Choosing the Right Charts and Graphs For Your Data
Data visualization is an essential aspect of data analysis. It allows us to present complex information in a visual format that is easy to understand and interpret. One of the key decisions in data visualization is choosing the right charts and graphs to represent your data effectively. In this article, we will explore the different types of charts and graphs available, how to select the right one for your data, and the importance of making the right chart selection.
Visualizing data through effective charts, graphs, and diagrams makes insights more intuitive. Matching data types to appropriate visual encodings optimizes understanding. This guide provides an overview of common chart options, when to use specific visualization formats, tools for building charts, principles for quality dataviz design, and techniques for storytelling with data. Follow these best practices to present key findings clearly with purpose-driven data graphics.
Clarifying Your Audience, Context, and Communication Goals
Before choosing visuals, understand who they are for and what insights to emphasize.
Key Questions to Guide Design
- Who is the target audience? Their background shapes complexity.
- Where and how will visuals be consumed? Online? Print? Presentations?
- What discoveries or messages do you want to focus on?
- What story are you trying to convey about the data?
- Will the context be a quick glance or deep analytical dive?
- Do you want to provoke action or simply inform?
- What questions or objections are you trying to preemptively address?
Framing goals and audience orients design choices towards clarity and impact.
Overview of Key Chart and Graph Options
Many visualization formats exist. Each excels at different purposes.
Major Data Visualization Types
- Bar charts – Compare discrete categorical values
- Pie charts – Show part-to-whole relationships
- Line graphs – Track trends and patterns over time
- Scatter plots – Reveal correlations between two variables
- Bubble charts – Show relationships between three variables
- Funnels – Depict stages in conversion processes
- Maps – Display location-based data patterns
- Tables – Provide access to underlying dataset details
Matching chart types to use cases and data types optimizes communication.
Choosing Appropriate Visual Encodings
Leverage visual attributes tailored to data types for intuitive clarity.
Visual Encodings to Map Data Attributes
- Position – Ordinal data like ranking
- Length – Quantitative metrics and scales
- Angle – Cyclical or part-to-whole data
- Color hue – Categorical distinctions
- Color saturation – Part-to-whole relationships
- Size – Quantitative differences and importance
- Shape – Categorical variation
Aim for perceptually effective matches maximizing cognition. Test mappings with audience.
Bar and Column Charts for Clear Category Comparisons
Bar charts enable direct comparison between categories sharing a common metric.
When to Use Bar Charts
- Displaying levels across nominal categories
- Highlighting ranking differentials clearly
- Drawing attention to a leading or lagging category
- Charting grouped clusters for segmentation analysis
- Plotting time series data across sequential intervals
- Portraying sparse datasets concisely over tables
Design Principles for Effective Bar Charts
- Order categories intuitively from highest to lowest
- Start y-axis at zero rather than cropping to exaggerate
- Cap x-axis labels to keywords for cleanliness
- Add data labels directly on bars for precision
- Include explanatory caption summarizing key takeaway
Bars excel when categorical distinction matters most. Simplify patterns.
Timeline Charts For Spotting Trends and Patterns
Line charts make longitudinal fluctuations, spikes, and trajectories easy to spot.
Appropriate Uses of Timeline Charts
- Visualizing progress over longer time horizons
- Demonstrating seasonal ebbs and flows in metrics
- Linking events to decisive impacts through correlation
- Quantifying the extent and duration of peaks and troughs
- Forecasting future trajectories based on historical curves
- Comparing overlapping trends by overlaying lines
Design Principles for Reading Timeline Charts
- Space time ticks consistently along x-axis
- Start y-axis at zero to avoid exaggerating spikes
- Label time intervals and key events directly on chart
- Highlight pertinent plot points like spikes with callouts
- Annotate turning points where trends shift
- Only include lines needed for key comparison
Lines turn complex timeseries into easily digestible pictures.
Pie Charts for Depicting Part-to-Whole Relationships
Pie charts show proportional splits making up one unified whole.
Appropriate Uses of Pie Charts
- Breaking market share into constituent competitors
- Looking at percentages or hours comprising a full year
- Distilling survey response rates by segment
- Seeing breakthroughs in datasets by factors
- Comparing budget, time, or resource allocation
- Visualizing priorities split through focus mapping
Design Principles for Intuitive Pie Charts
- Order slices by size proportion for easy benchmarking
- Pull small slices into unified “other” to simplify
- Use varying hue instead of patterns to encode categories
- Include direct data labels on slices
- Add title summarizing key takeaway proportion
Pies slices provide an intuitive feel for composition that tables lack.
