== equal to!= not equal to> greater than>= greater than or equal to< less than<= less than or equal tox = c(1.0, 1.2, 1.4, 1.6, 1.8)
x == 1.2
#> [1] FALSE TRUE FALSE FALSE FALSE
x != 1.2
#> [1] TRUE FALSE TRUE TRUE TRUE
x > 1.2
#> [1] FALSE FALSE TRUE TRUE TRUE
x >= 1.2
#> [1] FALSE TRUE TRUE TRUE TRUE
x < 1.2
#> [1] TRUE FALSE FALSE FALSE FALSE
x <= 1.2
#> [1] TRUE TRUE FALSE FALSE FALSE== equal to!= not equal to%in% element of set (use: x %in% set)x = c("aa", "bb", "aa", "bb", "aa", "cc", "dd")
x == "aa"
#> [1] TRUE FALSE TRUE FALSE TRUE FALSE FALSE
x != "aa"
#> [1] FALSE TRUE FALSE TRUE FALSE TRUE TRUE
x %in% c("aa","bb")
#> [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE
!(x %in% c("aa","bb")) # x not in set
#> [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUENote: the above works for factors. But R also supports ordered factors that will recognize numeric-like relational operators
y = factor(x, ordered=TRUE)
y
#> [1] aa bb aa bb aa cc dd
#> Levels: aa < bb < cc < dd
y <= "bb"
#> [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE! negation (NOT)& elementwise AND (returns a vector)&& first element AND (most useful in if clauses)| elementwise OR (returns a vector)|| first element OR (most useful in if clauses)xor elementwise exclusive ORisTRUE(x) returns TRUE only if argument x = TRUE (single value)which(x) returns the indices that are TRUEall(x) are all values TRUE?any(x) are some values TRUE?x = c(TRUE,FALSE,TRUE,FALSE)
y = c(TRUE,FALSE,FALSE,TRUE)
!x
#> [1] FALSE TRUE FALSE TRUE
x & y
#> [1] TRUE FALSE FALSE FALSE
x && y
#> [1] TRUE
x | y
#> [1] TRUE FALSE TRUE TRUE
x || y
#> [1] TRUE
xor(x,y)
#> [1] FALSE FALSE TRUE TRUE
isTRUE(x==y)
#> [1] FALSE
which(x)
#> [1] 1 3
which(1:5>3)
#> [1] 4 5
all(x)
#> [1] FALSE
any(x)
#> [1] TRUEunion(x,y) in either x or y (or both)intersect(x,y) in both x and ysetdiff(x,y) in x, but not ysetequal(x,y) returns TRUE if same elements in each setis.element(element,set) returns logical vector if element(s) are in set%in% shortcut for is.element() (use: element %in% set)match(x,table) finds index of 1st match (NA if x not in table)x = c(1,2,3,4,5,5)
y = c(2,4,6)
union(x,y)
#> [1] 1 2 3 4 5 6
intersect(x,y)
#> [1] 2 4
setdiff(x,y)
#> [1] 1 3 5
setdiff(y,x)
#> [1] 6
setequal(x,y)
#> [1] FALSE
is.element(x,y)
#> [1] FALSE TRUE FALSE TRUE FALSE FALSE
x %in% y
#> [1] FALSE TRUE FALSE TRUE FALSE FALSE
match(x,y)
#> [1] NA 1 NA 2 NA NA
match(y,x)
#> [1] 2 4 NA
match(4,y)
#> [1] 2if(test) if test==TRUE, then execute following expression(s). Use: if(test) expr or if(test) cons.expr else alt.exprifelse(tests,yes,no) vectorized loop: for the TRUE elements of tests, return yes, no for FALSE elements.for loop over elements in seq. Use: for(var in seq) exprwhile loop under condition is FALSE. Use: while(cond) exprrepeat loop continues until a breakbreak breaks out of a loopnext stops current flow and advances to next looping indexswitch(expr,...) switch between expressions depending on value of exprx = c(1,2,3,4,5,5)
y = c(2,4,6)
if(any(x == 5)) print("There is a 5!")
#> [1] "There is a 5!"
if(all(x == 5)) print("All 5's ") else print("Not all 5's")
#> [1] "Not all 5's"
ifelse(x==5,x^2,x) # Square all fives.
#> [1] 1 2 3 4 25 25
if(TRUE & TRUE & NA) print("Hello World") # if doesn't work with NA's
#> Error in if (TRUE & TRUE & NA) print("Hello World"): missing value where TRUE/FALSE needed
if(TRUE && FALSE && NA) print("Hello World") # && stops evaluating at first FALSE so NA does not get evaluated
z = 0; for(i in 1:10) z[i] = 2*i; z
#> [1] 2 4 6 8 10 12 14 16 18 20
w = 0; i = 1; while(i<=10){ w[i] = 2*i; i = i+1}; w
#> [1] 2 4 6 8 10 12 14 16 18 20all.equal(x,y) tests if objects x and y are (nearly) equalidentical(x,y) tests if objects x and y are (exactly) equaldplyr::near(x,y,tol) tests if numeric objects x and y are within tolerancex = 1:10 # integer vector of length 10
y = matrix(x,nrow=1) # 1 x 10 matrix
identical(x,y) # they are not identical
#> [1] FALSE
all.equal(x,y) # because they are not same type (class) of object
#> [1] "Attributes: < target is NULL, current is list >"
#> [2] "target is numeric, current is matrix"
identical(x,as.integer(y)) # now they are identical
#> [1] TRUE
(sqrt(2) ^ 2) == 2
#> [1] FALSE
dplyr::near((sqrt(2) ^ 2), 2)
#> [1] TRUEwhich(x) returns the indices that are TRUEwhich.min() The index of the minimum value (first index if ties)which.max() The index of the maximum value (first index if ties)match(x,table) find position of x values in table%in% element of set (use: x %in% set)is.na() returns the indices that have missing (NA) values.#- subset with logicals or indices
big = (iris$Sepal.Length > 7.5) # logical vector
ind = which(iris$Sepal.Length > 7.5) # integer vector of indices
identical(iris[big,],iris[ind,])
#> [1] TRUE
#- Find element which is minimum or maximum
which.max(iris$Sepal.Length)
#> [1] 132
which(iris$Sepal.Length == max(iris$Sepal.Length))
#> [1] 132
#- match()
a = -5:5
b = 3:5
match(b,a)
#> [1] 9 10 11
#- match and combine data (see merge() for a better approach)
x = data.frame(uppercase=LETTERS[1:10],lowercase=letters[1:10],position=NA)
y = data.frame(uppercase=c("B","C","A","E","D"), position=c(2,3,1,5,4)) # out of order and partial
ind.x = match(y$uppercase,x$uppercase) # index of x
ind.x
#> [1] 2 3 1 5 4
x$position[ind.x] = y$position # add position info to x
x$uppercase %in% y$uppercase
#> [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#- missing
a[3] = NA # set the third element to missing (NA)
is.na(a)
#> [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE