buildingDB.R

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

deleteRepo( exampleRepoDir )

# data.frame object
data(iris)
exampleRepoDir <- getwd()
createEmptyRepo(repoDir = exampleRepoDir)
saveToRepo( iris, repoDir=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)
saveToRepo( myplot123, repoDir=exampleRepoDir )


# lm object
model <- lm(Sepal.Length~ Sepal.Width + Petal.Length + Petal.Width, 
           data= iris)
saveToRepo( model, repoDir=exampleRepoDir )

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


# 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)
saveToRepo( lda1, repoDir=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)
saveToRepo( qda1, repoDir=exampleRepoDir )


# glmnet object
library( glmnet )
zk=matrix(rnorm(100*20),100,20)
bk=rnorm(100)
glmnet1=glmnet(zk,bk)
saveToRepo( glmnet1, repoDir=exampleRepoDir )


# 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 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")
saveToRepo( trellis.plot, repoDir=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")
saveToRepo( this.test, repoDir=exampleRepoDir )

# 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  )
saveToRepo( myFit , repoDir=exampleRepoDir )

# saveToRepo in chaining code
library(dplyr)

data("hflights", package = "hflights")
hflights %>%
  group_by(Year, Month, DayofMonth) %>%
  select(Year:DayofMonth, ArrDelay, DepDelay) %>%
  saveToRepo( exampleRepoDir, chain = 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) %>%
  saveToRepo( exampleRepoDir )
  # chaining code is finished and after last operation the 
  # artifact is stored