Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = ">"
)
library(invctr)
## -----------------------------------------------------------------------------
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)
## -----------------------------------------------------------------------------
# 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]
## -----------------------------------------------------------------------------
# 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)[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
## ----echo=FALSE---------------------------------------------------------------
# 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)
## -----------------------------------------------------------------------------
# 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
## -----------------------------------------------------------------------------
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
## -----------------------------------------------------------------------------
# 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
## -----------------------------------------------------------------------------
# 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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