pacman::p_load(tidyverse, ggplot2, knitr, plotly, skimr, questionr, funModeling, sf, tmap, quanteda, NLP, ggiraph, ggstatsplot, dplyr, crosstalk, DT, ggdist, gganimate, ggpubr, corrplot, performance, parameters, see)Data Visualization Makeover 03
Putting Visual Analytics into Practical Use
Objective
The objective of this exercise is to uncover the salient patterns of the resale prices of public housing property by residential towns and estates in Singapore by using appropriate analytical visualisation techniques and apply appropriate interactive techniques to enhance user and data discovery experiences.
The visualization is created using the Resale flat princes based on registration date from Jan-2017 onwards (Data.gov.sg).
Scope and Limitation
For the purpose of this study, the focus should be on 3-ROOM, 4-ROOM and 5-ROOM types. You can choose to focus on either one housing type or multiple housing types. The study period should be on 2022.
Getting Started - Data Loading and Processing
Installing and loading the required libraries
Before we get started, it is important for us to ensure that the required R packages have been installed.
Importing Data
This code chunk is to import the data from resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv file to the Quarto/R page.
total_flat_data <- read_csv("data/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv")Rows: 146429 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): month, town, flat_type, block, street_name, storey_range, flat_mode...
dbl (3): floor_area_sqm, lease_commence_date, resale_price
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Filtering the Dataset
This section deals with limiting the data to the following criteria:
The year should be 2022
The flat_type should be only 3-ROOM, 4-ROOM and 5-ROOM
Note
This code uses the dplyr package to filter out the rows with year 2022 and correct flat type
filter_flat_data <- filter(total_flat_data, grepl('2022', month) & flat_type %in% c("3 ROOM", "4 ROOM","5 ROOM"))
filter_flat_data# A tibble: 24,374 × 11
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
1 2022-01 ANG MO… 3 ROOM 320 ANG MO… 07 TO … 73 New Ge… 1977 54 yea…
2 2022-01 ANG MO… 3 ROOM 225 ANG MO… 07 TO … 67 New Ge… 1978 55 yea…
3 2022-01 ANG MO… 3 ROOM 331 ANG MO… 07 TO … 68 New Ge… 1981 58 yea…
4 2022-01 ANG MO… 3 ROOM 534 ANG MO… 07 TO … 82 New Ge… 1980 57 yea…
5 2022-01 ANG MO… 3 ROOM 578 ANG MO… 04 TO … 67 New Ge… 1980 57 yea…
6 2022-01 ANG MO… 3 ROOM 452 ANG MO… 01 TO … 83 New Ge… 1979 56 yea…
7 2022-01 ANG MO… 3 ROOM 560 ANG MO… 01 TO … 67 New Ge… 1980 57 yea…
8 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
9 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
10 2022-01 ANG MO… 3 ROOM 560 ANG MO… 10 TO … 67 New Ge… 1980 57 yea…
# … with 24,364 more rows, 1 more variable: resale_price <dbl>, and abbreviated
# variable names ¹flat_type, ²street_name, ³storey_range, ⁴floor_area_sqm,
# ⁵flat_model, ⁶lease_commence_date, ⁷remaining_leaseExploring the Data
This section of the code aims to ensure that the scope and limitation are followed and the data are listed correctly
skimr::skim(filter_flat_data)| Name | filter_flat_data |
| Number of rows | 24374 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| month | 0 | 1 | 7 | 7 | 0 | 12 | 0 |
| town | 0 | 1 | 5 | 15 | 0 | 26 | 0 |
| flat_type | 0 | 1 | 6 | 6 | 0 | 3 | 0 |
| block | 0 | 1 | 1 | 4 | 0 | 2457 | 0 |
| street_name | 0 | 1 | 7 | 20 | 0 | 552 | 0 |
| storey_range | 0 | 1 | 8 | 8 | 0 | 17 | 0 |
| flat_model | 0 | 1 | 4 | 22 | 0 | 16 | 0 |
| remaining_lease | 0 | 1 | 8 | 18 | 0 | 638 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| floor_area_sqm | 0 | 1 | 94.07 | 19.32 | 51 | 81 | 93 | 110 | 159 | ▅▇▆▃▁ |
| lease_commence_date | 0 | 1 | 1997.46 | 14.98 | 1967 | 1985 | 1998 | 2014 | 2019 | ▂▆▅▃▇ |
| resale_price | 0 | 1 | 536391.17 | 157993.72 | 200000 | 428000 | 515000 | 610000 | 1418000 | ▅▇▂▁▁ |
glimpse(filter_flat_data, 60)Rows: 24,374
Columns: 11
$ month <chr> "2022-01", "2022-01", "2022-01…
$ town <chr> "ANG MO KIO", "ANG MO KIO", "A…
$ flat_type <chr> "3 ROOM", "3 ROOM", "3 ROOM", …
$ block <chr> "320", "225", "331", "534", "5…
$ street_name <chr> "ANG MO KIO AVE 1", "ANG MO KI…
$ storey_range <chr> "07 TO 09", "07 TO 09", "07 TO…
$ floor_area_sqm <dbl> 73, 67, 68, 82, 67, 83, 67, 67…
$ flat_model <chr> "New Generation", "New Generat…
$ lease_commence_date <dbl> 1977, 1978, 1981, 1980, 1980, …
$ remaining_lease <chr> "54 years 05 months", "55 year…
$ resale_price <dbl> 358000, 355000, 338000, 420000…unique(filter_flat_data$month) [1] "2022-01" "2022-02" "2022-03" "2022-04" "2022-05" "2022-06" "2022-07"
[8] "2022-08" "2022-09" "2022-10" "2022-11" "2022-12"unique(filter_flat_data$flat_type)[1] "3 ROOM" "4 ROOM" "5 ROOM"unique(filter_flat_data$storey_range) [1] "07 TO 09" "04 TO 06" "01 TO 03" "10 TO 12" "13 TO 15" "25 TO 27"
[7] "16 TO 18" "19 TO 21" "22 TO 24" "28 TO 30" "34 TO 36" "31 TO 33"
[13] "37 TO 39" "40 TO 42" "43 TO 45" "49 TO 51" "46 TO 48"
Critical!
storey_range is listed as a string and needed for the arrangement to be corrected moving forward
unique(filter_flat_data$floor_area_sqm) [1] 73.0 67.0 68.0 82.0 83.0 75.0 74.0 60.0 81.0 92.0 99.0 93.0
[13] 98.0 100.0 91.0 106.0 90.0 85.0 119.0 120.0 118.0 125.0 110.0 112.0
[25] 69.0 59.0 70.0 66.0 65.0 88.0 94.0 95.0 87.0 104.0 103.0 107.0
[37] 84.0 105.0 108.0 123.0 122.0 121.0 139.0 115.0 133.0 113.0 126.0 64.0
[49] 116.0 114.0 111.0 130.0 77.0 102.0 117.0 101.0 96.0 149.0 62.0 63.0
[61] 80.0 132.0 89.0 128.0 97.0 138.0 79.0 109.0 124.0 127.0 134.0 131.0
[73] 57.0 56.0 61.0 52.0 72.0 58.0 129.0 136.0 140.0 53.0 76.0 86.0
[85] 71.0 137.0 135.0 150.0 60.3 78.0 54.0 142.0 55.0 159.0 141.0 154.0
[97] 63.1 144.0 155.0 157.0 51.0 152.0 143.0unique(filter_flat_data$remaining_lease) [1] "54 years 05 months" "55 years 01 month" "58 years"
[4] "57 years 02 months" "57 years 01 month" "56 years 07 months"
[7] "56 years 01 month" "56 years 05 months" "61 years 09 months"
[10] "57 years" "56 years 06 months" "54 years 04 months"
[13] "55 years 10 months" "54 years 03 months" "54 years 09 months"
[16] "55 years 09 months" "56 years" "55 years 05 months"
[19] "58 years 04 months" "63 years 01 month" "62 years"
[22] "63 years" "57 years 06 months" "58 years 03 months"
[25] "57 years 08 months" "57 years 05 months" "56 years 08 months"
[28] "56 years 04 months" "58 years 01 month" "59 years 01 month"
[31] "57 years 11 months" "57 years 07 months" "70 years 04 months"
[34] "95 years 05 months" "83 years 04 months" "80 years 01 month"
[37] "78 years 09 months" "79 years" "88 years 08 months"
[40] "87 years 05 months" "55 years 11 months" "56 years 09 months"
[43] "54 years 07 months" "55 years 04 months" "63 years 03 months"
[46] "55 years 03 months" "59 years 08 months" "58 years 07 months"
[49] "63 years 02 months" "62 years 08 months" "64 years"
[52] "59 years 07 months" "59 years 06 months" "53 years 01 month"
[55] "55 years 08 months" "55 years" "60 years 04 months"
[58] "93 years 11 months" "49 years 07 months" "57 years 04 months"
[61] "57 years 03 months" "83 years" "82 years 11 months"
[64] "91 years 10 months" "91 years 08 months" "62 years 09 months"
[67] "62 years 07 months" "64 years 03 months" "78 years"
[70] "62 years 04 months" "62 years 03 months" "62 years 02 months"
[73] "54 years 08 months" "52 years 01 month" "60 years 07 months"
[76] "61 years" "62 years 05 months" "73 years 11 months"
[79] "91 years 09 months" "64 years 02 months" "60 years 01 month"
[82] "59 years 05 months" "61 years 08 months" "62 years 06 months"
[85] "70 years 05 months" "93 years 10 months" "66 years 04 months"
