Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.
Key observations
Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Globe by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Globe. The dataset can be utilized to understand the population distribution of Globe by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Globe. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Globe.
Key observations
Largest age group (population): Male # 40-44 years (386) | Female # 50-54 years (413). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Globe Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘What Do Men Think It Means To Be A Man?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/masculinity-surveye on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This directory contains data behind the story What Do Men Think It Means To Be A Man?.
masculinity-survey.csv
contains the results of a survey of 1,615 adult men conducted by SurveyMonkey in partnership with FiveThirtyEight from May 10-22, 2018. The modeled error estimate for this survey is plus or minus 2.5 percentage points. The percentages have been weighted for age, race, education, and geography using the Census Bureau’s American Community Survey to reflect the demographic composition of the United States age 18 and over. Crosstabs with less than 100 respondents have been left blank because responses would not be statistically significant.The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.
Source: https://github.com/fivethirtyeight/data
This dataset was created by FiveThirtyEight and contains around 200 samples along with Adult Men, No Children, technical information and other features such as: - Age 35 64 - Race White - and more.
- Analyze Sexual Orientation Gay/ Bisexual in relation to Has Children
- Study the influence of Race Non White on Age 18 34
- More datasets
If you use this dataset in your research, please credit FiveThirtyEight
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about book subjects. It has 5 rows and is filtered where the books is The last survivor : the incredible story of the man who survived three concentration camps and a major maritime disaster near the end of World War II. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.
As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.
Teens and social media
As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Gross Domestic Product (GDP) in Isle Of Man was worth 7.43 billion US dollars in 2022, according to official data from the World Bank. The GDP value of Isle Of Man represents 0.01 percent of the world economy. This dataset provides - Isle Of Man Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.
As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.
Instagram’s Global Audience
As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.
Who is winning over the generations?
Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Isle of Man recorded 38008 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Isle of Man reported 110 Coronavirus Deaths. This dataset includes a chart with historical data for Isle of Man Coronavirus Cases.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
Dive into the heart of the global volleyball universe with this comprehensive dataset! Spanning players, teams, matches, transfers, awards, and more, this dataset offers an unparalleled look into professional volleyball.
From the strength behind the scenes to the stars of the show, explore the profiles of bodybuilders, physiotherapists, statisticians, and players who make each game possible. Follow the trajectories of players as they move between teams and achieve recognition for their skills. Discover the leaders who guide each team to success, including coaches, presidents, team managers, and sports directors.
Go beyond the court to explore the broader context of the volleyball world. Learn about the countries participating in this global sport and the stadiums hosting electrifying matches. Study the intricate details of each game, down to the number of sets won by each team.
Whether you're a volleyball enthusiast, a data scientist looking for a unique dataset to analyze, or simply curious about the inner workings of a global sport, this dataset provides a wealth of information to explore and investigate. Welcome to the world of volleyball!
The difference between the earnings of women and men shrank slightly over the past years. Considering the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women earned one U.S. cent less. By comparison, the uncontrolled gender pay gap measures the median salary for all men and all women across all sectors and industries and regardless of location and qualification. In 2025, the uncontrolled gender pay gap in the world stood at 0.83, meaning that women earned 0.83 dollars for every dollar earned by men.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Isle of Man recorded 312 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Isle of Man reported 67 Coronavirus Deaths. This dataset includes a chart with historical data for Isle of Man Coronavirus Recovered.
SECOND is a well-annotated semantic change detection dataset. To ensure data diversity, we firstly collect 4662 pairs of aerial images from several platforms and sensors. These pairs of images are distributed over the cities such as Hangzhou, Chengdu, and Shanghai. Each image has size 512 x 512 and is annotated at the pixel level. The annotation of SECOND is carried out by an expert group of earth vision applications, which guarantees high label accuracy. For the change category in the SECOND dataset, we focus on 6 main land-cover classes, i.e. , non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds , that are frequently involved in natural and man-made geographical changes. It is worth noticing that, in the new dataset, non-vegetated ground surface ( n.v.g. surface for short) mainly corresponds to impervious surface and bare land. In summary, these 6 selected land-cover categories result in 30 common change categories (including non-change ). Through the random selection of image pairs, the SECOND reflects real distributions of land-cover categories when changes occur.
