README.md
In averissimo/verissimo-rpackage: A lot of generic functions that can be reused
Verissimo r-package
With personal functions I like to reuse everytime!
- proper() : Capitalize string using regexpression
- draw.kaplan() : Draw kaplan curves based on Prognostic Index of Risk (calculated by coxph or something else)
- my.colors() : My own pallete
- my.symbols() : Same with symbols to plots
- draw.empty.plot() : Draws an empty plot with grid to add data points or lines afterwards
- balanced_data: get balanced train/test sets and cv folds.
- generate random matrix with pre-determined covariance
- ... check out rest of R folder
Proper
One of such is a proper function that capitalizes a string.
x <- "OnE oF sUcH iS a proPer function that capitalizes a string."
proper(x)
#> [1] "One Of Such Is A Proper Function That Capitalizes A String."
my.colors & my.symbols & draw.empty.plot
my.colors() and my.symbols() can be used to improve plot readability.
In this example, draw.empty.plot is also used to create an empty plot to show data points after.
xdata <- -10:10
draw.empty.plot(xlim = c(min(xdata),max(xdata)), ylim = c(0,23))
for (ix in 1:22) {
points(xdata, 1/10 * xdata * xdata + ix, pch = my.symbols(ix), col = my.colors(ix), cex = .9)
}
draw.kaplan
suppressPackageStartupMessages(library(survival))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(ggfortify))
suppressPackageStartupMessages(library(gridExtra))
data(flchain)
ydata <- data.frame( time = flchain$futime, status = flchain$death)
xdata <- cbind(flchain$age, as.numeric(flchain$sex == 'M') * 2 + 1, flchain$sample.yr, flchain$kappa)
page <- draw.kaplan(list(Age= c(1,0,0,0)), xdata = xdata, ydata = ydata)$plot
psex <- draw.kaplan(list(Sex= c(0,1,0,0)), xdata = xdata, ydata = ydata)$plot
grid.arrange(page, psex, ncol = 2)
#
draw.kaplan(list(Age= c(1,0,0,0), Sex = c(0,1,0,0), yr = c(0,0,1,0), kappa = c(0,0,0,1)), xdata = xdata, ydata = ydata)$plot
Balanced test/train dataset
This is specially relevant in survival or binary output with few cases of one category that need to be well distributed among test/train datasets or in cross-validation folds.
Example below sets aside 90% of the data to the training set. As samples are already divided in two sets (set1 and set2), it performs the 90% separation for each and then joins (with option join.all = T) the result.
set1 <- c(T,T,T,T,T,T,T,T,F,T,T,T,T,T,T,T,T,T,F,T)
set2 <- !set1
cat('Set1\n', set1, '\n\nSet2\n', set2, '\n\nTraining / Test set using logical indices\n\n')
set.seed(1985)
balanced.train.and.test(set1, set2, train.perc = .9)
#
set1 <- which(set1)
set2 <- which(set2)
cat('##### Same sets but using numeric indices\n\n', 'Set1\n', set1, '\n\nSet2\n', set2, '\n\nTraining / Test set using numeric indices\n')
set.seed(1985)
balanced.train.and.test(set1, set2, train.perc = .9)
