/
make_ames.R
614 lines (603 loc) · 19.3 KB
/
make_ames.R
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#' Create a Processed Version of the Ames Housing Data
#'
#' @details
#' For the processed version, the exact details can be found in
#' the code of `make_ames` but a summary of the differences between
#' these data sets and `ames_raw` is:
#'
#' * All factors are _unordered_.
#' * `PID` and `Order` are removed.
#' * Spaces and special characters in column names where changed
#' to snake case. To be consistent, `SalePrice` was changed to
#' `Sale_Price`.
#' * Many factor levels were changed to be more understandable
#' (e.g. `Split_or_Multilevel` instead of `080`)
#' * Many missing values were reset. For example, if the variable
#' `Bsmt_Qual` was missing, this implies that there is no basement
#' on the property. Instead of a missing value, the value of
#' `Bsmt_Qual` was changed to `No_Basement`. Similarly, numeric
#' data pertaining to basements were set to zero where appropriate
#' such as variables `Bsmt_Full_Bath` and `Total_Bsmt_SF`.
#' * `Garage_Yr_Blt` contained many missing data and was removed.
#' * Approximate longitude and latitude are included for the
#' properties. Also, note that there are 6 properties with
#' identical geotags. These are units within the same building.
#' For some properties, updated versions of the PID identifiers
#' were found and are replaced with new values.
#'
#' `make_ordinal_ames` is the same as `make_ames` but many factor
#' variables were changed to class `ordered` (see below).
#'
#' The documentation for [ames_raw()] contains descriptions of
#' the columns although, as noted above, the column names in
#' [ames_raw()] are slightly different from the processed
#' versions.
#'
#' `make_ames_new()` creates a data set of new properties. These were populated
#' using less data sources than the original and lack a number of the condition
#' and quality. Both properties were unsold at the time of this writing.
#' @return A tibble with the data.
#' @examples
#' ames <- make_ames()
#' nrow(ames)
#' summary(ames$Sale_Price)
#'
#' ames_ord <- make_ordinal_ames()
#' ord_vars <- vapply(ames_ord, is.ordered, logical(1))
#' names(ord_vars)[ord_vars]
#' @export
#' @importFrom dplyr add_rownames add_rownames vars contains
#' @importFrom dplyr funs rename_at rename mutate recode_factor
#' @importFrom dplyr recode filter select inner_join
#
make_ames <- function() {
process_ames(AmesHousing::ames_raw)
}
#' @export
#' @rdname make_ames
make_ames_new <- function() {
process_ames(AmesHousing::ames_new)
}
process_ames <- function(dat) {
out <- dat %>%
# Rename variables with spaces or begin with numbers.
# SalePrice would be inconsistently named so change that too.
dplyr::rename_with(
~ gsub(' ', '_', .),
dplyr::contains(' '),
) %>%
dplyr::rename(
Sale_Price = SalePrice,
Three_season_porch = `3Ssn_Porch`,
Year_Remod_Add = `Year_Remod/Add`,
First_Flr_SF = `1st_Flr_SF`,
Second_Flr_SF = `2nd_Flr_SF`,
Year_Sold = Yr_Sold
) %>%
# Remove leading zeros
dplyr::mutate(
MS_SubClass = as.character(as.integer(MS_SubClass))
) %>%
# Make more meaningful factor levels for some variables
dplyr::mutate(
MS_SubClass =
dplyr::recode_factor(
