Quick examples

Inspect and run your project.

library(drake)
load_basic_example()            # Get the code with drake_example("basic").
config <- drake_config(my_plan) # Master configuration list
vis_drake_graph(config)         # Hover, click, drag, zoom, pan.
make(my_plan)                   # Run the workflow.
outdated(config)                # Everything is up to date.

Debug errors.

failed()                 # Targets that failed in the most recent `make()`
diagnose()               # Targets that failed in any previous `make()`
error <- diagnose(large) # Most recent verbose error log of `large`
str(error)               # Object of class "error"
error$calls              # Call stack / traceback

Dive deeper into the built-in examples.

drake_example("basic") # Write the code files.
drake_examples()       # List the other examples.
vignette("quickstart") # This vignette

The motivation of the basic example

Is there an association between the weight and the fuel efficiency of cars? To find out, we use the mtcars dataset from the datasets package. The mtcars dataset originally came from the 1974 Motor Trend US magazine, and it contains design and performance data on 32 models of automobile.

# ?mtcars # more info
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Here, wt is weight in tons, and mpg is fuel efficiency in miles per gallon. We want to figure out if there is an association between wt and mpg. The mtcars dataset itself only has 32 rows, so we generate two larger bootstrapped datasets and then analyze them with regression models. We summarize the regression models to see if there is an association.

Set up the basic example

Before you run your project, you need to set up the workspace. In other words, you need to gather the “imports”: functions, pre-loaded data objects, and saved files that you want to be available before the real work begins.

library(knitr) # Drake knows which packages you load.
library(drake)

We need a function to bootstrap larger datasets from mtcars.

simulate <- function(n){
  # Pick a random set of cars to bootstrap from the mtcars data.
  index <- sample.int(n = nrow(mtcars), size = n, replace = TRUE)
  data <- mtcars[index, ]

  # x is the car's weight, and y is the fuel efficiency.
  data.frame(
    x = data$wt,
    y = data$mpg
  )
}

We also need functions to apply the regression models we need for detecting associations.

# Is fuel efficiency linearly related to weight?
reg1 <- function(d){
  lm(y ~ + x, data = d)
}

# Is fuel efficiency related to the SQUARE of the weight?
reg2 <- function(d){
  d$x2 <- d$x ^ 2
  lm(y ~ x2, data = d)
}

We want to summarize the final results in an R Markdown report, so we need the following report.Rmd source file.

path <- file.path("examples", "basic", "report.Rmd")
report_file <- system.file(path, package = "drake", mustWork = TRUE)
file.copy(from = report_file, to = getwd(), overwrite = TRUE)
## [1] TRUE

Here are the contents of the report. It will serve as a final summary of our work, and we will process it at the very end. Admittedly, some of the text spoils the punch line.

cat(readLines("report.Rmd"), sep = "\n")
## ---
## title: "Final results report for the basic example"
## author: You
## output: html_document
## ---
## 
## # The weight and fuel efficiency of cars
## 
## Is there an association between the weight and the fuel efficiency of cars? To find out, we use the `mtcars` dataset from the `datasets` package. The `mtcars` data originally came from the 1974 Motor Trend US magazine, and it contains design and performance data on 32 models of automobile.
## 
## ```{r showmtcars}
## # ?mtcars # more info
## head(mtcars)
## ```
## 
## Here, `wt` is weight in tons, and `mpg` is fuel efficiency in miles per gallon. We want to figure out if there is an association between `wt` and `mpg`. The `mtcars` dataset itself only has 32 rows, so we generated two larger bootstrapped datasets. We called them `small` and `large`.
## 
## ```{r example_chunk}
## library(drake)
## head(readd(small)) # 48 rows
## loadd(large)       # 64 rows
## head(large)
## ```
## 
## Then, we fit a couple regression models to the `small` and `large` to try to detect an association between `wt` and `mpg`. Here are the coefficients and p-values from one of the model fits.
## 
## ```{r second_example_chunk}
## readd(coef_regression2_small)
## ```
## 
## Since the p-value on `x2` is so small, there may be an association between weight and fuel efficiency after all.
## 
## # A note on knitr reports in drake projects.
## 
## Because of the calls to `readd()` and `loadd()`, `drake` knows that `small`, `large`, and `coef_regression2_small` are dependencies of this R Markdown report. This dependency relationship is what causes the report to be processed at the very end.

