| amesHousing2018 {cheem} | R Documentation |
Ames housing data 2018
Description
House sales prices from Ames, Iowa, USA between 2006 and 2010. Only complete numeric observations remain.
Usage
amesHousing2018
amesHousing2018_raw
amesHousing2018_NorthAmes
Format
complete data.frame with 2291 rows and 18 numeric variables, SalesPrice, the response variable, and 3 class variables
An object of class data.frame with 2930 rows and 82 columns.
An object of class data.frame with 338 rows and 11 columns.
Details
- amesHousing2018
Complete data.frame, n = 2291, 18 numeric variable (including 2 temporal: MoSold, YrSold ), response variable SalePrice, 3 class factors.
- amesHousing2018_NorthAmes
A simplified subsample, just North Ames (largest neighborhood). Complete data.frame, n = 338, 9 numeric variables, response variable SalePrice, 1 class factor SubclassMS, a zoning subclass.
- amesHousing2018_raw
Original data from Kaggle, 2930 rows of 82 variables. Sparse rows (639) and sparse/defaulted columns (64) are removed.
No data dictionary is provided on Kaggle, but amesHousing2018 variables are inferred to be:
LotFrontage, Length of the front (street facing) side of the lot in yards (0.914m)
LotArea, Area of the lot in square yards (0.836m^2)
OverallQual, Overall quality (of the house?), integer in (1, 10)
OverallCond, Overall condition (of the lot?), integer in (1, 10)
YearBuild, The year the house was originally built
BsmtUnfArea, Unfinished basement area, in square yards (0.836m^2)
TotBsmtArea, Total basement area, in square yards (0.836m^2)
1stFlrArea, First (ground) floor living area in square yards (0.836m^2)
LivingArea, Total living area in square yards (0.836m^2)
Bathrms, The number of bathrooms
Bedrms, The number of bedrooms
TotRms, The total number of rooms
GarageYrBlt, The year the garage was build
GarageCars, The number of car spaces in the garage
GarageArea, The area of the garage in square yards (0.836m^2)
MoSold, The number of the month of the house sale
YrSold, The number of the year of the house sale
SalePrice, The sale of the house in USD (as of the year of sale?)
SubclassMS, Factor subclass of construction zone, 16 levels
SubclassMS, Factor major class of construction zone, 7 levels
Neighborhd, Factor neighborhood of Ames, IA, 28 levels
Source
De Cock, D. (2011). "Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project," Journal of Statistics Education, Volume 19, Number 3. http://jse.amstat.org/v19n3/decock/DataDocumentation.txt http://jse.amstat.org/v19n3/decock.pdf
Kaggle, Ames Housing Dataset https://www.kaggle.com/prevek18/ames-housing-dataset
Replicating this dataset:
if(FALSE) ## Don't accidentally open the URL.
browseURL("https://www.kaggle.com/prevek18/ames-housing-dataset")
ames <- readr::read_csv("./buildignore/AmesHousing.csv")
amesHousing2018_raw <- data.frame(ames)
## save(amesHousing2018_raw, file = "./data/amesHousing2018_raw.rda")
## Complete rows and numeric variables
ames1 <- ames[, unlist(lapply(ames, is.numeric))]
ames1$Bathrooms <- ames1$`Full Bath` + ames1$`Half Bath`
ames1 <- ames1[, c(1:18, 38, 19:37)]
col_idx <- !(colnames(ames1) %in% c(
"Order", "Mas Vnr Area", "BsmtFin SF 1", "BsmtFin SF 2",
"Bsmt Full Bath", "Bsmt Half Bath", "Fireplaces",
"Wood Deck SF", "Open Porch SF", "Enclosed Porch",
"3Ssn Porch", "Screen Porch", "Pool Area", "Misc Val", "2nd Flr SF",
"Low Qual Fin SF", "Full Bath", "Half Bath", "Kitchen AbvGr"))
row_idx <- !is.na(ames1$"Garage Yr Blt") &
!is.na(ames1$"Lot Frontage") &
!is.na(ames1$"Bsmt Unf SF") &
!is.na(ames1$"Total Bsmt SF")
ames2 <- as.data.frame(ames1[row_idx, col_idx])
## Looking for character classes to keep:
ames_char <- ames[, unlist(lapply(ames, is.character))]
ames_clas <- as.data.frame(lapply(ames_char, factor))[, -1]
ames_clasint <- data.frame(lapply(ames_clas, as.integer))
col_idx_char <- which(names(ames_clas) %in%
c("MS.SubClass", "MS.Zoning", "Neighborhood"))
classes <- ames_clas[row_idx, col_idx_char]
amesHousing2018 <- cbind(ames2, classes)
names(amesHousing2018) <- c(
"LotFrontage", "LotArea","OverallQual", "OverallCond", "YearBuild",
"YearRemod", "BsmtUnfArea", "TotBsmtArea", "1stFlrArea", "LivingArea",
"Bathrms", "Bedrms", "TotRms", "GarageYrBlt", "GarageCars", "GarageArea",
"MoSold", "YrSold", "SalePrice", "SubclassMS", "ZoneMS", "Neighborhd")
## save(amesHousing2018, file = "./data/amesHousing2018.rda")
.thin_col_idx <- names(amesHousing2018) %in% c(
"LotArea", "OverallQual", "YearBuild",
"LivingArea", "Bathrms", "Bedrms", "TotRms",
"GarageYrBlt", "GarageArea", "SalePrice", "SubclassMS")
amesHousing2018_thin <- amesHousing2018[, .thin_col_idx]
## subset to north ames, and only 5 largest subclasses
r_idx <- amesHousing2018$Neighborhd == "NAmes" &
amesHousing2018$SubclassMS %in% c("020", "050", "080", "090", "060")
amesHousing2018_NorthAmes <- amesHousing2018_thin[r_idx, ]
amesHousing2018_NorthAmes$SubclassMS <- factor(
amesHousing2018_NorthAmes$SubclassMS,
unique(amesHousing2018_NorthAmes$SubclassMS))
if(F){ ## Don't accidentally save
save(amesHousing2018_NorthAmes, file = "./data/amesHousing2018_NorthAmes.rda")
Examples
library(cheem)
## Regression setup:
dat <- amesHousing2018_NorthAmes
X <- dat[, 1:9]
Y <- dat$SalePrice
clas <- dat$SubclassMS
## Cheem list
ames_rf_chm <- cheem_ls(X, Y, ames_rf_shap, ames_rf_pred, clas,
label = "North Ames, RF, SHAP")
## Cheem visuals
if(interactive()){
prim <- 1
comp <- 2
global_view(ames_rf_chm, primary_obs = prim, comparison_obs = comp)
bas <- sug_basis(ames_rf_chm, prim, comp)
mv <- sug_manip_var(ames_rf_chm, primary_obs = prim, comp)
ggt <- radial_cheem_tour(ames_rf_chm, basis = bas, manip_var = mv)
animate_plotly(ggt)
}
## Save for use with shiny app (expects an rds file)
if(FALSE){ ## Don't accidentally save.
saveRDS(ames_rf_chm, "./chm_NAmes_rf_tshap.rds")
run_app() ## Select the saved rds file from the data drop down.
}