melb_data <- read.csv("../../../../data/melb_data.csv")Mel Property
R
25Winter
data: melb_data.csv
Melbourne Housing Snapshot
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[1] 4head(melb_data) X Suburb Address Rooms Type Price Method SellerG Date
1 1 Abbotsford 85 Turner St 2 h 1480000 S Biggin 2016-12-03
2 2 Abbotsford 25 Bloomburg St 2 h 1035000 S Biggin 2016-02-04
3 3 Abbotsford 5 Charles St 3 h 1465000 SP Biggin 2017-03-04
4 4 Abbotsford 40 Federation La 3 h 850000 PI Biggin 2017-03-04
5 5 Abbotsford 55a Park St 4 h 1600000 VB Nelson 2016-06-04
6 6 Abbotsford 129 Charles St 2 h 941000 S Jellis 2016-05-07
Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
1 2.5 3067 2 1 1 202 NA NA
2 2.5 3067 2 1 0 156 79 1900
3 2.5 3067 3 2 0 134 150 1900
4 2.5 3067 3 2 1 94 NA NA
5 2.5 3067 3 1 2 120 142 2014
6 2.5 3067 2 1 0 181 NA NA
CouncilArea Lattitude Longtitude Regionname Propertycount
1 Yarra -37.7996 144.9984 Northern Metropolitan 4019
2 Yarra -37.8079 144.9934 Northern Metropolitan 4019
3 Yarra -37.8093 144.9944 Northern Metropolitan 4019
4 Yarra -37.7969 144.9969 Northern Metropolitan 4019
5 Yarra -37.8072 144.9941 Northern Metropolitan 4019
6 Yarra -37.8041 144.9953 Northern Metropolitan 4019Median House Price
median(melb_data$Price [melb_data$YearBuilt >= 2000 & melb_data$YearBuilt <= 2018], na.rm = TRUE)[1] 750000library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionDescriptive Stats
| mean_price | sd_price | min_price | max_price | mean_land | sd_landsize | min_landsize | max_landsize |
|---|---|---|---|---|---|---|---|
| 1075684 | 639310.7 | 85000 | 9e+06 | 558.4161 | 3990.669 | 0 | 433014 |
melb_data |>
summarise(
mean_price = mean(Price, na.rm = TRUE),
sd_price = sd(Price, na.rm = TRUE),
min_price = min(Price, na.rm = TRUE),
max_price = max(Price, na.rm = TRUE),
mean_landsize = mean(Landsize, na.rm = TRUE),
sd_landsize = sd(Landsize, na.rm = TRUE),
min_landsize = min(Landsize, na.rm = TRUE),
max_landsize = max(Landsize, na.rm = TRUE)
) mean_price sd_price min_price max_price mean_landsize sd_landsize
1 1075684 639310.7 85000 9e+06 558.4161 3990.669
min_landsize max_landsize
1 0 433014Price by number of rooms
library(ggplot2)ggplot(melb_data, aes(x=factor(Rooms), y=Price)) +
geom_boxplot() +
labs(title="Price by Number of Rooms", x="Rooms", y="Price") +
theme_minimal()
Correlation matrix
library(corrplot)corrplot 0.95 loadednum_data <- melb_data %>% select_if(is.numeric)
cor_matrix <- cor(num_data, use="complete.obs")install.packages("corrplot")The following package(s) will be installed:
- corrplot [0.95]
These packages will be installed into "~/tech_training/training-intensives/renv/library/linux-ubuntu-jammy/R-4.5/x86_64-pc-linux-gnu".
# Installing packages --------------------------------------------------------
- Installing corrplot ... OK [linked from cache]
Successfully installed 1 package in 5.2 milliseconds.library(corrplot)
corrplot(cor_matrix, method = "color")
This heatmap shows correlations between property features. Price is moderately correlated with the number of rooms, bathrooms, car spaces, and building area, indicating that larger, more feature-rich homes tend to be more expensive. Location variables (latitude/longitude) and distance from the city show weaker relationships.
Build year and Prices
library(ggplot2)ggplot(data = melb_data |> filter(YearBuilt > 1950, !is.na(YearBuilt), !is.na(Price)),
mapping = aes(x = YearBuilt, y = Price)) +
geom_line() +
geom_point(aes(colour = "pink")) +
theme(legend.position="none")
melb_data_small <- melb_data %>% filter(Landsize < 100, Price < 200000)
print(melb_data_small) X Suburb Address Rooms Type Price Method SellerG
1 2653 Footscray 202/51 Gordon St 1 u 85000 PI Burnham
2 7304 Albion 8/6 Ridley St 1 u 145000 PI Biggin
3 7306 Albion 5/25 Ridley St 2 u 190000 SP Burnham
4 8505 West Footscray 8/132 Rupert St 1 u 185000 PI Jas
5 8812 Footscray 10/30 Pickett St 1 u 170000 PI Burnham
Date Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea
1 2016-09-03 6.4 3011 1 1 0 0 NA
2 2016-05-28 13.9 3020 2 1 1 36 NA
3 2016-07-30 13.9 3020 2 1 1 0 NA
4 2017-04-29 8.2 3012 1 1 1 0 NA
5 2017-07-01 5.1 3011 1 1 0 30 26
YearBuilt CouncilArea Lattitude Longtitude Regionname Propertycount
1 2007 Maribyrnong -37.79110 144.8900 Western Metropolitan 7570
2 NA Brimbank -37.78330 144.8266 Western Metropolitan 2185
3 NA Brimbank -37.78390 144.8239 Western Metropolitan 2185
4 NA Maribyrnong -37.79800 144.8672 Western Metropolitan 5058
5 2013 Maribyrnong -37.80141 144.8959 Western Metropolitan 7570Summary by property type
melb_data %>%
group_by(Type) %>%
summarise(
count = n(),
mean_price = mean(Price, na.rm = TRUE),
mean_landsize = mean(Landsize, na.rm = TRUE)
)# A tibble: 3 × 4
Type count mean_price mean_landsize
<chr> <int> <dbl> <dbl>
1 h 9449 1242665. 617.
2 t 1114 933735. 280.
3 u 3017 605127. 477.melb_data |> filter(Date >= 2015, !is.na(Date), !is.na(Price)) |> ggplot(aes(x=Date, y=Price)) + geom_line(stat = “summary” , fun = mean, color = “blue”) + geom_point(alpha = 0.3 , color = “darkred”) + labs(title = “Housing Prices from 2015”, x = “Date”, Y = “Price (AUD)”) + theme_minimal() ```
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