---
title: "Example 4: AE Table"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Example 4: AE Table}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

### Create an Adverse Events Table

The fourth example produces an Adverse Events table by severity, with
treatment groups on separate pages. The report shows statistics for All 
Adverse Events and System Organ Class/Preferred Term.   

#### Program

Note the following about this example:

  * The `libname()` function from the **[libr](https://libr.r-sassy.org)** 
  package makes it easy to load the entire data library.
  * Functions from the **[sassy](https://sassy.r-sassy.org)**
  system integrate nicely with those from the 
  **[tidyverse](https://www.tidyverse.org/)**.   
  * The **[reporter](https://reporter.r-sassy.org)** package is 
  able to wrap treatment groups to separate pages using the _page_wrap_
  property.  
  * The **[reporter](https://reporter.r-sassy.org)** package is also
  able to easily handle page breaks, while still respecting the 
  established margins.

```{r eval=FALSE, echo=TRUE}
library(tidyverse)
library(sassy)

options("logr.autolog" = TRUE, 
        "logr.notes" = FALSE)


# Get Data ----------------------------------------------------------------


# Get path to temp directory
tmp <- tempdir() 

# Get path to sample data
pkg <- system.file("extdata", package = "sassy")

# Open log
lgpth <- log_open(file.path(tmp, "example4.log"))

sep("Prepare Data")

# Create libname for csv data
libname(sdtm, pkg, "csv") 

# Load data into workspace
lib_load(sdtm) 


sep("Prepare table data")
dat <- sdtm.AE %>% 
  inner_join(select(sdtm.DM, USUBJID, ARM, ARMCD),  
             c("USUBJID" = "USUBJID"), 
             keep = FALSE) %>% 
  filter(ARM != "SCREEN FAILURE")

put("Get population counts")
arm_pop <- dat %>% 
  select(USUBJID, ARM) %>%  
  distinct() %>% 
  count(ARM)  %>% 
  deframe() %>% put()

put ("Create lookup for AE severity")
sevn <- c(MILD = 1, MODERATE = 2, SEVERE = 3) %>% put()

put("Subset ADAE for needed rows and columns")
df_sub <- dat %>% 
  mutate(AESEVN = sevn[AESEV]) %>% 
  select(USUBJID, ARM, AESEV, AESEVN, AESOC, AEDECOD) %>% 
  distinct() %>% 
  put()


# Perform Calculations ----------------------------------------------------


put("Create template for needed ARM columns")
col_template <- paste0(c(rep("ARM A_", 3), rep("ARM B_", 3), rep("ARM C_", 3),
                         rep("ARM D_", 3)), rep(c(1, 2, 3), 3))



put("Identify and count highest severity grade for each subject")
df1 <- df_sub %>% arrange(USUBJID, AESOC, AEDECOD, AESEVN) %>% 
  group_by(USUBJID, ARM, AESOC, AEDECOD) %>% 
  mutate(max_sev = ifelse(row_number() == n(), TRUE, FALSE)) %>% 
