Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

This release contains experimental data on the pozzolanic of calcined coal gangue. "The data on the strength" presents the compressive and flexural strength data of cement mortar specimens (40×40×160 cm) containing 30% calcined coal gangue at different temperatures and curing times (3 days, 7 days, and 28 days). "Column chart of strength" visually represents the flexural and compressive strength data mentioned above in the form of bar charts, with temperature intervals on the x-axis and flexural strength and compressive strength on the y-axis. "R3 activity test data" displays the weights before and after calcination, along with the weight difference representing the combined water content measured through R3 activity testing. "The bar chart of R3 activity test" visually represents the combined water content in the form of bar charts, with temperature intervals on the x-axis and combined water content on the y-axis. Thermogravimetric data show the changes in TG and DTG concerning temperature(T). FTIR curve data at different temperatures include Wavenumber and absorbance values. XRD curve data display Degrees and Intensity, along with 80 scanning electron microscope images capturing different temperature coal gangue powder photos.

About Datasets: - Domain : Finance - Project: Bank loan of customers - Datasets: Finance_1.xlsx & Finance_2.xlsx - Dataset Type: Excel Data - Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revol_bal 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation methods. Each project has been classified according to visualisation and interaction techniques, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.
The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

Narrativity. It reports the presence of narratives employing information visualisation techniques. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is composed of user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:

• non_narrative (boolean)

• narrative (boolean)

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:

• domain (categorical):

• History and archaeology

• Art and art history

• Language and literature

• Music and musicology

• Multimedia and performing arts

• Philosophy and religion

• Other: both extra-list domains and cases of collections without a unique or specific thematic focus.

Visualisation of uncertainty and interpretation. Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:

• uncertainty_interpretation (categorical):

• Interactive distinction

• Visual distinction

• Ambiguation

• Interpretative metrics

Critical adaptation. We identify projects in which, for what concerns at least a visualisation, the following criteria are fulfilled: 1) avoid uncritical repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid extreme simplifications to embraces and depict complexity promoting time-spending visualisation-based inquiry. Column:

• critical_adaptation (boolean)

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:

• plot (boolean): visual representations that map data points onto a two-dimensional coordinate system.

• cluster_or_set (bool): sets or cluster-based visualisations used to unveil possible inter-object similarities.

• map (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.

• network (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.

• hierarchical_diagram (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.

• treemap (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.

• word_cloud (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context

• bars (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.

• line_chart (boolean): the display of information as sequential data points connected by straight-line segments.

• area_chart (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.

• pie_chart (boolean): circular graphs divided into slices which can also use multi-level solutions.

• plot_3d (boolean): plots that use a third dimension to encode an additional variable.

• proportional_area (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.

• other (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:

• timeline (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.

• temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.

• animation (boolean): temporality is perceived through an animation changing the visualisation according to time flow.

• visual_variable (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).

Interaction techniques. A set of categories to assess affordable interaction techniques based on the concept of user intent [8] and user-allowed data actions [9]. The following categories roughly match the “processing”, “mapping”, and “presentation” actions from [9] and the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the visual representation or the aspect of data points, symbols, and glyphs are taken into consideration. Columns:

• basic_selection (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.

• advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.

• navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.

• arrangement (boolean): methods to organise visualisation elements (symbols, glyphs, etc.) or

This comprehensive dataset presents the global refugee landscape by providing a detailed overview of refugee and displacement statistics from various countries and territories over a span of time. With a total of 107,980 rows and 11 columns, this dataset delves into the complexities of forced migration and human displacement, offering insights into the movements of refugees, asylum-seekers, internally displaced persons (IDPs), returned refugees and IDPs, stateless individuals, and other populations of concern.

Columns in the dataset:

• Year: The year of data collection.
• Country / territory of asylum/residence: The host country or territory where refugees seek asylum or residence.
• Origin: The country of origin from which refugees are fleeing.
• Refugees (incl. refugee-like situations): The number of refugees, including those in refugee-like situations.
• Asylum-seekers (pending cases): The count of individuals seeking asylum whose cases are pending.
• Returned refugees: The number of refugees who have returned to their country of origin.
• Internally displaced persons (IDPs): The count of people who have been displaced within their own country due to conflict or other reasons.
• Returned IDPs: The number of internally displaced persons who have returned to their previous locations.
• Stateless persons: Individuals who do not have the legal recognition of any country.
• Others of concern: Additional populations in need of humanitarian assistance.
• Total Population: The sum of all above categories, representing the overall population affected by displacement. The dataset serves as a valuable resource for studying global refugee trends, assessing the impact of conflicts and crises on displacement, and understanding the challenges faced by vulnerable populations worldwide.

