Data_607_Project_2_Anthony_Arroyo_Untidy_Dataset_Rental
Enid Roman
2022-10-08
About the dataset:
This dataset was taken from the following website:
DOF: Cooperative Comparable Rental Income – Queens – FY 2011/2012
The Department of Finance (DOF) is required by NY State law to value condominiums or cooperatives as if they were residential rental apartment buildings. DOF uses income information from rental properties similar in physical features and location to the condominiums or cooperatives. DOF applies this income data to the condominium or cooperative and determine its value in the same way DOF values rental apartment buildings. This is update annually.
What is the Net Value per Rental? Which is the most expensive and which is the cheapest?
# Upload the libraries.
library(tidyr)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ dplyr 1.0.9
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)
library(ggplot2)# Import the data from github.
# Link is provided to the csv file below:
# https://github.com/enidroman/data_607_data_aquisition_and_management_project/blob/main/Anthony%20Arroyo%20Untidy%20Dataset%20Rental.csv
urlfile <- "https://raw.githubusercontent.com/enidroman/data_607_data_aquisition_and_management_project/main/Anthony%20Arroyo%20Untidy%20Dataset%20Rental.csv"
comp_rental <- read.csv(urlfile)
comp_rental## COMPARABLE.RENTAL...1...Boro.Block.Lot COMPARABLE.RENTAL...1...Neighborhood
## 1 4-08276-0658 DOUGLASTON
## 2 4-03173-0013 REGO PARK
## 3 4-02134-0013 FOREST HILLS
## 4 4-01268-0001 JACKSON HEIGHTS
## 5 4-05046-0040 FLUSHING-NORTH
## 6 4-03155-0027 REGO PARK
## 7 4-03329-0054 KEW GARDENS
## 8 4-08276-0658 DOUGLASTON
## COMPARABLE.RENTAL...1...Total.Units COMPARABLE.RENTAL...1...Year.Built
## 1 54 1977
## 2 61 1949
## 3 123 1963
## 4 66 1951
## 5 63 1959
## 6 286 1959
## 7 54 1924
## 8 54 1977
## COMPARABLE.RENTAL...1...Gross.SqFt
## 1 27690
## 2 70910
## 3 135400
## 4 57888
## 5 58399
## 6 275197
## 7 54300
## 8 27690
## COMPARABLE.RENTAL...1...Estimated.Gross.Income
## 1 390932
## 2 1130670
## 3 1872338
## 4 860870
## 5 899224
## 6 4668060
## 7 814069
## 8 390932
## COMPARABLE.RENTAL...2...Boro.Block.Lot COMPARABLE.RENTAL...2...Neighborhood
## 1 4-03205-0024 FOREST HILLS
## 2 4-02135-0013 FOREST HILLS
## 3 4-01279-0001 JACKSON HEIGHTS
## 4 4-05046-0006 FLUSHING-NORTH
## 5 4-03234-0051 FOREST HILLS
## 6 4-03322-0014 KEW GARDENS
## 7 4-15555-0001 FAR ROCKAWAY
## 8 4-01268-0001 JACKSON HEIGHTS
## COMPARABLE.RENTAL...2...Total.Units COMPARABLE.RENTAL...2...Year.Built
## 1 20 1931
## 2 135 1960
## 3 96 1950
## 4 45 1968
## 5 228 1929
## 6 84 1928
## 7 108 1950
## 8 66 1951
## COMPARABLE.RENTAL...2...Gross.SqFt
## 1 24408
## 2 137700
## 3 95000
## 4 41398
## 5 174480
## 6 83727
## 7 87926
## 8 57888
## COMPARABLE.RENTAL...2...Estimated.Gross.Income
## 1 372944
## 2 1889772
## 3 1035405
## 4 605749
## 5 3520549
## 6 1220740
## 7 1077972
## 8 860870DATA CLEANING AND TRANSFORMATION
In observing the dataset I see that:
1. The first 6 columns are Comparable Rental 1 and the 6 columns after are Comparable Rental 2. Seem to have been 2 different datasets joined together. In order to combine the columns I need to delete the extra 6 columns and bring down the obersavations from the those columns to the first 6 columns. To do that I had to create 2 dataframes, one with the Comparable Rental 1 and the second with the Comparable Rental 2.
2. The columns need to be renamed. I then renamed each columns from both data frames. Then I was able to rebind the 2 dataframes and combine the first 6 columns and the second 6 columns.
3. The analysis that Anthony Arroyo wanted to make with this dataframe is to determine the net value of each unit. Need to create and add a column for Estimated Expense and Net Operating Income with observations to perform the analysis.
4. Need to calculate, create, and add a column called Net Value of Rental.
5. Need to check the classification for the observation in each column to make sure that the class is correct. Otherwise need to convert them to the correct class.
# Created a new dataframe witn Comparable Rental 1 only.
comp_rental1 <- select(comp_rental, COMPARABLE.RENTAL...1...Boro.Block.Lot, COMPARABLE.RENTAL...1...Neighborhood, COMPARABLE.RENTAL...1...Total.Units, COMPARABLE.RENTAL...1...Year.Built, COMPARABLE.RENTAL...1...Gross.SqFt, COMPARABLE.RENTAL...1...Estimated.Gross.Income)
comp_rental1## COMPARABLE.RENTAL...1...Boro.Block.Lot COMPARABLE.RENTAL...1...Neighborhood
## 1 4-08276-0658 DOUGLASTON
## 2 4-03173-0013 REGO PARK
## 3 4-02134-0013 FOREST HILLS
## 4 4-01268-0001 JACKSON HEIGHTS
## 5 4-05046-0040 FLUSHING-NORTH
## 6 4-03155-0027 REGO PARK
## 7 4-03329-0054 KEW GARDENS
## 8 4-08276-0658 DOUGLASTON
## COMPARABLE.RENTAL...1...Total.Units COMPARABLE.RENTAL...1...Year.Built
## 1 54 1977
## 2 61 1949
## 3 123 1963
## 4 66 1951
## 5 63 1959
## 6 286 1959
## 7 54 1924
## 8 54 1977
## COMPARABLE.RENTAL...1...Gross.SqFt
## 1 27690
## 2 70910
## 3 135400
## 4 57888
## 5 58399
## 6 275197
## 7 54300
## 8 27690
## COMPARABLE.RENTAL...1...Estimated.Gross.Income
## 1 390932
## 2 1130670
## 3 1872338
## 4 860870
## 5 899224
## 6 4668060
## 7 814069
## 8 390932# Renamed the columns in comp_rental1.
rename_comp_rental1 <- comp_rental1 %>%
rename(Boro_Block_Lot = COMPARABLE.RENTAL...1...Boro.Block.Lot,
Neighborhood = COMPARABLE.RENTAL...1...Neighborhood,
Total_Units = COMPARABLE.RENTAL...1...Total.Units,
Year_Built = COMPARABLE.RENTAL...1...Year.Built,
Gross_Sq_Ft = COMPARABLE.RENTAL...1...Gross.SqFt,
Gross_Income = COMPARABLE.RENTAL...1...Estimated.Gross.Income)
rename_comp_rental1## Boro_Block_Lot Neighborhood Total_Units Year_Built Gross_Sq_Ft
