R and bioconductor manual pdf

Due to the rapid development of most packages, it is also important to be aware that this manual will often not be fully up-to-date. Because of this and many other reasons, it is absolutely critical to use the original documentation of each package PDF manual or vignette as primary source of documentation.

Users are welcome to send suggestions for improving this manual directly to its author. Your email address will not be published. Post by Flavio Check the manual here. Helder Nakaya de pesquisadores, alunos.

C Install package from source: Linux: install. Instructions to fully build an R package under Windows can be found here and here. R" imports functions, methods and classes from myscript. R prompt myfct writes help file myfct. Rd promptClass "myclass" writes file myclass-class. Rd promptMethods "mymeth" writes help file mymeth. Rd files as they look in final help pages checkRd ".

Rd help file for problems. The best way of sharing an R package with the community is to submit it to one of the main R package repositories, such as CRAN or Bioconductor. Download on of the above exercise files, then start editing this R source file with a programming text editor, such as Vim, Emacs or one of the R GUI text editors. Here is the HTML version of the code with syntax coloring. This way one can organize file names by external table.

R execute from shell. The script ' sequenceAnalysis. R ' demonstrates how R can be used as a powerful tool for managing and analyzing large sets of biological sequences. Translation of this Page. This site was accessed times detailed access stats. Venables and B. Ripley Programming with Data , by John M.

If Statements If statements operate on length-one logical vectors. Less common are repeat loops. The break function is used to break out of loops, and next halts the processing of the current iteration and advances the looping index.

For Loop For loops are controlled by a looping vector. In every iteration of the loop one value in the looping vector is assigned to a variable that can be used in the statements of the body of the loop. Usually, the number of loop iterations is defined by the number of values stored in the looping vector and they are processed in the same order as they are stored in the looping vector.

Syntax tapply vector, factor, FUN Example Computes mean values of vector agregates defined by factor tapply as. This means there needs to be a second statement to test whether or not to break from the loop. However, this limitation can be overcome by eliminating certain operations in loops or avoiding loops over the data intensive dimension in an object altogether.

The latter can be achieved by performing mainly vector-to-vecor or matrix-to-matrix computations which run often over times faster than the corresponding for or apply loops in R. For this purpose, one can make use of the existing speed-optimized R functions e. Alternatively, one can write programs that will perform all time consuming computations on the C-level.

In fact, most of the R software can be viewed as a series of R functions. Naming Function names can be almost anything. Arguments It is often useful to provide default values for arguments e. Calling functions Functions are called by their name followed by parentheses containing possible argument names.

Scope Variables created inside a function exist only for the life time of a function. Stop To stop the action of a function and print an error message, one can use the stop function. Warning To print a warning message in unexpected situations without aborting the evaluation flow of a function, one can use the function warning " The Debugging in R page provides an overview of the available resources. The following example demonstrates the retrieval of specific lines from an external file with a regular expression.

First, an external file is created with the cat function, all lines of this file are imported into a vector with readLines , the specific elements lines are then retieved with the grep function, and the resulting lines are split into vector fields with strsplit.

Second, the files are imported one-by-one using a for loop where the original names are assigned to the generated data frames with the assign function. Consult help with? R" Table of Contents 2. R the following statement:! R [outfile] The output file lists the commands from the script file and their outputs. If no outfile is specified, the name used is that of infile and. Rout is appended to outfile.

R , then nothing will be saved in the. Rdata file which can get often very large. R 10 In the given example the number 10 is passed on from the command-line as an argument to the R script which is used to return to STDOUT the first 10 rows of the iris sample data.

If several arguments are provided, they will be interpreted as one string that needs to be split it in R with the strsplit function.

R This script doesn't need to have executable permissions. R is located. To utilize several CPUs on the Linux cluster, one can divide the input data into several smaller subsets and execute for each subset a separate process from a dedicated directory. An older S3 system and a more recently introduced S4 system. The latter is more formal, supports multiple inheritance, multiple dispatch and introspection. Many of these features are not available in the older S3 system.

