cleaning_company_labels.qmd

cleaning_company_labels.qmd

## Renaming long names

Load `tidyverse` and data from TidyTuesday.

```{r}
library(tidyverse)
big_tech_companies <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-02-07/big_tech_companies.csv')
big_tech_companies
# # A tibble: 14 × 2
#    stock_symbol company                                  
#    <chr>        <chr>                                    
#  1 AAPL         Apple Inc.                               
#  2 ADBE         Adobe Inc.                               
#  3 AMZN         Amazon.com, Inc.                         
#  4 CRM          Salesforce, Inc.                         
#  5 CSCO         Cisco Systems, Inc.                      
#  6 GOOGL        Alphabet Inc.                            
#  7 IBM          International Business Machines Corporat…
#  8 INTC         Intel Corporation                        
#  9 META         Meta Platforms, Inc.                     
# 10 MSFT         Microsoft Corporation                    
# 11 NFLX         Netflix, Inc.                            
# 12 NVDA         NVIDIA Corporation                       
# 13 ORCL         Oracle Corporation                       
# 14 TSLA         Tesla, Inc.  
```

First, notice that IBM has a suuuuper long name.
We can rename just that name in the `company` column with `mutate()` and `if_else()`.


```{r}
big_tech_companies |> 
  mutate(
    company = if_else(
      stock_symbol == 'IBM',
      'IBM',
      company
    )
  )
# # A tibble: 14 × 2
#    stock_symbol company              
#    <chr>        <chr>                
#  1 AAPL         Apple Inc.           
#  2 ADBE         Adobe Inc.           
#  3 AMZN         Amazon.com, Inc.     
#  4 CRM          Salesforce, Inc.     
#  5 CSCO         Cisco Systems, Inc.  
#  6 GOOGL        Alphabet Inc.        
#  7 IBM          IBM                  
#  8 INTC         Intel Corporation    
#  9 META         Meta Platforms, Inc. 
# 10 MSFT         Microsoft Corporation
# 11 NFLX         Netflix, Inc.        
# 12 NVDA         NVIDIA Corporation   
# 13 ORCL         Oracle Corporation   
# 14 TSLA         Tesla, Inc.        
```

## Remove superfluous words

Now, let's get rid of words like "Platforms" or "Corporation".
Similarly, we can get rid of commas.
We can do all that that with `str_remove()`.

```{r}
big_tech_companies |> 
  mutate(
    company = if_else(
      stock_symbol == 'IBM',
      'IBM',
      company
    ),
    company = str_remove(
      company,
      # Put multiple things that need to be removed into parantheses and keep words apart with |
      '(Platforms|Corporation|,)'
    )
  )
# # A tibble: 14 × 2
#    stock_symbol company             
#    <chr>        <chr>               
#  1 AAPL         "Apple Inc."        
#  2 ADBE         "Adobe Inc."        
#  3 AMZN         "Amazon.com Inc."   
#  4 CRM          "Salesforce Inc."   
#  5 CSCO         "Cisco Systems Inc."
#  6 GOOGL        "Alphabet Inc."     
#  7 IBM          "IBM"               
#  8 INTC         "Intel "            
#  9 META         "Meta , Inc."       
# 10 MSFT         "Microsoft "        
# 11 NFLX         "Netflix Inc."      
# 12 NVDA         "NVIDIA "           
# 13 ORCL         "Oracle "           
# 14 TSLA         "Tesla Inc."  
```

Notice how this did not remove everything we wanted?
That's a good use case for `str_remove_all()`.
You want to get rid of all possible matches.

```{r}
big_tech_companies |> 
  mutate(
    company = if_else(
      stock_symbol == 'IBM',
      'IBM',
      company
    ),
    company = str_remove_all(
      company,
      '(Platforms|Corporation|,)'
    )
  )
# # A tibble: 14 × 2
#    stock_symbol company             
#    <chr>        <chr>               
#  1 AAPL         "Apple Inc."        
#  2 ADBE         "Adobe Inc."        
#  3 AMZN         "Amazon.com Inc."   
#  4 CRM          "Salesforce Inc."   
#  5 CSCO         "Cisco Systems Inc."
#  6 GOOGL        "Alphabet Inc."     
#  7 IBM          "IBM"               
#  8 INTC         "Intel "            
#  9 META         "Meta  Inc."        
# 10 MSFT         "Microsoft "        
# 11 NFLX         "Netflix Inc."      
# 12 NVDA         "NVIDIA "           
# 13 ORCL         "Oracle "           
# 14 TSLA         "Tesla Inc." 
```



## Remove words with special characters


```{r}
big_tech_companies |> 
  mutate(
    company = if_else(
      stock_symbol == 'IBM',
      'IBM',
      company
    ),
    company = str_remove_all(
      company,
      '(Platforms|Corporation|Inc\\.|\\.com|,)'
    )
  )
# # A tibble: 14 × 2
#    stock_symbol company         
#    <chr>        <chr>           
#  1 AAPL         "Apple "        
#  2 ADBE         "Adobe "        
#  3 AMZN         "Amazon "       
#  4 CRM          "Salesforce "   
#  5 CSCO         "Cisco Systems "
#  6 GOOGL        "Alphabet "     
#  7 IBM          "IBM"           
#  8 INTC         "Intel "        
#  9 META         "Meta  "        
# 10 MSFT         "Microsoft "    
# 11 NFLX         "Netflix "      
# 12 NVDA         "NVIDIA "       
# 13 ORCL         "Oracle "       
# 14 TSLA         "Tesla " 
```


## Remove trailing white space

Here, `str_trim()` will help you.

```{r}
big_tech_companies |> 
  mutate(
    company = if_else(
      stock_symbol == 'IBM',
      'IBM',
      company
    ),
    company = str_remove_all(
      company,
      '(Platforms|Corporation|Inc\\.|\\.com|,)'
    ),
    company = str_trim(company)
  )
# # A tibble: 14 × 2
#    stock_symbol company      
#    <chr>        <chr>        
#  1 AAPL         Apple        
#  2 ADBE         Adobe        
#  3 AMZN         Amazon       
#  4 CRM          Salesforce   
#  5 CSCO         Cisco Systems
#  6 GOOGL        Alphabet     
#  7 IBM          IBM          
#  8 INTC         Intel        
#  9 META         Meta         
# 10 MSFT         Microsoft    
# 11 NFLX         Netflix      
# 12 NVDA         NVIDIA       
# 13 ORCL         Oracle       
# 14 TSLA         Tesla     
```