## 1 4-08276-0658 DOUGLASTON 54 1977 27690
## 2 4-03173-0013 REGO PARK 61 1949 70910
## 3 4-02134-0013 FOREST HILLS 123 1963 135400
## 4 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## 5 4-05046-0040 FLUSHING-NORTH 63 1959 58399
## 6 4-03155-0027 REGO PARK 286 1959 275197
## 7 4-03329-0054 KEW GARDENS 54 1924 54300
## 8 4-08276-0658 DOUGLASTON 54 1977 27690
## Gross_Income
## 1 390932
## 2 1130670
## 3 1872338
## 4 860870
## 5 899224
## 6 4668060
## 7 814069
## 8 390932# Created a new dataframe witn Comparable Rental 2 only.
comp_rental2 <- select(comp_rental, COMPARABLE.RENTAL...2...Boro.Block.Lot, COMPARABLE.RENTAL...2...Neighborhood, COMPARABLE.RENTAL...2...Total.Units, COMPARABLE.RENTAL...2...Year.Built, COMPARABLE.RENTAL...2...Gross.SqFt, COMPARABLE.RENTAL...2...Estimated.Gross.Income)
comp_rental2## COMPARABLE.RENTAL...2...Boro.Block.Lot COMPARABLE.RENTAL...2...Neighborhood
## 1 4-03205-0024 FOREST HILLS
## 2 4-02135-0013 FOREST HILLS
## 3 4-01279-0001 JACKSON HEIGHTS
## 4 4-05046-0006 FLUSHING-NORTH
## 5 4-03234-0051 FOREST HILLS
## 6 4-03322-0014 KEW GARDENS
## 7 4-15555-0001 FAR ROCKAWAY
## 8 4-01268-0001 JACKSON HEIGHTS
## COMPARABLE.RENTAL...2...Total.Units COMPARABLE.RENTAL...2...Year.Built
## 1 20 1931
## 2 135 1960
## 3 96 1950
## 4 45 1968
## 5 228 1929
## 6 84 1928
## 7 108 1950
## 8 66 1951
## COMPARABLE.RENTAL...2...Gross.SqFt
## 1 24408
## 2 137700
## 3 95000
## 4 41398
## 5 174480
## 6 83727
## 7 87926
## 8 57888
## COMPARABLE.RENTAL...2...Estimated.Gross.Income
## 1 372944
## 2 1889772
## 3 1035405
## 4 605749
## 5 3520549
## 6 1220740
## 7 1077972
## 8 860870# Renamed the columns in comp_rental2.
rename_comp_rental2 <- comp_rental2 %>%
rename(Boro_Block_Lot = COMPARABLE.RENTAL...2...Boro.Block.Lot,
Neighborhood = COMPARABLE.RENTAL...2...Neighborhood,
Total_Units = COMPARABLE.RENTAL...2...Total.Units,
Year_Built = COMPARABLE.RENTAL...2...Year.Built,
Gross_Sq_Ft = COMPARABLE.RENTAL...2...Gross.SqFt,
Gross_Income = COMPARABLE.RENTAL...2...Estimated.Gross.Income)
rename_comp_rental2## Boro_Block_Lot Neighborhood Total_Units Year_Built Gross_Sq_Ft
## 1 4-03205-0024 FOREST HILLS 20 1931 24408
## 2 4-02135-0013 FOREST HILLS 135 1960 137700
## 3 4-01279-0001 JACKSON HEIGHTS 96 1950 95000
## 4 4-05046-0006 FLUSHING-NORTH 45 1968 41398
## 5 4-03234-0051 FOREST HILLS 228 1929 174480
## 6 4-03322-0014 KEW GARDENS 84 1928 83727
## 7 4-15555-0001 FAR ROCKAWAY 108 1950 87926
## 8 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## Gross_Income
## 1 372944
## 2 1889772
## 3 1035405
## 4 605749
## 5 3520549
## 6 1220740
## 7 1077972
## 8 860870# Merged both dataframes to make it into 1 dataframe again.
new_comp_rental <- rbind(rename_comp_rental1, rename_comp_rental2)
new_comp_rental## Boro_Block_Lot Neighborhood Total_Units Year_Built Gross_Sq_Ft
## 1 4-08276-0658 DOUGLASTON 54 1977 27690
## 2 4-03173-0013 REGO PARK 61 1949 70910
## 3 4-02134-0013 FOREST HILLS 123 1963 135400
## 4 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## 5 4-05046-0040 FLUSHING-NORTH 63 1959 58399
## 6 4-03155-0027 REGO PARK 286 1959 275197
## 7 4-03329-0054 KEW GARDENS 54 1924 54300
## 8 4-08276-0658 DOUGLASTON 54 1977 27690
## 9 4-03205-0024 FOREST HILLS 20 1931 24408
## 10 4-02135-0013 FOREST HILLS 135 1960 137700
## 11 4-01279-0001 JACKSON HEIGHTS 96 1950 95000
## 12 4-05046-0006 FLUSHING-NORTH 45 1968 41398
## 13 4-03234-0051 FOREST HILLS 228 1929 174480
## 14 4-03322-0014 KEW GARDENS 84 1928 83727
## 15 4-15555-0001 FAR ROCKAWAY 108 1950 87926
## 16 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## Gross_Income
## 1 390932
## 2 1130670
## 3 1872338
## 4 860870
## 5 899224
## 6 4668060
## 7 814069
## 8 390932
## 9 372944
## 10 1889772
## 11 1035405
## 12 605749
## 13 3520549
## 14 1220740
## 15 1077972
## 16 860870ANALYSIS
In order to do the analysis I had to search on the origional dataset, DOF: Cooperative Comparable Rental Income – Queens – FY 2011/2012, for Estimated Expense and Net Operating Income for each Boro Block Lot and create and add a colomn called Estimated Expense and Net Operating Income with the observations to the dataframe.
