Central Tendency for Housing Data in R

In this project, you will find the mean, median, and mode cost of one-bedroom apartments in three of the five New York City boroughs: Brooklyn, Manhattan, and Queens.

Using your findings, you will make conclusions about the cost of living in each of the boroughs. We will also discuss an important assumption that we make when we point out differences between the boroughs.

We worked with Streeteasy.com to collect this data. While we will only focus on the cost of one-bedroom apartments, the dataset includes a lot more information if you’re interested in asking your own questions about the Brooklyn, Manhattan, and Queens housing market.

Observing your Data

1.We’ve imported data about one-bedroom apartments in three of New York City’s boroughs: Brooklyn, Manhattan, and Queens. We saved the values to:

1.brooklyn_one_bed

2.manhattan_one_bed

3.queens_one_bed

In this project, we only care about the price of apartments, so we saved the price of apartments in each borough to:

1.brooklyn_price

2.manhattan_price

3.queens_price

If you want to see what the data stored in these variables looks like, you can type the variable names in a code block. When you click run, the variables (and their contents) will appear.

# Load libraries
library(readr)
library(dplyr)
library(DescTools)

# Read in housing data
brooklyn_one_bed <- read_csv('brooklyn.csv')
brooklyn_price <- brooklyn_one_bed$rent

manhattan_one_bed <- read_csv('manhattan.csv')
manhattan_price <- manhattan_one_bed$rent

