What is a Data Table? A Comprehensive Guide on Using Data Tables in Excel

Data tables are an extremely useful tool in Excel for analyzing how changes in input values affect the output of formulas But what exactly is a data table and how do you create and use one? In this comprehensive guide, I’ll explain everything you need to know about data tables in Excel.

What is a Data Table?

A data table is a structured range of cells that shows how changing one or two variables in your formula affects the outcome. Data tables allow you to quickly simulate multiple scenarios by substituting different input values in your formula.

For example, say you have a formula to calculate total profit based on number of units sold and cost per unit. You could create a data table to see projected profit for different combinations of units sold and cost per unit.

Data tables fall into two categories:

One-variable data tables Show how changes in one input value affect the formula result
Two-variable data tables: Show how changes in two input values affect the formula result.

Now let’s look at how to create and use these two types of data tables in Excel.

One-Variable Data Table

A one-variable data table lets you analyze how different values of a single variable in your formula affect the final outcome. Follow these steps to create a one-variable data table:

Set up your original data and formula. In this example, we’ll calculate total profit based on number of units sold and cost per unit.
Determine which variable you want to substitute different values for. Here we’ll vary the number of units sold.
Build your input value column. Enter the different values to substitute for the variable.
In the column beside the input values, enter a formula that refers to the result cell of your original formula.
Select the input value column and result formula column.
Go to Data > What-If Analysis > Data Table.
In the Data Table dialog box, enter the cell reference for your original input value (units sold).
Click OK. Excel populates the result column, substituting the different input values.

Now you can see your formula results for each potential value of the input variable.

Two-Variable Data Table

A two-variable data table lets you analyze how different values of two input variables affect the result. Here are the steps to build a two-variable data table:

Set up your original formula. We’ll use the profit formula again.
Determine which two variables you want to substitute values for. Let’s vary units sold and cost per unit.
Build your input value table. The two variables form the left and top axes.
In the top left cell, enter a formula that refers to the result cell of your original formula.
Select the entire input value table.
Go to Data > What-If Analysis > Data Table.
In the Data Table dialog box, enter the cell references for your two input value cells from the original formula.
Click OK. Excel populates the table with results for all combinations of the two input variables.

The two-variable data table allows you to see the combined effect of different values for two inputs. This is extremely helpful for sensitivity analysis.

Key Tips for Data Tables

Here are some key tips to maximize the effectiveness of your Excel data tables:

Give your data table a descriptive name using table name referencing for easy identification.
Format the data table nicely to improve readability – bold column headers, apply borders, etc.
Use structured references for your data table headers to avoid formula errors when adding/deleting rows.
Document your data table assumptions and methodology in a separate notes tab.
Try utilizing data table results to create charts or use conditional formatting for visual analysis.
Change one variable at a time when troubleshooting unexpected data table results.
Protect your original formula cells to avoid accidental changes that impact the data table.
Data tables rely on array formulas, so edit the entire table array to change values rather than editing individual cells.

Examples of Using Data Tables

Data tables have many helpful applications for analysis and forecasting. Here are a few examples:

Sales projections – Estimate total profit for different sales volume and price scenarios.

Financial modeling – Model how interest rate changes impact loan payments.

Production planning – See how resource constraints affect output capacity.

Budgeting – Forecast how revenue changes impact net income.

Risk analysis – Model how uncertainty in key variables affects financial outcomes.

Engineering design – Test performance across different parameters and specifications.

Optimizing input combinations – Find the optimal balance of resource allocation for maximum profit.

Scenario planning – Evaluate strategic plans under different assumed conditions.

Dynamic Data Tables Using Formulas

While standard Excel data tables are static, you can also create dynamic data tables using formulas like OFFSET and INDEX.

For example, this formula dynamically returns a 5×5 data table based on an input cell:

excel

=OFFSET($A$1,MATCH(A13,$A:$A)-1,MATCH(B$12,$B$1:$F$1)-1,COUNTA($A:$A),COUNTA($1:$1))

Dynamic data tables update automatically when you change the source data, giving you more flexibility.

Data Tables vs. Other Excel Tools

Data tables serve a distinct purpose, but can be combined with other Excel tools for even more powerful analysis:

Scenarios – Like data tables, allow multiple input values. But scenarios provide named narrative explanations.
Goal Seek – Solves for a single input value needed to achieve a target formula result. Handy before creating data tables.
Solver – Finds the optimal input values out of thousands of combinations to achieve the best formula result. Great after data table analysis.
PivotTables – Summarize data table results for easy reporting. Combine your data table with a PivotTable for interactive analysis.

Limitations of Data Tables

Data tables unlock significant analysis capabilities, but they aren’t perfect:

Scalability issues – Data tables grow exponentially as you add more input values. Larger tables take longer to calculate and can strain memory.
Difficult to update – Modifying the original formula or adding/deleting input values breaks the data table.
Output-focused – Data tables only show the final result values rather than intermediate calculated steps.
Limited data visualization – Data tables provide raw analysis but limited charts, formatting and customization.