The Indonesia Demographic and Health Survey (IDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health. The 2002-2003 IDHS follows a sequence of several previous surveys: the 1987 National Indonesia Contraceptive Prevalence Survey (NICPS), the 1991 IDHS, the 1994 IDHS, and the 1997 IDHS. The 2002-2003 IDHS is expanded from the 1997 IDHS by including a collection of information on the participation of currently married men and their wives and children in the health care.
The main objective of the 2002-2003 IDHS is to provide policymakers and program managers in population and health with detailed information on population, family planning, and health. In particular, the 2002-2003 IDHS collected information on the female respondents’ socioeconomic background, fertility levels, marriage and sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, childhood and adult mortality including maternal mortality, maternal and child health, and awareness and behavior regarding AIDS and other sexually transmitted infections in Indonesia.
The 2002-2003 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality, and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs - Measure trends in fertility and contraceptive prevalence rates, analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception - Evaluate achievement of goals previously set by the national health programs, with special focus on maternal and child health - Assess men’s participation and utilization of health services, as well as of their families - Assist in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the area of family planning, fertility, and health in general.
National
Sample survey data
SAMPLE DESIGN AND IMPLEMENTATION
Administratively, Indonesia is divided into 30 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts and each subdistrict is divided into villages. The entire village is classified as urban or rural.
The primary objective of the 2002-2003 IDHS is to provide estimates with acceptable precision for the following domains: · Indonesia as a whole; · Each of 26 provinces covered in the survey. The four provinces excluded due to political instability are Nanggroe Aceh Darussalam, Maluku, North Maluku and Papua. These provinces cover 4 percent of the total population. · Urban and rural areas of Indonesia; · Each of the five districts in Central Java and the five districts in East Java covered in the Safe Motherhood Project (SMP), to provide information for the monitoring and evaluation of the project. These districts are: - in Central Java: Cilacap, Rembang, Jepara, Pemalang, and Brebes. - in East Java: Trenggalek, Jombang, Ngawi, Sampang and Pamekasan.
The census blocks (CBs) are the primary sampling unit for the 2002-2003 IDHS. CBs were formed during the preparation of the 2000 Population Census. Each CB includes approximately 80 households. In the master sample frame, the CBs are grouped by province, by regency/municipality within a province, and by subdistricts within a regency/municipality. In rural areas, the CBs in each district are listed by their geographical location. In urban areas, the CBs are distinguished by the urban classification (large, medium and small cities) in each subdistrict.
Note: See detailed description of sample design in APPENDIX B of the survey report.
Face-to-face
The 2002-2003 IDHS used three questionnaires: the Household Questionnaire, the Women’s Questionnaire for ever-married women 15-49 years old, and the Men’s Questionnaire for currently married men 15-54 years old. The Household Questionnaire and the Women’s Questionnaire were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. In consultation with the NFPCB and MOH, BPS modified these questionnaires to reflect relevant issues in family planning and health in Indonesia. Inputs were also solicited from potential data users to optimize the IDHS in meeting the country’s needs for population and health data. The questionnaires were translated from English into the national language, Bahasa Indonesia.
The Household Questionnaire was used to list all the usual members and visitors in the selected households. Basic information collected for each person listed includes the following: age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, the Household Questionnaire also identifies unmarried women and men age 15-24 who are eligible for the individual interview in the Indonesia Young Adult Reproductive Health Survey (IYARHS). Information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, construction materials used for the floor and outer walls of the house, and ownership of various durable goods were also recorded in the Household Questionnaire. These items reflect the household’s socioeconomic status.
The Women’s Questionnaire was used to collect information from all ever-married women age 15-49. These women were asked questions on the following topics: • Background characteristics, such as age, marital status, education, and media exposure • Knowledge and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Marriage and sexual activity • Woman’s work and husband’s background characteristics • Childhood mortality • Awareness and behavior regarding AIDS and other sexually transmitted infections (STIs) • Sibling mortality, including maternal mortality.
The Men’s Questionnaire was administered to all currently married men age 15-54 in every third household in the IDHS sample. The Men’s Questionnaire collected much of the same information included in the Women’s Questionnaire, but was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, and maternal mortality. Instead, men were asked about their knowledge and participation in the health-seeking practices for their children.