[88] "62 years 11 months" "65 years 01 month" "61 years 11 months"
[91] "50 years 11 months" "50 years 09 months" "50 years 10 months"
[94] "64 years 08 months" "65 years" "69 years 08 months"
[97] "69 years 07 months" "69 years 05 months" "69 years 04 months"
[100] "75 years 06 months" "67 years 01 month" "51 years 07 months"
[103] "64 years 10 months" "65 years 07 months" "64 years 05 months"
[106] "64 years 01 month" "64 years 07 months" "63 years 06 months"
[109] "67 years 02 months" "62 years 01 month" "62 years 10 months"
[112] "95 years 03 months" "65 years 04 months" "63 years 04 months"
[115] "65 years 06 months" "63 years 08 months" "64 years 11 months"
[118] "94 years 07 months" "61 years 07 months" "94 years 06 months"
[121] "74 years 05 months" "75 years 02 months" "75 years 07 months"
[124] "63 years 10 months" "64 years 06 months" "65 years 09 months"
[127] "82 years 02 months" "81 years 09 months" "80 years 10 months"
[130] "72 years 11 months" "74 years 11 months" "74 years 10 months"
[133] "75 years 01 month" "75 years" "82 years 01 month"
[136] "74 years 03 months" "93 years 01 month" "60 years"
[139] "60 years 08 months" "61 years 04 months" "94 years 11 months"
[142] "90 years 05 months" "90 years 04 months" "46 years 06 months"
[145] "47 years" "52 years 05 months" "58 years 09 months"
[148] "48 years 01 month" "48 years" "50 years"
[151] "90 years 02 months" "52 years 03 months" "60 years 05 months"
[154] "94 years 04 months" "53 years 03 months" "82 years 10 months"
[157] "93 years" "50 years 01 month" "47 years 06 months"
[160] "96 years 05 months" "96 years 04 months" "74 years 01 month"
[163] "80 years 08 months" "80 years 09 months" "86 years"
[166] "86 years 01 month" "80 years" "74 years 06 months"
[169] "80 years 06 months" "90 years 01 month" "78 years 02 months"
[172] "78 years 07 months" "78 years 06 months" "55 years 07 months"
[175] "94 years 05 months" "53 years 08 months" "53 years 09 months"
[178] "73 years 08 months" "79 years 11 months" "72 years 01 month"
[181] "72 years" "79 years 01 month" "65 years 11 months"
[184] "66 years" "92 years 07 months" "92 years"
[187] "89 years 11 months" "65 years 02 months" "79 years 06 months"
[190] "66 years 09 months" "94 years" "74 years 08 months"
[193] "66 years 01 month" "65 years 08 months" "75 years 04 months"
[196] "75 years 05 months" "75 years 09 months" "80 years 05 months"
[199] "66 years 07 months" "65 years 10 months" "79 years 04 months"
[202] "79 years 07 months" "89 years 10 months" "79 years 10 months"
[205] "91 years" "92 years 11 months" "92 years 10 months"
[208] "76 years 02 months" "81 years 10 months" "90 years 11 months"
[211] "79 years 05 months" "78 years 10 months" "66 years 05 months"
[214] "66 years 03 months" "75 years 10 months" "76 years 05 months"
[217] "75 years 11 months" "79 years 03 months" "92 years 06 months"
[220] "78 years 11 months" "53 years 04 months" "51 years 04 months"
[223] "66 years 06 months" "54 years 11 months" "56 years 03 months"
[226] "61 years 06 months" "54 years 02 months" "54 years 01 month"
[229] "88 years 01 month" "88 years" "60 years 10 months"
[232] "61 years 01 month" "94 years 09 months" "93 years 06 months"
[235] "90 years 07 months" "95 years 02 months" "95 years 04 months"
[238] "61 years 10 months" "94 years 03 months" "67 years 05 months"
[241] "76 years 06 months" "67 years 07 months" "69 years 06 months"
[244] "77 years 03 months" "94 years 02 months" "94 years 10 months"
[247] "76 years 10 months" "77 years" "80 years 07 months"
[250] "67 years 03 months" "67 years 09 months" "72 years 05 months"
[253] "73 years 05 months" "73 years 03 months" "95 years 01 month"
[256] "66 years 02 months" "76 years 07 months" "76 years 04 months"
[259] "93 years 05 months" "80 years 02 months" "76 years 11 months"
[262] "76 years 09 months" "80 years 04 months" "79 years 08 months"
[265] "70 years 06 months" "73 years 04 months" "71 years 10 months"
[268] "71 years 04 months" "72 years 08 months" "94 years 08 months"
[271] "61 years 03 months" "56 years 02 months" "90 years 09 months"
[274] "55 years 06 months" "95 years 08 months" "91 years 11 months"
[277] "56 years 11 months" "57 years 10 months" "57 years 09 months"
[280] "90 years 08 months" "58 years 08 months" "56 years 10 months"
[283] "52 years 07 months" "50 years 07 months" "44 years"
[286] "46 years 01 month" "44 years 01 month" "46 years"
[289] "84 years 01 month" "59 years" "53 years 06 months"
[292] "49 years 08 months" "77 years 05 months" "58 years 06 months"
[295] "59 years 04 months" "88 years 11 months" "82 years 03 months"
[298] "67 years 06 months" "83 years 11 months" "77 years 06 months"
[301] "89 years 05 months" "60 years 02 months" "52 years 06 months"
[304] "60 years 03 months" "70 years 03 months" "63 years 07 months"
[307] "63 years 05 months" "63 years 09 months" "93 years 03 months"
[310] "66 years 08 months" "64 years 09 months" "74 years 04 months"
[313] "93 years 02 months" "74 years 07 months" "69 years 10 months"
[316] "69 years 09 months" "76 years 03 months" "92 years 05 months"
[319] "60 years 09 months" "66 years 11 months" "76 years 01 month"
[322] "60 years 06 months" "61 years 05 months" "58 years 05 months"
[325] "58 years 11 months" "59 years 09 months" "52 years 10 months"
[328] "52 years 11 months" "52 years 09 months" "91 years 05 months"
[331] "91 years 04 months" "53 years 11 months" "51 years 10 months"
[334] "49 years 01 month" "89 years 03 months" "87 years 03 months"
[337] "92 years 01 month" "92 years 04 months" "79 years 02 months"
[340] "67 years 11 months" "66 years 10 months" "78 years 03 months"
[343] "78 years 01 month" "79 years 09 months" "77 years 07 months"
[346] "77 years 08 months" "77 years 11 months" "78 years 04 months"
[349] "68 years 01 month" "67 years 08 months" "83 years 05 months"
[352] "89 years 02 months" "87 years 04 months" "77 years 09 months"
[355] "61 years 02 months" "81 years 04 months" "77 years 10 months"
[358] "85 years 06 months" "67 years 04 months" "73 years 02 months"
[361] "51 years 09 months" "81 years 06 months" "53 years 10 months"
[364] "47 years 05 months" "51 years 01 month" "95 years"
[367] "59 years 03 months" "51 years" "49 years"
[370] "80 years 03 months" "64 years 04 months" "82 years"
[373] "51 years 02 months" "87 years 11 months" "53 years 07 months"
[376] "81 years 05 months" "77 years 04 months" "81 years 07 months"
[379] "52 years 04 months" "53 years" "72 years 03 months"
[382] "72 years 06 months" "73 years 07 months" "72 years 07 months"
[385] "70 years" "69 years 11 months" "71 years"
[388] "70 years 08 months" "72 years 10 months" "73 years 09 months"
[391] "72 years 04 months" "70 years 07 months" "70 years 09 months"
[394] "72 years 02 months" "71 years 03 months" "73 years 10 months"
[397] "91 years 03 months" "91 years 06 months" "81 years"
[400] "80 years 11 months" "85 years" "90 years"
[403] "89 years 07 months" "93 years 08 months" "84 years 11 months"
[406] "86 years 03 months" "88 years 03 months" "90 years 03 months"
[409] "90 years 06 months" "89 years 06 months" "88 years 02 months"
[412] "89 years 09 months" "84 years 07 months" "92 years 03 months"
[415] "93 years 09 months" "81 years 08 months" "47 years 07 months"
[418] "93 years 04 months" "93 years 07 months" "51 years 05 months"
[421] "47 years 01 month" "50 years 05 months" "51 years 06 months"
[424] "83 years 07 months" "83 years 10 months" "83 years 08 months"
[427] "88 years 07 months" "85 years 07 months" "89 years 08 months"
[430] "83 years 06 months" "85 years 08 months" "78 years 08 months"