Scatterplots For Revealing Correlations
Scatterplots map paired values to assess potential correlation or causation.
When to Use Scatterplots
- Identifying positive and negative correlations
- Visualizing clustering and outliers through point proximity
- Evaluating the strength of predictive relationships
- Inferring models for defining relationships
- Comparing correlation variances by factor through overlap
- Extracting intersecting insights across multiple comparisons
Design Principles for Scatterplots
- Choose visually distinct marks like color, shape, or size to encode meaningful attributes
- Draw trendline if directional relationship exists
- Label outliers and clusters warranting qualitative investigation
- Add jitter to prevent marks overlapping at intersections
- Show distribution along axes through histograms
Scatterplots surface hidden connections worthy of closer inquiry.
Maps For Plotting Location-Specific Patterns
Maps overlay metrics onto geography revealing regional insights.
Appropriate Uses of Data Maps
- Pinpointing regional clusters, hotspots, and variations
- Comparing contiguous areas side-by-side visually
- Linking location-based differences to demographic factors
- Revealing patterns like urban/suburban/rural divides
- Adding geographic context to non-location based data
- Guiding resource allocation decisions to physical areas
- Planning expansion, logistics, and distribution for local markets
Map Design Principles
- Use color to encode metric values over regions consistently
- Include legend quantifying ranges for color buckets
- Label landmarks and geographic features as reference points
- Allow zooming into local granular views
- Only visualizing regions relevant to your audience and purpose
- Add context through borders, boundaries, and basemaps
Maps merge location and data for insights with geographic influence.
Tables For Displaying Complete Datasets
Tables serve as precise references for underlying numbers.
When to Use Data Tables
- Showing full collection of raw values across fields
- Providing complete details where precision matters
- Looking up specific values like totals or peaks
- Exporting and delivering data summaries for analysis
- Including contextual categorical attributes not conveyed visually
- Serving as data source for generating visualizations
- Allowing custom filtering, sorting, and calculations
Table Design Best Practices
- Organize columns and rows intuitively
- Sort most significant fields leftmost
- Right align numeric data for easy vertical scanning
- Highlight key fields like headers and totals through bolding
- Only show essential fields to avoid clutter
- Summarize page with descriptive caption
Tables provide ready access to complete numbers underlying visuals.
Choosing Appropriate Chart Scales and Axes
Set axes and scales to provide appropriate context for key points.
Scale and Axis Considerations
- Start axis at zero unless intentionally cropping to focus
- Make scale intervals and units consistent across charts
- Size axes to give data space without excessive whitespace
- Label axis concisely indicating metrics and units
- Include enough tick marks for precision without clutter
- shorten tick labels like “Sept” instead of “September”
- Note truncations like “Values over 1,000” if cutting top end
Well-scaled axes frame data fairly. Avoid distorting perception.
Highlighting Insights Through Annotations
Callouts, labels, arrows, and overlays make insights obvious.
Types of Annotations to Guide Viewing
- Labels – Name titles, units, scales, sources, events
- Arrows – Pinpoint specific values or inflection points
- Callouts – Pull and explain key numbers worth noting
- Threshold lines – Mark signal levels like goals and averages
- Color & icons – Enhance encoding through redundant emphasis
- Details on hover – Reveal precise values on mouseover
Annotations focus attention on what matters most. Don’t rely solely on implicit interpretation.
Choosing Optimal Chart Sizes and Layouts
Balance information density with legibility and surrounding space.
Principles for Effective Chart Sizing
- Minimum size for discernible details and labels
- Consistent relative sizing across discrete charts
- White space providing cleanliness and visual breathing room
- Alignment and positioning aiding natural flow
- Size proportionate to significance and complexity
- Maximum size for context – don’t crowd out other content
Purpose and placement should guide proportions. Enhance readability.
Designing Color Palettes and Themes
Use color deliberately to augment understanding. Map hues to meaning.
Tips for Intentional Coloring
- Limit total distinct colors to avoid unnecessary cognitive load
- Map colors to categorical meanings consistently across charts
- Consider cultural associations and color blindness in mappings
- Vary saturation and value rather than hue for some encodings
- Use naturally ordered spectral sequences like red-yellow-green
- Make accessible and printable friendly palettes with distinct grays
- Brand with organization, product, or category color schemes
Coloring is powerful when systematic. Use restraint and significance.
