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In this post, We’ll see 3 functions from `tidyr`

that’s useful for handling Missing Values (`NA`

s) in the dataset. Please note: This post isn’t going to be about Missing Value Imputation.

### tidyr

According to the documentation of tidyr,

The goal of tidyr is to help you create tidy data. Tidy data is data where:

```
+ Every column is variable.
+ Every row is an observation..
+ Every cell is a single value.
```

Let’s start with loading `tidyr`

library. `tidyr`

is also one of the packages present in `tidyverse`

.

`library(tidyr)`

### tidyr functions

Following are the 3 tidyr functions that are handy for processing Missing Values

- drop_na()
- fill()
- replace_na()

### Dataset with Missing Value

To get a dataset with missing values, let’s take `mtcars`

and make some missing values in it.

```
df <- mtcars
df$hp[2] <- NA
df$cyl[5] <- NA
df$gear[5] <- NA
df$mpg[10] <- NA
# counting number of missing values
paste("Number of Missing Values", sum(is.na(df)))
```

`## [1] "Number of Missing Values 4"`

```
# dimensions
paste("Number of Rows",nrow(df))
```

`## [1] "Number of Rows 32"`

`paste("Number of Columns",ncol(df))`

`## [1] "Number of Columns 11"`

Now that we’ve got a dataset with Missing Values (`NA`

s) in it.

### drop_na()

`drop_na()`

drops/removes the rows/entries with Missing Values

`library(dplyr) #just in-case if we need to some dplyr verbs`

`## Warning: package 'dplyr' was built under R version 3.5.2`

```
##
## Attaching package: 'dplyr'
```

```
## The following objects are masked from 'package:stats':
##
## filter, lag
```

```
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
```

```
df_no_na <- drop_na(df)
# counting number of missing values
paste("Number of Missing Values", sum(is.na(df_no_na)))
```

`## [1] "Number of Missing Values 0"`

```
# dimensions
paste("Number of Rows",nrow(df_no_na))
```

`## [1] "Number of Rows 29"`

`paste("Number of Columns",ncol(df_no_na))`

`## [1] "Number of Columns 11"`

### fill()

`fill()`

fills the `NA`

s (missing values) in selected columns (`dplyr::select()`

options could be used like in the below example with `everything()`

).

It also lets us select the `.direction`

either `down`

(default) or `up`

or `updown`

or `downup`

from where the missing value must be filled.

Quite Naive, but could be handy in a lot of instances like let’s say Time Series data.

```
df_na_filled <- df %>%
fill(
dplyr::everything()
)
# counting number of missing values
paste("Number of Missing Values", sum(is.na(df_na_filled)))
```

`## [1] "Number of Missing Values 0"`

```
# dimensions
paste("Number of Rows",nrow(df_na_filled))
```

`## [1] "Number of Rows 32"`

`paste("Number of Columns",ncol(df_na_filled))`

`## [1] "Number of Columns 11"`

### replace_na()

`replace_na()`

is to be used when you have got the replacement value which the `NA`

s should be filled with.

Below is an example of how we have replaced all `NA`

s with just zero (`0`

)

```
df_na_replaced <- df %>%
mutate_all(replace_na,0)
# counting number of missing values
paste("Number of Missing Values", sum(is.na(df_na_replaced)))
```

`## [1] "Number of Missing Values 0"`

```
# dimensions
paste("Number of Rows",nrow(df_na_replaced))
```

`## [1] "Number of Rows 32"`

`paste("Number of Columns",ncol(df_na_replaced))`

`## [1] "Number of Columns 11"`

Alternatively, We could’ve simply identified numeric / continous values and replaced their values with `NA`

s like this:

```
df_na_replaced <- df %>%
mutate_if(is.numeric, replace_na,0)
```

Hopefully, this post would have thrown some light on those three functions of `tidyr`

to handle missing values: `drop_na()`

, `fill()`

, `replace_na()`

.

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