library(drake)
load_basic_example() # Also (over)writes report.Rmd.
plot_graph(my_plan) # Hover, click, drag, zoom, pan.
make(my_plan) # Run the workflow.
make(my_plan) # Check that everything is already up to date.Dive deeper into the built-in examples.
example_drake("basic") # Write the code files.
examples_drake() # List the other examples.
vignette("quickstart") # This vignetteLet’s establish the building blocks of a data analysis workflow.
library(knitr)
library(drake)First, we will generate a few datasets.
simulate <- function(n){
data.frame(
x = stats::rnorm(n),
y = rpois(n, 1)
)
}Then, we will analyze each dataset with multiple analysis methods.
reg1 <- function(d){
lm(y ~ + x, data = d)
}
reg2 <- function(d){
d$x2 <- d$x ^ 2
lm(y ~ x2, data = d)
}We need the source file report.Rmd for the end.k
lines <- c(
"---",
"title: Example Report",
"author: You",
"output: html_document",
"---",
"",
"Look how I read outputs from the drake cache.",
"Drake notices that `small`, `coef_regression2_small`,",
"and `large` are dependencies of the",
"future compiled output report file target, `report.md`.",
"Just be sure that the workflow plan command for the target `'report.md'`",
"has an explicit call to `knit()`, something like `knit('report.Rmd')` or",
"`knitr::knit(input = 'report.Rmd', quiet = TRUE)`.",
"",
"```{r example_chunk}",
"library(drake)",
"readd(small)",
"readd(coef_regression2_small)",
"loadd(large)",
"head(large)",
"```")
writeLines(lines, "report.Rmd")The workflow plan lists the intermediate steps of your project.
load_basic_example()
my_plan## target command
## 1 'report.md' knit('report.Rmd', quiet = TRUE)
## 2 small simulate(5)
## 3 large simulate(50)
## 4 regression1_small reg1(small)
## 5 regression1_large reg1(large)
## 6 regression2_small reg2(small)
## 7 regression2_large reg2(large)
## 8 summ_regression1_small suppressWarnings(summary(regression1_small))
## 9 summ_regression1_large suppressWarnings(summary(regression1_large))
## 10 summ_regression2_small suppressWarnings(summary(regression2_small))
## 11 summ_regression2_large suppressWarnings(summary(regression2_large))
## 12 coef_regression1_small coefficients(regression1_small)
## 13 coef_regression1_large coefficients(regression1_large)
## 14 coef_regression2_small coefficients(regression2_small)
## 15 coef_regression2_large coefficients(regression2_large)Each row is an intermediate step, and each command generates a target. A target is an output R object (cached when generated) or output file (specified with single quotes), and a command just an ordinary piece of R code (not necessarily a single function call). As input, commands may take objects imported from your workspace, targets generated by other commands, or initial input files. These dependencies give your project an underlying network.
# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px")See also dataframes_graph(), render_graph(), and config() for faster and more customized regraphing.
You can also check the dependencies of individual targets.
deps(reg2)## [1] "lm"deps(my_plan$command[1]) # Files like report.Rmd are single-quoted.## [1] "'report.Rmd'" "coef_regression2_small"
## [3] "knit" "large"
## [5] "small"deps(my_plan$command[nrow(my_plan)])## [1] "coefficients" "regression2_large"List all the reproducibly-tracked objects and files, including imports and targets.
tracked(my_plan, targets = "small")## Unloading targets from environment:
## small
## large
## coef_regression2_small## [1] "small" "simulate" "data.frame" "rpois"
## [5] "stats::rnorm"tracked(my_plan)## [1] "'report.md'" "small"
## [3] "large" "regression1_small"
## [5] "regression1_large" "regression2_small"
## [7] "regression2_large" "summ_regression1_small"
## [9] "summ_regression1_large" "summ_regression2_small"
## [11] "summ_regression2_large" "coef_regression1_small"
## [13] "coef_regression1_large" "coef_regression2_small"
## [15] "coef_regression2_large" "reg1"
## [17] "reg2" "simulate"
## [19] "'report.Rmd'" "knit"
## [21] "summary" "suppressWarnings"
## [23] "coefficients" "lm"
## [25] "data.frame" "rpois"
## [27] "stats::rnorm"Check for cycles, missing input files, and other pitfalls.
check(my_plan)The data frame my_plan would be a pain to write by hand, so drake has functions to help you.
