Examples

library(tfrmt)
library(dplyr)
library(gt)
library(tidyr)

Demography Table

For this demography table we are going to use data_demog, an example analysis results dataset found in the package, which is based on the CDISC pilot data. This dataset has two different row label columns, rowlbl1 and rowlbl2 because we are building a table with group and row labels. There are also two order columns which will be used to set the row order of the output. There is a single column to define our table’s columns (multiple column columns are used when there is column spanning). Finally there is a param column, a value column and an additional grouping column, grp, which we can use for more complex formatting.

#> # A tibble: 6 × 8
#> # Groups:   rowlbl1 [1]
#>   rowlbl1 rowlbl2 param grp    ord1  ord2 column                 value
#>   <chr>   <chr>   <chr> <chr> <dbl> <dbl> <chr>                  <dbl>
#> 1 Age (y) n       n     cont      1     1 Placebo               86    
#> 2 Age (y) n       n     cont      1     1 Xanomeline Low Dose   84    
#> 3 Age (y) n       n     cont      1     1 Xanomeline High Dose  84    
#> 4 Age (y) n       n     cont      1     1 Total                254    
#> 5 Age (y) n       p     cont      1     1 p-value                0.593
#> 6 Age (y) Mean    Mean  cont      1     2 Placebo               75.2

The mock we are going to match looks like this:

		Placebo	Xanomeline Low Dose	Xanomeline High Dose	Total	p-value
Age (y)	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
	<65 yrs	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	x.xxx
	65-80 yrs	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	>80 yrs	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
Sex	n	xxx	xxx	xxx	xxx	x.xxx
	Male	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	Female	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
Race (Origin)	n	xxx	xxx	xxx	xxx	x.xxx
	Caucasian	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	African Descent	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	Hispanic	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	Other	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
MMSE	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
Duration of disease	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
	<12 months	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	x.xxx
	>=12 months	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
Years of education	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
Baseline weight(kg)	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
Baseline height(cm)	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
Baseline BMI	n	xxx	xxx	xxx	xxx	x.xxx
	Mean	xxx.x	xxx.x	xxx.x	xxx.x
	SD	xxx.xx	xxx.xx	xxx.xx	xxx.xx
	Median	xxx.x	xxx.x	xxx.x	xxx.x
	Min	xxx.x	xxx.x	xxx.x	xxx.x
	Max	xxx.x	xxx.x	xxx.x	xxx.x
	<25	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	x.xxx
	25-<30	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)
	>=30	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)	xxx (xx.x %)

For this table, we have three columns for each of the treatment groups, a total column for all groups combined, and a p-value column. The table also contains a mix of categorical and continuous analysis.

The first thing we are going to do when building out the tfrmt is specify all our columns

tfrmt(
  # specify columns in the data
  group = c(rowlbl1,grp),
  label = rowlbl2,
  column = column, 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2)) %>% 
  print_to_gt(data_demog) %>% 
  tab_options(
    container.width = 900
  )

	ord1	ord2	Placebo	Xanomeline Low Dose	Xanomeline High Dose	Total	p-value
Age (y)
cont
n	1	1	86	84	84	254	0.593435775283097
Mean	1	2	75.2093023255814	75.6666666666667	74.3809523809524	75.0866141732283	NA
SD	1	3	8.59016712714193	8.28605059954093	7.88609384869824	8.24623389621606	NA
Median	1	4	76	77.5	76	77	NA
Min	1	5	52	51	56	51	NA
Max	1	6	89	88	88	89	NA
cat
<65 yrs	1	7	14, 16.2790697674419	8, 9.52380952380952	11, 13.0952380952381	33, 12.992125984252	0.143917025502502
65-80 yrs	1	8	42, 48.8372093023256	47, 55.9523809523809	55, 65.4761904761905	144, 56.6929133858268	NA
>80 yrs	1	9	30, 34.8837209302326	29, 34.5238095238095	18, 21.4285714285714	77, 30.3149606299213	NA
Sex
cat
n	2	1	86	84	84	254	0.140859828596478
Male	2	2	33, 38.3720930232558	34, 40.4761904761905	44, 52.3809523809524	111, 43.7007874015748	NA
Female	2	3	53, 61.6279069767442	50, 59.5238095238095	40, 47.6190476190476	143, 56.2992125984252	NA
Race (Origin)
cat
n	3	1	86	84	84	254	0.647674941661787
Caucasian	3	2	75, 87.2093023255814	72, 85.7142857142857	71, 84.5238095238095	218, 85.8267716535433	NA
African Descent	3	3	8, 9.30232558139535	6, 7.14285714285714	9, 10.7142857142857	23, 9.05511811023622	NA
Hispanic	3	4	3, 3.48837209302326	6, 7.14285714285714	3, 3.57142857142857	12, 4.7244094488189	NA
Other	3	5	NA, NA	NA, NA	1, 1.19047619047619	1, 0.393700787401575	NA
MMSE
cont
n	4	1	86	84	84	254	0.59465975941027
Mean	4	2	18.046511627907	17.8690476190476	18.5119047619048	18.1417322834646	NA
SD	4	3	4.2727783404855	4.22208696726523	4.15800591577738	4.21032874412179	NA
Median	4	4	19.5	18	20	19	NA
Min	4	5	10	10	10	10	NA
Max	4	6	23	24	24	24	NA
Duration of disease
cont
n	5	1	86	84	84	254	0.152960564175341
Mean	5	2	42.65	48.6916666666667	40.5071428571429	43.9393700787402	NA
SD	5	3	30.241571504451	29.5841711516636	24.6935472070355	28.3973156262964	NA
Median	5	4	35.3	40.25	35.95	36.25	NA
Min	5	5	7.2	7.8	2.2	2.2	NA
Max	5	6	183.1	130.8	135	183.1	NA
cat
<12 months	5	7	5, 5.81395348837209	3, 3.57142857142857	4, 4.76190476190476	12, 4.7244094488189	0.788536928535063
>=12 months	5	8	81, 94.1860465116279	81, 96.4285714285714	80, 95.2380952380952	242, 95.2755905511811	NA
Years of education
cont
n	6	1	86	84	84	254	0.38750874992316
Mean	6	2	12.5813953488372	13.1666666666667	12.5119047619048	12.751968503937	NA
SD	6	3	2.94843973206596	4.14738510544312	2.91854910157261	3.3829227112292	NA
Median	6	4	12	12	12	12	NA
Min	6	5	6	3	6	3	NA
Max	6	6	21	24	20	24	NA
Baseline weight(kg)
cont
n	7	1	86	83	84	253	0.00304006274608553
Mean	7	2	62.7593023255814	67.2795180722892	70.0047619047619	66.6478260869565	NA
SD	7	3	12.7715435329253	14.1235986486909	14.6534333717795	14.1314255372792	NA
Median	7	4	60.55	64.9	69.2	66.7	NA
Min	7	5	34	45.4	41.7	34	NA
Max	7	6	86.2	106.1	108	108	NA
Baseline height(cm)
cont
n	8	1	86	84	84	254	0.126217916960126
Mean	8	2	162.573255813953	163.433333333333	165.820238095238	163.931496062992	NA
SD	8	3	11.5223611185188	10.4192400034262	10.1313515524819	10.7604472686284	NA
Median	8	4	162.6	162.6	165.1	162.85	NA
Min	8	5	137.2	135.9	146.1	135.9	NA
Max	8	6	185.4	195.6	190.5	195.6	NA
Baseline BMI
cont
n	9	1	86	83	84	253	0.0133190726378392
Mean	9	2	23.6360465116279	25.0626506024096	25.347619047619	24.6723320158103	NA
SD	9	3	3.67192569419556	4.27050893303881	4.15826876019846	4.09218492698334	NA
Median	9	4	23.4	24.3	24.8	24.2	NA
Min	9	5	15.1	17.7	13.7	13.7	NA
Max	9	6	33.3	40.1	34.5	40.1	NA
cat
<25	9	7	59, 68.6046511627907	47, 55.9523809523809	44, 52.3809523809524	150, 59.0551181102362	0.232621461976889
25-<30	9	8	21, 24.4186046511628	27, 32.1428571428571	28, 33.3333333333333	76, 29.9212598425197	NA
>=30	9	9	6, 6.97674418604651	10, 11.9047619047619	12, 14.2857142857143	28, 11.0236220472441	NA

While this makes a table, it isn’t a very nice table and definitely doesn’t match the mock. So let’s start with formatting all the numbers. To do this we are going to build a body_plan to add to our tfrmt. This will be a fairly quick explanation of body_plans but if you would like more information see vignettes("Body Plan")

Body plans are made up of a series of frmt_stuctures where each frmt_stucture represents the formatting of a cell within the table. The order of the frmt_structures matter; they are always applied latest to oldest. This means the first frmt_stucture in the body_plan should be the most generic. You can use the groups, labels and parameters to specify which formatting applies to which values.

