Program

The previous examples in the logr documentation were intentionally simplified to focus on the workings of a particular function. It is helpful, however, to also view logr functions in the context of a complete program. The following example shows a complete program. The example illustrates how logr functions work together, and interact with tidyverse and sassy functions to create a comprehensive log.

This example has been chosen because it incorporates many of the functions that will log automatically. If you want to maximize the auto-generation features of logr, take note of these functions.

The data for this example has been included in the logr package as an external data file. It may be accessed using the system.file() function as shown below, or downloaded directly from the logr GitHub site here

library(tidyverse)
library(sassy)

options("logr.autolog" = TRUE)

# Get temp location for log and report output
tmp <- tempdir()

# Open the log
lf <- log_open(file.path(tmp, "example1.log"))

# Send code to the log
log_code()

sep("Load the data")

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

# Define data library
libname(sdtm, pkg, "csv") 

# Load the library into memory
lib_load(sdtm)


# Prepare Data -------------------------------------------------------------
sep("Prepare the data")

# Define format for age groups
ageg <- value(condition(x > 18 & x <= 29, "18 to 29"),
              condition(x >= 30 & x <= 44, "30 to 44"),
              condition(x >= 45 & x <= 59, "45 to 59"),
              condition(TRUE, "60+"))


# Manipulate data
final <- sdtm.DM %>% 
  select(USUBJID, BRTHDTC, AGE) %>% 
  mutate(AGEG = fapply(AGE, ageg)) %>% 
  arrange(AGEG, AGE) %>% 
  group_by(AGEG) %>% 
  datastep(retain = list(SEQ = 0), 
           calculate = {AGEM <- mean(AGE)},
           attrib = list(USUBJID = dsattr(label = "Universal Subject ID"),
                         BRTHDTC = dsattr(label = "Subject Birth Date", 
                                          format = "%m %B %Y"),
                         AGE = dsattr(label = "Subject Age in Years", 
                                      justify = "center"),
                         AGEG = dsattr(label = "Subject Age Group", 
                                       justify = "left"), 
                         AGEB = dsattr(label = "Age Group Boundaries"),
                         SEQ = dsattr(label = "Subject Age Group Sequence", 
                                      justify = "center"),
                         AGEM = dsattr(label = "Mean Subject Age", 
                                       format = "%1.2f"),
                         AGEMC = dsattr(label = "Subject Age Mean Category",
                                        format = c(B = "Below", A = "Above"), 
                                        justify = "right")), 
           {
             
             # Start and end of Age Groups
             if (first. & last.)
               AGEB <- "Start - End"
             else if (first.)
               AGEB <- "Start"
             else if (last.)
               AGEB <- "End"
             else 
               AGEB <- "-"
             
             # Sequence within Age Groups
             if (first.)
               SEQ <- 1
             else 
               SEQ <- SEQ + 1
             
             # Above or Below the mean age
             if (AGE > AGEM)
               AGEMC <- "A"
             else 
               AGEMC <- "B"
             
           }) %>% 
  ungroup() %>% 
  put()

# Put dictionary to log
dictionary(final) %>% put()

# Create Report ------------------------------------------------------------
sep("Create report")


# Create table
tbl <- create_table(final)

# Create report
rpt <- create_report(file.path(tmp, "./output/example1.rtf"), 
                     output_type = "RTF", font = "Arial") %>% 
  titles("Our first SASSY report", bold = TRUE) %>% 
  add_content(tbl)

# write out the report
res <- write_report(rpt)


# Clean Up -----------------------------------------------------------------
sep("Clean Up")

# Unload libname
lib_unload(sdtm)

# Close the log
log_close()

# View log
writeLines(readLines(lf, encoding = "UTF-8"))

# View Report
# file.show(res$modified_path)

Log

The above program produces the following log:

=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/Rtmp6DW7BF/log/example1.log
Program Path: C:\packages\logr\vignettes\logr-example1.Rmd
Working Directory: C:/packages/logr
User Name: dbosa
R Version: 4.1.2 (2021-11-01)
Machine: SOCRATES x86-64
Operating System: Windows 10 x64 build 19041
Base Packages: stats graphics grDevices utils datasets methods base
Other Packages: tidylog_1.0.2 reporter_1.2.6 libr_1.2.1 fmtr_1.5.3 sassy_1.0.5
                forcats_0.5.1 stringr_1.4.0 purrr_0.3.4 readr_2.0.2 tidyr_1.1.4
                tibble_3.1.5 ggplot2_3.3.5 tidyverse_1.3.1 logr_1.2.7 dplyr_1.0.7
Log Start Time: 2021-11-16 08:40:18
=========================================================================

