diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e99b1e9..1cc612f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -73,3 +73,11 @@ writeLines(pak::pkg_system_requirements("devtools", "ubuntu", "22.04")) To remove the local virtual Python environment, delete the `r-acro` folder. On GNU/Linux this is typically located in `~/.virtualenvs` + +## CRAN Submission + +A useful command to run before submitting: + +```shell +R CMD build . && R CMD check --as-cran $(ls -t . | head -n1) +``` diff --git a/acro_demo_2026.R b/acro_demo_2026.R new file mode 100644 index 0000000..5366009 --- /dev/null +++ b/acro_demo_2026.R @@ -0,0 +1,100 @@ +## ----------------------------------------------------------------------------- +# Check if acro is installed +if (!requireNamespace("acro", quietly = TRUE)) { + # If not installed, install it + install.packages("acro") +} + + +## ----------------------------------------------------------------------------- +library("acro") + + +## ----------------------------------------------------------------------------- +acro_init() + + +## ----------------------------------------------------------------------------- +data <- farff::readARFF("data/nursery.arff") +data <- as.data.frame(data) + +names(data)[names(data) == "class"] <- "recommendation" + + +## ----------------------------------------------------------------------------- +data$children <- as.numeric(as.character(data$children)) +data[is.na(data)] <- round(runif(sum(is.na(data)), min = 4, max = 10), 0) +unique(data$children) + + +## ----------------------------------------------------------------------------- +head(data) + + +## ----------------------------------------------------------------------------- +rows <- data[, c("recommendation")] +columns <- data[, c("parents")] + +table <- acro_table(index = rows, columns = columns, deparse.level = 1) +table + + +## ----------------------------------------------------------------------------- + +acro_enable_suppression() +table <- acro_table(index = rows, columns = columns, deparse.level = 1) +table + + +## ----------------------------------------------------------------------------- +myrows <- list(data[, c("parents")], data[, c("finance")]) +mycolumns <- data[, c("recommendation")] +myvalues <- data[, c("children")] +# convert the values to an array +myvalues <- matrix(myvalues, ncol = 1) + + +table4 <- acro_crosstab( + index = myrows, + columns = mycolumns, + values = myvalues, + aggfunc <- list("mean") +) +table4 + + +## ----------------------------------------------------------------------------- + +help(package = "acro") + + +## ----------------------------------------------------------------------------- +details <- acro_print_outputs() + + +## ----------------------------------------------------------------------------- +acro_remove_output("output_0") + + +## ----------------------------------------------------------------------------- +acro_rename_output("output_1", " crosstab_recommendation_vs_parents") +acro_rename_output("output_2", "mean_children_by_parents_finance_vs_recommendation") + + +## ----------------------------------------------------------------------------- + +acro_add_comments( + "mean_children_by_parents_finance_vs_recommendation", + "too few cases of recommend to report" +) + + +## ----------------------------------------------------------------------------- +acro_custom_output("acro_demo_2026.R", "This is the code that produced this session") + + +## ----------------------------------------------------------------------------- +myfolder <- "Routputs" +suffix <- format(Sys.time(), "%e_%m_%Y_%H_%M") +foldername <- paste(myfolder, suffix, sep = "_") +acro_finalise(foldername, ext = "json") diff --git a/acro_demo_2026.Rmd b/acro_demo_2026.Rmd new file mode 100644 index 0000000..20ce367 --- /dev/null +++ b/acro_demo_2026.Rmd @@ -0,0 +1,497 @@ +--- +title: "acro R demonstration 2026" +editor_options: + markdown: + wrap: 90 + chunk_output_type: inline +--- + +# ACRO Demonstration + +This is a simple notebook to get you started with using the `acro` package to add +disclosure risk control to your analysis. + +## A: The basic concepts + +### 1: A research *session*: + +by which we mean the activity of running a series of commands (interactively or via a +script) that: - ingest some data, - manipulate it, and then - produce (and store) some +outputs. + +### 2: Types of commands: + +Whether interactive, or just running a final script, we can think of the commands that get +run in a session as dividing into: + +\- *manipulation* commands that load and transform data into the shape you want + +\- *feedback* commands that report on your data - but are never intended to be exported. +For example, running a `head()` command to make sure your manipulations have got the data +into the format you want. + +\- *query* commands that produce an output from your data (table/plot/regression model +etc.) that you might want to export from the Trusted Research Environment (TRE) + +### 3: Risk Assessment vs decision making: + +SACRO stands for Semi-Automated Checking of Research Outputs.