Useful R functions (not on exam)

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 297 418 381 669 152 780  53 310 652 823 584 395 718 192 814 828 742 847 338 426

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 584  NA 718 426  NA 847  53 669 814 310 395  NA 152 828 297 823 652 338 192 381 780 418 742

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 4 3 3 2 4 2 1 4 5 2

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "t" "i" "n" "u" "h" "W" "X" "M" "P" "F"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

integer(0)

which( manyNumbersWithNA > 900 )

integer(0)

which( is.na( manyNumbersWithNA ) )

[1]  2  5 12

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

integer(0)

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

integer(0)

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

integer(0)

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "W" "X" "M" "P" "F"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "t" "i" "n" "u" "h"

manyNumbers %in% 300:600

 [1] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE

which( manyNumbers %in% 300:600 )

[1]  2  3  8 11 12 19 20

sum( manyNumbers %in% 300:600 )

[1] 7

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "large" NA      "large" "small" NA      "large" "small" "large" "large" "small" "small" NA      "small" "large" "small" "large"
[17] "large" "small" "small" "small" "large" "small" "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "large"   "UNKNOWN" "large"   "small"   "UNKNOWN" "large"   "small"   "large"   "large"   "small"   "small"   "UNKNOWN"
[13] "small"   "large"   "small"   "large"   "large"   "small"   "small"   "small"   "large"   "small"   "large"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1] 584  NA 718   0  NA 847   0 669 814   0   0  NA   0 828   0 823 652   0   0   0 780   0 742

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 4 3 2 1 5

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  4  3  2  1  5

duplicated( duplicatedNumbers )

 [1] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 6

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 847

which.min( manyNumbersWithNA )

[1] 7

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 53

range( manyNumbersWithNA, na.rm = TRUE )

[1]  53 847

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 584  NA 718 426  NA 847  53 669 814 310 395  NA 152 828 297 823 652 338 192 381 780 418 742

sort( manyNumbersWithNA )

 [1]  53 152 192 297 310 338 381 395 418 426 584 652 669 718 742 780 814 823 828 847

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  53 152 192 297 310 338 381 395 418 426 584 652 669 718 742 780 814 823 828 847  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 847 828 823 814 780 742 718 669 652 584 426 418 395 381 338 310 297 192 152  53  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 584  NA 718 426  NA

order( manyNumbersWithNA[1:5] )

[1] 4 1 3 2 5

rank( manyNumbersWithNA[1:5] )

[1] 2 4 3 1 5

sort( mixedLetters )

 [1] "F" "h" "i" "M" "n" "P" "t" "u" "W" "X"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1]  8.5  5.0  3.0 10.0  1.0  8.5  3.0  3.0  6.5  6.5

rank( manyDuplicates, ties.method = "min" )

 [1]  8  5  2 10  1  8  2  2  6  6

rank( manyDuplicates, ties.method = "random" )

 [1]  9  5  4 10  1  8  2  3  6  7

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000  0.43349143  0.23883635 -1.45709650  0.65440546  0.07343791
[11]  3.01517130 -1.34815822 -1.52177241  0.61025248  2.12930673

round( v, 0 )

 [1] -1  0  0  0  1  0  0 -1  1  0  3 -1 -2  1  2

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  0.4  0.2 -1.5  0.7  0.1  3.0 -1.3 -1.5  0.6  2.1

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  0.43  0.24 -1.46  0.65  0.07  3.02 -1.35 -1.52  0.61  2.13

floor( v )

 [1] -1 -1  0  0  1  0  0 -2  0  0  3 -2 -2  0  2

ceiling( v )

 [1] -1  0  0  1  1  1  1 -1  1  1  4 -1 -1  1  3

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 × 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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