Save time and effort by using emend to clean and standardise your data. The emend package is your artificial intelligence (AI) assistant that contain a collection of functions to help you with your data cleaning tasks.
WARNING: validate that the method works for your own data context.
The default setting has been set so that the results are reproducible and less prone to “creativity”, as desired for data processing. The reproducibility is achieved within the same system (i.e. computer) only and not necessarily across different systems.
Installation
Package
You can install emend from CRAN like below:
install.packages("emend")You can install the development version of emend from GitHub like below:
# install.packages("pak")
pak::pak("anuopensci/emend")Examples
Specifying the LLM
Before using any functions in emend, you need to set up a “Chat” object which is from the package ellmer, then use this “Chat” object as an input argument for functions in emend.
If you have set up a local LLM using Ollama:
You could run the below code to set up your Chat object. Our function is tested with the LLM llama3.1:8b and with a seed of 0, so these will be the preferred parameters.
chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none") If you want to use LLM via provider APIs: Follow the instructions on the ellmer website to set up the API keys and create a desired Chat object.
Categorise text
Some categorical variables can have simple typos or alternative representations. For example below we have “UK” written also as “United Kingdom”.
messy$country
#> [1] "UK" "US" "Canada" "UK"
#> [5] "US" "Canada" "United Kingdom" "USA"
#> [9] "New Zealand" "NZ" "Australia" "New Zealand"
#> [13] "UK" "United Kingdom" "UK" "US"
#> [17] "United Kingdom" "Australia" "US" "Australia"While you can manually fix this, again, this can be tedious. We can map this automatically using emend_fct_match().
emend_fct_match(messy$country, levels = c("UK", "USA", "Canada", "Australia", "NZ"), chat = chat)
#> [1] UK USA Canada UK USA Canada UK
#> [8] USA NZ NZ Australia NZ UK UK
#> [15] UK USA UK Australia USA Australia
#> Levels: UK USA Canada Australia NZThe function actually works to match a continent as well! Let’s use emend_lvl_match() to more easily see the conversion on the levels alone.
emend_lvl_match(messy$country, levels = c("Asia", "Europe", "North America", "Oceania", "South America"), chat = chat)
#> UK US Canada United Kingdom USA
#> "Europe" "North America" "North America" "Europe" "North America"
#> New Zealand NZ Australia
#> "Oceania" "Oceania" "Oceania"
#>
#> ── Converted by emend: ─────────────────────────────────────────────────────────
#> original converted
#> 1 UK Europe
#> 2 United Kingdom Europe
#> 3 US North America
#> 4 Canada North America
#> 5 USA North America
#> 6 New Zealand Oceania
#> 7 NZ Oceania
#> 8 Australia OceaniaThe above process required specification of all the levels but sometimes you may not know ahead all of the levels. The emend_get_levels() function will attempt to clean up the levels.
levels <- emend_lvl_unique(messy$country, chat = chat)
print(levels)
#> [1] "United Kingdom" "United States" "Canada" "New Zealand"
#> [5] "Australia"Then you can use the cleaned levels to map the messy data to the correct levels.
emend_fct_match(messy$country, levels = levels, chat = chat)
#> [1] United Kingdom United States Canada United Kingdom United States
#> [6] Canada United Kingdom United States New Zealand New Zealand
#> [11] Australia New Zealand United Kingdom United Kingdom United Kingdom
#> [16] United States United Kingdom Australia United States Australia
#> Levels: United Kingdom United States Canada New Zealand AustraliaCorrecting order of levels for ordinal variables
The levels of categorical variables by default are ordered alphabetically. This can be problematic when the levels have a natural order.
factor(likerts$likert1)
#> [1] Strongly Disagree Neutral Strongly Agree Strongly Disagree
#> [5] Disagree Somewhat Agree Strongly Agree Somewhat Disagree
#> [9] Agree Disagree Somewhat Disagree Somewhat Disagree
#> [13] Strongly Disagree Somewhat Agree Somewhat Agree Disagree
#> [17] Agree Agree Disagree Strongly Agree
#> [21] Strongly Disagree Strongly Agree Somewhat Agree Somewhat Agree
#> [25] Strongly Disagree Strongly Disagree Agree Somewhat Agree
#> [29] Somewhat Agree Disagree Disagree Agree
#> [33] Strongly Disagree Neutral Strongly Agree Strongly Disagree
#> [37] Neutral Somewhat Disagree Agree Disagree
#> 7 Levels: Agree Disagree Neutral Somewhat Agree ... Strongly DisagreeA correct order may need to be manually specified like below, but it can be a tedious task.