Now, all our imports are set up. When the real work begins, drake will import functions and data objects from your R session environment

ls()
##  [1] "Produc"                 "analysis_methods"      
##  [3] "analysis_plan"          "b"                     
##  [5] "bad_plan"               "coef_regression2_small"
##  [7] "combos"                 "command"               
##  [9] "commands"               "config"                
## [11] "data_plan"              "dataset_plan"          
## [13] "datasets"               "debug_plan"            
## [15] "envir"                  "error"                 
## [17] "f"                      "files"                 
## [19] "get_rmspe"              "good_plan"             
## [21] "large"                  "local"                 
## [23] "make_my_plot"           "make_my_table"         
## [25] "model_plan"             "my_plan"               
## [27] "myplan"                 "output_plan"           
## [29] "output_types"           "package_list"          
## [31] "path"                   "plot_rmspe"            
## [33] "predictors"             "recent"                
## [35] "reg1"                   "reg2"                  
## [37] "report_file"            "report_plan"           
## [39] "reportfile"             "results"               
## [41] "rmspe"                  "rmspe_plan"            
## [43] "rmspe_results_plan"     "rules"                 
## [45] "simulate"               "small"                 
## [47] "targets"                "tmp"                   
## [49] "whole_plan"             "x"

and saved files from your file system.

list.files()
##  [1] "best-practices.R"      "best-practices.Rmd"   
##  [3] "best-practices.html"   "best-practices.md"    
##  [5] "caution.R"             "caution.Rmd"          
##  [7] "caution.html"          "caution.md"           
##  [9] "debug.R"               "debug.Rmd"            
## [11] "debug.html"            "debug.md"             
## [13] "drake.R"               "drake.Rmd"            
## [15] "drake.html"            "drake.md"             
## [17] "example-gsp.R"         "example-gsp.Rmd"      
## [19] "example-gsp.html"      "example-gsp.md"       
## [21] "example-packages.R"    "example-packages.Rmd" 
## [23] "example-packages.html" "example-packages.md"  
## [25] "figure"                "graph.R"              
## [27] "graph.Rmd"             "graph.html"           
## [29] "graph.md"              "logo-vignettes.png"   
## [31] "parallelism.R"         "parallelism.Rmd"      
## [33] "parallelism.html"      "parallelism.md"       
## [35] "quickstart.R"          "quickstart.Rmd"       
## [37] "report.R"              "report.Rmd"           
## [39] "storage.Rmd"           "timing.Rmd"

The workflow plan data frame

Now that your workspace of imports is prepared, we can outline the real work step by step in a workflow plan data frame.

load_basic_example() # Get the code with drake_example("basic").
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## Unloading targets from environment:
##   small
##   large
##   coef_regression2_small
## connect 47 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
my_plan
##                    target
## 1             'report.md'
## 2                   small
## 3                   large
## 4       regression1_small
## 5       regression1_large
## 6       regression2_small
## 7       regression2_large
## 8  summ_regression1_small
## 9  summ_regression1_large
## 10 summ_regression2_small
## 11 summ_regression2_large
## 12 coef_regression1_small
## 13 coef_regression1_large
## 14 coef_regression2_small
## 15 coef_regression2_large
##                                                      command
## 1                           knit('report.Rmd', quiet = TRUE)
## 2                                               simulate(48)
## 3                                               simulate(64)
## 4                                                reg1(small)
## 5                                                reg1(large)
## 6                                                reg2(small)
## 7                                                reg2(large)
## 8     suppressWarnings(summary(regression1_small$residuals))
## 9     suppressWarnings(summary(regression1_large$residuals))
## 10    suppressWarnings(summary(regression2_small$residuals))
## 11    suppressWarnings(summary(regression2_large$residuals))
## 12 suppressWarnings(summary(regression1_small))$coefficients
## 13 suppressWarnings(summary(regression1_large))$coefficients
## 14 suppressWarnings(summary(regression2_small))$coefficients
## 15 suppressWarnings(summary(regression2_large))$coefficients

Each row is an intermediate step, and each command generates a single target. A target is an output R object (cached when generated) or an output file (specified with single quotes), and a command just an ordinary piece of R code (not necessarily a single function call). Commands make use of R objects imported from your workspace, targets generated by other commands, and initial input files. These dependencies give your project an underlying network representation.