my_datasets <- plan(
small = simulate(5),
large = simulate(50))
my_datasets## target command
## 1 small simulate(5)
## 2 large simulate(50)For multiple replicates:
expand(my_datasets, values = c("rep1", "rep2"))## target command
## 1 small_rep1 simulate(5)
## 2 small_rep2 simulate(5)
## 3 large_rep1 simulate(50)
## 4 large_rep2 simulate(50)Each dataset is analyzed multiple ways.
methods <- plan(
regression1 = reg1(..dataset..), # nolint
regression2 = reg2(..dataset..)) # nolint
methods## target command
## 1 regression1 reg1(..dataset..)
## 2 regression2 reg2(..dataset..)We evaluate the ..dataset.. wildcard.
my_analyses <- analyses(methods, data = my_datasets)
my_analyses## target command
## 1 regression1_small reg1(small)
## 2 regression1_large reg1(large)
## 3 regression2_small reg2(small)
## 4 regression2_large reg2(large)Next, we summarize each analysis of each dataset using summary statistics and regression coefficients.
summary_types <- plan(
summ = suppressWarnings(summary(..analysis..)), # nolint
coef = coefficients(..analysis..)) # nolint
summary_types## target command
## 1 summ suppressWarnings(summary(..analysis..))
## 2 coef coefficients(..analysis..)results <- summaries(summary_types, analyses = my_analyses,
datasets = my_datasets, gather = NULL)
results## target command
## 1 summ_regression1_small suppressWarnings(summary(regression1_small))
## 2 summ_regression1_large suppressWarnings(summary(regression1_large))
## 3 summ_regression2_small suppressWarnings(summary(regression2_small))
## 4 summ_regression2_large suppressWarnings(summary(regression2_large))
## 5 coef_regression1_small coefficients(regression1_small)
## 6 coef_regression1_large coefficients(regression1_large)
## 7 coef_regression2_small coefficients(regression2_small)
## 8 coef_regression2_large coefficients(regression2_large)The gather feature groups summaries into a smaller number of more manageable targets. I shut it off here to make the data frames more readable.
For the dynamic report, we have to make sure the files are single-quoted. Single quotes denote file targets and file imports, and double quotes denote literal strings that should not be treated as dependencies where they are mentioned. Also, knit() needs to be somewhere visible in the workflow plan command so that drake knows to dig into the active code chunks of 'report.Rmd' and look for dependencies mentioned in calls to loadd() and readd().
report <- plan(
report.md = knit('report.Rmd', quiet = TRUE), # nolint
file_targets = TRUE, strings_in_dots = "filenames")
report## target command
## 1 'report.md' knit('report.Rmd', quiet = TRUE)Finally, gather your workflow together with rbind(). Row order does not matter.
my_plan <- rbind(report, my_datasets, my_analyses, results)
my_plan## target command
## 1 'report.md' knit('report.Rmd', quiet = TRUE)
## 2 small simulate(5)
## 3 large simulate(50)
## 4 regression1_small reg1(small)
## 5 regression1_large reg1(large)
## 6 regression2_small reg2(small)
## 7 regression2_large reg2(large)
## 8 summ_regression1_small suppressWarnings(summary(regression1_small))
## 9 summ_regression1_large suppressWarnings(summary(regression1_large))
## 10 summ_regression2_small suppressWarnings(summary(regression2_small))
## 11 summ_regression2_large suppressWarnings(summary(regression2_large))
## 12 coef_regression1_small coefficients(regression1_small)
## 13 coef_regression1_large coefficients(regression1_large)
## 14 coef_regression2_small coefficients(regression2_small)
## 15 coef_regression2_large coefficients(regression2_large)If your workflow does not fit the rigid datasets/analyses/summaries framework, check out functions expand(), evaluate(), and gather().
