Package 'emend'

Airbnb listings and reviews

Description

A sample dataset of Airbnb listings and reviews of properties from Sydney, Australia.

Usage

airbnb_listings

airbnb_reviews

Format

airbnb_listings

A data.frame with 1623 rows and 68 columns

id

Airbnb's unique identifier for the listing.

name

Name of the listing.

description

Detailed description of the listing.

neighborhood_overview

Host's description of the neighbourhood.

picture_url

URL to the Airbnb hosted regular sized image for the listing.

host_id

Airbnb's unique identifier for the host/user.

host_name

Name of the host. Usually just the first name(s).

host_since

The date the host/user was created. For hosts that are Airbnb guests this could be the date they registered as a guest.

host_location

The host's self reported location.

host_about

Description about the host.

host_response_time

The time interval between when a host responds to an inquiry from a guest.

host_response_rate

Percentage of inquiries from potential guests that are responded to by hosts.

host_acceptance_rate

That rate at which a host accepts booking requests.

host_is_superhost

Whether the host is a super host or not.

host_thumbnail_url

A thumbnail of the host.

host_picture_url

A URL to the picture of the host.

host_neighbourhood

The host neighbourhood.

host_listings_count

The number of listings the host has.

host_total_listings_count

The number of listings the host has.

host_verifications

Host communication verifications.

host_has_profile_pic

Whether the host has a profile pic.

host_identity_verified

Whether the host has their identity verified.

neighbourhood_cleansed

The neighbourhood as geocoded using the latitude and longitude against neighborhoods as defined by open or public digital shapefiles.

latitude

Uses the World Geodetic System (WGS84) projection for latitude and longitude.

longitude

Uses the World Geodetic System (WGS84) projection for latitude and longitude.

property_type

Self selected property type. Hotels and Bed and Breakfasts are described as such by their hosts in this field.

room_type

Entire home/apt, Private room, Shared room, or Hotel. Entire places are best if you're seeking a home away from home. With an entire place, you'll have the whole space to yourself. This usually includes a bedroom, a bathroom, a kitchen, and a separate, dedicated entrance. Hosts should note in the description if they'll be on the property or not (ex: "Host occupies first floor of the home"), and provide further details on the listing. Private rooms are great for when you prefer a little privacy, and still value a local connection. When you book a private room, you'll have your own private room for sleeping and may share some spaces with others. You might need to walk through indoor spaces that another host or guest may occupy to get to your room. Shared rooms are for when you don't mind sharing a space with others. When you book a shared room, you'll be sleeping in a space that is shared with others and share the entire space with other people. Shared rooms are popular among flexible travelers looking for new friends and budget-friendly stays.

accommodates

The maximum capacity of the listing.

bathrooms

The number of bathrooms in the listing.

bathrooms_text

The text of the number of bathsroom in the listings.

bedrooms

The number of bedrooms.

beds

The number of bed(s).

amenities

The amenities.

price

Daily price in local currency.

minimum_nights

Minimum number of night stay for the listing.

maximum_nights

Maximum number of night stay for the listing.

minimum_minimum_nights

The smallest minimum_night value from the calender (looking 365 nights in the future).

maximum_minimum_nights

The largest minimum_night value from the calender (looking 365 nights in the future).

minimum_maximum_nights

The smallest maximum_night value from the calender (looking 365 nights in the future).

maximum_maximum_nights

The largest maximum_night value from the calender (looking 365 nights in the future).

minimum_nights_avg_ntm

The average minimum_night value from the calender (looking 365 nights in the future).

maximum_nights_avg_ntm

The average maximum_night value from the calender (looking 365 nights in the future).

has_availability

Whether there is availability or not.

availability_30

The availability of the listing x days in the future as determined by the calendar. Note a listing may not be available because it has been booked by a guest or blocked by the host.

availability_60

availability_90

availability_365

number_of_reviews

The number of reviews the listing has.

number_of_reviews_ltm

The number of reviews the listing has (in the last 12 months).

number_of_reviews_l30d

The number of reviews the listing has (in the last 30 days).

first_review

The date of the first/oldest review.

last_review

The date of the last/newest review.

review_scores_rating

The review score for ratings of the listing.

review_scores_accuracy

The review score for accuracy of the listing.

review_scores_cleanliness

The review score for cleanliness of the listing.

review_scores_checkin

The review score for checkin experience of the listing.

review_scores_communication

The review score for communication of the listing.

review_scores_location

The review score for location of the listing.

review_scores_value

The review score for value of the listing.

license

The licence/permit/registration number.

instant_bookable

Whether the guest can automatically book the listing without the host requiring to accept their booking request. An indicator of a commercial listing.

calculated_host_listings_count

The number of listings the host has in the current scrape, in the city/region geography.

calculated_host_listings_count_entire_homes

The number of Entire home/apt listings the host has in the current scrape, in the city/region geography.

calculated_host_listings_count_private_rooms

The number of Private room listings the host has in the current scrape, in the city/region geography.

