk.com/apartment_prices/apts-1513607533.txt")
BR_1_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1516280053.txt")
BR_2_21_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1519173184.txt")
BR_3_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1521392156.txt")
BR_4_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1524056383.txt")
BR_4_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1524059974.txt")
BR_5_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1526663745.txt")
BR_7_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1531942677.txt")
BR_8_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1534594761.txt")
BR_9_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1537269695.txt")
BR_10_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1539895062.txt")
BR_11_19_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1542637382.txt")
BR_12_18_2018 <-read.table("https://www.jefftk.com/apartment_prices/apts-1545151888.txt")
BR_1_27_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1548600831.txt")
BR_2_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1550519514.txt")
BR_3_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1552916163.txt")
BR_4_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1555593780.txt")
BR_5_20_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1558364572.txt")
BR_6_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1560875990.txt")
BR_8_3_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1564836478.txt")
BR_8_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1566158813.txt")
BR_9_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1568827984.txt")
BR_10_20_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1571532666.txt")
BR_11_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1574092365.txt")
BR_12_18_2019 <-read.table("https://www.jefftk.com/apartment_prices/apts-1576687349.txt")
BR_1_18_2020 <-read.table("https://www.jefftk.com/apartment_prices/apts-1579389225.txt")
BR_2_18_2020 <-read.table("https://www.jefftk.com/apartment_prices/apts-1582039669.txt")
BR_3_20_2020 <-read.table("https://www.jefftk.com/apartment_prices/apts-1584708383.txt")

Add Year

BR_2_18_2013$Year <-"2013"
BR_3_18_2013$Year <-"2013"
BR_4_18_2013$Year <-"2013"
BR_5_18_2013$Year <-"2013"
BR_6_18_2013$Year <-"2013"
BR_7_18_2013$Year <-"2013"
BR_8_18_2013$Year <-"2013"
BR_9_18_2013$Year <-"2013"
BR_10_18_2013$Year <-"2013"
BR_11_18_2013$Year <-"2013"
BR_11_21_2013$Year <-"2013"
BR_12_18_2013$Year <-"2013"
BR_1_18_2014$Year <-"2014"
BR_2_18_2014$Year <-"2014"
BR_3_18_2014$Year <-"2014"
BR_4_18_2014$Year <-"2014"
BR_5_18_2014$Year <-"2014"
BR_6_18_2014$Year <-"2014"
BR_7_18_2014$Year <-"2014"
BR_8_18_2014$Year <-"2014"
BR_9_18_2014$Year <-"2014"
BR_10_18_2014$Year <-"2014"
BR_11_18_2014$Year <-"2014"
BR_12_18_2014$Year <-"2014"
BR_1_18_2015$Year <-"2015"
BR_2_18_2015$Year <-"2015"
BR_3_18_2015$Year <-"2015"
BR_4_18_2015$Year <-"2015"
BR_5_18_2015$Year <-"2015"
BR_6_18_2015$Year <-"2015"
BR_7_18_2015$Year <-"2015"
BR_8_18_2015$Year <-"2015"
BR_9_18_2015$Year <-"2015"
BR_10_18_2015$Year <-"2015"
BR_11_18_2015$Year <-"2015"
BR_12_18_2015$Year <-"2015"
BR_1_18_2016$Year <-"2016"
BR_2_18_2016$Year <-"2016"
BR_3_18_2016$Year <-"2016"
BR_4_18_2016$Year <-"2016"
BR_5_18_2016$Year <-"2016"
BR_6_18_2016$Year <-"2016"
BR_7_18_2016$Year <-"2016"
BR_8_18_2016$Year <-"2016"
BR_9_18_2016$Year <-"2016"
BR_10_28_2016$Year <-"2016"
BR_11_18_2016$Year <-"2016"
BR_12_19_2016$Year <-"2016"
BR_1_18_2017$Year <-"2017"
BR_2_19_2017$Year <-"2017"
BR_3_19_2017$Year <-"2017"
BR_4_18_2017$Year <-"2017"
BR_5_18_2017$Year <-"2017"
BR_6_18_2017$Year <-"2017"
BR_7_18_2017$Year <-"2017"
BR_8_18_2017$Year <-"2017"
BR_9_19_2017$Year <-"2017"
BR_10_18_2017$Year <-"2017"
BR_11_18_2017$Year <-"2017"
BR_12_18_2017$Year <-"2017"
BR_1_18_2018$Year <-"2018"
BR_2_21_2018$Year <-"2018"
BR_3_18_2018$Year <-"2018"
BR_4_18_2018$Year <-"2018"
BR_4_18_2018$Year <-"2018"
BR_5_18_2018$Year <-"2018"
BR_7_18_2018$Year <-"2018"
BR_8_18_2018$Year <-"2018"
BR_9_18_2018$Year <-"2018"
BR_10_18_2018$Year <-"2018"
BR_11_19_2018$Year <-"2018"
BR_12_18_2018$Year <-"2018"
BR_1_27_2019$Year <-"2019"
BR_2_18_2019$Year <-"2019"
BR_3_18_2019$Year <-"2019"
BR_4_18_2019$Year <-"2019"
BR_5_20_2019$Year <-"2019"
BR_6_18_2019$Year <-"2019"
BR_8_3_2019$Year <-"2019"
BR_8_18_2019$Year <-"2019"
BR_9_18_2019$Year <-"2019"
BR_10_20_2019$Year <-"2019"
BR_11_18_2019$Year <-"2019"
BR_12_18_2019$Year <-"2019"
BR_1_18_2020$Year <-"2020"
BR_2_18_2020$Year <-"2020"
BR_3_20_2020$Year <-"2020"

