diff --git a/DESCRIPTION b/DESCRIPTION index fe4620a..abee5f9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: sassy Type: Package Title: Makes 'R' Easier for Everyone -Version: 1.2.4 +Version: 1.2.5 Author: David J. Bosak Maintainer: David Bosak Description: A meta-package that aims to make 'R' easier for everyone, @@ -23,10 +23,10 @@ Suggests: tidylog, magrittr, covr -Imports: fmtr(>= 1.6.3), +Imports: fmtr(>= 1.6.4), common(>= 1.1.3), - logr(>= 1.3.7), - libr(>= 1.3.2), + logr(>= 1.3.8), + libr(>= 1.3.3), reporter(>= 1.4.4), procs(>= 1.0.6), datasets, diff --git a/NEWS.md b/NEWS.md index a0abf2a..5c20068 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# sassy 1.2.5 + +* Updated required versions. +* Updated demographics table example. + # sassy 1.2.4 * Added oq validation for `proc_reg()`. diff --git a/docs/404.html b/docs/404.html index 5bc022f..86962b8 100644 --- a/docs/404.html +++ b/docs/404.html @@ -39,7 +39,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index ac1f52f..5e27423 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,7 +17,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/index.html b/docs/articles/index.html index a18e3af..c333975 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-ae.html b/docs/articles/sassy-ae.html index b7322cb..836e9d2 100644 --- a/docs/articles/sassy-ae.html +++ b/docs/articles/sassy-ae.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-ae2.html b/docs/articles/sassy-ae2.html index 8e24265..d4eb0f1 100644 --- a/docs/articles/sassy-ae2.html +++ b/docs/articles/sassy-ae2.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-chgbase.html b/docs/articles/sassy-chgbase.html index 1e43585..264a742 100644 --- a/docs/articles/sassy-chgbase.html +++ b/docs/articles/sassy-chgbase.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 @@ -388,7 +388,7 @@

Program ) put("Patch together plot and tables") - plts <- p + t1 + t2 + plot_layout(ncol = 1, nrow = 3, + plts <- p + t1 + t2 + plot_layout(ncol = 1, nrow = 3, widths = c(1, 2, 2), heights = c(8, 2, 2)) put("Create plot content") diff --git a/docs/articles/sassy-disclaimers.html b/docs/articles/sassy-disclaimers.html index cf8000c..85f26f5 100644 --- a/docs/articles/sassy-disclaimers.html +++ b/docs/articles/sassy-disclaimers.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-dm.html b/docs/articles/sassy-dm.html index 6a2b755..e8c807b 100644 --- a/docs/articles/sassy-dm.html +++ b/docs/articles/sassy-dm.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 @@ -201,19 +201,19 @@

Program condition(x >=30 & x <= 39, "30 to 39"), condition(x >=40 & x <=49, "40 to 49"), condition(x >= 50, ">= 50"), - condition(TRUE, "Out of range")) + as.factor = TRUE) put("Sex decodes") -fmt_sex <- value(condition(is.na(x), "Missing"), - condition(x == "M", "Male"), +fmt_sex <- value(condition(x == "M", "Male"), condition(x == "F", "Female"), - condition(TRUE, "Other")) + condition(TRUE, "Other"), + as.factor = TRUE) put("Race decodes") -fmt_race <- value(condition(is.na(x), "Missing"), - condition(x == "WHITE", "White"), +fmt_race <- value(condition(x == "WHITE", "White"), condition(x == "BLACK", "Black or African American"), - condition(TRUE, "Other")) + condition(TRUE, "Other"), + as.factor = TRUE) put("Compile format catalog") @@ -223,11 +223,7 @@

Program CNT = "%2d", PCT = "(%5.1f%%)", AGECAT = agecat, SEX = fmt_sex, - RACE = fmt_race, - AOV.F = "%5.3f", - AOV.P = "(%5.3f)", - CHISQ = "%5.3f", - CHISQ.P = "(%5.3f)") + RACE = fmt_race) # Load and Prepare Data --------------------------------------------------- @@ -294,29 +290,8 @@

Programproc_transpose(age_comb, var = names(age_comb), copy = VAR, id = BY, - name = LABEL) -> age_trans + name = LABEL) -> age_block -put("Calculate aov") -age_aov <- aov(AGE ~ ARM, data = adsl) |> - summary() - -put("Get aov into proper data frame") - -age_aov <- age_aov[[1]][1, c("F value", "Pr(>F)")] -names(age_aov) <- c("AOV.F", "AOV.P") -age_aov <- as.data.frame(age_aov) |> put() - -put("Combine aov statistics") -datastep(age_aov, - keep = PVALUE, - format = fc, - { - PVALUE <- fapply2(AOV.F, AOV.P) - - }) -> age_aov_comb - -put("Append aov") -datastep(age_trans, merge = age_aov_comb, {}) -> age_block # Sex Block --------------------------------------------------------------- @@ -342,37 +317,19 @@

Programput("Transpose ARMs into columns") proc_transpose(sex_comb, id = BY, var = CNTPCT, - copy = VAR, by = LABEL) -> sex_trans + copy = VAR, by = LABEL, + options = noname) -> sex_trans -put("Clean up") -datastep(sex_trans, drop = NAME, +put("Apply formats") +datastep(sex_trans, { LABEL <- fapply(LABEL, fc$SEX) - LABEL <- factor(LABEL, levels = levels(fc$SEX)) }) -> sex_cnts put("Sort by label") -proc_sort(sex_cnts, by = LABEL) -> sex_cnts - -put("Get sex chisq") -proc_freq(adsl, tables = v(SEX * ARM), - options = v(chisq, notable)) -> sex_chisq - -put("Combine chisq statistics") -datastep(sex_chisq, - format = fc, - keep = PVALUE, - { - - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> sex_chisq_comb - -put("Append chisq") -datastep(sex_cnts, - merge = sex_chisq_comb, - {}) -> sex_block +proc_sort(sex_cnts, by = LABEL) -> sex_block # Race block -------------------------------------------------------------- @@ -399,36 +356,18 @@

Program put("Transpose ARMs into columns") proc_transpose(race_comb, id = BY, var = CNTPCT, - copy = VAR, by = LABEL) -> race_trans + copy = VAR, by = LABEL, options = noname) -> race_trans put("Clean up") - -datastep(race_trans, drop = NAME, - where = expression(del == FALSE), +datastep(race_trans, { LABEL <- fapply(LABEL, fc$RACE) - LABEL <- factor(LABEL, levels = levels(fc$RACE)) }) -> race_cnts put("Sort by label") -proc_sort(race_cnts, by = LABEL) -> race_cnts - -put("Get race chisq") -proc_freq(adsl, tables = RACE * ARM, - options = v(chisq, notable)) -> race_chisq +proc_sort(race_cnts, by = LABEL) -> race_block -put("Combine chisq statistics") -datastep(race_chisq, - format = fc, - keep = c("PVALUE"), - { - - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> race_chisq_comb - -put("Append chisq") -datastep(race_cnts, merge = race_chisq_comb, {}) -> race_block # Age Group Block ---------------------------------------------------------- @@ -444,11 +383,11 @@

Program put("Combine counts and percents and assign age group factor for sorting") datastep(ageg_freq, - format = fc, + format = fc, keep = v(VAR, LABEL, BY, CNTPCT), { CNTPCT <- fapply2(CNT, PCT) - LABEL <- factor(CAT, levels = levels(fc$AGECAT)) + LABEL <- CAT }) -> ageg_comb @@ -460,29 +399,8 @@

Program var = CNTPCT, copy = VAR, id = BY, - by = LABEL) -> ageg_trans - -put("Some clean up") -datastep(ageg_trans, - drop = NAME, - {}) -> ageg_cnts - -put("Get ageg chisq") -proc_freq(adsl, tables = AGECAT * ARM, - options = v(chisq, notable)) -> ageg_chisq - -put("Combine chisq statistics") -datastep(ageg_chisq, - format = fc, - keep = c("PVALUE"), - { - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> ageg_chisq_comb - -put("Append chisq") -datastep(ageg_cnts, merge = ageg_chisq_comb, - {}) -> ageg_block - + by = LABEL, + options = noname) -> ageg_trans put("Combine blocks into final data frame") datastep(age_block, @@ -496,8 +414,6 @@

Program var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race") -plbl <- "Tests of Association{supsc('1')}\n Value (P-Value)" - # Create Table tbl <- create_table(final, first_row_blank = TRUE) |> column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |> @@ -509,13 +425,10 @@

Program define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |> define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |> define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |> - define(PVALUE, label = plbl, width = 2, dedupe = TRUE, align = "center") |> titles("Table 1.0", "Analysis of Demographic Characteristics", "Safety Population", bold = TRUE) |> footnotes("Program: DM_Table.R", - "NOTE: Denominator based on number of non-missing responses.", - "{supsc('1')}Pearson's Chi-Square tests will be used for " - %p% "Categorical variables and ANOVA tests for continuous variables.") + "NOTE: Denominator based on number of non-missing responses.") rpt <- create_report(file.path(tmp, "example2.rtf"), output_type = "RTF", @@ -552,17 +465,17 @@

Log

And here is the log:

