Issue while making summary - non corresponding categories are coming in categorical analysis

[Vatsal9195]

Want to remove all the rows with complete 0 which occur while generating the summary

can Add trim_rows() at the end but if a sub-group is not present in group it should not appear.

adae <- adae %>%
  filter(AESOC %in% c('CARDIAC DISORDERS', 'EYE DISORDERS')) %>% 
  select(USUBJID, TRTA, AESOC, AETERM) %>% 
  mutate(AETERM = factor(AETERM))

basic_table(title = "Demo table", main_footer = "Notes") %>%
  split_cols_by("TRTA") %>%
  add_colcounts() %>%
  split_rows_by("AESOC", split_label = "AESOC", label_pos = "topleft") %>%
  analyze_vars(vars = "AETERM", .stats = "count_fraction", .labels = "hello", na.rm = TRUE) %>%
  rtables::append_topleft(" n (%)") %>%
  build_table(adae)

Output:

[edelarua]

Hi @Vatsal9195,

Please try applying the prune_table() function on your table after it is constructed instead of trim_rows(). You can see a reproducible example using the function with dummy data below. If this is not the result you'd like, you can find a list of other pruning functions here which can be used as input to the prune_func argument of prune_table() to customize the pruning behaviour.

library(tern)

adae <- random.cdisc.data::cadae

adae <- adae %>%
  dplyr::select(USUBJID, TRT01A, AESOC, AETERM) %>%
  dplyr::mutate(AETERM = factor(AETERM))

tbl <- basic_table(title = "Demo table", main_footer = "Notes") %>%
  split_cols_by("TRT01A") %>%
  add_colcounts() %>%
  split_rows_by("AESOC", split_label = "AESOC", label_pos = "topleft") %>%
  analyze_vars(vars = "AETERM", .stats = "count_fraction", .labels = "hello", na.rm = TRUE) %>%
  append_topleft("  n (%)") %>%
  build_table(adae)

tbl
#> Demo table
#> 
#> ——————————————————————————————————————————————————————————
#> AESOC             A: Drug X    B: Placebo   C: Combination
#>   n (%)            (N=609)      (N=622)        (N=703)    
#> ——————————————————————————————————————————————————————————
#> cl A                                                      
#>   trm A.1.1.1.1   64 (48.5%)   62 (47.7%)      88 (55%)   
#>   trm A.1.1.1.2   68 (51.5%)   68 (52.3%)      72 (45%)   
#>   trm B.1.1.1.1       0            0              0       
#>   trm B.2.1.2.1       0            0              0       
#>   trm B.2.2.3.1       0            0              0       
#>   trm C.1.1.1.3       0            0              0       
#>   trm C.2.1.2.1       0            0              0       
#>   trm D.1.1.1.1       0            0              0       
#>   trm D.1.1.4.2       0            0              0       
#>   trm D.2.1.5.3       0            0              0       
#> cl B                                                      
#>   trm A.1.1.1.1       0            0              0       
#>   trm A.1.1.1.2       0            0              0       
#>   trm B.1.1.1.1   56 (30.3%)   60 (30.3%)     62 (30.2%)  
#>   trm B.2.1.2.1   65 (35.1%)   62 (31.3%)     66 (32.2%)  
#>   trm B.2.2.3.1   64 (34.6%)   76 (38.4%)     77 (37.6%)  
#>   trm C.1.1.1.3       0            0              0       
#>   trm C.2.1.2.1       0            0              0       
#>   trm D.1.1.1.1       0            0              0       
#>   trm D.1.1.4.2       0            0              0       
#>   trm D.2.1.5.3       0            0              0       
#> cl C                                                      
#>   trm A.1.1.1.1       0            0              0       
#>   trm A.1.1.1.2       0            0              0       
#>   trm B.1.1.1.1       0            0              0       
#>   trm B.2.1.2.1       0            0              0       
#>   trm B.2.2.3.1       0            0              0       
#>   trm C.1.1.1.3   55 (53.4%)   63 (54.3%)     64 (49.6%)  
#>   trm C.2.1.2.1   48 (46.6%)   53 (45.7%)     65 (50.4%)  
#>   trm D.1.1.1.1       0            0              0       
#>   trm D.1.1.4.2       0            0              0       
#>   trm D.2.1.5.3       0            0              0       
#> cl D                                                      
#>   trm A.1.1.1.1       0            0              0       
#>   trm A.1.1.1.2       0            0              0       
#>   trm B.1.1.1.1       0            0              0       
#>   trm B.2.1.2.1       0            0              0       
#>   trm B.2.2.3.1       0            0              0       
#>   trm C.1.1.1.3       0            0              0       
#>   trm C.2.1.2.1       0            0              0       
#>   trm D.1.1.1.1   61 (32.3%)   51 (28.7%)      71 (34%)   
#>   trm D.1.1.4.2   66 (34.9%)   55 (30.9%)     64 (30.6%)  
#>   trm D.2.1.5.3   62 (32.8%)   72 (40.4%)     74 (35.4%)  
#> ——————————————————————————————————————————————————————————
#> 
#> Notes

tbl %>% prune_table()
#> Demo table
#> 
#> ——————————————————————————————————————————————————————————
#> AESOC             A: Drug X    B: Placebo   C: Combination
#>   n (%)            (N=609)      (N=622)        (N=703)    
#> ——————————————————————————————————————————————————————————
#> cl A                                                      
#>   trm A.1.1.1.1   64 (48.5%)   62 (47.7%)      88 (55%)   
#>   trm A.1.1.1.2   68 (51.5%)   68 (52.3%)      72 (45%)   
#> cl B                                                      
#>   trm B.1.1.1.1   56 (30.3%)   60 (30.3%)     62 (30.2%)  
#>   trm B.2.1.2.1   65 (35.1%)   62 (31.3%)     66 (32.2%)  
#>   trm B.2.2.3.1   64 (34.6%)   76 (38.4%)     77 (37.6%)  
#> cl C                                                      
#>   trm C.1.1.1.3   55 (53.4%)   63 (54.3%)     64 (49.6%)  
#>   trm C.2.1.2.1   48 (46.6%)   53 (45.7%)     65 (50.4%)  
#> cl D                                                      
#>   trm D.1.1.1.1   61 (32.3%)   51 (28.7%)      71 (34%)   
#>   trm D.1.1.4.2   66 (34.9%)   55 (30.9%)     64 (30.6%)  
#>   trm D.2.1.5.3   62 (32.8%)   72 (40.4%)     74 (35.4%)  
#> ——————————————————————————————————————————————————————————
#> 
#> Notes

^{Created on 2024-08-23 with reprex v2.1.1}