# Ames housing data 2018 House sales prices from Ames, Iowa, USA between 2006 and 2010. Only complete numeric observations remain. data ## 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. ## 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/DataDocumentation.txt) [http://jse.amstat.org/v19n3/decock.pdf](http://jse.amstat.org/v19n3/decock.pdf) Kaggle, Ames Housing Dataset [https://www.kaggle.com/prevek18/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") ``` ```r amesHousing2018 amesHousing2018_raw amesHousing2018_NorthAmes ``` ## 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 ## Examples ```r 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. } ```

amesHousing2018 dataset