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Some say that data scientists spend 80% of their time cleaning data, and 20% of their time actually analyzing it. Learn about that 80% cleaning time and how to do it in R with dplyr and tidyr.

Data Cleaning in R

Let's say we have a column called `"type"` with data entries in the format `"admin_US"` or `"user_Kenya"`, as shown in the table below.

<div class="narrative-table-container">

|*id*|*type*|
|-|-|
|1011|"user_Kenya"|
|1112|"admin_US"|
|1113|"moderator_UK"|

</div>
<br>

Just like we saw before, this column actually contains two types of data. One seems to be the user type (with values like "admin" or "user") and one seems to be the country this user is in (with values like "US" or "Kenya"). 

We can no longer just split along the first 4 characters because `admin` and `user` are of different lengths. Instead, we know that we want to split along the `"_"`. We can thus use the tidyr function `separate()` to split this column into two, separate columns:

```r
# Create the 'user_type' and 'country' columns
df %>%
  separate(type,c('user_type','country'),'_')
```
* `type` is the column to split
* `c('user_type','country')` is a vector with the names of the two new columns
* `'_'` is the character to split on

This would transform the table above into a table like:

<div class="narrative-table-container">

|*id*|*user_type*|*country*|
|-|-|-|
|1011|"user"|"Kenya"|
|1112|"admin"|"US"|
|1113|"moderator"|"UK"|

</div>
<br>

Splitting By Character

We often describe data that is easy to analyze and visualize as "tidy data". What does it mean to have tidy data?

For data to be tidy, it must have:
- Each variable as a separate column
- Each row as a separate observation

For example, we would want to reshape a table like: 

<div class="narrative-table-container">

| Account |Checkings | Savings |
| --- | --- | --- |
| "12456543" | 8500 | 8900
| "12283942" | 6410 | 8020
| "12839485" | 78000 | 92000

</div>

<br>

Into a table that looks more like: 

<div class="narrative-table-container">

| Account| Account Type | Amount |
| --- | --- | --- |
| "12456543" | "Checking" | 8500
| "12456543" | "Savings" | 8900
| "12283942" | "Checking" | 6410
| "12283942" | "Savings" | 8020
| "12839485" | "Checking" | 78000
| "12839485" | "Savings" | 92000

</div>
<br>

The first step of diagnosing whether or not a dataset is tidy is using base R and dplyr functions to explore and probe the dataset.

You've seen most of the functions we often use to diagnose a dataset for cleaning. Some of the most useful ones are:
- `head()` &mdash; display the first 6 rows of the table
- `summary()` &mdash; display the summary statistics of the table
- `colnames()` &mdash; display the column names of the table

Diagnose the Data

Sometimes we need to modify strings in our data frames to help us transform them into more meaningful metrics. For example, in our fruits table from before: 

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|"$1"|105|
|"apple"|"$0.75"|95|
|"peach"|"$3"|55|
|"peach"|"$4"|55|
|"clementine"|"$2.5"|35|

</div>
<br>

We can see that the `'price'` column is actually composed of character strings representing dollar amounts. This column could be much better represented as numeric, so that we could take the mean, calculate other aggregate statistics, or compare different fruits to one another in terms of price.
 
First, we can use a regular expression, a sequence of characters that describe a pattern of text to be matched, to remove all of the dollar signs. The base R function `gsub()` will remove the `$` from the `price` column, replacing the symbol with an empty string `''`:

```r
fruit %>%
  mutate(price=gsub('\\$','',price))
```

Then, we can use the base R function `as.numeric()` to convert character strings containing numerical values to numeric:

```r
fruit %>%
  mutate(price = as.numeric(price))
```

Now, we have a data frame that looks like: 

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|1|105|
|"apple"|0.75|95|
|"peach"|3|55|
|"peach"|4|55|
|"clementine"|2.5|35|

</div>

String Parsing

Often, you have the same data separated out into multiple files.

Let's say that you have a ton of files following the filename structure: `'file_1.csv'`, `'file_2.csv'`, `'file_3.csv'`, and so on. The power of dplyr and tidyr is mainly in being able to manipulate large amounts of structured data, so you want to be able to get all of the relevant information into one table so that you can analyze the aggregate data.

