geom_bar {ggplot.layers}

.NET clr documentation

geom_bar

Description

A bar plot, also known as a bar chart or bar graph, is a type of data visualization that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. Bar plots are one of the most common types of charts and are used in a wide variety of fields for their simplicity and effectiveness in conveying information. Here are the main components and characteristics of a bar plot: Components: 1. Axes: The horizontal axis (x-axis) and the vertical axis (y-axis) define the scales for the categories and the values, respectively. 2. Bars: The bars are the rectangular elements of the plot. Each bar represents a category, and its length or height corresponds to the value of that category. 3. Labels: The categories are usually labeled on the x-axis, and the y-axis is often labeled with the type of value being represented (e.g., frequency, count, percentage). 4. Title: The plot may have a title that describes the data being presented. Types of Bar Plots: - Vertical Bar Plot: The bars extend vertically, with the categories along the horizontal axis and the values along the vertical axis. - Horizontal Bar Plot: The bars extend horizontally, with the categories along the vertical axis and the values along the horizontal axis. - Grouped Bar Plot: Used to compare two or more related groups of data. The bars are grouped by category, and each group contains bars for the different subcategories being compared. - Stacked Bar Plot: Each bar represents the whole, and segments within the bar represent different parts of the whole. This is useful for showing the composition of each category. When to Use Bar Plots: - To compare frequencies, counts, or other quantities across different categories. - To display distributions of categorical data. - To illustrate changes over time if each bar represents a time period. Advantages: - Easy to understand and interpret. - Good for comparing different categories. - Can be used for a wide range of data types. Disadvantages: - Can become cluttered if there are too many categories. - May not be the best choice for representing continuous data.

Declare

            
# namespace ggplot.layers
export class geom_bar extends ggplotGroup {
   alpha: double;
   colorMap: ggplotColorMap;
   groupWidth: double;
   position: string;
   reader: ggplotReader;
   showLegend: boolean;
   # the bar height evaluation method, value could be 
   #  
   #  1. identity
   #  2. percentage
   stat: string;
   which: Expression;
   zindex: integer;
}

        

.NET clr type reference tree

1. this class extends from ggplotGroup class: ggplotGroup
2. use by property member colorMap: ggplotColorMap
3. use by property member reader: ggplotReader
4. use by property member which: Expression

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