You can also build excellent apps with the help of this data visualization tool. The Ember Charts is more like a charting library built with the Ember. With Ember Charts, you can make graphics like time series, scatter, pie, and bar charts. Moreover, it helps you to easily extend and modify the charts since it offers great customizability options.
Ember Charts is suitable for your organization if you require statistical graphs pretty often. The NVD3 allows you to build reusable charts along with chart components for d3. If you prefer neat and customizable data charts, then you might want to try out NVD3. The Google Charts is another great data visualization tool on this list. It also gives you more control over the charts you create and will enable you to zoom the charts.
FusionCharts is a JavaScript-based data visualization software that comes with an extensive charting library. It helps you extract raw data from numerous databases and turn it into meaningful reports.
Moreover, it provides over 90 inbuilt charts and more than maps to help you process the data into interactive dashboards. FusionCharts is best for organizations that need to create dashboards within their project or product.
The good part is that you can get a free trial that you can use before making the final purchase. Highcharts helps you set up JavaScript-based charts in your web pages. It makes data visualization decidedly easy by providing high customizability. Other prominent features include the ability to visualize data in eight different ways, excellent scale granularity, and more.
Leaflet is an open-source JavaScript library that provides mobile-friendly, interactive maps. All in all, this tool is designed with simplicity, performance, and usability in mind.
The Leaflet is best for organizations that need specialized big data mapping solutions. It also helps organizations save plenty of time and resources and get more things done. You can check out this article from ScienceSoft on using Microsoft Business Intelligence to drive analytics and reporting. Handling such a humongous amount of data and interpreting it is a challenge. The right tool can attack any complex dataset by their horns, and break them down into simplified steps that even a layman can understand easily.
The technique graphically depicts the relationship between data. For that, it uses elements like charts, tables, histograms, bar graphs, and maps to correlate between different sets of data. It also enables us to identify the outliers among the data. The significant part about data visualization is that it lets you use colors to separate two or more datasets visually. Humans can identify nearly 7,, different colors , and data visualization techniques make use of this feature.
You also have the option to use different shapes and sizes to distinguish trends and facilitate decision making. Basically, data visualization techniques use our inherent nature to look for outliers and hidden trends in data, even subconsciously. Interactive data visualization tools help you visualize a huge amount of data by transforming numbers into diagrams within minutes.
Since humans interpret and understand diagrams better than numbers, these tools are externally useful. So how do you use these tools? You can choose from a lot of options, depending on your business requirements and objectives. For example, the monthly sale of the commodity X as the particular financial year progresses.
Bar Charts are a simple way to take data sources and depict quantitative data in a differentiable manner. With Pie Charts, you break down data into portions of a pie.
Venn diagrams demonstrate the possible type of relationships between 2 or more datasets. It helps in assessing data and displaying how it correlates to one another. Basically, Venn diagrams help depict the commonalities between two data sources.
Gantt charts are essential in the course of any business, be it B2B or B2C. It demonstrates the different stages of the advancement of a project and sheds insight on the rate of completion of projects on deadlines.
Source: teamgantt. This chart depicts the time it takes for a business to complete different phases of its operations. Make sure to check your data visualization tool to see if they have Gantt charts, or opt for a Gantt chart maker for your projects. If you want to study the trends of a dataset over time and notice crests and troughs, line charts can be helpful.
They are mostly popular for financial studies and stock markets. One of the best ways to judge a visualization tool is by analyzing how efficient and user-friendly the dashboard is. For example, some business intelligence tools offer advanced charts like treemaps, vector graphics, and custom reports via JavaScript and JSON which require a steeper learning curve.
It presents charts, bar diagrams, infographics, timelines, and more in a single frame, facilitating actionable insights and fast decision making. This app enables you to share data visualizations with others. Google Charts is an interactive cloud service that creates graphical charts from the information supplied by users.
You can use it to make a simple line chart or complex hierarchical tree. Sisense is a data visualization software that enables you to simplify complex data from multiple sources. It is one of the best data visualization tool which helps you to transform data into actionable applicable components or visualizations. These apps allow you to perform data analysis for organizations or clients.
Chartblocks is an app that helps you to build charts. This cloud-based tool enables you to embed charts to any website. You can use it to customize any charts and synch with any data source. Ember Charts is a charting library built-in JavaScript. It is one of the best open source data visualization tools which helps you to create a bar, pie, and many other editable charts. Polymaps is a JavaScript library for creating interactive and dynamic maps with ease.
Leaflet is an open-source data visualization tool that works efficiently across major mobile platforms and Desktop PCs. It can also be executed with the help of API. Sigmajs is an online app that is made for creating a graph. This app helps you to customize your drawing. You can also publish the final result on any website. Looker is a data visualization platform that enables you to explore, analyze, and share analytics with ease.
You can use this program to convert your data into useful diagrams. Data visualization is a process of graphical representation of data and information. It utilizes various graphs, charts, and maps to visualize the data in order to identify trends and patterns in the data. It helps businesses to analyze a huge amount of information and data to derive accurate business decisions. A Data visualization tool is a cloud-based software application that helps you to represent raw data in easy-to-understand graphical formats.
It is used to produce customizable bar charts, pie charts, column charts, and more using a huge amount of data. It helps to identify trends, patterns, and outliers in the information. The mean and standard deviation of the VAS scores are expressed as whiskers on the bars Fig. By comparing the endpoints of bars, one can identify the largest and the smallest categories, and understand gradual differences between each category.
It is advised to start the x - and y -axes from 0. Illustration of comparison results in the x - and y -axes that do not start from 0 can deceive readers' eyes and lead to overrepresentation of the results. One form of vertical bar graph is the stacked vertical bar graph. A stack vertical bar graph is used to compare the sum of each category, and analyze parts of a category. While stacked vertical bar graphs are excellent from the aspect of visualization, they do not have a reference line, making comparison of parts of various categories challenging Fig.
A pie chart, which is used to represent nominal data in other words, data classified in different categories , visually represents a distribution of categories. It is generally the most appropriate format for representing information grouped into a small number of categories. It is also used for data that have no other way of being represented aside from a table i. A pie chart is also commonly used to illustrate the number of votes each candidate won in an election.
A line plot is useful for representing time-series data such as monthly precipitation and yearly unemployment rates; in other words, it is used to study variables that are observed over time. Line graphs are especially useful for studying patterns and trends across data that include climatic influence, large changes or turning points, and are also appropriate for representing not only time-series data, but also data measured over the progression of a continuous variable such as distance.
As can be seen in Fig. If data are collected at a regular interval, values in between the measurements can be estimated. In a line graph, the x-axis represents the continuous variable, while the y-axis represents the scale and measurement values.
It is also useful to represent multiple data sets on a single line graph to compare and analyze patterns across different data sets. A box and whisker chart does not make any assumptions about the underlying statistical distribution, and represents variations in samples of a population; therefore, it is appropriate for representing nonparametric data. AA box and whisker chart consists of boxes that represent interquartile range one to three , the median and the mean of the data, and whiskers presented as lines outside of the boxes.
Whiskers can be used to present the largest and smallest values in a set of data or only a part of the data i. Data that are excluded from the data set are presented as individual points and are called outliers.
The spacing at both ends of the box indicates dispersion in the data. The relative location of the median demonstrated within the box indicates skewness Fig. The box and whisker chart provided as an example represents calculated volumes of an anesthetic, desflurane, consumed over the course of the observation period Fig. Most of the recently introduced statistical packages and graphics software have the three-dimensional 3D effect feature. The 3D effects can add depth and perspective to a graph.
However, since they may make reading and interpreting data more difficult, they must only be used after careful consideration. The application of 3D effects on a pie chart makes distinguishing the size of each slice difficult.
Even if slices are of similar sizes, slices farther from the front of the pie chart may appear smaller than the slices closer to the front Fig. Finally, we explain how to create a graph by using a line graph as an example Fig. In Fig. In many graphs, the x- and y-axes meet at the zero point Fig. The data can be clearly exposed by separating the zero point Fig. Separating the data sets and presenting standard deviations in a single direction prevents overlapping and, therefore, reduces the visual inconvenience.