[433] "81 years 03 months" "87 years 08 months" "78 years 05 months"
[436] "83 years 01 month" "92 years 09 months" "92 years 08 months"
[439] "84 years 08 months" "88 years 10 months" "85 years 05 months"
[442] "83 years 02 months" "86 years 07 months" "86 years 06 months"
[445] "86 years 05 months" "81 years 11 months" "87 years"
[448] "86 years 11 months" "91 years 07 months" "76 years 08 months"
[451] "71 years 11 months" "65 years 03 months" "73 years 06 months"
[454] "72 years 09 months" "60 years 11 months" "71 years 02 months"
[457] "85 years 11 months" "73 years" "74 years 02 months"
[460] "48 years 07 months" "44 years 06 months" "45 years 06 months"
[463] "86 years 04 months" "89 years 04 months" "74 years 09 months"
[466] "52 years 08 months" "59 years 10 months" "54 years 06 months"
[469] "75 years 03 months" "77 years 02 months" "81 years 01 month"
[472] "71 years 06 months" "58 years 02 months" "76 years"
[475] "77 years 01 month" "90 years 10 months" "70 years 02 months"
[478] "92 years 02 months" "65 years 05 months" "63 years 11 months"
[481] "53 years 02 months" "55 years 02 months" "54 years"
[484] "49 years 06 months" "52 years" "50 years 08 months"
[487] "69 years 03 months" "67 years" "49 years 11 months"
[490] "52 years 02 months" "82 years 09 months" "54 years 10 months"
[493] "75 years 08 months" "51 years 03 months" "94 years 01 month"
[496] "74 years" "43 years 11 months" "49 years 10 months"
[499] "45 years 11 months" "81 years 02 months" "95 years 06 months"
[502] "67 years 10 months" "49 years 05 months" "53 years 05 months"
[505] "70 years 11 months" "70 years 10 months" "87 years 07 months"
[508] "91 years 02 months" "86 years 02 months" "47 years 04 months"
[511] "51 years 11 months" "46 years 11 months" "47 years 08 months"
[514] "87 years 06 months" "88 years 09 months" "87 years 02 months"
[517] "45 years 05 months" "44 years 05 months" "46 years 04 months"
[520] "73 years 01 month" "71 years 05 months" "58 years 10 months"
[523] "88 years 06 months" "88 years 05 months" "59 years 11 months"
[526] "69 years 02 months" "47 years 10 months" "96 years 03 months"
[529] "48 years 04 months" "44 years 03 months" "46 years 05 months"
[532] "46 years 10 months" "82 years 07 months" "85 years 10 months"
[535] "82 years 08 months" "48 years 03 months" "59 years 02 months"
[538] "87 years 10 months" "71 years 09 months" "47 years 11 months"
[541] "45 years 10 months" "83 years 09 months" "70 years 01 month"
[544] "68 years 02 months" "89 years 01 month" "87 years 01 month"
[547] "85 years 04 months" "85 years 03 months" "83 years 03 months"
[550] "48 years 10 months" "91 years 01 month" "84 years 10 months"
[553] "84 years 06 months" "84 years 05 months" "43 years 10 months"
[556] "89 years" "86 years 10 months" "71 years 08 months"
[559] "85 years 09 months" "50 years 04 months" "45 years 04 months"
[562] "48 years 11 months" "49 years 04 months" "44 years 04 months"
[565] "46 years 03 months" "88 years 04 months" "68 years 11 months"
[568] "49 years 09 months" "47 years 09 months" "44 years 02 months"
[571] "47 years 03 months" "96 years 02 months" "87 years 09 months"
[574] "71 years 01 month" "71 years 07 months" "50 years 03 months"
[577] "43 years 09 months" "45 years 09 months" "84 years 09 months"
[580] "50 years 02 months" "84 years 04 months" "85 years 02 months"
[583] "45 years 03 months" "49 years 03 months" "46 years 09 months"
[586] "45 years 02 months" "50 years 06 months" "69 years"
[589] "46 years 08 months" "96 years" "82 years 06 months"
[592] "96 years 01 month" "45 years 08 months" "68 years 04 months"
[595] "85 years 01 month" "48 years 08 months" "47 years 02 months"
[598] "51 years 08 months" "84 years 03 months" "48 years 02 months"
[601] "45 years 01 month" "48 years 09 months" "69 years 01 month"
[604] "95 years 07 months" "46 years 02 months" "82 years 05 months"
[607] "45 years 07 months" "49 years 02 months" "43 years 08 months"
[610] "86 years 09 months" "46 years 07 months" "68 years 10 months"
[613] "43 years 07 months" "43 years 06 months" "68 years 03 months"
[616] "84 years 02 months" "48 years 06 months" "45 years"
[619] "68 years 09 months" "95 years 10 months" "82 years 04 months"
[622] "48 years 05 months" "84 years" "44 years 11 months"
[625] "86 years 08 months" "68 years 08 months" "95 years 09 months"
[628] "43 years 05 months" "43 years 04 months" "68 years"
[631] "68 years 07 months" "68 years 06 months" "43 years 03 months"
[634] "44 years 09 months" "68 years 05 months" "43 years 02 months"
[637] "43 years 01 month" "44 years 07 months"
Critical!
remaining lease is listed as a string and needed for the arrangement to be corrected moving foreward
summary(filter_flat_data$resale_price) Min. 1st Qu. Median Mean 3rd Qu. Max.
200000 428000 515000 536391 610000 1418000 Correcting the Data
This section deals with correcting the data last inspected previously.
Correcting the Storey Arrangement
The code adds corrects the order of the storey arrangement
storey_correct <- c("01 TO 03", "04 TO 06", "07 TO 09", "10 TO 12", "13 TO 15", "16 TO 18", "19 TO 21", "22 TO 24", "25 TO 27", "28 TO 30", "31 TO 33", "34 TO 36", "37 TO 39", "40 TO 42", "43 TO 45", "46 TO 48", "49 TO 51")
use_flat_data <- filter_flat_data %>%
mutate (storey_range = factor(storey_range, levels = storey_correct)) %>%
ungroup()ggplot(data = use_flat_data,
aes(y = storey_range)) +
geom_bar() 
Converting Remaining Lease from String to Num
The code adds a new integer column derived from the remaining_lease string column.
Note
This code uses the gsub-regex function to get the integer within the string.
lease_ryear <- as.numeric(gsub("([0-9]+).*$", "\\1", filter_flat_data$remaining_lease))
use_flat_data$lease_ryear <- lease_ryear
use_flat_data# A tibble: 24,374 × 12
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <dbl> <chr>
1 2022-01 ANG MO… 3 ROOM 320 ANG MO… 07 TO … 73 New Ge… 1977 54 yea…
2 2022-01 ANG MO… 3 ROOM 225 ANG MO… 07 TO … 67 New Ge… 1978 55 yea…
3 2022-01 ANG MO… 3 ROOM 331 ANG MO… 07 TO … 68 New Ge… 1981 58 yea…
4 2022-01 ANG MO… 3 ROOM 534 ANG MO… 07 TO … 82 New Ge… 1980 57 yea…
5 2022-01 ANG MO… 3 ROOM 578 ANG MO… 04 TO … 67 New Ge… 1980 57 yea…
6 2022-01 ANG MO… 3 ROOM 452 ANG MO… 01 TO … 83 New Ge… 1979 56 yea…
7 2022-01 ANG MO… 3 ROOM 560 ANG MO… 01 TO … 67 New Ge… 1980 57 yea…
8 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
9 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
10 2022-01 ANG MO… 3 ROOM 560 ANG MO… 10 TO … 67 New Ge… 1980 57 yea…
# … with 24,364 more rows, 2 more variables: resale_price <dbl>,
# lease_ryear <dbl>, and abbreviated variable names ¹flat_type, ²street_name,
# ³storey_range, ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date,
# ⁷remaining_leaseData Exploration through Statistical Visualization
This section presents the statistical visualization and explanation of the data.
Analysis on the Type of Flat
This section presents insights with regards to the relationship between Type of Flat and other variables.
One-way ANOVA Test on Type of Flat and Resale Price
Code
ggbetweenstats(
data = use_flat_data,
x = flat_type,
y = resale_price,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)
Coming from One-way ANOVA test between Type of Flat and the Resale Price, overall p-value of the ANOVA test is 0.00 < 0.05 means that we can reject the null hypotheses meaning each flat type have different mean resale price coming from the ANOVA hypothesis:
Ho = there’s no difference between the means and conclude that a significant difference does not exist.
Ha = there’s difference between the means and conclude that a significant difference does exist.
