R: Error in contrasts when fitting linear models with `lm` - r

Question

I've found Error in contrasts when defining a linear model in R and have followed the suggestions there, but none of my factor variables take on only one value and I am still experiencing the same issue.
This is the dataset I'm using: https://www.dropbox.com/s/em7xphbeaxykgla/train.csv?dl=0.
This is the code I'm trying to run:
simplelm <- lm(log_SalePrice ~ ., data = train)
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
What is the issue?

Answer 1

Thanks for providing your dataset (I hope that link will forever be valid so that everyone can access). I read it into a data frame train.
Using the debug_contr_error, debug_contr_error2 and NA_preproc helper functions provided by How to debug "contrasts can be applied only to factors with 2 or more levels" error?, we can easily analyze the problem.
info <- debug_contr_error2(log_SalePrice ~ ., train)
## the data frame that is actually used by `lm`
dat <- info$mf
## number of cases in your dataset
nrow(train)
#[1] 1460
## number of complete cases used by `lm`
nrow(dat)
#[1] 1112
## number of levels for all factor variables in `dat`
info$nlevels
# MSZoning Street Alley LotShape LandContour
# 4 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 5
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
As you can see, Utilities is the offending variable here as it has only 1 level.
Since you have many character / factor variables in train, I wonder whether you have NA for them. If we add NA as a valid level, we could possibly get more complete cases.
new_train <- NA_preproc(train)
new_info <- debug_contr_error2(log_SalePrice ~ ., new_train)
new_dat <- new_info$mf
nrow(new_dat)
#[1] 1121
new_info$nlevels
# MSZoning Street Alley LotShape LandContour
# 5 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 6
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
We do get more complete cases, but Utilities still has one level. This means that most incomplete cases are actually caused by NA in your numerical variables, which we can do nothing (unless you have a statistically valid way to impute those missing values).
As you only have one single-level factor variable, the same method as given in How to do a GLM when "contrasts can be applied only to factors with 2 or more levels"? will work.
new_dat$Utilities <- 1
simplelm <- lm(log_SalePrice ~ 0 + ., data = new_dat)
The model now runs successfully. However, it is rank-deficient. You probably want to do something to address it, but leaving it as it is is fine.
b <- coef(simplelm)
length(b)
#[1] 301
sum(is.na(b))
#[1] 9
simplelm$rank
#[1] 292