If you run into limitations, Excel add-ins like Power BI and Power Pivot can provide more robust modeling and analysis functionality.

While simple in concept, Excel data tables enable robust analysis of formula results under different scenarios. Use one-variable data tables to analyze how changes to one input impact your formula. Employ two-variable data tables to simultaneously analyze two inputs.

Data tables help identify optimal solutions, model financials, evaluate strategies, improve forecasts and much more. Just be mindful of scalability and updatability limitations in large, complex models.

Combine data tables with other Excel tools like PivotTables, Solver and Goal Seek to unlock even deeper insights. With the right approach, data tables provide immense analytical power, directly from within Excel.

Now that you have a solid understanding of data table concepts, it’s time to start using them to analyze your own business data to drive informed decision making. The insights you unlock could be game-changing. Give it a try!

what is a data table

g) Great! But how can I refer to columns by names in j (like in a data.frame)?

If youâre writing out the column names explicitly, thereâs no difference vis-a-vis data.frame (since v1.9.8).

If youâve stored the desired columns in a character vector, there are two options: Using the .. prefix, or using the with argument.

For those familiar with the Unix terminal, the .. prefix should be reminiscent of the âup-one-levelâ command, which is analogous to whatâs happening here â the .. signals to data.table to look for the select_cols variable âup-one-levelâ, i.e., in the global environment in this case.

The argument is named with after the R function with() because of similar functionality. Suppose you have a data.frame DF and youâd like to subset all rows where x > 1. In base R you can do the following:

Using with() in (2) allows using DFâs column x as if it were a variable. Hence the argument name with in data.table. Setting with = FALSE disables the ability to refer to columns as if they are variables, thereby restoring the âdata.frame modeâ.

We can also deselect columns using - or !. For example:

## not run # returns all columns except arr_delay and dep_delay ans <- flights[, !c("arr_delay", "dep_delay")] # or ans <- flights[, -c("arr_delay", "dep_delay")]

From v1.9.5+, we can also select by specifying start and end column names, e.g., year:day to select the first three columns.

## not run # returns year,month and day ans <- flights[, year:day] # returns day, month and year ans <- flights[, day:year] # returns all columns except year, month and day ans <- flights[, -(year:day)] ans <- flights[, !(year:day)]

This is particularly handy while working interactively.

with = TRUE is the default in data.table because we can do much more by allowing j to handle expressions – especially when combined with by, as weâll see in a moment.

Weâve already seen i and j from data.tableâs general form in the previous section. In this section, weâll see how they can be combined together with by to perform operations by group. Letâs look at some examples.

Data analysis using data.table

Data manipulation operations such as subset, group, update, join etc., are all inherently related. Keeping these related operations together allows for:

concise and consistent syntax irrespective of the set of operations you would like to perform to achieve your end goal.
performing analysis fluidly without the cognitive burden of having to map each operation to a particular function from a potentially huge set of functions available before performing the analysis.
automatically optimising operations internally, and very effectively, by knowing precisely the data required for each operation, leading to very fast and memory efficient code.

Briefly, if you are interested in reducing programming and compute time tremendously, then this package is for you. The philosophy that data.table adheres to makes this possible. Our goal is to illustrate it through this series of vignettes.

In this vignette, we will use NYC-flights14 data obtained by flights package (available on GitHub only). It contains On-Time flights data from the Bureau of Transporation Statistics for all the flights that departed from New York City airports in 2014 (inspired by nycflights13). The data is available only for Jan-Octâ14.

We can use data.tableâs fast-and-friendly file reader fread to load flights directly as follows:

Aside: fread accepts http and https URLs directly as well as operating system commands such as sed and awk output. See ?fread for examples.

In this vignette, we will

Start with basics – what is a data.table, its general form, how to subset rows, how to select and compute on columns;
Then we will look at performing data aggregations by group

Excel What-If Analysis Data Table | Easy to Use Once you Learn This☝️

What is a data table in Excel?

An Excel Data table is a What-if Analysis tool. It allows users to use different input values for a variable and assess the changes to the output value. These are especially of help if you are operating a formula in Excel where the output depends on several variables. And you are keen to compare the results for different inputs to the formula.

What is a database table?

A database table is a structure that organises data into rows and columns – forming a grid. Tables are similar to a worksheets in spreadsheet applications. The rows run horizontally and represent each record. The columns run vertically and represent a specific field. The rows and columns intersect, forming a grid.

What is a data table & how does it work?

They store information that people can retrieve later and update as needed. The data table title, column headers and row headers can help a user understand the information in the table more clearly. The document also processes the data through various operations, such as ordering, arranging, filtering and searching.

What is a table in a spreadsheet?

Tables are similar to a worksheets in spreadsheet applications. The rows run horizontally and represent each record. The columns run vertically and represent a specific field. The rows and columns intersect, forming a grid. The intersection of the rows and columns defines each cell in the table.