RPR-OBJECTS-Data frames

R "data frames""

(R data frames)

 

Abstract:

Introduction to data frames: how to create, and modify them and how to retrieve data.

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Objectives: This unit will ... ... introduce R data frames; ... cover a number of basic operations.

Outcomes: After working through this unit you ... ... know how to create and manipulate data frames; ... can access and change individual elements; ... can extract rows, columns, and append new data rows;

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Deliverables:

• Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
• Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
• Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

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Prerequisites:
This unit builds on material covered in the following prerequisite units:

• RPR-Objects-Vectors (R scalars and vectors)

 

 

 

Evaluation

Evaluation: NA

This unit is not evaluated for course marks.

Contents

Task:

• Load the R-Exercise_BasicSetup project in RStudio if you don't already have it open.
• Type init() as instructed after the project has loaded.
• Continue below.

Data frames

Data frames are the most frequently used type of data object for bioinformatics in R; they emulate our mental model of data in a spreadsheet and can be used to implement entity-relationship datamodels. They are more flexible than vectors or matrices, but they are easier to work with than lists.

Usually the result of reading external data from an input file is a data frame. The file below is included with the R-Exercise-BasicSetup project files - it is called plasmidData.tsv, and you can click on it in the Files Pane to open and inspect it.

Name	Size	Marker	Ori	Sites
pUC19	2686	Amp	ColE1	EcoRI, SacI, SmaI, BamHI, XbaI, PstI, HindIII
pBR322	4361	Amp, Tet	ColE1	EcoRI, ClaI, HindIII
pACYC184	4245	Tet, Cam	p15A	ClaI, HindIII

This data set uses tabs as value separators and it has a header line. Similar files can be exported from Excel or other spreadsheet programs. Note that the file contains commas within fields. Read this as a data frame as follows:

( plasmidData <- read.table("plasmidData.tsv",
                            sep = "\t",
                            header = TRUE )
objectInfo(plasmidData)

You can view the data frame contents by clicking on the spreadsheet icon behind its name in the Environment Pane.

 

Basic operations

Here are some basic operations with the data frame. Try them, and experiment. If you break the object by mistake, you can just recreate it by reading the source file again:

rownames(plasmidData) <- plasmidData[ , 1]  # assigns the contents of column 1 as rownames
nrow(plasmidData)
ncol(plasmidData)
objectInfo(plasmidData)


x <- plasmidData[2, ]  # assign one row to a variable
objectInfo(x)  # This is also a data frame! One row. It has to be, because
               # it contains elements of type chr and of type int!

plasmidData["pBR322", ]  # retrieve one row: different syntax, same thing


plasmidData <- plasmidData[-2, ]  # remove one row
objectInfo(plasmidData)

plasmidData <- rbind(plasmidData, x)  # add it back at the end
objectInfo(plasmidData)

# add a new row from scratch:
plasmidData <- rbind(plasmidData, data.frame(Name = "pMAL-p5x",
                                                     Size = 5752,
                                                     Marker = "Amp",
                                                     Ori = "pMB1",
                                                     Sites = "SacI, AvaI, ..., HindIII",
                                                     stringsAsFactors = FALSE))
objectInfo(plasmidData)

( x <- plasmidData[ , 2] )    # retrieve one column by index
  plasmidData[ , "Size"]      # retrieve one column by name
objectInfo(plasmidData)       # now a vector!

# That may be surprising behaviour. When you retrieve a single column from a
# dataframe it is (silently) turned into a vector (unless you explicitly
# tell R not to do that - e.g. plasmidData[ , "Size", drop = FALSE]). To make the
# nature of this data as a vector more expolicit, I usually use a different
# and equivalent syntax:
( x <- plasmidData$Size )
objectInfo(x)

# Note: the $ operator always returns a vector. And, the column name is _NOT_
# placed in quotation marks.

Task:
The rowname of the new row of plasmidData is now "1". It should be "pMAL-p5x". Fix this.

 

Self-evaluation

Notes

 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2020-09-18

Version:

1.1

Version history:

• 1.1 Remove stringsAsFactors, no longer an issue
• 1.0.1 Maintenance
• 1.0 Completed to first live version
• 0.1 Material collected from previous tutorial

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