How-To Guides

From detecting data types, to automatic binning, we explain the features that make AddMaple fast and easy to use

When you load a data file into AddMaple, a lot of calculations happen instantly in your browser. Below are the details for the major ones - all of which save you time and enable you to start exploring your data without manually cleaning your data or configuring columns.

Automatic data cleansing and interpretation

AddMaple automatically detects your data types for you. We detect numbers, currencies, percentages, dates, opinion scales, categorical data, tags, etc.

This happens instantly even on large datasets and is an important feature as it enables us to provide instant summaries of your data which differ depending on the detected data type.

AddMaple automatically cleans your data for you, for example if you have the values: 

AddMaple will understand it as a numeric column even though some of the values have the text "N/A".

Dealing with tags or survey questions that can have multiple responses to the same question is difficult to do in spreadsheets. 

This is where AddMaple shines. We automatically detect this data type whether the data is separated by commas (,), semicolons (;), colons (:), or pipes (|). In a spreadsheet a common approach of dealing with this data would be to separate it using formulas into mutiple columns - but that makes filtering and pivoting much harder. 

In AddMaple the data stays in the same column and you can easily explore, filter and pivot.

After a data set has been loaded and the data types automatically detected, AddMaple produces instant summaries of each column. This enables you to see an overview of your data at a glance.

Because the column types are automically detected, we can display different summaries for each data type. 

AddMaple performs intelligent bucketing (binning or grouping) of numeric data.

Our algorithm handles large, small and negative numeric ranges. The buckets that we create are rounded to sensible values and are not distorted by outliers.

As filters are applied the buckets are recalculated in an instant making it easy to dive into a particular range.

The below data was imported as raw numeric values, but AddMaple automatically grouped it into ranges.

There is a tresure trove of interesting data available from the Pew Research Center

From the American Trends Panel Datasets to surveys exploring how we interact and relate to technology.

The data is freely available from the Pew Research Center, however it is only available in the .sav (spss) format. This format is designed for IBM SPSS software, which is expensive and hard to use.

If you are keen to explore the Pew Research data, but don't have IBM SPSS, then this guide is for you. We'll show you how to open, analyse, explore and create shareable charts and reports from Pew Research data in just a few clicks with AddMaple.

Pew Research Center, founded in 1996, is a nonpartisan fact tank that provides information on the issues, attitudes, and trends shaping the world. Their research spans numerous topics, from politics and media to science and society. Their commitment to public service and open access means that many of their data sets are freely available for download. However, while the data is available, accessing its insights isn't always straightforward for the layperson. That's where AddMaple comes into play.

Step-by-Step Guide: From Download to Insight

Navigate to Pew Research's Data Download section.

Browse the available datasets and choose one that sparks your interest.

Sign in or create an account if you're a new user.

In the AddMaple dashboard, select "New Project.

Select the .sav file you downloaded from Pew Research and let AddMaple work its magic.

Once uploaded, AddMaple will automatically detect the column types of your dataset.

Navigate through the provided summaries, charts, and visuals that AddMaple has created for you.

Customize and dig deeper into specific data points, filter information, or perform comparative analyses.

If you've found an intriguing insight or pattern, use AddMaple's sharing features to send your findings to colleagues or embed them in presentations.

Creating and sharing interesting charts is quick and intuitive in AddMaple.

For example, we downloaded the Jan 2021 Core Trends Survey from Pew and within a few clicks created an "Age vs Internet Usage" interactive chart.

After creating the chart, we clicked "Share Chart" to get a sharable url to an interactive version of the chart. 

The link to the chart can be embedded in tools like Notion, or shared on Slack with automatic image previews.

Below is an embedded version of the interactive chart created above.

We've only scratch the surface of the type of analysis that you do in AddMaple.

For a more detailed guide to analysing surveys, check out our full guide.

in your browser, without SPSS

Discover how to open .sav files without SPSS. We show you how to convert .sav files to excel or .csv, as well as how you can instantly explore and analyse .sav files in the cloud without SPSS.

How to open .sav files without SPSS

Generate interactive graphs from your raw survey data and embed them in a report

Firstly, here’s a 🥳 for getting your survey out there and responded to. Now let's get it analyzed.

1. Get an overview of ALL your data in one easy step

When AddMaple reads your data file in your browser, it will automatically show you a graph summary page of ALL your data. That means all your columns will be analyzed and visualized as graphs in seconds. This happens automatically, you don’t need to do anything other than give AddMaple read-only permission. The column order from your raw data from left to right, will be represented in the same order on the graph summary from top to bottom. 

