div_section inside analyze call #835

iaugusty · 2024-03-04T12:25:27Z

Reporting an Issue with rtables

I'd like to get section divider only in between different variables from an analyze call, and not in between the multiple row stats from the analyze function.
As current behavior of div_section = "~" in an analyze call is to have this divider in between the rows as well as in between the stats, the only way to accomplish the wanted behavior is to use the setter method for div_section.

However, this approach does not always work as expected.

library(rtables)
library(tern)


lyt1 <- basic_table() %>%
  split_rows_by("ARM", section_div = "+") %>%
  analyze(c("AGE","BMRKR1","RACE"), 
          afun = a_summary, 
          extra_args=list(
            .stats=c("n","count_fraction","mean","sd")),
          section_div = "~") 

tbl1 <- build_table(lyt1, DM)

print(tbl1)


sd <- section_div(tbl1)
print(sd)

rpsl <- row_paths_summary(tbl1)$label


lrace <- levels(DM$RACE)[1:(length(levels(DM$RACE))-1)]

rpslx <- which(rpsl %in%  c("Mean","n",lrace) )


sd_orig <- sd

# update section_divs when rowlabel = Mean
sd[rpslx] <- NA_character_

# setter method
section_div(tbl1) <- sd

# getter method
new_sd <- section_div(tbl1)

any(new_sd != sd,na.rm=TRUE)

new_sd[which(sd=="+")]


print(tbl1)

The dividers where character + has been used are incorrectly overwritten by NA.

The setter example from help function, works as expected.

# Data
df <- data.frame(
  cat = c(
    "really long thing its so ", "long"
  ),
  value = c(6, 3, 10, 1)
)
fast_afun <- function(x) list("m" = rcell(mean(x), format = "xx."), "m/2" = max(x) / 2)


tbl <- basic_table() %>%
  split_rows_by("cat", section_div = "~") %>%
  analyze("value", afun = fast_afun, section_div = " ") %>%
  build_table(df)

# Getter
section_div(tbl)

xx <- section_div(tbl)

xl <- which(!is.na(xx))

xll <- letters[1:length(xl)]

xx[xl] <- xll

xx[4] <- "~"

# Setter
section_div(tbl) <- xx
section_div(tbl)
tbl

SessionInfo:
stringr_1.5.0 tern_0.9.3.9006 rtables_0.6.6.9010 formatters_0.5.5.9009 dplyr_1.1.3

Melkiades · 2024-03-04T14:00:59Z

I think the issue here is that when I wrote it I did not test it for cases with multiple analyzed variables, hence the behavior is not as expected. Can I ask you what you expect with the analyze and multiple variables? In other words, do you need to have "~" between the c("AGE", "BMRKR1", "RACE") or the single analysis outputs?

@edelarua @shajoezhu do you have any preference here? Surely needs to be fixed but which behavior you think would be best?

iaugusty · 2024-03-04T14:12:47Z

I'd prefer to have the divider to show up between the different variables, rather than between the rows within outputs from afun

Melkiades · 2024-03-06T18:30:10Z

@iaugusty I fixed the analyze call in the linked PR (does it work for you? added reprex here), but I think that section_div() function is doing what it is supposed to do, i.e. modify the trailing separator line by line (beside last line). Can you double check your code knowing that is modifying the trailing seperator line?

library(rtables)
#> Loading required package: formatters
#> Loading required package: magrittr
#> 
#> Attaching package: 'rtables'
#> The following object is masked from 'package:utils':
#> 
#>     str
# Regression test for #835
lyt <- basic_table() %>%
  split_rows_by("Species", section_div = "|") %>%
  analyze(c("Petal.Width", "Petal.Length"), 
          afun = function(x) list("m" = mean(x), "sd" = sd(x)), section_div = "-")
tbl <- build_table(lyt, iris)
tbl
#>                       all obs     
#> ——————————————————————————————————
#> setosa                            
#>   Petal.Width                     
#>     m                  0.246      
#>     sd           0.105385589380046
#> ----------------------------------
#>   Petal.Length                    
#>     m                  1.462      
#>     sd           0.173663996480184
#> ||||||||||||||||||||||||||||||||||
#> versicolor                        
#>   Petal.Width                     
#>     m                  1.326      
#>     sd           0.197752680004544
#> ----------------------------------
#>   Petal.Length                    
#>     m                  4.26       
#>     sd           0.469910977239958
#> ||||||||||||||||||||||||||||||||||
#> virginica                         
#>   Petal.Width                     
#>     m                  2.026      
#>     sd           0.274650055636667
#> ----------------------------------
#>   Petal.Length                    
#>     m                  5.552      
#>     sd           0.551894695663983

# One-var still works
lyt <- basic_table() %>%
  split_rows_by("Species", section_div = "|") %>%
  analyze("Petal.Width", 
          afun = function(x) list("m" = mean(x), "sd" = sd(x)), section_div = "-")

tbl <- build_table(lyt, iris)
tbl
#>                   all obs     
#> ——————————————————————————————
#> setosa                        
#>   m                0.246      
#> ------------------------------
#>   sd         0.105385589380046
#> ||||||||||||||||||||||||||||||
#> versicolor                    
#>   m                1.326      
#> ------------------------------
#>   sd         0.197752680004544
#> ||||||||||||||||||||||||||||||
#> virginica                     
#>   m                2.026      
#> ------------------------------
#>   sd         0.274650055636667

^{Created on 2024-03-06 with reprex v2.1.0}

iaugusty · 2024-03-07T10:21:20Z

@Melkiades, I'm not yet that experienced in how to get your code updates available in my R environment. If you could provide me some guidance on that I can test it out for my situation.

Melkiades · 2024-03-07T11:19:24Z

@Melkiades, I'm not yet that experienced in how to get your code updates available in my R environment. If you could provide me some guidance on that I can test it out for my situation.

To test a feature by checking out a specific branch in Git and then installing an R package from that branch, follow the steps below. These steps assume you have Git and R installed on your machine, and you're somewhat familiar with using the command line for Git operations and R for package installation.

Step 1: Clone the Repository (if you haven't already) - If you do just do `git pull`

First, you need to clone the repository if you haven't already done so. Open your terminal or command prompt and navigate to the directory where you want to clone the repository. Then, use the git clone command followed by the repository URL. For example:

git clone <repository-URL>

Replace <repository-URL> with the actual URL of the Git repository.

Step 2: Checkout the Specific Branch

Navigate into the cloned repository's directory:

cd <repository-name>

Checkout the specific branch you want to work with by using the git checkout command:

git checkout 835_fix_section_div_analysis_multivar@main

Step 3: Install the R Package

Once you've checked out the correct branch, you can proceed to install the R package. This process can vary depending on how the package is set up. If the package is ready to be installed directly from the source, you can use the devtools package in R to install it. If you don't have devtools installed, you can install it first by running:

install.packages("devtools")

Then, you can install the package from the source. Assuming you're in the root directory of the R package, you can use:

devtools::install()

Or if the package is in a subdirectory of the Git repository, you would use:

devtools::install("path/to/package")

Replace "path/to/package" with the actual path to the package directory.

Step 4: Test the Feature

After installing the package, you can test the feature you're interested in. How you do this depends on the specifics of the package and the feature. Generally, you would load the library in R and use the functions or data that are part of the feature you're testing:

library(packageName)
# Replace packageName with the actual name of the R package

# Use the package's functions or data to test the feature

These instructions should help you get started with testing a feature by checking out a branch with Git and installing an R package. Adjustments might be needed depending on the specifics of the Git repository and R package you're working with.

iaugusty · 2024-03-07T13:50:09Z

@Melkiades

Thanks for the guidance on this.
Here is my outcome of testing: Only the section_div specified in a first analyze call is being used, ie, section_div settings from later calls are ignored.
If the first analyze call is a single analysis var, and these are followed by multivar analyze calls, the behavior is not always as desired (see layout2 and 3 in the below examples)
If the first analyze call is a multivar analysis then the outcome is what I was hoping for.

library(rtables)
#> Loading required package: formatters
#> Loading required package: magrittr
#> 
#> Attaching package: 'rtables'
#> The following object is masked from 'package:utils':
#> 
#>     str
library(tern)
#> Registered S3 method overwritten by 'tern':
#>   method   from 
#>   tidy.glm broom



lyt <- basic_table() %>%
  split_rows_by("Species", section_div = "|") %>%
  analyze(c("Petal.Width", "Petal.Length"), 
          afun = function(x) list("m" = mean(x), "sd" = sd(x)), section_div = "-")
tbl <- build_table(lyt, iris)
tbl
#>                       all obs     
#> ——————————————————————————————————
#> setosa                            
#>   Petal.Width                     
#>     m                  0.246      
#>     sd           0.105385589380046
#> ----------------------------------
#>   Petal.Length                    
#>     m                  1.462      
#>     sd           0.173663996480184
#> ||||||||||||||||||||||||||||||||||
#> versicolor                        
#>   Petal.Width                     
#>     m                  1.326      
#>     sd           0.197752680004544
#> ----------------------------------
#>   Petal.Length                    
#>     m                  4.26       
#>     sd           0.469910977239958
#> ||||||||||||||||||||||||||||||||||
#> virginica                         
#>   Petal.Width                     
#>     m                  2.026      
#>     sd           0.274650055636667
#> ----------------------------------
#>   Petal.Length                    
#>     m                  5.552      
#>     sd           0.551894695663983




lyt0 <- basic_table() %>%
  split_rows_by("ARM", section_div = "+") %>%
  analyze(c("AGE","BMRKR1","RACE"), 
          afun = a_summary, 
          extra_args=list(
            .stats=c("n","count_fraction","mean","sd")),
          section_div = "~") 

tbl0 <- build_table(lyt0, DM)

print(tbl0)
#>                                                  all obs  
#> ——————————————————————————————————————————————————————————
#> A: Drug X                                                 
#>   AGE                                                     
#>     n                                              121    
#>     Mean                                           34.9   
#>     SD                                             7.8    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              121    
#>     Mean                                           5.8    
#>     SD                                             3.0    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              121    
#>     ASIAN                                       79 (65.3%)
#>     BLACK OR AFRICAN AMERICAN                   28 (23.1%)
#>     WHITE                                       14 (11.6%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> B: Placebo                                                
#>   AGE                                                     
#>     n                                              106    
#>     Mean                                           33.0   
#>     SD                                             6.3    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              106    
#>     Mean                                           6.1    
#>     SD                                             3.2    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              106    
#>     ASIAN                                       68 (64.2%)
#>     BLACK OR AFRICAN AMERICAN                   24 (22.6%)
#>     WHITE                                       14 (13.2%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> C: Combination                                            
#>   AGE                                                     
#>     n                                              129    
#>     Mean                                           34.6   
#>     SD                                             6.5    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              129    
#>     Mean                                           5.7    
#>     SD                                             3.4    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              129    
#>     ASIAN                                       84 (65.1%)
#>     BLACK OR AFRICAN AMERICAN                   27 (20.9%)
#>     WHITE                                        18 (14%) 
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0
### section divider from multivar analyze call is a divider in between of vars, rather than stats: as I'd prefer

### also when extra analyze calls are added, the same divider is used, even if no longer explicitely mentioned: OK
lyt1 <- basic_table() %>%
  split_rows_by("ARM", section_div = "+") %>%
  analyze(c("AGE","BMRKR1","RACE"), 
          afun = a_summary, 
          extra_args=list(
            .stats=c("n","count_fraction","mean","sd")),
          section_div = "~") %>% 
  analyze(c("STRATA1")) %>% 
  analyze(c("SEX")) %>% 
  analyze(c("STRATA1","SEX"),table_names=c("STRATAv2","SEXv2")) 

tbl1 <- build_table(lyt1, DM)

print(tbl1)
#>                                                  all obs  
#> ——————————————————————————————————————————————————————————
#> A: Drug X                                                 
#>   AGE                                                     
#>     n                                              121    
#>     Mean                                           34.9   
#>     SD                                             7.8    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              121    
#>     Mean                                           5.8    
#>     SD                                             3.0    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              121    
#>     ASIAN                                       79 (65.3%)
#>     BLACK OR AFRICAN AMERICAN                   28 (23.1%)
#>     WHITE                                       14 (11.6%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               36    
#>     B                                               41    
#>     C                                               44    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               70    
#>     M                                               51    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               36    
#>     B                                               41    
#>     C                                               44    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               70    
#>     M                                               51    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> B: Placebo                                                
#>   AGE                                                     
#>     n                                              106    
#>     Mean                                           33.0   
#>     SD                                             6.3    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              106    
#>     Mean                                           6.1    
#>     SD                                             3.2    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              106    
#>     ASIAN                                       68 (64.2%)
#>     BLACK OR AFRICAN AMERICAN                   24 (22.6%)
#>     WHITE                                       14 (13.2%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               33    
#>     B                                               40    
#>     C                                               33    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               56    
#>     M                                               50    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               33    
#>     B                                               40    
#>     C                                               33    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               56    
#>     M                                               50    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> C: Combination                                            
#>   AGE                                                     
#>     n                                              129    
#>     Mean                                           34.6   
#>     SD                                             6.5    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   BMRKR1                                                  
#>     n                                              129    
#>     Mean                                           5.7    
#>     SD                                             3.4    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   RACE                                                    
#>     n                                              129    
#>     ASIAN                                       84 (65.1%)
#>     BLACK OR AFRICAN AMERICAN                   27 (20.9%)
#>     WHITE                                        18 (14%) 
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               45    
#>     B                                               38    
#>     C                                               46    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               61    
#>     M                                               68    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0     
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   STRATA1                                                 
#>     A                                               45    
#>     B                                               38    
#>     C                                               46    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   SEX                                                     
#>     F                                               61    
#>     M                                               68    
#>     U                                               0     
#>     UNDIFFERENTIATED                                0



#### situation which would be more problematic:
### first analyze call is a single var analysis, followed by a multivar analyze call
### in order to get a section_div working at the second, you need to specify a section_div in the first, but this will be a divider in between stats

lyt2 <- basic_table() %>%
  split_rows_by("ARM", section_div = "+") %>%
  analyze(c("STRATA1")) %>% 
  analyze(c("AGE","BMRKR1","RACE"), 
          afun = a_summary, 
          extra_args=list(
            .stats=c("n","count_fraction","mean","sd")),section_div = "~") 


tbl2 <- build_table(lyt2, DM)

print(tbl2)
#>                                                  all obs  
#> ——————————————————————————————————————————————————————————
#> A: Drug X                                                 
#>   STRATA1                                                 
#>     A                                               36    
#>     B                                               41    
#>     C                                               44    
#>   AGE                                                     
#>     n                                              121    
#>     Mean                                           34.9   
#>     SD                                             7.8    
#>   BMRKR1                                                  
#>     n                                              121    
#>     Mean                                           5.8    
#>     SD                                             3.0    
#>   RACE                                                    
#>     n                                              121    
#>     ASIAN                                       79 (65.3%)
#>     BLACK OR AFRICAN AMERICAN                   28 (23.1%)
#>     WHITE                                       14 (11.6%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> B: Placebo                                                
#>   STRATA1                                                 
#>     A                                               33    
#>     B                                               40    
#>     C                                               33    
#>   AGE                                                     
#>     n                                              106    
#>     Mean                                           33.0   
#>     SD                                             6.3    
#>   BMRKR1                                                  
#>     n                                              106    
#>     Mean                                           6.1    
#>     SD                                             3.2    
#>   RACE                                                    
#>     n                                              106    
#>     ASIAN                                       68 (64.2%)
#>     BLACK OR AFRICAN AMERICAN                   24 (22.6%)
#>     WHITE                                       14 (13.2%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> C: Combination                                            
#>   STRATA1                                                 
#>     A                                               45    
#>     B                                               38    
#>     C                                               46    
#>   AGE                                                     
#>     n                                              129    
#>     Mean                                           34.6   
#>     SD                                             6.5    
#>   BMRKR1                                                  
#>     n                                              129    
#>     Mean                                           5.7    
#>     SD                                             3.4    
#>   RACE                                                    
#>     n                                              129    
#>     ASIAN                                       84 (65.1%)
#>     BLACK OR AFRICAN AMERICAN                   27 (20.9%)
#>     WHITE                                        18 (14%) 
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0

### single analyze call without a section_div followed by a multivar analyze call with a section_div is not as I would expect it


lyt3 <- basic_table() %>%
  split_rows_by("ARM", section_div = "+") %>%
  analyze(c("STRATA1"),section_div = "~") %>% 
  analyze(c("AGE","BMRKR1","RACE"), 
          afun = a_summary, 
          extra_args=list(
            .stats=c("n","count_fraction","mean","sd"))) 


tbl3 <- build_table(lyt3, DM)

