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 #>    <chr>                            <int> #>  1 Australian house and garden       1469 #>  2 New scientist (Australasian ed.)  1380 #>  3 Australian home beautiful         1331 #>  4 Country style                     1229 #>  5 The New idea                      1186 #>  6 Hello                             1133 #>  7 Woman's day                       1096 #>  8 Country life                      1056 #>  9 Better homes and gardens. (AU)    1041 #> 10 Yi Zhou Kan                        884 #> # … with 121,036 more rows count(bris_libs, item_type, sort = TRUE) #> # A tibble: 69 x 2 #>    item_type       n #>    <chr>       <int> #>  1 PICTURE-BK 121126 #>  2 DVD         98283 #>  3 AD-PBK      91671 #>  4 JU-PBK      88402 #>  5 NONFICTION  76168 #>  6 AD-MAGS     60516 #>  7 AD-FICTION  53090 #>  8 LARGEPRINT  19113 #>  9 JU-FICTION  17261 #> 10 LOTE-BOOK   12303 #> # … with 59 more rows count(bris_libs, age, sort = TRUE) #> # A tibble: 5 x 2 #>   age           n #>   <chr>     <int> #> 1 ADULT    420287 #> 2 JUVENILE 283902 #> 3 YA        13715 #> 4 <NA>        147 #> 5 UNKNOWN      36 count(bris_libs, library, sort = TRUE) #> # A tibble: 38 x 2 #>    library     n #>    <chr>   <int> #>  1 SBK     49154 #>  2 BSQ     45968 #>  3 CNL     45642 #>  4 IPY     44569 #>  5 GCY     43090 #>  6 CDE     42775 #>  7 ASH     42086 #>  8 WYN     35124 #>  9 KEN     33947 #> 10 MTO     31201 #> # … with 28 more rows 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.