Visualization Ideas: Time Series Analysis: Plot the trends in different refugee populations over the years, such as refugees, asylum-seekers, IDPs, returned refugees, etc. Geographic Analysis: Create heatmaps or choropleth maps to visualize refugee flows between different countries and regions. Origin and Destination Analysis: Show the top countries of origin and the top host countries for refugees using bar charts. Pie Charts: Visualize the distribution of different refugee populations (refugees, asylum-seekers, IDPs, etc.) as a percentage of the total population. Stacked Area Chart: Display the cumulative total of different refugee populations over time to observe changes and trends.

Data Modeling and Machine Learning Ideas: Time Series Forecasting: Use machine learning algorithms like ARIMA or LSTM to predict future refugee trends based on historical data. Clustering: Group countries based on similar refugee patterns using clustering algorithms such as K-Means or DBSCAN. Classification: Build a classification model to predict whether a country will experience a significant increase in refugee inflow based on historical and socio-political factors. Sentiment Analysis: Analyze social media or news data to determine the sentiment around refugee-related topics and how it correlates with migration patterns. Network Analysis: Construct a network graph to visualize the connections and interactions between countries in terms of refugee flows.

These visualization and modeling ideas can provide meaningful insights into the global refugee crisis and aid in decision-making, policy formulation, and humanitarian efforts.

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

"Classification and Quantification of Strawberry Fruit Shape" is a dataset that includes raw RGB images and binary images of strawberry fruit. These folders contain JPEG images taken from the same experimental units on 2 different harvest dates. Images in each folder are labeled according to the 4 digit plot ID from the field experiment (####_) and the 10 digit individual ID (_##########).

"H1" and "H2" folders contain RGB images of multiple fruits. Each fruit was extracted and binarized to become the images in "H1_indiv" and "H2_indiv".

"H1_indiv" and "H2_indiv" folders contain images of individual fruit. Each fruit is bordered by ten white pixels. There are a total of 6,874 images between these two folders. The images were used then resized and scaled to be the images in "ReSized".

"ReSized" contains 6,874 binary images of individual berries. These images are all square images (1000x1000px) with the object represented by black pixels (0) and background represented with white pixels (1). Each image was scaled so that it would take up the maximum number of pixels in a 1000 x 1000px image and would maintain the aspect ratio.

"Fruit_image_data.csv" contains all of the morphometric features extracted from individual images including intermediate values.

All images title with the form "B##_NA" were discarded prior to any analyses. These images come from the buffer plots, not the experimental units of the study.

"PPKC_Figures.zip" contains all figures (F1-F7) and supplemental figures (S1-S7_ from the manuscript. Captions for the main figures are found in the manuscript. Captions for Supplemental figures are below.

Fig. S1 Results of PPKC against original cluster assignments. Ordered centroids from k = 2 to k = 8. On the left are the unordered assignments from k-means, and the on the right are the order assignments following PPKC. Cluster position indicated on the right [1, 8].

Fig. S2 Optimal Value of k. (A) Total within clusters sum of squares. (B) The inverse of the Adjusted R . (C) Akaike information criterion (AIC). (D) Bayesian information criterion (AIC). All metrics were calculated on a random sample of 3, 437 images (50%). 10 samples were randomly drawn. The vertical dashed line in each plot represents the optimal value of k. Reported metrics are standardized to be between [0, 1].

Fig. S3 Hierarchical clustering and distance between classes on PC1. The relationship between clusters at each value of k is represented as both a dendrogram and as bar plot. The labels on the dendrogram (i.e., V1, V2, V3,..., V10) represent the original cluster assignment from k-means. The barplot to the right of each dendrogram depicts the elements of the eigenvector associated with the largest eigenvalue form PPKC. The labels above each line represent the original cluster assignment.

Fig. S4 BLUPs for 13 selected features. For each plot, the X-axis is the index and the Y-axis is the BLUP value estimated from a linear mixed model. Grey points represent the mean feature value for each individual. Each point is the BLUP for a single genotype.