Choosing Appropriate Chart Types For Data
Beyond general principles, match visuals carefully to specifics of each dataset.
Assessing Dataset Characteristics
- Data types – categorical, ordinal, quantitative, etc
- Directionality – trajectories, correlations, part-to-whole
- Continuity – discrete events vs time series
- Density – sparse points vs dense continuous spectrum
- Multivariate – singles or multiples to encode
- Uncertainty – samples or populations
Let innate data traits guide design choices. Fit form to content.
Testing Visualization Effectiveness
Confirm charts communicate intended insights through validation with audience.
- Comprehension testing – Can users correctly read key insights?
- Time trials – How quickly are insights apparent?
- Memory testing – What sticks after viewing?
- Emotional response surveys – Do visuals positively engage?
- Eye-tracking – Where do viewers focus first?
- Qualitative feedback – Which elements resonate or confuse?
Don’t assume your charts work. Watch people struggle or succeed. Iterate.
Tools and Platforms for Building Visualizations
Leverage software accelerating the creation process.
Data Visualization Software Capabilities
- Chart and graph galleries
- Direct data linkage and live updating
- Drag-and-drop customization
- Style and theme templates
- Exporting via images, PDFs, APIs
- Interactive drill-downs into data
- Mapping and geospatial features
- Collaborative editing
- Animation and motion graphics
Automate repetitive work for efficiency and consistency. Save time with smart tools.
Using Visuals to Tell Stories and Persuade
Shape narrative and meaning through curation, sequence, reference, and pacing.
Principles for Persuasive Visual Storytelling
- Curate views purposefully leading viewers
- Sequence direct attention through logical flow
- Use anchoring visual references readers recognize
- Reinforce relationships between steps numerically
- Build tempo and suspense to engaging crescendo
- Shed color-coded light on key turning points
- Balance objective data with subjective surprise
The whole storyline should transcend the sum of its charts. Design engaging flow.
Animating Data Visualizations For Video
Motion overlays narrative progression atop data. Draw viewers in dynamically.
Bringing Data Visuals to Life Through Video
- Pan and zoom focus from big picture to granular
- Animate chart axes and data streaming in real-time
- Transition between views fluidly maintaining context
- Revisit key anchors and motifs across segments
- Guide eyes by sequencing visual attention
- Change时间查询/statuses to illustrate processes unfolding
- Use voiceover to supplement and pace graphical details
- Incorporate text, captions, and highlights dynamically
Video provides a temporal canvas for data storytelling. Lead viewers through key insights smoothly.
Top Mistakes to Avoid When Visualizing Data
Sidestep common charting pitfalls through intentional design and testing.
Visualization Mistakes to Watch Out For
- Embellishing beyond supportable conclusions
- Centering personal agenda over objective data
- Using meaningless visual embellishments and flashy effects
- Citing data without providing proper context
- Misrepresenting by abusing scales, omitting baselines, etc.
- Inventing arbitrary color mappings with no meaning
- Cluttering with unnecessary elements competing for attention
- Choosing default settings rather than customizing intentionally
Great dataviz informs. Faulty dataviz misinforms. Respect the evidence. Foster clarity over persuasion.
What are the different types of charts and graphs?
There are several different types of charts and graphs that can be used to present data. Some of the most common ones include: 1. Bar chart: A bar chart is used to compare different categories of data by representing them as rectangular bars. The height of each bar corresponds to the value being represented.
2. Line graph: A line graph shows the relationship between two variables by plotting data points and connecting them with a line. It is often used to show trends over time.
3. Pie chart: A pie chart is a circular graph that represents data as slices of a whole. Each slice represents a proportion or percentage of the total.
4. Scatter plot: A scatter plot shows the relationship between two variables by representing each data point as a dot on a graph. It is useful for identifying patterns or correlations between variables.
5. Area chart: An area chart is similar to a line graph, but the area between the line and the x-axis is filled with color. This can be used to show the cumulative effect of multiple variables over time.
6. Histogram: A histogram is used to display the distribution of a continuous variable by dividing it into bins and showing the frequency or count of data points within each bin.
7. Radar chart: A radar chart is a graphical method of displaying multivariate data in the form of a two-dimensional chart with multiple axes. Each variable is represented by a separate axis that starts from the center and extends outwards.
8. Box and whisker plot: A box and whisker plot represents the distribution of a dataset using quartiles. It displays the minimum, maximum, median, and quartile values, providing a summary of the data’s spread and central tendency.
9. Gantt chart: A Gantt chart is a horizontal bar chart used for project management. It displays the project tasks along with their durations and dependencies, allowing for scheduling and progress tracking.
10. Waterfall chart: A waterfall chart shows how an initial value is affected by positive and negative changes, visually depicting cumulative total changes.
These are just a few examples of the various types of charts and graphs available, and there are many more specialized types that can be used for specific purposes. The choice of chart or graph depends on the type of data being presented and the story you want to convey. 1. Bar Charts: These charts use rectangular bars to represent data. They are useful for comparing the values of different categories or groups.
2. Line Charts: Line charts are used to show trends over time. They connect data points with lines, allowing for the visualization of changes and patterns.
3. Pie Charts: Pie charts are circular graphs that divide data into parts of a whole. They are effective in displaying percentages or proportions.
4. Scatter Plots: Scatter plots display the relationship between two variables. Each point on the graph represents a data point, and the distribution of points indicates the strength and direction of the relationship.
5. Histograms: Histograms are used to display the distribution of continuous data. They group data into intervals or bins and represent the frequency or count within each interval.
6. Area Charts: Area charts are similar to line charts but show the cumulative total of a variable over time. They are effective in illustrating changes in total quantities.
7. Gantt Charts: Gantt charts are used in project management to display the timeline of tasks and their dependencies. They show the start and end dates of each task and help in tracking progress.
8. Box and Whisker Plots: Box and whisker plots present a summary of a data set’s distribution. They show the minimum, maximum, median, and quartiles, providing insights into the spread and skewness of the data.
9. Bubble Charts: Bubble charts are similar to scatter plots but include an additional dimension by representing data points as bubbles of varying sizes. They are particularly useful for visualizing three variables simultaneously.
10. Radar Charts: Radar charts, also known as spider charts, display multiple data points on a two-dimensional graph with axes radiating from a common center point. They are used to compare different variables across multiple categories.
These are just some of the many types of charts and graphs available, and the choice of the appropriate one depends on the data being presented and the insights desired.
How do you choose the right chart for your data?
Choosing the right chart for your data depends on several factors:
1. Type of data: The first step is to determine the type of data you have. Is it categorical or numerical? Categorical data is divided into distinct categories, such as different types of fruits or customer segments. Numerical data represents quantities or measurements.
2. Purpose: Consider the purpose of your chart. Are you trying to show a comparison, distribution, relationship, or composition? Each purpose may require a different type of chart.
3. Relationships: Analyze the relationships between different variables in your data. Do you want to compare values across categories, show trends over time, or display the correlation between two variables? Understanding the relationships will help determine the appropriate chart type.
4. Features and dimensions: Consider the features and dimensions of your data. Are you dealing with one variable or multiple variables? Are there any specific patterns or trends you want to highlight? Understanding the features and dimensions will help you identify the most suitable chart type.
5. Data characteristics: Consider the characteristics of your data, such as its size, range, and variability. Large datasets with many data points may require charts that can efficiently display the information, such as scatter plots or histograms. On the other hand, small datasets with fewer data points may work well with simpler charts like bar graphs or pie charts.
6. Audience: Lastly, consider your audience and their familiarity with different chart types. Choose a chart that is easy to understand and interpret for your specific audience. Avoid complex or unfamiliar charts that may confuse or mislead the audience.
Overall, selecting the right chart for your data requires considering the type of data, purpose, relationships, features and dimensions, data characteristics, and audience. It may also involve trial and error to find the best chart that effectively communicates your message.
What charts are best for comparing values?
When comparing values, some of the best charts to use are:
1. Bar Charts: Bar charts are particularly effective for comparing values between different categories or groups. The length of the bars can represent the value being compared, making it easy to compare and understand the differences visually.
2. Column Charts: Similar to bar charts, column charts are useful for comparing values across different categories or groups. They work well when the labels for the categories are too long to display horizontally on a bar chart.
3. Line Charts: Line charts are great for comparing trends over time. By plotting the values on a continuous line, it becomes easy to compare the relative changes in the values.
4. Scatter Plots: Scatter plots are especially useful for comparing values that have a relationship or correlation. By plotting the values on an x-y axis, it becomes easy to see patterns or clusters.