=========================================================================
-Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpEBPgPu/log/example2.log
-Program Path: C:/packages/Testing/procs/ProcsDemog.R
-Working Directory: C:/packages/Testing/procs
+Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpAXQUo8/log/example2.log
+Program Path: C:/Projects/Archytas/Westat/Tutorial2/Project/sassy-dm.R
+Working Directory: C:/Projects/Archytas/Westat/Tutorial2/Project
 User Name: dbosa
-R Version: 4.3.1 (2023-06-16 ucrt)
+R Version: 4.4.0 (2024-04-24 ucrt)
 Machine: SOCRATES x86-64
-Operating System: Windows 10 x64 build 22621
-Base Packages: stats graphics grDevices utils datasets methods base Other
-Packages: tidylog_1.0.2 stringr_1.5.0 procs_1.0.3 reporter_1.4.1 libr_1.2.8
-fmtr_1.5.9 logr_1.3.4 common_1.0.8 sassy_1.1.0
-Log Start Time: 2023-09-05 22:11:55.376641
+Operating System: Windows 10 x64 build 22631
+Base Packages: stats graphics grDevices utils datasets methods base
+Other Packages: tidylog_1.0.2 ggplot2_3.5.1 procs_1.0.7 reporter_1.4.4
+libr_1.3.3 logr_1.3.7 fmtr_1.6.4 common_1.1.3 sassy_1.2.4
+Log Start Time: 2024-05-28 12:52:44.619869
 =========================================================================
 
 =========================================================================
@@ -571,40 +484,35 @@ 
Log
 
 Age categories
 
-# A user-defined format: 5 conditions
-  Name Type        Expression        Label Order
-1  obj    U x >= 18 & x <= 29     18 to 29    NA
-2  obj    U x >= 30 & x <= 39     30 to 39    NA
-3  obj    U x >= 40 & x <= 49     40 to 49    NA
-4  obj    U           x >= 50        >= 50    NA
-5  obj    U              TRUE Out of range    NA
+# A user-defined format: 4 conditions
+- as.factor: TRUE
+  Name Type        Expression    Label Order
+1  obj    U x >= 18 & x <= 29 18 to 29    NA
+2  obj    U x >= 30 & x <= 39 30 to 39    NA
+3  obj    U x >= 40 & x <= 49 40 to 49    NA
+4  obj    U           x >= 50    >= 50    NA
 
 Sex decodes
 
-# A user-defined format: 4 conditions
-  Name Type Expression   Label Order
-1  obj    U   is.na(x) Missing    NA
-2  obj    U   x == "M"    Male    NA
-3  obj    U   x == "F"  Female    NA
-4  obj    U       TRUE   Other    NA
+# A user-defined format: 3 conditions
+- as.factor: TRUE
+  Name Type Expression  Label Order
+1  obj    U   x == "M"   Male    NA
+2  obj    U   x == "F" Female    NA
+3  obj    U       TRUE  Other    NA
 
 Race decodes
 
-# A user-defined format: 4 conditions
-  Name Type   Expression                     Label
-1  obj    U     is.na(x)                   Missing
-2  obj    U x == "WHITE"                     White
-3  obj    U x == "BLACK" Black or African American
-4  obj    U         TRUE                     Other
-  Order
-1    NA
-2    NA
-3    NA
-4    NA
+# A user-defined format: 3 conditions
+- as.factor: TRUE
+  Name Type   Expression                     Label Order
+1  obj    U x == "WHITE"                     White    NA
+2  obj    U x == "BLACK" Black or African American    NA
+3  obj    U         TRUE                     Other    NA
 
 Compile format catalog
 
-# A format catalog: 15 formats
+# A format catalog: 11 formats
 - $MEAN: type S, "%.1f"
 - $STD: type S, "(%.2f)"
 - $Q1: type S, "%.1f"
@@ -613,13 +521,9 @@ 
Log
 - $MAX: type S, "%d"
 - $CNT: type S, "%2d"
 - $PCT: type S, "(%5.1f%%)"
-- $AGECAT: type U, 5 conditions
-- $SEX: type U, 4 conditions
-- $RACE: type U, 4 conditions
-- $AOV.F: type S, "%5.3f"
-- $AOV.P: type S, "(%5.3f)"
-- $CHISQ: type S, "%5.3f"
-- $CHISQ.P: type S, "(%5.3f)"
+- $AGECAT: type U, 4 conditions
+- $SEX: type U, 3 conditions
+- $RACE: type U, 3 conditions
 
 =========================================================================
 Prepare Data
@@ -683,16 +587,11 @@ 
Log
 
 datastep: columns decreased from 10 to 7
 
-     BY VAR N   Mean (SD) Median     Q1 - Q3
-1 ARM A AGE 5 34.4 (8.71)   37.0 37.0 - 39.0
-2 ARM B AGE 4 30.0 (6.16)   32.5 26.0 - 34.0
-3 ARM C AGE 4 32.0 (6.16)   34.5 28.0 - 36.0
-4 ARM D AGE 3 32.0 (5.29)   30.0 28.0 - 38.0
-  Min - Max
-1   19 - 40
-2   21 - 34
-3   23 - 36
-4   28 - 38
+     BY VAR N   Mean (SD) Median     Q1 - Q3 Min - Max
+1 ARM A AGE 5 34.4 (8.71)   37.0 37.0 - 39.0   19 - 40
+2 ARM B AGE 4 30.0 (6.16)   32.5 26.0 - 34.0   21 - 34
+3 ARM C AGE 4 32.0 (6.16)   34.5 28.0 - 36.0   23 - 36
+4 ARM D AGE 3 32.0 (5.29)   30.0 28.0 - 38.0   28 - 38
 
 Transpose ARMs into columns
 
@@ -703,49 +602,12 @@ 
Log
                 name: LABEL
                 output dataset 5 rows and 6 columns
 
-  VAR     LABEL       ARM A       ARM B
-1 AGE         N           5           4
-2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16)
-3 AGE    Median        37.0        32.5
-4 AGE   Q1 - Q3 37.0 - 39.0 26.0 - 34.0
-5 AGE Min - Max     19 - 40     21 - 34
-        ARM C       ARM D
-1           4           3
-2 32.0 (6.16) 32.0 (5.29)
-3        34.5        30.0
-4 28.0 - 36.0 28.0 - 38.0
-5     23 - 36     28 - 38
-
-Calculate aov
-
-Get aov into proper data frame
-
-                AOV.F     AOV.P
-ARM         0.2983651 0.8259486
-
-Combine aov statistics
-
-datastep: columns decreased from 2 to 1
-
-         PVALUE
-1 0.298 (0.826)
-
-Append aov
-
-datastep: columns increased from 6 to 7
-
-  VAR     LABEL       ARM A       ARM B
-1 AGE         N           5           4
-2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16)
-3 AGE    Median        37.0        32.5
-4 AGE   Q1 - Q3 37.0 - 39.0 26.0 - 34.0
-5 AGE Min - Max     19 - 40     21 - 34
-        ARM C       ARM D        PVALUE
-1           4           3 0.298 (0.826)
-2 32.0 (6.16) 32.0 (5.29)          <NA>
-3        34.5        30.0          <NA>
-4 28.0 - 36.0 28.0 - 38.0          <NA>
-5     23 - 36     28 - 38          <NA>
+  VAR     LABEL       ARM A       ARM B       ARM C       ARM D
+1 AGE         N           5           4           4           3
+2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
+3 AGE    Median        37.0        32.5        34.5        30.0
+4 AGE   Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
+5 AGE Min - Max     19 - 40     21 - 34     23 - 36     28 - 38
 
 =========================================================================
 Create frequency counts for SEX
@@ -791,25 +653,19 @@ 
Log
                 id: BY
                 copy: VAR
                 name: NAME
-                output dataset 2 rows and 7 columns
+                output dataset 2 rows and 6 columns
 
-  VAR LABEL   NAME       ARM A       ARM B
-1 SEX     F CNTPCT  2 ( 40.0%)  2 ( 50.0%)
-2 SEX     M CNTPCT  3 ( 60.0%)  2 ( 50.0%)
-        ARM C       ARM D
-1  2 ( 50.0%)  2 ( 66.7%)
-2  2 ( 50.0%)  1 ( 33.3%)
+  VAR LABEL       ARM A       ARM B       ARM C       ARM D
+1 SEX     F  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
+2 SEX     M  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
 
-Clean up
+Apply formats
 
-datastep: columns decreased from 7 to 6
+datastep: columns started with 6 and ended with 6
 
-  VAR  LABEL       ARM A       ARM B       ARM C
-1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)
-2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)
-        ARM D
-1  2 ( 66.7%)
-2  1 ( 33.3%)
+  VAR  LABEL       ARM A       ARM B       ARM C       ARM D
+1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
+2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
 
 Sort by label
 
@@ -819,40 +675,9 @@ 
Log
            order: a
            output data set 2 rows and 6 columns
 
-  VAR  LABEL       ARM A       ARM B       ARM C
-2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)
-1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)
-        ARM D
-2  1 ( 33.3%)
-1  2 ( 66.7%)
-
-Get sex chisq
-
-proc_freq: input data set 16 rows and 6 columns
-           tables: SEX * ARM
-           view: TRUE
-           output: 1 datasets
-
-      CHISQ CHISQ.DF   CHISQ.P
-1 0.5333333        3 0.9115095
-
-Combine chisq statistics
-
-datastep: columns decreased from 3 to 1
-
-         PVALUE
-1 0.533 (0.912)
-
-Append chisq
-
-datastep: columns increased from 6 to 7
-
-  VAR  LABEL       ARM A       ARM B       ARM C
-1 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)
-2 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)
-        ARM D        PVALUE
-1  1 ( 33.3%) 0.533 (0.912)
-2  2 ( 66.7%)          <NA>
+  VAR  LABEL       ARM A       ARM B       ARM C       ARM D
+2 SEX   Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
+1 SEX Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
 
 =========================================================================
 Create frequency counts for RACE
@@ -898,25 +723,19 @@ 
Log
                 id: BY
                 copy: VAR
                 name: NAME
-                output dataset 2 rows and 7 columns
+                output dataset 2 rows and 6 columns
 