To start, we are going to use all the rows that are “n (%)” as the default. This way we don’t need to list out every row that is an “n (%)” row. These rows are made up of two different values, so we will need to use frmt_combine. Next, we can format the continuous variables, which is just a straightforward one value per row so we can just use the label to filter and frmt to define the look. Finally, we want to format the p-values. This is a bit more complicated, since the p-value sits in the same row as other parameters; therefore the group and label value are not specific enough and we need something more granular. As such, we will need to specify the parameter in the frmt_structure like so: frmt_structure(group_val = ".default", label_val = ".default", p = frmt("x.xx"). Further, we also need to make sure it never displays a rounded p-value of 0 or 1. So we can use frmt_when to specify the formatting based on the value.

tfrmt(
  # specify columns in the data
  group = c(rowlbl1,grp),
  label = rowlbl2,
  column = column, 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default", frmt_combine("{n} ({pct} %)", 
                                                                                n = frmt("xxx"),
                                                                                pct = frmt("xx.x"))),
    frmt_structure(group_val = ".default", label_val = "n", frmt("xxx")),
    frmt_structure(group_val = ".default", label_val = c("Mean", "Median", "Min","Max"), frmt("xxx.x")),
    frmt_structure(group_val = ".default", label_val = "SD", frmt("xxx.xx")),
    frmt_structure(group_val = ".default", label_val = ".default", p = frmt_when(">0.99" ~ ">0.99",
                                                                                 "<0.001" ~ "<0.001",
                                                                                 TRUE ~ frmt("x.xxx", missing = "")))
  )) %>% 
  print_to_gt(data_demog) %>% 
  tab_options(
    container.width = 900
  )

	ord1	ord2	Placebo	Xanomeline Low Dose	Xanomeline High Dose	Total	p-value
Age (y)
cont
n	1	1	86	84	84	254	0.593
Mean	1	2	75.2	75.7	74.4	75.1
SD	1	3	8.59	8.29	7.89	8.25
Median	1	4	76.0	77.5	76.0	77.0
Min	1	5	52.0	51.0	56.0	51.0
Max	1	6	89.0	88.0	88.0	89.0
cat
<65 yrs	1	7	14 (16.3 %)	8 ( 9.5 %)	11 (13.1 %)	33 (13.0 %)	0.144
65-80 yrs	1	8	42 (48.8 %)	47 (56.0 %)	55 (65.5 %)	144 (56.7 %)
>80 yrs	1	9	30 (34.9 %)	29 (34.5 %)	18 (21.4 %)	77 (30.3 %)
Sex
cat
n	2	1	86	84	84	254	0.141
Male	2	2	33 (38.4 %)	34 (40.5 %)	44 (52.4 %)	111 (43.7 %)
Female	2	3	53 (61.6 %)	50 (59.5 %)	40 (47.6 %)	143 (56.3 %)
Race (Origin)
cat
n	3	1	86	84	84	254	0.648
Caucasian	3	2	75 (87.2 %)	72 (85.7 %)	71 (84.5 %)	218 (85.8 %)
African Descent	3	3	8 ( 9.3 %)	6 ( 7.1 %)	9 (10.7 %)	23 ( 9.1 %)
Hispanic	3	4	3 ( 3.5 %)	6 ( 7.1 %)	3 ( 3.6 %)	12 ( 4.7 %)
Other	3	5			1 ( 1.2 %)	1 ( 0.4 %)
MMSE
cont
n	4	1	86	84	84	254	0.595
Mean	4	2	18.0	17.9	18.5	18.1
SD	4	3	4.27	4.22	4.16	4.21
Median	4	4	19.5	18.0	20.0	19.0
Min	4	5	10.0	10.0	10.0	10.0
Max	4	6	23.0	24.0	24.0	24.0
Duration of disease
cont
n	5	1	86	84	84	254	0.153
Mean	5	2	42.6	48.7	40.5	43.9
SD	5	3	30.24	29.58	24.69	28.40
Median	5	4	35.3	40.2	36.0	36.2
Min	5	5	7.2	7.8	2.2	2.2
Max	5	6	183.1	130.8	135.0	183.1
cat
<12 months	5	7	5 ( 5.8 %)	3 ( 3.6 %)	4 ( 4.8 %)	12 ( 4.7 %)	0.789
>=12 months	5	8	81 (94.2 %)	81 (96.4 %)	80 (95.2 %)	242 (95.3 %)
Years of education
cont
n	6	1	86	84	84	254	0.388
Mean	6	2	12.6	13.2	12.5	12.8
SD	6	3	2.95	4.15	2.92	3.38
Median	6	4	12.0	12.0	12.0	12.0
Min	6	5	6.0	3.0	6.0	3.0
Max	6	6	21.0	24.0	20.0	24.0
Baseline weight(kg)
cont
n	7	1	86	83	84	253	0.003
Mean	7	2	62.8	67.3	70.0	66.6
SD	7	3	12.77	14.12	14.65	14.13
Median	7	4	60.5	64.9	69.2	66.7
Min	7	5	34.0	45.4	41.7	34.0
Max	7	6	86.2	106.1	108.0	108.0
Baseline height(cm)
cont
n	8	1	86	84	84	254	0.126
Mean	8	2	162.6	163.4	165.8	163.9
SD	8	3	11.52	10.42	10.13	10.76
Median	8	4	162.6	162.6	165.1	162.8
Min	8	5	137.2	135.9	146.1	135.9
Max	8	6	185.4	195.6	190.5	195.6
Baseline BMI
cont
n	9	1	86	83	84	253	0.013
Mean	9	2	23.6	25.1	25.3	24.7
SD	9	3	3.67	4.27	4.16	4.09
Median	9	4	23.4	24.3	24.8	24.2
Min	9	5	15.1	17.7	13.7	13.7
Max	9	6	33.3	40.1	34.5	40.1
cat
<25	9	7	59 (68.6 %)	47 (56.0 %)	44 (52.4 %)	150 (59.1 %)	0.233
25-<30	9	8	21 (24.4 %)	27 (32.1 %)	28 (33.3 %)	76 (29.9 %)
>=30	9	9	6 ( 7.0 %)	10 (11.9 %)	12 (14.3 %)	28 (11.0 %)

Now that all the numbers look correct, we can drop the order columns and the grp column (note that while we do not want to display the grp column, it plays a role behind the scenes, which will be addressed in the next step). To do this we use a col_plan which uses tidy-select nomenclature to drop/move columns.

tfrmt(
  # specify columns in the data
  group = c(rowlbl1,grp),
  label = rowlbl2,
  column = column, 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default", frmt_combine("{n} {pct}", 
                                                                                n = frmt("xxx"),
                                                                                pct = frmt_when("==100" ~ "",
                                                                                                "==0" ~ "",
                                                                                                TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = "n", frmt("xxx")),
    frmt_structure(group_val = ".default", label_val = c("Mean", "Median", "Min","Max"), frmt("xxx.x")),
    frmt_structure(group_val = ".default", label_val = "SD", frmt("xxx.xx")),
    frmt_structure(group_val = ".default", label_val = ".default", p = frmt("")),
    frmt_structure(group_val = ".default", label_val = c("n","<65 yrs","<12 months","<25"), p = frmt_when(">0.99" ~ ">0.99",
                                                                                 "<0.001" ~ "<0.001",
                                                                                 TRUE ~ frmt("x.xxx", missing = "")))
  ),
  # remove extra cols
  col_plan = col_plan(-grp, 
                      -starts_with("ord") )) %>% 
  print_to_gt(data_demog) %>% 
  tab_options(
    container.width = 900
  )

	Placebo	Xanomeline Low Dose	Xanomeline High Dose	Total	p-value
Age (y)
n	86	84	84	254	0.593
Mean	75.2	75.7	74.4	75.1
SD	8.59	8.29	7.89	8.25
Median	76.0	77.5	76.0	77.0
Min	52.0	51.0	56.0	51.0
Max	89.0	88.0	88.0	89.0
<65 yrs	14 (16.3 %)	8 ( 9.5 %)	11 (13.1 %)	33 (13.0 %)	0.144
65-80 yrs	42 (48.8 %)	47 (56.0 %)	55 (65.5 %)	144 (56.7 %)
>80 yrs	30 (34.9 %)	29 (34.5 %)	18 (21.4 %)	77 (30.3 %)
Sex
n	86	84	84	254	0.141
Male	33 (38.4 %)	34 (40.5 %)	44 (52.4 %)	111 (43.7 %)
Female	53 (61.6 %)	50 (59.5 %)	40 (47.6 %)	143 (56.3 %)
Race (Origin)
n	86	84	84	254	0.648
Caucasian	75 (87.2 %)	72 (85.7 %)	71 (84.5 %)	218 (85.8 %)
African Descent	8 ( 9.3 %)	6 ( 7.1 %)	9 (10.7 %)	23 ( 9.1 %)
Hispanic	3 ( 3.5 %)	6 ( 7.1 %)	3 ( 3.6 %)	12 ( 4.7 %)
Other			1 ( 1.2 %)	1 ( 0.4 %)
MMSE
n	86	84	84	254	0.595
Mean	18.0	17.9	18.5	18.1
SD	4.27	4.22	4.16	4.21
Median	19.5	18.0	20.0	19.0
Min	10.0	10.0	10.0	10.0
Max	23.0	24.0	24.0	24.0
Duration of disease
n	86	84	84	254	0.153
Mean	42.6	48.7	40.5	43.9
SD	30.24	29.58	24.69	28.40
Median	35.3	40.2	36.0	36.2
Min	7.2	7.8	2.2	2.2
Max	183.1	130.8	135.0	183.1
<12 months	5 ( 5.8 %)	3 ( 3.6 %)	4 ( 4.8 %)	12 ( 4.7 %)	0.789
>=12 months	81 (94.2 %)	81 (96.4 %)	80 (95.2 %)	242 (95.3 %)
Years of education
n	86	84	84	254	0.388
Mean	12.6	13.2	12.5	12.8
SD	2.95	4.15	2.92	3.38
Median	12.0	12.0	12.0	12.0
Min	6.0	3.0	6.0	3.0
Max	21.0	24.0	20.0	24.0
Baseline weight(kg)
n	86	83	84	253	0.003
Mean	62.8	67.3	70.0	66.6
SD	12.77	14.12	14.65	14.13
Median	60.5	64.9	69.2	66.7
Min	34.0	45.4	41.7	34.0
Max	86.2	106.1	108.0	108.0
Baseline height(cm)
n	86	84	84	254	0.126
Mean	162.6	163.4	165.8	163.9
SD	11.52	10.42	10.13	10.76
Median	162.6	162.6	165.1	162.8
Min	137.2	135.9	146.1	135.9
Max	185.4	195.6	190.5	195.6
Baseline BMI
n	86	83	84	253	0.013
Mean	23.6	25.1	25.3	24.7
SD	3.67	4.27	4.16	4.09
Median	23.4	24.3	24.8	24.2
Min	15.1	17.7	13.7	13.7
Max	33.3	40.1	34.5	40.1
<25	59 (68.6 %)	47 (56.0 %)	44 (52.4 %)	150 (59.1 %)	0.233
25-<30	21 (24.4 %)	27 (32.1 %)	28 (33.3 %)	76 (29.9 %)
>=30	6 ( 7.0 %)	10 (11.9 %)	12 (14.3 %)	28 (11.0 %)