Fig. S5 Effects of Eigenfruit, Vertical Biomass, and Horizontal Biomass Analyses. (A) Effects of PC [1, 7] from the Eigenfruit analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the horizontal pixel position. The vertical axis is the vertical pixel position. (B) Effects of PC [1, 3] from the Horizontal Biomass analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the vertical position from the image (height). The vertical axis is the number of activated pixels (RowSum) at the given vertical position. (C) Effects of PC [1, 3] from the Vertical Biomass analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the horizontal position from the image (width). The vertical axis is the number of activated pixels (ColSum) at the given horizontal position.

Fig. S6 PPKC with variable sample size. Ordered centroids from k = 2 to k = 5 using different image sets for clustering. For all k = [2, 5], k-means clustering was performed using either 100, 80, 50%, or 20% of the total number of images; 6,874, 5, 500, 3, 437, and 1, 374 respectively. Cluster position indicated on the right [1, 5].

Fig. S7 Comparison of scale and continuous features. (A.) PPKC 4-unit ordinal scale. (B.) Distributions of the selected features with each level of k = 4 from the PPKC 4-unit ordinal scale. The light gray line is cluster 1, the medium gray line is cluster 2, the dark gray line is cluster 3, and the black line is cluster 4.

Submarine Cable Features Dataset

Submarine Cable Features: Scale, Description, and Effective Dates

By Homeland Infrastructure Foundation [source]

About this dataset

The Submarine Cables dataset provides a comprehensive collection of features related to submarine cables. It includes information such as the scale band, description, and effective dates of these cables. These data are specifically designed to support coastal planning at both regional and national scales.

The dataset is derived from 2010 NOAA Electronic Navigational Charts (ENCs), along with 2009 NOAA Raster Navigational Charts (RNCs) which were updated in 2013 using the most recent RNCs as a reference point. The source material's scale varied significantly, resulting in discontinuities between multiple sources that were resolved with minimal spatial adjustments.

Polyline features representing submarine cables were extracted from the original sources while excluding 'cable areas' noted within the data. The S-57 data model was modified for improved readability and performance purposes.

Overall, this dataset provides valuable information regarding the occurrence and characteristics of submarine cables in and around U.S. navigable waters. It serves as an essential resource for coastal planning efforts at various geographic scales

How to use the dataset

Here's a guide on how to effectively utilize this dataset:

1. Familiarize Yourself with the Columns

The dataset contains multiple columns that provide important information:

• scaleBand: This categorical column indicates the scale band of each submarine cable.
• description: The text column provides a description of each submarine cable.
• effectiveDate: Indicates the effective date of the information about each submarine cable.

Understanding these columns will help you navigate and interpret the data effectively.

2. Explore Scale Bands

Start by analyzing the distribution of different scale bands in the dataset. The scale band categorizes submarines cables based on their size or capacity. Identifying patterns or trends within specific scale bands can provide valuable insights into how submarine cables are deployed.

For example, you could analyze which scale bands are most commonly used in certain regions or countries, helping coastal planners understand infrastructure needs and potential connectivity gaps.

3. Analyze Cable Descriptions

The description column provides detailed information about each submarine cable's characteristics, purpose, or intended use. By examining these descriptions, you can uncover specific attributes related to each cable.

This information can be crucial when evaluating potential impacts on marine ecosystems, identifying areas prone to damage or interference with other maritime activities, or understanding connectivity options for coastal regions.

4. Consider Effective Dates

While excluding dates from this analysis as per your request (as we exclude them here), effective dates play an important role in keeping track of when information about a particular cable was collected or updated.

By considering effective dates over time: - You can monitor changes in infrastructure deployment strategies. - Identify areas where new cables have been installed. - Track outdated infrastructure that may need replacements or upgrades.

5. Combine with Other Datasets

To gain a comprehensive understanding and unlock deeper insights, consider integrating this dataset with other relevant datasets. For example: - Population density data can help identify areas in high need of improved connectivity. - Coastal environmental data can help assess potential ecological impacts of submarine cables.

By merging datasets, you can explore relationships, draw correlations, and make more informed decisions based on the available information.

6. Visualize the Data

Create meaningful visualizations to better understand and communicate insights from the dataset. Utilize scatter plots, bar charts, heatmaps, or GIS maps

Research Ideas

• Coastal Planning: The dataset can be used for coastal planning at both regional and national scales. By analyzing the submarine cable features, planners can assess the impact of these cables on coastal infrastructure development and design plans accordingly.
• Communication Network Analysis: The dataset can be utilized to analyze the connectivity and coverage of submarine cable networks. This information is valuable for telecommunications companies and network providers to understand gaps in communication infras...

NFL Injury Analysis 2012-2017

NFL Injuries 2012-2017: Yearly, injury type, scenario, and season type data

By Throwback Thursday [source]

About this dataset

This dataset provides comprehensive information on injuries that occurred in the National Football League (NFL) during the period from 2012 to 2017. The dataset includes details such as the type of injury sustained by players, the specific situation or event that led to the injury, and the type of season (regular season or playoffs) during which each injury occurred.

The Injury Type column categorizes the various types of injuries suffered by players, providing insights into specific anatomical areas or specific conditions. For example, it may include injuries like concussions, ankle sprains, knee ligament tears, shoulder dislocations, and many others.

The Scenario column offers further granularity by describing the specific situation or event that caused each injury. It can provide context about whether an injury happened during a tackle, collision with another player or object on field (such as goalposts), blocking maneuvers gone wrong, falls to the ground resulting from being off-balance while making plays, and other possible scenarios leading to player harm.

The Season Type column classifies when exactly each injury occurred within a particular year. It differentiates between regular season games and playoff matches – identifying whether an incident took place during high-stakes postseason competition or routine games throughout the regular season.

The Injuries column represents numeric data detailing how many times a particular combination of year-injury type-scenario-season type has occurred within this dataset's timeframe – measuring both occurrence frequency and severity for each unique combination.

Overall, this extensive dataset provides valuable insight into NFL injuries over a six-year span. By understanding which types of injuries are most prevalent under certain scenarios and during different seasons of play - such as regular seasons versus playoffs - stakeholders within professional football can identify potential areas for improvement in safety measures and develop strategies aimed at reducing player harm on-field

How to use the dataset

The dataset contains six columns:

• Year: This column represents the year in which the injury occurred. It allows you to filter and analyze data based on specific years.

• Injury Type: This column indicates the specific type of injury sustained by players. It includes various categories such as concussions, fractures, sprains, strains, etc.

• Scenario: The scenario column describes the situation or event that led to each injury. It provides context for understanding how injuries occur during football games.

• Season Type: This column categorizes injuries based on whether they occurred during regular season games or playoff games.

• Injuries: The number of injuries recorded for each specific combination of year, injury type, scenario, and season type is mentioned in this column's numeric values.

Using this dataset effectively involves several steps:

• Data Exploration: Start by examining all available columns carefully and making note of their meanings and data types (categorical or numeric).

• Filtering Data by Year or Season Type: If you are interested in analyzing injuries during a particular year(s) or specific seasons (regular vs playoffs), apply filters accordingly using either one or both these columns respectively.

3a. Analyzing Injury Types: To gain insights into different types of reported injuries over time periods specified by your filters (e.g., a given year), group data based on Injury Type and calculate aggregate statistics like maximum occurrences or average frequency across years/seaso

3b.Scenario-based Analysis:/frequency across years/seasons. Group the data based on Scenario and calculate aggregate values to determine which situations or events lead to more injuries.

• Exploring Injury Trends: Explore the overall trend of injuries throughout the 2012-2017 period to identify any significant patterns, spikes, or declines in injury occurrence.

• Visualizing Data: Utilize appropriate visualization techniques such as bar graphs, line charts, or pie charts to present your findings effectively. These visualizations will help you communicate your analysis concisely and provide clear insights into both common injuries and specific scenarios.

• Drawing Conclusions: Based on your analysis of the

Research Ideas

• Understanding trends in NFL injuries: This dataset can be used to analyze the number and types of in...

To get high quality singers:

First we have to create a Google sheet. Name it as Project 3. then we have to create 23 sheets. name it from 1992 to 2014. now go to the website and copy the link. then by using importhtml function import the data to all the sheets from 1992 to 2014. create a sheet name it as merged data and copy the data from second row from all the 23 sheets and paste it in merged data. create the column names as Rank, Artist, Title, Year. we will get 2300 rows. now create a new google sheet name it as prolific-1. to get unique artist use unique function. and to get frequency use countif function. And sort them in descending order. now plot the bar. before we made with frequency now we make it with score. create a column score in merged data and use 101-rank function to get the scores. now create a google sheet as prolific-2. use artist and score columns. now use unique function to get the data of artists. for score use arrayfunction(). now sort the data and plot the bar