#
#> Set1
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#>
#> Set2
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#>
#> Training / Test set using logical indices
#>
#> $train
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18
#>
#> $test
#> [1] 12 19 20
#>
#> ##### Same sets but using numeric indices
#>
#> Set1
#> 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 20
#>
#> Set2
#> 9 19
#>
#> Training / Test set using numeric indices
#> $train
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18
#>
#> $test
#> [1] 12 19 20
Generate synthetic matrix with covariance
xdata1 <- gen.synth.xdata(10, 5, .2)
xdata2 <- gen.synth.xdata(10, 5, .75)
#> Using .2^|i-j| to generate co-variance matrix
#> X generated
#> X1 X2 X3 X4 X5
#> 1 0.09689175 0.9468821 1.3552340 1.2824676 0.4190769
#> 2 0.19084771 -0.4300693 1.5705338 0.2006243 -1.2941223
#> 3 0.93644208 -0.3945288 -0.0570492 -0.5691152 0.2626026
#> 4 0.07218033 0.3558250 -1.5559584 -1.0373982 -1.6702955
#> 5 0.49333811 -0.8816076 -0.9225416 -0.4309264 1.4476294
#> 6 0.24457253 -1.6353932 -0.6294087 1.3615857 -0.2112758
#> 7 1.59587882 1.7799506 0.1623247 -0.2918553 -0.1509117
#> 8 -1.42564382 -0.7091118 0.5150353 -1.7232931 -0.1749990
#> 9 -0.48595798 0.6337266 0.3455646 0.3907845 1.4462497
#> 10 -1.71854953 0.3343264 -0.7837344 0.8171261 -0.0739543
#> cov(X)
#> X1 X2 X3 X4 X5
#> 1 1.0000 0.200 0.04 0.008 0.0016
#> 2 0.2000 1.000 0.20 0.040 0.0080
#> 3 0.0400 0.200 1.00 0.200 0.0400
#> 4 0.0080 0.040 0.20 1.000 0.2000
#> 5 0.0016 0.008 0.04 0.200 1.0000
#> Using .75^|i-j| to generate co-variance matrix (plotting correlation)
#> X generated
#> X1 X2 X3 X4 X5
#> 1 0.4208010 0.9286722 1.45831161 1.67371415 1.17461275
#> 2 -1.1038121 -1.2636549 -0.07741947 0.41873089 1.06764561
#> 3 1.7356855 0.4526740 -0.37879089 -0.02327963 0.30411766
#> 4 -1.0318495 -0.6538382 -1.18185378 -0.08377025 -0.08410934
#> 5 0.1145984 -0.2168866 -0.21616338 -0.26522464 -1.74803648
#> 6 1.0900261 1.1550673 1.12052081 -0.12683280 0.60808321
#> 7 -0.2929580 1.1762793 0.21909445 -0.13175354 -0.07821635
#> 8 0.8276833 0.7144643 1.11215892 1.37228251 0.75499461
#> 9 -0.9345230 -1.1943438 -0.46819589 -1.49889929 -1.43608946
#> 10 -0.8256519 -1.0984336 -1.58766238 -1.33496740 -0.56300222
#> cov(X)
#> X1 X2 X3 X4 X5
#> 1 1.0000000 0.750000 0.5625 0.421875 0.3164063
#> 2 0.7500000 1.000000 0.7500 0.562500 0.4218750
#> 3 0.5625000 0.750000 1.0000 0.750000 0.5625000
#> 4 0.4218750 0.562500 0.7500 1.000000 0.7500000
#> 5 0.3164063 0.421875 0.5625 0.750000 1.0000000
Save in cache
Uses a cache to save and retrieve results. The cache is automatically created with the arguments and source code for function, so that if any of those changes, the cache is regenerated.
Caution: Files are not deleted so the cache directory can become rather big.