df <- plan(data = simulate(center = MU, scale = SIGMA))
df## target command
## 1 data simulate(center = MU, scale = SIGMA)df <- expand(df, values = c("rep1", "rep2"))
df## target command
## 1 data_rep1 simulate(center = MU, scale = SIGMA)
## 2 data_rep2 simulate(center = MU, scale = SIGMA)evaluate(df, wildcard = "MU", values = 1:2)## target command
## 1 data_rep1_1 simulate(center = 1, scale = SIGMA)
## 2 data_rep1_2 simulate(center = 2, scale = SIGMA)
## 3 data_rep2_1 simulate(center = 1, scale = SIGMA)
## 4 data_rep2_2 simulate(center = 2, scale = SIGMA)evaluate(df, wildcard = "MU", values = 1:2, expand = FALSE)## target command
## 1 data_rep1 simulate(center = 1, scale = SIGMA)
## 2 data_rep2 simulate(center = 2, scale = SIGMA)evaluate(df, rules = list(MU = 1:2, SIGMA = c(0.1, 1)), expand = FALSE)## target command
## 1 data_rep1 simulate(center = 1, scale = 0.1)
## 2 data_rep2 simulate(center = 2, scale = 1)evaluate(df, rules = list(MU = 1:2, SIGMA = c(0.1, 1, 10)))## target command
## 1 data_rep1_1_0.1 simulate(center = 1, scale = 0.1)
## 2 data_rep1_1_1 simulate(center = 1, scale = 1)
## 3 data_rep1_1_10 simulate(center = 1, scale = 10)
## 4 data_rep1_2_0.1 simulate(center = 2, scale = 0.1)
## 5 data_rep1_2_1 simulate(center = 2, scale = 1)
## 6 data_rep1_2_10 simulate(center = 2, scale = 10)
## 7 data_rep2_1_0.1 simulate(center = 1, scale = 0.1)
## 8 data_rep2_1_1 simulate(center = 1, scale = 1)
## 9 data_rep2_1_10 simulate(center = 1, scale = 10)
## 10 data_rep2_2_0.1 simulate(center = 2, scale = 0.1)
## 11 data_rep2_2_1 simulate(center = 2, scale = 1)
## 12 data_rep2_2_10 simulate(center = 2, scale = 10)gather(df)## target command
## 1 target list(data_rep1 = data_rep1, data_rep2 = data_rep2)gather(df, target = "my_summaries", gather = "rbind")## target command
## 1 my_summaries rbind(data_rep1 = data_rep1, data_rep2 = data_rep2)You may want to check for outdated or missing targets/imports first.
outdated(my_plan, verbose = FALSE) # Targets that need to be (re)built.## [1] "'report.md'" "coef_regression1_large"
## [3] "coef_regression1_small" "coef_regression2_large"
## [5] "coef_regression2_small" "large"
## [7] "regression1_large" "regression1_small"
## [9] "regression2_large" "regression2_small"
## [11] "small" "summ_regression1_large"
## [13] "summ_regression1_small" "summ_regression2_large"
## [15] "summ_regression2_small"missed(my_plan, verbose = FALSE) # Checks your workspace.Then just make(my_plan).
make(my_plan)## check 9 items: 'report.Rmd', knit, summary, suppressWarnings, coefficients, l...