Now this table looks just about right. There are two problems, (1) alignment and (2) spacing between the continuous and categorical values. To take care of the alignment we are going to add a col_style_plan which accepts a series of col_style_structures. This allows columns to be aligned differently if needed. For this table, we want all the columns to align on either “.”, “,” or ” ” so our col_style_structure looks like col_style_structure(align = c(".",","," "), col = vars(everything())). After the alignment is sorted we can move on to the spacing. In order to match the spacing of the mock we need to use the extra grp column from our data. If we look at our data, we can see we want a space any time either of the groups change.

data_demog %>% 
  distinct(rowlbl1,grp)
#> # A tibble: 12 × 2
#> # Groups:   rowlbl1 [9]
#>    rowlbl1                grp  
#>    <chr>                  <chr>
#>  1 "Age (y)"              cont 
#>  2 "Age (y)"              cat  
#>  3 "Sex"                  cat  
#>  4 "Race (Origin)"        cat  
#>  5 "MMSE"                 cont 
#>  6 "Duration of disease " cont 
#>  7 "Duration of disease " cat  
#>  8 "Years of education"   cont 
#>  9 "Baseline weight(kg)"  cont 
#> 10 "Baseline height(cm)"  cont 
#> 11 "Baseline BMI"         cont 
#> 12 "Baseline BMI"         cat

This means that we can use a row_grp_plan with just a ".default" as the group value and it should handle all of the spacing. In addition to the spacing, row_grp_plan will let us move the spanning group labels to a separate column by changing the label_loc to “column”.

tfrmt(
  # specify columns in the data
  group = c(rowlbl1,grp),
  label = rowlbl2,
  column = column, 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default", frmt_combine("{n} {pct}", 
                                                                                n = frmt("xxx"),
                                                                                pct = frmt_when("==100" ~ "",
                                                                                                "==0" ~ "",
                                                                                                TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = "n", frmt("xxx")),
    frmt_structure(group_val = ".default", label_val = c("Mean", "Median", "Min","Max"), frmt("xxx.x")),
    frmt_structure(group_val = ".default", label_val = "SD", frmt("xxx.xx")),
    frmt_structure(group_val = ".default", label_val = ".default", p = frmt("")),
    frmt_structure(group_val = ".default", label_val = c("n","<65 yrs","<12 months","<25"), p = frmt_when(">0.99" ~ ">0.99",
                                                                                 "<0.001" ~ "<0.001",
                                                                                 TRUE ~ frmt("x.xxx", missing = "")))
  ),
  # remove extra cols
  col_plan = col_plan(-grp, 
                      -starts_with("ord") ),
  # Specify column styling plan
  col_style_plan = col_style_plan(
    col_style_structure(align = c(".",","," "), col = c("Placebo", "Xanomeline Low Dose",
                                                        "Xanomeline High Dose", "Total", "p-value")),
    col_style_structure(align = "left", col = c("rowlbl1","rowlbl2"))
  ),
  
    # Specify row group plan
  row_grp_plan = row_grp_plan(
    row_grp_structure(group_val = ".default", element_block(post_space = " ")),
    label_loc = element_row_grp_loc(location = "column")
  )
  
  ) %>% 
  print_to_gt(data_demog) %>% 
  tab_options(
    container.width = 900
  )

		Placebo	Xanomeline Low Dose	Xanomeline High Dose	Total	p-value
Age (y)	n	86	84	84	254	0.593
	Mean	75.2	75.7	74.4	75.1
	SD	8.59	8.29	7.89	8.25
	Median	76.0	77.5	76.0	77.0
	Min	52.0	51.0	56.0	51.0
	Max	89.0	88.0	88.0	89.0
	<65 yrs	14 (16.3 %)	8 ( 9.5 %)	11 (13.1 %)	33 (13.0 %)	0.144
	65-80 yrs	42 (48.8 %)	47 (56.0 %)	55 (65.5 %)	144 (56.7 %)
	>80 yrs	30 (34.9 %)	29 (34.5 %)	18 (21.4 %)	77 (30.3 %)
Sex	n	86	84	84	254	0.141
	Male	33 (38.4 %)	34 (40.5 %)	44 (52.4 %)	111 (43.7 %)
	Female	53 (61.6 %)	50 (59.5 %)	40 (47.6 %)	143 (56.3 %)
Race (Origin)	n	86	84	84	254	0.648
	Caucasian	75 (87.2 %)	72 (85.7 %)	71 (84.5 %)	218 (85.8 %)
	African Descent	8 ( 9.3 %)	6 ( 7.1 %)	9 (10.7 %)	23 ( 9.1 %)
	Hispanic	3 ( 3.5 %)	6 ( 7.1 %)	3 ( 3.6 %)	12 ( 4.7 %)
	Other			1 ( 1.2 %)	1 ( 0.4 %)
MMSE	n	86	84	84	254	0.595
	Mean	18.0	17.9	18.5	18.1
	SD	4.27	4.22	4.16	4.21
	Median	19.5	18.0	20.0	19.0
	Min	10.0	10.0	10.0	10.0
	Max	23.0	24.0	24.0	24.0
Duration of disease	n	86	84	84	254	0.153
	Mean	42.6	48.7	40.5	43.9
	SD	30.24	29.58	24.69	28.40
	Median	35.3	40.2	36.0	36.2
	Min	7.2	7.8	2.2	2.2
	Max	183.1	130.8	135.0	183.1
	<12 months	5 ( 5.8 %)	3 ( 3.6 %)	4 ( 4.8 %)	12 ( 4.7 %)	0.789
	>=12 months	81 (94.2 %)	81 (96.4 %)	80 (95.2 %)	242 (95.3 %)
Years of education	n	86	84	84	254	0.388
	Mean	12.6	13.2	12.5	12.8
	SD	2.95	4.15	2.92	3.38
	Median	12.0	12.0	12.0	12.0
	Min	6.0	3.0	6.0	3.0
	Max	21.0	24.0	20.0	24.0
Baseline weight(kg)	n	86	83	84	253	0.003
	Mean	62.8	67.3	70.0	66.6
	SD	12.77	14.12	14.65	14.13
	Median	60.5	64.9	69.2	66.7
	Min	34.0	45.4	41.7	34.0
	Max	86.2	106.1	108.0	108.0
Baseline height(cm)	n	86	84	84	254	0.126
	Mean	162.6	163.4	165.8	163.9
	SD	11.52	10.42	10.13	10.76
	Median	162.6	162.6	165.1	162.8
	Min	137.2	135.9	146.1	135.9
	Max	185.4	195.6	190.5	195.6
Baseline BMI	n	86	83	84	253	0.013
	Mean	23.6	25.1	25.3	24.7
	SD	3.67	4.27	4.16	4.09
	Median	23.4	24.3	24.8	24.2
	Min	15.1	17.7	13.7	13.7
	Max	33.3	40.1	34.5	40.1
	<25	59 (68.6 %)	47 (56.0 %)	44 (52.4 %)	150 (59.1 %)	0.233
	25-<30	21 (24.4 %)	27 (32.1 %)	28 (33.3 %)	76 (29.9 %)
	>=30	6 ( 7.0 %)	10 (11.9 %)	12 (14.3 %)	28 (11.0 %)

AE table

For the adverse events (AE) table, we will use the data_ae analysis results data, which is also based on the CDISC pilot data. This dataset has two different row label columns, AEBODSYS and AETERM, for system organ class and preferred term, respectively. There are also two order columns which will be used to set the row order of the output. Because this table has column spanners, we have two column variables, col2 and col1 to define the hierarchy of columns. Finally, there is a param column and a value column. For brevity, we will subset to AEs with >10% prevalence in the High Dose group.

