Just KNIME It!
Prove your KNIME knowledge and practice your workflow building skills by solving our weekly challenges! Can you become one of our top Just KNIME It! KNinjas?
Challenge 2: Linguistic Diversity
Description: You are a linguist studying linguistic diversity around the world. You have found a dataset that includes information about countries, such as the number of languages spoken, area, and population. The dataset also contains a column called MGS, which refers to the mean growing season in each country (i.e., for how many months per year crops can be grown on average). What are the top 5 countries by the number of languages spoken? What are the top 5 countries by the ratio of languages spoken to population? What are the top 5 countries by the ratio of languages spoken to land area? Finally, do you notice any patterns between the numbers of languages spoken and the MGS values?
Objective 1 (Easy): Learn how to import a CSV file into KNIME.
Objective 2 (Easy): Perform ratio calculations between columns (e.g., number of languages spoken and population size ratio).
Objective 3 (Easy): Sort the resulting table using specific criteria to select top 5 countries.
Objective 4 (Easy): Filter the top rows based on your selected criteria.
Author: Michele Bassanelli
Dataset: Linguistic in the KNIME Community Hub
Remember to upload your solution with tag JKISeason4-2 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: Medium
Description: You have an EV and want to live in a place that has many available charging stations, and where it is also cheap to charge your vehicle. Given a dataset on chargers around the world, you need to find out the top ten cities that have the most EV chargers. You also want to consider which of those ten cities offer, on average, the cheapest KwH rates in cost. You should be narrowing down your city of choice to five after taking into account the costs.
Objective 1 (Easy): Clean data by removing addresses without real city names and extract country out of their addresses.
Objective 2 (Easy): Count the total number of EV charging stations by city and find the top ten cities.
Objective 3 (Easy): Of the top ten cities, find out which cities have the cheapest average cost to charge per kwH and show the five cheapest cities.
Objective 4 (Medium): Create a bar chart that allows you to compare the top ten cities in terms of average cost to charge per kwH. Create a widget that lets you select the cities you want to see in this plot and control the plotting with flow variables.
Author: Thor Landstrom
Dataset: EV data in the KNIME Community Hub
Solution Summary: We solve this problem by grouping the data by city, so that we can count every city's unique EV stations and also calculate their average cost. We sort and filter the data and create visualizations that allow users to compare cities' average EV prices interactively.
Solution Details: After reading the dataset with the CSV Reader node, we preprocess the data. First, we remove rows without proper addresses (Row Filter node) and then extract the addresses in the remaining rows for grouping (Expression node). The next step is to group the data by address with the GroupBy node, and sort the resulting data in descending order by count (Sorter node). We then extract the top 10 cities with the most EV charging stations with the Row Filter node. We use the Sorter and the Row Filter nodes again to extract the top 5 cheapest cities out of these 10, and visualize their average costs with the Table View node. In parallel, we create a component that allows users to compare the top 10 cities with the most EV charging stations in terms of average charging cost. This component has a widget that lets users select the cities they want to see in this plot, which turns into a flow variable that controls the plotting.
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