Pairwise P-Test also concluded the following:
| Relation | P-Value | Remark |
|---|---|---|
| 3 Room - 4 Room | 3.25 e-08 < 0.05 | Reject null hypothesis, Significant difference |
| 3 Room - 5 Room | 0 < 0.05 | Reject null hypothesis, Significant difference |
| 4 Room - 5 Room | 0 < 0.05 | Reject null hypothesis, Significant difference |
P-Value = 0
Please note that P-Value cannot be equals to 0, the 0 value is a reference that p-value is very close to 0
It can also be noted that in 2022, around 46% (n = 11,312 rooms) of the total scoped rooms belong to the 4 Room category and with 54% of the rooms split almost evenly between 3 and 5 Room categories.
One-way ANOVA Test on Type of Flat and Floor Sqr Area
Code
ggbetweenstats(
data = use_flat_data,
x = flat_type,
y = floor_area_sqm,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)
Coming from One-way ANOVA test between Type of Flat and the Floor Area, overall p-value of the ANOVA test is 0.00 < 0.05 means that we can reject the null hypotheses meaning each flat type have different mean resale price coming from the ANOVA hypothesis:
Ho = there’s no difference between the means and conclude that a significant difference does not exist.
Ha = there’s difference between the means and conclude that a significant difference does exist.
Pairwise P-Test also concluded the following:
| Relation | P-Value | Remark |
|---|---|---|
| 3 Room - 4 Room | 1.19 e-08 < 0.05 | Reject null hypothesis, Significant difference |
| 3 Room - 5 Room | 0 < 0.05 | Reject null hypothesis, Significant difference |
| 4 Room - 5 Room | 1.07 e-09 < 0.05 | Reject null hypothesis, Significant difference |
Correlation between Resale Price, Remaining Lease Years and Floor Area
Code
ggstatsplot::ggcorrmat(
data = use_flat_data,
cor.vars = c(resale_price, lease_ryear, floor_area_sqm)
)
Based on the correlation coefficients, there is moderate correlation between resale price and floor area and resale price and remaining lease year while weak correlation between the floor area and remaining lease year. This concludes that there is a relationship between resale price and remaining lease year and resale price and floor area though this does not mean causality between both relationship.
Model Diagnostic
Code
model <- lm(resale_price ~ flat_type + lease_ryear + floor_area_sqm, data = use_flat_data)
check_model(model)Variable `Component` is not in your data frame :/
Analysis on 3 Room Flats
room_3 <- filter(use_flat_data, grepl("3 ROOM", flat_type))
room_3# A tibble: 6,346 × 12
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <dbl> <chr>
1 2022-01 ANG MO… 3 ROOM 320 ANG MO… 07 TO … 73 New Ge… 1977 54 yea…
2 2022-01 ANG MO… 3 ROOM 225 ANG MO… 07 TO … 67 New Ge… 1978 55 yea…
3 2022-01 ANG MO… 3 ROOM 331 ANG MO… 07 TO … 68 New Ge… 1981 58 yea…
4 2022-01 ANG MO… 3 ROOM 534 ANG MO… 07 TO … 82 New Ge… 1980 57 yea…
5 2022-01 ANG MO… 3 ROOM 578 ANG MO… 04 TO … 67 New Ge… 1980 57 yea…
6 2022-01 ANG MO… 3 ROOM 452 ANG MO… 01 TO … 83 New Ge… 1979 56 yea…
7 2022-01 ANG MO… 3 ROOM 560 ANG MO… 01 TO … 67 New Ge… 1980 57 yea…
8 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
9 2022-01 ANG MO… 3 ROOM 435 ANG MO… 04 TO … 67 New Ge… 1979 56 yea…
10 2022-01 ANG MO… 3 ROOM 560 ANG MO… 10 TO … 67 New Ge… 1980 57 yea…
# … with 6,336 more rows, 2 more variables: resale_price <dbl>,
# lease_ryear <dbl>, and abbreviated variable names ¹flat_type, ²street_name,
# ³storey_range, ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date,
# ⁷remaining_leaseOne-way ANOVA Test on Town and Resale Price
Code
ggbetweenstats(
data = room_3,
x = town,
y = resale_price,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)Warning: Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
res.aov <- aov(resale_price~town, data = room_3)
summary (res.aov) Df Sum Sq Mean Sq F value Pr(>F)
town 25 6.746e+12 2.699e+11 43.27 <2e-16 ***
Residuals 6320 3.942e+13 6.237e+09
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(res.aov) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = resale_price ~ town, data = room_3)
$town
diff lwr upr p adj
BEDOK-ANG MO KIO -18620.3726 -36284.7679 -955.9773 0.0248933
BISHAN-ANG MO KIO 28273.3450 -9921.2322 66467.9221 0.5371259
BUKIT BATOK-ANG MO KIO -15676.8676 -37133.0345 5779.2993 0.5659993
BUKIT MERAH-ANG MO KIO 60593.6676 40297.5004 80889.8348 0.0000000
BUKIT PANJANG-ANG MO KIO -4396.5473 -34850.5958 26057.5011 1.0000000
BUKIT TIMAH-ANG MO KIO 54741.7603 -26833.4109 136316.9316 0.7379320
CENTRAL AREA-ANG MO KIO 68699.3131 34911.0399 102487.5864 0.0000000
CHOA CHU KANG-ANG MO KIO 4861.0927 -26828.0770 36550.2623 1.0000000
CLEMENTI-ANG MO KIO 42478.2353 20895.5975 64060.8730 0.0000000
GEYLANG-ANG MO KIO -31842.7295 -52570.7040 -11114.7549 0.0000053
HOUGANG-ANG MO KIO 836.9030 -20126.7331 21800.5391 1.0000000
JURONG EAST-ANG MO KIO -17969.7384 -43153.6616 7214.1849 0.6172205
JURONG WEST-ANG MO KIO -31329.1798 -51894.3527 -10764.0068 0.0000069
KALLANG/WHAMPOA-ANG MO KIO 61021.8457 40924.0372 81119.6543 0.0000000
MARINE PARADE-ANG MO KIO 38314.0790 5333.6982 71294.4598 0.0052470
PASIR RIS-ANG MO KIO 97581.9765 32926.1656 162237.7873 0.0000093
PUNGGOL-ANG MO KIO 57573.7158 33827.6516 81319.7801 0.0000000
QUEENSTOWN-ANG MO KIO 60308.2460 39899.1626 80717.3293 0.0000000
SEMBAWANG-ANG MO KIO 46015.3881 7006.7132 85024.0631 0.0039681
SENGKANG-ANG MO KIO 50753.9583 25570.0350 75937.8815 0.0000000
SERANGOON-ANG MO KIO 3423.2353 -29403.8988 36250.3693 1.0000000
TAMPINES-ANG MO KIO 27478.7721 8005.6287 46951.9155 0.0000669
TOA PAYOH-ANG MO KIO -3272.2922 -22701.8498 16157.2654 1.0000000
WOODLANDS-ANG MO KIO -16066.6297 -38591.1261 6457.8667 0.6179462
YISHUN-ANG MO KIO -5407.6457 -23199.4038 12384.1124 0.9999965
BISHAN-BEDOK 46893.7175 8801.1125 84986.3226 0.0017246
BUKIT BATOK-BEDOK 2943.5049 -18330.6093 24217.6191 1.0000000
BUKIT MERAH-BEDOK 79214.0402 59110.4275 99317.6528 0.0000000
BUKIT PANJANG-BEDOK 14223.8252 -16102.2346 44549.8851 0.9939382
BUKIT TIMAH-BEDOK 73362.1329 -8165.3434 154889.6093 0.1532958
CENTRAL AREA-BEDOK 87319.6857 53646.7255 120992.6458 0.0000000
CHOA CHU KANG-BEDOK 23481.4652 -8084.7241 55047.6546 0.5262089