5. Bubble Charts: Bubble charts are similar to scatter plots but with an added dimension. In addition to the x and y values, bubble charts use the size of the bubbles to represent a third variable. This can be useful for comparing values with an additional level of depth or complexity.
6. Radar Charts: Radar charts are effective for comparing multiple values across different dimensions or attributes. By using a circular layout with axes radiating from a center point, radar charts make it easy to compare values in a way that highlights strengths and weaknesses across different dimensions.
Ultimately, the choice of the best chart depends on the type of data being compared and the specific goal of the comparison. It is important to consider the nature of the data, the audience, and the level of detail required when choosing the appropriate chart for comparing values.
Which charts are suitable for showing part-to-whole relationships?
To show part-to-whole relationships in your data, consider using:
1. Pie charts: Pie charts are ideal for illustrating the proportions of different parts in relation to the whole. Each slice of the pie represents a specific component, and the size of the slice is proportionate to its value or percentage in the whole.
2. Stacked bar charts: Stacked bar charts show the composition of different components within a whole. Each bar represents the total, and the different sections of the bar represent the various parts in relation to the whole. The length of each section indicates the proportion of that component within the total.
3. Treemap charts: Treemap charts use rectangles or squares to represent the different parts of a whole. The size of each rectangle is determined by the proportion of the component in relation to the whole. The charts are divided into smaller rectangles, with each one representing a specific part.
4. Stacked area charts: Stacked area charts show the proportion of different components within a whole over time. The chart is segmented into different areas, with each area representing a specific part. The total area represents the whole, and the changing heights of the areas show the changing proportions of the components.
5. 100% stacked bar charts: 100% stacked bar charts are similar to stacked bar charts, but the total height of each bar is standardized to 100%. This type of chart shows the relative proportions of the different components within the total, making it easy to compare the parts to the whole across categories.
These chart types are useful for visually representing part-to-whole relationships and helping viewers understand the relative sizes or proportions of different components within a dataset.
How do you visualize geographical data?
When visualizing geographical data, you can use:
1. Maps: Maps are one of the most common and effective ways to visualize geographical data. They provide a visual representation of the geographic area, showing boundaries, land features, and other spatial details. You can use different types of maps, such as choropleth maps (color-coded maps based on specific data values), dot maps (representing individual data points as dots on a map), or thematic maps (highlighting specific themes or patterns).
2. Heatmaps: Heatmaps show the density or intensity of geographic data using colors or shading. They are useful for visualizing data such as population density, crime rates, or temperature variations. Heatmaps can provide a quick overview of data patterns and hotspots in a particular area.
3. Geospatial visualizations: Geospatial visualizations represent data on a three-dimensional model of the Earth’s surface. These visualizations can show terrain, elevation, and other geographical features. They are often used for scientific purposes, such as visualizing climate patterns or topographic data.
4. Geographical Information Systems (GIS): GIS software allows you to create, manage, analyze, and visualize geographical data. It provides a wide range of tools and techniques for creating maps, conducting spatial analysis, and incorporating multiple layers of data. GIS software can be used to create customized visualizations that suit specific data analysis needs.
5. Interactive tools: Interactive visualizations allow users to explore geographical data on their own. These tools often provide zooming, panning, and filtering options, enabling users to focus on specific regions or areas of interest. Interactive visualizations can be particularly useful when presenting data to a diverse audience or when conducting exploratory data analysis.
6. 3D modeling and virtual reality: For more immersive experiences, 3D modeling and virtual reality techniques can be used to visualize geographical data. This approach allows users to navigate through virtual representations of geographic areas, providing a more realistic and immersive visualization experience.
It is important to select visualization techniques based on the specific goals, type of data, and target audience.
What factors should be considered when choosing a chart or graph?
Several factors should be considered when choosing a chart or graph:
1. Purpose: The first factor to consider is the purpose of the chart or graph. Determine whether the goal is to compare quantitative data, show trends over time, display proportions or relationships, or explore patterns or distributions. The purpose will help determine the most appropriate type of chart or graph.
2. Data Type: Consider the type of data being presented. Is it quantitative or qualitative? If the data is quantitative, determine whether it is discrete or continuous. If the data is qualitative, consider whether it is nominal or ordinal. Different chart types are better suited for different types of data.
3. Number of Variables: Consider the number of variables you need to represent. If you have only one variable, a simple chart like a bar chart or line graph might be appropriate. If you have multiple variables or want to show the relationship between two variables, you may need to use more complex charts like scatter plots or heat maps.
