Tags

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.

Details

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

Note

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:

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

Contact

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

References

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

See also

Functions using Tags are:

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

Examples



# 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 )
# }