Using R to Fix Data Quality: Section 7

Section 7: Fix missing data

Overview

In previous sections, we have mentioned how to find missing data in our table. In this section, we are going to use linear regression to restore missing data.

Read CSV Data

In this demo, we use the hours.csv to be our data.

> data=read.csv("hours.csv")

> head(data)

Hours Score Questions.Posted Days.Missed

1 1 NA 0 3

2 1 55 2 0

3 3 NA 0 2

4 5 60 0 1

5 5 65 0 2

6 5 70 1 3

In this table, there are four columns. The Hours, Questions.Posted and Days.Missed are complete, but there are some missing data in the Score. Thus, we need a solution to restore the missing data in Score.

Because the names of each column are too long if we always use $ operation, we can attach data:

> attach(data)

After this command, we can just type “Score” to replace “data$Score”.

Linear Regression

An easy way to restore the missing data is just using random imputation to replace NA, but it is bad for the accuracy of our data. Linear Regression can use the existent data to predict the value of each missing data. Linear Regression is a complex functionality
to code if we use other programming language, such as Java, C, and Python. Fortunately, R includes the Linear Regression function, so that we can use it directly.

Use Hours, Questions.Posted and Days.Missed to make a linear Regression for Score:

> lmod=lm(Score ~ Hours + Questions.Posted + Days.Missed)

> lmod

The “lmod” is the result of our linear regression. We can use it to make a prediction of Score. For example, we want to predict the score when Hours = 3, Questions.Posted=50, and Days.Missed =2.

The code to predict:

> predict(lmod, data.frame(Hours=c(3), Questions.Posted=c(50), Days.Missed=c(2)))

1

31.65578

As can be seen, it predict the Score = 31.65578

Deterministic Regression Imputation

We have used linear regression to make a predicting Score based on the other three variables. The next thing we need to do is just to replace each NA to our prediction value.

In fact the function predict() can be used in data table directly:

> p=predict(lmod,data)

We should make a function impute() to replace NA:

impute <- function (a, a.impute){

ifelse (is.na(a), a.impute, a)

}

Replace each NA with our prediction value:

> data$Score=impute(data$Score,p)

Congratulations! You have fixed the missing data problem in your data table.

Practice Question

1. What are the 5 new values in the table after our regression imputation?

2. What is the benefit of this regression imputation than random imputation?