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2020/4/14 RESULTS 2020 User Survey - General - ROS Discourse

https://discourse.ros.org/t/results-2020-user-survey/13494/2 1/16

ROS Resources: Documentation | Support | Discussion Forum | Service Status | Q&A answers.ros.org

RESULTS 2020 User Survey

ros2,

community,

survey,

results,

data

Katherine_Scott

11d

2020 User Survey

Back in January Marya and I put together a survey of the ROS community . Our goal was to gather some data to

help us model what the ROS community looks like, and what it needs in terms in documentation. More specifically we

wanted some data to guide our documentation development. We put together a brief survey and left it up for about six

weeks. All in all it was reasonably successful. We got 116 responses in total. Last week I pulled down the data from

the survey (it is still up) and dropped it into an ipython notebook to play with results. Below you’ll find the raw data as

well as a cleaned up dataset. As stated in the original post I have removed the free form text sections and the e-mail

addresses for privacy reasons.

If you haven’t already, please take the survey before you read the

results. It takes three minutes. It is also better that you take it before

you read the results. If we get a few hundred more responses I’ll do an

update.

Open Data and Jupyter Notebook

We have released the data (sans personally identifiable information) in this repository . There are two versions, the

raw data and what I call the scrubbed results. The scrubbed results are just what was left over after I processed all

the data and cleaned it up so it will be easier for someone else to do more in depth analysis. If you want to take a

crack at analyzing the data or see how I put this together I put my Python Jupyter Notebook up on Google

Collaboratory . Be aware that I removed the word cloud code because of the redacted data. Feel free to fork it,

tweak it, and post your results. The rest of this post basically follows the notebook so feel free to follow along in a

second tab.

Raw Results

The first step with any data analysis is to clean up the data. Google sheets returns both a written value and a

numerical value for categorical questions. To make things easier to understand and work with I removed the

numerical values and then shorted the strings. For example the “3 (advanced)” became just “advanced.” Similarly

multiple choice questions come as strings and I wrote a little function to move that data to a python list of strings.

Skill Level Data

The first thing we asked survey participants to do was to self report their skill levels on a variety of ROS related topics

such as C++, Python, Shell Scripting, Robotics, etc. You can see this plot below:

2020/4/14 RESULTS 2020 User Survey - General - ROS Discourse

https://discourse.ros.org/t/results-2020-user-survey/13494/2 2/16

There are three things in this plot that I think are interesting:

The plot either has some sample bias towards advanced users or the ROS community on Discourse are all

brilliant and experts on everything except ROS 2.

Most of the community is still uncomfortable with ROS 2.

Asking the community about a specific topic, like C++, returns more normally distributed results than asking

about a more general topic like “robotics.”

Combined Skills

I reached out to Steve Macenski to review this work before I released it. He said one burning question he and others

have had is how to appropriately split their time and resources between C++ and Python. He was curious if there was

a bias in the community towards one or the other. Since we had all the data ready to go it was trivial to plot the

results. I created a self-reported skill matrix for two skills and then normalized it to the total number of respondents. I

didn’t look at all the permutations as I don’t think that is valuable, but I did take a look at a few of the most relevant

ones.

The way you read these plots is lower left is less skilled at both aspects, while the top right is high skill at both

aspects. The color indicates the number of respondents. The first thing that jumps out to me for most of these plots is

the sample bias, that is to say the top right corner is almost always the brightest square. As to the C++ versus Python

question the answer seems to be that most individuals who responded are roughly equally skilled at both. This

pattern holds for most of the other skill comparisons I looked at. Another way to interperet this data is to say that

perhaps being well skilled with ROS 1 requires mastery, or at least proficiency with C++, Python, shell scripting, and

robotics and software engineering fundementals.

The lone exception in all of the plots below is the ROS 1 versus ROS 2 skill level plot. The plot indicates that even the

most skilled ROS 1 users are still having trouble mastering ROS 2.

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