## import 'report.Rmd'
## import knit
## import summary
## import suppressWarnings
## import coefficients
## import lm
## import data.frame
## import rpois
## import stats::rnorm
## check 3 items: reg1, reg2, simulate
## import reg1
## import reg2
## import simulate
## check 2 items: small, large
## target small
## target large
## check 4 items: regression1_small, regression1_large, regression2_small, regre...
## target regression1_small
## target regression1_large
## target regression2_small
## target regression2_large
## check 8 items: summ_regression1_small, summ_regression1_large, summ_regressio...
## target summ_regression1_small
## target summ_regression1_large
## target summ_regression2_small
## target summ_regression2_large
## target coef_regression1_small
## target coef_regression1_large
## target coef_regression2_small
## target coef_regression2_large
## check 1 item: 'report.md'
## unload 11 items: regression1_small, regression1_large, regression2_small, reg...
## target 'report.md'The non-file dependencies of your last target are already loaded in your workspace.
ls()## [1] "coef_regression2_small" "command"
## [3] "datasets" "df"
## [5] "envir" "f"
## [7] "files" "large"
## [9] "lines" "methods"
## [11] "my_analyses" "my_datasets"
## [13] "my_plan" "myplan"
## [15] "reg1" "reg2"
## [17] "report" "results"
## [19] "rules" "simulate"
## [21] "small" "summary_types"outdated(my_plan, verbose = FALSE) # Everything is up to date.
build_times(digits = 4) # How long did it take to make each target?## item type elapsed user system
## 1 'report.Rmd' import 0.003s 0.001s 0.002s
## 2 'report.md' target 0.037s 0.035s 0.002s
## 3 coef_regression1_large target 0.004s 0.003s 0.001s
## 4 coef_regression1_small target 0.004s 0.003s 0.001s
## 5 coef_regression2_large target 0.003s 0.002s 0.001s
## 6 coef_regression2_small target 0.004s 0.004s 0s
## 7 coefficients import 0.003s 0.003s 0s
## 8 data.frame import 0.005s 0.005s 0s
## 9 knit import 0.004s 0.005s 0s
## 10 large target 0.004s 0.004s 0s
## 11 lm import 0.003s 0.003s 0s
## 12 reg1 import 0.003s 0.003s 0s
## 13 reg2 import 0.002s 0.003s 0s
## 14 regression1_large target 0.004s 0.005s 0s
## 15 regression1_small target 0.007s 0.006s 0.001s
## 16 regression2_large target 0.005s 0.005s 0s
## 17 regression2_small target 0.005s 0.005s 0s
## 18 rpois import 0.003s 0.003s 0s
## 19 simulate import 0.002s 0.001s 0.001s
## 20 small target 0.004s 0.004s 0s
## 21 stats::rnorm import 0.003s 0.003s 0.001s
## 22 summ_regression1_large target 0.004s 0.002s 0.002s
## 23 summ_regression1_small target 0.005s 0.005s 0s
## 24 summ_regression2_large target 0.004s 0.002s 0.002s
## 25 summ_regression2_small target 0.004s 0.003s 0.001s
## 26 summary import 0.005s 0.005s 0s
## 27 suppressWarnings import 0.004s 0.003s 0.001sSee also predict_runtime() and rate_limiting_times().
In the new graph, the red nodes from before are now green.
# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px")Optionally, get visNetwork nodes and edges so you can make your own plot with visNetwork or render_graph().
dataframes_graph(my_plan)Use readd() and loadd() to load more targets. (They are cached in the hidden .drake/ folder using storr). Other functions interact and view the cache.
readd(coef_regression2_large)## (Intercept) x2
## 1.0158230 -0.1823097loadd(small)
head(small)## x y
## 1 1.1134271 0
## 2 -0.3582157 2
## 3 0.3513026 1
## 4 0.5556090 0
## 5 0.5466293 2rm(small)
cached(small, large)## small large