> library(tidyverse)
> library(sassy)
> 
> options("logr.autolog" = TRUE)
> 
> # Open the log
> lf <- log_open()
> 
> # Send code to the log
> log_code()
> 
> sep("Load the data")
> 
> # Get path to sample data
> pkg <- system.file("extdata", package = "logr")
> 
> # Define data library
> libname(sdtm, pkg, "csv") 
> 
> # Load the library into memory
> lib_load(sdtm)
> 
> 
> # Prepare Data -------------------------------------------------------------
> sep("Prepare the data")
> 
> # Define format for age groups
> ageg <- value(condition(x > 18 & x <= 29, "18 to 29"),
>               condition(x >= 30 & x <= 44, "30 to 44"),
>               condition(x >= 45 & x <= 59, "45 to 59"),
>               condition(TRUE, "60+"))
> 
> 
> # Manipulate data
> final <- sdtm.DM %>% 
>   select(USUBJID, BRTHDTC, AGE) %>% 
>   mutate(AGEG = fapply(AGE, ageg)) %>% 
>   arrange(AGEG, AGE) %>% 
>   group_by(AGEG) %>% 
>   datastep(retain = list(SEQ = 0), 
>            calculate = {AGEM <- mean(AGE)},
>            attrib = list(USUBJID = dsattr(label = "Universal Subject ID"),
>                          BRTHDTC = dsattr(label = "Subject Birth Date", 
>                                           format = "%m %B %Y"),
>                          AGE = dsattr(label = "Subject Age in Years", 
>                                       justify = "center"),
>                          AGEG = dsattr(label = "Subject Age Group", 
>                                        justify = "left"), 
>                          AGEB = dsattr(label = "Age Group Boundaries"),
>                          SEQ = dsattr(label = "Subject Age Group Sequence", 
>                                       justify = "center"),
>                          AGEM = dsattr(label = "Mean Subject Age", 
>                                        format = "%1.2f"),
>                          AGEMC = dsattr(label = "Subject Age Mean Category",
>                                         format = c(B = "Below", A = "Above"), 
>                                         justify = "right")), 
>            {
>              
>              # Start and end of Age Groups
>              if (first. & last.)
>                AGEB <- "Start - End"
>              else if (first.)
>                AGEB <- "Start"
>              else if (last.)
>                AGEB <- "End"
>              else 
>                AGEB <- "-"
>              
>              # Sequence within Age Groups
>              if (first.)
>                SEQ <- 1
>              else 
>                SEQ <- SEQ + 1
>              
>              # Above or Below the mean age
>              if (AGE > AGEM)
>                AGEMC <- "A"
>              else 
>                AGEMC <- "B"
>              
>            }) %>% 
>   ungroup() %>% 
>   put()
> 
> # Put dictionary to log
> dictionary(final) %>% put()
> 
> # Create Report ------------------------------------------------------------
> sep("Create report")
> 
> 
> # Create table
> tbl <- create_table(final)
> 
> # Create report
> rpt <- create_report("./output/example1.rtf", 
>                      output_type = "RTF", font = "Arial") %>% 
>   titles("Our first SASSY report", bold = TRUE) %>% 
>   add_content(tbl)
> 
> # write out the report
> res <- write_report(rpt)
> 
> 
> # Clean Up -----------------------------------------------------------------
> sep("Clean Up")
> 
> # Unload libname
> lib_unload(sdtm)
> 
> # Close the log
> log_close()
> 
> # View log
> writeLines(readLines(lf, encoding = "UTF-8"))
> 
> # View Report
> # file.show(res$modified_path)

=========================================================================
Load the data
=========================================================================

# library 'sdtm': 8 items
- attributes: csv not loaded
- path: C:/Users/dbosa/Documents/R/win-library/4.1/logr/extdata
- items:
  Name Extension Rows Cols     Size        LastModified
1   AE       csv  150   27  88.3 Kb 2021-10-08 15:02:15
2   DA       csv 3587   18 528.1 Kb 2021-10-08 15:02:15
3   DM       csv   87   24  45.4 Kb 2021-10-08 15:02:15
4   DS       csv  174    9  33.9 Kb 2021-10-08 15:02:15
5   EX       csv   84   11  26.2 Kb 2021-10-08 15:02:15
6   IE       csv    2   14  13.2 Kb 2021-10-08 15:02:15
7   SV       csv  685   10  70.2 Kb 2021-10-08 15:02:15
8   VS       csv 3358   17 467.3 Kb 2021-10-08 15:02:15

