Posts Tagged 'csv' 4

Plotting Microtiter Plate Maps

Plotting Microtiter Plate Maps

May 1, 2014

I recently wrote about my workflow for Analyzing Microbial Growth with R. Perhaps the most important part of that process is the plate map, which describes the different experimental variables and where they occur. In the example case, the plate map described which strain was growing and in which environment for each of the wells used in a 96-well microtiter plate. Until recently, I’ve always created two plate maps. The first one is hand-drawn using pens and markers and sat on the bench with me when I started an experiment. By marking the wells with different colors, line types, and whatever other hieroglyphics I decide on, I can keep track of where everything is and how to inoculate the wells.

Read more

Analyzing Microbial Growth with R

Analyzing Microbial Growth with R

April 9, 2014

In experimental evolution research, few things are more important than growth. Both the rate of growth and the resulting yield can provide direct insights into a strain or species’ fitness. Whether one strain with a trait of interest can outgrow (and outcompete) another that possesses a variation of that trait often depends primarily on the fitnesses of the two strains.

Read more

Summarizing Data in Python with Pandas

Summarizing Data in Python with Pandas

October 22, 2013

Like many, I often divide my computational work between Python and R. For a while, I’ve primarily done analysis in R. And with the power of data frames and packages that operate on them like reshape, my data manipulation and aggregation has moved more and more into the R world as well. Perhaps my favorite tool of all has been plyr, which allows you to easily split up a data set into subsets based on some criteria, apply a function or set of functions to those pieces, and combine those results back together (a.k.a. “split-apply-combine”). For example, I often use this to split up a data set by treatment, calculate some summary stats for each treatment, and put these statistics back together for comparison. With R and these excellent packages, these steps are about as painless (I actually enjoy them, but that’s probably not normal) as it gets. Because of this, R has long been the choice for doing this kind of work.

Read more

Working with CSVs on the Command Line

Working with CSVs on the Command Line

September 23, 2013

Comma-separated values (CSV), and its close relatives (e.g., Tab-separated values) play a very important role in open access science. CSV is an informally-defined file format that stores tabular data (think spreadsheets) in plain text. Within the file, each row contains a record, and each field in that record is separated by a comma, tab, or some other character. This format offers several significant advantages. Because they are plain text, these files can be easily read and edited without the need for specialized or proprietary software. CSVs are also version-independent, so ten years down the road you won’t have to track down some ancient piece of software in order to revisit your data (or do the same for someone else’s data). Support for CSV files is built into most data analysis software, programming languages, and online services (see Some Useful Resources at the end of this article for links for your software of choice).

Read more