R Language Note 3: Extracts a subset of R objects, fuzzy matching and removing data box missing values _r

Three basic methods for subsetting R Objects subset [:] The single bracket "Returns an object that is the same, such as a subset of vectors or a vector, and can also be used to select multiple elements in an object [[:" Both brackets "can only be used to extract one element, and to extract elements from a list or a data box. However, because the category of elements in a list or data box is not unique, it returns the type of the object that is not necessarily a list or a data box $: "Dollar sign" is an element in a named list or data box (one) vector subset

Example single bracket + Digital Index:

> x <-C ("A", "B", "C", "C", "D", "a")  
> X[2]            # # Extract the second element
[1] "B"
> X[1:4]          # # Extract contiguous multiple elements
[1] "a" "B" "C" "C"
> X[c (1, 3, 4)]   # # Extract discontinuous multiple elements
[1] "a" "C" "C"

2. Single bracket + logical index (sorted alphabetically):

> x <-C ("A", "B", "C", "C", "D", "a")  
> X[x > "a"]      # # Extract First-letter sort greater than A's element
[1] "B" "C" "C" "D"

3. Creating Logical vectors

> U <-x > "a"
> U
[1] FALSE true True True  false
> X[u]
[1 ] "B" "C" "C" "D"

(ii) subset of matrices

The subset of matrices can be completed by row and column indexes.

For example, the following 2*3 matrices

> x <-Matrix (1:6, 2, 3)
> x
     [, 1] [, 2] [, 3] [
1,]    1 3 5
[2,]    2    4 6
> X[1, 2]            
[1] 3
> x[, 1]
[1] 1 2
> X[1, 2, drop = FALSE]   # # Modify the drop parameter, return the matrix form
     [, 1]
[1,]    3
  > x[, 1, drop = FALSE]
     [, 1]
[1,]    1
[2,]    2

(iii) A subset of the list

A subset of the list can be used either "[", "[["], or "$"

> # Create a list containing two elements
> x <-list (foo = 1:4, bar = 0.6)
> x
$foo
[1] 1 2 3 4

$bar
[ 1] 0.6
> # Three methods to extract the first element
> X[1]            # # The single parenthesis Returns a list
$foo
[1] 1 2 3 4
> X[[1]]
[1 ] 1 2 3 4
> X$foo
[1] 1 2 3 4

The advantage of a list-taking subset method is that you don't need to memorize the order, as long as you have a name.

However, if you want to extract more than one element from the list, you can only extract individual elements using single brackets (both brackets and dollar signs), and you know the order.

> x <-list (foo = 1:4, bar = 0.6, Baz = "Hello")
> X[c (1, 3)]
$foo
[1] 1 2 3 4

$baz
[1] "Hello"

The difference between the two brackets and the dollar sign:

The dollar sign must be the element name that exists in the list;

The two brackets can be after the subsequent assignment variable

> x <-list (foo = 1:4, bar = 0.6, Baz = "Hello")
> Name <-"foo"
>
> # Computed index for " Foo "
> X[[name]]  
[1] 1 2 3 4
>
> # element ' name ' doesn ' t exist! (but no error here)
> X$name     
NULL
>
> # # element "foo" does exist
> X$foo      
[1] 1 2 3 4

In addition, both brackets can take an integer sequence, not just a number

> x <-List (a = List (a), B = C (3.14, 2.81))
> > # Get the 
3rd element of the 1st Element
  > x[[c (1, 3)]  
[1]
> 
> # Same as above
> X[[1]][[3]]   
[1]
> 
> # 1st element of the 2nd element
> X[[c (2, 1)]]  
[1] 3.14

(iv) Fuzzy matching

Dollar sign $ and both brackets [[have fuzzy matching functionality.] In this way, you can quickly find elements at the command line.

Cases:

> x <-List (aardvark = 1:5)
> x$a
[1] 1 2 3 4 5
> x[["a"]]                 # parameter defaults to exact match
NULL
; X[["a", exact = FALSE]]  # # parameter set to exact match
[1] 1 2 3 4 5

(v) Deletion of missing data and missing values (NA)

Most of the real data contains a lot of missing data, whether it's a vector, a matrix, or a data box, creating a subset to delete them by creating a logical vector that tells you where the missing values are.

The Is.na () function can find missing values in a vector, for example:

> x <-C (1, 2, NA, 4, NA, 5)
> Bad <-is.na (x)
> Print (Bad)
[1] false  TRUE  false TRUE FALSE
> X[!bad]         # #!bad represents a non-missing value
[1] 1 2 4 5

If you have more than one vector, multiple objects, and NA is distributed in different places to remove all missing values and create a new subset, you can use the complete.cases () function, as in the following example:

> x <-C (1, 2, NA, 4, NA, 5)
> y <-C ("A", "B", Na, "D", Na, "f")
> Good <-complete.cases (x, y)
> Good
[1]  True True  false true
> X[good]
[1] 1 2 4 5
> Y[good]
[1] "a" "B" "D" " F

Similarly, if the missing value position of the X,y two vectors is different, the complete.cases () function takes the set to determine the missing position:

> x <-C (1, 2, NA, 4, NA, 5)
> y <-C ("A", "B", Na, Na, "D", "F")
> Good <-complete.cases (x, Y)
> Good
[1] True to true false  true
> 
> X[good]
[1] 1 2 5< c30/>> Y[good]
[1] "a" "B" "F"

The

complete.cases () function can also be used to remove missing values from the data box

> # # Create a Data box airquality > Ozone <-C (+,%, NA, MB) > SOLAR.R <-C (190, 118, 149, 313, NA, NA  , 244, 222) > Wind <-C (7.4, 8.0, 12.1, 11.2, 14.3, 13.9, 14.1, 15.2) > Temp <-C (67, 72, 74, 62, 56, 66, 22, > Month <-C (5, 5, 5, 5, 5, 5, 5, 5) > Day <-C (1, 2, 3, 4, 5, 6, 7, 8) > Airquality <-data.frame (     Ozone, SOLAR.R, Wind, Temp, Month, day) > Airquality Ozone SOLAR.R Wind Temp Month Day 1 41 190 7.4 67      5 1 2 118 8.0 (5 2 3 149 12.1) 5 3 4 313 11.2 5 4 5 NA   Na 14.3 5 5 6 na 13.9 66 5 6 7 13 244 14.1 22 5 7 8 15 222 15.2      5 8 > > # # Top six row data in Data box > head (airquality) Ozone SOLAR.R Wind Temp Month Day 1 41 190 7.4 67    5 1 2 36 118 8.0 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4-5  Na Na 14.3 56   5 5 6 NA 14.9 5 6 > > # to find rows without missing data by creating logical vectors > Good <-complete.cases (airquality) &G T      Head (Airquality[good,]) Ozone SOLAR.R wind Temp Month Day 1 41 190 7.4 67 5 1 2 36 118 8.0 72    5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 7 23 299 8.6 65 5 7-8 19 99 13.8 59 5 8