In general, the OOP approach taken by R is to separate the class specifications from the specifications of generic functions function-centric system. This R tutorial provides a condensed introduction into the usage of the R environment and its utilities for general data analysis and clustering. It also introduces a subset of packages from the Bioconductor project.

Many packages were chosen, because the author uses them often for his own teaching and research. To obtain a broad overview of available R packages, it is strongly recommended to consult the official Bioconductor and R project sites. Due to the rapid development of most packages, it is also important to be aware that this manual will often not be fully up-to-date.

Because of this and many other reasons, it is absolutely critical to use the original documentation of each package PDF manual or vignette as primary source of documentation. Users are welcome to send suggestions for improving this manual directly to its author.

In this format all commands are represented in code boxes, where the comments are given in blue color. To save space, often several commands are concatenated on one line and separated with a semicolon ' ; '. This way several commands can be pasted with their comment text into the R console to demo the different functions and analysis steps. Windows users can simply ignore them. Commands highlighted in red color are considered essential knowledge.

They are important for someone interested in a quick start with R and Bioconductor. Where relevant, the output generated by R is given in green color. Both of them work the same way and in both directions. For consistency reasons one should use only one of them. R Startup Behavior The R environment is controlled by hidden files in the startup directory:.

Rhistory and. Rprofile optional. The link 'Packages' provides a list of all installed packages. After initiating 'start. The generated output should be provided when sending questions or bug reports to the R and BioC mailing lists. Basics on Functions and Packages. R for loading into R IDE e. RData' when exiting R and the workspace is saved. Removes objects. This is sometimes useful to clean up memory allocations after deleting large objects.

More details on this topic can be found here. This option is intended to support programs which use R to compute results for them. The output file lists the commands from the script file and their outputs. If no outfile is specified, the name used is that of 'infile' and '. Rout' is appended to outfile.

R', then nothing will be saved in the. Rdata file which can get often very large. Remember, single escapes e. If the 'header' argument is set to FALSE, then the first line of the data set will not be used as column titles. In this example an external file is created with the 'cat' function, all lines of this file are imported into a vector with 'readLines', the specific elements lines are then retieved with the 'grep' function, and the resulting lines are split into sub-fields with 'strsplit'.

Export to files write. It writes the data of an R data frame object into the clipbroard from where it can be pasted into other applications. The argument 'col.

Second, the files are imported one-by-one using a for loop where the original names are assigned to the generated data frames with the 'assign' function. Subsequent exports to the same file will arrange several tables in one HTML document. This library is usually not installed by default. Data and Object Types. Assigning values to object components. Calculations [ Function Index ] Four basic arithmetic functions: addition, subtraction, multiplication and division.

A list of the basic R functions can be found on the function and variable index page. Iterative calculations. With the argument setting '1', row-wise iterations are performed and with '2' column-wise iterations. Generates the same result as 'sqrt x '. Regular expressions R's regular expression utilities work similar as in other languages. Vectors are ordered collection of 'atomic' same data type components or modes of the following four types: numeric, character, complex and logical.

Missing values are indicated by 'NA'. R inserts them automatically in blank fields. The sort function sorts the items by size. The rev function reverses the order. The order function returns the corresponding indices for a sorted object. The order function is usually the one that needs to be used for sorting complex objects, such as data frames or lists. For retrieving indices of several strings provided by query vector, use the following 'match' function.

If the query vector here 'c "c","g" ' contains entries that are duplicated in the target vector, then this syntax returns only the first occurence s for each duplicate.

This syntax allows the subsetting of vectors and data frames with a query vector 'y' containing entries that are duplicated in the target vector 'x'. The resulting logical vector can be used for the actual subsetting step of vectors and data frames. Note: if the argument names are not used, as in the second example, then the order of the arguments is important. Factors are vector objects that contain grouping classification information of its components. The argument 'byrow' defines whether the matrix is filled by row or columns.

If something doesn't work then try to convert the object into a matrix with the as. Appending arrays and matrices cbind matrix1, matrix2 Appends columns of matrices with same number of rows. The function 'as.

Data Frames Data frames are two dimensional data objects that are composed of rows and columns. They are very similar to matrices. The main difference is that data frames can store different data types, whereas matrices allow only one data type e. These names need to be unique. By adding a "-" sign one can reverse the sort order. In this example the corresponding column is first assigned to a vector and then the desired field is accessed by its index number.

This syntax returns for duplicates only the index of their first occurence. To return all, use the following syntax. This returns all occurences of duplicates.

The results are returned as vectors. In this example, they are appended to the original data frame with the data. The argument '1' in the apply function specifies row-wise calculations.

If '2' is selected, then the calculations are performed column-wise. First, an example matrix 'x' is created. Second, the correlation values for the "August" row against all other rows are calculated. Finally, the resulting vector with the correlation values is merged with the original matrix 'x' and sorted by decreasing correlation values. To merge on row names indices , refer to it with "row.

However, this will be very slow for data frames with millions of rows. However, this step will be still very slow for very large data sets, due to the sapply loop over the row elements. This approach is about times faster than the loop-based alternatives: sd t myDF or apply myDF, 1, sd. By default rows with "NA" values will be ignored. To work around this limitation, one can replace the NA fields with a value that doesn't affect the result, e.

Reformatting data frames with reshape. Length Sepal. Width Petal. Length Petal. Width 1 setosa 5. Species Samples value 1 setosa Sepal. Length 5. Length 6. Width 3. Width 2. Species Sepal. Length , summarize Species mean 1 setosa 5. Length , transform Sepal. Width Species mean 1 5. In this example the list component names are prepended to the corresponding vectors.

Length The first step returns the unique entries for the Sepal. Length column in the iris data set and the second step counts the number of its unique entries. Length column of the iris data set and aligns the counts with the original data frame.

The number of samples to consider in the comparisons can be controlled with the 'm' argument. A much faster alternative is given in the data frame section.

R" Imports the colAg function. The columns in the resulting object are named after the chosen aggregates. Note: the function can only perform those calculations that can be applied to sets of two or more values, such as mean, sum, sd, min and max. Much faster to compute, but less flexible, alternatives are given in the data frame section.

To merge on specific columns, refer to them by their position numbers or their column names e. The following list provides an overview of some very useful plotting functions in R's base graphics. To get familiar with their usage, it is recommended to carefully read their help documentation with?

The environment greatly simplifies many complicated high-level plotting tasks, such as automatically arranging complex graphical features in one or several plots. The syntax of the package is similar to R's base graphics; however, high-level lattice functions return an object of class "trellis", that can be either plotted directly or stored in an object. Important functions for accessing and changing global parameters are:?

The environment streamlines many graphics routines for the user to generate with minimum effort complex multi-layered plots. Its syntax is centered around the main ggplot function, while the convenience function qplot provides many shortcuts.

The ggplot function accepts two arguments: the data set to be plotted and the corresponding aesthetic mappings provided by the aes function. Additional plotting parameters such as geometric objects e. Their settings can be changed with the opts function. The following graphics sections demonstrate how to generate different types of plots first with R's base graphics device and then with the lattice and ggplot2 packages. The 'mar' argument specifies the margin sizes around the plotting area in this order: c bottom, left, top, right.

The color of the plotted symbols can be controlled with the 'col' argument. The plotting symbols can be selected with the 'pch' argument, while their size is controlled by the 'lwd' argument. A selection palette for 'pch' plotting symbols can be opened with the command 'example points '. As alternative, one can plot any character string by passing it on to 'pch', e.

The font sizes of the different text components can be changed with the 'cex. Please consult the '? The column headers of the matrix or data frame are used as axis titles.

Scatter Plot Generated with Base Graphics. The argument as. Change plotting parameters show. Length, Sepal. The 'split. More details on this topic are provided in the 'Arranging Plots' section. A very nice line plot function for time series data is available in the Mfuzz library.

Line Plot Generated with Base Graphics lattice. Scatter Plot Generated with lattice. Scatter Plot Generated with ggplot2. The barplot function expects as input format a matrix, and it uses by default the column titles for labeling the bars. The argument 'ncol' controls the number of columns that are used for printing the legend.

The arguments 'x' and 'y' control the placement of the legend and the 'mar' argument specifies the margin sizes around the plotting area in this order: c bottom, left, top, right. The example in the 'barplot' documentation provides a very useful outline of this function.

