The initial scatterplot conveys the fitness of each individual in a population only after the simulation has concluded.
I’m going to keep this post brief so that the steps are clear and concise. The reason for writing this post is that I wanted to get iPython Notebook, a powerful tool for data analysis, to run with plotting and pandas in Mac OS X 10.8. When I initially tried to get this running, I would encounter errors where there were conflicts between 32-bit and 64-bit installations of different packages. After a good deal of trial and error, I found the following steps resulted in a full iPython Notebook environment with Pandas and Matplotlib functioning flawlessly.
Working with R, I was looking for functionality to easily subset my data based on a sequence of numbers. After writing a for loop and using rbind to do it initially (terrible to do in R!), I finally found a way to do this efficiently. Using a command called %in%, you can easily apply it as a filter in the subset command to get data filtered based on your sequence. Enjoy!
# Generate sample data based to test.
sample_data <- data.frame(ID=seq(1,100,1),
# Plot the scores, see that there is a score for each id.
# Create a filter to apply.
look_at <- seq(1,100,10)
# Filter the sample data by look_at using the %in% command.
subset_data <- subset(sample_data, ID %in% look_at)
# Plot the scores, note the filtered data.
Okay, okay, the title might be a little sensationalised. I have been using the R statistics package for processing the results of evolutionary runs since beginning my PhD 2 years ago. In that time, I have become familiar with the basic process to importing data, performing basic population statistics, mean, confidence intervals, etc, and plotting using ggplot. I’ve always felt that I could streamline the process though as I perform a great deal of preprocessing using Python. This typically involves combining multiple replicate runs into one data file and possibly even doing some basic statistics using the built-in functionality of Python.
Growing up on a healthy diet of Microsoft Office products, I am well versed in Word, Excel and Powerpoint. As I have transitioned into the research world, these products still have their place, however, I sometimes find that the habits I developed for organizing data doesn’t necessarily transfer to statistical analysis. Recently, I ran into a situation where I was evaluating the performance of solutions in multiple different environments. Organizing this data appeared straightforward to me at first, I would simply group the different environments into one row grouped by the id of the individual. My data then looked something like this: