The lab notebook

Drawing completed by using SketchUp http://www.sketchup.com/ Maludam National Park in its glory, 3-D perception. Acknowledgement: Thank you Raini!

And to view multiple plots in one page, I referred to the below: > old.par <- par(mfrow=c(1, 2)) > plot(faithful, main="Faithful eruptions") > plot(large.islands, main="Islands", ylab="Area") > par(old.par) (Source: http://www.dummies.com/programming/r/how-to-put-multiple-plots-on-a-single-page-in-r/ ( inserted with my data: chns.par<-par(mfrow=c( 2 ,2)) barplot(as.matrix(Maludam.December.2014.Station.1CHNS), main="Elemental Analysis for Station 1", ylab= "Carbon Ratio", beside=TRUE, col=rainbow(5)) + legend("topright", c("Top","Middle","Bottom"), cex=0.6,bty="n", fill=rainbow(5)) barplot(as.matrix(Maludam.December.2014.Station.2CHNS), main="Elemental Analysis for Station 2", ylab= "Carbon Ratio", beside=TRUE, col=rainbow(5)) + legend("topright", c("Top","Middle","Bottom"), cex=0.6,bty="n", f

Correlation.R # ============================================================ # Tutorial on drawing a correlation map using ggplot2 # by Umer Zeeshan Ijaz (http://userweb.eng.gla.ac.uk/umer.ijaz) # =============================================================   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 )   #Filter out samples with fewer counts abund_table<-abund_table [ rowSums ( abund_table ) > 200 , ]   #Extract the corresponding meta_table for the samples in abund_table meta_table<-meta_table [ rownames ( abund_table ) , ]   I changed the parameters #You can use sel_env to specify the variables you want to use and sel_env_label to specify the labes for the pannel sel_env<- c ( "pH" , "Temp" , "TS

So this works for my data! I had started with the commands under the section NMDS from http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/ecological.html the sample and data samples were prepared according to the above. and proceed with the following: library("vegan", lib.loc="~/R/win-library/3.2") library("lattice", lib.loc="C:/Program Files/R/R-3.2.5/library") set.seed(2) abund_table<-read.csv("Sample.csv",row.names=1,check.names=FALSE) #Transpose the data to have sample names on rows abund_table<-t(abund_table) abund_table<-t(abund_table) example_NMDS=metaMDS(abund_table, k=2) plot(example_NMDS) ordiplot(example_NMDS,type="n") orditorp(example_NMDS,display="species",col="red",air=0.01) orditorp(example_NMDS,display="sites",cex=1.25,air=0.01) And I changed "Treatment 1" and "Treatment 2" to: treat=c(rep("pH",5),rep("Cond

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