-   VAR LABEL   NAME       ARM A       ARM B
-1 RACE BLACK CNTPCT  1 ( 20.0%)  0 (  0.0%)
-2 RACE WHITE CNTPCT  4 ( 80.0%)  4 (100.0%)
-        ARM C       ARM D
-1  1 ( 25.0%)  1 ( 33.3%)
-2  3 ( 75.0%)  2 ( 66.7%)
+   VAR LABEL       ARM A       ARM B       ARM C       ARM D
+1 RACE BLACK  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
+2 RACE WHITE  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
 
 Clean up
 
-datastep: columns decreased from 7 to 6
+datastep: columns started with 6 and ended with 6
 
-   VAR                     LABEL       ARM A
-1 RACE Black or African American  1 ( 20.0%)
-2 RACE                     White  4 ( 80.0%)
-        ARM B       ARM C       ARM D
-1  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
-2  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
+   VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
+1 RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
+2 RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
 
 Sort by label
 
@@ -926,43 +745,9 @@ 
Log
            order: a
            output data set 2 rows and 6 columns
 
-   VAR                     LABEL       ARM A
-2 RACE                     White  4 ( 80.0%)
-1 RACE Black or African American  1 ( 20.0%)
-        ARM B       ARM C       ARM D
-2  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
-1  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
-
-Get race chisq
-
-proc_freq: input data set 16 rows and 6 columns
-           tables: RACE * ARM
-           view: TRUE
-           output: 1 datasets
-
-     CHISQ CHISQ.DF   CHISQ.P
-1 1.449573        3 0.6939569
-
-Combine chisq statistics
-
-datastep: columns decreased from 3 to 1
-
-         PVALUE
-1 1.450 (0.694)
-
-Append chisq
-
-datastep: columns increased from 6 to 7
-
-   VAR                     LABEL       ARM A
-1 RACE                     White  4 ( 80.0%)
-2 RACE Black or African American  1 ( 20.0%)
-        ARM B       ARM C       ARM D
-1  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
-2  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
-         PVALUE
-1 1.450 (0.694)
-2          <NA>
+   VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
+2 RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
+1 RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
 
 =========================================================================
 Create frequency counts for Age Group
@@ -980,15 +765,19 @@ 
Log
 1  ARM A AGECAT 18 to 29   1 20.00000
 2  ARM A AGECAT 30 to 39   3 60.00000
 3  ARM A AGECAT 40 to 49   1 20.00000
-4  ARM B AGECAT 18 to 29   1 25.00000
-5  ARM B AGECAT 30 to 39   3 75.00000
-6  ARM B AGECAT 40 to 49   0  0.00000
-7  ARM C AGECAT 18 to 29   1 25.00000
-8  ARM C AGECAT 30 to 39   3 75.00000
-9  ARM C AGECAT 40 to 49   0  0.00000
-10 ARM D AGECAT 18 to 29   1 33.33333
-11 ARM D AGECAT 30 to 39   2 66.66667
-12 ARM D AGECAT 40 to 49   0  0.00000
+4  ARM A AGECAT    >= 50   0  0.00000
+5  ARM B AGECAT 18 to 29   1 25.00000
+6  ARM B AGECAT 30 to 39   3 75.00000
+7  ARM B AGECAT 40 to 49   0  0.00000
+8  ARM B AGECAT    >= 50   0  0.00000
+9  ARM C AGECAT 18 to 29   1 25.00000
+10 ARM C AGECAT 30 to 39   3 75.00000
+11 ARM C AGECAT 40 to 49   0  0.00000
+12 ARM C AGECAT    >= 50   0  0.00000
+13 ARM D AGECAT 18 to 29   1 33.33333
+14 ARM D AGECAT 30 to 39   2 66.66667
+15 ARM D AGECAT 40 to 49   0  0.00000
+16 ARM D AGECAT    >= 50   0  0.00000
 
 Combine counts and percents and assign age group factor for sorting
 
@@ -998,143 +787,79 @@ 
Log
 1  AGECAT 18 to 29 ARM A  1 ( 20.0%)
 2  AGECAT 30 to 39 ARM A  3 ( 60.0%)
 3  AGECAT 40 to 49 ARM A  1 ( 20.0%)
-4  AGECAT 18 to 29 ARM B  1 ( 25.0%)
-5  AGECAT 30 to 39 ARM B  3 ( 75.0%)
-6  AGECAT 40 to 49 ARM B  0 (  0.0%)
-7  AGECAT 18 to 29 ARM C  1 ( 25.0%)
-8  AGECAT 30 to 39 ARM C  3 ( 75.0%)
-9  AGECAT 40 to 49 ARM C  0 (  0.0%)
-10 AGECAT 18 to 29 ARM D  1 ( 33.3%)
-11 AGECAT 30 to 39 ARM D  2 ( 66.7%)
-12 AGECAT 40 to 49 ARM D  0 (  0.0%)
+4  AGECAT    >= 50 ARM A  0 (  0.0%)
+5  AGECAT 18 to 29 ARM B  1 ( 25.0%)
+6  AGECAT 30 to 39 ARM B  3 ( 75.0%)
+7  AGECAT 40 to 49 ARM B  0 (  0.0%)
+8  AGECAT    >= 50 ARM B  0 (  0.0%)
+9  AGECAT 18 to 29 ARM C  1 ( 25.0%)
+10 AGECAT 30 to 39 ARM C  3 ( 75.0%)
+11 AGECAT 40 to 49 ARM C  0 (  0.0%)
+12 AGECAT    >= 50 ARM C  0 (  0.0%)
+13 AGECAT 18 to 29 ARM D  1 ( 33.3%)
+14 AGECAT 30 to 39 ARM D  2 ( 66.7%)
+15 AGECAT 40 to 49 ARM D  0 (  0.0%)
+16 AGECAT    >= 50 ARM D  0 (  0.0%)
 
 Sort by age group factor
 
-proc_sort: input data set 12 rows and 4 columns
+proc_sort: input data set 16 rows and 4 columns
            by: BY LABEL
            keep: VAR LABEL BY CNTPCT
            order: a a
-           output data set 12 rows and 4 columns
+           output data set 16 rows and 4 columns
 
       VAR    LABEL    BY      CNTPCT
 1  AGECAT 18 to 29 ARM A  1 ( 20.0%)
 2  AGECAT 30 to 39 ARM A  3 ( 60.0%)
 3  AGECAT 40 to 49 ARM A  1 ( 20.0%)
-4  AGECAT 18 to 29 ARM B  1 ( 25.0%)
-5  AGECAT 30 to 39 ARM B  3 ( 75.0%)
-6  AGECAT 40 to 49 ARM B  0 (  0.0%)
-7  AGECAT 18 to 29 ARM C  1 ( 25.0%)
-8  AGECAT 30 to 39 ARM C  3 ( 75.0%)
-9  AGECAT 40 to 49 ARM C  0 (  0.0%)
-10 AGECAT 18 to 29 ARM D  1 ( 33.3%)
-11 AGECAT 30 to 39 ARM D  2 ( 66.7%)
-12 AGECAT 40 to 49 ARM D  0 (  0.0%)
+4  AGECAT    >= 50 ARM A  0 (  0.0%)
+5  AGECAT 18 to 29 ARM B  1 ( 25.0%)
+6  AGECAT 30 to 39 ARM B  3 ( 75.0%)
+7  AGECAT 40 to 49 ARM B  0 (  0.0%)
+8  AGECAT    >= 50 ARM B  0 (  0.0%)
+9  AGECAT 18 to 29 ARM C  1 ( 25.0%)
+10 AGECAT 30 to 39 ARM C  3 ( 75.0%)
+11 AGECAT 40 to 49 ARM C  0 (  0.0%)
+12 AGECAT    >= 50 ARM C  0 (  0.0%)
+13 AGECAT 18 to 29 ARM D  1 ( 33.3%)
+14 AGECAT 30 to 39 ARM D  2 ( 66.7%)
+15 AGECAT 40 to 49 ARM D  0 (  0.0%)
+16 AGECAT    >= 50 ARM D  0 (  0.0%)
 
 Tranpose age group block
 
-proc_transpose: input data set 12 rows and 4 columns
+proc_transpose: input data set 16 rows and 4 columns
                 by: LABEL
                 var: CNTPCT
                 id: BY
                 copy: VAR
                 name: NAME
-                output dataset 3 rows and 7 columns
-
-     VAR    LABEL   NAME       ARM A       ARM B
-1 AGECAT 18 to 29 CNTPCT  1 ( 20.0%)  1 ( 25.0%)
-2 AGECAT 30 to 39 CNTPCT  3 ( 60.0%)  3 ( 75.0%)
-3 AGECAT 40 to 49 CNTPCT  1 ( 20.0%)  0 (  0.0%)
-        ARM C       ARM D
-1  1 ( 25.0%)  1 ( 33.3%)
-2  3 ( 75.0%)  2 ( 66.7%)
-3  0 (  0.0%)  0 (  0.0%)
-
-Some clean up
-
-datastep: columns decreased from 7 to 6
-
-     VAR    LABEL       ARM A       ARM B
-1 AGECAT 18 to 29  1 ( 20.0%)  1 ( 25.0%)
-2 AGECAT 30 to 39  3 ( 60.0%)  3 ( 75.0%)
-3 AGECAT 40 to 49  1 ( 20.0%)  0 (  0.0%)
-        ARM C       ARM D
-1  1 ( 25.0%)  1 ( 33.3%)
-2  3 ( 75.0%)  2 ( 66.7%)
-3  0 (  0.0%)  0 (  0.0%)
-
-Get ageg chisq
-
-proc_freq: input data set 16 rows and 6 columns
-           tables: AGECAT * ARM
-           view: TRUE
-           output: 1 datasets
-
-     CHISQ CHISQ.DF   CHISQ.P
-1 2.436364        6 0.8755205
-
-Combine chisq statistics
+                output dataset 4 rows and 6 columns
 