factor(MS_SubClass),
'20' = 'One_Story_1946_and_Newer_All_Styles',
'30' = 'One_Story_1945_and_Older',
'40' = 'One_Story_with_Finished_Attic_All_Ages',
'45' = 'One_and_Half_Story_Unfinished_All_Ages',
'50' = 'One_and_Half_Story_Finished_All_Ages',
'60' = 'Two_Story_1946_and_Newer',
'70' = 'Two_Story_1945_and_Older',
'75' = 'Two_and_Half_Story_All_Ages',
'80' = 'Split_or_Multilevel',
'85' = 'Split_Foyer',
'90' = 'Duplex_All_Styles_and_Ages',
'120' = 'One_Story_PUD_1946_and_Newer',
'150' = 'One_and_Half_Story_PUD_All_Ages',
'160' = 'Two_Story_PUD_1946_and_Newer',
'180' = 'PUD_Multilevel_Split_Level_Foyer',
'190' = 'Two_Family_conversion_All_Styles_and_Ages'
)
) %>%
dplyr::mutate(
MS_Zoning =
dplyr::recode_factor(
factor(MS_Zoning),
'A' = 'Agriculture',
'C' = 'Commercial',
'FV' = 'Floating_Village_Residential',
'I' = 'Industrial',
'RH' = 'Residential_High_Density',
'RL' = 'Residential_Low_Density',
'RP' = 'Residential_Low_Density_Park',
'RM' = 'Residential_Medium_Density',
'A (agr)' = 'A_agr',
'C (all)' = 'C_all',
'I (all)' = 'I_all'
)
) %>%
dplyr::mutate(
Lot_Shape =
dplyr::recode_factor(
factor(Lot_Shape),
'Reg' = 'Regular',
'IR1' = 'Slightly_Irregular',
'IR2' = 'Moderately_Irregular',
'IR3' = 'Irregular'
)
) %>%
dplyr::mutate(Bldg_Type =
dplyr::recode_factor(factor(Bldg_Type),
'1Fam' = 'OneFam',
'2fmCon' = 'TwoFmCon')) %>%
# Change some factor levels so that they make valid R variable names
dplyr::mutate(
House_Style = gsub("^1.5", "One_and_Half_", House_Style),
House_Style = gsub("^1", "One_", House_Style),
House_Style = gsub("^2.5", "Two_and_Half_", House_Style),
House_Style = gsub("^2", "Two_", House_Style),
House_Style = factor(House_Style)
) %>%
# Some characteristics that houses lack (e.g. garage, pool) are
# coded as missing instead of "No_pool" or "No_Garage". Change these
# and also cases where the number of missing (e.g. garage size)
dplyr::mutate(
Bsmt_Exposure = ifelse(is.na(Bsmt_Exposure), "No_Basement", Bsmt_Exposure),
Bsmt_Exposure = factor(Bsmt_Exposure),
BsmtFin_Type_1 = ifelse(is.na(BsmtFin_Type_1), "No_Basement", BsmtFin_Type_1),
BsmtFin_Type_1 = factor(BsmtFin_Type_1),
BsmtFin_SF_1 = ifelse(is.na(BsmtFin_SF_1), 0, BsmtFin_Type_1),
BsmtFin_Type_2 = ifelse(is.na(BsmtFin_Type_2), "No_Basement", BsmtFin_Type_2),
BsmtFin_Type_2 = factor(BsmtFin_Type_2),
BsmtFin_SF_2 = ifelse(is.na(BsmtFin_SF_2), 0, BsmtFin_SF_2),
Bsmt_Unf_SF = ifelse(is.na(Bsmt_Unf_SF), 0, Bsmt_Unf_SF),
Total_Bsmt_SF = ifelse(is.na(Total_Bsmt_SF), 0, Total_Bsmt_SF),
Bsmt_Full_Bath = ifelse(is.na(Bsmt_Full_Bath), 0, Bsmt_Full_Bath),
Bsmt_Half_Bath = ifelse(is.na(Bsmt_Half_Bath), 0, Bsmt_Half_Bath),
Electrical = ifelse(is.na(Electrical), "Unknown", Electrical),
) %>%
dplyr::mutate(Garage_Type =
dplyr::recode(Garage_Type,
'2Types' = 'More_Than_Two_Types')) %>%
dplyr::mutate(
Garage_Type = ifelse(is.na(Garage_Type), "No_Garage", Garage_Type),
Garage_Finish = ifelse(is.na(Garage_Finish), "No_Garage", Garage_Finish),
Garage_Cars = ifelse(is.na(Garage_Cars), 0, Garage_Cars),
Garage_Area = ifelse(is.na(Garage_Area), 0, Garage_Area),
Bsmt_Full_Bath = ifelse(is.na(Bsmt_Full_Bath), 0, Bsmt_Full_Bath),
Bsmt_Half_Bath = ifelse(is.na(Bsmt_Half_Bath), 0, Bsmt_Half_Bath),
