Bump actions/checkout from 4 to 5 in the actions group

.github/workflows/R-CMD-check.yaml

@@ -39,7 +39,7 @@ jobs:
 
     steps:
       - name: Checkout ACRO
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Setup Python
         uses: actions/setup-python@v5

.github/workflows/pkgdown.yaml

@@ -22,7 +22,7 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Setup Python
         uses: actions/setup-python@v5

.github/workflows/test-coverage.yaml

@@ -18,7 +18,7 @@ jobs:
 
     steps:
       - name: Checkout ACRO
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Setup Python
         uses: actions/setup-python@v5

Installation Guides.md

@@ -6,36 +6,36 @@ Keeping this comprehensive will require input from the community.
 
 So please email sacro.contact@uwe.ac.uk, or [raise an issue on the GitHub repository](https://github.com/AI-SDC/ACRO-R/issues/new/choose) if:
  - you have a setting that is not covered, or
- - the steps outlined below do not work for you, 
+ - the steps outlined below do not work for you,
 
-**Please note**: most of the scenarios below assume that 
+**Please note**: most of the scenarios below assume that
 - you have a working version of Python 3 (version 3.9 or higher) on your system
 - you are able to access a terminal or command prompt to write and execute some commands.
 
 ---
 
 ## Step 1 create a python virtual environment and install the base python package *acro*
-**In every case** we recommend that you create what is called a 'python virtual environment' called **r-acro**.  
-Virtual environments (*venv's*) are recommended best practice.  
-This is because they isolate the impact of any changes you make in one venv - such as adding or updating a package- from the rest of your system.  
+**In every case** we recommend that you create what is called a 'python virtual environment' called **r-acro**.
+Virtual environments (*venv's*) are recommended best practice.
+This is because they isolate the impact of any changes you make in one venv - such as adding or updating a package- from the rest of your system.
 
 There are many tutorials available on the web if you get stuck.
 We do not endorse any particular site, but here are some examples:
 - [an overview with examples  for windows/linux/mac](https://python.land/virtual-environments/virtualenv)
 - [another that also contains instructions for VSCode and Pycharm](https://realpython.com/python-virtual-environments-a-primer/)
-  
-**For individual users** we suggest that you do this in your home directory where you should have write permission.  
+
+**For individual users** we suggest that you do this in your home directory where you should have write permission.
 
 **To install site-wide** we assume you have access rights and know where your organisation's preferred locations are (for example, this might be ```/usr/local``` on a linux system).
 
 ### Make a dedicated virtual environment
 You can make a new virtual environment  via:
 - the Anaconda GUI interface to the conda system
-- command line access - by opening a terminal or command prompt and entering the command: 
+- command line access - by opening a terminal or command prompt and entering the command:
  ```sh
   conda create --n r-acro
   ```
-if you have a version of conda installed or   
+if you have a version of conda installed or
  ```sh
 python -m venv ./r-acro
 ```
@@ -74,16 +74,16 @@ source r-acro/bin/activate
 #you should see the your command prompt change to show (r-acro)
 python -m pip install acro
 #assuming this completes successfuly you can now exit the virtual environment
-deactivate 
+deactivate
 ```
 ---
 
 ## Step 2 Install the R packages *reticulate* and *acro*
 
 The *reticulate* package is the industry-standard method for supporting communications between R and Python.
-It provides the `plumbing` between the R `front-end' 
+It provides the `plumbing` between the R `front-end'
 
-These commands should work whether you are 
+These commands should work whether you are
 - working on a machine outside the TRE: in which case packages should install from a mirror of the CRAN service
 - working on a machine inside a TRE: in which case the administrator should have set up a local mirror of approved packages from CRAN
 
@@ -128,7 +128,7 @@ If you follow the menu items from ```Tools->Project Options ->Python``` or ```To
   library(reticulate)
   library("acro)"
    ```
-   
+
 ### Option 3 - Editing your personal R preferences
 In your home directory create (or edit) the file ```.Rprofile``` file, adding the lines
 
@@ -139,5 +139,5 @@ Sys.setenv(RETICULATE_PYTHON_ENV=file.path(Sys.getenv("USERPROFILE"),"r-acro"))
 
 
 
-### Option 4- Making site-wide changes 
+### Option 4- Making site-wide changes
  You can also edit the [site-wide Rprofile]() file to add these global environment variables, using replacing *~/r-acro* with the path to wherever you created the dedicated virtual environment.

example-notebook.Rmd

@@ -25,21 +25,21 @@ acro_init()
 ```
 
 ### 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 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.
 ```{r}
-data = farff::readARFF("nursery_data/nursery.arff")
-data = as.data.frame(data)
+data <- farff::readARFF("nursery_data/nursery.arff")
+data <- as.data.frame(data)
 
 names(data)[names(data) == "class"] <- "recommend"
 ```
 
 - 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)
+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)
 ```
 
@@ -48,35 +48,35 @@ unique(data$children)
 #### ACRO Crosstab
 
 ```{r}
-index = data[, c("recommend")]
-columns = data[, c("parents")]
-values = data[, c("children")]
+index <- data[, c("recommend")]
+columns <- data[, c("parents")]
+values <- data[, c("children")]
 
 # convert the values to an array
-values = matrix(values, ncol=1)
+values <- matrix(values, ncol = 1)
 
-table = acro_crosstab(index = index, columns= columns, values = values,  aggfunc = "sum")
+table <- acro_crosstab(index = index, columns = columns, values = values, aggfunc = "sum")
 table
 ```
 
 #### ACRO table
 
 ```{r}
-index = data[, c("parents")]
-columns = data[, c("social")]
+index <- data[, c("parents")]
+columns <- data[, c("social")]
 
-table = acro_table(index=index, columns=columns, deparse.level=1)
+table <- acro_table(index = index, columns = columns, deparse.level = 1)
 table
 ```
 
 #### ACRO pivot table
 
 ```{r}
-index = "parents"
-values = "children"
-aggfunc = list("mean", "std")
+index <- "parents"
+values <- "children"
+aggfunc <- list("mean", "std")
 
-table = acro_pivot_table(data, values=values, index=index, aggfunc=aggfunc)
+table <- acro_pivot_table(data, values = values, index = index, aggfunc = aggfunc)
 table
 ```
 #### ACRO histogram
@@ -91,9 +91,9 @@ In this example a different data set will be used. The lung dataset from the sur
 ```{r}
 # Load the lung dataset
 data(lung)
-#head(lung)
+# head(lung)
 
-acro_surv_func(time=lung$time, status=lung$status, output ="plot")
+acro_surv_func(time = lung$time, status = lung$status, output = "plot")
 ```
 
 ### Examples of producing regression outputs using acro
@@ -104,27 +104,27 @@ acro_surv_func(time=lung$time, status=lung$status, output ="plot")
 
 ```{r}
 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$recommend[which(data$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)
 ```
 
 ```{r}
 # extract relevant columns
-df = data[, c("recommend", "children")]
+df <- data[, c("recommend", "children")]
 # drop rows with missing values
-df = df[complete.cases(df), ]
+df <- df[complete.cases(df), ]
 # formula to fit
-formula = "recommend ~ children"
+formula <- "recommend ~ children"
 ```
 
 #### ACRO Linear Model
 
 ```{r}
-acro_lm(formula=formula, data=df)
+acro_lm(formula = formula, data = df)
 ```
 
 #### ACRO Logit Model
@@ -133,25 +133,25 @@ We use a different combination of variables from the original dataset.
 
 ```{r}
 # extract relevant columns
-df = data[, c("finance", "children")]
+df <- data[, c("finance", "children")]
 # drop rows with missing values
-df = df[complete.cases(df), ]
+df <- df[complete.cases(df), ]
 # convert finance to numeric
-df = transform(df, finance = as.numeric(finance))
+df <- transform(df, finance = as.numeric(finance))
 # subtract 1 to make 1s and 2S into 0a and 1s
-df$finance <- df$finance -1
+df$finance <- df$finance - 1
 # formula to fit
-formula = "finance ~ children"
+formula <- "finance ~ children"
 ```
 
 ```{r}
-acro_glm(formula=formula, data=df, family="logit")
+acro_glm(formula = formula, data = df, family = "logit")
 ```
 
 #### ACRO Probit Model
 
 ```{r}
-acro_glm(formula=formula, data=df, family="probit")
+acro_glm(formula = formula, data = df, family = "probit")
 ```
 
 ### Examples of functionality to let users manage their output
@@ -185,12 +185,12 @@ acro_add_comments("output_1", "This is a crosstab on the nursery dataset.")
 ```
 
 #### Finalise
-- The users must call finalise() at the end of each session.  
-- Each output is saved to a CSV file.    
-- The SDC analysis for each output is saved to a json file or Excel file  
+- The users must call finalise() at the end of each session.
+- Each output is saved to a CSV file.
+- The SDC analysis for each output is saved to a json file or Excel file
   (depending on the extension of the name of the file provided as an input to the function)
 
 ```{r}
-#acro_finalise("RTEST", "xlsx")
+# acro_finalise("RTEST", "xlsx")
 acro_finalise("RTEST", "json")
 ```