# Hover, click, drag, zoom, and pan.
config <- drake_config(my_plan)
vis_drake_graph(config, width = "100%", height = "500px") # Also drake_graph()

You can also check the dependencies of individual targets and imported functions.

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] "regression2_large" "summary"           "suppressWarnings"

List all the reproducibly-tracked objects and files.

tracked(my_plan, targets = "small")
## connect 47 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
## [1] "small"      "simulate"   "data.frame" "mtcars"     "nrow"      
## [6] "sample.int"

tracked(my_plan)
## connect 47 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
##  [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] "data.frame"             "lm"                    
## [25] "mtcars"                 "nrow"                  
## [27] "sample.int"

Check for circular reasoning, missing input files, and other pitfalls.

check_plan(my_plan)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## connect 47 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...

Generate the workflow plan

The workflow plan data frame my_plan would be a pain to write by hand, so drake has functions to help you. Here are the commands to generate the bootstrapped datasets.

my_datasets <- drake_plan(
  small = simulate(48),
  large = simulate(64))
my_datasets
##   target      command
## 1  small simulate(48)
## 2  large simulate(64)

For multiple replicates:

expand_plan(my_datasets, values = c("rep1", "rep2"))
##       target      command
## 1 small_rep1 simulate(48)
## 2 small_rep2 simulate(48)
## 3 large_rep1 simulate(64)
## 4 large_rep2 simulate(64)

Here is a template for applying our regression models to our bootstrapped datasets.

methods <- drake_plan(
  regression1 = reg1(dataset__),
  regression2 = reg2(dataset__))
methods
##        target         command
## 1 regression1 reg1(dataset__)
## 2 regression2 reg2(dataset__)

We evaluate the dataset__ wildcard to generate all the regression commands we need.

my_analyses <- plan_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. We calculate descriptive statistics on the residuals, and we collect the regression coefficients and their p-values.

summary_types <- drake_plan(
  summ = suppressWarnings(summary(analysis__$residuals)),
  coef = suppressWarnings(summary(analysis__))$coefficients
)
summary_types
##   target                                            command
## 1   summ    suppressWarnings(summary(analysis__$residuals))
## 2   coef suppressWarnings(summary(analysis__))$coefficients

results <- plan_summaries(summary_types, analyses = my_analyses,
  datasets = my_datasets, gather = NULL)
results
##                   target
## 1 summ_regression1_small
## 2 summ_regression1_large
## 3 summ_regression2_small
## 4 summ_regression2_large
## 5 coef_regression1_small
## 6 coef_regression1_large
## 7 coef_regression2_small
## 8 coef_regression2_large
##                                                     command
## 1    suppressWarnings(summary(regression1_small$residuals))
## 2    suppressWarnings(summary(regression1_large$residuals))
## 3    suppressWarnings(summary(regression2_small$residuals))
## 4    suppressWarnings(summary(regression2_large$residuals))
## 5 suppressWarnings(summary(regression1_small))$coefficients
## 6 suppressWarnings(summary(regression1_large))$coefficients
## 7 suppressWarnings(summary(regression2_small))$coefficients
## 8 suppressWarnings(summary(regression2_large))$coefficients

The gather feature reduces a collection of targets to a single target. The resulting commands are long, so gathering is deactivated for the sake of readability.

For the dynamic report 'report.Rmd'/'report.md', be sure the file names are single-quoted. Single quotes denote file targets/imports, and double quotes denote literal strings that should not be treated as dependencies. To tell drake to look for the other dependencies of 'report.md', be sure the source file 'report.Rmd' exists and knit() is in the workflow plan command. That way, drake searches the active code chunks in 'report.Rmd' for any targets/imports mentioned in calls to loadd() and readd().

report <- drake_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, consolidate your workflow using rbind(). Row order does not matter.

my_plan <- rbind(report, my_datasets, my_analyses, results)
my_plan
##                    target
## 1             'report.md'
## 2                   small
## 3                   large
## 4       regression1_small
## 5       regression1_large
## 6       regression2_small
## 7       regression2_large
## 8  summ_regression1_small
## 9  summ_regression1_large
## 10 summ_regression2_small
## 11 summ_regression2_large
## 12 coef_regression1_small
## 13 coef_regression1_large
## 14 coef_regression2_small
## 15 coef_regression2_large
##                                                      command
## 1                           knit('report.Rmd', quiet = TRUE)
## 2                                               simulate(48)
## 3                                               simulate(64)
## 4                                                reg1(small)
## 5                                                reg1(large)
## 6                                                reg2(small)
## 7                                                reg2(large)
## 8     suppressWarnings(summary(regression1_small$residuals))
## 9     suppressWarnings(summary(regression1_large$residuals))
## 10    suppressWarnings(summary(regression2_small$residuals))
## 11    suppressWarnings(summary(regression2_large$residuals))
## 12 suppressWarnings(summary(regression1_small))$coefficients
## 13 suppressWarnings(summary(regression1_large))$coefficients
## 14 suppressWarnings(summary(regression2_small))$coefficients
## 15 suppressWarnings(summary(regression2_large))$coefficients

Flexible workflow plan generation

If your workflow does not fit the rigid datasets/analyses/summaries framework, consider using functions expand_plan(), evaluate_plan(), and gather_plan().

df <- drake_plan(data = simulate(center = MU, scale = SIGMA))
df
##   target                              command
## 1   data simulate(center = MU, scale = SIGMA)

df <- expand_plan(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_plan(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_plan(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_plan(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_plan(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_plan(df)
##   target                                            command
## 1 target list(data_rep1 = data_rep1, data_rep2 = data_rep2)

gather_plan(df, target = "my_summaries", gather = "rbind")
##         target                                             command
## 1 my_summaries rbind(data_rep1 = data_rep1, data_rep2 = data_rep2)

Run the workflow

You may want to check for outdated or missing targets/imports first.

config <- drake_config(my_plan, verbose = FALSE)
outdated(config) # 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(config) # Checks your workspace.
## character(0)

Then just make(my_plan).

make(my_plan)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## connect 53 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
## check 9 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, sample.int, ...
## check 3 items: reg1, reg2, simulate
## check 2 items: large, small
## target large
## target small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## target regression1_large
## target regression1_small
## target regression2_large
## target regression2_small
## check 8 items: coef_regression1_large, coef_regression1_small, coef_regressio...
## target coef_regression1_large
## target coef_regression1_small
## target coef_regression2_large
## target coef_regression2_small
## target summ_regression1_large
## target summ_regression1_small
## target summ_regression2_large
## target summ_regression2_small
## check 1 item: 'report.md'
## unload 11 items: regression1_small, regression1_large, regression2_small, reg...
## target 'report.md'

For the reg2() model on the small dataset, the p-value on x2 is so small that there may be an association between weight and fuel efficiency after all.

readd(coef_regression2_small)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##               Estimate Std. Error  t value     Pr(>|t|)
## (Intercept) 27.1005134  0.9653295 28.07385 1.364319e-30
## x2          -0.6200367  0.0677864 -9.14692 6.410805e-12

The non-file dependencies of your last target are already loaded in your workspace.

ls()
##  [1] "Produc"                 "analysis_methods"      
##  [3] "analysis_plan"          "b"                     
##  [5] "bad_plan"               "coef_regression2_small"
##  [7] "combos"                 "command"               
##  [9] "commands"               "config"                
## [11] "data_plan"              "dataset_plan"          
## [13] "datasets"               "debug_plan"            
## [15] "df"                     "envir"                 
## [17] "error"                  "f"                     
## [19] "files"                  "get_rmspe"             
## [21] "good_plan"              "large"                 
## [23] "local"                  "make_my_plot"          
## [25] "make_my_table"          "methods"               
## [27] "model_plan"             "my_analyses"           
## [29] "my_datasets"            "my_plan"               
## [31] "myplan"                 "output_plan"           
## [33] "output_types"           "package_list"          
## [35] "path"                   "plot_rmspe"            
## [37] "predictors"             "recent"                
## [39] "reg1"                   "reg2"                  
## [41] "report"                 "report_file"           
## [43] "report_plan"            "reportfile"            
## [45] "results"                "rmspe"                 
## [47] "rmspe_plan"             "rmspe_results_plan"    
## [49] "rules"                  "simulate"              
## [51] "small"                  "summary_types"         
## [53] "targets"                "tmp"                   
## [55] "whole_plan"             "x"
outdated(config) # Everything is up to date.
## character(0)