Misc_Feature = ifelse(is.na(Misc_Feature), "None", Misc_Feature),
Mas_Vnr_Type = ifelse(is.na(Mas_Vnr_Type), "None", Mas_Vnr_Type),
Mas_Vnr_Area = ifelse(is.na(Mas_Vnr_Area), 0, Mas_Vnr_Area),
Lot_Frontage = ifelse(is.na(Lot_Frontage), 0, Lot_Frontage)
) %>%
mutate(
Overall_Qual =
dplyr::recode(
Overall_Qual,
`10` = "Very_Excellent",
`9` = "Excellent",
`8` = "Very_Good",
`7` = "Good",
`6` = "Above_Average",
`5` = "Average",
`4` = "Below_Average",
`3` = "Fair",
`2` = "Poor",
`1` = "Very_Poor"
)
) %>%
mutate(
Overall_Cond =
dplyr::recode(
Overall_Cond,
`10` = "Very_Excellent",
`9` = "Excellent",
`8` = "Very_Good",
`7` = "Good",
`6` = "Above_Average",
`5` = "Average",
`4` = "Below_Average",
`3` = "Fair",
`2` = "Poor",
`1` = "Very_Poor"
)
) %>%
mutate(
Exter_Qual =
dplyr::recode(
Exter_Qual,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor"
)
) %>%
mutate(
Exter_Cond =
dplyr::recode(
Exter_Cond,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor"
)
) %>%
mutate(
Bsmt_Qual =
dplyr::recode(
Bsmt_Qual,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Basement"
)
) %>%
mutate(
Bsmt_Cond =
dplyr::recode(
Bsmt_Cond,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Basement"
)
) %>%
mutate(
Heating_QC =
dplyr::recode(
Heating_QC,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor"
)
) %>%
mutate(
Kitchen_Qual =
dplyr::recode(
Kitchen_Qual,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor"
)
) %>%
mutate(
Fireplace_Qu =
dplyr::recode(
Fireplace_Qu,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Fireplace"
)
) %>%
mutate(
Garage_Qual =
dplyr::recode(
Garage_Qual,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Garage"
)
) %>%
mutate(
Garage_Cond =
dplyr::recode(
Garage_Cond,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Garage"
)
) %>%
mutate(
Pool_QC =
dplyr::recode(
Pool_QC,
"Ex" = "Excellent",
"Gd" = "Good",
"TA" = "Typical",
"Fa" = "Fair",
"Po" = "Poor",
.missing = "No_Pool"
)
) %>%
mutate(
Neighborhood =
dplyr::recode(
Neighborhood,
"Blmngtn" = "Bloomington_Heights",
"Bluestem" = "Bluestem",
"BrDale" = "Briardale",
"BrkSide" = "Brookside",
"ClearCr" = "Clear_Creek",
"CollgCr" = "College_Creek",
"Crawfor" = "Crawford",
"Edwards" = "Edwards",
"Gilbert" = "Gilbert",
"Greens" = "Greens",
"GrnHill" = "Green_Hills",
"IDOTRR" = "Iowa_DOT_and_Rail_Road",
"Landmrk" = "Landmark",
"MeadowV" = "Meadow_Village",
"Mitchel" = "Mitchell",
"NAmes" = "North_Ames",
"NoRidge" = "Northridge",
"NPkVill" = "Northpark_Villa",
"NridgHt" = "Northridge_Heights",
"NWAmes" = "Northwest_Ames",
"OldTown" = "Old_Town",
"SWISU" = "South_and_West_of_Iowa_State_University",
"Sawyer" = "Sawyer",
"SawyerW" = "Sawyer_West",
"Somerst" = "Somerset",
"StoneBr" = "Stone_Brook",
"Timber" = "Timberland",
"Veenker" = "Veenker",
"Hayden Lake" = "Hayden_Lake"
)
) %>%
mutate(
Alley =
dplyr::recode(
Alley,
"Grvl" = "Gravel",
"Pave" = "Paved",
.missing = "No_Alley_Access"
)
) %>%
mutate(
Paved_Drive =
dplyr::recode(
Paved_Drive,
"Y" = "Paved",
"P" = "Partial_Pavement",
"N" = "Dirt_Gravel"
)
) %>%
mutate(
Fence =
dplyr::recode(
Fence,
"GdPrv" = "Good_Privacy",
"MnPrv" = "Minimum_Privacy",
"GdWo" = "Good_Wood",
"MnWw" = "Minimum_Wood_Wire",
.missing = "No_Fence"
