4 datasets found
  1. Iris Flower Data Set Cleaned

    • kaggle.com
    zip
    Updated Mar 27, 2020
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    Data-Science Sean (2020). Iris Flower Data Set Cleaned [Dataset]. https://www.kaggle.com/larsen0966/iris-flower-data-set-cleaned
    Explore at:
    zip(2624 bytes)Available download formats
    Dataset updated
    Mar 27, 2020
    Authors
    Data-Science Sean
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    If this data Set is useful, and upvote is appreciated. British Statistician Ronald Fisher introduced the Iris Flower in 1936. Fisher published a paper that described the use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.

  2. Gene Expression DEconvolution Pipeline in R

    • figshare.com
    application/gzip
    Updated Aug 31, 2021
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    Slim Karkar (2021). Gene Expression DEconvolution Pipeline in R [Dataset]. http://doi.org/10.6084/m9.figshare.16545708.v1
    Explore at:
    application/gzipAvailable download formats
    Dataset updated
    Aug 31, 2021
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Slim Karkar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    gedepir is an R package that simplifies the use of deconvolution tools within a complete transcriptomics analysis pipeline. It simplify the definition of a end-to-end analysis pipeline with a set of base functions that are connected through the pipe syntax used in magrittr, tidyr or dplyr packages.This dataset example is comprised of 50 pseudo-bulk samples.

  3. Z

    Brisbane Library Checkout Data

    • data.niaid.nih.gov
    Updated Jan 24, 2020
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    Nicholas Tierney (2020). Brisbane Library Checkout Data [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_2437859
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    Dataset updated
    Jan 24, 2020
    Dataset authored and provided by
    Nicholas Tierney
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brisbane
    Description

    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

    >

    > 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

    >

    > 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

    >

    > 1 ADULT 420287

    > 2 JUVENILE 283902

    > 3 YA 13715

    > 4 147

    > 5 UNKNOWN 36

    count(bris_libs, library, sort = TRUE)

    > # A tibble: 38 x 2

    > library n

    >

    > 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.

  4. Figure 2B source data.xlsx

    • figshare.com
    xlsx
    Updated Jan 4, 2025
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    Dong Huang (2025). Figure 2B source data.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.28137722.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jan 4, 2025
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Dong Huang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    the codelibrary(ggplot2)library(readxl)library(dplyr)library(tidyr)library(gridExtra)library(ggpubr)data_long % pivot_longer(cols = starts_with("value"), names_to = "measurement", values_to = "value")comparison_list

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Data-Science Sean (2020). Iris Flower Data Set Cleaned [Dataset]. https://www.kaggle.com/larsen0966/iris-flower-data-set-cleaned
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Iris Flower Data Set Cleaned

This is the Iris Flower Data Set Cleaned with tidyr

Explore at:
zip(2624 bytes)Available download formats
Dataset updated
Mar 27, 2020
Authors
Data-Science Sean
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

If this data Set is useful, and upvote is appreciated. British Statistician Ronald Fisher introduced the Iris Flower in 1936. Fisher published a paper that described the use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.

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