queens_one_bed <- read_csv('queens.csv')
queens_price <- queens_one_bed$rent


brooklyn_one_bed

manhattan_one_bed

queens_one_bed

brooklyn_price

   [1]  3600  3900  2700  4900  3900  2750  2700  4905  2000  3200  2950  1950  1799  5317  2300  2400  3045  3790
  [19]  2500  2600  3226  4995  6950  3500  3080  6992  6000  2700  4246  2299  2400  2485  4000  2995  1500  4370
  [37]  2400  3200  3500  1600  1650  2600  2400  1800  2295  4700  1875  2900  3600  2450  3200  3300  8900  2195
  [55]  2900  2400  2000  3100  1650  3090  4120  4079  1984  2745  1695  2850  2123  2250  4995  2600  5300  3786
  [73]  5300  2900  2975  1500  4000  2200  6300  2400  2350  5145  2900  1400  2900  1995  2650  2300  5000  5195
  [91]  3600  4850  3500  3800  2575  3000  2200  2428  1600  4488  4950  3495  1950  3500  2650  5050  2200  2150
 [109] 15750  4215  2348  1581  2850  3171  3300  3225  3000  1675  2225  5800  1550  5346  3155  2400  2250  2500
 [127]  1500 11000  3800  2395  2395  3200  4550  4615  2850  2800  3000  3900  2814  5299  1550  2200  6875  2978
 [145]  5550  1400  3875  1650  4800  1750  3305  3300  4695  2200  3070  3445  2250  1775  4675  3795  5000  4695
 [163]  3348  2395  2970  2840  3000  3461  2450  2599  2100  1950  3900  2300  2300  3000  2400  1350  3400  2150
 [181]  2575  2995  6000  2850  1325  2650  4595  2799  2200  3800  4361  1900  3200  3300  2650  4299  2400  2640
 [199]  2300  2400  2300  2950  2300  2600  3385  1800  4500  4595  2760  2530  2495  3495  3850 13500  4500  2500
 [217]  3500  4195  9400  2550  5100  3369  2775  3029  4800  5295  2395  2995  5250  2975  4795  2450  4500  2650
 [235]  4350  3100  3370  1832  6500  2780  3067  1600  2650  3375  2850  3994  3875  3600  3500  2615  2200  3120
 [253]  3299  3000  4545  1400  3300  5995  1900  2630  1895  1895  4500  4095  3400  2775  2700  2925  2400  1900
 [271]  1650  4145  2500  2300  3195  2750  1650  4500  3199  4300  2000  2534  2500  3750  3400  3225  4200  2488
 [289]  2550  3250  3411  1500  1995  2510  3900  3600  3000  3320  3400  2495  6600  2000  2570  2900  2000  3204
 [307]  6895  3499  3800  3700  3540  2704  1425  1950  1581  4500  2000  2500  3995  5320  2590  5500  4350  3950
 [325]  2350  3415  2500  4650  3340  1700  2450  2100  7200  1950  2300  3725  3100  3300  2775  3600  2500  3600
 [343]  3099  3995  2500  1900  2729  2500  2417  6250  2550  1700  2195  3805  2750  4000  3950  2446  3800  2635
 [361]  2600  1800  1850  2500  2900  3337  2150  3000  4850  3295  3695  2200  3385  4395  4500  3000  4390  5000
 [379]  2695  6100  3675  3000  3600  2800  2200  2835  2199  1750  3795  1425  9000  2200  3600  2200  6250  2600
 [397]  3150  3195  4314  2800  3750  2910  2650  1800  3500  4600  4350  3092  1938  1995  4500  5500  3450  3950
 [415]  2650  4000  2400  3000  4200  4000  5995  4950  2300  3000  6565  3100  3333  5250  2650 12000 10000  2200
 [433]  2625  1550  2700  2500  4750  2150  2700  3875  2100  4400  2800  2650  2300  3630  4416  2000  2275  2999
 [451]  2975  2854  2700  3750  3800  3600  4400  3400  3200  3400  2650  4350  6995  8900  3200  2500  1800  1600
 [469]  4100  1800  2899  2400  1900  3475  4600  3000  2750  2700  2200  4899  3200  3300  5300  3150  5630  4349