All completed questionnaires for IDHS, accompanied by their control forms, were returned to the BPS central office in Jakarta for data processing. This process consisted of office editing, coding of open-ended questions, data entry, verification, and editing computer-identified errors. A team of about 40 data entry clerks, data editors, and two data entry supervisors processed the data. Data entry and editing started on November 4, 2002 using a computer package program called CSPro, which was specifically designed to process DHS-type survey data. To prepare the data entry programs, two BPS staff spent three weeks in ORC Macro offices in Calverton, Maryland in April 2002.
A total of 34,738 households were selected for the survey, of which 33,419 were found. Of the encountered households, 33,088 (99 percent) were successfully interviewed. In these households, 29,996 ever-married women 15-49 were identified, and complete interviews were obtained from 29,483 of them (98 percent). From the households selected for interviews with men, 8,740 currently married men 15-54 were identified, and complete interviews were obtained from 8,310 men, or 95 percent of all eligible men. The generally high response rates for both household and individual interviews (for eligible women and men) were due mainly to the strict enforcement of the rule to revisit the originally selected household if no one was at home initially. No substitution for the originally selected households was allowed. Interviewers were instructed to make at least three visits in an effort to contact the household, eligible women, and eligible men.
Note: See summarized response rates by place of residence in Table 1.2 of the survey report.
The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2002-2003 Indonesia Demographic and Health Survey (IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents
This Dataset contains the detail about the batting average and all details of all batsman who played in ICC Men 2022 World Cup.
Under the Freedom of Information Act 2000, I request the following information: The number of individuals of all ages who were prescribed contraceptives in the financial years 2019-2020, 2021-2020, 2020-2021, 2021-2022 and 2022-2023 in community settings (GP surgeries and pharmacies) broken down by contraceptive method. I would also like the proportion these represent of contraception users. For example, X proportion of those on contraception are using the Mirena coil. If possible, I would also appreciate if this were broken down by age of those prescriptions too. To clarify, I mean patients. I also mean both contraceptive drugs and appliances/devices Response A copy of the information is attached. Please read the following information to ensure correct understanding of the data. Fewer than five Please be aware that I have decided not to release the full details where the total number of individuals falls below five. This is because the individuals could be identified, when combined with other information that may be in the public domain or reasonably available. This information falls under the exemption in section 40 subsections 2 and 3 (a) of the Freedom of Information Act (FOIA). This is because it would breach the first data protection principle as: a - It is not fair to disclose individual’s personal details to the world and is likely to cause damage or distress. b - These details are not of sufficient interest to the public to warrant an intrusion into the privacy of the individual. Please click the weblink to see the exemption in full: www.legislation.gov.uk/ukpga/2000/36/section/40 NHS Business Services Authority (NHSBSA) - NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors, and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over one billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care (DHSC) users. Data Source: ePACT2 - Data in ePACT2 is sourced from the NHSBSA Data Warehouse and is derived from products prescribed on prescriptions and dispensed in the Community. The data captured from prescription processing is used to calculate reimbursement and remuneration. It includes items prescribed in England, Wales, Scotland, Northern Ireland, Guernsey/Alderney, Jersey, and Isle of Man which have been dispensed in the community in England. English prescribing that has been dispensed in Wales, Scotland, Guernsey/Alderney, Jersey, and Isle of Man is also included. The data excludes: • Items not dispensed, disallowed and those returned to the contractor for further clarification. • Prescriptions prescribed and dispensed in prisons, hospitals, and private prescriptions. • Items prescribed but not presented for dispensing or not submitted to NHS Prescription Services by the dispenser. Dataset - The data is limited to presentations prescribed in BNF sections 0703 Contraceptives and BNF section 2104 Contraceptive Devices. Data is presented at BNF Sub Paragraph and BNF Presentation level. Time Period - Financial years 2019/20, 2020/21, 2021/22, 2022/23 and 2023/24 (April 2023 - January 2024). Data is currently available up to and including January 2024. Organisation Data - The data is for prescribing in England regardless of where dispensed in the community. British National Formulary (BNF) Sub Paragraph and Presentation Code – The BNF Code is a 15-digit code in which the first seven digits are allocated according to the categories in the BNF, and the last eight digits represent the medicinal product, form, strength and the link to the generic equivalent product. NHS Prescription Services has created pseudo BNF chapters, which are not published, for items not included in BNF chapters 1 to 15. Most of such items are dressings and appliances which NHS Prescription Services has classified into four pseudo BNF chapters (20 to 23). Patient Identification - Where patient identifiable figures have been reported they