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()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.