## TRUE TRUEcached()## [1] "'report.Rmd'" "'report.md'"
## [3] "coef_regression1_large" "coef_regression1_small"
## [5] "coef_regression2_large" "coef_regression2_small"
## [7] "coefficients" "data.frame"
## [9] "knit" "large"
## [11] "lm" "reg1"
## [13] "reg2" "regression1_large"
## [15] "regression1_small" "regression2_large"
## [17] "regression2_small" "rpois"
## [19] "simulate" "small"
## [21] "stats::rnorm" "summ_regression1_large"
## [23] "summ_regression1_small" "summ_regression2_large"
## [25] "summ_regression2_small" "summary"
## [27] "suppressWarnings"built()## [1] "'report.md'" "coef_regression1_large"
## [3] "coef_regression1_small" "coef_regression2_large"
## [5] "coef_regression2_small" "large"
## [7] "regression1_large" "regression1_small"
## [9] "regression2_large" "regression2_small"
## [11] "small" "summ_regression1_large"
## [13] "summ_regression1_small" "summ_regression2_large"
## [15] "summ_regression2_small"imported()## [1] "'report.Rmd'" "coefficients" "data.frame"
## [4] "knit" "lm" "reg1"
## [7] "reg2" "rpois" "simulate"
## [10] "stats::rnorm" "summary" "suppressWarnings"head(read_plan())## target command
## 1 'report.md' knit('report.Rmd', quiet = TRUE)
## 2 small simulate(5)
## 3 large simulate(50)
## 4 regression1_small reg1(small)
## 5 regression1_large reg1(large)
## 6 regression2_small reg2(small)head(progress()) # See also in_progress()## 'report.Rmd' 'report.md' coef_regression1_large
## "finished" "finished" "finished"
## coef_regression1_small coef_regression2_large coef_regression2_small
## "finished" "finished" "finished"progress(large)## large
## "finished"session() # of the last call to make()## R version 3.4.0 (2017-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.9 (Santiago)
##
## Matrix products: default
## BLAS: /lrlhps/apps/R/qualified/R-3.4.0/lib64/R/lib/libRblas.so
## LAPACK: /lrlhps/apps/R/qualified/R-3.4.0/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US LC_NUMERIC=C LC_TIME=en_US
## [4] LC_COLLATE=C LC_MONETARY=en_US LC_MESSAGES=en_US
## [7] LC_PAPER=en_US LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.17 magrittr_1.5 drake_4.3.0
##
## loaded via a namespace (and not attached):
## [1] igraph_1.1.2 Rcpp_0.12.13 R6_2.2.2
## [4] rlang_0.1.2 stringr_1.2.0 storr_1.1.2
## [7] plyr_1.8.4 visNetwork_2.0.1 tools_3.4.0
## [10] parallel_3.4.0 R.oo_1.21.0 eply_0.1.0
## [13] withr_2.0.0 htmltools_0.3.6 yaml_2.1.14
## [16] rprojroot_1.2 digest_0.6.12 crayon_1.3.4
## [19] htmlwidgets_0.9 clisymbols_1.2.0 R.utils_2.5.0
## [22] codetools_0.2-15 testthat_1.0.2.9000 evaluate_0.10.1
## [25] rmarkdown_1.6 stringi_1.1.5 compiler_3.4.0
## [28] backports_1.1.0 R.methodsS3_1.7.1 jsonlite_1.5
## [31] lubridate_1.6.0 pkgconfig_2.0.1The next time you run make(my_plan), nothing will be built because drake knows everything is up to date.
make(my_plan)## check 9 items: 'report.Rmd', knit, summary, suppressWarnings, coefficients, d...
## import 'report.Rmd'
## import knit
## import summary
## import suppressWarnings
## import coefficients
## import data.frame
## import rpois
## import stats::rnorm
## import lm
## check 3 items: simulate, reg1, reg2
## import simulate
## import reg1
## import reg2
## check 2 items: small, large
## check 4 items: regression1_small, regression1_large, regression2_small, regre...
## check 8 items: summ_regression1_small, summ_regression1_large, summ_regressio...
## check 1 item: 'report.md'But if you change one of your functions, commands, or other dependencies, drake will update the affected parts of the workflow. Let’s say we want to change the quadratic term to a cubic term in our reg2() function.
reg2 <- function(d) {
d$x3 <- d$x ^ 3
lm(y ~ x3, data = d)
}The targets depending on reg2() need to be rebuilt and everything else is left alone.
outdated(my_plan, verbose = FALSE)## [1] "'report.md'" "coef_regression2_large"
## [3] "coef_regression2_small" "regression2_large"
## [5] "regression2_small" "summ_regression2_large"
## [7] "summ_regression2_small"# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px")make(my_plan)## check 9 items: 'report.Rmd', knit, summary, suppressWarnings, coefficients, d...