calculated_host_listings_count_shared_rooms

The number of Shared room listings the host has in the current scrape, in the city/region geography.

reviews_per_month

The average number of reviews per month the listing has over the lifetime of the listing.

airbnb_reviews

A data.frame with 5679 rows and 6 columns

listing_id

Unique identifier for the listing

id

Unique identifier for the review

date

Date of the review

reviewer_id

Unique identifier for the reviewer

reviewer_name

Name of the reviewer

comments

Text of the review

Source

https://insideairbnb.com/get-the-data/

---

Alcohol warehouse and retail sales

Description

year

Year

month

Month

supplier

Supplier

item_code

Item code

item_description

Item description

item_type

Item type

retail_sales

Retail sales

retail_transfers

Retail transfers

warehouse_sales

Warehouse sales

Usage

alcohol

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 1000 rows and 9 columns.

Source

https://catalog.data.gov/dataset/warehouse-and-retail-sales

---

Amazon consumer reviews

Description

A sample of reviews from Amazon in the applicances category.

Usage

consumer

Format

A data.frame with 4549 rows and 11 columns

overall

Overall rating of the product.

verified

Whether the reviewer is verified or not.

review_date

Review date.

review_id

A unique identifier of the reviewer.

reviewer_name

The name of the reviewer.

review_text

The text of the review.

summary

Summary of the review.

vote_helpful

The number of helpful votes of the review.

image

The images that the reviewer post after they receive the product.

Source

Jianmo Ni, Jiacheng Li, Julian McAuley (2019) Justifying recommendations using distantly-labeled reviews and fined-grained aspects. Empirical Methods in Natural Language Processing .

---

Standardise address format

Description

This function standardise inconsistent address formats to a standard format.

Usage

emend_clean_address(address_vector, chat = get_default_chat())

Arguments

address_vector	A character vector that is assumed to be addresses.
chat	A chat object defined by ellmer.

Value

A character vector with converted addresses.

Examples

# Convert a vector of inconsistent formatted address to a standard format
options(ellmer_timeout_s = 3600) 
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")
chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_clean_address(x, chat = chat)

---

Standardise date format

Description

This function standardise inconsistent date formats.

Usage

emend_clean_date(dates_vector, chat = get_default_chat())

Arguments

dates_vector	A character vector that is assumed to be dates.
chat	A chat object defined by ellmer.

Value

A vector of Date objects.

Examples

x <- c("16/02/1997", "20 November 2024", "24 Mar 2022", "2000-01-01", "Jason", 
       "Dec 25, 2030", "11/05/2024", "March 10, 1999")
chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_clean_date(x, chat = chat)

---

Match input factor to specified levels.

Description

Match input factor to specified levels.

Usage

emend_fct_match(.f, levels = NULL, chat = get_default_chat())

Arguments

.f	A factor.
levels	The levels of the factor
chat	A chat object defined by ellmer.

Value

A factor with levels matching the provided levels argument.

Examples

chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_fct_match(messy$country, levels = c("UK", "USA", "Canada", "Australia", "NZ"), chat = chat)

---

Reorder the levels of the input factor in a meaningful way.

Description

Reorder the levels of the input factor in a meaningful way.

Usage

emend_fct_reorder(.f, chat = get_default_chat())

Arguments

.f	A vector of characters or a factor.
chat	A chat object defined by ellmer.

Value

A factor with standardized category labels.

Examples

chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_fct_reorder(likerts$likert1, chat = chat) |> levels()

---

Match the input factor to supplied levels.

Description

Match the input factor to supplied levels.

Usage

emend_lvl_match(.f, levels = NULL, chat = get_default_chat())

Arguments

.f	A vector of characters or a factor.
levels	The levels of the factor.
chat	The chat object defined by ellmer.

Value

A named character vector of standardised category labels, with the class "emend_lvl_match". The names correspond to the original messy categories, and the values are the cleaned versions.

Examples

chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_lvl_match(messy$country,
                levels = c("Asia", "Europe", "North America", "Oceania",
                           "South America"),
                chat = chat)

---

Get the unique levels of messy categorical data

Description

The returned value is a vector. The LLM will return full names instead of abbreviations. You can use this functions to clean up your categorical data and obtain unique levels. Double check if the output from LLM is true to your data. This function is generally suitable for categories, not working well with sentences and too many categories.

Usage

emend_lvl_unique(.f, chat = get_default_chat())

Arguments

.f	A vector of characters or a factor.
chat	A chat object defined by ellmer.

Value

A character vector of standardised category names.

Examples

options(ellmer_timeout_s = 3600)
chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_lvl_unique(messy$country, chat = chat)

---

Translate text from one language to another.

Description

Translate text from one language to another.

Usage

emend_translate(text, to = "English", chat = get_default_chat())

Arguments

text	The text to translate.
to	The language to translate to. The default is "English".
chat	An ellmer Chat object.

Value

A character vector of translated text.

