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Data visualization is
creating a visual representation of your data, for example in the form of a chart or a graph. Choosing the right format to visualize your data is critical to effectively communicating the results of your study. Good visualizations can be more memorable and persuasive than pure text.
Data visualization is a visual of data , for the or a . Choosing the right format data is criticalvisualizations can more than text.
Read First ==
Specific code for data visualization is available on the software-specific tools (e. g. [[ iegraph] ]). This page discusses general principles for data visualization.
for data visualization on the . . [ .visualization .
=== What type of data visualization should I use? ===
The best format for data visualization will depend on the type of data and results you wish to display, as well as the medium in which they will be displayed. For example, online interfaces allow for more dynamic visualizations than printed articles.
and , the in
* [https:// www. data- to- viz. com/ |[Data to Viz] provides a handy decision tree.
* The [http://www.visual-literacy.org/periodic_table/periodic_table.html|Periodic Table of Visualization] provides a catalogue of all data visualization types with visual examples.
* Gapminder. org [https:// www.gapminder. org/ tools/ #$chart- type=bubbles|interactive visualization tools] provide beautiful examples of effective visualizations.
. [https://.//-visualization] of .
===Stata Visual Library===
a [://../---/---] .
The DIME Analytics team has created a [ https:// worldbank. github. io/ Stata- IE- Visual- Library/|Stata Visual Library for Impact Evaluation] which shows examples of graphs and provides the codes used to create them. You can contribute to the library [https://github.com/ worldbank/Stata- IE- Visual- Library|on our github].
Data Visualization in R ===
R has many options for data visualization ; the ggplot package is one of the best. Here is a list of [ http:// r-statistics. co/ Top50-Ggplot2-Visualizations-MasterList-R-Code. html|50 ggplot2 Visualizations with full R code] .
data visualization the the . Here is a [://./.].
== Back to Parent ==
to of the 
This article is part of the topic [ [Data Analysis]]
Additional Resources ==
* Harvard Business Review Article on [https: //hbr.org/2016/06/visualizations-that-really-work|Visualizations that Really Work]
[[Category: Data Analysis]]
[[Category: Data Analysis]]
Data visualization is a method of expressing descriptive statistics and analytical results through visual representations of the data (i.e. charts, graphs, etc.). It can also be a useful tool for the research team during exploratory analysis to better understand the data. During or after data analysis, the research team may use data visualizations to present results to a broader audience. Choosing the right format for data visualization is critical: good visualizations can often communicate results and persuade audiences more effectively than text. This page outlines provides resources on what data visualization to use, what colors to use, and which features to include.
- DIME Analytics has prepared a checklist on reviewing graphs before dissemination.
- Use color strategically to differentiate groups or highlight trends; remember that color choices matter and can influence how effectively the visualization communicates information.
- The title, annotation and citation of the visualization depends on where the visualization is used.
Deciding on a Data Visualization
The best format for data visualization depends on the type of data, the results you wish to display, and the medium in which they will be displayed. For example, online interfaces allow for more dynamic visualizations than printed articles. To help decide which data visualization to use Data to Viz provides a handy decision tree, while The Periodic Table of Visualization provides a catalogue of all data visualization types with visual examples. Further, Gapminder’s visualization tools provide beautiful examples of effective visualizations.
Colors not only set the mood for your visualization, but can also draw attention to certain features of your visualization. Thanks to digital interfaces, we can visualize quantitative data in a more dynamic way. In this case, colors can encode a great deal of information if we make intelligent use of them. For a general guide about colors, see What to Consider When Choosing Colors for Data Visualization and Your Friendly Guide to Colors in Data Visualization. In general, follow these tips when deciding whether to use colors in visualizations:
- Only add colors if they add useful information by, for example, differentiating groups or highlighting information.
- For numeric variables, color can show differences, but they hide absolute values.
- If many different colors are necessary to display what you want, consider using a different way to display information: adding too many colors will make the graph difficult to read.
- Be consistent color use: for example, if you are using two colors, one for treatment and one for control, use the same colors for all graphs in the same document. This will save readers both time and confusion.
- Don't forget to add legends indicating what the colors mean.
- Consider that color contrast is important: it will make the difference when telling colors apart.
- Use intuitive colors will save time: good is green, blue is water, darker shades are higher values than lighter shades.
- Use sequential or diverging colors scales to represent numeric variables.
- Use distinctive colors to represent categorical variables.
- Take color blindness and transition to gray scale into account.
If you are uncertain which colors to use to make your visualization fancier, then color palettes can be a handy option. This is the color wheel lets you create color palettes with specific color hex (like a color code so that you can easily find it). This free color generator even includes codes for generating color palettes in Java.
Titles and Annotations
Data visualizations should be intuitive: audiences should grasp whatever the data aims to convey in the first 20 seconds when they see a visualization. Thus, it is important to adjust some features of a visualization based on where it will be used. Usually, if a visualization is used in an academic report or research paper, it does not need detailed titles or annotations because the paper itself will include explanations for the visualization. Here is an example of what a visualization looks like in an academic report.
However, if a visualization is used in a website or presentation without supporting materials, then it should include detailed titles and annotations. In other words, it should help the audience with the key takeaway points by offering at least an annotative lead-in sentence. Here is an example of an independent visualization with detailed titles and annotations.
Data, like literature, should be cited. However, like titles and annotations, data source citation will not necessarily be included in the visualization. If you use the visualization in an academic report or research paper, you will cite data source in the reference page, so that you no longer need to include that citation again in the visualization. Here is a guide of data citation.
If a visualization is used in a presentation or website and you are NOT using primary data, it is important to include the citation in your visualization. Usually, we will add a footnote at right bottom or left bottom corner of the visualization in this format - Source: Data Name and Date. Here is an example of visualization with a data source citation in it.
Lastly, if you are using primary data, then there is no need to cite the data source.
Data Visualization in R
R has many options for data visualization. Here are some useful packages:
- ggplot2: this is the go-to package for static plots. Here is a list of 50 ggplot2 visualizations with full R code.
- plotly: creates interactive graphs, and is integrated with ggplot.
- gganimate: allows users to create animated GIFs from ggplot plots.
- Leaflet: an R wrapper to one of the most popular open-source libraries for interactive maps.
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This article is part of the topic Data Analysis