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The function groups insiders, outsiders and
extractors provide infix functions that can be used to
extract values from vectors.
insiders and outsidersThese functions return values inside or outside a given interval.
Inclusion or exclusion of interval endpoints follows the common notation
for open and closed intervals: [ and ] means
inclusion, and ( and ) means exclusion of
endpoints.
The syntax is always:
vector infix interval
Depending on which function is called, the return value is either a
logical vector indicating which values are inside or outside the
interval, or, the actual values (use the functions with a dot between
the operators %[.]%)
The syntax and function is similar to those provided in package
DescTools (I did not test whether they give the same
results).
x <- 0:9
# Inside open interval
x %()% c(5,9)
> [1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
# Inside closed interval
x %[]% c(5,9)
> [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
# Outside open interval
x %)(% c(5,9)
> [1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
# Outside closed interval
x %][% c(5,9)
> [1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
# All variations left/right open/closed are possible
x %[)% c(5,9)
> [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
x %](% c(5,9)
> [1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSEIndices are commonly used to extract values, if you add a dot
. inbetween the the interval symbols, values will be
extracted.
# Regular indexing works, but is a bit 'wordy'
x[x %[]% c(5,9)]
> [1] 5 6 7 8 9
# Easier to use the special functions
x %[.]% c(5,9)
> [1] 5 6 7 8 9
# Extract first, last, or, middle value of x
x %:% "f"
> [1] 0
x %:% "m"
> [1] 4
x %:% "l"
> [1] 9
# Simulate a sample from a standard normal distribution
set.seed(4321)
Zscore <- rnorm(100)
# Find Z-scores that are 'significant' at alpha = .05
Zscore %).(% c(-1.96,1.96)
> [1] 2.080248 -2.450016 -2.439320
# Old indexing has a lot of repetition, so does tidyverse, e.g. using filter()
Zscore[Zscore < -1.96 | Zscore > 1.96]
> [1] 2.080248 -2.450016 -2.439320extractorsExtracting a subset of values from the front or rear of a vector is a
common task and the base functions head() and
tail() can do this. The infix functions in the
extractors group mimic some of this behaviour and add the
ability to extract from - to, or, up -and-untill, a
specific value.
# A character vector
z <- letters
# Extract front by first occurrence of value "n"
z %[f% "n"
> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n"
# Extact first, middle, last of z
z %:% "f"
> [1] "a"
z %:% "m"
> [1] "m"
z %:% "l"
> [1] "z"
# Extract by percentile
seq(1,10,.5) %(q% .5 # infix
> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
seq(1,10,.5)[seq(1,10,.5) < quantile(seq(1,10,.5),.5)] # regular syntax
> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
seq(1,10,.5) %q]% .5 # infix
> [1] 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
seq(1,10,.5)[seq(1,10,.5) >= quantile(seq(1,10,.5),.5)] # regular syntax
> [1] 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
# Random uniform integers
set.seed(123)
x <- round(runif(100,1,100))
# Extract front up and untill index 10
x%[%10 # infix
> [1] 29 79 41 88 94 6 53 89 56 46