a <- run.cache(sum, 1, 2)
#> Loading from cache (not calculating): /tmp/runCache/561a/cache-generic_cache-H_561a43a3af7b265aed512a7995a46f89c382f78fdba4170e569495892b0076ba.RData
b <- run.cache(sum, 1, 2)
#> Loading from cache (not calculating): /tmp/runCache/561a/cache-generic_cache-H_561a43a3af7b265aed512a7995a46f89c382f78fdba4170e569495892b0076ba.RData
all(a == b)
#> [1] TRUE
a <- run.cache(rnorm, 5, seed = 1985)
#> Saving in cache: /tmp/runCache/9636/cache-generic_cache-H_96360922babcb9eeb480fabc9811eab598abaf087c10f3ef49e9093607089531.RData
b <- run.cache(rnorm, 5, seed = 2000)
#> Saving in cache: /tmp/runCache/ab76/cache-generic_cache-H_ab768ab59eab0e3848e3f5b8c133baaa381eb1e6d5fda439f10847d911b0ace7.RData
all(a == b)
#> [1] FALSE
RunCache was originaly intended to be used to calculate big correlation matrix
n.rows <- 1000
n.cols <- 50000
xdata <- matrix(rnorm(n.rows * n.cols), ncol = n.cols)
# making sure cache is saved
.Last.value <- run.cache(sapply, 2:n.cols, function(ix) {cor(xdata[,1], xdata[,ix])})
#> Saving in cache: /tmp/runCache/1ca4/cache-generic_cache-H_1ca4cc293760a485015a91c0cfb2e633d366ca6e7317a11309c37dd05926ef0d.RData
runCache.digest <- list(verissimo::digest.cache(xdata))
my.fun <- function(ix) {cor(xdata[,1], xdata[,ix])}
microbenchmark::microbenchmark(
runCache.non.cached = run.cache(sapply, 2:n.cols, my.fun, show.message = FALSE, force.recalc = T),
runCache.cached = run.cache(sapply, 2:n.cols, my.fun, show.message = FALSE),
runCache.cached.speed = run.cache(sapply, 2:n.cols, my.fun, cache.digest = runCache.digest, show.message = FALSE),
actual.function = sapply(2:n.cols, my.fun),
actual.4cores = unlist(parallel::mclapply(2:n.cols, my.fun, mc.cores = 4)),
times = 5)
#> Unit: milliseconds
#> expr min lq mean median
#> runCache.non.cached 3597.887922 3830.686961 4947.4225 3962.969784
#> runCache.cached 12.151663 14.631728 621.8065 15.051005
#> runCache.cached.speed 6.954156 7.332214 989.1665 9.592484
#> actual.function 3988.803092 4286.941875 5834.0053 4686.555011
#> actual.4cores 2599.503680 3317.719993 3297.8353 3438.453918
#> uq max neval cld
#> 6132.18401 7213.384 5 b
#> 19.24390 3047.954 5 a
#> 10.44369 4911.510 5 a
#> 6914.68445 9293.042 5 b
#> 3546.38849 3587.110 5 ab
averissimo/verissimo-rpackage documentation built on May 11, 2019, 4:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Verissimo r-package
With personal functions I like to reuse everytime!
- proper() : Capitalize string using regexpression
- draw.kaplan() : Draw kaplan curves based on Prognostic Index of Risk (calculated by coxph or something else)
- my.colors() : My own pallete
- my.symbols() : Same with symbols to plots
- draw.empty.plot() : Draws an empty plot with grid to add data points or lines afterwards
- balanced_data: get balanced train/test sets and cv folds.
- generate random matrix with pre-determined covariance
- ... check out rest of R folder
Proper
One of such is a proper function that capitalizes a string.
x <- "OnE oF sUcH iS a proPer function that capitalizes a string."
proper(x)
#> [1] "One Of Such Is A Proper Function That Capitalizes A String."
my.colors & my.symbols & draw.empty.plot
my.colors() and my.symbols() can be used to improve plot readability.
In this example, draw.empty.plot is also used to create an empty plot to show data points after.
xdata <- -10:10
draw.empty.plot(xlim = c(min(xdata),max(xdata)), ylim = c(0,23))
for (ix in 1:22) {
points(xdata, 1/10 * xdata * xdata + ix, pch = my.symbols(ix), col = my.colors(ix), cex = .9)
}
draw.kaplan
suppressPackageStartupMessages(library(survival))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(ggfortify))
suppressPackageStartupMessages(library(gridExtra))
data(flchain)
ydata <- data.frame( time = flchain$futime, status = flchain$death)
xdata <- cbind(flchain$age, as.numeric(flchain$sex == 'M') * 2 + 1, flchain$sample.yr, flchain$kappa)
page <- draw.kaplan(list(Age= c(1,0,0,0)), xdata = xdata, ydata = ydata)$plot
psex <- draw.kaplan(list(Sex= c(0,1,0,0)), xdata = xdata, ydata = ydata)$plot
grid.arrange(page, psex, ncol = 2)
#
draw.kaplan(list(Age= c(1,0,0,0), Sex = c(0,1,0,0), yr = c(0,0,1,0), kappa = c(0,0,0,1)), xdata = xdata, ydata = ydata)$plot
Balanced test/train dataset
This is specially relevant in survival or binary output with few cases of one category that need to be well distributed among test/train datasets or in cross-validation folds.