How to give AddMaple read-only permission:

Click on New Project and select the raw data file you want to analyze. 

Don't worry about cleaning the data or configuring it. AddMaple will do that for you.

Get-a-graph-summary-from-raw-data-files.mp4

2. Toggle between viewing data in graphs, a table or as reader-friendly row-by-row entries

Use the top left "VIEW DATASET AS" menu to toggle between graphs, table and rows.

Read on below for further details on how the graph, table and row-by-row pages work to help you analyze your data. 

View-data-as-graphs-tables-or-rows1.mp4

3. Your data in graphs. 
Add filters, pivots and edit column titles/types. 

See all your data in graphs that you can interact with:

AddMaple automatically creates graphs from your data without you needing to clean the data or format it. This is done in seconds in your browser. 

Watch the tutorial video below showing you how to do the following: 

Edit long column titles and column types [video timestamp 0:05 - 0:23]

Filter by a bar in a graph. You must expand the graph first for this feature [0:28]

Pivot your data in 3 easy steps [0:40]. Read on to see the 3 steps broken down clearly. 

What-you-can-do-from-the-graph-page2.mp4

In this pivot example above we want to know how the different types of developers (full stack, front end, mobile etc) in our dataset learned to code. 

The column containing the type of developers is titled "DevType". This is the column we want to pivot which is why we expand it first. 

The column containing the pivot criteria, how the developers learned to code is titled "LearnCode". 

You can easily swap the pivot parameters with each other, thereby swapping which column pivots the data. Swap these by clicking on the existing pivoted columns in the black sentence bar, and select the first "swap" option. 

An example pivot with step-by-step instructions

Expand the graph you would like to add pivots to. In our example this is the type of developer column, "DevType".



Use the + icon to add a pivot to the expanded graph. You can add filters via the + icon too.  

Select the column you would like to pivot your data by, which in our example is "LearnCode".

Voila. Your pivot graph is produced. You can now add filters by hovering on segments you'd like to filter by. Or you could share your graph, or add it to your report as we explain below. 

You can easily swap the pivot columns thus swapping the column being pivoted (sliced in segments) with the column applying the pivoting. Below we pivot the type of developer column with how they learned to code. And then we swap these two around, to look at how developers learned to code by the type of development work they do.  

This is the pivoted graph we produced in our example. Next we want to swap the "DevType" and "LearnCode" columns. 

Click on either of the columns in the black box. Then click on the first option in the drop down menu which reads, "Swap with ...". That's all you need to do. Done. 

You're now looking at the LearnCode column pivoted by DevType. 

Add interactive graphs to your report using the three dotted vertical ellipsis menu at the top right of the black box. 

Add-graphs-to-your-reports.mp4

4. Your data in tables. 
Add filters, select columns, expand on individual data rows.

Our tables aren't just static presentations of your data. They are interactive.  
AddMaple turns any raw file into a table. But unlike Excel, we don't include all columns and overwhelm you with information. We allow you to select and deselect which columns you need in your table. 

Watch the tutorial video below showing you how to do the following: 

Add columns [timestamp 0:04 - 0:12]. Select columns to add to your table by clicking on them. 

Remove columns [0:17]. Simply click on any selected columns, to remove them from your table. Selected columns have a tick beside them. 

Segment your data [0:23]. Add Filters to your table using the top + icon in the top menu. 

Expand individual records [0:52] . If you see an interesting response in the table and want to read that record in full, click on the corresponding record number on the left. This brings up that record's row-by-row view where you can read free-text responses too. Here you can also see how that respondent's answers or that data record, compares to the overall data cohort you're looking at. Return to the table/graph view via the top left "View DataSet As menu".

Table-view-all-you-can-do.mp4

Filter data by clicking on a response in your table

Being able to filter by responses in a table is more intuitive. 

In the example video we click on "Master's degree" in the "EdLevel" column to add "Master's degree" as a filter. We then only see responses where candidates selected that option in their response. 

Then we add "Hybrid" from the "RemoteWork" column as a filter.   

You can specify whether you want to include or exclude the filtered results from your table. If you choose to exclude the filtered results, you won't see responses containing the filters you applied. The default is to include the applied filters as you see from the video. 

Filter-using-table-data.mp4

Columns containing quantitive data (multiple choice questions, opinion scales etc) are automatically colour-coded. This helps you spot matching responses.

5. Your data in individual rows. Add filters, compare responses to cohort, filter by free-text 

Reading individual records can help you understand the context of that specific record. 
However, we show you how that record compares with the overall cohort, so that you can easily spot outliers. A deep dive with a helping of big picture perspective. This applies to survey data and other quantitive data. The free-text filters you can apply is probably one of the most useful aspects of the row-by-row view. This way you can only read records containing certain words to save time. 


