As a little holiday project, I got to working on mini terrariums. I had a few different ideas that didn’t all work together. So I ended up making three different variants. For one, I have some leafy plants and an animal prop. For the second one, I have moss and an toadstools. And finally cactus/dry succulents. I wanted a tiny sign post prop for the cacti but didn’t manage to get one. I bought the ~10cm glass balls from eBay, and the props from toy stores. The glass balls were filled with a small amount of charcoal followed by soil. The exposed soil on the top is covered with a thin layer of fine gravel. in hindsight, these glass were too small to work well. Also finding suitable plants of the right size can be tricky. Propagating cacti can be a bit of a pain.
I had this idea of using some of my travel photos to create a photo calendar. I would normally go about it using Adobe Photoshop or Adobe Illustrator. But, that would involve a lot of manual work placing dates and days for each month. I would also like to mark some public holidays and friend’s birthdays. So, I wondered if it might be possible to do it with R. After fiddling about with it over the weekend, I managed to make it work. It went better than I expected. And here I am recreating the calendar using some stock photos. All stock photos are royalty-free from Pexels. For the impatient ones, the whole code and images are available at this Github repository. For detailed guide, keep reading.
Scientific graphs are key in science to presenting usually complex data in an attractive and concise manner. Scientific graphs are supposed to be a visually summary of your data.
Data collection > Data analysis > Data visualisation
The most important part of a plot is of course its content. Once you have good content/data, you need to think about how to represent this data. Which sort of plot to use. How to best convey this information. See this article for most common types of data visualisation. Some of the popular programming environments for plotting include
Julia being the latest addition. Other options include Excel, Tableau, Plotly and more.
R is a great tool for graphics as evident from the numerous images, blogs and publications over the past years. There are several resources that can help you find code to create all sorts of graphs in
But, just managing to create a graph is not sufficient in my opinion. The graphic has to be beautiful, elegant, user-friendly and attractive. Getting from data to a plot is one thing, but creating a high-quality, publishable and professional looking figure is a different story. A Basic plot is the initial basic output figure from any plotting software. This uses the default setting and default looks. Most people stop at this point because they have gone through considerable effort to get the data, analyse it and finally figure out the code to plot it. But a basic plot is usually not going to look professional or elegant. It will need some level of customisation to make it attractive. The
ggplot2 plotting package in
R, for example, produces a pretty decent default output, but they are overused and the graphics are not unique or catchy. I haven’t come across many sources that go into the fine details of the making a professional looking graph. We will go into plot customisation in
R in a future post, but, in this post, we explore some examples of customised plots.