build_times(digits = 4) # How long did it take to make each target?
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##                      item   type elapsed   user system
## 1            'report.Rmd' import      0s     0s     0s
## 2             'report.md' target  0.037s 0.036s     0s
## 3  coef_regression1_large target  0.004s 0.004s     0s
## 4  coef_regression1_small target  0.011s 0.008s 0.004s
## 5  coef_regression2_large target  0.004s 0.004s     0s
## 6  coef_regression2_small target  0.003s 0.004s     0s
## 7              data.frame import  0.005s 0.008s     0s
## 8                    knit import  0.002s     0s     0s
## 9                   large target  0.004s 0.004s     0s
## 10                     lm import  0.001s 0.004s     0s
## 11                 mtcars import      0s     0s     0s
## 12                   nrow import  0.004s 0.004s     0s
## 13                   reg1 import      0s 0.004s     0s
## 14                   reg2 import  0.001s     0s     0s
## 15      regression1_large target  0.005s 0.008s     0s
## 16      regression1_small target  0.004s 0.004s     0s
## 17      regression2_large target  0.004s     0s 0.004s
## 18      regression2_small target  0.005s 0.004s     0s
## 19             sample.int import  0.003s 0.004s     0s
## 20               simulate import  0.001s     0s     0s
## 21                  small target  0.004s 0.004s     0s
## 22 summ_regression1_large target  0.004s 0.004s     0s
## 23 summ_regression1_small target  0.003s 0.004s     0s
## 24 summ_regression2_large target  0.004s 0.004s     0s
## 25 summ_regression2_small target  0.003s 0.004s     0s
## 26                summary import  0.003s 0.004s     0s
## 27       suppressWarnings import  0.004s 0.004s     0s

See also predict_runtime() and rate_limiting_times().

In the new graph, the black nodes from before are now green.

# Hover, click, drag, zoom, and pan.
vis_drake_graph(config, width = "100%", height = "500px")

Optionally, get visNetwork nodes and edges so you can make your own plot with visNetwork() or render_drake_graph().

dataframes_graph(config)

Use readd() and loadd() to load targets into your workspace. (They are cached in the hidden .drake/ folder using storr). There are many more functions for interacting with the cache.

readd(coef_regression2_large)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##               Estimate Std. Error   t value     Pr(>|t|)
## (Intercept) 30.0226962 0.76499386  39.24567 1.698338e-45
## x2          -0.8558643 0.06718172 -12.73954 5.759347e-19

loadd(small)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake

head(small)
##      x    y
## 1 2.20 32.4
## 2 3.52 15.5
## 3 3.15 22.8
## 4 5.25 10.4
## 5 3.78 15.2
## 6 2.62 21.0

rm(small)
cached(small, large)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## small large 
##  TRUE  TRUE

cached()
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##  [1] "'report.Rmd'"           "'report.md'"           
##  [3] "coef_regression1_large" "coef_regression1_small"
##  [5] "coef_regression2_large" "coef_regression2_small"
##  [7] "data.frame"             "knit"                  
##  [9] "large"                  "lm"                    
## [11] "mtcars"                 "nrow"                  
## [13] "reg1"                   "reg2"                  
## [15] "regression1_large"      "regression1_small"     
## [17] "regression2_large"      "regression2_small"     
## [19] "sample.int"             "simulate"              
## [21] "small"                  "summ_regression1_large"
## [23] "summ_regression1_small" "summ_regression2_large"
## [25] "summ_regression2_small" "summary"               
## [27] "suppressWarnings"

