TOP 10 Best Charts for Applications
Tags: webdev, javascript, programming, design
Published: 2024-06-11 13:07:11

Charts are essential tools for visualizing data in applications, making it easier to understand trends, patterns, and insights. This guide will explore 10 different types of charts, detailing their purposes, advantages, and best use cases.

## 1. Line Charts

**Overview**

Line charts display data points connected by straight lines. They are ideal for showing trends over time, such as stock prices, weather patterns, or website traffic.

**Advantages**

- _**Clarity:**_ Easily shows trends and changes over time.
- _**Simplicity:**_ Simple to understand and create.
- _**Comparability:**_ Can compare multiple data sets on the same axis.

**Best Use Cases**

- _**Time-series Data:**_ Ideal for displaying how data changes over time.
- _**Performance Metrics:**_ Useful in tracking performance metrics such as sales, production levels, or user activity.

**Application**

**[Microsoft Excel:](https://www.microsoft.com/fr-ch/microsoft-365/excel?ef_id=_k_1076b6a1f8331530a09cd0c9c6a9de68_k_&OCID=AIDcmmpeftwgl4_SEM__k_1076b6a1f8331530a09cd0c9c6a9de68_k_&msclkid=1076b6a1f8331530a09cd0c9c6a9de68)** A popular tool for creating line charts with customizable options.


![Microsoft Excel charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xxstbew12uxsp2z36lxu.png)



## 2. Bar Charts

**Overview**

Bar charts represent data with rectangular bars. Each bar’s length is proportional to the value it represents. They are excellent for comparing quantities across different categories.

**Advantages**

- _**Versatility:**_ Can represent both positive and negative values.
- _**Comparison:**_ Easy to compare different groups or categories.
- _**Clarity:**_ Clearly shows differences in values.

**Best Use Cases**

- _**Categorical Data:**_ Ideal for comparing different categories such as sales by region or product popularity.
- _**Surveys and Polls:**_ Effective in displaying survey results and opinion polls.

**Application**

**[Google Charts:](https://developers.google.com/)** Provides an easy way to create interactive bar charts.


![Google Charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/878d7v6p6tibfx1jas4j.png)



## 3. Pie Charts

**Overview**

Pie charts show proportions of a whole. Each slice represents a category’s contribution to the total.

**Advantages**

- _**Proportional Insight:**_ Quickly shows the proportion of each category.
- _**Visual Appeal:**_ Engaging and easy to understand at a glance.

**Best Use Cases**

- _**Market Share:**_ Showing the market share of different companies.
- _**Budget Allocation:**_ Displaying how a budget is divided among departments.

**Application**

**[Tableau:](https://www.tableau.com/)** A powerful tool for creating detailed and interactive pie charts.


![Tableau pie charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/rvnomm0mvqosg1ksaj5s.png)



## 4. Gantt Charts

**Overview**

Gantt charts are used for project management, illustrating project schedules. They show the start and end dates of project elements and their relationships.

**Advantages**

- _**Project Tracking:**_ Clearly displays project timelines and progress.
- _**Resource Management:**_ Helps in managing resources and dependencies.
- _**Schedule Visualization:**_ Visualizes the entire project schedule in one view.

**Best Use Cases**

- _**Project Management:**_ Tracking the progress of project tasks and deadlines.
- _**Resource Allocation:**_ Managing team workloads and resource allocation.
- _**Drag-and-Drop:**_ Easily adjust task durations and dependencies.
- _**Customization:**_ Highly customizable to fit different project needs.
- _**Real-Time Updates:**_ Supports real-time updates and collaboration.
- _**Resource Management:**_ Integrated tools for managing resources and their availability.

**Application**

**[ScheduleJS:](https://schedulejs.com/)** Specifically designed for creating advanced and interactive Gantt charts.


![Schedule JS Gantt Charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/w8fqqqwx9l3whxzoojp2.png)



## 5. Area Charts

**Overview**

Area charts are similar to line charts but fill the area below the line. They are useful for showing cumulative totals over time.

**Advantages**

- _**Cumulative Data:**_ Shows how a measure progresses over time.
- _**Visual Impact:**_ More visually impactful than line charts for certain data types.

**Best Use Cases**

- _**Resource Usage:**_ Tracking cumulative resource usage like energy consumption.
- _**Financial Data:**_ Displaying cumulative financial data such as revenue over time.

