Useful R functions

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Sandbox vectors

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

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

 [1] 734  99 847 203 695 259 871  87 211  52 998 945 889 594 509  18  21 106 796 391

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

 [1]  99 203  NA  21  52 106 211 734 847  NA  NA 259 889 391 945  87 998 695 871 509  18 796 594

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

 [1] 5 5 1 1 2 1 5 3 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"
[25] "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"
[25] "Y" "Z"

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

 [1] "c" "a" "p" "q" "i" "J" "W" "H" "Y" "L"

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 )

[1] 11 12

which( manyNumbersWithNA > 900 )

[1] 15 17

which( is.na( manyNumbersWithNA ) )

[1]  3 10 11

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

[1] 998 945

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

[1] 998 945

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

[1] 998 945

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] "J" "W" "H" "Y" "L"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "c" "a" "p" "q" "i"

manyNumbers %in% 300:600

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

which( manyNumbers %in% 300:600 )

[1] 14 15 20

sum( manyNumbers %in% 300:600 )

[1] 3

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] "small" "small" NA      "small" "small" "small" "small" "large" "large" NA      NA      "small"
[13] "large" "small" "large" "small" "large" "large" "large" "large" "small" "large" "large"

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

 [1] "small"   "small"   "UNKNOWN" "small"   "small"   "small"   "small"   "large"   "large"  
[10] "UNKNOWN" "UNKNOWN" "small"   "large"   "small"   "large"   "small"   "large"   "large"  
[19] "large"   "large"   "small"   "large"   "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]   0   0  NA   0   0   0   0 734 847  NA  NA   0 889   0 945   0 998 695 871 509   0 796 594

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 5 1 2 3

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  5  1  2  3

duplicated( duplicatedNumbers )

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

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 17

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 998

which.min( manyNumbersWithNA )

[1] 21

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 18

range( manyNumbersWithNA, na.rm = TRUE )

[1]  18 998

Sorting/ordering of vectors

manyNumbersWithNA

 [1]  99 203  NA  21  52 106 211 734 847  NA  NA 259 889 391 945  87 998 695 871 509  18 796 594

sort( manyNumbersWithNA )

 [1]  18  21  52  87  99 106 203 211 259 391 509 594 695 734 796 847 871 889 945 998

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  18  21  52  87  99 106 203 211 259 391 509 594 695 734 796 847 871 889 945 998  NA  NA  NA

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

 [1] 998 945 889 871 847 796 734 695 594 509 391 259 211 203 106  99  87  52  21  18  NA  NA  NA

manyNumbersWithNA[1:5]

[1]  99 203  NA  21  52

order( manyNumbersWithNA[1:5] )

[1] 4 5 1 2 3

rank( manyNumbersWithNA[1:5] )

[1] 3 4 5 1 2

sort( mixedLetters )

 [1] "a" "c" "H" "i" "J" "L" "p" "q" "W" "Y"

Ranking of vectors

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

 [1]  4.5 10.0  1.5  6.0  3.0  8.0  8.0  1.5  8.0  4.5

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

 [1]  4 10  1  6  3  7  7  1  7  4

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

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

Rounding numbers

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

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000  1.5968327 -0.6236429 -0.4301821
 [9]  0.1527396  1.2952801 -0.1241376  0.8545873 -1.4958236 -1.2404464 -1.2228682

round( v, 0 )

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

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  1.6 -0.6 -0.4  0.2  1.3 -0.1  0.9 -1.5 -1.2 -1.2

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  1.60 -0.62 -0.43  0.15  1.30 -0.12  0.85 -1.50 -1.24 -1.22

floor( v )

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

ceiling( v )

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

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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