Add Month

BR_2_18_2013$Month <-"Feb"
BR_3_18_2013$Month <-"Mar"
BR_4_18_2013$Month <-"Apr"
BR_5_18_2013$Month <-"May"
BR_6_18_2013$Month <-"Jun"
BR_7_18_2013$Month <-"Jul"
BR_8_18_2013$Month <-"Aug"
BR_9_18_2013$Month <-"Sep"
BR_10_18_2013$Month <-"Oct"
BR_11_18_2013$Month <-"Nov"
BR_11_21_2013$Month <-"Nov"
BR_12_18_2013$Month <-"Dec"
BR_1_18_2014$Month <-"Jan"
BR_2_18_2014$Month <-"Feb"
BR_3_18_2014$Month <-"Mar"
BR_4_18_2014$Month <-"Apr"
BR_5_18_2014$Month <-"May"
BR_6_18_2014$Month <-"Jun"
BR_7_18_2014$Month <-"Jul"
BR_8_18_2014$Month <-"Aug"
BR_9_18_2014$Month <-"Sep"
BR_10_18_2014$Month <-"Oct"
BR_11_18_2014$Month <-"Nov"
BR_12_18_2014$Month <-"Dec"
BR_1_18_2015$Month <-"Jan"
BR_2_18_2015$Month <-"Feb"
BR_3_18_2015$Month <-"Mar"
BR_4_18_2015$Month <-"Apr"
BR_5_18_2015$Month <-"May"
BR_6_18_2015$Month <-"Jun"
BR_7_18_2015$Month <-"Jul"
BR_8_18_2015$Month <-"Aug"
BR_9_18_2015$Month <-"Sep"
BR_10_18_2015$Month <-"Oct"
BR_11_18_2015$Month <-"Nov"
BR_12_18_2015$Month <-"Dec"
BR_1_18_2016$Month <-"Jan"
BR_2_18_2016$Month <-"Feb"
BR_3_18_2016$Month <-"Mar"
BR_4_18_2016$Month <-"Apr"
BR_5_18_2016$Month <-"May"
BR_6_18_2016$Month <-"Jun"
BR_7_18_2016$Month <-"Jul"
BR_8_18_2016$Month <-"Aug"
BR_9_18_2016$Month <-"Sep"
BR_10_28_2016$Month <-"Oct"
BR_11_18_2016$Month <-"Nov"
BR_12_19_2016$Month <-"Dec"
BR_1_18_2017$Month <-"Jan"
BR_2_19_2017$Month <-"Feb"
BR_3_19_2017$Month <-"Mar"
BR_4_18_2017$Month <-"Apr"
BR_5_18_2017$Month <-"May"
BR_6_18_2017$Month <-"Jun"
BR_7_18_2017$Month <-"Jul"
BR_8_18_2017$Month <-"Aug"
BR_9_19_2017$Month <-"Sep"
BR_10_18_2017$Month <-"Oct"
BR_11_18_2017$Month <-"Nov"
BR_12_18_2017$Month <-"Dec"
BR_1_18_2018$Month <-"Jan"
BR_2_21_2018$Month <-"Feb"
BR_3_18_2018$Month <-"Mar"
BR_4_18_2018$Month <-"Apr"
BR_4_18_2018$Month <-"Apr"
BR_5_18_2018$Month <-"May"
BR_7_18_2018$Month <-"Jul"
BR_8_18_2018$Month <-"Aug"
BR_9_18_2018$Month <-"Sep"
BR_10_18_2018$Month <-"Oct"
BR_11_19_2018$Month <-"Nov"
BR_12_18_2018$Month <-"Dec"
BR_1_27_2019$Month <-"Jan"
BR_2_18_2019$Month <-"Feb"
BR_3_18_2019$Month <-"Mar"
BR_4_18_2019$Month <-"Apr"
BR_5_20_2019$Month <-"May"
BR_6_18_2019$Month <-"Jun"
BR_8_3_2019$Month <-"Aug"
BR_8_18_2019$Month <-"Aug"
BR_9_18_2019$Month <-"Sep"
BR_10_20_2019$Month <-"Oct"
BR_11_18_2019$Month <-"Nov"
BR_12_18_2019$Month <-"Dec"
BR_1_18_2020$Month <-"Jan"
BR_2_18_2020$Month <-"Feb"
BR_3_20_2020$Month <-"Mar"

Merge by Year

# 2013
BR_2013 <- rbind(BR_2_18_2013,
BR_3_18_2013,
BR_4_18_2013,
BR_5_18_2013,
BR_6_18_2013,
BR_7_18_2013,
BR_8_18_2013,
BR_9_18_2013,
BR_10_18_2013,
BR_11_18_2013,
BR_11_21_2013,
BR_12_18_2013)
# 2014
BR_2014 <- rbind(BR_1_18_2014,
BR_2_18_2014,
BR_3_18_2014,
BR_4_18_2014,
BR_5_18_2014,
BR_6_18_2014,
BR_7_18_2014,
BR_8_18_2014,
BR_9_18_2014,
BR_10_18_2014,
BR_11_18_2014,
BR_12_18_2014)
# 2015
BR_2015 <- rbind(BR_1_18_2015,
BR_2_18_2015,
BR_3_18_2015,
BR_4_18_2015,
BR_5_18_2015,
BR_6_18_2015,
BR_7_18_2015,
BR_8_18_2015,
BR_9_18_2015,
BR_10_18_2015,
BR_11_18_2015,
BR_12_18_2015)
# 2016  
BR_2016 <- rbind(BR_1_18_2016,
BR_2_18_2016,
BR_3_18_2016,
BR_4_18_2016,
BR_5_18_2016,
BR_6_18_2016,
BR_7_18_2016,
BR_8_18_2016,
BR_9_18_2016,
BR_10_28_2016,
BR_11_18_2016,
BR_12_19_2016)
# 2017
BR_2017 <- rbind(BR_1_18_2017,
BR_2_19_2017,
BR_3_19_2017,
BR_4_18_2017,
BR_5_18_2017,
BR_6_18_2017,
BR_7_18_2017,
BR_8_18_2017,
BR_9_19_2017,
BR_10_18_2017,
BR_11_18_2017,
BR_12_18_2017)
# 2018
BR_2018 <- rbind(BR_1_18_2018,
BR_2_21_2018,
BR_3_18_2018,
BR_4_18_2018,
BR_4_18_2018,
BR_5_18_2018,
BR_7_18_2018,
BR_8_18_2018,
BR_9_18_2018,
BR_10_18_2018,
BR_11_19_2018,
BR_12_18_2018)
# 2019
BR_2019 <- rbind(BR_1_27_2019,
BR_2_18_2019,
BR_3_18_2019,
BR_4_18_2019,
BR_5_20_2019,
BR_6_18_2019,
BR_8_3_2019,
BR_8_18_2019,
BR_9_18_2019,
BR_10_20_2019,
BR_11_18_2019,
BR_12_18_2019)
# 2020
BR_2020 <- rbind(BR_1_18_2020,
BR_2_18_2020,
BR_3_20_2020)

Update Column Names

# 2020
names(BR_2020)[1] <- "Rent"
names(BR_2020)[2] <- "Bedrooms"
names(BR_2020)[3] <- "ID"
names(BR_2020)[4] <- "Lon"
names(BR_2020)[5] <- "Lat"
# 2019
names(BR_2019)[1] <- "Rent"
names(BR_2019)[2] <- "Bedrooms"
names(BR_2019)[3] <- "ID"
names(BR_2019)[4] <- "Lon"
names(BR_2019)[5] <- "Lat"
# 2018
names(BR_2018)[1] <- "Rent"
names(BR_2018)[2] <- "Bedrooms"
names(BR_2018)[3] <- "ID"
names(BR_2018)[4] <- "Lon"
names(BR_2018)[5] <- "Lat"
# 2017
names(BR_2017)[1] <- "Rent"
names(BR_2017)[2] <- "Bedrooms"
names(BR_2017)[3] <- "ID"
names(BR_2017)[4] <- "Lon"
names(BR_2017)[5] <- "Lat"
# 2016
names(BR_2016)[1] <- "Rent"
names(BR_2016)[2] <- "Bedrooms"
names(BR_2016)[3] <- "ID"
names(BR_2016)[4] <- "Lon"
names(BR_2016)[5] <- "Lat"
# 2015
names(BR_2015)[1] <- "Rent"
names(BR_2015)[2] <- "Bedrooms"
names(BR_2015)[3] <- "ID"
names(BR_2015)[4] <- "Lon"
names(BR_2015)[5] <- "Lat"
# 2014
names(BR_2014)[1] <- "Rent"
names(BR_2014)[2] <- "Bedrooms"
names(BR_2014)[3] <- "ID"
names(BR_2014)[4] <- "Lon"
names(BR_2014)[5] <- "Lat"
# 2013
names(BR_2013)[1] <- "Rent"
names(BR_2013)[2] <- "Bedrooms"
names(BR_2013)[3] <- "ID"
names(BR_2013)[4] <- "Lon"
names(BR_2013)[5] <- "Lat"

Merge into one dataframe

Download data.frame

write.csv(BR_2013_2020, "BR_2013_2020.csv")

Distribution of Boston Rent Prices Nominal Prices (2013 - 2020)

rent_value_hist <- ggplot(BR_2013_2019, aes(Rent)) + 
  geom_histogram(fill=palette_1_colors) +
  xlab("Rent Price($)") + ylab("Count") +
  scale_fill_manual(values=palette_1_colors) +
  plotTheme() + 
  labs(x="Rent Price($)", y="Count", title="Distribution of Boston Rent prices",
       subtitle="Nominal prices (2013 - 2020)", 
       caption="Source: Padmapper UI\n@JeffKaufman")
# Plotting it:
rent_value_hist

It seems as though there may be some outliers. We’ll remove anything greater than 2.5 standard deviations from the mean.

check out the distribution of prices for each year using a violin plot

rent_value_violin <- ggplot(BR_13_29, aes(x=Year, y=Rent, fill=Year)) + 
  geom_violin(color = "grey50") +
  xlab("Rent Price($)") + ylab("Count") +
  scale_fill_manual(values=palette_7_colors) +
  stat_summary(fun.y=mean, geom="point", size=2, colour="white") +
  plotTheme() + theme(legend.position="none") +
  scale_y_continuous(labels = comma) +
  labs(x="Year",y="Rent Price($)",title="Distribution of Boston Rent prices",
       subtitle="Nominal prices (2013 - 2019); Rent price means visualized as points",
       caption="Source: Padmapper UI\n@JeffKaufman")

`fun.y` is deprecated. Use `fun` instead.

rent_value_violin

The white circles denote sale price means for each year.

Distribution of Northeastern Rent Prices

# Add Boston Shape file with names of neighborhoods

Boston_neighb <- readOGR("Boston_Neighborhoods",layer = "Boston_Neighborhoods")
bbox <- Boston_neighb@bbox
bos_bbox <- c(left = bbox[1, 1] - .01, bottom = bbox[2, 1] - .005, 
             right = bbox[1, 2] + .01, top = bbox[2, 2] + .005)
basemap <- get_stamenmap(
  bbox = bos_bbox,
  zoom = 12,
  maptype = "toner-lite")
# Map it
bmMap <- ggmap(basemap) + mapTheme() + 
  labs(title="Boston basemap")
bmMap

Distribution of Boston Rent Prices

library(mapproj)

Loading required package: maps

Attaching package: ‘maps’

The following object is masked from ‘package:plyr’:

    ozone

The following object is masked from ‘package:purrr’:

    map

prices_mapped_by_year <- ggmap(basemap) + 
  geom_point(data = BR_13_19, aes(x = Lon, y = Lat, color = Rent), 
             size = .25, alpha = 0.6) +
  facet_wrap(~Year, scales = "fixed", ncol = 4) +
  coord_map() +
  mapTheme() + theme(legend.position = c(.85, .25)) +
  scale_color_gradientn("Rent Price", 
                        colors = palette_8_colors,
                        labels = scales::dollar_format(prefix = "$")) +
  labs(title="Distribution of Boston Rent prices",
       subtitle="Nominal prices (2013 - 2019)",
       caption="Source: Padmapper UI\n@JeffKaufman")

