archivist (version 1.0)

Tags: Tags

Description

Tags are attributes of an artifact, i.e., a class, a name, names of artifact's parts, etc.. The list of artifact tags vary across artifact's classes. To learn more about artifacts visit archivist-package.

Arguments

Details

Tags are attributes of an artifact. Tags can be an artifact's name, class or archiving date. Furthermore, for various artifact's classes more different Tags are available and can be searched by searchInLocalRepo or searchInGithubRepo functions.

Tags are stored in the Repository. If data is extracted from artifact a special Tag named relationWith is created, and specifies with which artifact this data is related to.

So far supported artifact list is presented below. Objects are divided thematically. The newest list is also available on archivist wiki on https://github.com/pbiecek/archivist/wiki/archivist-package---Tags{{Github}}.

Regression Models

[object Object],[object Object],[object Object] Plots

[object Object],[object Object] Results of Agglomeration Methods

[object Object],[object Object],[object Object],[object Object] Statistical Tests

[object Object]

When non of above is specified, tags are corresponded by default

[object Object],[object Object]

See Also

Functions using Tags are:

Other archivist: Repository; archivist-package; copyGithubRepo, copyLocalRepo; createEmptyRepo; deleteRepo; loadFromGithubRepo, loadFromLocalRepo; md5hash; rmFromRepo; saveToRepo; searchInGithubRepo, searchInLocalRepo; showGithubRepo, showLocalRepo; summaryGithubRepo, summaryLocalRepo

Examples

Run this code
# examples
# data.frame object
data(iris)
exampleRepoDir <- tempdir()
createEmptyRepo(repoDir = exampleRepoDir)
saveToRepo( iris, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# ggplot/gg object
library(ggplot2)
df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),y = rnorm(30))
library(plyr)
ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))
myplot123 <- ggplot(df, aes(x = gp, y = y)) +
  geom_point() +  geom_point(data = ds, aes(y = mean),
                             colour = 'red', size = 3)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( myplot123, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# lm object
model <- lm(Sepal.Length~ Sepal.Width + Petal.Length + Petal.Width,
           data= iris)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( model, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# agnes (twins) object
library(cluster)
data(votes.repub)
agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( agn1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# fanny (partition) object
x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),
          cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5)),
          cbind(rnorm( 3,3.2,0.5), rnorm( 3,3.2,0.5)))
fannyx <- fanny(x, 2)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( fannyx, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# lda object
library(MASS)

Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
                   Sp = rep(c("s","c","v"), rep(50,3)))
train <- c(8,83,115,118,146,82,76,9,70,139,85,59,78,143,68,
           134,148,12,141,101,144,114,41,95,61,128,2,42,37,
           29,77,20,44,98,74,32,27,11,49,52,111,55,48,33,38,
           113,126,24,104,3,66,81,31,39,26,123,18,108,73,50,
           56,54,65,135,84,112,131,60,102,14,120,117,53,138,5)
lda1 <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( lda1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# qda object
tr <- c(7,38,47,43,20,37,44,22,46,49,50,19,4,32,12,29,27,34,2,1,17,13,3,35,36)
train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
qda1 <- qda(train, cl)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( qda1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )


# glmnet object
library( glmnet )

zk=matrix(rnorm(100*20),100,20)
bk=rnorm(100)
glmnet1=glmnet(zk,bk)
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( glmnet1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# trellis object
require(stats)

## Tonga Trench Earthquakes

Depth <- equal.count(quakes$depth, number=8, overlap=.1)
xyplot(lat ~ long | Depth, data = quakes)
update(trellis.last.object(),
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       par.strip.text = list(cex = 0.75),
       aspect = "iso")

## Examples with data from `Visualizing Data' (Cleveland, 1993) obtained
## from http://cm.bell-labs.com/cm/ms/departments/sia/wsc/

EE <- equal.count(ethanol$E, number=9, overlap=1/4)

## Constructing panel functions on the fly; prepanel
trellis.plot <- xyplot(NOx ~ C | EE, data = ethanol,
                       prepanel = function(x, y) prepanel.loess(x, y, span = 1),
                       xlab = "Compression Ratio", ylab = "NOx (micrograms/J)",
                       panel = function(x, y) {
                         panel.grid(h = -1, v = 2)
                         panel.xyplot(x, y)
                         panel.loess(x, y, span=1)
                       },
                       aspect = "xy")
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( trellis.plot, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# htest object

x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
this.test <- wilcox.test(x, y, paired = TRUE, alternative = "greater")
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( this.test, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteRepo( exampleRepoDir )

# survfit object

# Create the simplest test data set
test1 <- list(time=c(4,3,1,1,2,2,3),
              status=c(1,1,1,0,1,1,0),
             x=c(0,2,1,1,1,0,0),
             sex=c(0,0,0,0,1,1,1))
# Fit a stratified model
myFit <-  survfit( coxph(Surv(time, status) ~ x + strata(sex), test1), data = test1  )
exampleRepoDir <- tempdir()
createEmptyRepo( repoDir = exampleRepoDir )
saveToRepo( myFit , repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )[,-3]
deleteRepo( exampleRepoDir )

rm( exampleRepoDir )

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