x[1:10] # regular [saves just 1 char]
> [1] 29 79 41 88 94 6 53 89 56 46
# Extract from index 90 to rear
x%]%90 # infix
> [1] 18 14 66 35 66 33 20 78 10 47 52
x[90:length(x)] # regular
> [1] 18 14 66 35 66 33 20 78 10 47 52
# Extract numbers from front to first occurrence of 11
x%[f%11 # infix
> [1] 29 79 41 88 94 6 53 89 56 46 96 46 68 58 11
x[1:which(x==11)[1]] # regular
> [1] 29 79 41 88 94 6 53 89 56 46 96 46 68 58 11
# Extract numbers from last occurrence of 11 to rear
x%l]%11 # infix
> [1] 11 44 99 89 89 18 14 66 35 66 33 20 78 10 47 52
x[which(x==11)[length(which(x==11))]:length(x)] # regular
> [1] 11 44 99 89 89 18 14 66 35 66 33 20 78 10 47 52
# Extract by indices if an index range provided
# This is a clear case in which the infix is less sensible to use than regular indexing:
x%]%c(6,10) # infix
> [1] 6 53 89 56 46
x[6:10] # regular
> [1] 6 53 89 56 46
z%[%c(6,10) #infix
> [1] "f" "g" "h" "i" "j"
z[6:10] #regular
> [1] "f" "g" "h" "i" "j"The fINDexers group provides infix functions that can
return column and row names based on indices, or, indices based on
column and row names. Take for instance data frame d:
| x | y | txt | |
|---|---|---|---|
| ri5 | 1 | 6 | delta = 5 |
| ri4 | 2 | 6 | delta = 4 |
| ri3 | 3 | 6 | delta = 3 |
| ri2 | 4 | 6 | delta = 2 |
| ri1 | 5 | 6 | delta = 1 |
We can use the infix functions to get names and indices of
d:
# Columns
"txt"%ci%d # infix
> [1] 3
which(colnames(d)%in%"txt") # regular
> [1] 3
2%ci%d # infix
> [1] "y"
colnames(d)[2] # regular
> [1] "y"
# Rows
"ri4"%ri%d # infix
> [1] 2
which(rownames(d)%in%"ri4") # regular
> [1] 2
2%ri%d # infix
> [1] "ri4"
rownames(d)[2] # regular
> [1] "ri4"
# Change column name
colnames(d)["y"%ci%d] <- "Yhat" # infix
colnames(d)[colnames(d)%in%"y"] <- "Yhat" # regularFor 1D list and vector objects %ri% and
%ci% return the same value.
l <- list(a=1:100, b=LETTERS)
2%ci%l == 2%ri%l
> [1] TRUE
"a"%ci%l == "a"%ri%l
> [1] TRUE
# Named vector
v <- c("first" = 1, "2nd" = 1000)
1%ci%v == 1%ri%v
> [1] TRUE
"2nd"%ci%v == "2nd"%ri%v
> [1] TRUEFunction %mi% will return row and/or column names on 2D
objects: data frames, matrices, tibbles, etc.
# Data frame d
c(5,2) %mi% d
> [1] "ri1" "Yhat"
list(r="ri1",c=2) %mi% d
> $r
> [1] 5
>
> $c
> [1] "Yhat"
# matrix row and column indices
(m <- matrix(1:10,ncol=2, dimnames = list(paste0("ri",0:4),c("xx","yy"))))
> xx yy
> ri0 1 6
> ri1 2 7
> ri2 3 8
> ri3 4 9
> ri4 5 10
1 %ci% m
> [1] "xx"
5 %ci% m # no column 5
> [1] NA
1 %ri% m
> [1] "ri0"
5 %ri% m
> [1] "ri4"
c(5,1)%mi%m
> [1] "ri4" "xx"
c(1,5)%mi%m
> [1] "ri0" NAFunction %ai% is a version of %in% that
returns the indices of all occurrences of one or more values in an
object.
# get all indices of the number 1 in v
1 %ai% v
> nv first
> 1 1 1
# get all indices of the number 3 and 6 in d
c(3,6) %ai% d
> nv row col
> 1 3 3 1
> 2 6 1 2
> 3 6 2 2
> 4 6 3 2
> 5 6 4 2
> 6 6 5 2
# Simulate a sample from a standard normal distribution
set.seed(1234)
Zscores <- rnorm(100)
Zscores%).(%c(-1.96,1.96) %ai% Zscores # returns a data frame with values and indices
> nv V1
> 1 -2.34569770262935 4
> 2 2.41583517848934 20
> 3 -2.18003964894867 37
> 4 2.54899107071786 62
> 5 2.07027086133094 75
> 6 2.12111710537568 100
which(Zscores%)(%c(-1.96,1.96)) # returns an index vector
> [1] 4 20 37 62 75 100These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.