For multiple records, var widths for each record must be in separate vectors. The negative numbers on record 2 show we are skipping vars. Although the foreign package is the most widely documented approach, it lacks important capabilities. Functions in the Hmisc package add the ability to read formatted values, variable labels and lengths.
SAS users rarely use the length statement, accepting the default storage method of double precision. This wastes a bit of disk space but saves programmer time. However since R saves all its data in memory, space limitations are far more important. If you use the length statement in SAS to save space, the sasxport. You will need the foreign package for this example. It comes with R but must be loaded using the library foreign function.
You also need the Hmisc package, which does not come with R but is very easy to install. The example below assumes you have a SAS xport format file.
It comes with R so you don't have to install it, but you do have to load it with the library command. However I have seen it work only intermittently on. Portable format files seem to work every time. This loads the needed package. The first example uses the default write.
Tabs have an advantage over commas as commas are used by some countries as decimal points, and exported text strings may also contain commas. By default it writes out the "m" and "f" for gender, including the quotes. Enter help write. The second two examples use the write. They require a library called foreign, which is loaded first with the library function. Note that these two examples write out the gender values as 1 and 2 for f and m respectively.
SPSS library foreign write. In R, these two processes are almost identical. As a result, variable selection in R is both more flexible and quite a bit more complex. However since you need to learn that complexity to select observations, it is not much added effort. Our example dataset contains the variables: workshop, gender, q1, q2, q3, q4. SAS lets you refer to them by individual name or in contiguous order separated by double dashes as in workshop--q4. SAS also uses a single dash to request variables that share a numeric suffix, q1-q4, regardless of their order in the data set.
Selecting any variable beginning with a q is done with q:. You never use that logic to select variables. With R, it is best to dive in and see them all because understanding them is the key to understanding other documentation, especially the help files.
This section focuses only on selecting variables. In R you can abbreviate this as Entering at the R console will cause it to actually generate the sequence, 1,2,3,4,5,6. They are stored within our data frame in an object called the names vector. The names function accesses that vector, so entering names mydata will cause R to display them. These are referred to using square brackets as mydata[rows,columns]. This section focuses on the second parameter, the columns variables.
You can address the elements of the list using two square brackets as in mydata[[3]] to select our third variable, q1.
R offers many ways to select variables columns from a data frame to use in an analysis. If you perform an analysis without selecting any variables, the R function will use all the variables if it can. For example, to get summary statistics on all variables and all observations or rows , use summary mydata. You can substitute any of the examples below to choose a subset of variables. For example, summary mydata[ "q1"] would get a summary for just variable q1 using the data frame, mydata.
For example, mydata[ ,3] selects all rows for the third variable or column, q1. If you leave out an index, it will assume you want them all. If you leave the comma out completely, R assumes you want a column, so mydata[3] is almost the same as mydata[ ,3] — both refer to our third variable, q1. Some functions require one approach or the other. See the section on Converting Data Structures for details.
To select more than one variable using indexes, you must combine them into a numeric vector using the c function. So mydata[ c 3,4,5,6 ] selects variable 3 through 6. You will see this approach used many ways in R. You combine multiple objects into a single one in several ways to feed into functions that require a single object. If you use a negative sign on an index, you will exclude those columns.
For example, mydata[-c 3,4,5,6 , ] will exclude those variables. The colon operator can generate longer strings of numbers, but it's tricky. The isolate function I in R exists to clarify such occasional confusion. They do not have to have names. R is still expecting the form mydata[row,column], but when you supply only one parameter, it assumes it is the column.
So mydata[ ,"q1"]works as well. If you have more than one name, you must combine them into a single character vector using the combine or c function. For example, mydata[ c "q1","q2","q3","q4" ]. Unfortunately, the colon operator does not work directly with character prefixes, but you can paste the letter "q" onto the numbers you generate using that operator. This code generates the same list as the paragraph above and stores it in a character vector called myqs. You can use this approach to generate variable names to use in a variety of circumstances.
Note that merely changing the 4 below to would generate the sequence q1 to q In R, the as. It would stack them both into a single variable with twice as many observations! You can actually correlate X from one data frame with Y stored in another! After you submit the function, attach mydata , you can refer to just q1 and R will know which one you mean. This works when selecting existing variables but is best avoided when creating them.
So when adding new variables to a data frame, you need to use any of the above methods that make it absolutely clear where you want the variable stored. With this approach getting summary statistics on multiple variables might look like, summary data. R ignores them. This approach is usually used for under other circumstances. With this approach you cannot use the colon operator, so mydata[[]] is invalid. The examples below demonstrate many ways to select variables.
To make it easier to see the result of the selection, we will use the print function. When working interactively, this is the default function, so mydata["q1"] and print mydata["q1"] are equivalent. However to give you the feel how the selection works in all functions, I use the longer form. R R Program for Selecting Variables.
Uses many of the same methods as selecting observations. Rdata" This refers to no particular variables, so all are printed. These all select the variables q1,q2,q3 and q4 by indexes. If you use a range of columns repeatedly, it is helpful to store the whole range in a numeric vector.
The "which" function can find an index number for you. Column names are stored in mydata as a character vector. The "names" function extracts those names. The data. The subset function makes selecting contiguous variables easy using the colon operator.
This demonstrates again that we can select contiguous variables by their indexes. We repeat this here as a prelude to getting indexes by name. That vector is then used to choose variables. It will just be in the workspace. Manually create a vector to get just q1. You probably would not do this, but it demonstrates the basis for the next example. The as. This generates the same logical vector automatically.
It is much shorter than the one above that uses OR. This approach is fine for reading data, but not advisable for writing it. These four are equivalent as long as the data frame is attached. This section focuses only on selecting observations. They are stored within our data frame in an object in called the row names vector. Note that the quotes around them show that they are stored as characters, not as numbers.
