Module 2

We will be working with data about every tax lot in NYC, in a zip file. Please download it from http://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page. You will need to combine the data from each boro into one file called ‘all_PLUTO_data.R’ in order to complete this assignment. I encourage you to do this via code but it is not necessary to submit such code.

This assignment must be done in a single R script with ggplot2. Use of bigvis is encouraged but not required.
Please zip your code and images into a file, named ‘Last_First_hw2.zip’ (mine would be ‘Ferrari_Charley_hw2.zip’.
I have created some codes in the sample code file for you- this assumes the data is in a file called ‘all_PLUTO_data’ which you will need to create (as mentioned above).

Extract or download data

if (file.exists("HWData")) {
  
  unzip("HWData")
BK = read.csv("BORO_zip_files_csv\\BK.csv")
BX = read.csv("BORO_zip_files_csv\\BX.csv")
MN = read.csv("BORO_zip_files_csv\\MN.csv")
QN = read.csv("BORO_zip_files_csv\\QN.csv")
SI = read.csv("BORO_zip_files_csv\\SI.csv")
all_pluto  = rbind(BK,BX,MN,QN,SI)
} else{

download.file("http://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nyc_pluto_16v2%20.zip", destfile="HWData")
unzip("HWData")
BK = read.csv("BORO_zip_files_csv\\BK.csv")
BX = read.csv("BORO_zip_files_csv\\BX.csv")
MN = read.csv("BORO_zip_files_csv\\MN.csv")
QN = read.csv("BORO_zip_files_csv\\QN.csv")
SI = read.csv("BORO_zip_files_csv\\SI.csv")
all_pluto  = rbind(BK,BX,MN,QN,SI)
}

Question 1

After a few building collapses, the City of New York is going to begin investigating older buildings for safety. However, the city has a limited number of inspectors, and wants to find a ‘cut-off’ date before most city buildings were constructed. Build a graph to help the city determine when most buildings were constructed. Is there anything in the results that causes you to question the accuracy of the data? (note: only look at buildings built since 1850)

#Verify number of Columns in data set
ncol(all_pluto)

## [1] 84

# a quick summary identifies min being 0 no null present and a max year of 2040 in data set.
summary(all_pluto$YearBuilt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    1920    1930    1841    1955    2040

# Quick plot to verify data distribution
hist(all_pluto$YearBuilt,25)

# I will subset the data to remove data with 0,2040.

q1data <- all_pluto %>%
  filter(YearBuilt >= 1850, YearBuilt != 2040) %>%
  select(YearBuilt)
q1data$Count = 1
summary(q1data)

##    YearBuilt        Count  
##  Min.   :1850   Min.   :1  
##  1st Qu.:1920   1st Qu.:1  
##  Median :1931   Median :1  
##  Mean   :1941   Mean   :1  
##  3rd Qu.:1960   3rd Qu.:1  
##  Max.   :2016   Max.   :1

plotq1 = condense(bin(q1data$YearBuilt,10))
#autoplot(plotq1)+ ylab('YearBuilt')+theme_stata() theme_tufte() 
#label = unit_format(unit = "K")

The majority of the buildings build after 1850 occurred between 1920 and 1939. Missing values did not raise concern regarding the accuracy of data because of the large sample size.

ggplot(plotq1, aes(x=plotq1$q1data.YearBuilt,y=plotq1$.count)) +
  geom_bar(stat="identity",fill="sky blue")+
  scale_x_continuous(breaks = seq(min(1850), max(2016), by = 10))+
  scale_y_continuous(label = comma,breaks = seq(min(0), max(200000), by = 25000))+
  labs(title = "Buildings Constructed 1850-2016", x= "Year", y= "Count")+
  theme(plot.title = element_text(hjust = 0.5,size=20))+theme_pander()

ggsave('Question1.png')

Question 2

The city is particularly worried about buildings that were unusually tall when they were built, since best-practices for safety hadn’t yet been determined. Create a graph that shows how many buildings of a certain number of floors were built in each year (note: you may want to use a log scale for the number of buildings). It should be clear when 20-story buildings, 30-story buildings, and 40-story buildings were first built in large numbers.

q2data = all_pluto %>%
  filter(YearBuilt >= 1850, YearBuilt != 2040,NumFloors >= 20) %>%
  mutate(NumFloors = round(NumFloors,0)) %>%
  select(YearBuilt,NumFloors)


plotq2 = condense(bin(q2data$YearBuilt, 1), bin(q2data$NumFloors, 1))

The chart below identifies that 1895 was the start of high rise buildings. This trend pick up from 1920-1935, slowed down between the great depression and World War 2.

autoplot(plotq2)+
  scale_fill_gradient("Count", low = "sky blue", high = "red")+
  scale_x_continuous(breaks = seq(min(1850), max(2016), by = 10))+
  scale_y_continuous(label = comma,breaks = seq(min(20), max(120), by = 10))+
  labs(title = "Buildings Constructed by Number of Floors", x= "Year", y= "Floors")+
  theme(plot.title = element_text(hjust = 0.5,size=20))

ggsave('Question2.png')

Question 3

Your boss suspects that buildings constructed during the US’s involvement in World War II (1941-1945) are more poorly constructed than those before and after the way due to the high cost of materials during those years. She thinks that, if you calculate assessed value per floor, you will see lower values for buildings at that time vs before or after. Construct a chart/graph to see if she’s right.

q3data = all_pluto %>%
  filter(YearBuilt >= 1932,YearBuilt <= 1954,NumFloors > 1,AssessTot > 0) %>%
  mutate(NumFloors = round(NumFloors,0),FloorValN = round(AssessTot/NumFloors,0),FloorValM = (AssessTot/NumFloors)/1000000 )%>%
  select(YearBuilt,FloorValN,FloorValM)

#write.csv(q3data,file = "mydata.csv")
#plotq2 = condense(bin(q2data$YearBuilt, 1), bin(q2data$NumFloors, 1))

Running a cumulative plot of value per floor indicates that the suspicion was true, and the values surrounding years 1941-1945 were lower with the exception of 1945.

ggplot(q3data, aes(x=q3data$YearBuilt,y=q3data$FloorValM)) +
  geom_bar(stat="identity",fill="sky blue")+
  scale_x_continuous(breaks = seq(min(1932), max(1954), by = 2))+
  scale_y_continuous(label = comma,breaks = seq(min(0), max(1300), by = 50))+
  labs(title = "Cumulative Value Per Floor by Year", x= "Year", y= "Value Millions")+
  theme(plot.title = element_text(hjust = 0.5,size=20))+theme_pander()

ggsave('Question3a.png')

#gc() 1941-1945
q3bdata = all_pluto %>%
  filter(YearBuilt >= 1937,YearBuilt <= 1949,NumFloors > 1,AssessTot > 0) %>%
  mutate(NumFloors = round(NumFloors,0),FloorValM = (AssessTot/NumFloors)/1000000 )%>%
  select(YearBuilt,FloorValM)

plotq4 = condense(bin(q3bdata$YearBuilt, 1), bin(q3bdata$FloorValM, 1))

autoplot(plotq4)+
  scale_fill_gradient("Count", low = "sky blue", high = "red")+
  scale_x_continuous(breaks = seq(min(1937), max(1949), by = 1))+
  scale_y_continuous(label = comma,breaks = seq(min(0), max(50), by = 5))+
  labs(title = "Value Per Building", x= "Year", y= "Value Millions")+
  theme(plot.title = element_text(hjust = 0.5,size=20))+theme_pander()

ggsave('Question3b.png')

Data 608

Homework 2

Christopher Estevez

Module 2

Extract or download data

Question 1

Question 2

Question 3