The lab notebook

After much eye squinting from all viewers, it was decided during the meeting with great panel judges that the graph needed a face lift.  The best Simple Graph tutorial that was perfect for my data derived from: http://www.harding.edu/fmccown/r/#autosdatafile So, the changes were as per below: # Graph autos with adjacent bars using rainbow colors barplot( as.matrix(Maludam_Mac2014) , main="Elemental Analysis", ylab= "Carbon Ratio", beside=TRUE, col=rainbow(5) ) # Place the legend at the top-left corner with no frame # using rainbow colors legend("topleft", c("S1","S4","S6","S8","S9"), cex=0.6, bty="n", fill=rainbow(5)); After much thinking and staring at my PC for one day, giving opportunity to bloodshot eyes, I've realized the data was the key. Therefore, I changed the raw data to C:N C:S H:C 0.40   1.81 1.13 2.42   17.66 0.33 0.67   4.

It began with MATLAB and since the software was limited, Zee introduced me to R.   First step -> https://cran.r-project.org/ (download the software!) Second stop->Do not panic! Third step->There is R 3.2.5 (I am using that now, to download phyloseq) and there is R Studio that had four displays.  The source, The console window where the magic happens and the Apps Store (right to the console window)- I am constantly installing new packages to beautify my data. My first hands on: Producing simple graphs for my chemical data manifested by http://www.harding.edu/fmccown/r/#autosdatafile Hence, the result: Special acknowledgement:  Thanks Zee ! And congrats on your 1st Anniversary as a PhD student ;) He will be studying on  Navier-Stokes equation and using Ansys Fluent to simulate a system.   Like what is that? Jk.

So, we have tried. From SILVA NGS, RDP Pipeline, MG-RAST to MEGA, and MEGAN...6.  Two made the cut with my data so far; MEGAN and R.  As a note, I would say the data choose the analysis software. R yet again gave a beautiful taxa plot.  abund_table<- read.csv ( "SPE_pitlatrine.csv" , row.names = 1 , check.names= FALSE )   #Transpose the data to have sample names on rows abund_table<- t ( abund_table ) meta_table<- read.csv ( "ENV_pitlatrine.csv" , row.names = 1 , check.names= FALSE )   #Just a check to ensure that the samples in meta_table are in the same order as in abund_table meta_table<-meta_table [ rownames ( abund_table ) , ]   #Get grouping information grouping_info<- data.frame ( row.names = rownames ( abund_table ) , t ( as.data.frame ( strsplit ( rownames ( abund_table ) , "_" ) ) ) ) # > head(grouping_info) # X1 X2 X3 # T_2_1 T 2 1 # T_2_10 T 2 10 # T_2_12 T 2 12 # T_2_2 T 2 2 # T_2_3