  filter(max_sev == TRUE) %>% 
  group_by(ARM, AESOC, AEDECOD, AESEVN) %>% 
  summarize(cnt = n()) %>% 
  pivot_wider(names_from = c(ARM, AESEVN),
              values_from = cnt, 
              values_fill = 0) %>% 
  put()


put("Fill in missing columns where there were no events.")
for (nm in col_template) {
  if (!nm %in% names(df1))
    df1[[nm]] <- 0
}
put(df1)

put("Format counts and percents for each column")
df_events <- df1 %>% 
  transmute(AESOC = AESOC, 
            AEDECOD = str_to_title(AEDECOD),
            `ARM A_1` = fmt_cnt_pct(`ARM A_1`, arm_pop["ARM A"]),
            `ARM A_2` = fmt_cnt_pct(`ARM A_2`, arm_pop["ARM A"]),
            `ARM A_3` = fmt_cnt_pct(`ARM A_3`, arm_pop["ARM A"]),
            `ARM B_1` = fmt_cnt_pct(`ARM B_1`, arm_pop["ARM B"]),
            `ARM B_2` = fmt_cnt_pct(`ARM B_2`, arm_pop["ARM B"]),
            `ARM B_3` = fmt_cnt_pct(`ARM B_3`, arm_pop["ARM B"]),
            `ARM C_1` = fmt_cnt_pct(`ARM C_1`, arm_pop["ARM C"]),
            `ARM C_2` = fmt_cnt_pct(`ARM C_2`, arm_pop["ARM C"]),
            `ARM C_3` = fmt_cnt_pct(`ARM C_3`, arm_pop["ARM C"]),
            `ARM D_1` = fmt_cnt_pct(`ARM D_1`, arm_pop["ARM D"]), 
            `ARM D_2` = fmt_cnt_pct(`ARM D_2`, arm_pop["ARM D"]), 
            `ARM D_3` = fmt_cnt_pct(`ARM D_3`, arm_pop["ARM D"])) %>% 
  arrange(AESOC, AEDECOD) %>% 
  ungroup() %>% 
  put()


put("Get counts for All Adverse Events")
df2 <- df_sub %>% 
  select(USUBJID, ARM, AESEVN) %>% 
  arrange(USUBJID, AESEVN) %>% 
  group_by(USUBJID) %>% 
  mutate(max_sev = ifelse(row_number() == n(), TRUE, FALSE)) %>% 
  filter(max_sev == TRUE) %>% 
  group_by(ARM, AESEVN) %>% 
  summarize(cnt = n()) %>% 
  pivot_wider(names_from = c(ARM, AESEVN),
              values_from = cnt, 
              values_fill = 0) %>% 
  ungroup() %>% 
  put()

put("Fill in missing columns where there were no events.")
for (nm in col_template) {
  if (!nm %in% names(df2))
    df2[[nm]] <- 0
}
put(df2)

put("Format counts and percents for all adverse events.")
df_all <- df2 %>% 
  transmute(AESOC = "All System Organ Classes",
            AEDECOD = "All Adverse Events", 
            `ARM A_1` = fmt_cnt_pct(`ARM A_1`, arm_pop["ARM A"]),
            `ARM A_2` = fmt_cnt_pct(`ARM A_2`, arm_pop["ARM A"]),
            `ARM A_3` = fmt_cnt_pct(`ARM A_3`, arm_pop["ARM A"]),
            `ARM B_1` = fmt_cnt_pct(`ARM B_1`, arm_pop["ARM B"]),
            `ARM B_2` = fmt_cnt_pct(`ARM B_2`, arm_pop["ARM B"]),
            `ARM B_3` = fmt_cnt_pct(`ARM B_3`, arm_pop["ARM B"]),
            `ARM C_1` = fmt_cnt_pct(`ARM C_1`, arm_pop["ARM C"]),
            `ARM C_2` = fmt_cnt_pct(`ARM C_2`, arm_pop["ARM C"]),
            `ARM C_3` = fmt_cnt_pct(`ARM C_3`, arm_pop["ARM C"]),
