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R Data Frames

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You can think of a data frame as structure similar to Excel spreadsheet.

It is essentially a list of vectors. A data frame is more general than a matrix as different columns can contain different modes of data (numeric, character, etc.).   Data frames are the most common data structure  in R.

Like Excel spreadsheet R data frame is two dimensional object comprising of rows and columns, and you can address columns by names. The rows are referred by the first (left-hand] subscripts, while columns by the second (right-hand) subscript or name. Each element can be addressed by two indexes provided in square brackets, for example


You can use intervals instead of single index, for example


you can drop a raw or a column from the data set with minus operation, for example

gld[,-2] # drops the second column
gld[-(1:n),] # drop rows from 1 to n in x

You can select multiple columns with c operator


To select all the entries in a column, the syntax is "comma, number of column", for example


Construction of a data frame from several vectors of the same length

A data frame is created with the data.frame() function:

mydata <- data.frame(vector1, vector2, ...)

 Assign the result to the metals  variable three vectors:

metals <- data.frame(date, open, close, )

Now, try printing metals to see its contents using statement print(metals) 

 > print(metals) date open close ... ... ...

There's your new data frame, neatly organized into rows, with column names (derived from the variable names) across the top.

You can get individual columns by providing their index number in double-brackets. Try getting the second column (prices) of metals:


You could instead provide a column name as a string in double-brackets. (This is often more readable.) Retrieve the "close" column:


Reading CSVs

There are numerous ways to download data into data.frame. The simplest and the most common is downloading and then reading comma separated values (CSV) files. You can use read.csv to do that. It actually calls read.table with some arguments preset. The result of using read.table is a data.frame.

The first argument to read.table is the full path of the file to be loaded or URL. If you specify just the name of the file, it is assumed to be in your project folder.

yahooUrl <- ""
theGold<- read.table (file = yahooUrl, header = TRUE, sep = ",")

The result can now be seen using head.

> head(theGold)

the first argument is the file name in quotes (or as a character variable). Notice how we explicitly used the argument names file, header and sep.  The second argument, header, indicates that the first row of data holds the column names. The third argument gives the delimiter separating data cells. Changing this to other values such as “\t” (tab delimited) or “;” (semicolon delimited) allows it to read other types of files.

There is another little argument that is helpful to use is stringsAsFactors. Setting this to FALSE (the default is TRUE) prevents character columns from being converted to factor columns. This saves computation time, which can be substantial in case of a large dataset with large number of rows and several character columns with many unique values. Also keeping the columns as character data in many case makes them easier to work with. Conversion to factors is often overkill unless we are dealing with a members of a set and can benefit from set-style operations. 

BTW stringsAsFactors argument can also be used in data.frame function for blocking conversion of strings into factors: 

theGold <- data.frame(datet=d, close=cc, volume=v, stringsAsFactors=FALSE)

There are several other arguments to read.table function. Among them the most useful are quote and colClasses. The former specifies the character used for enclosing cells and the latter the data type for each column, respectively.

When comma delimited files are poorly built, for example the cell separator has been used inside a cell you can try to use functions read.csv2 (or read.delim2) instead of read.table.

Loading Data Frames from a file

Typing in all your data by hand only works up to a point, obviously, which is why R was given the capability to easily load data in from external files.

You can create a couple data files to experiment with in you project directory. To check what files your project directory contains use function list.files():

> list.files() 

Let's assume that there is a CSV (Comma Separated Values) file "gld150730.csv"  in your project directory. You can export such a file from any spreadsheet programs or download from any web site that provide stock quotes such as .  For example:

Date,Open,High,Low,Close,Volume,Adj Close

You can load a CSV file's content into a data frame by passing the file name to the read.csv  function. Try it with the "gld150730.csv"  file:

theGold <- read.csv("gld150730.csv")

Fields in a file can be separated by tab characters rather than commas.  

For files that use separator strings other than commas, you can use the read.table  function. The sep  argument defines the separator character, and you can specify a tab character with "\t".

theGold <- read.table("gld150730.csv", sep="\t", header=TRUE)

Selecting columns

To get a single column of data from data frame you need to specify the row and do not specify any rows. For example to access the first column in data frame theGold you can use index of this column:


In general each index can be a vector. That means that you can use ranges to select set of consecutive columns (or not consequtive if  the step in sequnce is larger then 1):


Unlike most other programming language, in R you also can use column names, which is more convenient, then using numeric indexes. Remember that column names are a factor vector so each name has its numeric equivalent. You can get the list of names of the columns for particular data frame using the function names, for example


To access a single column using this "column name" feature just put a name of the column instead of numeric index:


To access multiple columns by name, make the column argument a character vector of the names of the columns you want to be in the output.

goldSelectedCol <- theGold[, c("date", "open", "close", "volume")]
When you are selecting a single column R converts it into a vector and displays values horizontally. If you want the values to be displayed vertically as you used to in viewing data frames,  you need to ensure that result is still a data frame, despite having just a single column. That can be achieved using an argument drop=false

You can check the mode of result: it will be a data frame not a vector. For example:


The $ notation for selection of columns

Typing all those brackets can get tedious and error prone, so in R there is a shorthand notation: the data frame name, a dollar sign, and the column name (without quotes). Try using it to get the "close"  column:


The $ notation selects a particular column (vector) from a given data frame.