Example below sets aside 90% of the data to the training set. As samples are already divided in two sets (set1 and set2), it performs the 90% separation for each and then joins (with option join.all = T) the result.
set1 <- c(T,T,T,T,T,T,T,T,F,T,T,T,T,T,T,T,T,T,F,T)
set2 <- !set1
cat('Set1\n', set1, '\n\nSet2\n', set2, '\n\nTraining / Test set using logical indices\n\n')
set.seed(1985)
balanced.train.and.test(set1, set2, train.perc = .9)
#
set1 <- which(set1)
set2 <- which(set2)
cat('##### Same sets but using numeric indices\n\n', 'Set1\n', set1, '\n\nSet2\n', set2, '\n\nTraining / Test set using numeric indices\n')
set.seed(1985)
balanced.train.and.test(set1, set2, train.perc = .9)
#
#> Set1
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#>
#> Set2
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#>
#> Training / Test set using logical indices
#>
#> $train
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18
#>
#> $test
#> [1] 12 19 20
#>
#> ##### Same sets but using numeric indices
#>
#> Set1
#> 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 20
#>
#> Set2
#> 9 19
#>
#> Training / Test set using numeric indices
#> $train
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18
#>
#> $test
#> [1] 12 19 20
Generate synthetic matrix with covariance
xdata1 <- gen.synth.xdata(10, 5, .2)
xdata2 <- gen.synth.xdata(10, 5, .75)
#> Using .2^|i-j| to generate co-variance matrix
#> X generated
#> X1 X2 X3 X4 X5
#> 1 0.09689175 0.9468821 1.3552340 1.2824676 0.4190769
#> 2 0.19084771 -0.4300693 1.5705338 0.2006243 -1.2941223
#> 3 0.93644208 -0.3945288 -0.0570492 -0.5691152 0.2626026
#> 4 0.07218033 0.3558250 -1.5559584 -1.0373982 -1.6702955
#> 5 0.49333811 -0.8816076 -0.9225416 -0.4309264 1.4476294
#> 6 0.24457253 -1.6353932 -0.6294087 1.3615857 -0.2112758
#> 7 1.59587882 1.7799506 0.1623247 -0.2918553 -0.1509117
#> 8 -1.42564382 -0.7091118 0.5150353 -1.7232931 -0.1749990
#> 9 -0.48595798 0.6337266 0.3455646 0.3907845 1.4462497
#> 10 -1.71854953 0.3343264 -0.7837344 0.8171261 -0.0739543
#> cov(X)
#> X1 X2 X3 X4 X5
#> 1 1.0000 0.200 0.04 0.008 0.0016
#> 2 0.2000 1.000 0.20 0.040 0.0080
#> 3 0.0400 0.200 1.00 0.200 0.0400
#> 4 0.0080 0.040 0.20 1.000 0.2000
#> 5 0.0016 0.008 0.04 0.200 1.0000
#> Using .75^|i-j| to generate co-variance matrix (plotting correlation)
#> X generated
#> X1 X2 X3 X4 X5
#> 1 0.4208010 0.9286722 1.45831161 1.67371415 1.17461275
#> 2 -1.1038121 -1.2636549 -0.07741947 0.41873089 1.06764561
#> 3 1.7356855 0.4526740 -0.37879089 -0.02327963 0.30411766
#> 4 -1.0318495 -0.6538382 -1.18185378 -0.08377025 -0.08410934
#> 5 0.1145984 -0.2168866 -0.21616338 -0.26522464 -1.74803648
#> 6 1.0900261 1.1550673 1.12052081 -0.12683280 0.60808321
#> 7 -0.2929580 1.1762793 0.21909445 -0.13175354 -0.07821635
#> 8 0.8276833 0.7144643 1.11215892 1.37228251 0.75499461
#> 9 -0.9345230 -1.1943438 -0.46819589 -1.49889929 -1.43608946
#> 10 -0.8256519 -1.0984336 -1.58766238 -1.33496740 -0.56300222
#> cov(X)
#> X1 X2 X3 X4 X5
#> 1 1.0000000 0.750000 0.5625 0.421875 0.3164063
#> 2 0.7500000 1.000000 0.7500 0.562500 0.4218750
#> 3 0.5625000 0.750000 1.0000 0.750000 0.5625000
#> 4 0.4218750 0.562500 0.7500 1.000000 0.7500000
#> 5 0.3164063 0.421875 0.5625 0.750000 1.0000000
Save in cache
Uses a cache to save and retrieve results. The cache is automatically created with the arguments and source code for function, so that if any of those changes, the cache is regenerated.
