Finding and extracting values and indices

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Finding and extracting values

The function groups insiders, outsiders and extractors provide infix functions that can be used to extract values from vectors.

insiders and outsiders

These 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)

# Inside closed interval
x %[]% c(5,9)

# Outside open interval
x %)(% c(5,9)

# Outside closed interval
x %][% c(5,9)

# All variations left/right open/closed are possible
x %[)% c(5,9)
x %](% c(5,9)

How to use...

Indices 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)]

# Easier to use the special functions
x %[.]% c(5,9)

# Extract first, last, or, middle value of x
x %:% "f"
x %:% "m"
x %:% "l"

# Simulate a sample from a standard normal distribution
Zscore <- rnorm(100)

# Find Z-scores that are 'significant' at alpha = .05
Zscore %).(% c(-1.96,1.96)

# Old indexing has a lot of repetition, so does tidyverse, e.g. using filter()
Zscore[Zscore < -1.96 | Zscore > 1.96]


Extracting 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"

# Extact first, middle, last of z
z %:% "f"
z %:% "m"
z %:% "l"

# Extract by percentile
seq(1,10,.5) %(q% .5 # infix
seq(1,10,.5)[seq(1,10,.5) < quantile(seq(1,10,.5),.5)] # regular syntax

seq(1,10,.5) %q]% .5 # infix
seq(1,10,.5)[seq(1,10,.5) >= quantile(seq(1,10,.5),.5)] # regular syntax

# Random uniform integers
x <- round(runif(100,1,100))

# Extract front up and untill index 10
x%[%10 # infix
x[1:10] # regular [saves just 1 char]

# Extract from index 90 to rear
x%]%90 # infix
x[90:length(x)] # regular

# Extract numbers from front to first occurrence of 11
x%[f%11 # infix
x[1:which(x==11)[1]] # regular

# Extract numbers from last occurrence of 11 to rear
x%l]%11 # infix
x[which(x==11)[length(which(x==11))]:length(x)] # regular

# 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
x[6:10] # regular

z%[%c(6,10) #infix
z[6:10] #regular

Finding and extracting indices

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:

# data frame
d <- data.frame(x=1:5,y=6,txt=paste0("delta = ",6-1:5),row.names=paste0("ri",5:1))

We can use the infix functions to get names and indices of d:

# Columns
 "txt"%ci%d # infix
 which(colnames(d)%in%"txt") # regular

 2%ci%d # infix
 colnames(d)[2] # regular

# Rows
 "ri4"%ri%d # infix
 which(rownames(d)%in%"ri4") # regular

 2%ri%d # infix
 rownames(d)[2] # regular

# Change column name
 colnames(d)["y"%ci%d] <- "Yhat" # infix
 colnames(d)[colnames(d)%in%"y"] <- "Yhat" # regular

For 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
 "a"%ci%l == "a"%ri%l

# Named vector
 v <- c("first" = 1, "2nd" = 1000)

 1%ci%v == 1%ri%v
 "2nd"%ci%v == "2nd"%ri%v

Function %mi% will return row and/or column names on 2D objects: data frames, matrices, tibbles, etc.

# Data frame d
 c(5,2) %mi% d

 list(r="ri1",c=2) %mi% d

# matrix row and column indices
(m <- matrix(1:10,ncol=2, dimnames = list(paste0("ri",0:4),c("xx","yy"))))

 1 %ci% m
 5 %ci% m # no column 5

 1 %ri% m
 5 %ri% m


Function %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

# get all indices of the number 3 and 6 in d
 c(3,6) %ai% d

 # Simulate a sample from a standard normal distribution
 Zscores <- rnorm(100)

 Zscores%).(%c(-1.96,1.96) %ai% Zscores # returns a data frame with values and indices

 which(Zscores%)(%c(-1.96,1.96)) # returns an index vector

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invctr documentation built on May 1, 2019, 10:53 p.m.