-datastep: columns decreased from 3 to 1
-
-         PVALUE
-1 2.436 (0.876)
-
-Append chisq
-
-datastep: columns increased from 6 to 7
-
-     VAR    LABEL       ARM A       ARM B
-1 AGECAT 18 to 29  1 ( 20.0%)  1 ( 25.0%)
-2 AGECAT 30 to 39  3 ( 60.0%)  3 ( 75.0%)
-3 AGECAT 40 to 49  1 ( 20.0%)  0 (  0.0%)
-        ARM C       ARM D        PVALUE
-1  1 ( 25.0%)  1 ( 33.3%) 2.436 (0.876)
-2  3 ( 75.0%)  2 ( 66.7%)          <NA>
-3  0 (  0.0%)  0 (  0.0%)          <NA>
+     VAR    LABEL       ARM A       ARM B       ARM C       ARM D
+1 AGECAT 18 to 29  1 ( 20.0%)  1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%)
+2 AGECAT 30 to 39  3 ( 60.0%)  3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)
+3 AGECAT 40 to 49  1 ( 20.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
+4 AGECAT    >= 50  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
 
 Combine blocks into final data frame
 
-datastep: columns started with 7 and ended with 7
-
-      VAR                     LABEL       ARM A
-1     AGE                         N           5
-2     AGE                 Mean (SD) 34.4 (8.71)
-3     AGE                    Median        37.0
-4     AGE                   Q1 - Q3 37.0 - 39.0
-5     AGE                 Min - Max     19 - 40
-6  AGECAT                  18 to 29  1 ( 20.0%)
-7  AGECAT                  30 to 39  3 ( 60.0%)
-8  AGECAT                  40 to 49  1 ( 20.0%)
-9     SEX                      Male  3 ( 60.0%)
-10    SEX                    Female  2 ( 40.0%)
-11   RACE                     White  4 ( 80.0%)
-12   RACE Black or African American  1 ( 20.0%)
-         ARM B       ARM C       ARM D
-1            4           4           3
-2  30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
-3         32.5        34.5        30.0
-4  26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
-5      21 - 34     23 - 36     28 - 38
-6   1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%)
-7   3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)
-8   0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
-9   2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
-10  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
-11  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
-12  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
-          PVALUE
-1  0.298 (0.826)
-2           <NA>
-3           <NA>
-4           <NA>
-5           <NA>
-6  2.436 (0.876)
-7           <NA>
-8           <NA>
-9  0.533 (0.912)
-10          <NA>
-11 1.450 (0.694)
-12          <NA>
+datastep: columns started with 6 and ended with 6
+
+      VAR                     LABEL       ARM A       ARM B       ARM C       ARM D
+1     AGE                         N           5           4           4           3
+2     AGE                 Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29)
+3     AGE                    Median        37.0        32.5        34.5        30.0
+4     AGE                   Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0
+5     AGE                 Min - Max     19 - 40     21 - 34     23 - 36     28 - 38
+6  AGECAT                  18 to 29  1 ( 20.0%)  1 ( 25.0%)  1 ( 25.0%)  1 ( 33.3%)
+7  AGECAT                  30 to 39  3 ( 60.0%)  3 ( 75.0%)  3 ( 75.0%)  2 ( 66.7%)
+8  AGECAT                  40 to 49  1 ( 20.0%)  0 (  0.0%)  0 (  0.0%)  0 (  0.0%)
+9     SEX                      Male  3 ( 60.0%)  2 ( 50.0%)  2 ( 50.0%)  1 ( 33.3%)
+10    SEX                    Female  2 ( 40.0%)  2 ( 50.0%)  2 ( 50.0%)  2 ( 66.7%)
+11   RACE                     White  4 ( 80.0%)  4 (100.0%)  3 ( 75.0%)  2 ( 66.7%)
+12   RACE Black or African American  1 ( 20.0%)  0 (  0.0%)  1 ( 25.0%)  1 ( 33.3%)
 
 =========================================================================
 Create and print report
@@ -1143,17 +868,17 @@ 
Log
 Write out the report
 
 # A report specification: 1 pages
-- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpEBPgPu/example2.rtf'
+- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpAXQUo8/example2.rtf'
 - output_type: RTF
 - units: inches
 - orientation: landscape
 - margins: top 1 bottom 1 left 1 right 1
 - line size/count: 9/36
 - page_header: left=Sponsor: Company right=Study: ABC
-- page_footer: left=Date Produced: 2023-09-05 center= right=Page [pg] of [tpg]
+- page_footer: left=Date Produced: 2024-05-28 center= right=Page [pg] of [tpg]
 - content: 
 # A table specification:
-- data: data.frame 'final' 12 rows 7 cols
+- data: data.frame 'final' 12 rows 6 cols
 - show_cols: all
 - use_attributes: all
 - title 1: 'Table 1.0'
@@ -1161,7 +886,6 @@ 
Log
 - title 3: 'Safety Population'
 - footnote 1: 'Program: DM_Table.R'
 - footnote 2: 'NOTE: Denominator based on number of non-missing responses.'
-- footnote 3: '¹Pearson's Chi-Square tests will be used for Categorical variables and ANOVA tests for continuous variables.'
 - stub: VAR LABEL 'Variable' width=2.5 align='left' 
 - define: VAR 'Variable' dedupe='TRUE' 
 - define: LABEL 'Demographic Category' 
@@ -1169,8 +893,6 @@ 
Log
 - define: ARM B 'Drug 50mg' 
 - define: ARM C 'Drug 100mg' 
 - define: ARM D 'Competitor' 
-- define: PVALUE 'Tests of Association¹
- Value (P-Value)' width=2 align='center' dedupe='TRUE' 
 
 =========================================================================
 Clean Up
@@ -1179,10 +901,11 @@ 
Log
 Close log
 
 =========================================================================
-Log End Time: 2023-09-05 22:11:56.276799
+Log End Time: 2024-05-28 12:52:45.234875
 Log Elapsed Time: 0 00:00:00
 =========================================================================
 
+

Next: Example 3: Figures

diff --git a/docs/articles/sassy-ds.html b/docs/articles/sassy-ds.html index c495e2a..41d2fc7 100644 --- a/docs/articles/sassy-ds.html +++ b/docs/articles/sassy-ds.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-faq.html b/docs/articles/sassy-faq.html index c669fb5..4d7ae92 100644 --- a/docs/articles/sassy-faq.html +++ b/docs/articles/sassy-faq.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-figure.html b/docs/articles/sassy-figure.html index 4e6438e..8e4ac31 100644 --- a/docs/articles/sassy-figure.html +++ b/docs/articles/sassy-figure.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-figureby.html b/docs/articles/sassy-figureby.html index 4826a9e..fe1a736 100644 --- a/docs/articles/sassy-figureby.html +++ b/docs/articles/sassy-figureby.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-forest.html b/docs/articles/sassy-forest.html index fbcbf96..7363f11 100644 --- a/docs/articles/sassy-forest.html +++ b/docs/articles/sassy-forest.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 @@ -220,7 +220,7 @@

Programsep("Create Plot") put("Assign Forest Properties") -p <- forest(dt[,c(1:3, 8:9)], +p <- forest(dt[,c(1:3, 8:9)], est = dt$est, lower = dt$low, upper = dt$hi, diff --git a/docs/articles/sassy-intext.html b/docs/articles/sassy-intext.html index 4e9a451..3d1bb01 100644 --- a/docs/articles/sassy-intext.html +++ b/docs/articles/sassy-intext.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-listing.html b/docs/articles/sassy-listing.html index f893201..bdb2192 100644 --- a/docs/articles/sassy-listing.html +++ b/docs/articles/sassy-listing.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-pfigure.html b/docs/articles/sassy-pfigure.html index 21b12e5..ed72081 100644 --- a/docs/articles/sassy-pfigure.html +++ b/docs/articles/sassy-pfigure.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-plisting.html b/docs/articles/sassy-plisting.html index 167690b..bde8ca1 100644 --- a/docs/articles/sassy-plisting.html +++ b/docs/articles/sassy-plisting.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-profile.html b/docs/articles/sassy-profile.html index 5aa89bf..5f4d5c4 100644 --- a/docs/articles/sassy-profile.html +++ b/docs/articles/sassy-profile.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-shift.html b/docs/articles/sassy-shift.html index 83f4981..0314b7a 100644 --- a/docs/articles/sassy-shift.html +++ b/docs/articles/sassy-shift.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-survival.html b/docs/articles/sassy-survival.html index 49df5eb..db8e997 100644 --- a/docs/articles/sassy-survival.html +++ b/docs/articles/sassy-survival.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy-vs.html b/docs/articles/sassy-vs.html index 75ec779..4b2043f 100644 --- a/docs/articles/sassy-vs.html +++ b/docs/articles/sassy-vs.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/articles/sassy.html b/docs/articles/sassy.html index 1a7b155..cc8c052 100644 --- a/docs/articles/sassy.html +++ b/docs/articles/sassy.html @@ -40,7 +40,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/authors.html b/docs/authors.html index c6b348b..a92237d 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ sassy - 1.2.4 + 1.2.5 @@ -130,13 +130,13 @@

Citation

Bosak DJ (2024). sassy: Makes 'R' Easier for Everyone. -R package version 1.2.4, https://github.com/dbosak01/sassy, https://r-sassy.org. +R package version 1.2.5, https://github.com/dbosak01/sassy, https://r-sassy.org. 

@Manual{,
   title = {sassy: Makes 'R' Easier for Everyone},
   author = {David J. Bosak},
   year = {2024},
-  note = {R package version 1.2.4, https://github.com/dbosak01/sassy},
+  note = {R package version 1.2.5, https://github.com/dbosak01/sassy},
   url = {https://r-sassy.org},
 }
diff --git a/docs/index.html b/docs/index.html index 6dfbf3c..39408ba 100644 --- a/docs/index.html +++ b/docs/index.html @@ -46,7 +46,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/man/images/dm.png b/docs/man/images/dm.png index 5fe922a..15cb761 100644 Binary files a/docs/man/images/dm.png and b/docs/man/images/dm.png differ diff --git a/docs/news/index.html b/docs/news/index.html index 4691b7a..94b53d4 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ sassy - 1.2.4 + 1.2.5 @@ -115,7 +115,12 @@

Changelog

-

sassy 1.2.4

+

sassy 1.2.5

+
+
+

sassy 1.2.42024-04-16

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 696f1f9..99cc670 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 3.1.1 +pandoc: 3.1.11 pkgdown: 2.0.7 pkgdown_sha: ~ articles: @@ -21,7 +21,7 @@ articles: sassy-survival: sassy-survival.html sassy-vs: sassy-vs.html sassy: sassy.html -last_built: 2024-04-16T17:59Z +last_built: 2024-05-28T19:17Z urls: reference: https://sassy.r-sassy.org/reference article: https://sassy.r-sassy.org/articles diff --git a/docs/reference/index.html b/docs/reference/index.html index 60951da..d5c8f19 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/reference/run_iq.html b/docs/reference/run_iq.html index cf6851a..5793cc4 100644 --- a/docs/reference/run_iq.html +++ b/docs/reference/run_iq.html @@ -22,7 +22,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/reference/run_oq.html b/docs/reference/run_oq.html index 89ab879..fc9d913 100644 --- a/docs/reference/run_oq.html +++ b/docs/reference/run_oq.html @@ -22,7 +22,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/docs/reference/sassy.html b/docs/reference/sassy.html index 06468f7..981496e 100644 --- a/docs/reference/sassy.html +++ b/docs/reference/sassy.html @@ -25,7 +25,7 @@ sassy - 1.2.4 + 1.2.5 diff --git a/man/images/dm.png b/man/images/dm.png index 5fe922a..15cb761 100644 Binary files a/man/images/dm.png and b/man/images/dm.png differ diff --git a/vignettes/sassy-dm.Rmd b/vignettes/sassy-dm.Rmd index 4f0eb2c..c008d9b 100644 --- a/vignettes/sassy-dm.Rmd +++ b/vignettes/sassy-dm.Rmd @@ -62,19 +62,19 @@ agecat <- value(condition(x >= 18 & x <= 29, "18 to 29"), condition(x >=30 & x <= 39, "30 to 39"), condition(x >=40 & x <=49, "40 to 49"), condition(x >= 50, ">= 50"), - condition(TRUE, "Out of range")) + as.factor = TRUE) put("Sex decodes") -fmt_sex <- value(condition(is.na(x), "Missing"), - condition(x == "M", "Male"), +fmt_sex <- value(condition(x == "M", "Male"), condition(x == "F", "Female"), - condition(TRUE, "Other")) + condition(TRUE, "Other"), + as.factor = TRUE) put("Race decodes") -fmt_race <- value(condition(is.na(x), "Missing"), - condition(x == "WHITE", "White"), +fmt_race <- value(condition(x == "WHITE", "White"), condition(x == "BLACK", "Black or African American"), - condition(TRUE, "Other")) + condition(TRUE, "Other"), + as.factor = TRUE) put("Compile format catalog") @@ -84,11 +84,7 @@ fc <- fcat(MEAN = "%.1f", STD = "(%.2f)", CNT = "%2d", PCT = "(%5.1f%%)", AGECAT = agecat, SEX = fmt_sex, - RACE = fmt_race, - AOV.F = "%5.3f", - AOV.P = "(%5.3f)", - CHISQ = "%5.3f", - CHISQ.P = "(%5.3f)") + RACE = fmt_race) # Load and Prepare Data --------------------------------------------------- @@ -155,29 +151,8 @@ put("Transpose ARMs into columns") proc_transpose(age_comb, var = names(age_comb), copy = VAR, id = BY, - name = LABEL) -> age_trans + name = LABEL) -> age_block -put("Calculate aov") -age_aov <- aov(AGE ~ ARM, data = adsl) |> - summary() - -put("Get aov into proper data frame") - -age_aov <- age_aov[[1]][1, c("F value", "Pr(>F)")] -names(age_aov) <- c("AOV.F", "AOV.P") -age_aov <- as.data.frame(age_aov) |> put() - -put("Combine aov statistics") -datastep(age_aov, - keep = PVALUE, - format = fc, - { - PVALUE <- fapply2(AOV.F, AOV.P) - - }) -> age_aov_comb - -put("Append aov") -datastep(age_trans, merge = age_aov_comb, {}) -> age_block # Sex Block --------------------------------------------------------------- @@ -203,37 +178,19 @@ datastep(sex_freq, put("Transpose ARMs into columns") proc_transpose(sex_comb, id = BY, var = CNTPCT, - copy = VAR, by = LABEL) -> sex_trans + copy = VAR, by = LABEL, + options = noname) -> sex_trans -put("Clean up") -datastep(sex_trans, drop = NAME, +put("Apply formats") +datastep(sex_trans, { LABEL <- fapply(LABEL, fc$SEX) - LABEL <- factor(LABEL, levels = levels(fc$SEX)) }) -> sex_cnts put("Sort by label") -proc_sort(sex_cnts, by = LABEL) -> sex_cnts - -put("Get sex chisq") -proc_freq(adsl, tables = v(SEX * ARM), - options = v(chisq, notable)) -> sex_chisq - -put("Combine chisq statistics") -datastep(sex_chisq, - format = fc, - keep = PVALUE, - { - - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> sex_chisq_comb - -put("Append chisq") -datastep(sex_cnts, - merge = sex_chisq_comb, - {}) -> sex_block +proc_sort(sex_cnts, by = LABEL) -> sex_block # Race block -------------------------------------------------------------- @@ -260,36 +217,18 @@ datastep(race_freq, put("Transpose ARMs into columns") proc_transpose(race_comb, id = BY, var = CNTPCT, - copy = VAR, by = LABEL) -> race_trans + copy = VAR, by = LABEL, options = noname) -> race_trans put("Clean up") - -datastep(race_trans, drop = NAME, - where = expression(del == FALSE), +datastep(race_trans, { LABEL <- fapply(LABEL, fc$RACE) - LABEL <- factor(LABEL, levels = levels(fc$RACE)) }) -> race_cnts put("Sort by label") -proc_sort(race_cnts, by = LABEL) -> race_cnts - -put("Get race chisq") -proc_freq(adsl, tables = RACE * ARM, - options = v(chisq, notable)) -> race_chisq +proc_sort(race_cnts, by = LABEL) -> race_block -put("Combine chisq statistics") -datastep(race_chisq, - format = fc, - keep = c("PVALUE"), - { - - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> race_chisq_comb - -put("Append chisq") -datastep(race_cnts, merge = race_chisq_comb, {}) -> race_block # Age Group Block ---------------------------------------------------------- @@ -305,11 +244,11 @@ proc_freq(adsl, put("Combine counts and percents and assign age group factor for sorting") datastep(ageg_freq, - format = fc, + format = fc, keep = v(VAR, LABEL, BY, CNTPCT), { CNTPCT <- fapply2(CNT, PCT) - LABEL <- factor(CAT, levels = levels(fc$AGECAT)) + LABEL <- CAT }) -> ageg_comb @@ -321,29 +260,8 @@ proc_transpose(ageg_sort, var = CNTPCT, copy = VAR, id = BY, - by = LABEL) -> ageg_trans - -put("Some clean up") -datastep(ageg_trans, - drop = NAME, - {}) -> ageg_cnts - -put("Get ageg chisq") -proc_freq(adsl, tables = AGECAT * ARM, - options = v(chisq, notable)) -> ageg_chisq - -put("Combine chisq statistics") -datastep(ageg_chisq, - format = fc, - keep = c("PVALUE"), - { - PVALUE = fapply2(CHISQ, CHISQ.P) - }) -> ageg_chisq_comb - -put("Append chisq") -datastep(ageg_cnts, merge = ageg_chisq_comb, - {}) -> ageg_block - + by = LABEL, + options = noname) -> ageg_trans put("Combine blocks into final data frame") datastep(age_block, @@ -357,8 +275,6 @@ sep("Create and print report") var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race") -plbl <- "Tests of Association{supsc('1')}\n Value (P-Value)" - # Create Table tbl <- create_table(final, first_row_blank = TRUE) |> column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |> @@ -370,13 +286,10 @@ tbl <- create_table(final, first_row_blank = TRUE) |> define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |> define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |> define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |> - define(PVALUE, label = plbl, width = 2, dedupe = TRUE, align = "center") |> titles("Table 1.0", "Analysis of Demographic Characteristics", "Safety Population", bold = TRUE) |> footnotes("Program: DM_Table.R", - "NOTE: Denominator based on