are based on the information captured during the prescription processing activities. Please note, patient details cannot be captured from every prescription form and based on the criteria used for this analysis, patient information (NHS number) was only available for 98.28% of prescription items. The unique patient count figures are based on a distinct count of NHS number as captured from the prescription image. Patient ages are based on the age as captured from the prescription image and relates to the patient's age at the time of prescribing/dispensing. Please note it is possible that a single patient may be included in the results for more than one age band where a patient has received prescribing at different ages during a financial year. The figures for the number of identifiable patients should not be combined and reported at any other level than provided as this may result in the double counting of patients. For example, a single patient could appear in the results for multiple presentations or both financial years. Patient Age - Shows the age of the patient, if recorded. Data Quality for patient age - NHSBSA stores information on the age of the recipient of each prescription as it was read by computer from images of paper prescriptions or as attached to messages sent through the electronic prescription system. The NHSBSA does not validate, verify or manually check the resulting information as part of the routine prescription processing. There are some data quality issues with the ages of patients prescribed the products. The NHSBSA holds prescription images for 18 months. A sample of the data was compared to the images of the paper prescription forms from which the data was generated where these images are still available. These checks revealed issues in the reliability of age data, in particular the quality of the stored age data was poor for patients recorded as aged two years and under. When considering the accuracy of age data, it is expected that a small number of prescriptions may be allocated against any given patient age incorrectly. Application of Disclosure Control to information services (prescriptions) products- ePACT 2 data is not published statistics - it is available to authorised NHS users who are subject to Caldicott Guardian approval. We have no plans to apply disclosure control to data released to ePACT 2 users. These users are under an obligation to protect the anonymity of any patients when reusing this data or releasing derived information publicly. All requests that fall under the FOI process are subject to the NHSBSA Anonymisation and Pseudonymisation Standard. The application of the techniques described in the standard is judged on a case-by-case basis (by NHSBSA Information Governance) in respect of what techniques should be applied. The ICO typically rules on a case-by-case basis too so each case or challenge or appeal is judged on its own merits. FOI rules apply to data that we hold as part of our normal course of business.
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.
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.
The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.
The dataset conforms to the SCAR Feature Catalogue which includes data quality information.
The data is included in the data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.
Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset provides an in-depth and comprehensive collection of match-by-match data from the ICC Men's T20 World Cup 2024. The dataset covers a wide range of statistics, including detailed batting and bowling performances, match summaries, and player information. It has been meticulously scraped from ESPNcricinfo, a trusted source for cricket statistics and live match data, using Python libraries such as Selenium and BeautifulSoup. I, Mustafa Sultan, am the primary data extractor and source for this dataset.
The dataset is ideal for cricket analysts, enthusiasts, data scientists, and machine learning practitioners who are looking to dive into the intricate details of the 2024 T20 World Cup and use this data for research, analysis, or prediction models.
Primary Source: This data has been scraped from ESPNcricinfo, and I am the sole extractor and compiler of the data, ensuring its completeness and accuracy for public use.
Use Cases: This dataset opens up several analytical possibilities, including: 1. Performance Analysis: Identify top performers, assess consistency, and discover key players for different roles (batting, bowling, all-rounders). 2. Match Outcome Predictions: Use historical data to predict future match outcomes or individual performances. 3. Data Visualization: Create graphs, charts, and dashboards to visualize key insights from the tournament. 4. Fantasy League Team Selections: Use this data to assist in selecting players for fantasy leagues based on performance trends. 5. Statistical Models: Build machine learning models to forecast player performance, predict match results, or analyze game strategies. By scraping this data and compiling it into a user-friendly format, I hope to provide a valuable resource for the cricket analytics community and anyone interested in diving deeper into the statistics of the T20 World Cup 2024.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.
Key observations
The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for United States Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Globe. The dataset can be utilized to gain insights into gender-based income distribution within the Globe population, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
Employment type classifications include:
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Globe median household income by race. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.
Key observations
Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here