CLEMENTI-BEDOK 61098.6078 39696.9470 82500.2687 0.0000000
GEYLANG-BEDOK -13222.3569 -33761.8254 7317.1116 0.8060836
HOUGANG-BEDOK 19457.2756 -1319.9927 40234.5438 0.1045683
JURONG EAST-BEDOK 650.6342 -24378.3655 25679.6339 1.0000000
JURONG WEST-BEDOK -12708.8072 -33083.9680 7666.3536 0.8494961
KALLANG/WHAMPOA-BEDOK 79642.2183 59738.8833 99545.5533 0.0000000
MARINE PARADE-BEDOK 56934.4516 24072.2186 89796.6846 0.0000001
PASIR RIS-BEDOK 116202.3490 51606.7245 180797.9736 0.0000000
PUNGGOL-BEDOK 76194.0884 52612.3920 99775.7848 0.0000000
QUEENSTOWN-BEDOK 78928.6185 58711.0143 99146.2228 0.0000000
SEMBAWANG-BEDOK 64635.7607 25726.9242 103544.5972 0.0000003
SENGKANG-BEDOK 69374.3308 44345.3311 94403.3306 0.0000000
SERANGOON-BEDOK 22043.6078 -10664.8248 54752.0405 0.7302170
TAMPINES-BEDOK 46099.1447 26826.7772 65371.5122 0.0000000
TOA PAYOH-BEDOK 15348.0804 -3880.2463 34576.4070 0.3698323
WOODLANDS-BEDOK 2553.7429 -19797.4042 24904.8900 1.0000000
YISHUN-BEDOK 13212.7269 -4359.0543 30784.5080 0.5021302
BUKIT BATOK-BISHAN -43950.2126 -83942.4968 -3957.9284 0.0133576
BUKIT MERAH-BISHAN 32320.3226 -7061.7812 71702.4264 0.3118034
BUKIT PANJANG-BISHAN -32669.8923 -78128.2773 12788.4927 0.6019101
BUKIT TIMAH-BISHAN 26468.4154 -61812.5240 114749.3548 0.9999973
CENTRAL AREA-BISHAN 40425.9682 -7330.3205 88182.2568 0.2519136
CHOA CHU KANG-BISHAN -23412.2523 -69707.1675 22882.6629 0.9834724
CLEMENTI-BISHAN 14204.8903 -25855.3886 54265.1693 0.9999347
GEYLANG-BISHAN -60116.0744 -99722.4455 -20509.7034 0.0000078
HOUGANG-BISHAN -27436.4420 -67166.6539 12293.7699 0.6841679
JURONG EAST-BISHAN -46243.0833 -88353.0072 -4133.1595 0.0135144
JURONG WEST-BISHAN -59602.5247 -99123.9371 -20081.1123 0.0000095
KALLANG/WHAMPOA-BISHAN 32748.5008 -6531.7437 72028.7452 0.2806273
MARINE PARADE-BISHAN 10040.7341 -37147.4129 57228.8811 1.0000000
PASIR RIS-BISHAN 69308.6315 -3625.5140 142242.7770 0.0897604
PUNGGOL-BISHAN 29300.3709 -11965.7323 70566.4740 0.6276142
QUEENSTOWN-BISHAN 32034.9010 -7405.5146 71475.3166 0.3330134
SEMBAWANG-BISHAN 17742.0432 -33839.8570 69323.9433 0.9999626
SENGKANG-BISHAN 22480.6133 -19629.3106 64590.5372 0.9681761
SERANGOON-BISHAN -24850.1097 -71931.2782 22231.0588 0.9721390
TAMPINES-BISHAN -794.5728 -39758.9028 38169.7572 1.0000000
TOA PAYOH-BISHAN -31545.6372 -70488.2026 7396.9283 0.3386057
WOODLANDS-BISHAN -44339.9746 -84915.4430 -3764.5062 0.0145896
YISHUN-BISHAN -33680.9906 -71832.8235 4470.8423 0.1810828
BUKIT MERAH-BUKIT BATOK 76270.5352 52765.3980 99775.6725 0.0000000
BUKIT PANJANG-BUKIT BATOK 11280.3203 -21400.0430 43960.6835 0.9999601
BUKIT TIMAH-BUKIT BATOK 70418.6280 -12013.5601 152850.8161 0.2354481
CENTRAL AREA-BUKIT BATOK 84376.1807 48568.2978 120184.0637 0.0000000
CHOA CHU KANG-BUKIT BATOK 20537.9603 -13296.3499 54372.2705 0.8810195
CLEMENTI-BUKIT BATOK 58155.1029 33530.5760 82779.6298 0.0000000
GEYLANG-BUKIT BATOK -16165.8618 -40044.8485 7713.1248 0.7219671
HOUGANG-BUKIT BATOK 16513.7706 -7570.0645 40597.6057 0.6979668
JURONG EAST-BUKIT BATOK -2292.8707 -30127.8678 25542.1264 1.0000000
JURONG WEST-BUKIT BATOK -15652.3121 -39390.1177 8085.4934 0.7682176
KALLANG/WHAMPOA-BUKIT BATOK 76698.7133 53364.6399 100032.7868 0.0000000
MARINE PARADE-BUKIT BATOK 53990.9467 18944.3692 89037.5241 0.0000048
PASIR RIS-BUKIT BATOK 113258.8441 47525.0556 178992.6326 0.0000001
PUNGGOL-BUKIT BATOK 73250.5834 46709.4335 99791.7334 0.0000000
QUEENSTOWN-BUKIT BATOK 75985.1136 52382.4070 99587.8202 0.0000000
SEMBAWANG-BUKIT BATOK 61692.2558 20921.7542 102462.7573 0.0000086
SENGKANG-BUKIT BATOK 66430.8259 38595.8288 94265.8230 0.0000000
SERANGOON-BUKIT BATOK 19100.1029 -15802.3011 54002.5069 0.9578702
TAMPINES-BUKIT BATOK 43155.6398 20357.3857 65953.8938 0.0000000
TOA PAYOH-BUKIT BATOK 12404.5754 -10356.4611 35165.6119 0.9597580
WOODLANDS-BUKIT BATOK -389.7620 -25843.8386 25064.3145 1.0000000
YISHUN-BUKIT BATOK 10269.2220 -11110.7624 31649.2063 0.9915860
BUKIT PANJANG-BUKIT MERAH -64990.2149 -96920.9751 -33059.4547 0.0000000
BUKIT TIMAH-BUKIT MERAH -5851.9072 -87989.7970 76285.9826 1.0000000
CENTRAL AREA-BUKIT MERAH 8105.6455 -27019.4422 43230.7333 1.0000000
CHOA CHU KANG-BUKIT MERAH -55732.5749 -88843.4168 -22621.7330 0.0000002
CLEMENTI-BUKIT MERAH -18115.4323 -41736.0723 5505.2076 0.4578283
GEYLANG-BUKIT MERAH -92436.3970 -115278.7548 -69594.0393 0.0000000
HOUGANG-BUKIT MERAH -59756.7646 -82813.1828 -36700.3464 0.0000000
JURONG EAST-BUKIT MERAH -78563.4059 -105514.3683 -51612.4436 0.0000000
JURONG WEST-BUKIT MERAH -91922.8474 -114617.5762 -69228.1185 0.0000000
KALLANG/WHAMPOA-BUKIT MERAH 428.1781 -21843.9185 22700.2748 1.0000000
MARINE PARADE-BUKIT MERAH -22279.5886 -56628.2406 12069.0635 0.7946202
PASIR RIS-BUKIT MERAH 36988.3089 -28376.0411 102352.6589 0.9396362
PUNGGOL-BUKIT MERAH -3019.9518 -28632.4477 22592.5442 1.0000000
QUEENSTOWN-BUKIT MERAH -285.4216 -22838.8041 22267.9609 1.0000000
SEMBAWANG-BUKIT MERAH -14578.2794 -54750.4228 25593.8639 0.9999009
SENGKANG-BUKIT MERAH -9839.7093 -36790.6716 17111.2530 0.9998897
SERANGOON-BUKIT MERAH -57170.4323 -91371.9690 -22968.8956 0.0000003
TAMPINES-BUKIT MERAH -33114.8954 -54824.9782 -11404.8127 0.0000066
TOA PAYOH-BUKIT MERAH -63865.9598 -85536.9562 -42194.9633 0.0000000
WOODLANDS-BUKIT MERAH -76660.2972 -101144.5231 -52176.0713 0.0000000
YISHUN-BUKIT MERAH -66001.3133 -86216.9269 -45785.6996 0.0000000
BUKIT TIMAH-BUKIT PANJANG 59138.3077 -26079.9275 144356.5429 0.6744804
CENTRAL AREA-BUKIT PANJANG 73095.8605 31271.3885 114920.3324 0.0000000
CHOA CHU KANG-BUKIT PANJANG 9257.6400 -30890.1186 49405.3986 1.0000000
CLEMENTI-BUKIT PANJANG 46874.7826 14111.2465 79638.3187 0.0000456
GEYLANG-BUKIT PANJANG -27446.1821 -59653.1375 4760.7732 0.2397921
HOUGANG-BUKIT PANJANG 5233.4503 -27125.6764 37592.5771 1.0000000
JURONG EAST-BUKIT PANJANG -13573.1910 -48813.3532 21666.9712 0.9997196
JURONG WEST-BUKIT PANJANG -26932.6324 -59035.0526 5169.7878 0.2686211
KALLANG/WHAMPOA-BUKIT PANJANG 65418.3931 33613.3470 97223.4391 0.0000000
MARINE PARADE-BUKIT PANJANG 42710.6264 1536.0643 83885.1885 0.0311163
PASIR RIS-BUKIT PANJANG 101978.5238 32783.0549 171173.9927 0.0000193
PUNGGOL-BUKIT PANJANG 61970.2632 27742.8675 96197.6588 0.0000000
QUEENSTOWN-BUKIT PANJANG 64704.7933 32702.1414 96707.4452 0.0000000
SEMBAWANG-BUKIT PANJANG 50411.9355 4267.4259 96556.4451 0.0146514
SENGKANG-BUKIT PANJANG 55150.5056 19910.3434 90390.6679 0.0000029
SERANGOON-BUKIT PANJANG 7819.7826 -33232.1330 48871.6982 1.0000000
TAMPINES-BUKIT PANJANG 31875.3195 461.2723 63289.3666 0.0416304
TOA PAYOH-BUKIT PANJANG 1124.2552 -30262.7924 32511.3027 1.0000000
WOODLANDS-BUKIT PANJANG -11670.0823 -45061.5780 21721.4134 0.9999499
YISHUN-BUKIT PANJANG -1011.0983 -31411.5210 29389.3244 1.0000000
CENTRAL AREA-BUKIT TIMAH 13957.5528 -72508.3130 100423.4186 1.0000000
CHOA CHU KANG-BUKIT TIMAH -49880.6677 -135548.0595 35786.7241 0.9194632
CLEMENTI-BUKIT TIMAH -12263.5251 -94728.7225 70201.6724 1.0000000
GEYLANG-BUKIT TIMAH -86584.4898 -168830.1430 -4338.8366 0.0253535
HOUGANG-BUKIT TIMAH -53904.8574 -136210.2191 28400.5044 0.7793168
JURONG EAST-BUKIT TIMAH -72711.4987 -156191.6073 10768.6099 0.2023762
JURONG WEST-BUKIT TIMAH -86070.9401 -168275.7142 -3866.1661 0.0273677
KALLANG/WHAMPOA-BUKIT TIMAH 6280.0854 -75809.0153 88369.1860 1.0000000
MARINE PARADE-BUKIT TIMAH -16427.6813 -102581.0565 69725.6939 1.0000000
PASIR RIS-BUKIT TIMAH 42840.2161 -59702.8110 145383.2432 0.9989278
PUNGGOL-BUKIT TIMAH 2831.9555 -80225.6998 85889.6108 1.0000000
QUEENSTOWN-BUKIT TIMAH 5566.4856 -76599.3785 87732.3497 1.0000000
SEMBAWANG-BUKIT TIMAH -8726.3722 -97362.5683 79909.8239 1.0000000
SENGKANG-BUKIT TIMAH -3987.8021 -87467.9106 79492.3065 1.0000000
SERANGOON-BUKIT TIMAH -51318.5251 -137413.3523 34776.3021 0.8992254
TAMPINES-BUKIT TIMAH -27262.9882 -109201.3910 54675.4146 0.9999799
TOA PAYOH-BUKIT TIMAH -58014.0525 -139942.1078 23914.0028 0.6333007
WOODLANDS-BUKIT TIMAH -70808.3900 -153525.0841 11908.3041 0.2317839
YISHUN-BUKIT TIMAH -60149.4060 -141704.5726 21405.7605 0.5452882
CHOA CHU KANG-CENTRAL AREA -63838.2205 -106570.4198 -21106.0211 0.0000128
CLEMENTI-CENTRAL AREA -26221.0779 -62104.8853 9662.7296 0.5657722
GEYLANG-CENTRAL AREA -100542.0426 -135918.3950 -65165.6901 0.0000000
HOUGANG-CENTRAL AREA -67862.4101 -103377.3566 -32347.4636 0.0000000
JURONG EAST-CENTRAL AREA -86669.0515 -124827.5024 -48510.6006 0.0000000
JURONG WEST-CENTRAL AREA -100028.4929 -135309.7021 -64747.2837 0.0000000
KALLANG/WHAMPOA-CENTRAL AREA -7677.4674 -42688.3128 27333.3780 1.0000000
MARINE PARADE-CENTRAL AREA -30385.2341 -74083.5510 13313.0829 0.6705238
PASIR RIS-CENTRAL AREA 28882.6633 -41843.6490 99608.9757 0.9992564
PUNGGOL-CENTRAL AREA -11125.5973 -48350.7629 26099.5683 0.9999975
QUEENSTOWN-CENTRAL AREA -8391.0672 -43581.5214 26799.3871 1.0000000
SEMBAWANG-CENTRAL AREA -22683.9250 -71093.7805 25725.9305 0.9940190
SENGKANG-CENTRAL AREA -17945.3548 -56103.8058 20213.0961 0.9937075
SERANGOON-CENTRAL AREA -65276.0779 -108858.8510 -21693.3047 0.0000118
TAMPINES-CENTRAL AREA -41220.5410 -75876.5751 -6564.5069 0.0033972
TOA PAYOH-CENTRAL AREA -71971.6053 -106603.1674 -37340.0432 0.0000000
WOODLANDS-CENTRAL AREA -84765.9428 -121224.0053 -48307.8802 0.0000000
YISHUN-CENTRAL AREA -74106.9588 -107846.9061 -40367.0115 0.0000000
CLEMENTI-CHOA CHU KANG 37617.1426 3702.4894 71531.7958 0.0115315
GEYLANG-CHOA CHU KANG -36703.8221 -70081.0955 -3326.5487 0.0132248
HOUGANG-CHOA CHU KANG -4024.1897 -37548.3226 29499.9433 1.0000000
JURONG EAST-CHOA CHU KANG -22830.8310 -59143.6860 13482.0240 0.8392466
JURONG WEST-CHOA CHU KANG -36190.2724 -69466.6873 -2913.8575 0.0157059
KALLANG/WHAMPOA-CHOA CHU KANG 56160.7531 23171.1281 89150.3781 0.0000001
MARINE PARADE-CHOA CHU KANG 33452.9864 -8643.3195 75549.2923 0.3793682
PASIR RIS-CHOA CHU KANG 92720.8838 22972.9998 162468.7678 0.0003110
PUNGGOL-CHOA CHU KANG 52712.6232 17381.7724 88043.4739 0.0000133
QUEENSTOWN-CHOA CHU KANG 55447.1533 22266.9765 88627.3301 0.0000003
SEMBAWANG-CHOA CHU KANG 41154.2955 -5814.5258 88123.1168 0.1927931
SENGKANG-CHOA CHU KANG 45892.8656 9580.0106 82205.7206 0.0009912
SERANGOON-CHOA CHU KANG -1437.8574 -43414.2100 40538.4952 1.0000000
TAMPINES-CHOA CHU KANG 22617.6795 -9995.1518 55230.5107 0.6757472
TOA PAYOH-CHOA CHU KANG -8133.3848 -40720.2098 24453.4401 0.9999999
WOODLANDS-CHOA CHU KANG -20927.7223 -55449.4023 13593.9577 0.8824086
YISHUN-CHOA CHU KANG -10268.7383 -41906.3758 21368.8992 0.9999875
GEYLANG-CLEMENTI -74320.9647 -98313.6544 -50328.2751 0.0000000
HOUGANG-CLEMENTI -41641.3323 -65837.9078 -17444.7568 0.0000001
JURONG EAST-CLEMENTI -60447.9736 -88380.5750 -32515.3723 0.0000000
JURONG WEST-CLEMENTI -73807.4150 -97659.5966 -49955.2334 0.0000000
KALLANG/WHAMPOA-CLEMENTI 18543.6105 -4906.8082 41994.0291 0.3901003
MARINE PARADE-CLEMENTI -4164.1562 -39288.3039 30959.9914 1.0000000
PASIR RIS-CLEMENTI 55103.7412 -10671.4373 120878.9197 0.2713411
PUNGGOL-CLEMENTI 15095.4805 -11548.0139 41738.9750 0.9388630
QUEENSTOWN-CLEMENTI 17830.0107 -5887.7234 41547.7448 0.5026374
SEMBAWANG-CLEMENTI 3537.1529 -37300.0477 44374.3535 1.0000000
SENGKANG-CLEMENTI 8275.7230 -19656.8783 36208.3244 0.9999979
SERANGOON-CLEMENTI -39055.0000 -74035.2939 -4074.7061 0.0103656
TAMPINES-CLEMENTI -14999.4631 -37916.7828 7917.8566 0.7803867
TOA PAYOH-CLEMENTI -45750.5275 -68630.8233 -22870.2317 0.0000000
WOODLANDS-CLEMENTI -58544.8649 -84105.6389 -32984.0909 0.0000000
YISHUN-CLEMENTI -47885.8809 -69392.7841 -26378.9778 0.0000000
HOUGANG-GEYLANG 32679.6324 9242.2063 56117.0586 0.0000924
JURONG EAST-GEYLANG 13872.9911 -13404.6352 41150.6174 0.9822562
JURONG WEST-GEYLANG 513.5497 -22568.1583 23595.2577 1.0000000
KALLANG/WHAMPOA-GEYLANG 92864.5752 70198.2827 115530.8676 0.0000000
MARINE PARADE-GEYLANG 70156.8085 35551.2537 104762.3633 0.0000000
PASIR RIS-GEYLANG 129424.7059 63924.9900 194924.4218 0.0000000
PUNGGOL-GEYLANG 89416.4453 63460.4349 115372.4556 0.0000000
QUEENSTOWN-GEYLANG 92150.9754 69208.2295 115093.7213 0.0000000
SEMBAWANG-GEYLANG 77858.1176 37466.0933 118250.1419 0.0000000
SENGKANG-GEYLANG 82596.6877 55319.0614 109874.3140 0.0000000
SERANGOON-GEYLANG 35265.9647 806.4285 69725.5009 0.0372594
TAMPINES-GEYLANG 59321.5016 37207.2026 81435.8006 0.0000000
TOA PAYOH-GEYLANG 28570.4373 6494.5089 50646.3656 0.0005842
WOODLANDS-GEYLANG 15776.0998 -9067.2463 40619.4459 0.8257573
YISHUN-GEYLANG 26435.0838 5785.9782 47084.1894 0.0007466
