BOL: Related items

A guide for complete R beginners :- Getting data into R

Archana Malhotra — Tue, 24 Feb 2015 20:15:08 -0600

For a beginner this can be is the hardest part, it is also the most important to get right.

It is possible to create a vector by typing data directly into R using the combine function ‘c’

x

same as

x

creates the vector x with the numbers between 1 and 5.

You can see what is in an object at any time by typing its name;

x

will produce the output ‘[1] 1 2 3 4 5′

Note that names need to be quoted

daysofweek ← c(‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’);

Usually however you want to input from a file. We have touched on the ‘read.table’ function already.

mydata

Now mydata is a data frame with multiple vectors

each vector can be identified by the default syntax

#if any of these are typed it will print to screen

mydata$V1 mydata$V2 mydata$V3

By default the function assumes certain things from the file

The file is a plain text file (there are function to read excel files: not covered here)
columns are separated by any number of tabs or spaces
there is the same number of data points in each column
there is no header row (labels for the columns)
there is no column with names for the rows** [I’ll explain].

If any of these are false, we need to tell that to the function

If it has a header column

mydata header=T also works

Note that there is a comma between different parts of the functions arguments

If there is one less column in the header row, then R assumes that the 1^st column of data after the header are the row names

Now the vectors (columns) are identified by their name

#if any of these are typed it will print to screen

mydata$A mydata$B mydata$C

# Summary about the whole data frame

summary(mydata)

# Summary information of column A

summary(mydata$A)

We can shortcut having to type the data frame each time by attaching it

attach(mydata)

# summary of column B as ‘mydata’ is attached

summary(B)

Two other important options for read.table

If is is separated only by tabs and has a header

mydata

Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.

If you know that the file has uneven columns

mydata

This causes R to fill empty spaces in a columns with ‘NA’ .

The last two examples will still work with our file and give the same result as with only headers=T

Graphs

to get an idea of what R is capable of type

demo(graphics)

steps through the examples, and the code is printed to the screen

We will work with simpler examples that have immediate use to biologists.

Remember to get more information about the options to a function type ‘?function’

Histogram of A

hist(mydata$A)

If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).

boxplot(mydata)

We can get rid of the need to type the data frame each time by using the attach function

# if not already done so

attach(mydata)
boxplot(mydata$A, mydata$B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

same as

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Scatter plot

# if not already done so

attach(mydata)
plot(A,B) # or plot(mydata$A, mydata$B)

SAVING an image

Windows users (Rgui) RIGHT click on image and select which you want.

These instructions work for everyone.

You need to create a new device of the type of file you need, then send the data to that device

to save as a png file (easy to load into the likes of powerpoint, also great for web applications.

png(‘filename’)
boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

or to save as a pdf

pdf(‘filename’)
boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Note

Nothing will appear on screen, the output is going to the file
Also it may not be saved immediately but will once the device (or R) is turned quit.

To quit R type

q() # If you save your session, next time you start R, you will have your data preloaded.

Or if you want to remain in R

dev.off() #turns of the png (or pdf etc) device, thus forces the data to save

BioScripts

Rahul Nayak — Sun, 28 Jun 2015 07:46:14 -0500

You are requested to please bookmark collection of bioinformatics tools, scripts, codes that can be pieced together in a very easy and flexible manner to perform both simple and complex bioinformatics tasks.

The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.

https://code.google.com/p/biowiki/

https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk

NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&projectsearch=Search+projects

NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&projectsearch=Search+projects

Address of the bookmark: https://code.google.com/hosting/search?q=bioinformatics&sa=Search

vcfR

Archana Malhotra — Fri, 19 Aug 2016 07:38:24 -0500

Most variant calling pipelines result in files containing large quantities of variant information. The variant call format (vcf) is an increasingly popular format for this data. The format of these files and their content is discussed in the vignette ‘vcf data.’ These files are typically intended to be post-processed (i.e., filtered) as an attempt to remove false positives or otherwise problematic sites. The R package vcfR provides tools to facilitate this filtering as well as to visualize the effects of choices made during this process.

Address of the bookmark: https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html

R Graphs !!

Jit — Fri, 04 Nov 2016 10:48:00 -0500

The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided. Enjoy nice graphs !!

Address of the bookmark: http://rgraphgallery.blogspot.be/

BioStats class tutorial

Shruti Paniwala — Thu, 16 Mar 2017 01:50:50 -0500

Nice biostat turorial by Ingo Ruczinski

Address of the bookmark: http://www.biostat.jhsph.edu/~iruczins/teaching/

Import R Data

Abhimanyu Singh — Wed, 12 Jul 2017 08:30:46 -0500

It is often necessary to import sample textbook data into R before you start working on your homework.