\ + +- The prefix 'Semi' is important here - because in a principles-based system humans + should make *decisions* about output requests. + +- To help with that we provide the SACRO-Viewer, which collates all the relevant + information for them. + +A key part of that information is the *Risk Assessment*. + +- Since it involves calculating metrics and comparing them to thresholds (the TRE's risk + appetite) it can be done automatically, at the time an output query runs on the data. + +- This is what the ACRO package does when you use it as part of your workflow. + +### 4: What acro does + +The acro package aims to support you in producing *Safe Outputs* within minimal changes to +your work flow. To do that we provide: + +1. drop-in replacements for the most commonly used *output commands*, + +- keeping the same syntax as the originals, and + +- supporting as many of the options as we can (features supported will increase over + time in response demand). + +2. a set of *session-management* commands to help you manage the set of files you request + for output. + +**Important to note** that currently acro outputs results (tables, details of regression +models etc.) as `.csv` files.\ +In other words we separate the processes of: + +- *creating* outputs - which *must* be done *inside* the TRE. +- *formatting* them for publication - which can be done outside the TRE with your + preferred toolchain. + +acro currently handles creation. We are interested in hearing from researchers whether it +is important to support them with formatting + +But if you format your outputs, and save them to your preferred type of file, you can add +them to your acro session as `custom_outputs`, including a comment to the reviewer to say +which of the risk-assessed outputs they are derived from. + +### 5: How acro works in R + +There are obvious benefits to maintaining a single *point of truth* for the key +functionality of the code base: + +- it makes it easier to maintain and extend. +- it makes it possible to validate the disclosure -control process against a published + ontology +- it enables rapid and consistent support for other languages + +Therefore the `acro` in R acts like a *skin* that communicates with the underlying python +via R's `reticulate` package. So behind-the scenes: + +- For **regressions** and other statistics acro uses the *statsmodels* package - which + has the benefit of happily accepting R-style equations. + +- For **tables** acro uses the industry standard *pandas* package, in particular the + *pivot_table()* and *crosstab()* functions. + +This approach lets acro: + +- support R's `help()` functions and and vignettes + +- provide acro versions of standard R commands like `table` (this directly maps on + directly onto `crosstab()` queries) + +- provide R users with access to powerful table-building commands like crosstab: + + - There are hundreds (thousands?) of web sites showing how to do this.\ + + - You can make (hierarchical) 2-D tables (or 1-D if you add a 'dummy' variable + containing the same value for each row) + + - You can specify what the table cells contain by specifying which variable to + report on and what statistic to report- for example: mean, count, std deviation, + median etc.(pandas calls these *aggregation functions*) + +**The acro version does not reimplement any statistical commands**. Instead it reuses +them - but it adds extra code that checks for disclosure risks depending on the statistic +you ask for + +## B: Getting Started with the demonstration + +### Step 1: Check if acro is installed and if it is not install it from CRAN + +```{r} +# Check if acro is installed +if (!requireNamespace("acro", quietly = TRUE)) { + # If not installed, install it + install.packages("acro") +} +``` + +### Step 2: Starting an ACRO session + +First of all, we need to load the package and then call acro_init() to initialise an acro +session. This function takes two optional parameters: + +- suppress which can be TRUE or FALSE (default) to choose whether to automatically apply + suppression to the results or not. + +- config: the name of a file in .yaml format the TRE may have given you to over-ride the + default risk appetite parameters. + +Note that when the cell runs it should report (possibly in a different coloured +font/background): + +- what version of acro is running: *this should be 0.4.12* + +- the TRE's risk appetite: that define the rules your outputs will be checked against. + +- whether suppression is automatically applied to disclosive outputs. + +#### Load the acro package + +```{r} +library("acro") +``` + +#### Initiate acro + +```{r} +acro_init() +``` + +### Step 3: Load the data + +- The dataset used in this example notebook is the nursery dataset from OpenML. +- The code below reads the data from a folder called "nursery_data" which we assume is + at the same level as the folder where you are working. +- The path might need to be changed if the data has been downloaded and stored + elsewhere. + +**There is no change to your usual workflow here** This is just loading and manipulating +data. + +```{r} +data <- farff::readARFF("data/nursery.arff") +data <- as.data.frame(data) + +names(data)[names(data) == "class"] <- "recommendation" +``` + +Convert the children column to integers, replacing 