Cleaning text data
Cleaning Data in R
Maggie Matsui
Content Developer @ DataCamp
What is text data?
| Type of data | Example values |
|---|---|
| Names | "Veronica Hopkins", "Josiah", ... |
| Phone numbers | "6171679912", "(868) 949-4489", ... |
| Emails | "[email protected]", "[email protected]", ... |
| Passwords | "JZY46TVG8SM", "iamjosiah21", ... |
| Comments/Reviews | "great service!", "This product broke after 2 days", ... |
Unstructured data problems
- Formatting inconsistency
"6171679912"vs."(868) 949-4489""9239 5849 3712 0039"vs."4490459957881031"
- Information inconsistency
+1 617-167-9912vs.617-167-9912"Veronica Hopkins"vs."Josiah"
- Invalid data
- Phone number
"0492"is too short - Zip code
"19888"doesn't exist
- Phone number
Customer data
customers
# A tibble: 99 x 3
name company credit_card
<chr> <chr> <chr>
1 Galena In Magna Associates 5171 5854 8986 1916
2 MacKenzie Iaculis Ltd 5128-5078-8008-5824
3 Megan Acosta Semper LLC 5502 4529 0732 1744
4 Phoebe Delacruz Sit Amet Nulla Limited 5419-7308-7424-0944
5 Jessica Pellentesque Sed Ltd 5419 2949 5508 9530
# ... with 95 more rows
Detecting hyphenated credit card numbers
str_detect(customers$credit_card, "-")
FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE ...
customers %>%
filter(str_detect(credit_card, "-"))
name company credit_card
1 MacKenzie Iaculis Ltd 5128-5078-8008-5824
2 Phoebe Delacruz Sit Amet Nulla Limited 5419-7308-7424-0944
3 Abel Lorem PC 5211-6023-0805-0217
...
Replacing hyphens
customers %>%mutate(credit_card_spaces = str_replace_all(credit_card, "-", " "))
name company credit_card_spaces
1 Galena In Magna Associates 5171 5854 8986 1916
2 MacKenzie Iaculis Ltd 5128 5078 8008 5824
3 Megan Acosta Semper LLC 5502 4529 0732 1744
4 Phoebe Delacruz Sit Amet Nulla Limited 5419 7308 7424 0944
5 Jessica Pellentesque Sed Ltd 5419 2949 5508 9530
...
Removing hyphens and spaces
credit_card_clean <- customers$credit_card %>%str_remove_all("-") %>% str_remove_all(" ")customers %>% mutate(credit_card = credit_card_clean)
name company credit_card
1 Galena In Magna Associates 5171585489861916
2 MacKenzie Iaculis Ltd 5128507880085824
3 Megan Acosta Semper LLC 5502452907321744
...
Finding invalid credit cards
str_length(customers$credit_card)
16 16 16 16 16 16 16 16 16 16 16 16 12 16 16 16 16 16 16 16 16 16 16 16 16 ...
customers %>%
filter(str_length(credit_card) != 16)
name company credit_card
1 Jerry Russell Sed Eu Company 516294099537
2 Ivor Christian Ut Tincidunt Incorporated 544571330015
3 Francesca Drake Etiam Consulting 517394144089
Removing invalid credit cards
customers %>%
filter(str_length(credit_card) == 16)
name company credit_card
1 Galena In Magna Associates 5171585489861916
2 MacKenzie Iaculis Ltd 5128507880085824
3 Megan Acosta Semper LLC 5502452907321744
4 Phoebe Delacruz Sit Amet Nulla Limited 5419730874240944
5 Jessica Pellentesque Sed Ltd 5419294955089530
...
More complex text problems
- A regular expression is a sequence of characters that allows for robust searching within a string.
- Certain characters are treated differently in a regular expression:
(,),[,],$,.,+,*, and others
stringrfunctions use regular expressions- Searching for these characters requires using
fixed():str_detect(column, fixed("$"))
Learn more in String Manipulation with stringr in R & Intermediate Regular Expressions in R
Let's practice!
Cleaning Data in R
