Data Visualization Ppt !EXCLUSIVE! Download
Gabrielle Brownlee
Data visualization is the term we use to describe all of the ways people transform data into visual representations. This could be a map, a bar chart, a timeline or an artistic rendering of data. For more definitions, examples, and helpful data visualization tools, see our Duke Data Visualization LibGuide.
Effective data visualizations can increase the impact of and engagement with your research. Visualizations with distortions or ineffective design choices, however, can confuse your audience or misrepresent your work. Increasingly, data visualization skills are necessary to succeed in a competitive job market.
Data visualization support can happen at any stage of your research. We can suggest ways to clean your data for visualization, brainstorm on visualization techniques, offer graphic design advice, or work with you on custom visualization training for your group or course.
The curriculum integrates theory and studio practice and provides students with a competitive edge as they enter the field of data visualization. The program may be completed in one year (full time) or two years (part time).
Graduates find success in applying their skill set in design, data analysis, and computing in a wide variety of fields such as advertising and branding; journalism; business consulting and analytics strategy; management; strategic planning; entrepreneurship; social enterprise; public policy; trend forecasting, and business intelligence.
Parsons faculty represent a broad range of expertise and are acknowledged as leading practitioners and scholars in their fields. Faculty also invite guest lecturers and critics to share their insights and expose students to new possibilities in data visualization and related career paths.
That one line of code loads the core tidyverse; the packages that you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded)1.
In addition to tidyverse, we will also use the palmerpenguins package, which includes the penguins dataset containing body measurements for penguins on three islands in the Palmer Archipelago, and the ggthemes package, which offers a colorblind safe color palette.
You can test your answers to those questions with the penguins data frame found in palmerpenguins (a.k.a. palmerpenguins::penguins). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). penguins contains 344 observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER2.
Type the name of the data frame in the console and R will print a preview of its contents. Note that it says tibble on top of this preview. In the tidyverse, we use special data frames called tibbles that you will learn more about soon.
Our ultimate goal in this chapter is to recreate the following visualization displaying the relationship between flipper lengths and body masses of these penguins, taking into consideration the species of the penguin.
Next, we need to tell ggplot() how the information from our data will be visually represented. The mapping argument of the ggplot() function defines how variables in your dataset are mapped to visual properties (aesthetics) of your plot. The mapping argument is always defined in the aes() function, and the x and y arguments of aes() specify which variables to map to the x and y axes. For now, we will only map flipper length to the x aesthetic and body mass to the y aesthetic. ggplot2 looks for the mapped variables in the data argument, in this case, penguins.
To do so, we need to define a geom: the geometrical object that a plot uses to represent data. These geometric objects are made available in ggplot2 with functions that start with geom_. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms (geom_bar()), line charts use line geoms (geom_line()), boxplots use boxplot geoms (geom_boxplot()), scatterplots use point geoms (geom_point()), and so on.
Since this is a new geometric object representing our data, we will add a new geom as a layer on top of our point geom: geom_smooth(). And we will specify that we want to draw the line of best fit based on a linear model with method = "lm".
You can set the width of the intervals in a histogram with the binwidth argument, which is measured in the units of the x variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. In the plots below a binwidth of 20 is too narrow, resulting in too many bars, making it difficult to determine the shape of the distribution. Similarly, a binwidth of 2,000 is too high, resulting in all data being binned into only three bars, and also making it difficult to determine the shape of the distribution. A binwidth of 200 provides a sensible balance.
Make a histogram of the carat variable in the diamonds dataset that is available when you load the tidyverse package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?
A box that indicates the range of the middle half of the data, a distance known as the interquartile range (IQR), stretching from the 25th percentile of the distribution to the 75th percentile. In the middle of the box is a line that displays the median, i.e. 50th percentile, of the distribution. These three lines give you a sense of the spread of the distribution and whether or not the distribution is symmetric about the median or skewed to one side.
However adding too many aesthetic mappings to a plot makes it cluttered and difficult to make sense of. Another way, which is particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.
The mpg data frame that is bundled with the ggplot2 package contains 234 observations collected by the US Environmental Protection Agency on 38 car models. Which variables in mpg are categorical? Which variables are numerical? (Hint: Type ?mpg to read the documentation for the dataset.) How can you see this information when you run mpg?
Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?
This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.
Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.
The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data.
Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks.
If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly.
Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information.
Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion.
Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.
At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics.
While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively.
The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely.
Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.
Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:
A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text.
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