R " Imports a function that plots a loan amortization table as bar plot. Bar Plot Generated with lattice. C Customizing colors library RColorBrewer ; display. Wind Rose Pie Chart Generated with ggplot2.

Several Heatmaps in One Plot Generated with lattice. The latter defines the height of each heatmap. R " Imports required functions. These names are used as sample labels in all subsequent data sets and plots. Such a file can be easily created from a spreadsheet program, such as Excel.

By default, duplicates are removed from the test sets. When assigning the value "intersects" to the type argument then the function will compute Regular Intersects instead of Venn Intersects. The Regular Intersect approach not compatible with Venn diagrams! The seperator used for naming the intersect samples can be specified under the sep argument.

In contrast to Venn diagrams, bar plots scale to larger numbers of sample sets. The minimum number of counts to consider in the plot can be set with the mincount argument default is 0.

Note: the vector lengths provided for the arguments ccol, lcol and lines should match the number of their corresponding features in the plot, e. The argument setlabels allows to provide a vector of custom sample labels. However, assigning the proper names in the original test set list is much more effective for tracking purposes.

The results from several Venn comparisons can be combined in a single Venn diagram by assigning to the count argument a list with several count vectors. The positonal offset of the count sets in the plot can be controlled with the yoffset argument. This representation misses two overlap sectors, but is sometimes easier to navigate than the default ellipse version.

This allows to identify the sample set combinations with the largest intersect within each complexity level. In the given example, only set 'F' contains duplications. Their frequency is provided in the result. This could be any data type! With the current implementation, the computation time is about 0. The results are returned as a list where each overlap component is labeled by the corresponding sample names.

The seperator used for naming the intersect samples can be specified under the 'sep' argument. The complexity level range to consider can be controlled with the 'complexity' argument, which can have values from 2 to the total number of samples. OLlist[[2]]; OLlist[[3]] Returns the corresponding intersect matrix and complexity levels. More details on this function are provided in the Venn diagram section.

This approach scales well up to 3several thousands of sample sets. The result is plotted as heatmap with two identical dendrograms representing the outcome of the hierarchical clustering. The latter is internally performed by calls of heatmap. Note: the distance matrix used for clustering in this examples is based on the row-to-row column-to-column similarities in the olMA object. The next example shows how one can use the olMA object directly as distance matrix for clustering after transforming the intersect counts into similarity measures, such as Jaccard or Rand indices.

This transformation can give reasonable results for sample sets with large size differences. Many alternative similarity measures for set comparisons could be considered here. This allows to identify the combinations of pairwise sample comparisons that have the largest or smallest intersects. R" Imports the required overLapper function. In this matrix the presence information is indicated by ones and its rows are sorted by the presence frequencies.

Alternative versions of the present-absent matrix can be returned by setting the 'type' argument to 1, 2 or 3. Histogram Generated with ggplot2. A box plot also known as a box-and-whisker diagram is a graphical representation of a five-number summary, which consists of the smallest observation, lower quartile, median, upper quartile and largest observation.

The featureMap. R script plots simple feature maps of biological sequences based on provided mapping coordinates. The usage of plotted values will connect the data points. More on this can be found in the documentation for 'par'. The last step sets the palette back to its default setting.

In the second plot a modified palette is called the same way. The start and end values need to be between 0 and 1. The wider their distance the more diverse are the resulting colors. The col2rgb can translates them into the RGB color code. System returns the corresponding x-y-coordinates after clicking on right mouse button. The actual image data are not written to the file until the 'dev. The pdf and svg formats provide often the best image quality, since they scale to any size without pixelation.

A much more detailed introduction into writing functions in R is available in the Programming in R section of this manual. The following exercises introduce a variety of useful data analysis utilities available in R. Import from spreadsheet programs e.

Download the following molecular weight and subcelluar targeting tables from the TAIR site, import the files into Excel and save them as tab delimited text files. Check tables and rename gene ID columns. Problem 1: How can the merge function in the previous step be executed so that only the common rows among the two data frames are returned? Prove that both methods - the two step version with na.