)
) %>%
# Convert everything else to factors
dplyr::mutate(
Alley = factor(Alley),
Bsmt_Qual = factor(Bsmt_Qual),
Bsmt_Cond = factor(Bsmt_Cond),
Central_Air = factor(Central_Air),
Condition_1 = factor(Condition_1),
Condition_2 = factor(Condition_2),
Electrical = factor(Electrical),
Exter_Cond = factor(Exter_Cond),
Exter_Qual = factor(Exter_Qual),
Exterior_1st = factor(Exterior_1st),
Exterior_2nd = factor(Exterior_2nd),
Fence = factor(Fence),
Fireplace_Qu = factor(Fireplace_Qu),
Foundation = factor(Foundation),
Functional = factor(Functional),
Garage_Cond = factor(Garage_Cond),
Garage_Finish = factor(Garage_Finish),
Garage_Qual = factor(Garage_Qual),
Garage_Type = factor(Garage_Type),
Heating = factor(Heating),
Heating_QC = factor(Heating_QC),
Kitchen_Qual = factor(Kitchen_Qual),
Land_Contour = factor(Land_Contour),
Land_Slope = factor(Land_Slope),
Lot_Config = factor(Lot_Config),
Mas_Vnr_Type = factor(Mas_Vnr_Type),
Misc_Feature = factor(Misc_Feature),
Paved_Drive = factor(Paved_Drive),
Pool_QC = factor(Pool_QC),
Roof_Matl = factor(Roof_Matl),
Roof_Style = factor(Roof_Style),
Sale_Condition = factor(Sale_Condition),
Sale_Type = factor(Sale_Type),
Street = factor(Street),
Utilities = factor(Utilities),
Overall_Qual = factor(Overall_Qual, levels = rev(ten_point)),
Overall_Cond = factor(Overall_Cond, levels = rev(ten_point))
) %>%
# see issue #2, updated PIDs for some properties
mutate(
PID = ifelse(PID == "0904351040", "0904351045,", PID),
PID = ifelse(PID == "0535300120", "0535300125,", PID),
PID = ifelse(PID == "0902401130", "0902401135,", PID),
PID = ifelse(PID == "0906226090", "0906226090,", PID),
PID = ifelse(PID == "0908154040", "0908154045,", PID),
PID = ifelse(PID == "0909129100", "0909129105,", PID),
PID = ifelse(PID == "0914465040", "0914465043,", PID),
PID = ifelse(PID == "0902103150", "0902103145,", PID),
PID = ifelse(PID == "0902401120", "0902401125,", PID),
PID = ifelse(PID == "0916253320", "0916256880,", PID),
PID = ifelse(PID == "0916477060", "0916477065,", PID),
PID = ifelse(PID == "0916325040", "0916325045,", PID)
) %>%
dplyr::inner_join(AmesHousing::ames_geo, by = "PID") %>%
# Garage_Yr_Blt is removed due to a fair amount of missing data
dplyr::select(-Order,-PID, -Garage_Yr_Blt)
out <- out %>%
dplyr::mutate(
Neighborhood = factor(Neighborhood, levels = AmesHousing::hood_levels)
)
out
}
ten_point <- c(
"Very_Excellent",
"Excellent",
"Very_Good",
"Good",
"Above_Average",
"Average",
"Below_Average",
"Fair",
"Poor",
"Very_Poor"
)
five_point <- c(
"Excellent",
"Good",
"Typical",
"Fair",
"Poor"
)
#' @rdname make_ames
#' @export
make_ordinal_ames <- function() {
get_no <- function(x)
grep("^No", levels(x), value = TRUE)
out <- make_ames()
out$Lot_Shape <- ordered(
as.character(out$Lot_Shape),
levels = c("Irregular", "Moderately_Irregular",
"Slightly_Irregular", "Regular")
)
out$Land_Contour <- ordered(
as.character(out$Land_Contour),
levels = c("Low", "HLS", "Bnk", "Lvl")
)
out$Utilities <- ordered(
as.character(out$Utilities),
levels = c("ELO", "NoSeWa", "NoSewr", "AllPub")
)
out$Land_Slope <- ordered(
as.character(out$Land_Slope),
levels = c("Sev", "Mod", "Gtl")
)
out$Overall_Qual <- ordered(
as.character(out$Overall_Qual),
