1. Using Excel to Automate Mundane (Simple) Data Analyses and Matlab to Construct Good-Looking Graphs
Anna Fedders
Department of Civil and Environmental Engineering, University of Illinois at Urbana Champaign
Generating Graphs in MATLAB
Issues to be Addressed
I analyze a lot of water samples for ammonia, phosphate, and dissolved organic carbon. I have the benefit of having lab instrumentation to expedite sample analysis, but the output files from these instruments still require manipulation and post-processing to convert instrument outputs (peak area) to the desired format (analyte concentration). The files that are exported from these instruments have a consistent format. To save time in executing these repetitive tasks, I generated several excel templates to post-process and summarize the data into tables.
Additionally, Excel graphs are quick to make, but I wanted to see if I could make something nicer looking by plotting in Matlab.
Data to be plotted was pasted from Excel into a .mat file in Matlab. The script shown in Figure 2 generates the plot shown in Figure 3.
Figure 1: This table contains the data which was used to graph below.
Excel Template Example:
Figure 2: Code for Matlab plot. Components of the code designate six lines, each with its own color and line style. Axis labels, legend text, and legend location were also specified.
Data output file
Data proccesing sheet B C D E A
Figure 3: The plot shows carbon to nitrogen ratio or freeze-dried algal biomass which was collected from 6 photobioreactors over the course of 42 days. Nitrogen-limited reactors (dashed lines) showed increased C:N as compared to phosphorus-limited reactors (solid lines). Increased C:N is an indicator of carbohydrate and/or lipid storage, which was the desired outcome of the experiment in which the biomass was collected. Carbohydrate and lipid content of the biomass will be confirmed using separate analyses.
Figure 1: Data output file is pasted into the first page of the Excel Template. Cells for standards and samples (A) on the data processing page auto-populate via cell references to the data output page. The slope and intercept (B) and plot (C) of the standard curve are automatically calculated. Sample concentrations are calculated by referencing absorbance values and standard curve slope and intercept (D). Identifying information for each sample is input by the user (E). A data summary page (F) references sample ID and final concentration information for the samples. F
Data summary sheet
This work was part of a Focal Point grant funded by the Graduate College at the University of Illinois at Urbana-Champaign