# [R] FOR TAKING PERCENTAGES of OTUS in each column (n=2910 COLUMNs)

Tue Mar 7 23:38:21 CET 2017

```Dear all,
Thank you very much for all of your inputs. I finally managed to get a
table with the percentages using R. I'm definitely going to invest more
time to learn R. Thank you again for taking your valuable time to answer my
question. Really appreciate it.

*Thilini Jayasinghe*
PhD Candidate
Liggins Institute
The University of Auckland
Building 503/201, 85 Park Road, Grafton, Auckland 2023
Mobile: +64 220211604

On 8 March 2017 at 02:41, David L Carlson <dcarlson at tamu.edu> wrote:

> If you read your data into R, it is simple to compute the percentages. Use
> Save As in Excel to save your data as a .csv (comma separated variables)
> file. Then use read.csv() to create a data frame in R as Jim indicated. Put
> it in the default directory that R is using (this depends on what operating
> system you are using). Then import the file with
>
>
> You may need to add some arguments in read.csv() depending on if you have
> column headings or not. Blank fields in Excel will be interpreted as
> missing values, not zeros, but you did not give us any of your data (even
> just the first 10, rows and columns) so it is impossible to be more
> specific. Once you have the data frame (and have replaced the missing
> values with zeros if necessary), the process is simple:
>
> pct_data <- prop.table(as.matrix(raw_data), 2) * 100
>
> will produce a matrix with percentages down each column and store it as a
> matrix object (variable) called pct_data. R uses different methods to store
> different kinds of data. The read.csv() function creates a data frame which
> can handle a mixture of character and numeric data, but the prop.table()
> function only accepts a matrix of numeric data and returns a matrix of
> numeric data. The data you described is all numeric so it is easy to switch
> the data frame to a matrix (and then back again if you want). If you are
> going to use R, you will need to spend some time reading about how it
> works, but as you can see, that time invested will make some operations
> much simpler than Excel and will allow you to conduct analyses that Excel
> does not even attempt.
>
> You can get details on these three functions by running the following
> commands in R:
>
> ?prop.table
> ?as.matrix
>
> -------------------------------------
> David L Carlson
> Department of Anthropology
> Texas A&M University
> College Station, TX 77840-4352
>
>
>
>
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Jim Lemon
> Sent: Tuesday, March 7, 2017 2:24 AM
> mailing list <r-help at r-project.org>
> Subject: Re: [R] FOR TAKING PERCENTAGES of OTUS in each column (n=2910
> COLUMNs)
>
> Hi Thilini,
> It is fairly simple in R once you have imported the data. Say you have
> a data frame obtained by exporting the Excel table to CSV and then
> importing it with "read.csv". I'm not sure whether you have a number
> in each cell or just a 0/1 absent/present value, but it may not
> matter. Assume the data frame is named "tjdf"
>
> for(column in 1:dim(tjdf)[2])
>  tjdf[,paste("pct",column,sep="")]<-100*tjdf[,column]/sum(tjdf[,column])
>
> Alternatively, you could create a new data frame with just the percentages.
>
> Jim
>
>
> On Tue, Mar 7, 2017 at 12:16 PM, Thilini Maddegoda Vidanelage
> > Hi,
> > I am analyzing a huge excel table with OTUs. In the table, I have 2910
> > columns and 365 rows.Each column represents one individual (n=2910). Rows
> > represent microbial species (n=365).
> > I have the total of all OTUs of microbial species under each column.
> Then I
> > need to get the percentages of each species in each individual.I started
> to
> > do this in excel but I have to repeat this for 2910 times which is going
> to
> > be very time-consuming.  I am sure there should be a smart way to do this
> > and just wondering whether there is any R script to do this.Any help is
> > much appreciated.
> > Many thanks, Thilini
> >
> > *Thilini Jayasinghe*
> > PhD Candidate
> > Liggins Institute
> > The University of Auckland
> > Building 503/201, 85 Park Road, Grafton, Auckland 2023
> > Mobile: +64 220211604
> > Email: tmad109 at aucklanduni.ac.nz
> >
> >         [[alternative HTML version deleted]]
> >
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