JURONG EAST-HOUGANG -18806.6413 -46263.7717 8650.4890 0.7000181
JURONG WEST-HOUGANG -32166.0828 -55459.6517 -8872.5138 0.0001194
KALLANG/WHAMPOA-HOUGANG 60184.9427 37302.9427 83066.9427 0.0000000
MARINE PARADE-HOUGANG 37477.1760 2729.9526 72224.3994 0.0178161
PASIR RIS-HOUGANG 96745.0735 31170.3993 162319.7476 0.0000187
PUNGGOL-HOUGANG 56736.8128 30592.2229 82881.4028 0.0000000
QUEENSTOWN-HOUGANG 59471.3430 36315.4647 82627.2213 0.0000000
SEMBAWANG-HOUGANG 45178.4852 4665.0215 85691.9488 0.0105705
SENGKANG-HOUGANG 49917.0553 22459.9250 77374.1856 0.0000000
SERANGOON-HOUGANG 2586.3323 -32015.4704 37188.1349 1.0000000
TAMPINES-HOUGANG 26641.8691 4306.5310 48977.2073 0.0032181
TOA PAYOH-HOUGANG -4109.1952 -26406.5431 18188.1527 1.0000000
WOODLANDS-HOUGANG -16903.5326 -41943.8392 8136.7739 0.7272232
YISHUN-HOUGANG -6244.5487 -27130.2057 14641.1084 0.9999975
JURONG WEST-JURONG EAST -13359.4414 -40513.5628 13794.6799 0.9884069
KALLANG/WHAMPOA-JURONG EAST 78991.5841 52189.6835 105793.4847 0.0000000
MARINE PARADE-JURONG EAST 56283.8174 18838.8513 93728.7835 0.0000106
PASIR RIS-JURONG EAST 115551.7148 48508.4911 182594.9385 0.0000001
PUNGGOL-JURONG EAST 75543.4542 45907.4029 105179.5054 0.0000000
QUEENSTOWN-JURONG EAST 78277.9843 51241.8853 105314.0833 0.0000000
SEMBAWANG-JURONG EAST 63985.1265 21135.4277 106834.8253 0.0000129
SENGKANG-JURONG EAST 68723.6966 37923.5350 99523.8583 0.0000000
SERANGOON-JURONG EAST 21392.9736 -15917.0880 58703.0352 0.9308973
TAMPINES-JURONG EAST 45448.5105 19111.7818 71785.2392 0.0000001
TOA PAYOH-JURONG EAST 14697.4462 -11607.0720 41001.9643 0.9471863
WOODLANDS-JURONG EAST 1903.1087 -26763.4730 30569.6904 1.0000000
YISHUN-JURONG EAST 12562.0927 -12556.9563 37681.1417 0.9856626
KALLANG/WHAMPOA-JURONG WEST 92351.0255 69833.5162 114868.5348 0.0000000
MARINE PARADE-JURONG WEST 69643.2588 35134.9723 104151.5453 0.0000000
PASIR RIS-JURONG WEST 128911.1562 63462.7782 194359.5343 0.0000000
PUNGGOL-JURONG WEST 88902.8956 63076.7096 114729.0816 0.0000000
QUEENSTOWN-JURONG WEST 91637.4257 68841.6586 114433.1929 0.0000000
SEMBAWANG-JURONG WEST 77344.5679 37035.8463 117653.2895 0.0000000
SENGKANG-JURONG WEST 82083.1380 54929.0167 109237.2594 0.0000000
SERANGOON-JURONG WEST 34752.4150 390.5605 69114.2696 0.0434070
TAMPINES-JURONG WEST 58807.9519 36846.1754 80769.7285 0.0000000
TOA PAYOH-JURONG WEST 28056.8876 6133.7486 49980.0266 0.0007520
WOODLANDS-JURONG WEST 15262.5501 -9445.1259 39970.2261 0.8613456
YISHUN-JURONG WEST 25921.5341 5435.8569 46407.2113 0.0009654
MARINE PARADE-KALLANG/WHAMPOA -22707.7667 -56939.5853 11524.0519 0.7581007
PASIR RIS-KALLANG/WHAMPOA 36560.1307 -28742.8995 101863.1610 0.9460582
PUNGGOL-KALLANG/WHAMPOA -3448.1299 -28903.7276 22007.4678 1.0000000
QUEENSTOWN-KALLANG/WHAMPOA -713.5998 -23088.6432 21661.4437 1.0000000
SEMBAWANG-KALLANG/WHAMPOA -15006.4576 -55078.7499 25065.8347 0.9998274
SENGKANG-KALLANG/WHAMPOA -10267.8874 -37069.7880 16534.0131 0.9997442
SERANGOON-KALLANG/WHAMPOA -57598.6105 -91682.8094 -23514.4115 0.0000002
TAMPINES-KALLANG/WHAMPOA -33543.0736 -55067.8314 -12018.3158 0.0000033
TOA PAYOH-KALLANG/WHAMPOA -64294.1379 -85779.4723 -42808.8035 0.0000000
WOODLANDS-KALLANG/WHAMPOA -77088.4754 -101408.5252 -52768.4256 0.0000000
YISHUN-KALLANG/WHAMPOA -66429.4914 -86445.9481 -46413.0347 0.0000000
PASIR RIS-MARINE PARADE 59267.8974 -11076.0388 129611.8337 0.2606000
PUNGGOL-MARINE PARADE 19259.6368 -17233.8018 55753.0754 0.9721751
QUEENSTOWN-MARINE PARADE 21994.1669 -12421.3264 56409.6602 0.8167589
SEMBAWANG-MARINE PARADE 7701.3091 -40148.1655 55550.7837 1.0000000
SENGKANG-MARINE PARADE 12439.8792 -25005.0868 49884.8453 0.9999805
SERANGOON-MARINE PARADE -34890.8438 -77850.3157 8068.6281 0.3331537
TAMPINES-MARINE PARADE -10835.3069 -44704.1541 23033.5403 0.9999905
TOA PAYOH-MARINE PARADE -41586.3712 -75430.1772 -7742.5652 0.0017910
WOODLANDS-MARINE PARADE -54380.7087 -90091.3294 -18670.0879 0.0000070
YISHUN-MARINE PARADE -43721.7247 -76652.5940 -10790.8554 0.0003207
PUNGGOL-PASIR RIS -40008.2607 -106524.7204 26508.1991 0.8904692
QUEENSTOWN-PASIR RIS -37273.7305 -102673.2300 28125.7690 0.9350120
SEMBAWANG-PASIR RIS -51566.5883 -124930.3435 21797.1669 0.6486317
SENGKANG-PASIR RIS -46828.0182 -113871.2419 20215.2055 0.6614774
SERANGOON-PASIR RIS -94158.7412 -164430.9589 -23886.5235 0.0002565
TAMPINES-PASIR RIS -70103.2043 -135216.6988 -4989.7098 0.0183019
TOA PAYOH-PASIR RIS -100854.2687 -165954.7415 -35753.7958 0.0000040
WOODLANDS-PASIR RIS -113648.6061 -179738.8232 -47558.3891 0.0000001
YISHUN-PASIR RIS -102989.6221 -167620.1916 -38359.0527 0.0000015
QUEENSTOWN-PUNGGOL 2734.5302 -22967.5365 28436.5968 1.0000000
SEMBAWANG-PUNGGOL -11558.3277 -53579.0638 30462.4085 0.9999995
SENGKANG-PUNGGOL -6819.7575 -36455.8088 22816.2937 1.0000000
SERANGOON-PUNGGOL -54150.4805 -90505.4840 -17795.4771 0.0000140
TAMPINES-PUNGGOL -30094.9437 -55060.2968 -5129.5905 0.0026248
TOA PAYOH-PUNGGOL -60846.0080 -85777.3788 -35914.6372 0.0000000
WOODLANDS-PUNGGOL -73640.3455 -101052.3589 -46228.3320 0.0000000
YISHUN-PUNGGOL -62981.3615 -86658.6122 -39304.1108 0.0000000
SEMBAWANG-QUEENSTOWN -14292.8578 -54522.1678 25936.4522 0.9999324
SENGKANG-QUEENSTOWN -9554.2877 -36590.3867 17481.8113 0.9999386
SERANGOON-QUEENSTOWN -56885.0107 -91153.6756 -22616.3458 0.0000003
TAMPINES-QUEENSTOWN -32829.4738 -54645.1557 -11013.7920 0.0000102
TOA PAYOH-QUEENSTOWN -63580.5382 -85357.3232 -41803.7531 0.0000000
WOODLANDS-QUEENSTOWN -76374.8756 -100952.7844 -51796.9669 0.0000000
YISHUN-QUEENSTOWN -65715.8916 -86044.8689 -45386.9144 0.0000000
SENGKANG-SEMBAWANG 4738.5701 -38111.1287 47588.2690 1.0000000
SERANGOON-SEMBAWANG -42592.1529 -90336.1308 5151.8251 0.1655197
TAMPINES-SEMBAWANG -18536.6160 -58299.2871 21226.0551 0.9944354
TOA PAYOH-SEMBAWANG -49287.6803 -89029.0241 -9546.3365 0.0014797
WOODLANDS-SEMBAWANG -62082.0178 -103424.7274 -20739.3082 0.0000109
YISHUN-SEMBAWANG -51423.0338 -90389.8576 -12456.2100 0.0003708
SERANGOON-SENGKANG -47330.7230 -84640.7846 -10020.6614 0.0009132
TAMPINES-SENGKANG -23275.1861 -49611.9149 3061.5426 0.1794610
TOA PAYOH-SENGKANG -54026.2505 -80330.7686 -27721.7323 0.0000000