Selecting rows

To get a single row you can use nation similar to getting a single column -- specify row and do not specify any columns:

theRow2<-theGold[2, ]
To specify multiple row, use a vector, for example
theRow2<-theGold[2:10, ]
If rows are not adjacent use c function to construct a vector, for example
theRow2<-theGold[c(2,5,10), ]
As nrow function provides number of rows you can calculate variables and use them instead of constants. For example to select the last 200 rows you can use the follwong: 
rMax <- nrow(theGold)
rMin <- nMax-200
theRow2<-theGold[rMin:rMax, ]

Functions head and tail

R contain two very useful functions for operating with rows called head and tail. Which are similar to Unix utilities with the same names:

Usually a data.frame has far too many rows to print them all to the screen, so thankfully the head function prints out only the first few rows.

Try the following command on our example data frame

head(theGold, n = 7)
tail(thegold, n = 10)

Getting attributes of a data frame

There are various ways to inspect a data frame, such as:

Browsing data

RStudio has a nice data browser (View(mydata)). The data frame will be displayed in nice spreadsheet like format in the upper left pane.

You can also use functions head()  and  tail()  to display rows that are interesting for you in command window.

Binding a new row or column to existing data frame

Most of the times when you are working with data frames, you are changing the data and one of the several changes you can do to a data frame is adding column or row and as the result increase the dimension of your data frame.

There are few different ways to do it but the easiest ones are cbind()  and rbind()  which are part of the base package:

mydata <- cbind(mydata, newVector)
mydata <- rbind(mydata, newVector)

Remember that the length of the newVector should match the length of the side of the data frame that you are attaching it to. For example in the cbind()  command the following statement should be TRUE:


To see more samples, you can always do ?base::cbind  and ?base::rbind.

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[Jul 30, 2015] attach() and detach() or with() functions

It can get tiresome typing patientdata$ at the beginning of every variable name, so shortcuts are available. You can use either the attach() and detach() or with() functions to simplify your code.

Attach, Detach, and With

The attach() function adds the data frame to the R search path. When a variable name is encountered, data frames in the search path are checked in order to locate the variable. Using the mtcars data frame from chapter 1 as an example, you could use the following code to obtain summary statistics for automobile mileage (mpg), and plot this variable against engine displacement (disp), and weight (wt):

plot(mtcars$mpg, mtcars$disp)
plot(mtcars$mpg, mtcars$wt)

This could also be written as

  plot(mpg, disp)
  plot(mpg, wt)

The detach() function removes the data frame from the search path. Note that detach() does nothing to the data frame itself. The statement is optional but is good programming practice and should be included routinely. (I'll sometimes ignore this sage advice in later chapters in order to keep code fragments simple and short.)

The limitations with this approach are evident when more than one object can have the same name. Consider the following code:

> mpg <- c(25, 36, 47)
> attach(mtcars)

The following object(s) are masked _by_ '.GlobalEnv':    mpg
> plot(mpg, wt)
Error in xy.coords(x, y, xlabel, ylabel, log) :
  'x' and 'y' lengths differ
> mpg
[1] 25 36 47

Here we already have an object named mpg in our environment when the mtcars data frame is attached. In such cases, the original object takes precedence, which isn't what you want. The plot statement fails because mpg has 3 elements and disp has 32 elements. The attach() and detach() functions are best used when you're analyzing a single data frame and you're unlikely to have multiple objects with the same name. In any case, be vigilant for warnings that say that objects are being masked.

An alternative approach is to use the with() function. You could write the previous example as

with(mtcars, {
  summary(mpg, disp, wt)
  plot(mpg, disp)
  plot(mpg, wt)

In this case, the statements within the {} brackets are evaluated with reference to the mtcars data frame. You don't have to worry about name conflicts here. If there's only one statement (for example, summary(mpg)), the {} brackets are optional.

The limitation of the with() function is that assignments will only exist within the function brackets. Consider the following:

> with(mtcars, {
   stats <- summary(mpg)
   Min. 1st Qu.  Median    Mean 3rd Qu.     Max.
  10.40   15.43   19.20   20.09   22.80    33.90
> stats
Error: object 'stats' not found

If you need to create objects that will exist outside of the with() construct, use the special assignment operator <<- instead of the standard one (<-). It will save the object to the global environment outside of the with() call. This can be demonstrated with the following code:

> with(mtcars, {
   nokeepstats <- summary(mpg)
   keepstats <<- summary(mpg)
> nokeepstats
Error: object 'nokeepstats' not found
> keepstats
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    10.40   15.43   19.20   20.09   22.80   33.90

Most books on R recommend using with() over attach(). I think that ultimately the choice is a matter of preference and should be based on what you're trying to achieve and your understanding of the implications. We'll use both in this book.

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