# Data Cleaning in R - Part 2

### Attributes with Zero Variance

Datasets can sometimes contain attributes (predictors) that have near-zero variance, or may have just one value. Such variables are considered to have less predictor power. Apart from being uninformative, these predictors may also sometimes break the model that you are trying to fit to your data. This can occur, for example, due to division by zero (if a standardization is performed in the data).

One quick solution is to remove all predictors that satisfy some threshold criterion related to their variance.

In our dataset, we will look for predictors that have zero variance and will remove them.

We will first define some generic functions that we will use later.

# Returns the Numeric columns from a dataset
getNumericColumns<-function(t){
tn = sapply(t,function(x){is.numeric(x)})
return(names(tn)[which(tn)])
}

# Returns the character columns from a dataset
getCharColumns<-function(t){
tn = sapply(t,function(x){is.character(x)})
return(names(tn)[which(tn)])
}

# Returns the factor columns in a dataset
getFactorColumns<-function(t){
tn = sapply(t,function(x){is.factor(x)})
return(names(tn)[which(tn)])
}

# Returns index of columns along with the column names
getIndexsOfColumns <- function(t,column_names){
return(match(column_names,colnames(t)))
}


Now we can find character columns with same value and numeric columns with zero-variance.

tmp = apply(data_train[getCharColumns(data_train)],2,function(x){length(unique(x))})
tmp = tmp[tmp==1]

tmp2 = apply(data_train[getNumericColumns(data_train)],2,function(x){(sd(x))})
tmp2 = tmp2[tmp2==0]

[1] "policy_code"
>


There is only one predictor that meets this criteria. We then proceed to drop this zero variance feature.

data_train = (data_train[,!(names(data_train) %in% discard_column)])


### Title, Desc, and Purpose

Let’s look at the attributes ’title’ and ‘purpose’.

> table(data_train$purpose) car credit_card debt_consolidation home_improvement house 424 9163 24604 2785 197 major_purchase medical moving other renewable_energy 939 480 281 2340 31 small_business vacation 404 261 > table(data_train$title)
Business           Car financing Credit card refinancing
3323                     372                     403                    8292
Debt consolidation              Green loan             Home buying        Home improvement
22614                      27                     187                    2614
Major purchase        Medical expenses   Moving and relocation                   Other
879                     453                     264                    2239
Vacation
242
>


The variable title and purpose have the same information. So, we can drop one of them. We will drop title.

> data_train$title = NULL  Let’s look at what we have in the desc column. > str(data_train$desc)
chr [1:41909] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" …


As you can see it looks mostly empty. We will drop this as well.

> data_train\$desc = NULL

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