Doing so also reduces the excessive number of ticks on the y-axis, increasing the legibility of the graph Fig. In the last graph, different shapes were used for the lines connecting different time points to further allow the data to be distinguished, and the y-axis was shortened to get rid of the unnecessary empty space present in the previous graphs Fig. A graph can be made easier to interpret by assigning each group to a different color, changing the shape of a point, or including graphs of different formats [ 10 ].
The use of random settings for the scale in a graph may lead to inappropriate presentation or presentation of data that can deceive readers' eyes Fig. Owing to the lack of space, we could not discuss all types of graphs, but have focused on describing graphs that are frequently used in scholarly articles.
We have summarized the commonly used types of graphs according to the method of data analysis in Table 3. For general guidelines on graph designs, please refer to the journal submission requirements 2. Text, tables, and graphs are effective communication media that present and convey data and information.
They aid readers in understanding the content of research, sustain their interest, and effectively present large quantities of complex information. As journal editors and reviewers will scan through these presentations before reading the entire text, their importance cannot be disregarded. For this reason, authors must pay as close attention to selecting appropriate methods of data presentation as when they were collecting data of good quality and analyzing them.
In addition, having a well-established understanding of different methods of data presentation and their appropriate use will enable one to develop the ability to recognize and interpret inappropriately presented data or data presented in such a way that it deceives readers' eyes [ 11 ]. Discovery and communication are the two objectives of data visualization.
In the discovery phase, various types of graphs must be tried to understand the rough and overall information the data are conveying. The communication phase is focused on presenting the discovered information in a summarized form.
During this phase, it is necessary to polish images including graphs, pictures, and videos, and consider the fact that the images may look different when printed than how appear on a computer screen. In this appendix, we discuss important concepts that one must be familiar with to print graphs appropriately.
The KJA asks that pictures and images meet the following requirement before submission 3. Submit files of figures and photographs separately from the text of the paper.
Width of figure should be 84 mm one column. Contrast of photos or graphs should be at least dpi. Contrast of line drawings should be at least 1, dpi. The Powerpoint file ppt, pptx is also acceptable. Unfortunately, without sufficient knowledge of computer graphics, it is not easy to understand the submission requirement above. Therefore, it is necessary to develop an understanding of image resolution, image format bitmap and vector images , and the corresponding file specifications.
The higher the resolution, the clearer and closer to reality the image is, while the opposite is true for low resolutions. The greater the number of dots, the higher the resolution. The KJA submission requirements recommend dpi for images, and 1, dpi 4 for graphs. In other words, resolutions in which or 1, dots constitute one inch are required for submission. There are requirements for the horizontal length of an image in addition to the resolution requirements.
While there are no requirements for the vertical length of an image, it must not exceed the vertical length of a page. The width of a column on one side of a printed page is 84 mm, or 3. Therefore, a graph must have a resolution in which 1, dots constitute 1 inch, and have a width of 3. Methods of image construction are important. Bitmap images can be considered as images drawn on section paper. Enlarging the image will enlarge the picture along with the grid, resulting in a lower resolution; in other words, aliasing occurs.
On the other hand, reducing the size of the image will reduce the size of the picture, while increasing the resolution. In other words, resolution and the size of an image are inversely proportionate to one another in bitmap images, and it is a drawback of bitmap images that resolution must be considered when adjusting the size of an image.
To enlarge an image while maintaining the same resolution, the size and resolution of the image must be determined before saving the image. An image that has already been created cannot avoid changes to its resolution according to changes in size.
Enlarging an image while maintaining the same resolution will increase the number of horizontal and vertical dots, ultimately increasing the number of pixels 5 of the image, and the file size.
To avoid this complexity, the width of an image can be set to 4 inches and its resolution to dpi to satisfy the submission requirements of most journals [ 12 ]. Vector images overcome the shortcomings of bitmap images.
Vector images are created based on mathematical operations of line segments and areas between different points, and are not affected by aliasing or pixelation. Furthermore, they result in a smaller file size that is not affected by the size of the image. They can also contain bitmap and vector images.
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