WOODLANDS-SENGKANG -66820.5879 -95487.1696 -38154.0062 0.0000000
YISHUN-SENGKANG -56161.6039 -81280.6529 -31042.5550 0.0000000
TAMPINES-SERANGOON 24055.5369 -9664.1016 57775.1754 0.6176415
TOA PAYOH-SERANGOON -6695.5275 -40390.0139 26998.9590 1.0000000
WOODLANDS-SERANGOON -19489.8649 -55059.0039 16079.2740 0.9572700
YISHUN-SERANGOON -8830.8809 -41608.2720 23946.5101 0.9999997
TOA PAYOH-TAMPINES -30751.0643 -51653.2412 -9848.8875 0.0000203
WOODLANDS-TAMPINES -43545.4018 -67351.8343 -19738.9693 0.0000000
YISHUN-TAMPINES -32886.4178 -52275.5886 -13497.2470 0.0000002
WOODLANDS-TOA PAYOH -12794.3375 -36565.1309 10976.4560 0.9650414
YISHUN-TOA PAYOH -2135.3535 -21480.7493 17210.0424 1.0000000
YISHUN-WOODLANDS 10658.9840 -11792.9552 33110.9231 0.9928273
Too Many Values
There is an estimated 55 areas as such would yield large amount of combination for pairwise analysis.
One-way ANOVA Test on Flat Model and Resale Price
Code
ggbetweenstats(
data = room_3,
x = flat_model,
y = resale_price,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)Warning: Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
Based on the Oneway ANOVA, overall ANOVA has a p-value less than 0.05 (4.36 e-107 < 0.05), indicating that the mean price is significantly different from each Flat Model.
Correlation between Resale Price, Remaining Lease Years and Floor Area
Code
ggstatsplot::ggcorrmat(
data = room_3,
cor.vars = c(resale_price, lease_ryear, floor_area_sqm)
)
Based on the correlation coefficients, there is moderate correlation between resale price and remaining lease year while weak to no correlation between the floor area and remaining lease year and resale price and floor area. This concludes that there is a relationship between resale price and remaining lease year though this does not mean causality between both relationship.
Analysis on 4 Room Flats
room_4 <- filter(use_flat_data, grepl("4 ROOM", flat_type))
room_4# A tibble: 11,312 × 12
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <dbl> <chr>
1 2022-01 ANG MO… 4 ROOM 438 ANG MO… 10 TO … 92 New Ge… 1979 56 yea…
2 2022-01 ANG MO… 4 ROOM 556 ANG MO… 13 TO … 92 New Ge… 1980 57 yea…
3 2022-01 ANG MO… 4 ROOM 419 ANG MO… 07 TO … 99 New Ge… 1979 56 yea…
4 2022-01 ANG MO… 4 ROOM 466 ANG MO… 04 TO … 93 New Ge… 1984 61 yea…
5 2022-01 ANG MO… 4 ROOM 472 ANG MO… 01 TO … 92 New Ge… 1979 56 yea…
6 2022-01 ANG MO… 4 ROOM 405 ANG MO… 01 TO … 92 New Ge… 1979 56 yea…
7 2022-01 ANG MO… 4 ROOM 547 ANG MO… 01 TO … 92 New Ge… 1981 58 yea…
8 2022-01 ANG MO… 4 ROOM 415 ANG MO… 01 TO … 92 New Ge… 1979 56 yea…
9 2022-01 ANG MO… 4 ROOM 324 ANG MO… 04 TO … 98 New Ge… 1978 55 yea…
10 2022-01 ANG MO… 4 ROOM 121 ANG MO… 10 TO … 92 New Ge… 1978 55 yea…
# … with 11,302 more rows, 2 more variables: resale_price <dbl>,
# lease_ryear <dbl>, and abbreviated variable names ¹flat_type, ²street_name,
# ³storey_range, ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date,
# ⁷remaining_leaseOne-way ANOVA Test on Flat Model and Resale Price
Code
ggbetweenstats(
data = room_4,
x = flat_model,
y = resale_price,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)Warning: Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
Based on the Oneway ANOVA, overall ANOVA has a p-value less than 0.05 (5.37 e-34 < 0.05), indicating that the mean price is significantly different from each Flat Model.
Correlation between Resale Price, Remaining Lease Years and Floor Area
Code
ggstatsplot::ggcorrmat(
data = room_4,
cor.vars = c(resale_price, lease_ryear, floor_area_sqm)
)
Based on the correlation coefficients, there is moderate correlation between resale price and remaining lease year while weak to no correlation between the floor area and remaining lease year and resale price and floor area. This concludes that there is a relationship between resale price and remaining lease year though this does not mean causality between both relationship.
Analysis on 5 Room Flats
room_5 <- filter(use_flat_data, grepl("5 ROOM", flat_type))
room_5# A tibble: 6,716 × 12
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <fct> <dbl> <chr> <dbl> <chr>
1 2022-01 ANG MO… 5 ROOM 402 ANG MO… 19 TO … 119 Improv… 1979 56 yea…
2 2022-01 ANG MO… 5 ROOM 460 ANG MO… 16 TO … 120 Improv… 1980 57 yea…
3 2022-01 ANG MO… 5 ROOM 539 ANG MO… 22 TO … 118 Improv… 1980 57 yea…
4 2022-01 ANG MO… 5 ROOM 521 ANG MO… 10 TO … 118 Improv… 1980 57 yea…
5 2022-01 ANG MO… 5 ROOM 648 ANG MO… 01 TO … 125 Improv… 1980 57 yea…
6 2022-01 ANG MO… 5 ROOM 354 ANG MO… 04 TO … 110 Improv… 2001 78 yea…
7 2022-01 ANG MO… 5 ROOM 353 ANG MO… 04 TO … 110 Improv… 2001 78 yea…
8 2022-01 ANG MO… 5 ROOM 350 ANG MO… 22 TO … 110 Improv… 2001 79 yea…
9 2022-01 ANG MO… 5 ROOM 588A ANG MO… 01 TO … 120 DBSS 2011 88 yea…
10 2022-01 ANG MO… 5 ROOM 588B ANG MO… 28 TO … 120 DBSS 2011 88 yea…
# … with 6,706 more rows, 2 more variables: resale_price <dbl>,
# lease_ryear <dbl>, and abbreviated variable names ¹flat_type, ²street_name,
# ³storey_range, ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date,
# ⁷remaining_leaseOne-way ANOVA Test on Flat Model and Resale Price
Code
ggbetweenstats(
data = room_5,
x = flat_model,
y = resale_price,
type = "p",
mean.ci = TRUE,
pairwise.comparisons = TRUE,
pairwise.display = "s",
p.adjust.method = "fdr",
messages = FALSE
)Warning: Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
Based on the Oneway ANOVA, overall ANOVA has a p-value less than 0.05 (3.88 e-17 < 0.05), indicating that the mean price is significantly different from each Flat Model.
Correlation between Resale Price, Remaining Lease Years and Floor Area
Code
ggstatsplot::ggcorrmat(
data = room_5,
cor.vars = c(resale_price, lease_ryear, floor_area_sqm)
)
Based on the correlation coefficients, for 5 Room Flats, the resale price, remaining lease years and floor area have weak to no correlation meaning each variable is independent of each other.
Learning and Insights
Size Limitations: due to the limitations of quarto size, visualization many variables may be difficult. Filters and selected values may be considered
Ease of Statistical Understanding: certain visualizations may not be easily understood by readers/users. Simplification may be a better option.