Caution: Files are not deleted so the cache directory can become rather big.
a <- run.cache(sum, 1, 2)
#> Loading from cache (not calculating): /tmp/runCache/561a/cache-generic_cache-H_561a43a3af7b265aed512a7995a46f89c382f78fdba4170e569495892b0076ba.RData
b <- run.cache(sum, 1, 2)
#> Loading from cache (not calculating): /tmp/runCache/561a/cache-generic_cache-H_561a43a3af7b265aed512a7995a46f89c382f78fdba4170e569495892b0076ba.RData
all(a == b)
#> [1] TRUE
a <- run.cache(rnorm, 5, seed = 1985)
#> Saving in cache: /tmp/runCache/9636/cache-generic_cache-H_96360922babcb9eeb480fabc9811eab598abaf087c10f3ef49e9093607089531.RData
b <- run.cache(rnorm, 5, seed = 2000)
#> Saving in cache: /tmp/runCache/ab76/cache-generic_cache-H_ab768ab59eab0e3848e3f5b8c133baaa381eb1e6d5fda439f10847d911b0ace7.RData
all(a == b)
#> [1] FALSE
RunCache was originaly intended to be used to calculate big correlation matrix
n.rows <- 1000
n.cols <- 50000
xdata <- matrix(rnorm(n.rows * n.cols), ncol = n.cols)
# making sure cache is saved
.Last.value <- run.cache(sapply, 2:n.cols, function(ix) {cor(xdata[,1], xdata[,ix])})
#> Saving in cache: /tmp/runCache/1ca4/cache-generic_cache-H_1ca4cc293760a485015a91c0cfb2e633d366ca6e7317a11309c37dd05926ef0d.RData
runCache.digest <- list(verissimo::digest.cache(xdata))
my.fun <- function(ix) {cor(xdata[,1], xdata[,ix])}
microbenchmark::microbenchmark(
runCache.non.cached = run.cache(sapply, 2:n.cols, my.fun, show.message = FALSE, force.recalc = T),
runCache.cached = run.cache(sapply, 2:n.cols, my.fun, show.message = FALSE),
runCache.cached.speed = run.cache(sapply, 2:n.cols, my.fun, cache.digest = runCache.digest, show.message = FALSE),
actual.function = sapply(2:n.cols, my.fun),
actual.4cores = unlist(parallel::mclapply(2:n.cols, my.fun, mc.cores = 4)),
times = 5)
#> Unit: milliseconds
#> expr min lq mean median
#> runCache.non.cached 3597.887922 3830.686961 4947.4225 3962.969784
#> runCache.cached 12.151663 14.631728 621.8065 15.051005
#> runCache.cached.speed 6.954156 7.332214 989.1665 9.592484
#> actual.function 3988.803092 4286.941875 5834.0053 4686.555011
#> actual.4cores 2599.503680 3317.719993 3297.8353 3438.453918
#> uq max neval cld
#> 6132.18401 7213.384 5 b
#> 19.24390 3047.954 5 a
#> 10.44369 4911.510 5 a
#> 6914.68445 9293.042 5 b
#> 3546.38849 3587.110 5 ab
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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