Enormous data is a very common need. Processing them helps describe changes in your business’s profitability, track customer activity, and better understand the marketing efforts. However, raw data is not easy to understand.
That’s why we need to visualize them. Data visualization tools help everyone from marketers to data scientists sort data into classes and demonstrate results and processing through charts, graphs, videos, and more. Naturally, the human eye is attracted to colors and patterns because 90% of the information that enters the brain is visual.
With the development of science and technology, meteorology has established close links with various industries and is becoming increasingly important for the visualization of meteorological data. Modular packaging of the main meteorological types provides data interfacing and visualization solutions so that massive meteorological data can be easily presented in the user’s field of view.
Today, we’ll learn the ways of visualization weathers stats with the help of a special plugin made for tables and charts named PubyDoc Data Table and Charts.
A heatmap table uses colors to represent data values in a table. You will find it most useful when you need to plot large and complex data. The Data Table plugin has specific conditional rules allowing creating heatmaps in a few clicks without digging into the code. Let’s create a climate table for London weather stats.
|Average temperature °C||4.8||4.9||6.7||9.4||12.7||15.7||17.8||17.3|
|Minimum temperature °C||2.1||2.0||3.1||5.1||8.4||11.4||13.6||13.3|
|Maximum temperature °C||7.3||7.8||10.3||13.6||16.7||19.6||21.8||21.1|
|Precipitation rate (mm)||59.0||50.0||47.0||54.0||57.0||60.0||60.0||59.0|
|Longitude of the day (hours)||3.3||3.8||7.0||7.0||7.6||8.3||8.8||7.8|
Data Table plugin also has such type of graph as a multi chart which can be a perfect tool for displaying weather statistics. The multi chart is usually used to display ups and downs for the data set, so it can graphically show the difference between climates. The plugin allows editing each type of graph used in a chart.
According to the chart data, we can see that the driest month is March with 47 mm with an average of 67 mm, the most precipitation occurs in November.
As an example, we are using the weather stats in London. With the help of a line chart, we may notice that the warmest month of the year is July with an average temperature of 17.8 °C. January has the lowest average temperature of the year. It is 4.8 °C.
The area chart is created based on the line chart, so it is also a great tool for visualization of the weather statistics. In our example, we’re gonna show the sunny days in London.
From the chart, we can summarize that the most sunshine per day was measured in July. In July there is an average of 8.75 hours. While the fewest hours of sunshine per day were measured in January. January averages 3.36 hours of sunshine per day.
What’s even more, you can use Data Table and Chart plugin to showcase weather forecasts because there is an opportunity to create different formats of cells needed for forecasts such as numeric, percentage, etc. Let’s create the forecast for a week.
|Date||Weather||Max.Temp||Min.Temp||Rain probability||Wind speed||Precipitation(mm)||Humidity|
|08-Feb-22||🌧||11 °C||6 °C||50%||24 km/h||2||78%|
|09-Feb-22||☁||9 °C||2 °C||20%||12 km/h||0||68%|
|10-Feb-22||☁||12 °C||8 °C||20%||15 km/h||0||83%|
|11-Feb-22||🌥||13 °C||7 °C||15%||13 km/h||0||80%|
|12-Feb-22||🌧||8 °C||4 °C||0%||15 km/h||0||58%|
|13-Feb-22||🌥||6 °C||2 °C||20%||14 km/h||0||62%|
|14-Feb-22||🌧||6 °C||-0 °C||0%||10 km/h||0||54%|
As you can see Data Table and Chart plugin has all the needed options for showing up the weather statistics in an attractive and eye-catching way. The only limitation you may face is your imagination.