04-missing.Rmd

# Missing values

## Load library

```{r echo=FALSE}
library(tidyverse)
library(extracat)
```

## Load data 

```{r echo=FALSE}
index = c('loan_amnt','issue_d','annual_inc','addr_state','grade','purpose','loan_status','home_ownership','emp_length')
df <- read.csv(file="loan.csv", header=TRUE, sep=",")[index]
df[df==""] <- NA
df[df=="n/a"] <- NA
```

## Missing column pattern graph

```{r echo=FALSE}
visna(df, sort = 'b')
```

## Analysis

This is the graph that describes the missing column pattern. It shows that we have some missing values in the variables 'emp_length' and 'annual_income'. The missing pattern shows that:  
- 1. Most of the missing values are in the variable 'emp_length'.  
- 2. There is no record that has missing values in these two variables at the same time.   
- 3. The missing values take only a small (about 5%) part in the total number of records.  
In our analysis, we will simply delete those records with missing values.