number of non-missing responses.", - "{supsc('1')}Pearson's Chi-Square tests will be used for " - %p% "Categorical variables and ANOVA tests for continuous variables.") + "NOTE: Denominator based on number of non-missing responses.") rpt <- create_report(file.path(tmp, "example2.rtf"), output_type = "RTF", @@ -402,6 +315,7 @@ log_close() # Uncomment to view log # file.show(lf) + ``` ## Output @@ -417,17 +331,17 @@ And here is the log: ``` ========================================================================= -Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpEBPgPu/log/example2.log -Program Path: C:/packages/Testing/procs/ProcsDemog.R -Working Directory: C:/packages/Testing/procs +Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpAXQUo8/log/example2.log +Program Path: C:/Projects/Archytas/Westat/Tutorial2/Project/sassy-dm.R +Working Directory: C:/Projects/Archytas/Westat/Tutorial2/Project User Name: dbosa -R Version: 4.3.1 (2023-06-16 ucrt) +R Version: 4.4.0 (2024-04-24 ucrt) Machine: SOCRATES x86-64 -Operating System: Windows 10 x64 build 22621 -Base Packages: stats graphics grDevices utils datasets methods base Other -Packages: tidylog_1.0.2 stringr_1.5.0 procs_1.0.3 reporter_1.4.1 libr_1.2.8 -fmtr_1.5.9 logr_1.3.4 common_1.0.8 sassy_1.1.0 -Log Start Time: 2023-09-05 22:11:55.376641 +Operating System: Windows 10 x64 build 22631 +Base Packages: stats graphics grDevices utils datasets methods base +Other Packages: tidylog_1.0.2 ggplot2_3.5.1 procs_1.0.7 reporter_1.4.4 +libr_1.3.3 logr_1.3.7 fmtr_1.6.4 common_1.1.3 sassy_1.2.4 +Log Start Time: 2024-05-28 12:52:44.619869 ========================================================================= ========================================================================= @@ -436,40 +350,35 @@ Prepare formats Age categories -# A user-defined format: 5 conditions - Name Type Expression Label Order -1 obj U x >= 18 & x <= 29 18 to 29 NA -2 obj U x >= 30 & x <= 39 30 to 39 NA -3 obj U x >= 40 & x <= 49 40 to 49 NA -4 obj U x >= 50 >= 50 NA -5 obj U TRUE Out of range NA +# A user-defined format: 4 conditions +- as.factor: TRUE + Name Type Expression Label Order +1 obj U x >= 18 & x <= 29 18 to 29 NA +2 obj U x >= 30 & x <= 39 30 to 39 NA +3 obj U x >= 40 & x <= 49 40 to 49 NA +4 obj U x >= 50 >= 50 NA Sex decodes -# A user-defined format: 4 conditions - Name Type Expression Label Order -1 obj U is.na(x) Missing NA -2 obj U x == "M" Male NA -3 obj U x == "F" Female NA -4 obj U TRUE Other NA +# A user-defined format: 3 conditions +- as.factor: TRUE + Name Type Expression Label Order +1 obj U x == "M" Male NA +2 obj U x == "F" Female NA +3 obj U TRUE Other NA Race decodes -# A user-defined format: 4 conditions - Name Type Expression Label -1 obj U is.na(x) Missing -2 obj U x == "WHITE" White -3 obj U x == "BLACK" Black or African American -4 obj U TRUE Other - Order -1 NA -2 NA -3 NA -4 NA +# A user-defined format: 3 conditions +- as.factor: TRUE + Name Type Expression Label Order +1 obj U x == "WHITE" White NA +2 obj U x == "BLACK" Black or African American NA +3 obj U TRUE Other NA Compile format catalog -# A format catalog: 15 formats +# A format catalog: 11 formats - $MEAN: type S, "%.1f" - $STD: type S, "(%.2f)" - $Q1: type S, "%.1f" @@ -478,13 +387,9 @@ Compile format catalog - $MAX: type S, "%d" - $CNT: type S, "%2d" - $PCT: type S, "(%5.1f%%)" -- $AGECAT: type U, 5 conditions -- $SEX: type U, 4 conditions -- $RACE: type U, 4 conditions -- $AOV.F: type S, "%5.3f" -- $AOV.P: type S, "(%5.3f)" -- $CHISQ: type S, "%5.3f" -- $CHISQ.P: type S, "(%5.3f)" +- $AGECAT: type U, 4 conditions +- $SEX: type U, 3 conditions +- $RACE: type U, 3 conditions ========================================================================= Prepare Data @@ -548,16 +453,11 @@ Combine stats datastep: columns decreased from 10 to 7 - BY VAR N Mean (SD) Median Q1 - Q3 -1 ARM A AGE 5 34.4 (8.71) 37.0 37.0 - 39.0 -2 ARM B AGE 4 30.0 (6.16) 32.5 26.0 - 34.0 -3 ARM C AGE 4 32.0 (6.16) 34.5 28.0 - 36.0 -4 ARM D AGE 3 32.0 (5.29) 30.0 28.0 - 38.0 - Min - Max -1 19 - 40 -2 21 - 34 -3 23 - 36 -4 28 - 38 + BY VAR N Mean (SD) Median Q1 - Q3 Min - Max +1 ARM A AGE 5 34.4 (8.71) 37.0 37.0 - 39.0 19 - 40 +2 ARM B AGE 4 30.0 (6.16) 32.5 26.0 - 34.0 21 - 34 +3 ARM C AGE 4 32.0 (6.16) 34.5 28.0 - 36.0 23 - 36 +4 ARM D AGE 3 32.0 (5.29) 30.0 28.0 - 38.0 28 - 38 Transpose ARMs into columns @@ -568,49 +468,12 @@ proc_transpose: input data set 4 rows and 7 columns name: LABEL output dataset 5 rows and 6 columns - VAR LABEL ARM A ARM B -1 AGE N 5 4 -2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) -3 AGE Median 37.0 32.5 -4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 -5 AGE Min - Max 19 - 40 21 - 34 - ARM C ARM D -1 4 3 -2 32.0 (6.16) 32.0 (5.29) -3 34.5 30.0 -4 28.0 - 36.0 28.0 - 38.0 -5 23 - 36 28 - 38 - -Calculate aov - -Get aov into proper data frame - - AOV.F AOV.P -ARM 0.2983651 0.8259486 - -Combine aov statistics - -datastep: columns decreased from 2 to 1 - - PVALUE -1 0.298 (0.826) - -Append aov - -datastep: columns increased from 6 to 7 - - VAR LABEL ARM A ARM B -1 AGE N 5 4 -2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) -3 AGE Median 37.0 32.5 -4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 -5 AGE Min - Max 19 - 40 21 - 34 - ARM C ARM D PVALUE -1 4 3 0.298 (0.826) -2 32.0 (6.16) 32.0 (5.29) -3 34.5 30.0 -4 28.0 - 36.0 28.0 - 38.0 -5 23 - 36 28 - 38 + VAR LABEL ARM A ARM B ARM C ARM D +1 AGE N 5 4 4 3 +2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) +3 AGE Median 37.0 32.5 34.5 30.0 +4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 +5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38 ========================================================================= Create frequency counts for SEX @@ -656,25 +519,19 @@ proc_transpose: input data set 8 rows and 4 columns id: BY copy: VAR name: NAME - output dataset 2 rows and 7 columns + output dataset 2 rows and 6 columns - VAR LABEL NAME ARM A ARM B -1 SEX F CNTPCT 2 ( 40.0%) 2 ( 50.0%) -2 SEX M CNTPCT 3 ( 60.0%) 2 ( 50.0%) - ARM C ARM D -1 2 ( 50.0%) 2 ( 66.7%) -2 2 ( 50.0%) 1 ( 33.3%) + VAR LABEL ARM A ARM B ARM C ARM D +1 SEX F 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) +2 SEX M 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) -Clean up +Apply formats -datastep: columns decreased from 7 to 6 +datastep: columns started with 6 and ended with 6 - VAR LABEL ARM A ARM B ARM C -1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) -2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) - ARM D -1 2 ( 66.7%) -2 1 ( 33.3%) + VAR LABEL ARM A ARM B ARM C ARM D +1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) +2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) Sort by label @@ -684,40 +541,9 @@ proc_sort: input data set 2 rows and 6 columns order: a output data set 2 rows and 6 columns - VAR LABEL ARM A ARM B ARM C -2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) -1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) - ARM D -2 1 ( 33.3%) -1 2 ( 66.7%) - -Get sex chisq - -proc_freq: input data set 16 rows and 6 columns - tables: SEX * ARM - view: TRUE - output: 1 datasets - - CHISQ CHISQ.DF CHISQ.P -1 0.5333333 3 0.9115095 - -Combine chisq statistics - -datastep: columns decreased from 3 to 1 - - PVALUE -1 0.533 (0.912) - -Append chisq - -datastep: columns increased from 6 to 7 - - VAR LABEL ARM A ARM B ARM C -1 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) -2 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) - ARM D PVALUE -1 1 ( 33.3%) 0.533 (0.912) -2 2 ( 66.7%) + VAR LABEL ARM A ARM B ARM C ARM D +2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) +1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) ========================================================================= Create frequency counts for RACE @@ -763,25 +589,19 @@ proc_transpose: input data set 8 rows and 4 columns id: BY copy: VAR name: NAME - output dataset 2 rows and 7 columns + output dataset 2 rows and 6 columns - VAR