            `ARM D_1` = fmt_cnt_pct(`ARM D_1`, arm_pop["ARM D"]), 
            `ARM D_2` = fmt_cnt_pct(`ARM D_2`, arm_pop["ARM D"]), 
            `ARM D_3` = fmt_cnt_pct(`ARM D_3`, arm_pop["ARM D"])) %>% 
  put()


# Final Data --------------------------------------------------------------

sep("Create final data frame")

final <- bind_rows(df_all, df_events) %>% put()


# Print Report ----------------------------------------------------------

sep("Create and print report")

put("Create table object")
tbl <- create_table(final, first_row_blank = TRUE, width = 9) %>% 
  column_defaults(from = `ARM A_1`, to = `ARM D_3`, width = 1) %>% 
  spanning_header("ARM A_1", "ARM A_3", label = "ARM A", n = arm_pop["ARM A"]) %>%
  spanning_header("ARM B_1", "ARM B_3", label = "ARM B", n = arm_pop["ARM B"]) %>%
  spanning_header("ARM C_1", "ARM C_3", label = "ARM C", n = arm_pop["ARM C"]) %>%
  spanning_header("ARM D_1", "ARM D_3", label = "ARM D", n = arm_pop["ARM D"]) %>%
  stub(vars = c("AESOC", "AEDECOD"), label = "System Organ Class\n   Preferred Term", width = 5) %>% 
  define(AESOC, blank_after = TRUE, label_row = TRUE) %>% 
  define(AEDECOD, indent = .25) %>% 
  define(`ARM A_1`, align = "center", label = "Mild") %>% 
  define(`ARM A_2`, align = "center", label = "Moderate") %>% 
  define(`ARM A_3`, align = "center", label = "Severe") %>% 
  define(`ARM B_1`, align = "center", label = "Mild", page_wrap = TRUE) %>% 
  define(`ARM B_2`, align = "center", label = "Moderate") %>% 
  define(`ARM B_3`, align = "center", label = "Severe") %>% 
  define(`ARM C_1`, align = "center", label = "Mild", page_wrap = TRUE) %>% 
  define(`ARM C_2`, align = "center", label = "Moderate") %>% 
  define(`ARM C_3`, align = "center", label = "Severe") %>% 
  define(`ARM D_1`, align = "center", label = "Mild", page_wrap = TRUE) %>% 
  define(`ARM D_2`, align = "center", label = "Moderate") %>% 
  define(`ARM D_3`, align = "center", label = "Severe") 


pth <- file.path(tmp, "output/example4.rtf")

put("Create report object")
rpt <- create_report(pth, output_type = "RTF") %>% 
  options_fixed(font_size = 10) %>% 
  page_header("Sponsor: Company", "Study: ABC") %>% 
  titles("Table 5.0", "Adverse Events by Maximum Severity") %>% 
  add_content(tbl) %>% 
  footnotes("Program: AE_Table.R",
            "Note: Adverse events were coded using MedDRA Version 9.1") %>% 
  page_footer(Sys.time(), "Confidential", "Page [pg] of [tpg]") 

put("Print report")
write_report(rpt) %>% put()



# Clean Up ----------------------------------------------------------------

# Remove library from workspace
lib_unload(sdtm)

# Close log
log_close()

# View files
# file.show(pth)
# file.show(lgpth)