print(tbl3)
#>                                                  all obs  
#> ——————————————————————————————————————————————————————————
#> A: Drug X                                                 
#>   STRATA1                                                 
#>     A                                               36    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     B                                               41    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     C                                               44    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   AGE                                                     
#>     n                                              121    
#>     Mean                                           34.9   
#>     SD                                             7.8    
#>   BMRKR1                                                  
#>     n                                              121    
#>     Mean                                           5.8    
#>     SD                                             3.0    
#>   RACE                                                    
#>     n                                              121    
#>     ASIAN                                       79 (65.3%)
#>     BLACK OR AFRICAN AMERICAN                   28 (23.1%)
#>     WHITE                                       14 (11.6%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> B: Placebo                                                
#>   STRATA1                                                 
#>     A                                               33    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     B                                               40    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     C                                               33    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   AGE                                                     
#>     n                                              106    
#>     Mean                                           33.0   
#>     SD                                             6.3    
#>   BMRKR1                                                  
#>     n                                              106    
#>     Mean                                           6.1    
#>     SD                                             3.2    
#>   RACE                                                    
#>     n                                              106    
#>     ASIAN                                       68 (64.2%)
#>     BLACK OR AFRICAN AMERICAN                   24 (22.6%)
#>     WHITE                                       14 (13.2%)
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0     
#> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#> C: Combination                                            
#>   STRATA1                                                 
#>     A                                               45    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     B                                               38    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>     C                                               46    
#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#>   AGE                                                     
#>     n                                              129    
#>     Mean                                           34.6   
#>     SD                                             6.5    
#>   BMRKR1                                                  
#>     n                                              129    
#>     Mean                                           5.7    
#>     SD                                             3.4    
#>   RACE                                                    
#>     n                                              129    
#>     ASIAN                                       84 (65.1%)
#>     BLACK OR AFRICAN AMERICAN                   27 (20.9%)
#>     WHITE                                        18 (14%) 
#>     AMERICAN INDIAN OR ALASKA NATIVE                0     
#>     MULTIPLE                                        0     
#>     NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER       0     
#>     OTHER                                           0     
#>     UNKNOWN                                         0


### conclusion: only the section divider from the first analyze call is used
### if the first is a single var analyze call, this is not always a desirable outcome
### if the fist analyze call is a multivar analyze call, the outcome is acceptable

^{Created on 2024-03-07 with reprex v2.1.0}

Melkiades · 2024-03-07T14:14:57Z

Thanks for your research! I think solutions to these would be complicated as you need each analysis split to be aware of other analysis splits. I prefer to keep each analysis call self-consistent. As it is now, you can still construct both scenarios (each analysis line separator) and var group separator if you do either add section_div for each single var call or put the multi var call first. I will double-check how complicated it is to make it a bit smarter, but I am not sure to find a straightforward solution; at the end, it all depends on the representation structure underneath and it is all dipendent on a recursive call of splits

iaugusty · 2024-03-08T07:49:57Z

I'm happy with the current version, a single analysis var, followed by other (multivar) analyze calls is not how I would be using this most often

Melkiades self-assigned this Mar 4, 2024

Melkiades added the sme label Mar 4, 2024

Melkiades mentioned this issue Mar 6, 2024

Fix section_div in case of multiple var analyzed (AnalyzeMultiVars) #836

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Melkiades closed this as completed in #836 Mar 12, 2024

Melkiades mentioned this issue Apr 24, 2024

section_div in single var analyze call puts divider between each row generated by afun, rather than only after the last one #863

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div_section inside analyze call #835

div_section inside analyze call #835

iaugusty commented Mar 4, 2024

Melkiades commented Mar 4, 2024 •

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Melkiades commented Mar 6, 2024 •

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iaugusty commented Mar 7, 2024

Melkiades commented Mar 7, 2024

iaugusty commented Mar 7, 2024

Melkiades commented Mar 7, 2024

iaugusty commented Mar 8, 2024

div_section inside analyze call #835

div_section inside analyze call #835

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iaugusty commented Mar 4, 2024

Reporting an Issue with rtables

Melkiades commented Mar 4, 2024 • edited Loading

iaugusty commented Mar 4, 2024

Melkiades commented Mar 6, 2024 • edited Loading

iaugusty commented Mar 7, 2024

Melkiades commented Mar 7, 2024

Step 1: Clone the Repository (if you haven't already) - If you do just do git pull

Step 2: Checkout the Specific Branch

Step 3: Install the R Package

Step 4: Test the Feature

iaugusty commented Mar 7, 2024

Melkiades commented Mar 7, 2024

iaugusty commented Mar 8, 2024

Melkiades commented Mar 4, 2024 •

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Melkiades commented Mar 6, 2024 •

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Step 1: Clone the Repository (if you haven't already) - If you do just do `git pull`