 [487]  1750  4500  2734  2000  2500  2550  2850  3570  2875  3256  3025  2699  1550  1975  1650  1800  2500  4100
 [505]  4620  3250  2200  3800  3680  2965  2600  2750  1800  1475  2400  3325  2300  2856  2840  3199  3400  2999
 [523]  2769  3500  4190  2999  2200  2750  3600  1900  1750  2100  4100  6320  3390  2300  2125  3784  6500  2000
 [541]  2400  5039  3515  3100  4350  5405  3195  4700  2995  2350  4095  4350  3500  5100  2650  1575  1800  4583
 [559]  5800  2149  1800  5200  2475  2999  1800  2875  4100  1825  3150  6250  3485  2300  3100  3025  2200  4033
 [577]  1500  3595  2300  3210  4200  3999  1650  3671  2125  2625  2200  1675  2000  2450  2000  4245  3350  2550
 [595]  1450  3000  1971  3434  3500  4200  2650  2500  5250 12000  2350  2550  3490  2891  2685  4200  2000  4500
 [613]  2950  4354  3300  3695  5195  3595  3200  2995  2850  2075  1725  2200  1550  2750  2500  4050  3200  2800
 [631]  2495  2665  4500  3825  3400  2745  9495  2550  2395  2300  4750  2500  3525  2600  3400  2215  5100  3300
 [649]  2800  5210  2300  2600  4750  4303  5300  2050  4995  2350  3450  4250  2400  2735  2500  2750  3200  4075
 [667]  2150  6500  3100  2995  2350  3450  4400  2250  2800  3299  4800  2250  3300  3400  2100  3349  3900  2800
 [685]  3650  2891  3500  4200  2815  3896  2800  2750  2900  4100  2995  1650  3495  3950  4000  4995  3175  3414
 [703]  3500  4250  2000  2850  2800  3000  1800  2800  2675  3100  3000  2300  2995  3758  9000  2600  2850  3100
 [721]  4350  5250  2580  1675  2600  3450  2995  4024  4100  2850  4400  3400  2850  5995  5262  3950 16000  6600
 [739]  2650  5600  3450  2699  3500  4300  2950  2400  2118  2900  3600  3300  2499  3600  2750  2500  2300  3337
 [757]  2800  3050  2720  3595  2500  2940  2550  3675  3100  3450  5000  3117  2600  2800  3050  2200 11500  2680
 [775]  3350  4500  3100  3099  4200  2474  8500  3650  1590  1675  3595  6426  2050  2150  2745  3500  5999  3300
 [793]  2123  3500  2250  3680  2445  5699  5860  2450  1400  3170  3350  4650  1850  4495  3400  1500  5000  5295
 [811]  4500  1900  1800  4100  2000  3465  2195  2650  1695  3700  4000  2850  3400  1850  7250  2891  4190  3300
 [829]  1995  5100  2550  1275  3225  2600  3800  3295  3750  2600  2395  4500  3200  1695  4325  2400  1700  1846
 [847]  2395  2700  3300  2251  4200  2675  4000  3029  2500  3138  3250  3700  5900  6650  2500  1450  1375  4300
 [865]  4800  2100  1875  3099  3942  2765  2400  2400  3080  2500  1795  2395  4595  1795  3000  2585  3495  2695
 [883]  3200  1875  3100  2930  4250  3100  1400  1650  3000  2383  2769  2764  2550  2658  4200  2800  1800  2735
 [901]  2800  1850  3200  2975  1846  3850  4350  3050  4900  2600  3100  2850  2450  6850  2000  1550  2200  3500
 [919]  4000  4600  4350  2999  2400  3000  1925  2400  2700  4395  2995  3295  2200  4395  3595  2700  2995  3700
 [937]  5900  3550  3241  2600  2999  3250  4500  1995  2100  3895  1625  3000  1475  2700  4025  3850  4190  4200
 [955]  2500  2480  2875  2150  3899  3500  2500  1850  3270  3100  3300  2635  4500  2250  3375  3350  3195  3184
 [973]  3099  6923  4595  2300  2660  2100  3950  2250  3300  1850  4600  3000  3800  4700  1750  4150  2300  2300
 [991]  1600  1850  4095  2750 16000  1900  4000  2850  1795  2580
 [ reached 'max' / getOption("max.print") -- omitted 13 entries ]

manhattan_price

   [1]  2550 11500  4500  4795 17500  3800  1995  2995 15000  4650  2950  6920  4875  4850  3700  4200  2195  4200
  [19]  9000  4950  3000  2450  1950  3375  2395  4990  7495 10000  3090 10904  2100  2499  3650  3800  9800  4350
  [37]  2495  3625  9000  5800  3295  2200  5485  2950  2750  8200  2000  4995  1875  3900  2400  5325  3500  3900
  [55]  3375  6400 12500  2595  2199  2430 10000  6400  5895  2600  4700  6000  4995  3388  3483  2900  4600  1695
  [73]  2700 11750  5080  4890  3800  5200  2800  6000  2100  2300 16750  6400  5595  2800  3219  8500 10000  3775
  [91]  4350  3995  4995  7195  7600  2995  4490  3450  3800 14662  2575  3750  6695  5500  5475  4910  5150  4950
 [109]  5225 13750  4500  2200  4000  4300  2750  5120  5895 11723  4270  2995  2150  3500  2150  2350  4499  3815
 [127]  3200  1625  3500  3495  2025  2700  3550  6000  4750  2800  5000  3500  6800  2775  7900  3995  9950  3400
 [145]  3300 11000  3250  5700  7246  5688  6350  2800  5740  3500 12500  2695  2895 16500  5000  6600  3490  4800
 [163]  4050  5850  2300  3275  4095  2995  5900  3920  4795  3800 12500  5000  4365  3950  4000  3905  4000  2750
 [181]  3631  2095  7995  2995 18450  4250  4995  4200  4300 14000  8250  3950  5995  4785  4800  3595  4500  5415
 [199]  5500  3700  1700  2799  9300  4990  7795  6000  2450  7000  3250  5495  4050  3900  3195  3000  3700  7950
 [217]  2995  2600  2095  5668  7995  3190  2300  4950  3100  6400  3950  4400  6450  3100  7700 18250 13500  5780
 [235]  6800  3300  5500  1750  8500  2650  3700  6000  5145  2600  1600  3058  4475  4950  3932  3000  5150  3450
 [253]  2250  4260  4375 10995  2900  4200  7500  3665  3800  2995  3935  2495  3400  5535  3600  5440  1950  4100
 [271]  2200  7000  4695  5300  7900  3600  1950  7500  5150  4730  1460  5300  9995  3950  2685  2695  3500  4000
 [289] 14000  9950  3675  7250  3425  2125  4200  2999  5100  5256  3688  2595  3850  4000  3290  2100  5200 10000
 [307]  6300  4870  4500  6000  2500 14500  4000  2150 12000  2300 10780  1650  5350  4495 14500  3800  1800  2450
 [325]  6000  2750  5050  2200  3085  3650  3800  3500 10500  7999  4185  3350  2850  3323  4300  5900  3200 10662
 [343]  6650  3800  4080  5000  3500  7500  4575  4000  3925  5500  8500  3300 15000  4950  9995  3495  2600  3000
 [361]  5855  4500  7995 18000  2450  3791  5600  3945  3000  7500  3950  3450  3200  5850 10995  2800  2575  1599
 [379]  3400  2995  3790  4195  9415  1795  6000  4765  5250 17500  5250  3845  8000  3560  1775  5100  8000  7650
 [397] 15000  3100  2445  5200  3285 10000  5250  4600  4450  9000  7000 15000  2995  4200  2600  9700  6058 11257
 [415]  5000  2400  3250 14000  4035  3395 14000  2825  2050  2995  5050  5900  2450  2200  3730  5600  2800 11000
 [433]  4350  3900  6500  7495  2999  2595  5250  5995  3946  7800  3200  4950  3650  2600  3100  4500  5250  3375
 [451]  4600  3675  3350  3500  6250  4700  2485 12500  4615  3250  4000  2000  3950  2099 10000  3195  2750  2950
 [469]  5400  5100  2150  4800  3415  5995  3695  2650  4600  2790  3700  4500 14500  3550  3535  9000  4000  3323
 [487]  6495  2400  4600  8000  4495  2900  6080  5100  3453  9000  9350  3510  4506  4000  3000  3370  6310  4995
 [505]  3495  3823 12500 12000  7995  3100  4610  4768  3650  2750  3900  4000  5500  1850  3150  2400 11500  2700
 [523]  9950  3200  4495  3150  5500  3950  4200  3195  4000  9995  5125  2625  3225  5950  2650  7500  5200  6650
 [541]  2450  6700  7195  3350  4686  4500  2600  3000  6950  4650  3350  6854  3800  8500  2950  6250  7350  4750
 [559]  2720  2495  2150  2950  2700  3341  5300  3400  6900  2690  2000  3950  2600 15000 13900  2990  4795  2850
 [577]  3500  6995  6500  2700  7500  5700  3900  4195  7290  3800  3410  2900  9250 13000 10000  4500  3000  2400
 [595]  3000  6861  6917  3800  3800  9250  9995  5995  4280  4000  4275  3700  2395  9995  6000  2050  3000  4565
 [613]  3800  2900 13500  1999  3100  9750  2500 11500  3900  5400  2425  5940 10495  3500  6055 11538  5900  4140
 [631]  3876  2879 13000  5995  8250  3595  6500 18500  6350  5250  3300  3750  8000  5200  7795  3500 13000  3642
 [649]  3100  3900  3500  7000  8095  7500  7995  3200  9000  3365  6465  2900  6700  3705  4216  3395  2735  2800
 [667]  8100  2650  2900  5555 15900  3575  8750  5275 15000  7500  1895  4750  4600  3150  4050  7085  3595  3900
 [685]  3595  5250  2495 12500  3645  7350  5110  2550  2900  4650  3800  3530  2795  5800  3500  2215  3325  4250
 [703]  8500  2935  8995  6594  9850  8200  6776  5999  2095  2595  3500  3300  6800 16000  3750  3195  4700  2805
 [721]  4150  3515  5850  3850  6350  4000  5250  3645  3725  2695  2650  1995  6500  3700  4500  3350  5999  2800
 [739]  4350  2600  3800  2500  2800  2850  6118  5995  3850  5395  3750  1443  2795  4500  3600  3750  3800 12000
 [757]  5155  2500  2250  3750  4995  4198  7500  3000  3093 11000  4195  2700  2499  3865  5935  4950  4600  8500
 [775]  3350  3550  4600  4850  3150  3400  2200  3300  7000  3850  6500 12000  2975  3100  2990  6995  3229  3250
 [793]  3254  8950  3495  4850  4995  2900  3184  3000 10400  2100  3160  3308  3500  4846  2300  2750  2695  6995
 [811]  6000  2620  4895  3250  3498  1990  2650  5795  7500  2600  6725  3200 20000  3640  4500  5600  3900  3285
 [829]  2850  3950  2775  3840  2100  2295  3700  3550  8500  5500  5345  5150  3965  4400  5340  2100  7250  2850
 [847]  1987  3600  3300  3795  2400  4410  9250  3230  3500  4000  3813  3750  4590  4056  7250  3595  8500  2950
 [865]  3600  3730  3100  3495  3700  3850  3600  2095  7650  2790  3295  3000  8900  2960  4400  2995  1950  3850
 [883]  4000  5819  4100  4195  4200  3825  5250  3400  2723  3800 15000  3800  3342  7880  4965 16000  1600  3187
 [901]  2750  5958  4100  3600  3700  2700  4000  3300  2350  3795  7500  5771  4600  3400  3715  2750  1600  6500
 [919]  1500  2795  3211  6900  2450  4400  3895  5260  7500  5495  2595  5500  5000  4150  3100  3995  3875  7000
 [937]  3775 10500 12000  2750  6800  3700  3167  3775  3495  2900  3805  5950  8500  6806  3195 10600  3300  2950
 [955]  2995  5300  4400  3100  3990  2499  4695  7100  5600  2400  4195  5450  3300  3405 12000  5800  7995  1700
 [973]  3900  2595  3465  2550  3500  6300  4500  3395 14000  6900  3505  2975  2100  9000  8500  5995  3700  6000
 [991] 16000  2050  3150  5018  5900  3025  3600  4585  3500  2500
 [ reached 'max' / getOption("max.print") -- omitted 2539 entries ]