Please Note: Gross Income - Estimated Expense = Net Operating Income.
I only had to add the column for Net Operating Income (which was already on the origional dataset) but I wanted you to show how Net Operating Income was calculated.
# Create and add columns with observation called Estimated Expense and Net Operating Income to be able to perform my analysis.
Estimated_Expense <- c(149526, 452085, 796014, 374654, 352611, 2367993, 357727, 149526, 165174, 1026891, 553582, 266036, 1461326, 647210, 484086, 374654)
Net_Operating_Income <- c(241406, 678585, 1076324, 486216, 546613, 2300067, 456342, 241406, 207770, 862881, 481823, 339713, 2059223, 573530, 593886, 486216)
new_comp_rental <- cbind(new_comp_rental, Estimated_Expense, Net_Operating_Income)
new_comp_rental## Boro_Block_Lot Neighborhood Total_Units Year_Built Gross_Sq_Ft
## 1 4-08276-0658 DOUGLASTON 54 1977 27690
## 2 4-03173-0013 REGO PARK 61 1949 70910
## 3 4-02134-0013 FOREST HILLS 123 1963 135400
## 4 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## 5 4-05046-0040 FLUSHING-NORTH 63 1959 58399
## 6 4-03155-0027 REGO PARK 286 1959 275197
## 7 4-03329-0054 KEW GARDENS 54 1924 54300
## 8 4-08276-0658 DOUGLASTON 54 1977 27690
## 9 4-03205-0024 FOREST HILLS 20 1931 24408
## 10 4-02135-0013 FOREST HILLS 135 1960 137700
## 11 4-01279-0001 JACKSON HEIGHTS 96 1950 95000
## 12 4-05046-0006 FLUSHING-NORTH 45 1968 41398
## 13 4-03234-0051 FOREST HILLS 228 1929 174480
## 14 4-03322-0014 KEW GARDENS 84 1928 83727
## 15 4-15555-0001 FAR ROCKAWAY 108 1950 87926
## 16 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## Gross_Income Estimated_Expense Net_Operating_Income
## 1 390932 149526 241406
## 2 1130670 452085 678585
## 3 1872338 796014 1076324
## 4 860870 374654 486216
## 5 899224 352611 546613
## 6 4668060 2367993 2300067
## 7 814069 357727 456342
## 8 390932 149526 241406
## 9 372944 165174 207770
## 10 1889772 1026891 862881
## 11 1035405 553582 481823
## 12 605749 266036 339713
## 13 3520549 1461326 2059223
## 14 1220740 647210 573530
## 15 1077972 484086 593886
## 16 860870 374654 486216Not sure if the calculation is correct to get the Net Value for each Unit by dividing Net Operating Income and Total Units. This formula I got when I googled.
# Calculate the Net Value for each Unit by dividing Net Operating Income and Total Units at the same time create a new column called Net Value Per Unit.