'more' with random int from range 4-10 + +```{r} +data$children <- as.numeric(as.character(data$children)) +data[is.na(data)] <- round(runif(sum(is.na(data)), min = 4, max = 10), 0) +unique(data$children) +``` + +Example of a Feedback command + +```{r} +head(data) +``` + +## C: Producing tables that are 'Safe Outputs' + +### Example 1: A simple 2-D table of frequencies stratified by two variables + +#### Using the acro version of R's table command + +We will produce a simple cross-tabulation of the number of records, stratified by values +for the *recommendation* and *parents* variables. + +```{r} +rows <- data[, c("recommendation")] +columns <- data[, c("parents")] + +table <- acro_table(index = rows, columns = columns, deparse.level = 1) +table +``` + +#### How to understand this output + +The output in the console (or click on left thumbnail above) is the risk analysis produced +by acro. It is telling us that: + +- the overall summary status is *fail* because 4 cells are failing the 'minimum + threshold' check + +- which cells failed so you can choose how to respond + +- finally it is telling us that is has saved the table and risk assessment to our acro + session with id "output_0" + +The part in the data.frame window (or click or right thumbnail above) is the normal output +produced by the R *table* function. + +- As this is such a small table it is not hard to spot the four problematic cells with + zero or low counts. + +- But of course this might be harder for a bigger table. + +#### How to respond to this input + +There are basically three choices: + +1. You might decide these low numbers reveal something where the public interest + outweighs the disclosure risk. + +- Rather than being a strict rules-based system, acro lets you attach an 'exception + request' to an output, to send a message to the output checkers. For example, you + could type: + +``` +acro.add_exception('output_0',"I think you should let me have this because...") +``` + +2. You could redesign/recode the data so that table so that none of the cells in the + resulting table represent fewer than *n* people (10 for the default risk appetite). + +- For example, you could recode *'very_recommend'* and *'priority'* into one label.But + maybe it is revealing that the *'recommend'* value is not used? + +3. You can redact the disclosive cells - and **acro will do this for you**. + + We simply enable the option to suppress disclosive cells and re-run the query. + +The code below shows option 3. When you run the cell below you should see that a new +output is created and added to the session: + +- the status now changes to `review` (so the output-checker knows what has been applied) + +- the code automatically adds an exception request saying that suppression has been + applied + +- and, most importantly, the cells are redacted. + +```{r} +acro_enable_suppression() +table <- acro_table(index = rows, columns = columns, deparse.level = 1) +table +``` + +#### ACRO Crosstab + +According to R's documentation, the \`table' command is typically only used to produce +contingency tables - i.e. report on frequencies. + +We could have produced the table above using the command +`table2 <- acro_crosstab(index=rows,columns=columns)` + +To illustrate the sort of tables that `crosstab()` can easily produce, the example below +produces something more complex. Going through the parameters in order: + +- passing a list of variable names to `index` (rather than a single variable/column + name) tells it we want a hierarchy within the rows. + + - we can do the same to columns as well (or instead) if we want to + +- to specify the cell contents we: + + - set `values` to be the column `children` to identify what variable to report on + + - setting `aggfunc<-"mean"` to specify the statistic to use. + + - In this case the mean number of children per sub-group. This introduces additional + risks of *dominance* for which there are two widely used tests. + +- + +```{r} +myrows <- list(data[, c("parents")], data[, c("finance")]) +mycolumns <- data[, c("recommendation")] +myvalues <- data[, c("children")] +# convert the values to an array +myvalues <- matrix(myvalues, ncol = 1) + + +table4 <- acro_crosstab( + index = myrows, + columns = mycolumns, + values = myvalues, + aggfunc <- list("mean") +) +table4 +``` + +## D: What other sorts of analysis does ACRO currently support? + +We are continually adding support for more types of analysis as users prioritise them. + +ACRO currently supports: + +- **Tables** via `acro_table()`, `acro_crosstab()` and `acro_pivot_table()`. + + - supported aggregation functions are: *mean*, *median*, *sum*, *std*, *count*, and + *mode*. + - you can also pass a list of aggregation functions e.g. + `aggfunc <-list("mean","median")` + - NB we have recently had reports of instability when automatically suppressing + tables with multiple aggregation functions, one of which is "std". A fix for this + will be issued shortly. + +- **Survival analysis** via: `acro_surv_function()`, + +- **Histograms** via:`acro_hist()` + +- **Regression** via: `acro_ols()`, `acro_logit()`,`acro_probit()` + +You can get help on using any of these using the standard