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

prices_mapped_by_year

We’ll stack the two maps and increase the point size

prices_mapped_2013_2019 <- ggmap(basemap) + 
  geom_point(data = subset(BR_13_19, BR_13_19$Year == 2013 | BR_13_19$Year == 2019), 
             aes(x = Lon, y = Lat, color = Rent), 
             size = 1, alpha = 0.75) +
  facet_wrap(~Year, scales = "fixed", ncol = 1) +
  coord_map() +
  mapTheme() +
  scale_color_gradientn("Sale Price", 
                        colors = palette_8_colors,
                        labels = scales::dollar_format(prefix = "$")) +
  labs(title="Distribution of Boston Rent prices",
       subtitle="Nominal prices (2013 & 2019)",
       caption="Source: Padmapper UI\n@JeffKaufman")

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

prices_mapped_2013_2019

Merging Rent Data with Boston Neighbrohood Polygon

Neighborhood—Fenway

Neighborhood—Mission Hill

Neighborhood—Roxbury

Neighborhood—South End

Distribution of Rent prices around Northeastern University

Boston Summarization

Boston_Rents_summarized <- ddply(Boston_Rent_Data, c("Name","Year"), summarise,
                                       medianRent = median(Rent),
                                       rentCount = length(Year),
                                       sdRent = sd(Rent),
                                       minusSd = medianRent - sdRent,
                                       plusSD = medianRent + sdRent,
                                       .progress = "text")

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yearly_rents <- ddply(Boston_Rents_summarized, ~Name, summarise, 
                      avg.yearly.rent = mean(rentCount))

Boston_Rents_summarized <- left_join(Boston_Rents_summarized, yearly_rents, by = "Name")

medByYear <- dcast(Boston_Rents_summarized, Name ~ Year, value.var = "medianRent")
medByYear$pctChange <- (medByYear$`2019` - medByYear$`2013`) / medByYear$`2013`
Boston_Rents_summarized <- left_join(Boston_Rents_summarized, medByYear[,c("Name", "pctChange")], 
                           by = "Name")
Boston_Rents_summarized

neighb.tidy <- tidy(Boston_neighb, region = c('Name'))

Ring Self-intersection at or near point -71.125747340000004 42.27233854SpP is invalid
Invalid objects found; consider using set_RGEOS_CheckValidity(2L)Unequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorUnequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vector

neighb.tidy$Name <- neighb.tidy$id
Boston_Rents_summarized <- join(Boston_Rents_summarized, neighb.tidy, by = "Name", match = "all")
Boston_Rents_summarized

NA

Northeastern Summeraization

Median Rent Price by Northeastern Neighborhood

neighb_map <- ggmap(basemap) +
  geom_polygon(data = Northeastern_Rents_summarized_tidy, 
               aes(x = long, y = lat, group = group, fill = medianRent), 
               colour = "white", alpha = 0.75, size = 0.25) + 
  scale_fill_gradientn("Neighborhood \nMedian \nRent Price", 
                       colors = palette_8_colors,
                       labels = scales::dollar_format(prefix = "$")) +
  mapTheme() + theme(legend.position = c(.85, .25)) + coord_map() +
  facet_wrap(~Year, nrow = 2) +
  labs(title="Median rent price by neighborhood, Northeastern ",
       subtitle="Nominal prices (2013 - 2019)",
       caption="Source: Padmapper UI\n@JeffKaufman")

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

neighb_map

Percent Change in Rent

Time Sereis Plots

Boston_Rents_summarized

# Lets look at the top 8 most appreciating neighborhoods.
topPctChange <- unique(Boston_Rents_summarized$pctChange) %>% sort(decreasing = TRUE) %>% head(8)
 
# Well use these percentages to subset our neighborhoods data frame
bosForTimeSeries <- Boston_Rents_summarized[which(Boston_Rents_summarized$pctChange %in% topPctChange), ] 

time.series <- ggplot(bosForTimeSeries, aes(x = Year, group=Name)) +
  geom_line(aes(y = medianRent)) +
  geom_ribbon(aes(ymin = minusSd, ymax = plusSD, fill = Name), alpha = 0.75) +
  facet_wrap(~Name, scales = "fixed", nrow = 4) +
  scale_y_continuous(labels = scales::dollar_format(prefix = "$")) +
  ylab("Neighborhood median rent price") + xlab(NULL) +
  plotTheme() +
  theme(
    legend.position = "none",
    panel.spacing.y = unit(1, "lines")
  ) +
  scale_fill_manual(values=palette_8_colors) +
  labs(title="Time series for highest growth neighborhoods, Boston",
       subtitle="Nominal prices (2013-2019); Median; Ribbon indicates 1 standard deviation",
       caption="Source:")
time.series