So they cannot be abbreviated simply You can abbreviate the with as. Row names can also be character values that are easier to identify, such as "Ann","Bob","Carla"…. These are referred to as mydata[rows,columns]. This section focuses on the first parameter, the rows. There are many ways to select observations rows from a data frame.
For example, to get summary statistics on all rows for all columns, use summary mydata. You can substitute any of the examples below to choose a subset of observations. The fact that the column position after the comma is blank tells R to use all the variables. For example, mydata[8 , ] selects all the variables for row 8. To select more than one variable using indexes, you must combine them into a numeric vector using the combine or c function.
So mydata[ c 5,6,7,8 , ] selects rows 5 through 8, which happen to be the males. The colon operator ":" can generate a numeric vector directly, so mydata[, ] selects the same observations. If you use a negative sign on an index, you will exclude those observations. The colon operator can abbreviate this as well, but it's tricky. The isolate function in R exists to clarify such occasional confusion. For example, mydata[ c "1","2","3","4" , ] or mydata[ c "Ann","Carla","Bob","Sue" , ] Note that even if your names appear to be numbers, they are still stored characters.
So you cannot abbreviate them using the form However, you could generate them using the colon operator and force them to become character using the as. When selecting observations, these two have no equivalents. R R Program to Select Observations. So this excludes the females in rows 1 through 4. The isolate function is used to apply the minus to 1,2,3,4 and prevent -1,0,1,2,3,4.
Otherwise how would you store the 5,6,7,8 values in a data frame that has 8 rows? Note that even though happyGuys is a variable name, it is not used in quotes. Since a logical vector is as long as the original variables, the new variable is a good match, so we'll save it there. You can use the saved logical vector to select observations. Note that when we use a saved logical vector to select, it is not put in quotes.
Since they're in quotes, they make up a character vector. This prints the first 4 cases selected by their row name. This assigns more useful row names. Note that this vector's length is not equal to the number of rows in the data frame, so it cannot be stored there. Note that it is not enclosed in quotes when used this way.
All the rules that work for rows also work for columns. Within that, there is only one structure, the variable. On the other hand, R has several data structures: data frames, vectors, lists and matrices. R takes advantage of these by having the same function procedure do different things depending on what you give it. We have seen several instances of this in our examples. However, some functions will indeed accept the first form.
In the section on Selecting Variables, I said that mydata[ ,3] and mydata[3] were almost the same. For many functions those approaches are interchangeable. But some procedures are pickier than others and require very specific data structures. These commands pass the data in the form of a data frame mydata[3], mydata["q1"]. These pass the data in the form of a vector: mydata[ ,3], mydata[ ,"q1"].
An exception to that is that selecting a column using the form mydata[ ,3] will pass the data as a data frame while selecting a row using the same type of notation, mydata[3, ] passes the data as a vector! If you have having a problem figuring out which form of data you have, there are functions that will tell you. So functions that require either form will work with it.
Some of the functions you can use to convert from one structure to another are below. It is more like SPSS where as long as you have data read in, you can modify it. In other words, although R has loops, they are not needed for this type of manipulation. The basic transformations include sqrt for square root, log for natural logarithm, log10 for the base 10 logarithm and so on. It is the equivalent to attaching a data frame, performing as many transformations as you like using short variable names and then detaching the data.
You can even use the shorter name form in the created variable s. This would create a variable 7 where previously we had only 6. R will also give it a column name of V7. If you're using the index approach, it is probably easier to initialize a new variable by binding a new variable to mydata.
R R Program for Transforming Variables. If any is missing, the result is missing. The result will be missing only if all qs are missing.
This selects the qs using the select function. Rdata" Saves the R file. For example, the formulas for recommended daily allowances of vitamins differ for males and females. R R Program for Conditional transformations. Rdata" print mydata attach mydata Makes this the default dataset. It identifies the people who strongly agree with question 4. It identifies people who agree with question 4. However, it specifies only the female condition, assuming male is true whenever female is false.
So if gender were missing, they would get the male code. However, it checks for the male gender so it will not assume missing genders are male. The nesting involved is quite a bit more complex than the example above. If workshop or q4 are missing, it will use the second formula.
The letters NA are also an object in R that you can use to assign missing values. But if you have other values, you will of course have to tell R which values are missing. However, it applies the values to all variables, which is unlikely to be of use.
Periods that represent missing values in SAS cause R to read the whole variable as a character vector. So you have to first fix the missing values and then convert it to numeric using the as.
So if you wanted to substitute another value such as the mean, you would need to use the is. NA is the missing value code in R.
There are no nines in the data, but you can put a few there in q1 manually in the data editor using fix mydata. The lapply function applies a function to every member of a list a data frame is a type of list. If you know their column numbers, use this approach. The data comes in two files, the raw data in a. Factor fct variables are what R calls categorical variables. Another thing to clean up are the blank cells. We would prefer these to be showing as NA in R, instead of an empty character.
Now we have the value labels showing up, but what do Q1, Q2, and Q3 mean? Even better. Now the tibble in R contains the descriptive variable names and values from the labels stored in the original SAS data files. To read in an SPSS. Also, because they are sequential engines, some procedures such as the PRINT procedure give a warning message that the engine is sequential.
With these engines, the physical filename that is associated with a libref is an actual filename, not a folder. This action is an exception to the rules concerning librefs. The following sections assume that you are familiar with the BMDP save file terminology. If the libref appears previously as a fileref, you can omit filename because the physical filename that is associated with the fileref is used. Then it prints the data for the first save file in the physical file:.
The dictionary-filename argument can also be an environment variable name or a fileref. Do not use quotation marks if it is an environment variable name or fileref. Therefore, you can use whatever member name you like.
0コメント