R `help()` syntax as shown in +the next cell. + +In RStudio this opens the help menu for the package in the sidebar. + +```{r} +help(package = "acro") +``` + +## E: ACRO functionality to let users manage their outputs + +As explained above, you need to create an "acro session" whenever your code is run. + +After that, every time you run an acro \`query' command both the output and the risk +assessment are saved as part of the acro session. + +But we recognise that: + +- You may not want to request release of all your outputs - for example, the first table + we produced above. + +- It is good practice to provide a more informative name than just *output_n* for the + .csv files that acro produces + +- It helps the output checker if you provide some comments saying what the outputs are. + +- You might want to add more things to the bundles of files you want to take out, such + as: + + - outputs from analyses that acro doesn't currently support + + - your code itself (which many journals want) + + - maybe a version of your paper in pdf/word format etc. + +Therefore acro provides the following commands for 'session management' + +### 1: Listing the current contents of an ACRO session + +This output is not beautiful (there's a GUI coming soon) but it should let you identify +outputs you want to rename,comment on, or delete. + +```{r} +details <- acro_print_outputs() +``` + +### 2: Remove some ACRO outputs before finalising + +At the start of this demo we made a disclosive output -it's the first one with status +*fail*. + +We don't want to waste the output checker's time so lets remove it. + +```{r} +acro_remove_output("output_0") +``` + +### 3: Rename ACRO outputs before finalising + +It's always a good idea to rename the outputs to provide more descriptive names. + +```{r} +acro_rename_output("output_1", " crosstab_recommendation_vs_parents") +acro_rename_output("output_2", "mean_children_by_parents_finance_vs_recommendation") +``` + +### 4: Add a comment to output + +This is an example of adding a comment to outputs.\ +It can be used to provide a description or to pass additional information to the TRE +staff. + +They will see it alongside your file in the output checking viewer - rather than having it +in an email somewhere. + +```{r} +acro_add_comments( + "mean_children_by_parents_finance_vs_recommendation", + "too few cases of recommend to report" +) +``` + +### 5. Request an exception + +An example of providing a reason why an exception should be made. + +There are none in this example but this is the syntax + +``` +acro_add_exception("output_n", "This is evidence of systematic bias?") +``` + +### 6: Adding a custom output. + +As mentioned above you might want to request release of all sorts of things - including +your code, - or outputs from analyses *acro* doesn't support (yet) + +In ACRO we can add a file to our session with a comment describing what it is. + +The following example includes the R code extracted from this notebook using knit::purl(). + +```{r} +acro_custom_output("acro_demo_2026.R", "This is the code that produced this session") +``` + +## F: Finishing your session and producing a folder of files to release. + +This is an example of the function *finalise()* which the users must call at the end of +each session. This function: + +- takes each output and saves it to a CSV file (or the original file type for custom + outputs) + +- saves the risk appetite used (minimum cell thresholds etc.) to a file called + *config.json* + +- saves the SDC analysis for each output to a json file *results.json* + +- adds checksums for everything - so we know they've not been edited. + +- puts all the above in a folder with the name you supply. + +**ACRO will not overwrite previous sessions** + +So every time you call finalise on a session you need to either: + +- manually delete the previous folder, or + +- provide a new folder name, or + +- create a unique string variable in your code and pass that to `finalise()` as shown + below. + +```{r} +myfolder <- "Routputs" +suffix <- format(Sys.time(), "%e_%m_%Y_%H_%M") +foldername <- paste(myfolder, suffix, sep = "_") +acro_finalise(foldername, ext = "json") +``` + +## G: Reminder about getting help while you work + +- If you can't remember the name of the command, from the R prompt type: + `help(package="acro")` + + - in RStudio this will open the interactive sidebar + + - from a terminal this will list the commands present in the acro package + +- If you can know the name of the command and want an explanation or to explain the + syntax, from the R prompt type: `` help(topic=`acro_command_name`, package="acro") `` + + for example: `` help(topic=`acro_crosstab`,package="acro") `` diff --git a/example-notebook.Rmd b/example-notebook-old.Rmd similarity index 100% rename from example-notebook.Rmd rename to example-notebook-old.Rmd diff --git a/example-notebook.nb.html b/example-notebook.nb.html deleted file mode 100644 index f9e7ccb..0000000 --- a/example-notebook.nb.html +++ /dev/null @@ -1,2125 +0,0 @@ - - - - - - - - - - - - - -acro R Notebook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Check if acro is installed and if it is not install it from -CRAN

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# Check if acro is installed
-if (!requireNamespace("acro", quietly = TRUE)) {
-  # If not installed, install it
-  install.packages("acro")
-}
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Load the acro package

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library("acro")
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Initiate acro

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First of all, we need to call acro_init() to initialise an acro -object. This function takes the parameter suppress which can be TRUE or -FALSE to choose whether to automatically apply suppression to the -results or not. The default is no suppression.

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acro_init()
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Load the data

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data = farff::readARFF("data/nursery.arff")
-data = as.data.frame(data)
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-names(data)[names(data) == "class"] <- "recommend"
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data$children <-as.numeric(as.character(data$children))
-data[is.na(data)] <-  round(runif(sum(is.na(data)), min = 4, max = 10),0)
-unique(data$children)
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Examples of producing tabular output using acro

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ACRO Crosstab

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index = data[, c("recommend")]
-columns = data[, c("parents")]
-values = data[, c("children")]
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-# convert the values to an array
-values = matrix(values, ncol=1)
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-table = acro_crosstab(index = index, columns= columns, values = values,  aggfunc = "sum")
-table
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ACRO table

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index = data[, c("parents")]
-columns = data[, c("social")]
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-table = acro_table(index=index, columns=columns, deparse.level=1)
-table
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ACRO pivot table

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index = "parents"
-values = "children"
-aggfunc = list("mean", "std")
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-table = acro_pivot_table(data, values=values, index=index, aggfunc=aggfunc)
-table
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ACRO histogram

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acro_hist(data, "children")
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ACRO survival analysis

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In this example a different data set will be used. The lung dataset -from the survival package is used.

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# Load the lung dataset
-data(lung)
-#head(lung)
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-acro_surv_func(time=lung$time, status=lung$status, output ="plot")
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Examples of producing regression outputs using acro

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Start by manipulating the nursery data to get two numeric -variables

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data$recommend <- as.character(data$recommend)
-data$recommend[which(data$recommend=="not_recom")] <- "0"
-data$recommend[which(data$recommend=="recommend")] <- "1"
-data$recommend[which(data$recommend=="very_recom")] <- "2"
-data$recommend[which(data$recommend=="priority")] <- "3"
-data$recommend[which(data$recommend=="spec_prior")] <- "4"
-data$recommend <- as.numeric(data$recommend)
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# extract relevant columns
-df = data[, c("recommend", "children")]
-# drop rows with missing values
-df = df[complete.cases(df), ]
-# formula to fit
-formula = "recommend ~ children"
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ACRO Linear Model

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acro_lm(formula=formula, data=df)
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ACRO Logit Model

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This is an example of logit regression using ACRO We use a different -combination of variables from the original dataset.

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# extract relevant columns
-df = data[, c("finance", "children")]
-# drop rows with missing values
-df = df[complete.cases(df), ]
-# convert finance to numeric
-df = transform(df, finance = as.numeric(finance))
-# subtract 1 to make 1s and 2S into 0a and 1s
-df$finance <- df$finance -1
-# formula to fit
-formula = "finance ~ children"
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acro_glm(formula=formula, data=df, family="logit")
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ACRO Probit Model

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acro_glm(formula=formula, data=df, family="probit")
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Examples of functionality to let users manage their output

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Rename Output

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This function can be used to rename the outputs to a more descriptive -name.

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acro_rename_output("output_0", "crosstab")
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Remove Output

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This function can be used to delete outputs from the acro object.

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acro_remove_output("output_3")
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Display Outputs

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This function can be used to list all the outputs created so far

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acro_print_outputs()
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Add Comments to Output

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This function is used to add comments to the outputs. It can be used -to provide a description or to pass additional information to the output -checkers.

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acro_add_comments("output_1", "This is a crosstab on the nursery dataset.")
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Finalise

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#acro_finalise("RTEST", "xlsx")
-acro_finalise("RTEST", "json")
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- - - - - - - - - - - - - - - - diff --git a/prepare_to_submit_to_CRAN.R b/prepare_to_submit_to_CRAN.R index 8977349..0a2105d 100644 --- a/prepare_to_submit_to_CRAN.R +++ b/prepare_to_submit_to_CRAN.R @@ -35,8 +35,7 @@ usethis::use_version(which = c("patch", "minor", "major", "dev")[1]) # When you are happy with the package and want to submit it to CRAN, you can do this manually by converting the package source directory into a single bundled file and submit this file to CRAN (https://xmpalantir.wu.ac.at/cransubmit/) devtools::build() -#good idea to run -R CMD build . && R CMD check --as-cran $(ls -t . | head -n1) +# Good idea to run: see CONTRIBUTING.md # Or if you decide to do the release automatically then you can run this command # devtools::release()