factor(likerts$likert1,
levels = c("Strongly Disagree", "Disagree", "Somewhat Disagree", "Neutral", "Somewhat Agree", "Agree", "Strongly Agree"))
#> [1] Strongly Disagree Neutral Strongly Agree Strongly Disagree
#> [5] Disagree Somewhat Agree Strongly Agree Somewhat Disagree
#> [9] Agree Disagree Somewhat Disagree Somewhat Disagree
#> [13] Strongly Disagree Somewhat Agree Somewhat Agree Disagree
#> [17] Agree Agree Disagree Strongly Agree
#> [21] Strongly Disagree Strongly Agree Somewhat Agree Somewhat Agree
#> [25] Strongly Disagree Strongly Disagree Agree Somewhat Agree
#> [29] Somewhat Agree Disagree Disagree Agree
#> [33] Strongly Disagree Neutral Strongly Agree Strongly Disagree
#> [37] Neutral Somewhat Disagree Agree Disagree
#> 7 Levels: Strongly Disagree Disagree Somewhat Disagree ... Strongly AgreeThe emend_fct_reorder() function will try to reorder the levels of the factor in a meaningful way automatically using a large language model.
emend_fct_reorder(likerts$likert1, chat = chat) |> levels()
#> [1] "Strongly Disagree" "Disagree" "Somewhat Disagree"
#> [4] "Neutral" "Somewhat Agree" "Agree"
#> [7] "Strongly Agree"Translate
The emend_translate() function can be used to translate text to another language (default English). The text can be a mix of different languages.
text <- c("猿も木から落ちる", "你好", "bon appetit")
emend_translate(text, chat = chat)
#> [1] "Even monkeys fall from trees." "Hello."
#> [3] "Enjoy your meal."You can also try to identify the language in the text.
emend_what_language(text, chat = chat)
#> [1] "Japanese" "Mandarin Chinese" "French"Dates
When combining data from different sources, inconsistencies in date formats can occur frequently. Reformatting dates to a single format using traditional programming requires listing all possible date formats and can be time-consuming. The emend_clean_date() function uses an LLM to standardise the dates to the international standard “YYYY-MM-DD”.
x <- c("16/02/1997", "20 November 2024", "24 Mar 2022", "2000-01-01", "Jason",
"Dec 25, 2030", "11/05/2024", "March 10, 1999")
emend_clean_date(x, chat = chat)
#> [1] "1997-02-16" "2024-11-20" "2022-03-24" "2000-01-01" NA
#> [6] "2020-12-25" "2024-05-11" "1999-03-10"Addresses
When scraping data from websites or APIs, especially property-related information, addresses can present challenges. The emend_clean_address() function uses an LLM to standardise addresses into a consistent format and returns an empty value for items that are not addresses.
x <- c("154 university avenue, acton act 2601",
"76/2 Cape Street, Dickson ACT 2602",
"Shop 4/96 Bunda St, Canberra ACT 2601",
"11 E Row, Canberra ACT 2601",
"173/46 Macquarie St, Barton ACT 2600",
"Unit 189/260 City walk, Canberra ACT 2601",
"the kebab place",
"i don't know the address")
emend_clean_address(x, chat = chat)
#> [1] "154 University Ave, Acton ACT 2601"
#> [2] "76/2 Cape St, Dickson ACT 2602"
#> [3] "Shop 4/96 Bunda St, Canberra ACT 2601"
#> [4] "11 E Row, Canberra ACT 2601"
#> [5] "173/46 Macquarie St, Barton ACT 2600"
#> [6] "189/260 City Walk, Canberra ACT 2601"
#> [7] "INVALID ADDRESS"
#> [8] "INVALID ADDRESS"