# Just KNIME It!

### CO₂ Emissions

Challenge 2

Level: Easy

Description: You are a climate scientist studying CO₂ emissions. To make your research insights more accessible to your colleagues, and then write a paper about it, you decide to build a report-enabled component in KNIME that allows users to check how emissions vary for different regions and sources. What are the most alarming insights illustrated in such report?

Authors: Armin Ghassemi Rudd and Marina Kobzeva

Dataset: CO₂ Emissions Data in the KNIME Community Hub

Remember to upload your solution with tag JKISeason3-2 to your public space on KNIME Community Hub. To increase the visibility of your solution, also post it to this challenge thread on KNIME Forum.

We will post our solution to this challenge here next Tuesday.

## Previous Challenges

Level: Easy

Description: You work in finance and one of your clients wants to understand the value of different company stocks over time. Given a dataset of stock prices, you decide to use simple moving averages (window length = 20) to tackle this task. What companies have an upward trend for the most recent data? And what companies have a downward trend?

Author: Thor Landstrom

Dataset: Stock Data in the KNIME Community Hub

Solution Summary: We propose two different solutions to this challenge. The simplest one involves manually filtering the data for a specific company, calculating its moving average, and then visualizing it with a line plot. The second one relies on a simple data app: a company is selected from a dropdown box and its stock prices are selected, a moving average is computed, and the final points are plotted as a line plot.

Solution Details: Both solutions have a core part in common. After the rows for a company are selected, we use the Column Filter node to isolate dates and close prices, do some typecasting with the String to Date&Time node, sort the data from oldest to most recent with the Sorter node, and then use the Moving Average node to compute simple moving averages (window length = 20). Next, we visualize the results with the Line Plot node. In the simplest solution, we use the configuration of the Row Filter node to select the data for a company. In the more complex solution, we get all company names with the "Get company names" metanode, and then pass them, along with the original data, to the "Visualize company stock prices" component. Inside this component, a Single Selection Widget node allows the selection of one of the company names, which in turn is used to control an instance of the Row Filter node. After that, this solution is basically equivalent to the simplest one.

## Here is how the challenges work:

We post a challenge on Wednesday.
You create a solution with KNIME Analytics Platform.

Our solution to the challenge comes out on the following Tuesday.

### Enjoying our challenges?

They are a great way of preparing for our certifications.