NOTE: Log Print Time:  2021-11-16 08:40:54
NOTE: Elapsed Time in seconds: 35.2692317962646

lib_load: library 'sdtm' loaded

NOTE: Log Print Time:  2021-11-16 08:41:00
NOTE: Elapsed Time in seconds: 5.96066999435425

=========================================================================
Prepare the data
=========================================================================

select: dropped 21 variables (STUDYID, DOMAIN, SUBJID, RFSTDTC, RFENDTC, <U+0085>)

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 9.39324498176575

mutate: new variable 'AGEG' (character) with 4 unique values and 0% NA

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 0.0129940509796143

group_by: one grouping variable (AGEG)

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 0.0119409561157227

datastep: columns increased from 4 to 8

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 0.0947761535644531

ungroup: no grouping variables

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 0.0029609203338623

# A tibble: 87 x 8
   USUBJID    BRTHDTC      AGE AGEG      AGEM AGEB    SEQ AGEMC
   <chr>      <date>     <dbl> <chr>    <dbl> <chr> <dbl> <chr>
 1 ABC-04-128 1987-05-24    19 18 to 29  49.4 Start     1 B    
 2 ABC-07-011 1985-01-18    21 18 to 29  49.4 -         2 B    
 3 ABC-09-139 1985-11-13    21 18 to 29  49.4 -         3 B    
 4 ABC-09-018 1984-08-29    22 18 to 29  49.4 -         4 B    
 5 ABC-04-074 1983-03-28    23 18 to 29  49.4 -         5 B    
 6 ABC-01-053 1980-04-07    26 18 to 29  49.4 -         6 B    
 7 ABC-06-070 1980-02-01    26 18 to 29  49.4 End       7 B    
 8 ABC-02-112 1976-11-01    30 30 to 44  49.4 Start     1 B    
 9 ABC-01-056 1975-05-02    31 30 to 44  49.4 -         2 B    
10 ABC-03-089 1975-10-02    31 30 to 44  49.4 -         3 B    
# ... with 77 more rows

NOTE: Data frame has 87 rows and 8 columns.

NOTE: Log Print Time:  2021-11-16 08:41:09
NOTE: Elapsed Time in seconds: 0.0398931503295898

# A tibble: 8 x 10
  Name  Column  Class     Label     Description Format  Width Justify  Rows   NAs
  <chr> <chr>   <chr>     <chr>     <chr>       <chr>   <int> <chr>   <int> <int>
1 final USUBJID character Universa~ <NA>         <NA>      10 <NA>       87     0
2 final BRTHDTC Date      Subject ~ <NA>        "%m %B~    NA <NA>       87     0
3 final AGE     numeric   Subject ~ <NA>         <NA>      NA center     87     0
4 final AGEG    character Subject ~ <NA>         <NA>       8 left       87     0
5 final AGEM    numeric   Mean Sub~ <NA>        "%1.2f"    NA <NA>       87     0
6 final AGEB    character Age Grou~ <NA>         <NA>       5 <NA>       87     0
7 final SEQ     numeric   Subject ~ <NA>         <NA>      NA center     87     0
8 final AGEMC   character Subject ~ <NA>        "Below~     1 right      87     0

NOTE: Data frame has 8 rows and 10 columns.

NOTE: Log Print Time:  2021-11-16 08:41:11
NOTE: Elapsed Time in seconds: 2.2303478717804

=========================================================================
Create report
=========================================================================

# A report specification: 3 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\Rtmp6DW7BF/./output/example1.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 9/46
- title 1: 'Our first SASSY report'
- content: 
# A table specification:
- data: tibble 'final' 87 rows 8 cols
- show_cols: all
- use_attributes: all

NOTE: Log Print Time:  2021-11-16 08:41:16
NOTE: Elapsed Time in seconds: 4.57250308990479

=========================================================================
Clean Up
=========================================================================

lib_sync: synchronized data in library 'sdtm'

NOTE: Log Print Time:  2021-11-16 08:41:17
NOTE: Elapsed Time in seconds: 1.38584780693054

lib_unload: library 'sdtm' unloaded

NOTE: Log Print Time:  2021-11-16 08:41:17
NOTE: Elapsed Time in seconds: 0.00399112701416016

=========================================================================
Log End Time: 2021-11-16 08:41:19
Log Elapsed Time: 0 00:00:01
=========================================================================

Output

Here is the report produced by the sample program above: