Data Visualization By Different Charts Graphical Representation
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Data Visualization By Different Charts Graphical Representation
There are different types of charts to represent different type of date or in other words there are different methods of data visualizations by graphs. The data may be of any type, it may discrete, quantitative, continuous or categorical. The graphs are used to represent various assumptions and data, which are discus below:
RANKING
This shows how two or more values compare to each other in relative magnitude. Example: Historic weather patterns, ranked from the hottest months to the coldest.
TIME-SERIES
This tracks changes in values of a consistent metric over time. Example: Monthly sales.
DEVIATION & CORRELATION
This examines how data points relate to each other, particularly how far any given data point differs from the mean. Example: Amusement park tickets sold on a rainy day vs. a regular day.
This is data with two or more variables that may demonstrate a positive or negative correlation to each other. Example: Salaries according to education level.
DISTRIBUTION
This shows data distribution, often around a central value. Example: Heights of players on a
basketball team.
PART-TO-WHOLE RELATIONSHIPS
This shows a subset of data compared to the larger whole. Example: Percentage of customerspurchasing specific products.
NOMINAL COMPARISON
This is a simple comparison of the quantitative
values of subcategories. Example: Number of
visitors to various websites.
Bar Charts: They are used to show changes over the time, for comparisons b/w different categories.
VERTICAL(COLUMN CHART)Best used for chronological data (time-series should always run left to right), or when visualizing negative values below the x-axis
HORIZONTALBest used for data with long category labels.
STACKED
Best used when there is a need to compare
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multiple part-to-whole relationships. These can use discrete or continuous data, oriented either vertically or horizontally.
100% STACKED
Best used when the total value of each Category is unimportant and percentage distribution of subcategories is the primary message
Pie Charts:
Pie charts are best used for making part-to-whole comparisons with discrete or continuous data. They are most impactful with a small data set.
STANDARD
Used to show part-to-whole relationships.
DONUT
Stylistic variation that enables the inclusion of a total value or design element in the center.
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Line Chart:
Line charts are used to show time-series relationships with continuous data. They help show trend, acceleration, deceleration, and volatility.
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Area Chart:
Area charts depict a time-series relationship, but they are different than line charts in that they can represent volume.
VARIATIONS OF AREA CHARTS
AREA CHART
Best used to show or compare a quantitative progression over time.
STACKED AREA
Best used to visualize part-to-whole relationships, helping show how each category contributes to the cumulative total.
100% STACKED AREA
Best used to show distribution of categories as part of a whole,
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where the cumulative total is unimportant.
Scatter Plot:
Scatter plots show the relationship between items based on two sets of variables. They are best used to show correlation in a large amount of data.
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Y-Values
Column1
Column2
Bubble Chart:
Bubble charts are good for displaying nominal comparisons or ranking relationships.
BUBBLE PLOT
This is a scatter plot with bubbles, best used to display an additional variable.
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Y-Values
We can use Bar graphs or infographic to represent the data.
Spatial/Axis Design
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