Excel File

Quite frequently, the sample data is in Excel format, and needs to be imported into R prior to use. For this, we can use the function read.xls from the gdata package. It reads from an Excel spreadsheet and returns a data frame. The following shows how to load an Excel spreadsheet named "mydata.xls". This method requires Perl runtime to be present in the system.

> library(gdata)                   # load gdata package
> help(read.xls)                   # documentation
> mydata = read.xls("mydata.xls")  # read from first sheet

Alternatively, we can use the function loadWorkbook from the XLConnect package to read the entire workbook, and then load the worksheets with readWorksheet. The XLConnect package requires Java to be pre-installed.

> library(XLConnect) # load XLConnect package
> wk = loadWorkbook("mydata.xls")
> df = readWorksheet(wk, sheet="Sheet1")

Minitab File

If the data file is in Minitab Portable Worksheet format, it can be opened with the function read.mtp from the foreign package. It returns a list of components in the Minitab worksheet.

> library(foreign)                 # load the foreign package
> help(read.mtp)                   # documentation
> mydata = read.mtp("mydata.mtp")  # read from .mtp file

SPSS File

For the data files in SPSS format, it can be opened with the function read.spss also from the foreign package. There is a "to.data.frame" option for choosing whether a data frame is to be returned. By default, it returns a list of components instead.

> library(foreign) # load the foreign package
> help(read.spss) # documentation
> mydata = read.spss("myfile", to.data.frame=TRUE)

Table File

A data table can resides in a text file. The cells inside the table are separated by blank characters. Here is an example of a table with 4 rows and 3 columns.

100   a1   b1
200   a2   b2
300   a3   b3
400   a4   b4

Now copy and paste the table above in a file named "mydata.txt" with a text editor. Then load the data into the workspace with the function read.table.

> mydata = read.table("mydata.txt")  # read text file
> mydata                             # print data frame
   V1 V2 V3
1 100 a1 b1
2 200 a2 b2
3 300 a3 b3
4 400 a4 b4

For further detail of the function read.table, please consult the R documentation.

> help(read.table)

CSV File

The sample data can also be in comma separated values (CSV) format. Each cell inside such data file is separated by a special character, which usually is a comma, although other characters can be used as well.

The first row of the data file should contain the column names instead of the actual data. Here is a sample of the expected format.

Col1,Col2,Col3
100,a1,b1
200,a2,b2
300,a3,b3

After we copy and paste the data above in a file named "mydata.csv" with a text editor, we can read the data with the function read.csv.

> mydata = read.csv("mydata.csv")  # read csv file
> mydata
  Col1 Col2 Col3
1  100   a1   b1
2  200   a2   b2
3  300   a3   b3

In various European locales, as the comma character serves as the decimal point, the function read.csv2 should be used instead. For further detail of the read.csv and read.csv2 functions, please consult the R documentation.

> help(read.csv)

Working Directory

Finally, the code samples above assume the data files are located in the R working directory, which can be found with the function getwd.

> getwd() # get current working directory

You can select a different working directory with the function setwd(), and thus avoid entering the full path of the data files.

> setwd("") # set working directory

Note that the forward slash should be used as the path separator even on Windows platform.

> setwd("C:/MyDoc")

R googleVis examples

Rahul Nayak — Sun, 10 Dec 2017 06:13:42 -0600

It may take a little while to load all charts. Please be patient. All charts require an Internet connection.

These examples are taken from the googleVis demo. You can execute the demo via

library(googleVis)
demo(googleVis)

For more details about the charts and further examples see the helpfiles of the individual googleVis function and review the Google Charts API documentation and Terms of Service.

Address of the bookmark: https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html

ASAR: Advanced metagenomic Sequence Analysis in R

Jit — Mon, 09 Jul 2018 05:20:50 -0500

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites

Neel — Mon, 05 Nov 2018 08:12:45 -0600

An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.

Address of the bookmark: https://github.com/olarerin/metaPlotR

Retrieving Taxonomic Information with R

Rahul Nayak — Thu, 29 Aug 2019 01:38:39 -0500

This vignette will introduce users to the retrieval of taxonomic information with myTAI. The taxonomy() function implemented in myTAI relies on the powerful package taxize. Nevertheless, taxonomic information retrieval has been customized for the myTAI standard and for organism specific information retrieval.

Specifically, the taxonomy() function implemented in myTAI can be used to classify genomes according to phylogenetic classification into Phylostrata (Phylostratigraphy) or to retrieve species specific taxonomic information when performing Divergence Stratigraphy (see Introduction for details).

Address of the bookmark: https://cran.r-project.org/web/packages/myTAI/vignettes/Taxonomy.html