Editing Bar chart (section)

==Usage==
{{multiple image |align=right |direction=horizontal |total_width=350
|image1=20210413 Carbon capture and storage - CCS - proposed vs implemented.svg |caption1=A vertical stacked bar chart with positive values
|image2=20210331 Global tree cover loss - World Resources Institute.svg |caption2=A vertical stacked bar chart with negative values
}}
{{multiple image |align=right |direction=horizontal |total_width=350
|image3=2010 homicide rates - gun PLUS non-gun - high-income countries.png |caption3=A horizontal stacked bar chart
|image4=Personal pronouns2.jpg |caption4=A vertical, grouped (clustered) [[3D computer graphics|3D]] bar chart
}}
Bar graphs/charts provide a visual presentation of categorical data.<ref name=KD>Kelley, W. M.; Donnelly, R. A. (2009) ''The Humongous Book of Statistics Problems''. New York, NY: Alpha Books {{ISBN|1592578659}}</ref> Categorical data is a grouping of data into discrete groups, such as months of the year, age group, shoe sizes, and animals. These categories are usually qualitative. In a column (vertical) bar chart, categories appear along the horizontal axis and the height of the bar corresponds to the value of each category.

Bar charts have a discrete domain of categories, and are usually scaled so that all the data can fit on the chart. When there is no natural ordering of the categories being compared, bars on the chart may be arranged in any order. Bar charts arranged from highest to lowest incidence are called Pareto charts.

===Grouped (clustered) and stacked===
Bar graphs can also be used for more complex comparisons of data with grouped (or "clustered") bar charts, and stacked bar charts.<ref name=KD/>

In '''grouped (clustered) bar charts''', for each categorical group there are two or more bars color-coded to represent a particular grouping. For example, a business owner with two stores might make a grouped bar chart with different colored bars to represent each store: the horizontal axis would show the months of the year and the vertical axis would show revenue.

Alternatively, '''Stacked bar charts''' (also known as '''Composite bar charts''') stack bars on top of each other so that the height of the resulting stack shows the combined result. Unlike a grouped bar chart where each factor is displayed next to another, each with their own bar, the stacked bar chart displays multiple data points stacked in a single row or column.  This may, for instance, take the form of uniform height bars charting a [[time series]] with internal stacked colours indicating the percentage participation of a sub-type of data.  Another example would be a time series displaying total numbers, with internal colors indicating participation in the total by sub-types. Stacked bar charts are not suited to data sets having both positive and negative values.

Grouped bar charts usually present the information in the same order in each grouping. Stacked bar charts present the information in the same sequence on each bar.

===Variable-width (variwide)===
[[File:20210626 Variwide chart of greenhouse gas emissions per capita by country.svg|thumb|'''Example:''' Variable-width bar chart relating:
<br>* countries' respective populations (along ''x'' axis), 
<br>* per-person CO2 emissions 1990-2018 (along ''y'' axis), and 
<br>* total emissions for that country (rectangle area = product ''<code>x*y</code>'' of sides' lengths)]]

Variable-width bar charts, sometimes abbreviated ''variwide'' (bar) charts, are bar charts having bars with non-uniform widths. Generally:
* Bars represent quantities with respective rectangles of areas ''A'' that are respective [[Product (mathematics)|arithmetic products]] of related pairs of
:::— vertical-axis quantities (''A/X'') and
:::— horizontal-axis quantities (''X'').
* Arithmetically, the area of each bar (rectangle) is determined a product of sides' lengths:
::''<code>(A/X)*X</code> = Area A'' for each bar
Roles of the vertical and horizontal axes may be reversed, depending on the desired application.

Examples of variable-width bar charts are shown at [https://commons.wikimedia.org/wiki/Category:Variable-width_bar_charts Wikimedia Commons].

===Advantages===

# Easy to read and interpret: Bar charts are easy to read and interpret, even for people without a background in statistics or data visualization. The bars make it easy to compare values and see trends, making it a useful tool for communicating information to a wide range of audiences.
# Can handle large amounts of data: Bar charts can handle large amounts of data and still provide a clear representation of the information. The bars can be made narrow or wide to fit a large number of categories or data points, and the use of color or patterns can make it easier to distinguish between them.
# Customizable: Bar charts can be customized to suit the needs of the user. For example, the color, width, and height of the bars can be adjusted to make the chart more visually appealing, and labels and annotations can be added to provide additional information.
# Useful for comparing values: Bar charts are particularly useful for comparing values between categories or data points. They allow for quick identification of differences and similarities, making it easy to draw conclusions and make decisions.<ref name=":0">{{Cite journal |last=Reid |first=Nathalie |date=2018-01-12 |title=Data Visualization: A Guide to Visual Storytelling for Libraries |url=http://jmla.pitt.edu/ojs/jmla/article/view/346 |journal=Journal of the Medical Library Association |volume=106 |issue=1 |pages=135 |doi=10.5195/jmla.2018.346 |issn=1558-9439 |pmc=5764581}}</ref><ref name=":1">{{Cite book |last=Healy |first=Kieran Joseph |url=https://www.worldcat.org/oclc/1032356534 |title=Data visualization : a practical introduction |date=2019 |isbn=978-0-691-18161-5 |location=Princeton, New Jersey |oclc=1032356534}}</ref>

===Limitations===

# Limited use for continuous data: Bar charts are not useful for displaying continuous data, such as temperature or time. For continuous data, a line chart or scatter plot may be more appropriate. Bar charts of continuous data with error bars are sometimes referred to as dynamite plots.<ref>{{Cite report |url=http://biorxiv.org/lookup/doi/10.1101/2022.03.14.484206 |title=Replacing bar graphs of continuous data with more informative graphics: Are we making progress? |last1=Riedel |first1=Nico |last2=Schulz |first2=Robert |date=2022-03-15 |publisher=Scientific Communication and Education |doi=10.1101/2022.03.14.484206 |language=en |last3=Kazezian |first3=Vartan |last4=Weissgerber |first4=Tracey}}</ref><ref>{{Cite journal |last1=Doggett |first1=Thomas J |last2=Way |first2=Connor |date=2024-01-08 |title=Dynamite plots in surgical research over 10 years: a meta-study using machine-learning analysis |url=https://doi.org/10.1093/postmj/qgad134 |journal=Postgraduate Medical Journal |volume=100 |issue=1182 |pages=262–266 |doi=10.1093/postmj/qgad134 |pmid=38190146 |issn=0032-5473|url-access=subscription }}</ref>
# Limited use for small sample sizes: Bar charts may not be useful for displaying small sample sizes, as the bars may not accurately represent the data. In such cases, a [[histogram]] or box plot may be more appropriate.
# May be misleading: Bar charts can be misleading if the scale is not appropriate or if the data is presented in a way that is designed to mislead the viewer. For example, if the y-axis is truncated, the differences between the bars may appear larger than they actually are.
# Limited scope for multivariate data: Bar charts can only display one or two variables at a time, making them less useful for displaying multivariate data. In such cases, a scatter plot or heat map may be more appropriate.<ref name=":0" /><ref name=":1" />