Tags: Tags
In archivist: Tools for Storing, Restoring and Searching for R Objects

Description Details Contact Note References See Also Examples

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.

Tags are attributes of an artifact. They can be the artifact's name, class or archiving date. Furthermore, for various artifact's classes more different Tags are available.

A Tag is represented as a string and usually has the following structure "TagKey:TagValue", e.g., "name:iris".

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

The list of supported artifacts which are divided thematically is presented below. The newest list is also available on archivist wiki on Github.

Regression Models

lm

name
class
coefname
rank
df.residual
date

summary.lm

name
class
sigma
df
r.squared
adj.r.squared
fstatistic
fstatistic.df
date

glmnet

name
class
dim
nulldev
npasses
offset
nobs
date

survfit

name
class
n
type
conf.type
conf.int
strata
date

Plots

ggplot

name
class
date
labelx
labely

trellis

date
name
class

Results of Agglomeration Methods

twins which is a result of agnes, diana or mona functions

date
name
class
ac

partition which is a result of pam, clara or fanny functions

name
class
memb.exp
dunn_coeff
normalized dunn_coeff
k.crisp
objective
tolerance
iterations
converged
maxit
clus.avg.widths
avg.width
date

lda

name
class
N
lev
counts
prior
svd
date

qda

name
class
N
lev
counts
prior
ldet
terms
date

Statistical Tests

htest

name
class
method
data.name
null.value
alternative
statistic
parameter
p.value
conf.int.
estimate
date

When none of above is specified, Tags are assigned by default

default

name
class
date

data.frame

name
class
date
varname

Bug reports and feature requests can be sent to https://github.com/pbiecek/archivist/issues

In the following way one can specify his own Tags for artifacts by setting artifact's attribute before call of the saveToLocalRepo function: attr(x, "tags" ) = c( "name1", "name2" ), where x is an artifact and name1, name2 are Tags specified by a user. It can be also done in a new, simpler way by using userTags parameter like this:

saveToLocalRepo(model, repoDir, userTags = c("my_model", "do not delete")).

Specifing additional Tags by attributes can be beneficial when one uses addHooksToPrint.

Biecek P and Kosinski M (2017). "archivist: An R Package for Managing, Recording and Restoring Data Analysis Results." _Journal of Statistical Software_, *82*(11), pp. 1-28. doi: 10.18637/jss.v082.i11 (URL: http://doi.org/10.18637/jss.v082.i11). URL https://github.com/pbiecek/archivist

Functions using Tags are:

addTagsRepo
getTagsLocal
getTagsRemote
saveToLocalRepo
searchInLocalRepo,
searchInRemoteRepo.

Other archivist: Repository, %a%(), addHooksToPrint(), addTagsRepo(), aformat(), ahistory(), alink(), aoptions(), archivist-package, areadLocal(), aread(), asearchLocal(), asearch(), asession(), atrace(), cache(), copyLocalRepo(), createLocalRepo(), createMDGallery(), deleteLocalRepo(), getRemoteHook(), getTagsLocal(), loadFromLocalRepo(), md5hash, removeTagsRepo(), restoreLibs(), rmFromLocalRepo(), saveToLocalRepo(), searchInLocalRepo(), setLocalRepo(), shinySearchInLocalRepo(), showLocalRepo(), splitTagsLocal(), summaryLocalRepo(), zipLocalRepo()

## Not run: 
# examples
# data.frame object
data(iris)
exampleRepoDir <- tempfile()
createLocalRepo(repoDir = exampleRepoDir)
saveToLocalRepo( iris, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, deleteRoot=TRUE )

# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( myplot123, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, deleteRoot=TRUE )

# lm object
model <- lm(Sepal.Length~ Sepal.Width + Petal.Length + Petal.Width, 
           data= iris)
exampleRepoDir <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
asave( model, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# agnes (twins) object
library(cluster)
data(votes.repub)
agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
exampleRepoDir <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( agn1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( fannyx, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
asave( lda1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( qda1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )


# glmnet object
library( glmnet )

zk=matrix(rnorm(100*20),100,20)
bk=rnorm(100)
glmnet1=glmnet(zk,bk)
exampleRepoDir <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( glmnet1, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# trellis object
require(stats)
library( lattice)
## 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 run; 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( trellis.plot, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( this.test, repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )
deleteLocalRepo( exampleRepoDir, TRUE )

# survfit object
library( survival )
# 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 <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
saveToLocalRepo( myFit , repoDir=exampleRepoDir )
showLocalRepo( exampleRepoDir, "tags" )[,-3]
deleteLocalRepo( exampleRepoDir, TRUE)

# origin of the artifacts stored as a name - chaining code
library(dplyr)
exampleRepoDir <- tempfile()
createLocalRepo( repoDir = exampleRepoDir )
data("hflights", package = "hflights")
hflights %>%
  group_by(Year, Month, DayofMonth) %>%
  select(Year:DayofMonth, ArrDelay, DepDelay) %>%
  saveToLocalRepo( exampleRepoDir, value = TRUE ) %>%
  # here the artifact is stored but chaining is not finished
  summarise(
    arr = mean(ArrDelay, na.rm = TRUE),
    dep = mean(DepDelay, na.rm = TRUE)
  ) %>%
  filter(arr > 30 | dep > 30) %>%
  saveToLocalRepo( exampleRepoDir ) 
  # chaining code is finished and after last operation the 
  # artifact is stored
showLocalRepo( exampleRepoDir, "tags" )[,-3]
showLocalRepo( exampleRepoDir )
deleteLocalRepo( exampleRepoDir, TRUE)

rm( exampleRepoDir )

## End(Not run)