queens_price

  [1] 3000 1950 3500 1725 1700 2550 2550 3100 1399 2275 3400 2695 4275 2000 2200 2400 1850 1600 2025 1900 2200
 [22] 2000 3295 2350 3450 1450 1975 2995 2900 2000 1495 3400 3000 2535 2945 1900 2450 1700 1995 1800 1600 1835
 [43] 1625 3450 1625 3275 2500 3410 1650 2050 1850 2295 1700 2595 2695 1550 2550 3300 2955 1575 3795 2550 2750
 [64] 2825 2995 2550 2650 2395 1741 4850 1950 2650 1950 2150 3500 1465 2300 2100 3700 2870 2395 4199 3200 2395
 [85] 1700 5700 4100 1600 4475 3500 1900 2495 2350 2000 2700 2300 1550 2950 1250 3500 2550 4450 5575 2795 1825
[106] 2225 2950 1590 2050 2800 2600 2700 4085 2200 2498 2650 1450 3325 1500 2750 2745 3911 2535 2250 1350 2907
[127] 2550 1950 2800 3400 2400 1400 1725 2800 1700 2275 2600 2500 2000 3800 3160 2650 1750 2000 1750 1750 1995
[148] 1550 3300 2200 2535 2300 3139 1950 1925 1650 2350 2600 1800 1475 3300 2200 1475 3495 3000 2200 3200 1850
[169] 2800 2650 2350 1650 1600 1650 1500 3950 1625 1800 2175 3350 2950 2499 1800 3125 5350 2550 1350 2450 4500
[190] 1450 3911 1900 1350 2850 2600 1550 2750 2200 3300 3300 2200 2725 2900 2550 2700 2200 2100 1925 3975 2195
[211] 3200 2250 3400 3200 2600 2700 3150 2325 1999 2900 1750 3117 1695 1799 1725 3475 1500 1900 2200 1450 2895
[232] 3350 2625 3225 2400 2380 1605 2200 1585 3750 1750 2000 3000 2950 1750 3250 1900 2600 2200 2800 3600 1975
[253] 2250 2295 1725 3000 1850 2575 8000 2175 2950 2485 1575 2100 3175 2050 1725 2095 2300 3193 3575 2900 1625
[274] 2795 1825 1700 2450 1750 2650 2000 3000 1750 2575 2200 3450 1750 2100 2569 2200 3125 1399 2750 1450 4700
[295] 1625 1950 2350 1850 1850 1900 2350 2000 2500 2050 1650 1750 2400 3125 1625 3964 4000 2350 2000 1600 2275
[316] 2100 2095 1950 2250 1895 2000 1975 1700 2000 1299 3300 3125 2650 2050 2700 1971 2995 1825 3325 2250 2300
[337] 2700 1250 2950 2995 1950 1700 3300 3500 2570 2595 2400 2600 3225 3700 3175 4150 2550 3400 6400 2500 1585
[358] 2600 2450 5057 2100 1875 2650 2700 1299 1800 2250 4707 1800 2000 2250 3110 2300 2950 1625 3300 2800 3095
[379] 1950 2100 2300 2125 2895 1975 2375 1695 2400 2299 1950 2975 2600 2000 3950 1650 1995 1550 1900 2150 2200
[400] 3500 3275 2600 2500 1750 2700 1585 1425 3229 2800 2545 2550 4750 2000 1999 1895 1650 2050 4350 1345 5125
[421] 2900 1895 3700 1845 3000 1800 1800 3300 1950 3700 2350 1795 1875 3050 2625 1600 2950 5300 3500 2350 4850
[442] 2650 2200 3750 2950 2200 2250 2650

Find the Mean

2.Find the average value of one-bedroom apartments in Brooklyn and save the value to brooklyn_mean.

