Choosing the Most Appropriate Type of Chart or Graph for Data Visualization

Bar Graphs for Categorical Data

1. Bar charts are useful for ranking categorical data by examining how two or more values or groups compare to each other in relative magnitude, at a given point in time.

When to use a Line Graph?

1. Line graphs allow a quick assessment of acceleration (lines curving upward), deceleration (lines curving downward), and volatility (up/down frequency). 2. Line graphs can also be used to show and compare several groups or variables over the same metric of time to see any correlation in trends4 .

Clustered Bar Charts

1. Clustered Bar Charts display categorical data next to each other, rather than stacked in the same bar, in order to easily compare values between groups.

Histograms

1. Histograms are a graphical representation of the distribution and frequency of numerical data 2.They show how often each different value occurs in a quantitative, continuous dataset. 3.Histograms group data into bins or ranges to show the distribution and frequency of each value

When to use a Scatter Plot?

Unlike other charts, scatter plots have the ability to show trends, clusters, patterns, and relationships in a cloud of data points - especially a very large dataset.

Bar Graphs for Longitudinal Data

1 Bar charts can be used to represent longitudinal data repeated over time to help identify temporal trends and patterns. 2. It allows the viewer to see temporal trends in the single dataset, such as high use during the school months and low use over the summer break.

Bar Charts

1. Bar charts use a horizontal (X) axis and a vertical (Y) axis to plot categorical data or longitudinal data 2. Bar charts compare or rank variables by grouping data by bars 3. The lengths of the bars are proportional to the values the group represents. 4. Bar charts can be plotted vertically or horizontally

Line Graphs

1. Line graphs are a commonly used visualization technique that use horizontal (X) and vertical (Y) axes to map quantitative, independent or dependent variables 2. Like scatter plots below, line graphs record individual data points; however, line graphs connect each data point together to determine local change from one point to the next 3.Line graphs are often used to display time-series relationships by tracking changes in continuous data, using equal intervals of time between each data point

Pie Charts

1. Pie charts are useful for cross-sectional visualizations, or for viewing a snapshot of categories at a single point in time 2.Pie charts divide categories into slices to illustrate numerical proportions of a whole, typically out of 100%. This data is usually only measured once. 3.One challenge with pie charts is the ability to compare the numerical values of each group

Scatter Plots

1. Scatter plots use horizontal (X) and vertical (Y) axes to plot quantitative, independent, or dependent variables in order to visualize the correlation between two variables. 2. Scatter plots are similar to line graphs in that they graph quantitative data points; however, scatter plots do not connect individual data points with a line but instead express a trend 3. This trend can be represented through the distribution of points or through the addition of a trend line/regression line5

Introduction to Word Clouds/Tag Clouds

1. Similar to a histogram, word clouds represent the frequency of certain words, terms or expressions. 2.Unlike histograms, word clouds use categories or terms from text. 3. They show the frequency of terms used in a paragraph, RSS feed, or other block of text by scaling the size of the terms and color to highlight the frequency of occurrence. 4. Unlike the other techniques for displaying information, word clouds are not easy to create in Excel; however, many webbased generators exist to create colorful and informative word clouds.

Summary Tables

1. Summary tables display data in simple, digestible ways. 2. The use of a summary table allows the viewer to assess data and to note significant values or relationships

When to use a Trend Line or Regression Line

1. Trend lines can help visualize correlations between the variables. 2. A regression line could be added, which is a calculated "best fit" line through the data points. 3. There are many trend line options, including linear, exponential, logarithmic, polynomial, power, or moving average. 4. Regression lines can help interpolate and extrapolate datasets for predicting values outside of observed data. 5. In addition to adding a regression line, you can add in its R-squared value

R-squared value

1. statistical measure of how close the observed data are fitted to the regression line. 2. R-squared is always between 0 and 100%. 0% indicates that the model explains none of the variability of the response data around its mean. 3.100% indicates that the model explains all the variability of the response data around its mean. 4. . In general, the higher the R-squared, the better the model fits the data.

Stacked Bar Charts

1.Stacked bar charts are useful when the sum of all the values is as important as the individual categories/groups. 2.Stacked bar charts show multiple values for individual categories, along with the total for all of the categories combined. 3. While stacked graphs are helpful for conveying multiple levels of meaning simultaneously, they also have some limitations a) While it's easy to interpret the values for the total bar and the first group of the bar, it is challenging to quantify the values for subsequent groups (strips) in the same bar, or to compare the groups within the same bar

When to Use a Word Cloud?

1.Word clouds are useful to qualitatively display the frequency of many categories or terms within a large body of text or data. 2.They are helpful when a dataset has many categories or terms beyond those that can easily be summarized in other charts or when using unstructured data. 3.Unstructured Data refers to information that either does not have a predefined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. 4.An example of unstructured data is a news article, report, journal article, etc.

Stacked Bar Charts vs Clustered Bar Charts

Bar charts can effectively display raw data over time. In the Stacked Bar Chart, each bar represents the total number of households in each district, with each color representing the number of households using a type of fuel source. This method shows how the total number of households varies by district, but is less effective at comparing the actual numbers for each fuel source over all districts. In the Clustered Bar Chart, the same data is depicted, but the cooking fuel sources are clustered next to each other. This allows for group comparisons over multiple districts, but makes it more challenging to see how the total number of households vary.

random placement of points

It means no correlation

Correlation=causation?

It's important to remember that correlation does not always equal causation, and other unnoticed variables could be influencing the data in a chart

exponential correlation

it looks like positive correlation