This has been copied from the README.md file

bris-lib-checkout

This provides tidied up data from the Brisbane library checkouts

Retrieving and cleaning the data

The script for retrieving and cleaning the data is made available in scrape-library.R.

The data

• The data/ folder contains the tidy data
• The data-raw/ folder contains the raw data

data/

This contains four tidied up dataframes:

• tidy-brisbane-library-checkout.csv
• metadata_branch.csv
• metadata_heading.csv
• metadata_item_type.csv

tidy-brisbane-library-checkout.csv contains the following columns, with the metadata file metadata_heading containing the description of these columns.

knitr::kable(readr::read_csv("data/metadata_heading.csv"))
#> Parsed with column specification:
#> cols(
#> heading = col_character(),
#> heading_explanation = col_character()
#> )

heading

heading_explanation

Title

Title of Item

Author

Author of Item

Call Number

Call Number of Item

Item id

Unique Item Identifier

Item Type

Type of Item (see next column)

Status

Current Status of Item

Language

Published language of item (if not English)

Age

Suggested audience

Checkout Library

Checkout branch

Date

Checkout date

We also added year, month, and day columns.

The remaining data are all metadata files that contain meta information on the columns in the checkout data:

library(tidyverse)
#> ── Attaching packages ────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
#> ✔ tibble 1.4.99.9006 ✔ dplyr 0.7.8
#> ✔ tidyr 0.8.2 ✔ stringr 1.3.1
#> ✔ readr 1.3.0 ✔ forcats 0.3.0
#> ── Conflicts ───────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
knitr::kable(readr::read_csv("data/metadata_branch.csv"))
#> Parsed with column specification:
#> cols(
#> branch_code = col_character(),
#> branch_heading = col_character()
#> )

branch_code

branch_heading

ANN

Annerley

ASH

Ashgrove

BNO

Banyo

BRR

BrackenRidge

BSQ

Brisbane Square Library

BUL

Bulimba

CDA

Corinda

CDE

Chermside

CNL

Carindale

CPL

Coopers Plains

CRA

Carina

EPK

Everton Park

FAI

Fairfield

GCY

Garden City

GNG

Grange

HAM

Hamilton

HPK

Holland Park

INA

Inala

IPY

Indooroopilly

MBG

Mt. Coot-tha

MIT

Mitchelton

MTG

Mt. Gravatt

MTO

Mt. Ommaney

NDH

Nundah

NFM

New Farm

SBK

Sunnybank Hills

SCR

Stones Corner

SGT

Sandgate

VAN

Mobile Library

TWG

Toowong

WND

West End

WYN

Wynnum

ZIL

Zillmere

knitr::kable(readr::read_csv("data/metadata_item_type.csv"))
#> Parsed with column specification:
#> cols(
#> item_type_code = col_character(),
#> item_type_explanation = col_character()
#> )

item_type_code

item_type_explanation

AD-FICTION

Adult Fiction

AD-MAGS

Adult Magazines

AD-PBK

Adult Paperback

BIOGRAPHY

Biography

BSQCDMUSIC

Brisbane Square CD Music

BSQCD-ROM

Brisbane Square CD Rom

BSQ-DVD

Brisbane Square DVD

CD-BOOK

Compact Disc Book

CD-MUSIC

Compact Disc Music

CD-ROM

CD Rom

DVD

DVD

DVD_R18+

DVD Restricted - 18+

FASTBACK

Fastback

GAYLESBIAN

Gay and Lesbian Collection

GRAPHICNOV

Graphic Novel

ILL

InterLibrary Loan

JU-FICTION

Junior Fiction

JU-MAGS

Junior Magazines

JU-PBK

Junior Paperback

KITS

Kits

LARGEPRINT

Large Print

LGPRINTMAG

Large Print Magazine

LITERACY

Literacy

LITERACYAV

Literacy Audio Visual

LOCSTUDIES

Local Studies

LOTE-BIO

Languages Other than English Biography

LOTE-BOOK

Languages Other than English Book

LOTE-CDMUS

Languages Other than English CD Music

LOTE-DVD

Languages Other than English DVD

LOTE-MAG

Languages Other than English Magazine

LOTE-TB

Languages Other than English Taped Book

MBG-DVD

Mt Coot-tha Botanical Gardens DVD

MBG-MAG

Mt Coot-tha Botanical Gardens Magazine

MBG-NF

Mt Coot-tha Botanical Gardens Non Fiction

MP3-BOOK

MP3 Audio Book

NONFIC-SET

Non Fiction Set

NONFICTION

Non Fiction

PICTURE-BK

Picture Book

PICTURE-NF

Picture Book Non Fiction

PLD-BOOK

Public Libraries Division Book

YA-FICTION

Young Adult Fiction

YA-MAGS

Young Adult Magazine

YA-PBK

Young Adult Paperback

Example usage

Let’s explore the data

bris_libs <- readr::read_csv("data/bris-lib-checkout.csv")
#> Parsed with column specification:
#> cols(
#> title = col_character(),
#> author = col_character(),
#> call_number = col_character(),
#> item_id = col_double(),
#> item_type = col_character(),
#> status = col_character(),
#> language = col_character(),
#> age = col_character(),
#> library = col_character(),
#> date = col_double(),
#> datetime = col_datetime(format = ""),
#> year = col_double(),
#> month = col_double(),
#> day = col_character()
#> )
#> Warning: 20 parsing failures.
#> row col expected actual file
#> 587795 item_id a double REFRESH 'data/bris-lib-checkout.csv'
#> 590579 item_id a double REFRESH 'data/bris-lib-checkout.csv'
#> 590597 item_id a double REFRESH 'data/bris-lib-checkout.csv'
#> 595774 item_id a double REFRESH 'data/bris-lib-checkout.csv'
#> 597567 item_id a double REFRESH 'data/bris-lib-checkout.csv'
#> ...... ....... ........ ....... ............................
#> See problems(...) for more details.

We can count the number of titles, item types, suggested age, and the library given:

library(dplyr)
count(bris_libs, title, sort = TRUE)
#> # A tibble: 121,046 x 2
#> title n
#>

License

This data is provided under a CC BY 4.0 license

It has been downloaded from Brisbane library checkouts, and tidied up using the code in data-raw.

Beach Volleyball

The data this week comes from Adam Vagnar who also blogged about this dataset. There's a LOT of data here - match-level results, player details, and match-level statistics for some matches. For all this dataset all the matches are played 2 vs 2, so there are columns for 2 winners (1 team) and 2 losers (1 team). The data is relatively ready for analysis and clean, although there are some duplicated columns and the data is wide due to the 2-players per team.

Check out the data dictionary, or Wikipedia for some longer-form details around what the various match statistics mean.

Most of the data is from the international FIVB tournaments but about 1/3 is from the US-centric AVP.

The FIVB Beach Volleyball World Tour (known between 2003 and 2012 as the FIVB Beach Volleyball Swatch World Tour for sponsorship reasons) is the worldwide professional beach volleyball tour for both men and women organized by the Fédération Internationale de Volleyball (FIVB). The World Tour was introduced for men in 1989 while the women first competed in 1992.

Winning the World Tour is considered to be one of the highest honours in international beach volleyball, being surpassed only by the World Championships, and the Beach Volleyball tournament at the Summer Olympic Games.

FiveThirtyEight examined the disadvantage of serving in beach volleyball, although they used Olympic-level data. Again, Adam Vagnar also covered this data on his blog.

What is Tidy Tuesday?

TidyTuesday A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.

Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy!

We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions. While we understand that the two are related, the focus of this practice is purely on building skills with real-world data.