## import 'report.Rmd'
## import knit
## import summary
## import suppressWarnings
## import coefficients
## import data.frame
## import rpois
## import stats::rnorm
## import lm
## check 3 items: simulate, reg1, reg2
## import simulate
## import reg1
## import reg2
## check 2 items: small, large
## check 4 items: regression1_small, regression1_large, regression2_small, regre...
## load 2 items: large, small
## target regression2_small
## target regression2_large
## check 8 items: summ_regression1_small, summ_regression1_large, summ_regressio...
## target summ_regression2_small
## target summ_regression2_large
## target coef_regression2_small
## target coef_regression2_large
## check 1 item: 'report.md'
## unload 5 items: regression2_small, regression2_large, summ_regression2_small,...
## target 'report.md'But trivial changes to whitespace and comments are totally ignored in your functions and in my_plan$command.
reg2 <- function(d) {
d$x3 <- d$x ^ 3
lm(y ~ x3, data = d) # I indented here.
}
outdated(my_plan, verbose = FALSE) # Everything is up to date.Need to add new work on the fly? Just append rows to the workflow plan. If the rest of your workflow is up to date, only the new work is run.
new_simulation <- function(n){
data.frame(x = rnorm(n), y = rnorm(n))
}
additions <- plan(
new_data = new_simulation(36) + sqrt(10))
additions## target command
## 1 new_data new_simulation(36) + sqrt(10)my_plan <- rbind(my_plan, additions)
my_plan## target command
## 1 'report.md' knit('report.Rmd', quiet = TRUE)
## 2 small simulate(5)
## 3 large simulate(50)
## 4 regression1_small reg1(small)
## 5 regression1_large reg1(large)
## 6 regression2_small reg2(small)
## 7 regression2_large reg2(large)
## 8 summ_regression1_small suppressWarnings(summary(regression1_small))
## 9 summ_regression1_large suppressWarnings(summary(regression1_large))
## 10 summ_regression2_small suppressWarnings(summary(regression2_small))
## 11 summ_regression2_large suppressWarnings(summary(regression2_large))
## 12 coef_regression1_small coefficients(regression1_small)
## 13 coef_regression1_large coefficients(regression1_large)
## 14 coef_regression2_small coefficients(regression2_small)
## 15 coef_regression2_large coefficients(regression2_large)
## 16 new_data new_simulation(36) + sqrt(10)make(my_plan)## check 11 items: 'report.Rmd', knit, summary, suppressWarnings, coefficients, ...
## import 'report.Rmd'
## import knit
## import summary
## import suppressWarnings
## import coefficients
## import sqrt
## import data.frame
## import rnorm
## import rpois
## import stats::rnorm
## import lm
## check 4 items: new_simulation, simulate, reg1, reg2
## import new_simulation
## import simulate
## import reg1
## import reg2
## check 3 items: small, large, new_data
## target new_data
## check 4 items: regression1_small, regression1_large, regression2_small, regre...
## check 8 items: summ_regression1_small, summ_regression1_large, summ_regressio...
## check 1 item: 'report.md'If you ever need to erase your work, use clean(). Any targets removed from the cache will have to be rebuilt on the next call to make(), so be careful.
clean(small, reg1) # uncaches individual targets and imported objects
clean() # cleans all targets out of the cache
clean(destroy = TRUE) # removes the cache entirelyAs you have seen with reg2(), drake reacts to changes. In other words, make() notices when your dependencies are different from last time, rebuilds any affected targets, and continues downstream. In particular, drake watches for nontrivial changes to
knit('your_report.Rmd') as a command in your workflow plan data frame), targets and imports mentioned in calls to readd() and loadd() in the code chunks to be evaluated. Drake treats these targets and imports as dependencies of the compiled output target (say, ‘report.md’).To explore the dependencies, please refer to the deps() and tracked() functions. Better yet, generate interactive graphs with plot_graph() as shown above. Hover over the nodes in the graph to see the content that drake watches.
There is more to reproducibility than just using drake to watch for dependencies. Packrat creates a tightly-controlled local library of packages to extend the shelf life of your project. And with Docker, you can execute your project on a virtual machine to ensure platform independence. Together, packrat and Docker can help others reproduce your work even if they have different software and hardware.
Drake has extensive high-performance computing support, from local multicore computing on your laptop to serious supercomputing across multiple nodes of a large cluster. In make(), just set the jobs argument to something greater than 1. That unlocks local multicore parallelism. For large-scale distributed parallelism, set parallelism to "Makefile" and follow the instructions in the parallelism vignette.