**Application**

**[Microsoft Power BI:](https://www.microsoft.com/fr-ch/power-platform/products/power-bi/landing/free-account?ef_id=_k_7cd505e282b41fded869638ac33fed89_k_&OCID=AIDcmmgdd45iwc_SEM__k_7cd505e282b41fded869638ac33fed89_k_&msclkid=7cd505e282b41fded869638ac33fed89)** Great for creating interactive and dynamic area charts.


![Microsoft Power BI Area Charts ](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/65r51cah5k0a617u9bv6.png)



## 6. Histogram

**Overview**

Histograms display the distribution of a dataset. They divide the data into bins and count the number of observations in each bin.

**Advantages**

- _**Distribution Analysis:**_ Shows the distribution and spread of data.
- _**Outlier Detection:**_ Helps in identifying outliers and anomalies.
- _**Frequency:**_ Displays the frequency of data points within ranges.

**Best Use Cases**

- _**Statistical Analysis:**_ Essential in statistics for analyzing data distributions.
- _**Quality Control:**_ Used in manufacturing to monitor process variations.

**Application**

[Python (with Matplotlib)](https://matplotlib.org/): A powerful library for creating detailed histograms.


![Python (with Matplotlib) Histogram](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/rjfv6buj2jz1j89r1nad.png)



## 7. Heatmaps

**Overview**

Heatmaps represent data in a matrix format, using color to indicate values. They are great for showing the magnitude of values in a two-dimensional space.

**Advantages**

- _**Density Display:**_ Shows the density and intensity of data.
- _**Pattern Recognition:**_ Helps in identifying patterns and correlations.

**Best Use Cases**

**[D3.js:](https://d3js.org/)** A JavaScript library for producing dynamic and interactive heatmaps.


![D3.js heatmaps](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/4pe861w9300w9x1figsq.png)



## 8. Bubble Charts

**Overview**

Bubble charts are a variation of scatter plots with an added dimension of data represented by the size of the bubbles.

**Advantages**

- _**Multidimensional Data:**_ Displays three dimensions of data in one chart.
- _**Comparison:**_ Allows comparison of relationships and magnitude.

**Best Use Cases**

- _**Market Analysis:**_ Showing market segments with different sizes.
- _**Risk Analysis:**_ Displaying risk factors where size indicates the level of risk.

**Application**

**[Plotly:](https://plotly.com/python/)** Ideal for creating interactive and visually appealing bubble charts.


![Plotly Bubble Charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/g6nmuqqhdq9le7xxn0gh.png)



## 9. Radar Charts

**Overview**

Radar charts, also known as spider charts, display multivariate data across multiple axes starting from the same point.

**Advantages**

- _**Multivariable Comparison:**_ Effective for comparing multiple variables at once.
- _**Performance Analysis:**_ Useful for performance analysis across different metrics.

**Best Use Cases**

- _**Skill Assessment:**_ Comparing skill levels across different competencies.
- _**Product Comparison:**_ Comparing product features and performance.

**Application**

**[Chart.js](https://www.chartjs.org/)**: A simple yet powerful tool for creating interactive radar charts.


![Chart.js Radar Charts](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vyxqii8mlo7esie87xbh.png)



## 10. Scatter Plots

**Overview**

Scatter plots use Cartesian coordinates to display values for two variables. Each point represents an observation in the data set.

**Advantages**

- _**Correlation Detection:**_ Helps identify relationships between variables.
- _**Outlier Identification:**_ Easily spot outliers in the data.
- _**Trend Analysis:**_ Can show trends or clusters in the data.

**Best Use Cases**

- _**Correlation Studies:**_ Ideal for exploring relationships between variables, such as height vs. weight.
- _**Scientific Data:**_ Useful in various scientific fields for analyzing experimental data.

**Application**

**[R (with ggplot2):](https://ggplot2.tidyverse.org/)** An excellent choice for creating customizable and detailed scatter plots.


![R (with ggplot2) Scatter Plots](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3th2psmchljcx6ecfmpl.png)



## Conclusion:
 

By understanding and utilizing these various types of charts, applications can effectively communicate data, making it easier for users to grasp complex information and make informed decisions.

If you'd like to see more Gantt aplications, have a look: [TOP 5 Best Javascript Gantt Chart Library](https://dev.to/lenormor/top-5-best-javascript-gantt-chart-library-fjg)