#Calculate Mean
brooklyn_mean <- mean(brooklyn_price)
brooklyn_mean

[1] 3327.404

3.Find the average value of one-bedroom apartments in Manhattan and save the value to manhattan_mean.

#Calculate Mean
manhattan_mean <- mean(manhattan_price)
manhattan_mean

[1] 5138.94

4.Find the average value of one-bedroom apartments in Queens and save the value to queens_mean.

#Calculate Mean
queens_mean <- mean(queens_price)
queens_mean

[1] 2516.147

Find the Median

5.Find the median value of one-bedroom apartments in Brooklyn and save the value to brooklyn_median.

#Calculate Median
brooklyn_median <- median(brooklyn_price)
brooklyn_median

[1] 3000

6.Find the median value of one-bedroom apartments in Manhattan and save the value to manhattan_median.

manhattan_median <- median(manhattan_price)
manhattan_median

[1] 4000

7.Find the median value of one-bedroom apartments in Queens and save the value to queens_median.

#Calculate Median
queens_median <- median(queens_price)
queens_median

[1] 2350

Find the Mode

8.Find the mode value of one-bedroom apartments in Brooklyn and save the value to brooklyn_mode.

#Calculate Mode
brooklyn_mode <- Mode(brooklyn_price)
brooklyn_mode

[1] 2500
attr(,"freq")
[1] 26

9.Find the mode value of one-bedroom apartments in Manhattan and save the value to manhattan_mode.

#Calculate Mode
manhattan_mode <- Mode(manhattan_price)
manhattan_mode

[1] 3500
attr(,"freq")
[1] 77

10.Find the mode value of one-bedroom apartments in Queens and save the value to queens_mode.

#Calculate Mode
queens_mode <- Mode(queens_price)
queens_mode

[1] 2200
attr(,"freq")
[1] 17

What does our data tell us?

11.Now what?

We don’t find the mean, median, and mode of a dataset for the sake of it.

The point is to make inferences from our data. What can you say about the housing prices in Brooklyn, Queens, and Manhattan? Besides, “It’s really expensive to live in any of them.”

Take a minute to think through it. We added our thoughts to the hint.

*Hint

It looks like the average cost of one-bedroom apartments in Manhattan is the most, and in Queens is the least. This pattern holds for the median and mode values as well.

While the mode is not the most important indicator of centrality, the fact that mean, median, and mode are within a few hundred dollars for each borough indicates the data is centered around:

1.$3,300 for Brooklyn

2.$3,900 for Manhattan

3.$2,300 for Queens

12.Did you make any assumptions when you drew inferences in the previous task?

If so, what assumptions did you make? We added our thoughts to the hint.

*Hint

We assumed that the data from Streeteasy is representative of housing prices for the entire borough. Given that Streeteasy is only used by a subset of property owners, this is not a fair assumption. A quick search on rentcafe.com will tell you the averages are more like:

1.$2,695 for Brooklyn one-bedroom apartments

2.$4,188 for Manhattan one-bedroom apartments

3.$2,178 for Queens one-bedroom apartments

This is an interesting finding. Why may the cost from rentcafe.com be higher in Manhattan than in Brooklyn or Queens?

Although we don’t have the answer to this question, it’s worth thinking about the possible differences between our Streeteasy data and where rentcafe is pulling their data.

13.Finally, think about what the histogram for each dataset will look like.

If you have the time, take a minute to make a rough sketch of the histograms for the cost of a one-bedroom apartment in Brooklyn, Manhattan, and Queens.