4. Data Distribution: Consider the distribution of the data. If the data is normally distributed, a histogram or a box plot can be used to show the spread and central tendency. If the data is skewed or has outliers, consider using transformation or alternative charts.
5. Audience: Consider the characteristics and preferences of your audience. Different chart types may be more familiar or easier to interpret for different audiences. Consider their level of expertise, familiarity with data visualization, and any cultural or contextual factors that may influence their understanding.
6. Accuracy and Precision: Consider the level of accuracy and precision required for the data representation. Some charts may be better at displaying precise values, while others may provide a more general overview or trends. Consider whether you need to show exact values, approximate trends, or both.
7. Visualization Effectiveness: Lastly, consider the visual effectiveness of the chart or graph. Ensure that the chosen chart type accurately represents the data, is easy to interpret, and effectively communicates the intended message. Avoid using overly complex or cluttered visualizations that may confuse or mislead the audience.
Why is selecting the right chart or graph pivotal for data visualization?
Selecting the right chart or graph is pivotal in data visualization for the following reasons:
1. Efficient data communication: Different types of data require different types of visualization. By selecting the right chart or graph, you can convey the information more effectively and efficiently. This ensures that the audience understands the data easily and accurately.
2. Clarity and simplicity: The appropriate choice of chart or graph can simplify complex data and make it more understandable. It helps in presenting the data in a clear and organized manner, reducing confusion and enhancing clarity.
3. Highlighting patterns and trends: A suitable chart or graph can help in highlighting patterns, trends, or relationships within the data. For example, a line graph can show trends over time, while a scatter plot can reveal correlations between variables. This enables the audience to easily identify and grasp important insights.
4. Emphasizing comparisons: Charts and graphs are ideal for presenting comparative data. Choosing the right type of visualization can emphasize the comparisons between different data points or categories, making it easier for the audience to draw conclusions or make decisions based on the data.
5. Audience engagement: Well-designed and relevant visualizations can enhance audience engagement and interest. By selecting the right chart or graph, you can present the data in a visually appealing and engaging way, capturing the attention of the audience and encouraging them to explore and understand the data more effectively.
Overall, the right choice of chart or graph in data visualization plays a crucial role in conveying information accurately, promoting understanding, and enabling effective decision-making.
Translating data into impactful charts and visuals requires thoughtful pairings of formats, encodings, annotations, and messaging tailored specifically to each dataset and audience. But when well executed, visuals become intuitive windows into revelations, trends, and insights buried in the numbers themselves. They frame narratives rather than just display information. Follow these chart design best practices to turn passive stats into engaging stories and calls to action that stick with audiences. Prioritize tailored relevance over decoration. Through quality visual analysis and presentation, you amplify understanding and enable data-driven decisions.
- 1 Choosing the Right Charts and Graphs For Your Data
- 1.1 Clarifying Your Audience, Context, and Communication Goals
- 1.2 Overview of Key Chart and Graph Options
- 1.3 Choosing Appropriate Visual Encodings
- 1.4 Bar and Column Charts for Clear Category Comparisons
- 1.5 Timeline Charts For Spotting Trends and Patterns
- 1.6 Pie Charts for Depicting Part-to-Whole Relationships
- 1.7 Scatterplots For Revealing Correlations
- 1.8 Maps For Plotting Location-Specific Patterns
- 1.9 Tables For Displaying Complete Datasets
- 1.10 Choosing Appropriate Chart Scales and Axes
- 1.11 Highlighting Insights Through Annotations
- 1.12 Choosing Optimal Chart Sizes and Layouts
- 1.13 Designing Color Palettes and Themes
- 1.14 Choosing Appropriate Chart Types For Data
- 1.15 Testing Visualization Effectiveness
- 1.16 Tools and Platforms for Building Visualizations
- 1.17 Using Visuals to Tell Stories and Persuade
- 1.18 Animating Data Visualizations For Video
- 1.19 Top Mistakes to Avoid When Visualizing Data
- 1.20 What are the different types of charts and graphs?
- 1.21 How do you choose the right chart for your data?
- 1.22 What charts are best for comparing values?
- 1.23 Which charts are suitable for showing part-to-whole relationships?
- 1.24 How do you visualize geographical data?
- 1.25 What factors should be considered when choosing a chart or graph?
- 1.26 Why is selecting the right chart or graph pivotal for data visualization?
- 1.27 Conclusion