Creating 2D graphs in Tableau

Downloading and importing the data into the tableau:

Download GHGRP (Greenhouse Gas Reporting Program) dataset: https://bit.ly/3heB6Zi

Import the downloaded dataset into the Tableau:

Finally choose GHGRP_data_2014_8_13_16.csv file to import it into the Tableau graphical interface. This dataset contains the emission of different gases from different plants across the US. If this dataset is loaded successfully, you should see the table sheet of the current dataset as:

Main Tableau interface:

The main Tableau interface is shown here:

  • Section A: "This pane contains all the headers of your data, split into Dimensions and Measures. Typically, dimensions are your main ID variables and are almost always discrete data. Measures are usually measurements, often continuous data that are dependent on dimensions."

  • Section B: "This pane contains the Columns and Rows shelf. Tableau is almost all drag-and-drop. You can drag dimensions and/or measures to these shelves to build a graph."

  • Section C: "This is the pane where a graph will show up. If empty, you can drag a dimension / measure directly to this area."

  • Section D: "This pane contains the Filters shelf and Mark control. Marks are Tableau’s name for data-points. You can modify the look of data-points by adding dimensions to “color” or “size” or just clicking on these buttons. Filters are an option to crop out or refine the data. Drag a dimension or measure to the Filters shelf in order to filter the data being graphed."

Source: https://sites.google.com/a/g.clemson.edu/clemsongeospatial/home/open-refine-and-tableau-for-visualization-reu/data-analytics-and-visualization-with-tableau/introduction

Bar graph:

Creating bar graphs in Tableau is easy. It only needs to define Columns and Rows. Here we plot the average CO2 emission of each plant versus industry type. In fact these plants are grouped together in the same industry type by averaging over their CO2 emission. As a result, you need to drag and drop the "reported CO2e emissions" into the Columns and "Industry type (sectors)" to the row. Make sure the measure is put on average to group or aggregate the dataset across different industry types:

Note that you can use other measures to aggregate data such as SUM that take summation over the CO2 emission of different plants instead of averaging.

Creating group:

Creating groups help to aggregate similar data points. Here we group different industry types:

Also, we choose the Industry groups as the name of created group here and make sure you group these categories together:

  • Chemicals

  • Import and Export

  • Industrial Gas Suppliers

  • Injection of CO2

  • Metals and Minerals

  • Natural Gas

  • Others

  • Petroleum

  • Power Plants

  • Pulp and Paper

  • Refineries

  • Waste

Finally, we use this group industries to plot their CO2 emission:

Scatter plot:

Scatter plots are useful for showing the correlation between two variables. One hypothesis about this dataset is that plants that have more CO2 emission probably would have more CH4 emission as well. Scatter plot would help to examine this hypothesis. In order to create scatter plot, first we need to make sure data would not be aggregated. We could disable aggregation by going to Analysis ---> Aggregate Measures:

Finally, we put the reported CO2e emission in Columns and Methane (CH4) emission in rows:

Due to above plot we see that this hypothesis is correct for few data points but might not be a general trend for all the plants. One option that might help to explore the data furthermore is to limit the range to smaller values for Methane emission. You can change the range of the y or x axes by double-clicking on them:

Finally, we have this scatter plot, which shows that we have indeed a correlation between CO2 and Methane emissions but it might not be a unitary correlation:

The last point in this section is that: as you might see in the above plots, Tableau tells us that we have 181 missing data points shown as "181 nulls". One useful option is to filter this data points:

In the next section, we will learn how to put multiple attributes on plots and graphs such as incorporating different variables as size or color.