knitr::opts_chunk$set( collapse = TRUE, comment = ">" ) library(invctr)
The function groups
extractors provide infix functions that can be used to extract values from vectors.
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:
] means inclusion, 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)
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 set.seed(4321) 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
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 set.seed(123) 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)] # 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
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
# data frame d <- data.frame(x=1:5,y=6,txt=paste0("delta = ",6-1:5),row.names=paste0("ri",5:1)) knitr::kable(d)
We can use the infix functions to get names and indices of
# Columns "txt"%ci%d # infix which(colnames(d)%in%"txt") # regular 2%ci%d # infix colnames(d) # regular # Rows "ri4"%ri%d # infix which(rownames(d)%in%"ri4") # regular 2%ri%d # infix rownames(d) # 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
%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
%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 c(5,1)%mi%m c(1,5)%mi%m
%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 set.seed(1234) 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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