You can combine the base R functions `list.files()` and `lapply()` with readr and dplyr to organize this data better, as shown below:

```r
files <- list.files(pattern = "file_.*csv")
df_list <- lapply(files,read_csv)
df <- bind_rows(df_list)
```
* The first line uses `list.files()` and a [regular expression](https://www.codecademy.com/courses/practical-data-cleaning/lessons/nlp-regex-conceptual/exercises/introduction), a sequence of characters describing a pattern of text that should be matched, to find any file in the current directory that starts with `'file_'` and has an extension of `csv`, storing the name of each file in a vector `files`
* The second line uses `lapply()` to read each file in `files` into a data frame with `read_csv()`, storing the data frames in `df_list`
* The third line then concatenates all of those data frames together with dplyr's `bind_rows()` function

Dealing with Multiple Files

Great! We have looked at a number of different methods we may use to get data into the format we want for analysis.

Specifically, we have covered:

* diagnosing the “tidiness” of data
* combining multiple files
* reshaping data
* changing the types of values
* manipulating strings to represent data better

You can use these methods to transform your datasets to be clean and easy to work with!

Review

Each column of a data frame can hold items of the same _data type_. The data types that R uses are: character, numeric (real or decimal), integer, logical, or complex. Often, we want to convert between types so that we can do better analysis. If a numerical category like `"num_users"` is stored as a vector of `character`s instead of `numeric`s, for example, it makes it more difficult to do something like make a line graph of users over time.

To see the types of each column of a data frame, we can use:

```py
str(df)
```

`str()` displays the internal structure of an R object. Calling `str()` with a data frame as an argument will return a variety of information, including the data types. For a data frame like this: 

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|"$1"|105|
|"apple"|"$0.75"|95|
|"peach"|"$3"|55|
|"clementine"|"$2.5"|35|

</div>
<br>

the data types would be:

```
#> $ item:        chr
#> $ price:       chr
#> $ calories:    num
```

We can see that the `price` column is made up of `character`s, which will probably make our analysis of price more difficult. We'll look at how to convert columns into numeric values in the next few exercises.


Looking at Data Types

In trying to get clean data, we want to make sure each column represents one type of measurement. Often, multiple measurements are recorded in the same column, and we want to separate these out so that we can do individual analysis on each variable.

Let's say we have a column "birthday" with data formatted in MMDDYYYY format. In other words, "11011993" represents a birthday of November 1, 1993. We want to split this data into day, month, and year so that we can use these columns as separate features.

In this case, we know the exact structure of these strings. The first two characters will always correspond to the month, the second two to the day, and the rest of the string will always correspond to year. We can easily break the data into three separate columns by splitting the strings into substrings using `str_sub()`, a helpful function from the stringr package:

```r
# Create the 'month' column
df %>%
  mutate(month = str_sub(birthday,1,2))

# Create the 'day' column
df %>%
  mutate(day = str_sub(birthday,3,4))

# Create the 'year' column
df %>%
  mutate(year = str_sub(birthday,5))
```

* The first command takes the characters starting at index `1` and ending at index `2` of each value in the `birthday` column and puts it into a `month` column.
* The second command takes the characters starting at index `3` and ending at index `4` of each value in the `birthday` column and puts it into a `day` column.
* The third command takes the characters starting at index `5` and ending at the end of the value in the `birthday` column and puts it into a `year` column.

This would transform a table like:

<div class="narrative-table-container">

|*id*|*birthday*|
|-|-|
|1011|"12241989"|
|1112|"10311966"|
|1113|"01052011"|

</div>
<br>

into a table like:

<div class="narrative-table-container">

|*id*|*birthday*|*month*|*day*|*year*|
|-|-|-|-|-|
|1011|"12241989"|"12"|"24"|"1989"|
|1112|"10311966"|"10"|"31"|"1966"|
|1113|"01052011"|"01"|"05"|"2011"|

</div>
<br>

We will practice changing string columns into numerical columns (like converting `"10"` to `10`) in a future exercise.



