# dby: Calculate summary statistics grouped by In mets: Analysis of Multivariate Event Times

 dby R Documentation

## Calculate summary statistics grouped by

### Description

Calculate summary statistics grouped by variable

### Usage

```dby(
data,
INPUT,
...,
ID = NULL,
ORDER = NULL,
SUBSET = NULL,
SORT = 0,
COMBINE = !REDUCE,
NOCHECK = FALSE,
ARGS = NULL,
NAMES,
COLUMN = FALSE,
REDUCE = FALSE,
REGEX = mets.options()\$regex,
ALL = TRUE
)
```

### Arguments

 `data` Data.frame `INPUT` Input variables (character or formula) `...` functions `ID` id variable `ORDER` (optional) order variable `SUBSET` (optional) subset expression `SORT` sort order (id+order variable) `COMBINE` If TRUE result is appended to data `NOCHECK` No sorting or check for missing data `ARGS` Optional list of arguments to functions (...) `NAMES` Optional vector of column names `COLUMN` If TRUE do the calculations for each column `REDUCE` Reduce number of redundant rows `REGEX` Allow regular expressions `ALL` if FALSE only the subset will be returned

### Details

Calculate summary statistics grouped by

dby2 for column-wise calculations

### Author(s)

Klaus K. Holst and Thomas Scheike

### Examples

```n <- 4
k <- c(3,rbinom(n-1,3,0.5)+1)
N <- sum(k)
d <- data.frame(y=rnorm(N),x=rnorm(N),id=rep(seq(n),k),num=unlist(sapply(k,seq)))
d2 <- d[sample(nrow(d)),]

dby(d2, y~id, mean)
dby(d2, y~id + order(num), cumsum)

dby(d,y ~ id + order(num), dlag)
dby(d,y ~ id + order(num), dlag, ARGS=list(k=1:2))
dby(d,y ~ id + order(num), dlag, ARGS=list(k=1:2), NAMES=c("l1","l2"))

dby(d, y~id + order(num), mean=mean, csum=cumsum, n=length)
dby(d2, y~id + order(num), a=cumsum, b=mean, N=length, l1=function(x) c(NA,x)[-length(x)])

dby(d, y~id + order(num), nn=seq_along, n=length)
dby(d, y~id + order(num), nn=seq_along, n=length)

d <- d[,1:4]
dby(d, x<0) <- list(z=mean)
d <- dby(d, is.na(z), z=1)

f <- function(x) apply(x,1,min)
dby(d, y+x~id, min=f)

dby(d,y+x~id+order(num), function(x) x)

f <- function(x) { cbind(cumsum(x[,1]),cumsum(x[,2]))/sum(x)}
dby(d, y+x~id, f)

## column-wise
a <- d
dby2(a, mean, median, REGEX=TRUE) <- '^[y|x]'~id
a
## wildcards
dby2(a,'y*'+'x*'~id,mean)

## subset
dby(d, x<0) <- list(z=NA)
d
dby(d, y~id|x>-1, v=mean,z=1)
dby(d, y+x~id|x>-1, mean, median, COLUMN=TRUE)

dby2(d, y+x~id|x>0, mean, REDUCE=TRUE)

dby(d,y~id|x<0,mean,ALL=FALSE)

a <- iris
a <- dby(a,y=1)
dby(a,Species=="versicolor") <- list(y=2)
```

mets documentation built on Jan. 17, 2023, 5:12 p.m.