 See how an individual record compares with the overall cohort you're looking at [0:08]

Add filters by clicking on a response in that record [0.28]. In the tutorial example, we filter by developers who learned to code using "Other online resources (e.g. videos, blogs, forums)" by clicking on that response. 

Switch between viewing individual records, graphs, table and report [0:36]

Read free text responses and apply word filters [0:43]. This enables you only to read records including/excluding specific words


Row-by-row-view-all-you-can-do.mp4

6. Watch a survey analysis video in full 

7. Apply filters to your entire dataset in two simple ways

We learn so much from survey data, both when we look at the responses as a whole, and when we apply filters to look at how specific segments answer questions, in order to compare their responses with other segments. This is often where deeper insights hide from plain sight. From demographics to user preferences, opinions, work history and more, our filters are easy to apply and remove. We even allow you to segment your data by specific words a respondent included in their free text response if you included open-ended questions!

You could also add filters using the information provided by your survey tool, e.g. Survey Monkey. Data such as survey completion times (perhaps filter out possible rushed responses), or IP regions, completion time stamps (find the night owls) or even devices used to complete the survey (find those who do everything on their phone) and so on. Needless to say, the reasons Why it is useful to segment your data are so numerous, they deserve a dedicated article.

Right, here's how to apply filters to your data

Method 1: Filter by the top "+" button. Watch the video tutorial below.

*This top black menu box serves as a sentence builder where the applied filters and pivots are written in plain english.

Select the column that you want to filter the data from

Select whether to include or exclude the values you will apply next. You could remove a segment from the dataset or you could only view the data where those values are applied.

The video tutorial below shows you how to segment your dataset by respondents who learned to code using online courses. With the filter applied, you can view every column graph automatically update with this applied segmentation filter.

This video tutorial shows you how to filter data using the "+" button found on the top menu

Method 2: Filter data directly from a bar in a chart

This method allows you to filter by the interesting metrics you see on a graph. Rather than decide up front what you want to filter by, this method allows you to filter by the interesting insights you see in the graphs themselves.

Expand a graph you want to explore further.

Hover over a bar in the graph (not the empty space) that you want to filter your data by. When you do this you will notice the following text appear, "filter data by this value" which you should click on.

Note, that filtering a single-response Multiple Choice question (the teal MC questions) work slightly differently to the multiple-response Multiple Choice questions (the blue MC+ questions). 
Single-response question filtering: Segment by one value OR other values. 
Multiple- response question filtering: Segment by one value AND other options. 

Clicking on a response to add a filter can also be done when you're in the table and individual row-by-row views. 

This video tutorial shows you how to filter data directly from a bar in a graph

AddMaple detects many different column types automatically including numbers, currency, dates, categories, etc. 

Learn the types of data that AddMaple can automatically detect and create charts for

What column types are detected?

AddMaple supports powerful instant filters that are applied across your entire dataset. Filters can be applied directly from charts.

Learn how to interactively and instantly filter your data in AddMaple

How to filter your data

How to share an embeddable chart from AddMaple

AddMaple makes it simple and intuitive to pivot your data. We help you cross-tabulate, pivot and aggregate your data without any complicated settings.

How to let anyone explore your data using AddMaple. You can choose which columns to share and allow your colleagues or the whole world to explore your raw data.

AddMaple analyses your data in your browser without uploading it to the cloud. We use modern web APIs to read and process your files all within your browser.

How does AddMaple work without uploading data?

AddMaple is one of the few cloud based software tools that can read SPSS (or SAV) data files. They have an unusual format and can't be opened by standard spreadsheets. 

Learn how to analyze SPSS (.sav) files in your browser, instantly, with AddMaple.

How to analyze SPSS or SAV files

No more pasting charts into powerpoint, AddMaple makes it easy to create a beautiful, responsive report that you can share with colleagues (or the world)

No more pasting ugly default charts into powerpoint, AddMaple makes it easy to create a beautiful, responsive report that you can share with colleagues (or the world)

How to create a shareable report from your data

AddMaple is designed to work with any data source - this includes data where column names or items of data may be long, or where there are many possible values for a column.

AddMaple automatically analyses your data from a file and generates charts for you. 

AddMaple can read large data files - including files that will crash a spreadsheet. On a modern computer AddMaple can easily read in data files of up to 200MB...

Read extensive how-to guides on how to pivot, vlookup, countif, make likert charts, etc in AddMaple

How-to guides