built()
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##  [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()
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##  [1] "'report.Rmd'"     "data.frame"       "knit"            
##  [4] "lm"               "mtcars"           "nrow"            
##  [7] "reg1"             "reg2"             "sample.int"      
## [10] "simulate"         "summary"          "suppressWarnings"

head(read_drake_plan())
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##              target                          command
## 1       'report.md' knit('report.Rmd', quiet = TRUE)
## 2             small                     simulate(48)
## 3             large                     simulate(64)
## 4 regression1_small                      reg1(small)
## 5 regression1_large                      reg1(large)
## 6 regression2_small                      reg2(small)

head(progress()) # See also in_progress()
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##           'report.Rmd'            'report.md' coef_regression1_large 
##             "finished"             "finished"             "finished" 
## coef_regression1_small coef_regression2_large coef_regression2_small 
##             "finished"             "finished"             "finished"

progress(large)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
##      large 
## "finished"

# drake_session() # sessionInfo() of the last make() # nolint

The next time you run make(my_plan), nothing will build because drake knows everything is already up to date.

config <- make(my_plan) # Will use config later. See also drake_config().
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## Unloading targets from environment:
##   large
##   coef_regression2_small
## connect 53 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
## check 9 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, sample.int, ...
## check 3 items: reg1, reg2, simulate
## check 2 items: large, small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## check 8 items: coef_regression1_large, coef_regression1_small, coef_regressio...
## check 1 item: 'report.md'
## All targets are already up to date.

But if you change one of your functions, commands, or other dependencies, drake will update the affected targets. Suppose we change the quadratic term to a cubic term in reg2(). We might want to do this if we suspect a cubic relationship between tons and miles per gallon.

reg2 <- function(d) {
  d$x3 <- d$x ^ 3
  lm(y ~ x3, data = d)
}

The targets that depend on reg2() need to be rebuilt.

outdated(config)
## check 9 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, sample.int, ...
## check 3 items: reg1, reg2, simulate
## check 2 items: large, small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## check 4 items: coef_regression1_large, coef_regression1_small, summ_regressio...
## [1] "'report.md'"            "coef_regression2_large"
## [3] "coef_regression2_small" "regression2_large"     
## [5] "regression2_small"      "summ_regression2_large"
## [7] "summ_regression2_small"

Advanced: To find out why a target is out of date, you can load the storr cache and compare the appropriate hash keys to the output of dependency_profile().

dependency_profile(target = "regression2_small", config = config)
## $cached_command
## [1] "{\n reg2(small) \n}"
## 
## $current_command
## [1] "{\n reg2(small) \n}"
## 
## $cached_file_modification_time
## NULL
## 
## $cached_dependency_hash
## [1] "864b4e5947c8753982fecde3c41d2f1e4bdba1eaf0711d870496a66523fa08b0"
## 
## $current_dependency_hash
## [1] "5c77977876a4292b634be67227691b8c3834501d0c5dfc56095fe01e3ada0137"
## 
## $hashes_of_dependencies
##               reg2              small 
## "d47109544c89ca7a" "62ca42a74bf5f8e6"

config$cache$get_hash(key = "small") # same
## [1] "62ca42a74bf5f8e6"

config$cache$get_hash(key = "reg2") # different
## [1] "cd89057e24fe00ae"
# Hover, click, drag, zoom, and pan.
# Same as drake_graph():
vis_drake_graph(config, width = "100%", height = "500px")

The next make() will rebuild the targets depending on reg2() and leave everything else alone.

make(my_plan)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## connect 53 imports: output_plan, data_plan, results, tmp, predictors, config,...
## connect 15 targets: 'report.md', small, large, regression1_small, regression1...
## check 9 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, sample.int, ...
## check 3 items: reg1, reg2, simulate
## check 2 items: large, small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## check 4 items: coef_regression1_large, coef_regression1_small, summ_regressio...
## load 2 items: large, small
## target regression2_large
## target regression2_small
## check 4 items: coef_regression2_large, coef_regression2_small, summ_regressio...
## target coef_regression2_large
## target coef_regression2_small
## target summ_regression2_large
## target summ_regression2_small
## check 1 item: 'report.md'
## unload 5 items: regression2_small, regression2_large, summ_regression2_small,...
## target 'report.md'

Trivial changes to whitespace and comments are totally ignored.

reg2 <- function(d) {
  d$x3 <- d$x ^ 3
    lm(y ~ x3, data = d) # I indented here.
}
outdated(config) # Everything is up to date.
## check 9 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, sample.int, ...
## check 3 items: reg1, reg2, simulate
## check 2 items: large, small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## check 8 items: coef_regression1_large, coef_regression1_small, coef_regressio...
## check 1 item: 'report.md'
## character(0)

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 <- drake_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
## 1             'report.md'
## 2                   small
## 3                   large
## 4       regression1_small
## 5       regression1_large
## 6       regression2_small
## 7       regression2_large
## 8  summ_regression1_small
## 9  summ_regression1_large
## 10 summ_regression2_small
## 11 summ_regression2_large
## 12 coef_regression1_small
## 13 coef_regression1_large
## 14 coef_regression2_small
## 15 coef_regression2_large
## 16               new_data
##                                                      command
## 1                           knit('report.Rmd', quiet = TRUE)
## 2                                               simulate(48)
## 3                                               simulate(64)
## 4                                                reg1(small)
## 5                                                reg1(large)
## 6                                                reg2(small)
## 7                                                reg2(large)
## 8     suppressWarnings(summary(regression1_small$residuals))
## 9     suppressWarnings(summary(regression1_large$residuals))
## 10    suppressWarnings(summary(regression2_small$residuals))
## 11    suppressWarnings(summary(regression2_large$residuals))
## 12 suppressWarnings(summary(regression1_small))$coefficients
## 13 suppressWarnings(summary(regression1_large))$coefficients
## 14 suppressWarnings(summary(regression2_small))$coefficients
## 15 suppressWarnings(summary(regression2_large))$coefficients
## 16                             new_simulation(36) + sqrt(10)

make(my_plan)
## cache /tmp/Rtmp7EbM7A/Rbuild75ca54d145b9/drake/vignettes/.drake
## Unloading targets from environment:
##   small
##   large
##   coef_regression2_small
## connect 55 imports: additions, output_plan, data_plan, results, tmp, predicto...
## connect 16 targets: 'report.md', small, large, regression1_small, regression1...
## check 11 items: 'report.Rmd', data.frame, knit, lm, mtcars, nrow, rnorm, samp...
## check 4 items: new_simulation, reg1, reg2, simulate
## check 3 items: large, new_data, small
## check 4 items: regression1_large, regression1_small, regression2_large, regre...
## check 8 items: coef_regression1_large, coef_regression1_small, coef_regressio...
## check 1 item: 'report.md'
## target new_data

If you ever need to erase your work, use clean(). The next make() will rebuild any cleaned targets, so be careful. You may notice that by default, the size of the cache does not go down very much. To purge old data, you could use clean(garbage_collection = TRUE, purge = TRUE). To do garbage collection without removing any important targets, use drake_gc().

# Uncaches individual targets and imported objects.
clean(small, reg1, verbose = FALSE)
clean(verbose = FALSE) # Cleans all targets out of the cache.
drake_gc(verbose = FALSE) # Just garbage collection.
clean(destroy = TRUE, verbose = FALSE) # removes the cache entirely

Automatic watching for changed dependencies

As you have seen with reg2(), drake reacts to changes in dependencies. 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 the following.

  1. Other imported functions, whether user-defined or from packages.
  2. For imported functions from your environment, any nested functions also in your environment or from packages.
  3. Commands in your workflow plan data frame.
  4. Global variables mentioned in the commands or imported functions.
  5. The output values of upstream targets.
  6. For dynamic knitr reports generated with the right commands, 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' or report.html). **To activate this feature, the command in your workflow plan data frame must call knitr::knit() or rmarkdown::render(). Examples of acceptable commands:

To enhance reproducibility beyond the scope of drake, you might consider packrat and Docker. 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.

Need more speed?

Drake has extensive high-performance computing support, from local multicore processing to serious distributed computing across multiple nodes of a cluster. See the parallelism vignette for detailed instructions.