Expand for the code used to produce this subset

data_ae2 <- data_ae %>% 
  group_by(AEBODSYS, AETERM) %>% 
  mutate(pct_high = value[col2=="Xanomeline High Dose" & param=="pct"]) %>% 
  ungroup %>% 
  filter(pct_high >10) %>% 
  select(-pct_high)

#> # A tibble: 6 × 8
#>   AEBODSYS        AETERM          col2             col1  param value  ord1  ord2
#>   <chr>           <chr>           <chr>            <chr> <chr> <dbl> <dbl> <dbl>
#> 1 ANY BODY SYSTEM ANY BODY SYSTEM Placebo          n_pct n      65       0     0
#> 2 ANY BODY SYSTEM ANY BODY SYSTEM Placebo          n_pct pct    75.6     0     0
#> 3 ANY BODY SYSTEM ANY BODY SYSTEM Placebo          AEs   AEs   281       0     0
#> 4 ANY BODY SYSTEM ANY BODY SYSTEM Xanomeline Low … n_pct n      77       0     0
#> 5 ANY BODY SYSTEM ANY BODY SYSTEM Xanomeline Low … n_pct pct    91.7     0     0
#> 6 ANY BODY SYSTEM ANY BODY SYSTEM Xanomeline Low … AEs   AEs   412       0     0

The mock we are going to match looks like this:

	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
	n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
CARDIAC DISORDERS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
GASTROINTESTINAL DISORDERS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
APPLICATION SITE PRURITUS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
APPLICATION SITE ERYTHEMA	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
APPLICATION SITE IRRITATION	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
INFECTIONS AND INFESTATIONS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
NERVOUS SYSTEM DISORDERS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
DIZZINESS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
PRURITUS	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
ERYTHEMA	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx
RASH	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	XXX (xx.x %)	[XXX]	x.xxx	x.xxx

For this table we have three treatment group columns (Placebo, Low, and High Dose) which each have the following values reported: # of subjects with at least one AE (n), percent of subjects with at least one AE (pct), and # of AEs (AEs). We also have two p-value columns (Low Dose vs. Placebo, High Dose vs. Placebo).

Like the demography example, the first thing we are going to do when building out the tfrmt is specify all our columns. Note that col2 contains our spanning labels and col1 contains our lower level column headers:

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2)) %>% 
  print_to_gt(data_ae2) %>% 
  tab_options(
    container.width = 1000
  )
#> The following rows of the given dataset have no format applied to them 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165
#> Multiple param listed for the same group/label values.
#> The following frmt_structures may be missing from the body_plan
#> or the order may need to be changed:
#> - `frmt_structure(group_val = "ANY BODY SYSTEM", label_val = "ANY BODY SYSTEM", frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "CARDIAC DISORDERS", label_val = "CARDIAC DISORDERS", frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "GASTROINTESTINAL DISORDERS", label_val = "GASTROINTESTINAL DISORDERS", frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS", label_val = c("APPLICATION SITE ERYTHEMA","APPLICATION SITE IRRITATION","APPLICATION SITE PRURITUS","GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS"), frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "INFECTIONS AND INFESTATIONS", label_val = "INFECTIONS AND INFESTATIONS", frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "NERVOUS SYSTEM DISORDERS", label_val = c("DIZZINESS","NERVOUS SYSTEM DISORDERS"), frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS", label_val = c("RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS"), frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`
#> - `frmt_structure(group_val = "SKIN AND SUBCUTANEOUS TISSUE DISORDERS", label_val = c("ERYTHEMA","PRURITUS","RASH","SKIN AND SUBCUTANEOUS TISSUE DISORDERS"), frmt_combine("{n}, {pct}",n = frmt("xx"), pct = frmt("xx")))`

	ord1	ord2	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
			n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	0	0	65, 75.5813953488372	281	77, 91.6666666666667	412	76, 90.4761904761905	433	0.00653312936477891	0.0136376915028284
CARDIAC DISORDERS	1	0	12, 13.953488372093	26	13, 15.4761904761905	30	15, 17.8571428571429	30	0.830838674053784	0.533664723024524
GASTROINTESTINAL DISORDERS	5	0	17, 19.7674418604651	26	14, 16.6666666666667	22	20, 23.8095238095238	36	0.692428919010868	0.579522945659183
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	6	0	21, 24.4186046511628	46	47, 55.9523809523809	118	40, 47.6190476190476	124	4.01936476971637e-05	0.00227387200885964
APPLICATION SITE PRURITUS	6	1	6, 6.97674418604651	10	22, 26.1904761904762	32	22, 26.1904761904762	35	0.000811758368600543	0.000811758368600543
APPLICATION SITE ERYTHEMA	6	1	3, 3.48837209302326	3	12, 14.2857142857143	20	15, 17.8571428571429	23	0.0151667414143154	0.00248031755239288
APPLICATION SITE IRRITATION	6	1	3, 3.48837209302326	7	9, 10.7142857142857	18	9, 10.7142857142857	16	0.078319062603502	0.078319062603502
INFECTIONS AND INFESTATIONS	9	0	16, 18.6046511627907	35	9, 10.7142857142857	16	13, 15.4761904761905	20	0.194038579805755	0.684680584558915
NERVOUS SYSTEM DISORDERS	15	0	8, 9.30232558139535	11	20, 23.8095238095238	40	25, 29.7619047619048	41	0.0129835630195297	0.000870133171350475
DIZZINESS	15	1	2, 2.32558139534884	3	8, 9.52380952380952	13	11, 13.0952380952381	15	0.0556186226520671	0.009253648876646
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	19	0	8, 9.30232558139535	12	9, 10.7142857142857	14	10, 11.9047619047619	22	0.802870189478092	0.625582203021787
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20	0	20, 23.2558139534884	45	39, 46.4285714285714	111	40, 47.6190476190476	104	0.00210032738584151	0.0012509423867864
PRURITUS	20	1	8, 9.30232558139535	11	21, 25	31	26, 30.952380952381	38	0.00784138633137627	0.000480743020332539
ERYTHEMA	20	1	8, 9.30232558139535	12	14, 16.6666666666667	22	14, 16.6666666666667	22	0.175425360790723	0.175425360790723
RASH	20	1	5, 5.81395348837209	9	13, 15.4761904761905	18	9, 10.7142857142857	15	0.048021070861557	0.276671966759128

Next, we need to format the values using the body_plan. Recall that our body plan will be made up of a series of frmt_stuctures where each frmt_stucture represents the formatting of a cell within the table. Our AE table boils down to the following values: # of subjects with at least one AE (n), percent of subjects with at least one AE (pct), # of AEs (AEs), and p-value (pval). Because our n and pct will be combined using frmt_combine, we will have 3 frmt_structure objects. Note the use of frmt_when to format the p-values.

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default",
                   frmt_combine("{n} {pct}",
                                n = frmt("XXX"),
                                pct = frmt_when(
                                  "==100" ~ "",
                                  "==0" ~ "",
                                  TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   AEs = frmt("[XXX]")),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   pval = frmt_when(">0.99" ~ ">0.99",
                                    "<0.001" ~ "<0.001",
                                    "<0.05" ~ frmt("x.xxx*"),
                                    TRUE ~ frmt("x.xxx", missing ="--")))
  )) %>% 
  print_to_gt(., data_ae2) %>% 
  tab_options(
    container.width = 1000
  )

	ord1	ord2	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
			n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	0	0	65 (75.6 %)	[281]	77 (91.7 %)	[412]	76 (90.5 %)	[433]	0.007*	0.014*
CARDIAC DISORDERS	1	0	12 (14.0 %)	[ 26]	13 (15.5 %)	[ 30]	15 (17.9 %)	[ 30]	0.831	0.534
GASTROINTESTINAL DISORDERS	5	0	17 (19.8 %)	[ 26]	14 (16.7 %)	[ 22]	20 (23.8 %)	[ 36]	0.692	0.580
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	6	0	21 (24.4 %)	[ 46]	47 (56.0 %)	[118]	40 (47.6 %)	[124]	<0.001	0.002*
APPLICATION SITE PRURITUS	6	1	6 ( 7.0 %)	[ 10]	22 (26.2 %)	[ 32]	22 (26.2 %)	[ 35]	<0.001	<0.001
APPLICATION SITE ERYTHEMA	6	1	3 ( 3.5 %)	[ 3]	12 (14.3 %)	[ 20]	15 (17.9 %)	[ 23]	0.015*	0.002*
APPLICATION SITE IRRITATION	6	1	3 ( 3.5 %)	[ 7]	9 (10.7 %)	[ 18]	9 (10.7 %)	[ 16]	0.078	0.078
INFECTIONS AND INFESTATIONS	9	0	16 (18.6 %)	[ 35]	9 (10.7 %)	[ 16]	13 (15.5 %)	[ 20]	0.194	0.685
NERVOUS SYSTEM DISORDERS	15	0	8 ( 9.3 %)	[ 11]	20 (23.8 %)	[ 40]	25 (29.8 %)	[ 41]	0.013*	<0.001
DIZZINESS	15	1	2 ( 2.3 %)	[ 3]	8 ( 9.5 %)	[ 13]	11 (13.1 %)	[ 15]	0.056	0.009*
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	19	0	8 ( 9.3 %)	[ 12]	9 (10.7 %)	[ 14]	10 (11.9 %)	[ 22]	0.803	0.626
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20	0	20 (23.3 %)	[ 45]	39 (46.4 %)	[111]	40 (47.6 %)	[104]	0.002*	0.001*
PRURITUS	20	1	8 ( 9.3 %)	[ 11]	21 (25.0 %)	[ 31]	26 (31.0 %)	[ 38]	0.008*	<0.001
ERYTHEMA	20	1	8 ( 9.3 %)	[ 12]	14 (16.7 %)	[ 22]	14 (16.7 %)	[ 22]	0.175	0.175
RASH	20	1	5 ( 5.8 %)	[ 9]	13 (15.5 %)	[ 18]	9 (10.7 %)	[ 15]	0.048*	0.277