Splitting By Index

A huge part of data science involves acquiring raw data and getting it into a form ready for analysis. Some have estimated that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it or building models from it.

When we receive raw data, we have to do a number of things before we're ready to analyze it, possibly including:

* diagnosing the "tidiness" of the data — how much data cleaning we will have to do
* reshaping the data — getting the right rows and columns for effective analysis
* combining multiple files
* changing the types of values — how we fix a column where numerical values are stored as strings, for example
* dropping or filling missing values - how we deal with data that is incomplete or missing
* manipulating strings to represent the data better

We will go through the techniques data scientists use to accomplish these goals by looking at some "unclean" datasets and trying to get them into a good, clean state. Along the way we will use the powerful tidyverse packages dplyr and tidyr to get our data squeaky clean!

Introduction

Often we see duplicated rows of data in the data frames we are working with. This could happen due to errors in data collection or in saving and loading the data.

To check for duplicates, we can use the base R function `duplicated()`, which will return a logical vector telling us which rows are duplicate rows.

Let's say we have a data frame `fruits` that represents this table:

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|"$1"|105|
|"apple"|"$0.75"|95|
|"apple"|"$0.75"|95|
|"peach"|"$3"|55|
|"peach"|"$4"|55|
|"clementine"|"$2.5"|35|

</div>
<br>

If we call `fruits %>% duplicated()`, we would get the following vector:

```r
>> [1] FALSE FALSE TRUE FALSE FALSE FALSE
```

We can see that the third row, which represents an `"apple"` with price `"$0.75"` and `95` calories, is a duplicate row. Every value in this row is the same as in another row (the previous row).

We can use the dplyr `distinct()` function to remove all rows of a data frame that are duplicates of another row.

If we call `fruits %>% distinct()`, we would get the table: 

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|"$1"|105|
|"apple"|"$0.75"|95|
|"peach"|"$3"|55|
|"peach"|"$4"|55|
|"clementine"|"$2.5"|35|

</div>
<br>

The `"apple"` row was deleted because it was exactly the same as another row. But the two `"peach"` rows remain because there is a difference in the *price* column.

If we wanted to remove every row with a duplicate value in the *item* column, we could specify a `subset`:

```r
fruits %>%
  distinct(item,.keep_all=TRUE)
```

By default, this keeps the first occurrence of the duplicate:

<div class="narrative-table-container">

|*item*|*price*|*calories*|
|-|-|-|
|"banana"|"$1"|105|
|"apple"|"$0.75"|95|
|"peach"|"$3"|55|
|"clementine"|"$2.5"|35|

</div>
<br>

Make sure that the columns you drop duplicates from are specifically the ones where duplicates don't belong. You wouldn't want to drop duplicates with the *`price`* column as a subset, for example, because it's okay if multiple items cost the same amount!

Dealing with Duplicates

Since we want
- Each variable as a separate column
- Each row as a separate observation

We would want to reshape a table like:

<div class="narrative-table-container">

|*Account*|*Checking*|*Savings*|
|-|-|-|
|"12456543"|8500|8900|
|"12283942"|6410|8020|
|"12839485"|78000|92000|

</div>
<br>

Into a table that looks more like: 

<div class="narrative-table-container">

|*Account*|*Account Type*|*Amount*|
|-|-|-|
|"12456543"|"Checking"|8500|
|"12456543"|"Savings"|8900|
|"12283942"|"Checking"|6410|
|"12283942"|"Savings"|8020|
|"12839485"|"Checking"|78000|
|"12839485"|"Savings"|920000|

</div>
<br>

We can use tidyr's `gather()` function to do this transformation. `gather()` takes a data frame and the columns to unpack:

```r
df %>%
  gather('Checking','Savings',key='Account Type',value='Amount')
```

The arguments you provide are:
- `df`: the data frame you want to gather, which can be piped into `gather()`
- `Checking` and `Savings`: the columns of the old data frame that you want to turn into variables
- `key`: what to call the column of the new data frame that stores the variables
- `value`: what to call the column of the new data frame that stores the values