levels = rev(ten_point)
)
out$Overall_Cond <- ordered(
as.character(out$Overall_Cond),
levels = rev(ten_point)
)
out$Exter_Qual <- ordered(
as.character(out$Exter_Qual),
levels = rev(five_point)
)
out$Exter_Cond <- ordered(
as.character(out$Exter_Cond),
levels = rev(five_point)
)
out$Bsmt_Qual <- ordered(
as.character(out$Bsmt_Qual),
levels = c(get_no(out$Bsmt_Qual), rev(five_point))
)
out$Bsmt_Cond <- ordered(
as.character(out$Bsmt_Cond),
levels = c(get_no(out$Bsmt_Cond), rev(five_point))
)
out$Bsmt_Exposure <- ordered(
as.character(out$Bsmt_Exposure),
levels = c(
"No_Basement", "No", "Mn", "Av", "Gd"
)
)
out$BsmtFin_Type_1 <- ordered(
as.character(out$BsmtFin_Type_1),
levels = c(
"No_Basement", "Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"
)
)
out$BsmtFin_Type_2 <- ordered(
as.character(out$BsmtFin_Type_2),
levels = c(
"No_Basement", "Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"
)
)
out$Heating_QC <- ordered(
as.character(out$Heating_QC),
levels = rev(five_point)
)
out$Electrical <- ordered(
as.character(out$Electrical),
levels = c("Mix", "FuseP", "FuseF", "FuseA", "SBrkr")
)
out$Kitchen_Qual <- ordered(
as.character(out$Kitchen_Qual),
levels = rev(five_point)
)
out$Functional <- ordered(
as.character(out$Functional),
levels = c(
"Sal", "Sev", "Maj2", "Maj1", "Mod", "Min2", "Min1", "Typ"
)
)
out$Fireplace_Qu <- ordered(
as.character(out$Fireplace_Qu),
levels = c(get_no(out$Fireplace_Qu), rev(five_point))
)
out$Garage_Finish <- ordered(
as.character(out$Garage_Finish),
levels = c(get_no(out$Garage_Finish), "Unf", "RFn", "Fin")
)
out$Garage_Qual <- ordered(
as.character(out$Garage_Qual),
levels = c(get_no(out$Garage_Qual), rev(five_point))
)
out$Garage_Cond <- ordered(
as.character(out$Garage_Cond),
levels = c(get_no(out$Garage_Cond), rev(five_point))
)
out$Paved_Drive <- ordered(
as.character(out$Paved_Drive),
levels = c("Dirt_Gravel", "Partial_Pavement", "Paved")
)
out$Pool_QC <- ordered(
as.character(out$Pool_QC),
levels = c(get_no(out$Pool_QC), rev(five_point))
)
out$Fence <- ordered(
as.character(out$Fence),
levels = c("No_Fence", "Minimum_Wood_Wire", "Good_Wood",
"Minimum_Privacy", "Good_Privacy")
)
out
}
ames_vars <-
c('.', 'SalePrice', '3Ssn_Porch', 'Year_Remod/Add', '1st_Flr_SF',
'2nd_Flr_SF', 'MS_SubClass', 'MS_Zoning', 'Alley', 'Lot_Shape',
'Bldg_Type', 'House_Style', 'Bsmt_Qual', 'Bsmt_Cond',
'Bsmt_Exposure', 'BsmtFin_Type_1', 'BsmtFin_SF_1',
'BsmtFin_Type_2', 'BsmtFin_SF_2', 'Bsmt_Unf_SF', 'Total_Bsmt_SF',
'Bsmt_Full_Bath', 'Bsmt_Half_Bath', 'Fireplace_Qu', 'Garage_Type',
'Garage_Finish', 'Garage_Qual', 'Garage_Cond', 'Garage_Cars',
'Garage_Area', 'Pool_QC', 'Fence', 'Misc_Feature', 'Mas_Vnr_Type',
'Mas_Vnr_Area', 'Lot_Frontage', 'Central_Air', 'Condition_1',
'Condition_2', 'Electrical', 'Exter_Cond', 'Exter_Qual',
'Exterior_1st', 'Exterior_2nd', 'Foundation', 'Functional',
'Heating', 'Heating_QC', 'Kitchen_Qual', 'Land_Contour',
'Land_Slope', 'Lot_Config', 'Neighborhood', 'Yr_Sold',
'Overall_Cond', 'Overall_Qual', 'Paved_Drive',
'Roof_Matl', 'Roof_Style', 'Sale_Condition', 'Sale_Type',
'Street', 'Utilities', 'Order', 'PID', 'Garage_Yr_Blt')
#' @importFrom utils globalVariables
utils::globalVariables(ames_vars)