Almost there! Our AE table contains data for both Preferred Terms and System Organ Classes. Therefore, we do not want a typical group-level header. Instead, we want to display the System Organ Class label inline with its data, and nest the Preferred Term data underneath. Fortunately, we are able to achieve this formatting with a row_grp_plan:

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default",
                   frmt_combine("{n} {pct}",
                                n = frmt("XXX"),
                                pct = frmt_when(
                                  "==100" ~ "",
                                  "==0" ~ "",
                                  TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   AEs = frmt("[XXX]")),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   pval = frmt_when(">0.99" ~ ">0.99",
                                    "<0.001" ~ "<0.001",
                                    "<0.05" ~ frmt("x.xxx*"),
                                    TRUE ~ frmt("x.xxx", missing ="--")))
  ),
  # Nest Preferred terms under SOC
  row_grp_plan = row_grp_plan(label_loc = element_row_grp_loc(location = "indented"))
  ) %>% 
  print_to_gt(data_ae2) %>% 
  tab_options(
    container.width = 1000
  )

	ord1	ord2	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
			n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	0	0	65 (75.6 %)	[281]	77 (91.7 %)	[412]	76 (90.5 %)	[433]	0.007*	0.014*
CARDIAC DISORDERS	1	0	12 (14.0 %)	[ 26]	13 (15.5 %)	[ 30]	15 (17.9 %)	[ 30]	0.831	0.534
GASTROINTESTINAL DISORDERS	5	0	17 (19.8 %)	[ 26]	14 (16.7 %)	[ 22]	20 (23.8 %)	[ 36]	0.692	0.580
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	6	0	21 (24.4 %)	[ 46]	47 (56.0 %)	[118]	40 (47.6 %)	[124]	<0.001	0.002*
APPLICATION SITE PRURITUS	6	1	6 ( 7.0 %)	[ 10]	22 (26.2 %)	[ 32]	22 (26.2 %)	[ 35]	<0.001	<0.001
APPLICATION SITE ERYTHEMA	6	1	3 ( 3.5 %)	[ 3]	12 (14.3 %)	[ 20]	15 (17.9 %)	[ 23]	0.015*	0.002*
APPLICATION SITE IRRITATION	6	1	3 ( 3.5 %)	[ 7]	9 (10.7 %)	[ 18]	9 (10.7 %)	[ 16]	0.078	0.078
INFECTIONS AND INFESTATIONS	9	0	16 (18.6 %)	[ 35]	9 (10.7 %)	[ 16]	13 (15.5 %)	[ 20]	0.194	0.685
NERVOUS SYSTEM DISORDERS	15	0	8 ( 9.3 %)	[ 11]	20 (23.8 %)	[ 40]	25 (29.8 %)	[ 41]	0.013*	<0.001
DIZZINESS	15	1	2 ( 2.3 %)	[ 3]	8 ( 9.5 %)	[ 13]	11 (13.1 %)	[ 15]	0.056	0.009*
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	19	0	8 ( 9.3 %)	[ 12]	9 (10.7 %)	[ 14]	10 (11.9 %)	[ 22]	0.803	0.626
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20	0	20 (23.3 %)	[ 45]	39 (46.4 %)	[111]	40 (47.6 %)	[104]	0.002*	0.001*
PRURITUS	20	1	8 ( 9.3 %)	[ 11]	21 (25.0 %)	[ 31]	26 (31.0 %)	[ 38]	0.008*	<0.001
ERYTHEMA	20	1	8 ( 9.3 %)	[ 12]	14 (16.7 %)	[ 22]	14 (16.7 %)	[ 22]	0.175	0.175
RASH	20	1	5 ( 5.8 %)	[ 9]	13 (15.5 %)	[ 18]	9 (10.7 %)	[ 15]	0.048*	0.277

Our column alignment looks good as-is, except for the p-values. We can use the col_style_plan to tweak those.

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default",
                   frmt_combine("{n} {pct}",
                                n = frmt("XXX"),
                                pct = frmt_when(
                                  "==100" ~ "",
                                  "==0" ~ "",
                                  TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   AEs = frmt("[XXX]")),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   pval = frmt_when(">0.99" ~ ">0.99",
                                    "<0.001" ~ "<0.001",
                                    "<0.05" ~ frmt("x.xxx*"),
                                    TRUE ~ frmt("x.xxx", missing ="--")))
  ),
  # Nest Preferred terms under SOC
  row_grp_plan = row_grp_plan(label_loc = element_row_grp_loc(location = "indented")),
  # alignment
  
  # Specify column styling plan
  col_style_plan = col_style_plan(
    col_style_structure(align = c(".",","," "), col = vars(starts_with("p_")))
  )
  ) %>% 
  print_to_gt(data_ae2) %>% 
  tab_options(
    container.width = 1000
  )

	ord1	ord2	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
			n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	0	0	65 (75.6 %)	[281]	77 (91.7 %)	[412]	76 (90.5 %)	[433]	0.007*	0.014*
CARDIAC DISORDERS	1	0	12 (14.0 %)	[ 26]	13 (15.5 %)	[ 30]	15 (17.9 %)	[ 30]	0.831	0.534
GASTROINTESTINAL DISORDERS	5	0	17 (19.8 %)	[ 26]	14 (16.7 %)	[ 22]	20 (23.8 %)	[ 36]	0.692	0.580
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	6	0	21 (24.4 %)	[ 46]	47 (56.0 %)	[118]	40 (47.6 %)	[124]	<0.001	0.002*
APPLICATION SITE PRURITUS	6	1	6 ( 7.0 %)	[ 10]	22 (26.2 %)	[ 32]	22 (26.2 %)	[ 35]	<0.001	<0.001
APPLICATION SITE ERYTHEMA	6	1	3 ( 3.5 %)	[ 3]	12 (14.3 %)	[ 20]	15 (17.9 %)	[ 23]	0.015*	0.002*
APPLICATION SITE IRRITATION	6	1	3 ( 3.5 %)	[ 7]	9 (10.7 %)	[ 18]	9 (10.7 %)	[ 16]	0.078	0.078
INFECTIONS AND INFESTATIONS	9	0	16 (18.6 %)	[ 35]	9 (10.7 %)	[ 16]	13 (15.5 %)	[ 20]	0.194	0.685
NERVOUS SYSTEM DISORDERS	15	0	8 ( 9.3 %)	[ 11]	20 (23.8 %)	[ 40]	25 (29.8 %)	[ 41]	0.013*	<0.001
DIZZINESS	15	1	2 ( 2.3 %)	[ 3]	8 ( 9.5 %)	[ 13]	11 (13.1 %)	[ 15]	0.056	0.009*
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	19	0	8 ( 9.3 %)	[ 12]	9 (10.7 %)	[ 14]	10 (11.9 %)	[ 22]	0.803	0.626
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20	0	20 (23.3 %)	[ 45]	39 (46.4 %)	[111]	40 (47.6 %)	[104]	0.002*	0.001*
PRURITUS	20	1	8 ( 9.3 %)	[ 11]	21 (25.0 %)	[ 31]	26 (31.0 %)	[ 38]	0.008*	<0.001
ERYTHEMA	20	1	8 ( 9.3 %)	[ 12]	14 (16.7 %)	[ 22]	14 (16.7 %)	[ 22]	0.175	0.175
RASH	20	1	5 ( 5.8 %)	[ 9]	13 (15.5 %)	[ 18]	9 (10.7 %)	[ 15]	0.048*	0.277

Notice that we still have our order columns and the column labels could benefit from some renaming. We can add a col_plan to help with the ordering:

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default",
                   frmt_combine("{n} {pct}",
                                n = frmt("XXX"),
                                pct = frmt_when(
                                  "==100" ~ "",
                                  "==0" ~ "",
                                  TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   AEs = frmt("[XXX]")),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   pval = frmt_when(">0.99" ~ ">0.99",
                                    "<0.001" ~ "<0.001",
                                    "<0.05" ~ frmt("x.xxx*"),
                                    TRUE ~ frmt("x.xxx", missing ="--")))
  ),
  # Nest Preferred terms under SOC
  row_grp_plan = row_grp_plan(label_loc = element_row_grp_loc(location = "indented")),

  # Specify column styling plan
  col_style_plan = col_style_plan(
    col_style_structure(align = c(".",","," "), col = vars(p_low, p_high))
  ),
  # columns
  col_plan = col_plan(
    -starts_with("ord")
  )
  ) %>% 
  print_to_gt(data_ae2)  %>% 
  tab_options(
    container.width = 1000
  )