You can see someone else’s attempt at a sketch of the Brooklyn histogram.

knitr::include_graphics("C:/Users/kuoan/Desktop/R Code/His1.png")

library(ggplot2)
#plot data
hist <- qplot(brooklyn_one_bed$rent,
      geom='histogram',
      binwidth = 500,  
      main = 'Brooklyn 1 bedroom price', 
      xlab = 'Cost',
      ylab = 'Count',
      fill=I("blue"), 
      col=I("black"), 
      alpha=I(.2)) +
  geom_vline(aes(xintercept=median(brooklyn_one_bed$rent),
                 color="median"), linetype="dashed",
             size=1) +
  geom_vline(aes(xintercept=mean(brooklyn_one_bed$rent),
                 color="mean"), linetype="solid",
             size=1) +
  geom_vline(aes(xintercept=Mode(brooklyn_one_bed$rent),
                 color="mode"), linetype="solid",
             size=1) +
  scale_color_manual(name = "statistics", values = c(median = "blue", mean = "red", mode="green"))


hist

#plot data
hist <- qplot(manhattan_one_bed$rent,
      geom='histogram',
      binwidth = 500,  
      main = 'Manhattan 1 bedroom price', 
      xlab = 'Cost',
      ylab = 'Count',
      fill=I("blue"), 
      col=I("black"), 
      alpha=I(.2)) +
  geom_vline(aes(xintercept=median(manhattan_one_bed$rent),
                 color="median"), linetype="dashed",
             size=1) +
  geom_vline(aes(xintercept=mean(manhattan_one_bed$rent),
                 color="mean"), linetype="solid",
             size=1) +
  geom_vline(aes(xintercept=Mode(manhattan_one_bed$rent),
                 color="mode"), linetype="solid",
             size=1) +
  scale_color_manual(name = "statistics", values = c(median = "blue", mean = "red", mode="green"))


hist

#plot data
hist <- qplot(queens_one_bed$rent,
      geom='histogram',
      binwidth = 500,  
      main = 'Queens 1 bedroom price', 
      xlab = 'Cost',
      ylab = 'Count',
      fill=I("blue"), 
      col=I("black"), 
      alpha=I(.2)) +
  geom_vline(aes(xintercept=median(queens_one_bed$rent),
                 color="median"), linetype="dashed",
             size=1) +
  geom_vline(aes(xintercept=mean(queens_one_bed$rent),
                 color="mean"), linetype="solid",
             size=1) +
  geom_vline(aes(xintercept=Mode(queens_one_bed$rent),
                 color="mode"), linetype="solid",
             size=1) +
  scale_color_manual(name = "statistics", values = c(median = "blue", mean = "red", mode="green"))


hist

# Don't look below here
# Mean
if(exists('brooklyn_mean')) {
  print(paste("The mean price in Brooklyn is" , round(brooklyn_mean, digits=2))) 
}else{
    print("The mean price in Brooklyn is not yet defined.")
}

[1] "The mean price in Brooklyn is 3327.4"

if(exists("manhattan_mean")) {
    print(paste("The mean price in Manhattan is", round(manhattan_mean,digits=2)))
} else {
    print("The mean in Manhattan is not yet defined.")
}

[1] "The mean price in Manhattan is 5138.94"

if(exists("queens_mean")) {
    print(paste("The mean price in Queens is" , round(queens_mean,digits=2)))
} else {
  print("The mean price in Queens is not yet defined.")
}   

[1] "The mean price in Queens is 2516.15"

    
# Median
if(exists("brooklyn_median")) {
  print(paste("The median price in Brooklyn is" , brooklyn_median)) 
}else{
    print("The median price in Brooklyn is not yet defined.")
}

[1] "The median price in Brooklyn is 3000"

if(exists("manhattan_median")) {
    print(paste("The median price in Manhattan is", manhattan_median))
} else {
    print("The median in Manhattan is not yet defined.")
}

[1] "The median price in Manhattan is 4000"

if(exists("queens_median")) {
    print(paste("The median price in Queens is" , queens_median))
} else {
  print("The median price in Queens is not yet defined.")
} 

[1] "The median price in Queens is 2350"

    
#Mode
if(exists("brooklyn_mode")) {
  print(paste("The mode price in Brooklyn is" , brooklyn_mode)) 
}else{
    print("The mode price in Brooklyn is not yet defined.")
}

[1] "The mode price in Brooklyn is 2500"

if(exists("manhattan_median")) {
    print(paste("The mode price in Manhattan is", manhattan_mode))
} else {
    print("The mode in Manhattan is not yet defined.")
}

[1] "The mode price in Manhattan is 3500"

if(exists("queens_median")) {
    print(paste("The mode price in Queens is" , queens_mode))
} else {
  print("The mode price in Queens is not yet defined.")
} 

[1] "The mode price in Queens is 2200"