Examples

chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_translate(c("\u733F\u3082\u6728\u304B\u3089\u843D\u3061\u308B", 
                  "\u4F60\u597D", "bon appetit"), chat = chat)

---

Identify the language in the text.

Description

Identify the language in the text.

Usage

emend_what_language(text, chat = get_default_chat())

Arguments

text	A string or a factor that contains text information.
chat	A chat object defined by ellmer

Value

A character vector of language names.

Examples

chat <- ellmer::chat_ollama(model = "llama3.1:8b", seed = 0, echo = "none")
emend_what_language(c("\u733F\u3082\u6728\u304B\u3089\u843D\u3061\u308B", 
                      "\u4F60\u597D", "bon appetit"), chat = chat)

---

Get or create the default chat object

Description

Get or create the default chat object

Usage

get_default_chat()

Value

A Chat object.

---

Hotel reviews

Description

A sample review of hotels.

Usage

hotel

Format

A data.frame with 13,193 rows and 8 columns

reviewer_name

The name of the reviewer.

reviewer_nationality

The nationality of the reviewer.

reviewer_rating

Reviewer's rating.

review_date

Date of the review.

review_title

Title of the review.

review_text

Text of the review.

hotel_name

Name of the hotel being reviewed.

avg_rating

Average rating of the hotel.

Source

https://www.kaggle.com/datasets/nikitaryabukhin/reviewshotel

---

A collection of different likert scales

Description

A data set containing 9 different likert scales.

Usage

likerts

Format

A data.frame with 40 rows and 9 columns

likert1

A 7-point agreeableness likert scale.

likert2

A 5-point agreeableness likert scale.

likert3

A 5-point agreeableness likert scale as a sentence.

likert4

A 5-point frequency likert scale.

likert5

A 5-point rating likert scale.

likert6

A 5-point likelihood likert scale.

likert7

A 5-point likert scale.

likert8

A 5-point satisfaction likert scale.

likert9

A 6-point priority likert scale.

---

A collection of messy inputs

Description

A synthetic dataset that contains inputs with some common standardisation issues.

Usage

messy

Format

A list of 3 character vectors

country

A character vector of countries.

suburb

A character vector of suburbs in Australia with various typos.

school

A character vector of schools or college (with typos) at the Australian National University.

---

Recipes

Description

A sample of 200 recipes.

Usage

recipes

Format

A data.frame with 200 rows and 7 columns.

name

The name of the dish.

ingredients

The list of ingredients.

url

The URL of the recipe.

image

An image of the dish.

cook_time

Cooking time.

prep_time

Preperation time.

servings

The number of servings in text.

Source

https://github.com/jakevdp/open-recipe-data/tree/main

---

Registration data for workshops

Description

A dataset containing registration information for a series of workshops The dataset includes columns for workshop name, affiliation, and the cleaned affiliation by hand.

Usage

registration

Format

A data frame with 221 rows and 3 columns:

Workshop

Character: Workshop name and time.

Affiliation

Character: Affiliation input by participants.

GroundTruth

Character: The cleaned affiliation type, cleaned manually by author.

Source

Real registration data from the Biological Data Science Institute, Australian National University.

---

Review of Iori restaurant

Description

Iori is a Japanese restaurant in Canberra, Australia. The data contains a sample of 20 reviews from Google maps.

Usage

restaurant

Format

A data.frame with 20 rows and 2 columns

review

The text of the review.

rating

A rating out of 5.

---

Ask A Manager Salary Survey 2021

Description

Ask A Manager Salary Survey 2021

Usage

salary

Format

A data.frame with 28,083 rows and 18 columns

timestamp

The timestampe of response.

age

Age cateogry. "How old are you?"

industry

Categorical but respondents can enter text for Other. "What industry do you work in?"

job_title

Text entry. "Job title"

job_title_context

Text entry. "If your job title needs additional context, please clarify here:"

salary_annual

Text entry. "What is your annual salary? (You'll indicate the currency in a later question. If you are part-time or hourly, please enter an annualized equivalent – what you would earn if you worked the job 40 hours a week, 52 weeks a year.)"

salarly_additional

Text entry. "How much additional monetary compensation do you get, if any (for example, bonuses or overtime in an average year)? Please only include monetary compensation here, not the value of benefits."

currency

Categorical entry. "Please indicate the currency"

currency_other

Text entry. "If "Other," please indicate the currency here:"

salary_context

Text entry. "If your income needs additional context, please provide it here:"

country

Text entry. "What country do you work in?"

state

Categorical entry. "If you're in the U.S., what state do you work in?"

city

Text entry. "What city do you work in?"

experience_overall

Categorical entry. "How many years of professional work experience do you have overall?"

experience_in_field

Categorical entry. "How many years of professional work experience do you have in your field?"

education

Categorical entry."What is your highest level of education completed?"

gender

Categorical entry. "What is your gender?"

race

Multiselect entry. "What is your race? (Choose all that apply.)"

Source

https://www.askamanager.org/2021/04/how-much-money-do-you-make-4.html

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