	Placebo		Xanomeline Low Dose		Xanomeline High Dose		fisher_pval
	n_pct	AEs	n_pct	AEs	n_pct	AEs	p_low	p_high
ANY BODY SYSTEM	65 (75.6 %)	[281]	77 (91.7 %)	[412]	76 (90.5 %)	[433]	0.007*	0.014*
CARDIAC DISORDERS	12 (14.0 %)	[ 26]	13 (15.5 %)	[ 30]	15 (17.9 %)	[ 30]	0.831	0.534
GASTROINTESTINAL DISORDERS	17 (19.8 %)	[ 26]	14 (16.7 %)	[ 22]	20 (23.8 %)	[ 36]	0.692	0.580
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	21 (24.4 %)	[ 46]	47 (56.0 %)	[118]	40 (47.6 %)	[124]	<0.001	0.002*
APPLICATION SITE PRURITUS	6 ( 7.0 %)	[ 10]	22 (26.2 %)	[ 32]	22 (26.2 %)	[ 35]	<0.001	<0.001
APPLICATION SITE ERYTHEMA	3 ( 3.5 %)	[ 3]	12 (14.3 %)	[ 20]	15 (17.9 %)	[ 23]	0.015*	0.002*
APPLICATION SITE IRRITATION	3 ( 3.5 %)	[ 7]	9 (10.7 %)	[ 18]	9 (10.7 %)	[ 16]	0.078	0.078
INFECTIONS AND INFESTATIONS	16 (18.6 %)	[ 35]	9 (10.7 %)	[ 16]	13 (15.5 %)	[ 20]	0.194	0.685
NERVOUS SYSTEM DISORDERS	8 ( 9.3 %)	[ 11]	20 (23.8 %)	[ 40]	25 (29.8 %)	[ 41]	0.013*	<0.001
DIZZINESS	2 ( 2.3 %)	[ 3]	8 ( 9.5 %)	[ 13]	11 (13.1 %)	[ 15]	0.056	0.009*
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	8 ( 9.3 %)	[ 12]	9 (10.7 %)	[ 14]	10 (11.9 %)	[ 22]	0.803	0.626
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20 (23.3 %)	[ 45]	39 (46.4 %)	[111]	40 (47.6 %)	[104]	0.002*	0.001*
PRURITUS	8 ( 9.3 %)	[ 11]	21 (25.0 %)	[ 31]	26 (31.0 %)	[ 38]	0.008*	<0.001
ERYTHEMA	8 ( 9.3 %)	[ 12]	14 (16.7 %)	[ 22]	14 (16.7 %)	[ 22]	0.175	0.175
RASH	5 ( 5.8 %)	[ 9]	13 (15.5 %)	[ 18]	9 (10.7 %)	[ 15]	0.048*	0.277

For better control over our column labels, we can make use of col_plan’s span_structures to define the column labels and spanners order and names:

tfrmt(
  # specify columns in the data
  group = AEBODSYS,
  label = AETERM,
  column = c(col2, col1), 
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  # specify value formatting 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default",
                   frmt_combine("{n} {pct}",
                                n = frmt("XXX"),
                                pct = frmt_when(
                                  "==100" ~ "",
                                  "==0" ~ "",
                                  TRUE ~ frmt("(xx.x %)")))),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   AEs = frmt("[XXX]")),
    frmt_structure(group_val = ".default", label_val = ".default", 
                   pval = frmt_when(">0.99" ~ ">0.99",
                                    "<0.001" ~ "<0.001",
                                    "<0.05" ~ frmt("x.xxx*"),
                                    TRUE ~ frmt("x.xxx", missing ="--")))
  ),
  # Nest Preferred terms under SOC
  row_grp_plan = row_grp_plan(label_loc = element_row_grp_loc(location = "indented")),
  
  # Specify column styling plan
  col_style_plan = col_style_plan(
    col_style_structure(align = c(".",","," "), col = c(p_low, p_high))
  ),
  
  # columns
  col_plan = col_plan(
    ## defines the spanning column order, and then beneath them the order of their contents
    -starts_with("ord"),
    span_structure(
      col2 = c(
        "Xanomeline High Dose (N=84)" = `Xanomeline High Dose`,
        "Xanomeline Low Dose (N=84)" = `Xanomeline Low Dose`,
        "Placebo (N=86)" = Placebo
      ),
      col1 = c(`n (%)` = `n_pct` ,
               `[AEs]` = `AEs`)
    ),
    span_structure(
      col2 = c("Fisher's Exact p-values" = fisher_pval),
      col1 = c(
        # add a line break to help with table formatting
        `Placebo vs.\n Low Dose` = `p_low` ,
       `Placebo vs.\n High Dose` = `p_high` 
      )
    ))
  ) %>% 
  print_to_gt(data_ae2)  %>% 
  tab_options(
    container.width = 1000
  )

	Placebo (N=86)		Xanomeline Low Dose (N=84)		Xanomeline High Dose (N=84)		Fisher’s Exact p-values
	n (%)	[AEs]	n (%)	[AEs]	n (%)	[AEs]	Placebo vs. Low Dose	Placebo vs. High Dose
ANY BODY SYSTEM	65 (75.6 %)	[281]	77 (91.7 %)	[412]	76 (90.5 %)	[433]	0.007*	0.014*
CARDIAC DISORDERS	12 (14.0 %)	[ 26]	13 (15.5 %)	[ 30]	15 (17.9 %)	[ 30]	0.831	0.534
GASTROINTESTINAL DISORDERS	17 (19.8 %)	[ 26]	14 (16.7 %)	[ 22]	20 (23.8 %)	[ 36]	0.692	0.580
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	21 (24.4 %)	[ 46]	47 (56.0 %)	[118]	40 (47.6 %)	[124]	<0.001	0.002*
APPLICATION SITE PRURITUS	6 ( 7.0 %)	[ 10]	22 (26.2 %)	[ 32]	22 (26.2 %)	[ 35]	<0.001	<0.001
APPLICATION SITE ERYTHEMA	3 ( 3.5 %)	[ 3]	12 (14.3 %)	[ 20]	15 (17.9 %)	[ 23]	0.015*	0.002*
APPLICATION SITE IRRITATION	3 ( 3.5 %)	[ 7]	9 (10.7 %)	[ 18]	9 (10.7 %)	[ 16]	0.078	0.078
INFECTIONS AND INFESTATIONS	16 (18.6 %)	[ 35]	9 (10.7 %)	[ 16]	13 (15.5 %)	[ 20]	0.194	0.685
NERVOUS SYSTEM DISORDERS	8 ( 9.3 %)	[ 11]	20 (23.8 %)	[ 40]	25 (29.8 %)	[ 41]	0.013*	<0.001
DIZZINESS	2 ( 2.3 %)	[ 3]	8 ( 9.5 %)	[ 13]	11 (13.1 %)	[ 15]	0.056	0.009*
RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS	8 ( 9.3 %)	[ 12]	9 (10.7 %)	[ 14]	10 (11.9 %)	[ 22]	0.803	0.626
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	20 (23.3 %)	[ 45]	39 (46.4 %)	[111]	40 (47.6 %)	[104]	0.002*	0.001*
PRURITUS	8 ( 9.3 %)	[ 11]	21 (25.0 %)	[ 31]	26 (31.0 %)	[ 38]	0.008*	<0.001
ERYTHEMA	8 ( 9.3 %)	[ 12]	14 (16.7 %)	[ 22]	14 (16.7 %)	[ 22]	0.175	0.175
RASH	5 ( 5.8 %)	[ 9]	13 (15.5 %)	[ 18]	9 (10.7 %)	[ 15]	0.048*	0.277

Our AE table is now complete!

Efficacy

For this example, we will use the data_efficacy dataset, an example analysis results dataset found in the package, which is based on the CDISC pilot data for the ADAS-Cog(11) score. The goal is to recreate table 14-3.01 from the CDISC pilot.

This data is relatively simple in that it contains only 1 group column and column column, but it adds complexity in that multiple analyses are stacked together - summary statistics of different values at different time points, and the results of several different ANCOVA models. Multiple treatment groups as well as the contrasts between groups are included.

#> # A tibble: 6 × 7
#>   group    label     column               param value  ord1  ord2
#>   <chr>    <chr>     <chr>                <chr> <dbl> <dbl> <dbl>
#> 1 Baseline n         Placebo              n      79       1     1
#> 2 Baseline n         Xanomeline Low Dose  n      81       1     1
#> 3 Baseline n         Xanomeline High Dose n      74       1     1
#> 4 Baseline Mean (SD) Placebo              mean   24.1     1     2
#> 5 Baseline Mean (SD) Xanomeline Low Dose  mean   24.4     1     2
#> 6 Baseline Mean (SD) Xanomeline High Dose mean   21.3     1     2

The mock we are going to match looks like this:

	Placebo	Xanomeline Low Dose	Xanomeline High Dose	ord1	ord2
Baseline
n	xx	xx	xx	1	1
Mean (SD)	xx.x (xx.xx)	xx.x (xx.xx)	xx.x (xx.xx)	1	2
Median (Range)	xx.x (xx;xx)	xx.x (xx;xx)	xx.x (xx;xx)	1	3
Week 24
n	xx	xx	xx	2	1
Mean (SD)	xx.x (xx.xx)	xx.x (xx.xx)	xx.x (xx.xx)	2	2
Median (Range)	xx.x (xx;xx)	xx.x (xx;xx)	xx.x (xx;xx)	2	3
Change from Baseline
n	xx	xx	xx	3	1
Mean (SD)	xx.x (xx.xx)	xx.x (xx.xx)	xx.x (xx.xx)	3	2
Median (Range)	xx.x (xx;xx)	xx.x (xx;xx)	xx.x (xx;xx)	3	3
				3	3
p-value (Dose Response)			x.xxx	4	4
				4	4
p-value (Xan - Placebo)		x.xxx	x.xxx	5	4
Diff of LS Means (SE)		xx.x (xx.xx)	xx.x (xx.xx)	5	5
95% CI		(xx.x;xx.x)	(xx.x;xx.x)	5	6
				5	6
p-value (Xan High - Xan Low)			x.xxx	6	4
Diff of LS Means (SE)			xx.x (xx.xx)	6	5
95% CI			(xx.x;xx.x)	6	6

Let’s first see how the table looks without any special formatting.

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value 
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  ) 
#> The following rows of the given dataset have no format applied to them 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70
#> Multiple param listed for the same group/label values.
#> The following frmt_structures may be missing from the body_plan
#> or the order may need to be changed:
#> - `frmt_structure(group_val = "Baseline", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "Change from Baseline", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "Week 24", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan - Placebo)", label_val = "95% CI", frmt_combine("{diff_lcl}, {diff_ucl}",diff_lcl = frmt("xx"), diff_ucl = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan - Placebo)", label_val = "Diff of LS Means (SE)", frmt_combine("{diff}, {diff_se}",diff = frmt("xx"), diff_se = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan High - Xan Low)", label_val = "95% CI", frmt_combine("{diff_lcl}, {diff_ucl}",diff_lcl = frmt("xx"), diff_ucl = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan High - Xan Low)", label_val = "Diff of LS Means (SE)", frmt_combine("{diff}, {diff_se}",diff = frmt("xx"), diff_se = frmt("xx")))`

	ord1	ord2	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	1	1	79	81	74
Mean (SD)	1	2	24.121780881711	24.4074074074074	21.2972972972973
Median (Range)	1	3	21, 5, 61	21, 5, 56.7241379310345	18, 3, 57
Mean (SD)	1	6	12.1863695136042	12.9224478515241	11.7365250390648
Week 24
n	2	1	79	81	74
Mean (SD)	2	2	26.6665211697948	26.4027245636441	22.7677850264057
Median (Range)	2	3	24, 5, 61.551724137931	25, 6, 62	20, 3, 61.551724137931
Mean (SD)	2	6	13.7942934074663	13.1806548367334	12.4835803751227
Change from Baseline
n	3	1	79	81	74
Mean (SD)	3	2	2.54474028808381	1.9953171562367	1.47048772910842
Median (Range)	3	3	2, -11, 16	2, -11, 17	1, -7, 13
Mean (SD)	3	6	5.80389919656815	5.55278623671741	4.26238487169685
p-value (Dose Response)	4	4			0.2447056738685
p-value (Xan - Placebo)	5	4		0.568846971341775	0.232641095885767
Diff of LS Means (SE)	5	5		-0.466782357500736, 0.818042222283683	-1.00601359773133, 0.840529356750352
95% CI	5	6		-2.07898454398439, 1.14541982898292	-2.66253355457861, 0.650506359115944
p-value (Xan High - Xan Low)	6	4			0.51964487082863
Diff of LS Means (SE)	6	5			-0.539231240230598, 0.836108901551478
95% CI	6	6			-2.18703933925105, 1.10857685878985

Through judicious use of body_plan’s frmt, frmt_combine, and frmt_when, we can conditionally format each of the different pieces of results. For the summary statistics, we have the number of observations (n), the mean and standard deviation (mean, sd), and the median and range (median, min, max). For the models, we have the p-value (p.value) and the least squares mean difference (diff) as well as its associated standard error (diff_se) and 95% confidence interval (diff_lcl, diff_ucl).

The label column indicates which row the various measures belong on. First, let’s format the stand-alone values: n and p-value. Notice that n always sits on a row labelled “n”; therefore we can reference it by label_val or param name in the frmt_structure. Our p-values have several different label values so it is more convenient to format them according to their param name in the frmt_structure.

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value, 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = "n", 
                   frmt("xx")),  # we could also do: label_val = ".default", n = frmt("xx")
    frmt_structure(group_val = ".default", label_val = ".default", 
                   p.value = frmt_when("<0.001" ~ "<0.001",
                                       ">0.99" ~ ">0.99",
                                       TRUE ~ frmt("x.xxx", missing = " ")))
  )
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  )
#> The following rows of the given dataset have no format applied to them 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70
#> Multiple param listed for the same group/label values.
#> The following frmt_structures may be missing from the body_plan
#> or the order may need to be changed:
#> - `frmt_structure(group_val = "Baseline", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "Change from Baseline", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "Week 24", label_val = "Median (Range)", frmt_combine("{median}, {min}, {max}",median = frmt("xx"), min = frmt("xx"), max = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan - Placebo)", label_val = "95% CI", frmt_combine("{diff_lcl}, {diff_ucl}",diff_lcl = frmt("xx"), diff_ucl = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan - Placebo)", label_val = "Diff of LS Means (SE)", frmt_combine("{diff}, {diff_se}",diff = frmt("xx"), diff_se = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan High - Xan Low)", label_val = "95% CI", frmt_combine("{diff_lcl}, {diff_ucl}",diff_lcl = frmt("xx"), diff_ucl = frmt("xx")))`
#> - `frmt_structure(group_val = "p-value (Xan High - Xan Low)", label_val = "Diff of LS Means (SE)", frmt_combine("{diff}, {diff_se}",diff = frmt("xx"), diff_se = frmt("xx")))`

	ord1	ord2	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	1	1	79	81	74
Mean (SD)	1	2	24.121780881711	24.4074074074074	21.2972972972973
Median (Range)	1	3	21, 5, 61	21, 5, 56.7241379310345	18, 3, 57
Mean (SD)	1	6	12.1863695136042	12.9224478515241	11.7365250390648
Week 24
n	2	1	79	81	74
Mean (SD)	2	2	26.6665211697948	26.4027245636441	22.7677850264057
Median (Range)	2	3	24, 5, 61.551724137931	25, 6, 62	20, 3, 61.551724137931
Mean (SD)	2	6	13.7942934074663	13.1806548367334	12.4835803751227
Change from Baseline
n	3	1	79	81	74
Mean (SD)	3	2	2.54474028808381	1.9953171562367	1.47048772910842
Median (Range)	3	3	2, -11, 16	2, -11, 17	1, -7, 13
Mean (SD)	3	6	5.80389919656815	5.55278623671741	4.26238487169685
p-value (Dose Response)	4	4			0.245
p-value (Xan - Placebo)	5	4		0.569	0.233
Diff of LS Means (SE)	5	5		-0.466782357500736, 0.818042222283683	-1.00601359773133, 0.840529356750352
95% CI	5	6		-2.07898454398439, 1.14541982898292	-2.66253355457861, 0.650506359115944
p-value (Xan High - Xan Low)	6	4			0.520
Diff of LS Means (SE)	6	5			-0.539231240230598, 0.836108901551478
95% CI	6	6			-2.18703933925105, 1.10857685878985

Next, the remaining param values are combined in twos or threes. Therefore, we use the frmt_combine utility to achieve desired formatting:

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value, 
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = "n", 
                   frmt("xx")),  # we could also do: label_val = ".default", n = frmt("xx")
    frmt_structure(group_val = ".default", label_val = ".default", 
                   p.value = frmt_when("<0.001" ~ "<0.001",
                                       ">0.99" ~ ">0.99",
                                       TRUE ~ frmt("x.xxx", missing = " "))),
    frmt_structure(group_val = ".default", label_val = "Median (Range)", 
                   frmt_combine("{median} ({min};{max})",
                                median = frmt("xx.x"),
                                min = frmt("xx"),
                                max = frmt("xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Mean (SD)",
                   frmt_combine("{mean} ({sd})",
                                mean = frmt("xx.x"),
                                sd = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Diff of LS Means (SE)", 
                   frmt_combine("{diff} ({diff_se})",
                                diff = frmt("xx.x"),
                                diff_se = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "95% CI", 
                   frmt_combine("({diff_lcl};{diff_ucl})",
                                diff_lcl = frmt("xx.x"),
                                diff_ucl = frmt("xx.x"), missing = " "))
  )
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  )

	ord1	ord2	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	1	1	79	81	74
Mean (SD)	1	2	24.1 (12.19)	24.4 (12.92)	21.3 (11.74)
Median (Range)	1	3	21.0 ( 5;61)	21.0 ( 5;57)	18.0 ( 3;57)
Week 24
n	2	1	79	81	74
Mean (SD)	2	2	26.7 (13.79)	26.4 (13.18)	22.8 (12.48)
Median (Range)	2	3	24.0 ( 5;62)	25.0 ( 6;62)	20.0 ( 3;62)
Change from Baseline
n	3	1	79	81	74
Mean (SD)	3	2	2.5 ( 5.80)	2.0 ( 5.55)	1.5 ( 4.26)
Median (Range)	3	3	2.0 (-11;16)	2.0 (-11;17)	1.0 (-7;13)
p-value (Dose Response)	4	4			0.245
p-value (Xan - Placebo)	5	4		0.569	0.233
Diff of LS Means (SE)	5	5		-0.5 ( 0.82)	-1.0 ( 0.84)
95% CI	5	6		(-2.1; 1.1)	(-2.7; 0.7)
p-value (Xan High - Xan Low)	6	4			0.520
Diff of LS Means (SE)	6	5			-0.5 ( 0.84)
95% CI	6	6			(-2.2; 1.1)

Now that our values are all formatted correctly, we can make sure the table is sorted appropriately by passing our order columns to sorting_cols. We can also drop these order columns from the final display using col_plan.