net_value_comp_rental <- new_comp_rental
net_value_comp_rental$Net_Value_Per_Rental <- Net_Operating_Income / new_comp_rental$Total_Units
net_value_comp_rental## Boro_Block_Lot Neighborhood Total_Units Year_Built Gross_Sq_Ft
## 1 4-08276-0658 DOUGLASTON 54 1977 27690
## 2 4-03173-0013 REGO PARK 61 1949 70910
## 3 4-02134-0013 FOREST HILLS 123 1963 135400
## 4 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## 5 4-05046-0040 FLUSHING-NORTH 63 1959 58399
## 6 4-03155-0027 REGO PARK 286 1959 275197
## 7 4-03329-0054 KEW GARDENS 54 1924 54300
## 8 4-08276-0658 DOUGLASTON 54 1977 27690
## 9 4-03205-0024 FOREST HILLS 20 1931 24408
## 10 4-02135-0013 FOREST HILLS 135 1960 137700
## 11 4-01279-0001 JACKSON HEIGHTS 96 1950 95000
## 12 4-05046-0006 FLUSHING-NORTH 45 1968 41398
## 13 4-03234-0051 FOREST HILLS 228 1929 174480
## 14 4-03322-0014 KEW GARDENS 84 1928 83727
## 15 4-15555-0001 FAR ROCKAWAY 108 1950 87926
## 16 4-01268-0001 JACKSON HEIGHTS 66 1951 57888
## Gross_Income Estimated_Expense Net_Operating_Income Net_Value_Per_Rental
## 1 390932 149526 241406 4470.481
## 2 1130670 452085 678585 11124.344
## 3 1872338 796014 1076324 8750.602
## 4 860870 374654 486216 7366.909
## 5 899224 352611 546613 8676.397
## 6 4668060 2367993 2300067 8042.192
## 7 814069 357727 456342 8450.778
## 8 390932 149526 241406 4470.481
## 9 372944 165174 207770 10388.500
## 10 1889772 1026891 862881 6391.711
## 11 1035405 553582 481823 5018.990
## 12 605749 266036 339713 7549.178
## 13 3520549 1461326 2059223 9031.680
## 14 1220740 647210 573530 6827.738
## 15 1077972 484086 593886 5498.944
## 16 860870 374654 486216 7366.909# Verified the class for each column.
as.data.frame(sapply(net_value_comp_rental, class))## sapply(net_value_comp_rental, class)
## Boro_Block_Lot character
## Neighborhood character
## Total_Units integer
## Year_Built integer
## Gross_Sq_Ft integer
## Gross_Income integer
## Estimated_Expense numeric
## Net_Operating_Income numeric
## Net_Value_Per_Rental numericI see the dataframe is 16 rows in length. Boro Block Lot, Neighborhood is class as characters. The Total Units Min is 20.00, 1st Quarter is 54.00, Median 66.00, Mean is 96.44, 3rd Quarter is 111.75, the Max is 286.00. The Year Built Min is 1924, 1st Quarter is 1944, Median 1951, Mean is 1952, 3rd Quarter is 1961, the Max is 1977. The Gross Sq Ft Min is 24408, 1st Quarter is 51075, Median 64655, Mean is 88125, 3rd Quarter is 105100, the Max is 275197. The Gross Income Min is 372944, 1st Quarter is 761989, Median 967314, Mean is 1350694, 3rd Quarter is 1383640, the Max is 4668060. The Gross Income Min is 372944, 1st Quarter is Min 761989, Median 967314, Mean is 1350694, 3rd Quarter is 1383640, the Max is 4668060. The Estimated Expense Min is 149526, 1st Quarter is 330967, Median 413370, Mean is 623693, 3rd Quarter is 684411, the Max is 2367993. The Operating Income Min is 207770, 1st Quarter is 427185, Median 5164150, Mean is 727000, 3rd Quarter is 724659, the Max is 2300067. The Net Value Per Rental Min is 4470, 1st Quarter is 66169, Median 7458, Mean is 7464, 3rd Quarter is 8695, the Max is 11124.
# Summary of each column.
summary(net_value_comp_rental)## Boro_Block_Lot Neighborhood Total_Units Year_Built