```

#### Log

Here is part of the log from the above example:

```
========================================================================= 
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpKC93lu/log/example4.log 
Working Directory: C:/packages/Testing 
User Name: dbosa 
R Version: 4.0.5 (2021-03-31) 
Machine: SOCRATES x86-64 
Operating System: Windows 10 x64 build 19041 
Log Start Time: 2021-06-27 11:30:01 
========================================================================= 

========================================================================= 
Prepare Data 
========================================================================= 

# library 'sdtm': 4 items
- attributes: csv not loaded
- path: C:/Users/dbosa/Documents/R/win-library/4.0/sassy/extdata
- items:
  Name Extension Rows Cols    Size        LastModified
1   AE       csv  150   27 88.1 Kb 2021-04-04 11:55:58
2   DM       csv   87   24 45.2 Kb 2021-04-04 11:55:58
3   SV       csv  685   10 69.9 Kb 2021-04-04 11:55:58
4   VS       csv 3358   17  467 Kb 2021-04-04 11:55:58

lib_load: library 'sdtm' loaded 

========================================================================= 
Prepare table data 
========================================================================= 

select: dropped 21 variables (STUDYID, DOMAIN, SUBJID, RFSTDTC, RFENDTC, …)

inner_join: added 2 columns (ARM, ARMCD)

            > rows only in x  (  0)

            > rows only in y  ( 30)

            > matched rows     150

            >                 =====

            > rows total       150

filter: removed 5 rows (3%), 145 rows remaining

Get population counts 

select: dropped 27 variables (STUDYID, DOMAIN, AESEQ, AETERM, AELLT, …)

distinct: removed 90 rows (62%), 55 rows remaining

count: now 4 rows and 2 columns, ungrouped

ARM A ARM B ARM C ARM D 
   14    14    14    13 

Create lookup for AE severity 

    MILD MODERATE   SEVERE 
       1        2        3 

Subset ADAE for needed rows and columns 

mutate: new variable 'AESEVN' (double) with 3 unique values and 0% NA

select: dropped 24 variables (STUDYID, DOMAIN, AESEQ, AETERM, AELLT, …)

distinct: removed 18 rows (12%), 127 rows remaining

# A tibble: 127 x 6
   USUBJID    ARM   AESEV    AESEVN AESOC                                           AEDECOD                         
   <chr>      <chr> <chr>     <dbl> <chr>                                           <chr>                           
 1 ABC-01-049 ARM D MODERATE      2 Investigations                                  BLOOD GLUCOSE INCREASED         
 2 ABC-01-049 ARM D MODERATE      2 Investigations                                  BLOOD TRIGLYCERIDES INCREASED   
 3 ABC-01-049 ARM D MILD          1 Nervous system disorders                        HEADACHE                        
 4 ABC-01-049 ARM D MODERATE      2 Investigations                                  LABORATORY TEST ABNORMAL        
 5 ABC-01-049 ARM D MILD          1 Musculoskeletal and connective tissue disorders MUSCULOSKELETAL DISCOMFORT      
 6 ABC-01-050 ARM B MILD          1 Skin and subcutaneous tissue disorders          RASH                            
 7 ABC-01-050 ARM B MILD          1 Respiratory, thoracic and mediastinal disorders UPPER RESPIRATORY TRACT CONGEST~
 8 ABC-01-051 ARM A MILD          1 Nervous system disorders                        HEADACHE                        
 9 ABC-01-051 ARM A MILD          1 General disorders and administration site cond~ INFLUENZA LIKE ILLNESS          
10 ABC-01-051 ARM A MILD          1 Respiratory, thoracic and mediastinal disorders LOWER RESPIRATORY TRACT INFLAMM~
# ... with 117 more rows

Create template for needed ARM columns 

Identify and count highest severity grade for each subject 

group_by: 4 grouping variables (USUBJID, ARM, AESOC, AEDECOD)

mutate (grouped): new variable 'max_sev' (logical) with 2 unique values and 0% NA

filter (grouped): removed one row (1%), 126 rows remaining

group_by: 4 grouping variables (ARM, AESOC, AEDECOD, AESEVN)

summarize: now 101 rows and 5 columns, 3 group variables remaining (ARM, AESOC, AEDECOD)

pivot_wider: reorganized (ARM, AESEVN, cnt) into (ARM A_2, ARM A_1, ARM B_1, ARM B_2, ARM B_3, …) [was 101x5, now 73x11]

...


========================================================================= 
Create final data frame 
========================================================================= 

# A tibble: 74 x 14
   AESOC AEDECOD `ARM A_1` `ARM A_2` `ARM A_3` `ARM B_1` `ARM B_2` `ARM B_3` `ARM C_1` `ARM C_2` `ARM C_3` `ARM D_1`
   <chr> <chr>   <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>    
 1 All ~ All Ad~ 6 ( 42.9~ 8 ( 57.1~ 0 (  0.0~ 8 ( 57.1~ 4 ( 28.6~ 2 ( 14.3~ 8 ( 57.1~ 6 ( 42.9~ 0 (  0.0~ 7 ( 53.8~