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = "n", 
                   frmt("xx")),  # we could also do: label_val = ".default", n = frmt("xx")
    frmt_structure(group_val = ".default", label_val = ".default", 
                   p.value = frmt_when("<0.001" ~ "<0.001",
                                       ">0.99" ~ ">0.99",
                                       TRUE ~ frmt("x.xxx", missing = " "))),
    frmt_structure(group_val = ".default", label_val = "Median (Range)", 
                   frmt_combine("{median} ({min};{max})",
                                median = frmt("xx.x"),
                                min = frmt("xx"),
                                max = frmt("xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Mean (SD)", 
                   frmt_combine("{mean} ({sd})",
                                mean = frmt("xx.x"),
                                sd = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Diff of LS Means (SE)", 
                   frmt_combine("{diff} ({diff_se})",
                                diff = frmt("xx.x"),
                                diff_se = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "95% CI", 
                   frmt_combine("({diff_lcl};{diff_ucl})",
                                diff_lcl = frmt("xx.x"),
                                diff_ucl = frmt("xx.x"), missing = " "))
  ),
  col_plan = col_plan(
    group, label, Placebo, contains("Low"), contains("High"), -starts_with("ord")
  )
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  )

	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	79	81	74
Mean (SD)	24.1 (12.19)	24.4 (12.92)	21.3 (11.74)
Median (Range)	21.0 ( 5;61)	21.0 ( 5;57)	18.0 ( 3;57)
Week 24
n	79	81	74
Mean (SD)	26.7 (13.79)	26.4 (13.18)	22.8 (12.48)
Median (Range)	24.0 ( 5;62)	25.0 ( 6;62)	20.0 ( 3;62)
Change from Baseline
n	79	81	74
Mean (SD)	2.5 ( 5.80)	2.0 ( 5.55)	1.5 ( 4.26)
Median (Range)	2.0 (-11;16)	2.0 (-11;17)	1.0 (-7;13)
p-value (Dose Response)			0.245
p-value (Xan - Placebo)		0.569	0.233
Diff of LS Means (SE)		-0.5 ( 0.82)	-1.0 ( 0.84)
95% CI		(-2.1; 1.1)	(-2.7; 0.7)
p-value (Xan High - Xan Low)			0.520
Diff of LS Means (SE)			-0.5 ( 0.84)
95% CI			(-2.2; 1.1)

Notice that our row labels are not quite right. First, we have a bit of a hierarchy with the label values nested under the group values, and it would be nice to add some indentation to make the nesting more obvious. Also, in some cases, the group values also contain summary data, which means the ARD contains a matching group and label value. For summary rows, we want to suppress the printing of the extra group-level header, and display the summary data in-line. The row_grp_plan can help us with both via the row_grp_loc argument:

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = "n", 
                   frmt("xx")),  # we could also do: label_val = ".default", n = frmt("xx")
    frmt_structure(group_val = ".default", label_val = ".default", 
                   p.value = frmt_when("<0.001" ~ "<0.001",
                                       ">0.99" ~ ">0.99",
                                       TRUE ~ frmt("x.xxx", missing = " "))),
    frmt_structure(group_val = ".default", label_val = "Median (Range)", 
                   frmt_combine("{median} ({min};{max})",
                                median = frmt("xx.x"),
                                min = frmt("xx"),
                                max = frmt("xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Mean (SD)", 
                   frmt_combine("{mean} ({sd})",
                                mean = frmt("xx.x"),
                                sd = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Diff of LS Means (SE)", 
                   frmt_combine("{diff} ({diff_se})",
                                diff = frmt("xx.x"),
                                diff_se = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "95% CI", 
                   frmt_combine("({diff_lcl};{diff_ucl})",
                                diff_lcl = frmt("xx.x"),
                                diff_ucl = frmt("xx.x"), missing = " "))
  ),
  col_plan = col_plan(
    group, label, Placebo, contains("Low"), contains("High"), -starts_with("ord")
  ),
  row_grp_plan = row_grp_plan(
    label_loc = element_row_grp_loc(location = "indented")
  )
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  )

	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	79	81	74
Mean (SD)	24.1 (12.19)	24.4 (12.92)	21.3 (11.74)
Median (Range)	21.0 ( 5;61)	21.0 ( 5;57)	18.0 ( 3;57)
Week 24
n	79	81	74
Mean (SD)	26.7 (13.79)	26.4 (13.18)	22.8 (12.48)
Median (Range)	24.0 ( 5;62)	25.0 ( 6;62)	20.0 ( 3;62)
Change from Baseline
n	79	81	74
Mean (SD)	2.5 ( 5.80)	2.0 ( 5.55)	1.5 ( 4.26)
Median (Range)	2.0 (-11;16)	2.0 (-11;17)	1.0 (-7;13)
p-value (Dose Response)			0.245
p-value (Xan - Placebo)		0.569	0.233
Diff of LS Means (SE)		-0.5 ( 0.82)	-1.0 ( 0.84)
95% CI		(-2.1; 1.1)	(-2.7; 0.7)
p-value (Xan High - Xan Low)			0.520
Diff of LS Means (SE)			-0.5 ( 0.84)
95% CI			(-2.2; 1.1)

Almost done! Notice that the spec also contains empty rows after different groups of data. We can mimic this behavior by passing row_grp_structure objects in our row_grp_plan. These objects define “blocks” of rows and describe how to format them. In this case, we want to add a post space after specific blocks of data. We can reference the locations of each block based on the values of the group variable.

tfrmt(
  group = group,
  label = label,
  column = column,
  param = param,
  value = value,
  sorting_cols = c(ord1, ord2),
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = "n", 
                   frmt("xx")),  # we could also do: label_val = ".default", n = frmt("xx")
    frmt_structure(group_val = ".default", label_val = ".default", 
                   p.value = frmt_when("<0.001" ~ "<0.001",
                                       ">0.99" ~ ">0.99",
                                       TRUE ~ frmt("x.xxx", missing = " "))),
    frmt_structure(group_val = ".default", label_val = "Median (Range)", 
                   frmt_combine("{median} ({min};{max})",
                                median = frmt("xx.x"),
                                min = frmt("xx"),
                                max = frmt("xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Mean (SD)", 
                   frmt_combine("{mean} ({sd})",
                                mean = frmt("xx.x"),
                                sd = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "Diff of LS Means (SE)", 
                   frmt_combine("{diff} ({diff_se})",
                                diff = frmt("xx.x"),
                                diff_se = frmt("xx.xx"), missing = " ")),
    frmt_structure(group_val = ".default", label_val = "95% CI", 
                   frmt_combine("({diff_lcl};{diff_ucl})",
                                diff_lcl = frmt("xx.x"),
                                diff_ucl = frmt("xx.x"), missing = " "))
  ),
  col_plan = col_plan(
    group, label, Placebo, contains("Low"), contains("High"), -starts_with("ord")
  ),
  row_grp_plan = row_grp_plan(
    row_grp_structure(group_val = list(group="Change from Baseline"), 
                      element_block(post_space = " ")),
    row_grp_structure(group_val = list(group="p-value (Dose Response)"),
                      element_block(post_space = " ")),
    row_grp_structure(group_val = list(group="p-value (Xan - Placebo)"), 
                      element_block(post_space = " ")),
    label_loc = element_row_grp_loc(location = "indented")
  )
) %>%
  print_to_gt(data_efficacy) %>% 
  tab_options(
    container.width = 800
  )

	Placebo	Xanomeline Low Dose	Xanomeline High Dose
Baseline
n	79	81	74
Mean (SD)	24.1 (12.19)	24.4 (12.92)	21.3 (11.74)
Median (Range)	21.0 ( 5;61)	21.0 ( 5;57)	18.0 ( 3;57)
Week 24
n	79	81	74
Mean (SD)	26.7 (13.79)	26.4 (13.18)	22.8 (12.48)
Median (Range)	24.0 ( 5;62)	25.0 ( 6;62)	20.0 ( 3;62)
Change from Baseline
n	79	81	74
Mean (SD)	2.5 ( 5.80)	2.0 ( 5.55)	1.5 ( 4.26)
Median (Range)	2.0 (-11;16)	2.0 (-11;17)	1.0 (-7;13)
p-value (Dose Response)			0.245
p-value (Xan - Placebo)		0.569	0.233
Diff of LS Means (SE)		-0.5 ( 0.82)	-1.0 ( 0.84)
95% CI		(-2.1; 1.1)	(-2.7; 0.7)
p-value (Xan High - Xan Low)			0.520
Diff of LS Means (SE)			-0.5 ( 0.84)
95% CI			(-2.2; 1.1)

There we have it, our efficacy table is complete!