## Length:16 Length:16 Min. : 20.00 Min. :1924
## Class :character Class :character 1st Qu.: 54.00 1st Qu.:1944
## Mode :character Mode :character Median : 66.00 Median :1951
## Mean : 96.44 Mean :1952
## 3rd Qu.:111.75 3rd Qu.:1961
## Max. :286.00 Max. :1977
## Gross_Sq_Ft Gross_Income Estimated_Expense Net_Operating_Income
## Min. : 24408 Min. : 372944 Min. : 149526 Min. : 207770
## 1st Qu.: 51075 1st Qu.: 761989 1st Qu.: 330967 1st Qu.: 427185
## Median : 64655 Median : 967314 Median : 413370 Median : 516415
## Mean : 88125 Mean :1350694 Mean : 623693 Mean : 727000
## 3rd Qu.:105100 3rd Qu.:1383640 3rd Qu.: 684411 3rd Qu.: 724659
## Max. :275197 Max. :4668060 Max. :2367993 Max. :2300067
## Net_Value_Per_Rental
## Min. : 4470
## 1st Qu.: 6169
## Median : 7458
## Mean : 7464
## 3rd Qu.: 8695
## Max. :11124# Created a dataframe with just Boro Block Lot, Neighborhood, Net Value Per Rentsl.
analysis <- net_value_comp_rental
select(net_value_comp_rental, Boro_Block_Lot, Neighborhood, Total_Units, Net_Operating_Income, Net_Value_Per_Rental)## Boro_Block_Lot Neighborhood Total_Units Net_Operating_Income
## 1 4-08276-0658 DOUGLASTON 54 241406
## 2 4-03173-0013 REGO PARK 61 678585
## 3 4-02134-0013 FOREST HILLS 123 1076324
## 4 4-01268-0001 JACKSON HEIGHTS 66 486216
## 5 4-05046-0040 FLUSHING-NORTH 63 546613
## 6 4-03155-0027 REGO PARK 286 2300067
## 7 4-03329-0054 KEW GARDENS 54 456342
## 8 4-08276-0658 DOUGLASTON 54 241406
## 9 4-03205-0024 FOREST HILLS 20 207770
## 10 4-02135-0013 FOREST HILLS 135 862881
## 11 4-01279-0001 JACKSON HEIGHTS 96 481823
## 12 4-05046-0006 FLUSHING-NORTH 45 339713
## 13 4-03234-0051 FOREST HILLS 228 2059223
## 14 4-03322-0014 KEW GARDENS 84 573530
## 15 4-15555-0001 FAR ROCKAWAY 108 593886
## 16 4-01268-0001 JACKSON HEIGHTS 66 486216
## Net_Value_Per_Rental
## 1 4470.481
## 2 11124.344
## 3 8750.602
## 4 7366.909
## 5 8676.397
## 6 8042.192
## 7 8450.778
## 8 4470.481
## 9 10388.500
## 10 6391.711
## 11 5018.990
## 12 7549.178
## 13 9031.680
## 14 6827.738
## 15 5498.944
## 16 7366.909Thought if I convert the Net Operating Income from double to interger the graph would fix but did not succeed. Line graph did not work at all for me no lines and doing the conversion from double to interger did not work either.
# Convert Net Operating Income and Net Value Per Rental to integer from double.
numbers <- analysis
numbers$Net_Operating_Income <- as.integer(numbers$Net_Operating_Income) # First column is a double.
numbers$Net_Value_Per_Rental <- as.integer(numbers$Net_Value_Per_Rental) # Second column is a double.
sapply(numbers, class)## Boro_Block_Lot Neighborhood Total_Units
## "character" "character" "integer"
## Year_Built Gross_Sq_Ft Gross_Income
## "integer" "integer" "integer"
## Estimated_Expense Net_Operating_Income Net_Value_Per_Rental
## "numeric" "integer" "integer"Please note for some reason my bar graph and scatter plot is only plotting 14 observations instead of 16.
Not sure if the calculation are correct the Net Value for each Unit by dividing Net Operating Income and Total Units .
As per the below graphs Bar Block Lot in Jackson Heights is has more expensive Net Value Per Rent at close to 15000. The lowest Net Value Per Rent is Bar Block Lot in Jackson Heights also at close to 5000. Looking at the numbers from the above dataframe Bar Block Lot in Rego Park has the most expensive Net Value Per Rent at 11124.344 and the lowest is Douglaston at 4470.481.
# Bar gaph showing Net Value per Boro Block Lot by Neighborhood.
#ggplot(numbers, aes(x = Net_Value_Per_Rental, y = Boro_Block_Lot, colour = Neighborhood)) +
#geom_line()
ggplot(numbers, aes(x = Net_Value_Per_Rental, y = Boro_Block_Lot, fill = Neighborhood)) +
geom_col()
ggplot(numbers, aes(x = Net_Value_Per_Rental, y = Boro_Block_Lot, colour = Neighborhood)) +
geom_point(size = 5) +
scale_shape_manual(values = c(1,2,3,4,5,6,7)) +
scale_colour_hue(l = 45)