 2 Bloo~ Neutro~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~
 3 Card~ Palpit~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.7~
 4 Card~ Sinus ~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.7~
 5 Cong~ Dermoi~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~
 6 Ear ~ Vertigo 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~
 7 Endo~ Parath~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~
 8 Gast~ Diarrh~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~
 9 Gast~ Food P~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~
10 Gast~ Tootha~ 0 (  0.0~ 1 (  7.1~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 0 (  0.0~ 2 ( 15.4~
# ... with 64 more rows, and 2 more variables: ARM D_2 <chr>, ARM D_3 <chr>

========================================================================= 
Create and print report 
========================================================================= 

Create table object 

Create report object 

Print report 

# A report specification: 16 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKC93lu/output/example4.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 107/47
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 5.0'
- title 2: 'Adverse Events by Maximum Severity'
- footnote 1: 'Program: AE_Table.R'
- footnote 2: 'Note: Adverse events were coded using MedDRA Version 9.1'
- page_footer: left=2021-06-27 11:30:02 center=Confidential right=Page [pg] of [tpg]
- content: 
# A table specification:
- data: tibble 'final' 74 rows 14 cols
- show_cols: all
- use_attributes: all
- width: 9
- spanning_header: from='ARM A_1' to='ARM A_3' 'ARM A' level=1 
- spanning_header: from='ARM B_1' to='ARM B_3' 'ARM B' level=1 
- spanning_header: from='ARM C_1' to='ARM C_3' 'ARM C' level=1 
- spanning_header: from='ARM D_1' to='ARM D_3' 'ARM D' level=1 
- stub: AESOC AEDECOD 'System Organ Class
   Preferred Term' width=5 align='left' 
- define: AESOC dedupe='TRUE' 
- define: AEDECOD 
- define: ARM A_1 'Mild' align='center' 
- define: ARM A_2 'Moderate' align='center' 
- define: ARM A_3 'Severe' align='center' 
- define: ARM B_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM B_2 'Moderate' align='center' 
- define: ARM B_3 'Severe' align='center' 
- define: ARM C_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM C_2 'Moderate' align='center' 
- define: ARM C_3 'Severe' align='center' 
- define: ARM D_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM D_2 'Moderate' align='center' 
- define: ARM D_3 'Severe' align='center' 

# A report specification: 16 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKC93lu/output/example4.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 107/47
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 5.0'
- title 2: 'Adverse Events by Maximum Severity'
- footnote 1: 'Program: AE_Table.R'
- footnote 2: 'Note: Adverse events were coded using MedDRA Version 9.1'
- page_footer: left=2021-06-27 11:30:02 center=Confidential right=Page [pg] of [tpg]
- content: 
# A table specification:
- data: tibble 'final' 74 rows 14 cols
- show_cols: all
- use_attributes: all
- width: 9
- spanning_header: from='ARM A_1' to='ARM A_3' 'ARM A' level=1 
- spanning_header: from='ARM B_1' to='ARM B_3' 'ARM B' level=1 
- spanning_header: from='ARM C_1' to='ARM C_3' 'ARM C' level=1 
- spanning_header: from='ARM D_1' to='ARM D_3' 'ARM D' level=1 
- stub: AESOC AEDECOD 'System Organ Class
   Preferred Term' width=5 align='left' 
- define: AESOC dedupe='TRUE' 
- define: AEDECOD 
- define: ARM A_1 'Mild' align='center' 
- define: ARM A_2 'Moderate' align='center' 
- define: ARM A_3 'Severe' align='center' 
- define: ARM B_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM B_2 'Moderate' align='center' 
- define: ARM B_3 'Severe' align='center' 
- define: ARM C_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM C_2 'Moderate' align='center' 
- define: ARM C_3 'Severe' align='center' 
- define: ARM D_1 'Mild' align='center' page_wrap='TRUE' 
- define: ARM D_2 'Moderate' align='center' 
- define: ARM D_3 'Severe' align='center' 

lib_sync: synchronized data in library 'sdtm' 

lib_unload: library 'sdtm' unloaded 

========================================================================= 
Log End Time: 2021-06-27 11:30:04 
Log Elapsed Time: 0 00:00:03 
========================================================================= 


```
#### Output

Here are the first four pages of the output report:

<img src="../man/images/ae1.png" align="center" />
<img src="../man/images/ae2.png" align="center" />
<img src="../man/images/ae3.png" align="center" />
<img src="../man/images/ae4.png" align="center" />


Next: [Example 5: VS Table](sassy-vs.html)