LABEL NAME ARM A ARM B -1 RACE BLACK CNTPCT 1 ( 20.0%) 0 ( 0.0%) -2 RACE WHITE CNTPCT 4 ( 80.0%) 4 (100.0%) - ARM C ARM D -1 1 ( 25.0%) 1 ( 33.3%) -2 3 ( 75.0%) 2 ( 66.7%) + VAR LABEL ARM A ARM B ARM C ARM D +1 RACE BLACK 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) +2 RACE WHITE 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) Clean up -datastep: columns decreased from 7 to 6 +datastep: columns started with 6 and ended with 6 - VAR LABEL ARM A -1 RACE Black or African American 1 ( 20.0%) -2 RACE White 4 ( 80.0%) - ARM B ARM C ARM D -1 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) -2 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) + VAR LABEL ARM A ARM B ARM C ARM D +1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) +2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) Sort by label @@ -791,43 +611,9 @@ proc_sort: input data set 2 rows and 6 columns order: a output data set 2 rows and 6 columns - VAR LABEL ARM A -2 RACE White 4 ( 80.0%) -1 RACE Black or African American 1 ( 20.0%) - ARM B ARM C ARM D -2 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) -1 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) - -Get race chisq - -proc_freq: input data set 16 rows and 6 columns - tables: RACE * ARM - view: TRUE - output: 1 datasets - - CHISQ CHISQ.DF CHISQ.P -1 1.449573 3 0.6939569 - -Combine chisq statistics - -datastep: columns decreased from 3 to 1 - - PVALUE -1 1.450 (0.694) - -Append chisq - -datastep: columns increased from 6 to 7 - - VAR LABEL ARM A -1 RACE White 4 ( 80.0%) -2 RACE Black or African American 1 ( 20.0%) - ARM B ARM C ARM D -1 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) -2 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) - PVALUE -1 1.450 (0.694) -2 + VAR LABEL ARM A ARM B ARM C ARM D +2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) +1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) ========================================================================= Create frequency counts for Age Group @@ -845,15 +631,19 @@ proc_freq: input data set 16 rows and 6 columns 1 ARM A AGECAT 18 to 29 1 20.00000 2 ARM A AGECAT 30 to 39 3 60.00000 3 ARM A AGECAT 40 to 49 1 20.00000 -4 ARM B AGECAT 18 to 29 1 25.00000 -5 ARM B AGECAT 30 to 39 3 75.00000 -6 ARM B AGECAT 40 to 49 0 0.00000 -7 ARM C AGECAT 18 to 29 1 25.00000 -8 ARM C AGECAT 30 to 39 3 75.00000 -9 ARM C AGECAT 40 to 49 0 0.00000 -10 ARM D AGECAT 18 to 29 1 33.33333 -11 ARM D AGECAT 30 to 39 2 66.66667 -12 ARM D AGECAT 40 to 49 0 0.00000 +4 ARM A AGECAT >= 50 0 0.00000 +5 ARM B AGECAT 18 to 29 1 25.00000 +6 ARM B AGECAT 30 to 39 3 75.00000 +7 ARM B AGECAT 40 to 49 0 0.00000 +8 ARM B AGECAT >= 50 0 0.00000 +9 ARM C AGECAT 18 to 29 1 25.00000 +10 ARM C AGECAT 30 to 39 3 75.00000 +11 ARM C AGECAT 40 to 49 0 0.00000 +12 ARM C AGECAT >= 50 0 0.00000 +13 ARM D AGECAT 18 to 29 1 33.33333 +14 ARM D AGECAT 30 to 39 2 66.66667 +15 ARM D AGECAT 40 to 49 0 0.00000 +16 ARM D AGECAT >= 50 0 0.00000 Combine counts and percents and assign age group factor for sorting @@ -863,143 +653,79 @@ datastep: columns decreased from 5 to 4 1 AGECAT 18 to 29 ARM A 1 ( 20.0%) 2 AGECAT 30 to 39 ARM A 3 ( 60.0%) 3 AGECAT 40 to 49 ARM A 1 ( 20.0%) -4 AGECAT 18 to 29 ARM B 1 ( 25.0%) -5 AGECAT 30 to 39 ARM B 3 ( 75.0%) -6 AGECAT 40 to 49 ARM B 0 ( 0.0%) -7 AGECAT 18 to 29 ARM C 1 ( 25.0%) -8 AGECAT 30 to 39 ARM C 3 ( 75.0%) -9 AGECAT 40 to 49 ARM C 0 ( 0.0%) -10 AGECAT 18 to 29 ARM D 1 ( 33.3%) -11 AGECAT 30 to 39 ARM D 2 ( 66.7%) -12 AGECAT 40 to 49 ARM D 0 ( 0.0%) +4 AGECAT >= 50 ARM A 0 ( 0.0%) +5 AGECAT 18 to 29 ARM B 1 ( 25.0%) +6 AGECAT 30 to 39 ARM B 3 ( 75.0%) +7 AGECAT 40 to 49 ARM B 0 ( 0.0%) +8 AGECAT >= 50 ARM B 0 ( 0.0%) +9 AGECAT 18 to 29 ARM C 1 ( 25.0%) +10 AGECAT 30 to 39 ARM C 3 ( 75.0%) +11 AGECAT 40 to 49 ARM C 0 ( 0.0%) +12 AGECAT >= 50 ARM C 0 ( 0.0%) +13 AGECAT 18 to 29 ARM D 1 ( 33.3%) +14 AGECAT 30 to 39 ARM D 2 ( 66.7%) +15 AGECAT 40 to 49 ARM D 0 ( 0.0%) +16 AGECAT >= 50 ARM D 0 ( 0.0%) Sort by age group factor -proc_sort: input data set 12 rows and 4 columns +proc_sort: input data set 16 rows and 4 columns by: BY LABEL keep: VAR LABEL BY CNTPCT order: a a - output data set 12 rows and 4 columns + output data set 16 rows and 4 columns VAR LABEL BY CNTPCT 1 AGECAT 18 to 29 ARM A 1 ( 20.0%) 2 AGECAT 30 to 39 ARM A 3 ( 60.0%) 3 AGECAT 40 to 49 ARM A 1 ( 20.0%) -4 AGECAT 18 to 29 ARM B 1 ( 25.0%) -5 AGECAT 30 to 39 ARM B 3 ( 75.0%) -6 AGECAT 40 to 49 ARM B 0 ( 0.0%) -7 AGECAT 18 to 29 ARM C 1 ( 25.0%) -8 AGECAT 30 to 39 ARM C 3 ( 75.0%) -9 AGECAT 40 to 49 ARM C 0 ( 0.0%) -10 AGECAT 18 to 29 ARM D 1 ( 33.3%) -11 AGECAT 30 to 39 ARM D 2 ( 66.7%) -12 AGECAT 40 to 49 ARM D 0 ( 0.0%) +4 AGECAT >= 50 ARM A 0 ( 0.0%) +5 AGECAT 18 to 29 ARM B 1 ( 25.0%) +6 AGECAT 30 to 39 ARM B 3 ( 75.0%) +7 AGECAT 40 to 49 ARM B 0 ( 0.0%) +8 AGECAT >= 50 ARM B 0 ( 0.0%) +9 AGECAT 18 to 29 ARM C 1 ( 25.0%) +10 AGECAT 30 to 39 ARM C 3 ( 75.0%) +11 AGECAT 40 to 49 ARM C 0 ( 0.0%) +12 AGECAT >= 50 ARM C 0 ( 0.0%) +13 AGECAT 18 to 29 ARM D 1 ( 33.3%) +14 AGECAT 30 to 39 ARM D 2 ( 66.7%) +15 AGECAT 40 to 49 ARM D 0 ( 0.0%) +16 AGECAT >= 50 ARM D 0 ( 0.0%) Tranpose age group block -proc_transpose: input data set 12 rows and 4 columns +proc_transpose: input data set 16 rows and 4 columns by: LABEL var: CNTPCT id: BY copy: VAR name: NAME - output dataset 3 rows and 7 columns - - VAR LABEL NAME ARM A ARM B -1 AGECAT 18 to 29 CNTPCT 1 ( 20.0%) 1 ( 25.0%) -2 AGECAT 30 to 39 CNTPCT 3 ( 60.0%) 3 ( 75.0%) -3 AGECAT 40 to 49 CNTPCT 1 ( 20.0%) 0 ( 0.0%) - ARM C ARM D -1 1 ( 25.0%) 1 ( 33.3%) -2 3 ( 75.0%) 2 ( 66.7%) -3 0 ( 0.0%) 0 ( 0.0%) + output dataset 4 rows and 6 columns -Some clean up - -datastep: columns decreased from 7 to 6 - - VAR LABEL ARM A ARM B -1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) -2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) -3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) - ARM C ARM D -1 1 ( 25.0%) 1 ( 33.3%) -2 3 ( 75.0%) 2 ( 66.7%) -3 0 ( 0.0%) 0 ( 0.0%) - -Get ageg chisq - -proc_freq: input data set 16 rows and 6 columns - tables: AGECAT * ARM - view: TRUE - output: 1 datasets - - CHISQ CHISQ.DF CHISQ.P -1 2.436364 6 0.8755205 - -Combine chisq statistics - -datastep: columns decreased from 3 to 1 - - PVALUE -1 2.436 (0.876) - -Append chisq - -datastep: columns increased from 6 to 7 - - VAR LABEL ARM A ARM B -1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) -2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) -3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) - ARM C ARM D PVALUE -1 1 ( 25.0%) 1 ( 33.3%) 2.436 (0.876) -2 3 ( 75.0%) 2 ( 66.7%) -3 0 ( 0.0%) 0 ( 0.0%) + VAR LABEL ARM A ARM B ARM C ARM D +1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) +2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) +3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) +4 AGECAT >= 50 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) Combine blocks into final data frame -datastep: columns started with 7 and ended with 7 - - VAR LABEL ARM A -1 AGE N 5 -2 AGE Mean (SD) 34.4 (8.71) -3 AGE Median 37.0 -4 AGE Q1 - Q3 37.0 - 39.0 -5 AGE Min - Max 19 - 40 -6 AGECAT 18 to 29 1 ( 20.0%) -7 AGECAT 30 to 39 3 ( 60.0%) -8 AGECAT 40 to 49 1 ( 20.0%) -9 SEX Male 3 ( 60.0%) -10 SEX Female 2 ( 40.0%) -11 RACE White 4 ( 80.0%) -12 RACE Black or African American 1 ( 20.0%) - ARM B ARM C ARM D -1 4 4 3 -2 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) -3 32.5 34.5 30.0 -4 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 -5 21 - 34 23 - 36 28 - 38 -6 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) -7 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) -8 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) -9 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) -10 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) -11 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) -12 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) - PVALUE -1 0.298 (0.826) -2 -3 -4 -5 -6 2.436 (0.876) -7 -8 -9 0.533 (0.912) -10 -11 1.450 (0.694) -12 +datastep: columns started with 6 and ended with 6 + + VAR LABEL ARM A ARM B ARM C ARM D +1 AGE N 5 4 4 3 +2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) +3 AGE Median 37.0 32.5 34.5 30.0 +4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 +5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38 +6 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) +7 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) +8 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) +9 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) +10 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) +11 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) +12 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) ========================================================================= Create and print report @@ -1008,17 +734,17 @@ Create and print report Write out the report # A report specification: 1 pages -- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpEBPgPu/example2.rtf' +- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpAXQUo8/example2.rtf' - output_type: RTF - units: inches - orientation: landscape - margins: top 1 bottom 1 left 1 right 1 - line size/count: 9/36 - page_header: left=Sponsor: Company right=Study: ABC -- page_footer: left=Date Produced: 2023-09-05 center= right=Page [pg] of [tpg] +- page_footer: left=Date Produced: 2024-05-28 center= right=Page [pg] of [tpg] - content: # A table specification: -- data: data.frame 'final' 12 rows 7 cols +- data: data.frame 'final' 12 rows 6 cols - show_cols: all - use_attributes: all - title 1: 'Table 1.0' @@ -1026,7 +752,6 @@ Write out the report - title 3: 'Safety Population' - footnote 1: 'Program: DM_Table.R' - footnote 2: 'NOTE: Denominator based on number of non-missing responses.' -- footnote 3: '¹Pearson's Chi-Square tests will be used for Categorical variables and ANOVA tests for continuous variables.' - stub: VAR LABEL 'Variable' width=2.5 align='left' - define: VAR 'Variable' dedupe='TRUE' - define: LABEL 'Demographic Category' @@ -1034,8 +759,6 @@ Write out the report - define: ARM B 'Drug 50mg' - define: ARM C 'Drug 100mg' - define: ARM D 'Competitor' -- define: PVALUE 'Tests of Association¹ - Value (P-Value)' width=2 align='center' dedupe='TRUE' ========================================================================= Clean Up @@ -1044,11 +767,12 @@ Clean Up Close log ========================================================================= -Log End Time: 2023-09-05 22:11:56.276799 +Log End Time: 2024-05-28 12:52:45.234875 Log Elapsed Time: 0 00:00:00 ========================================================================= + ``` Next: [Example 3: Figures](sassy-figure.html) diff --git a/vignettes/sassy-dm_bak.txt b/vignettes/sassy-dm_bak.txt new file mode 100644 index 0000000..e01df41 --- /dev/null +++ b/vignettes/sassy-dm_bak.txt @@ -0,0 +1,366 @@ +library(sassy) + +# Prepare Log ------------------------------------------------------------- + + +options("logr.autolog" = TRUE, + "logr.on" = TRUE, + "logr.notes" = FALSE, + "procs.print" = FALSE) + +# Get temp directory +tmp <- tempdir() + +# Open log +lf <- log_open(file.path(tmp, "example2.log")) + + + +# Prepare formats --------------------------------------------------------- + +sep("Prepare formats") + +put("Age categories") +agecat <- value(condition(x >= 18 & x <= 29, "18 to 29"), + condition(x >=30 & x <= 39, "30 to 39"), + condition(x >=40 & x <=49, "40 to 49"), + condition(x >= 50, ">= 50"), + condition(TRUE, "Out of range")) + +put("Sex decodes") +fmt_sex <- value(condition(is.na(x), "Missing"), + condition(x == "M", "Male"), + condition(x == "F", "Female"), + condition(TRUE, "Other")) + +put("Race decodes") +fmt_race <- value(condition(is.na(x), "Missing"), + condition(x == "WHITE", "White"), + condition(x == "BLACK", "Black or African American"), + condition(TRUE, "Other")) + + +put("Compile format catalog") +fc <- fcat(MEAN = "%.1f", STD = "(%.2f)", + Q1 = "%.1f", Q3 = "%.1f", + MIN = "%d", MAX = "%d", + CNT = "%2d", PCT = "(%5.1f%%)", + AGECAT = agecat, + SEX = fmt_sex, + RACE = fmt_race, + AOV.F = "%5.3f", + AOV.P = "(%5.3f)", + CHISQ = "%5.3f", + CHISQ.P = "(%5.3f)") + + +# Load and Prepare Data --------------------------------------------------- + +sep("Prepare Data") + + +put("Create sample ADSL data.") +adsl <- read.table(header = TRUE, text = ' + SUBJID ARM SEX RACE AGE + "001" "ARM A" "F" "WHITE" 19 + "002" "ARM B" "F" "WHITE" 21 + "003" "ARM C" "F" "WHITE" 23 + "004" "ARM D" "F" "BLACK" 28 + "005" "ARM A" "M" "WHITE" 37 + "006" "ARM B" "M" "WHITE" 34 + "007" "ARM C" "M" "WHITE" 36 + "008" "ARM D" "M" "WHITE" 30 + "009" "ARM A" "F" "WHITE" 39 + "010" "ARM B" "F" "WHITE" 31 + "011" "ARM C" "F" "BLACK" 33 + "012" "ARM D" "F" "WHITE" 38 + "013" "ARM A" "M" "BLACK" 37 + "014" "ARM B" "M" "WHITE" 34 + "015" "ARM C" "M" "WHITE" 36 + "016" "ARM A" "M" "WHITE" 40') + +put("Categorize AGE") +adsl$AGECAT <- fapply(adsl$AGE, agecat) + +put("Log starting dataset") +put(adsl) + + +put("Get ARM population counts") +proc_freq(adsl, tables = ARM, + output = long, + options = v(nopercent, nonobs)) -> arm_pop + +# Age Summary Block ------------------------------------------------------- + +sep("Create summary statistics for age") + +put("Call means procedure to get summary statistics for age") +proc_means(adsl, var = AGE, + stats = v(n, mean, std, median, q1, q3, min, max), + by = ARM, + options = v(notype, nofreq)) -> age_stats + +put("Combine stats") +datastep(age_stats, + format = fc, + drop = find.names(age_stats, start = 4), + { + `Mean (SD)` <- fapply2(MEAN, STD) + Median <- MEDIAN + `Q1 - Q3` <- fapply2(Q1, Q3, sep = " - ") + `Min - Max` <- fapply2(MIN, MAX, sep = " - ") + + + }) -> age_comb + +put("Transpose ARMs into columns") +proc_transpose(age_comb, + var = names(age_comb), + copy = VAR, id = BY, + name = LABEL) -> age_trans + +put("Calculate aov") +age_aov <- aov(AGE ~ ARM, data = adsl) |> + summary() + +put("Get aov into proper data frame") + +age_aov <- age_aov[[1]][1, c("F value", "Pr(>F)")] +names(age_aov) <- c("AOV.F", "AOV.P") +age_aov <- as.data.frame(age_aov) |> put() + +put("Combine aov statistics") +datastep(age_aov, + keep = PVALUE, + format = fc, + { + PVALUE <- fapply2(AOV.F, AOV.P) + + }) -> age_aov_comb + +put("Append aov") +datastep(age_trans, merge = age_aov_comb, {}) -> age_block + +# Sex Block --------------------------------------------------------------- + +sep("Create frequency counts for SEX") + +put("Get sex frequency counts") +proc_freq(adsl, tables = SEX, + by = ARM, + options = nonobs) -> sex_freq + + +put("Combine counts and percents.") +datastep(sex_freq, + format = fc, + rename = list(CAT = "LABEL"), + drop = v(CNT, PCT), + { + + CNTPCT <- fapply2(CNT, PCT) + + }) -> sex_comb + +put("Transpose ARMs into columns") +proc_transpose(sex_comb, id = BY, + var = CNTPCT, + copy = VAR, by = LABEL) -> sex_trans + +put("Clean up") +datastep(sex_trans, drop = NAME, + { + + LABEL <- fapply(LABEL, fc$SEX) + LABEL <- factor(LABEL, levels = levels(fc$SEX)) + + }) -> sex_cnts + +put("Sort by label") +proc_sort(sex_cnts, by = LABEL) -> sex_cnts + +put("Get sex chisq") +proc_freq(adsl, tables = v(SEX * ARM), + options = v(chisq, notable)) -> sex_chisq + +put("Combine chisq statistics") +datastep(sex_chisq, + format = fc, + keep = PVALUE, + { + + PVALUE = fapply2(CHISQ, CHISQ.P) + }) -> sex_chisq_comb + +put("Append chisq") +datastep(sex_cnts, + merge = sex_chisq_comb, + {}) -> sex_block + + +# Race block -------------------------------------------------------------- + + +sep("Create frequency counts for RACE") + +put("Get race frequency counts") +proc_freq(adsl, tables = RACE, + by = ARM, + options = nonobs) -> race_freq + + +put("Combine counts and percents.") +datastep(race_freq, + format = fc, + rename = list(CAT = "LABEL"), + drop = v(CNT, PCT), + { + + CNTPCT <- fapply2(CNT, PCT) + + }) -> race_comb + +put("Transpose ARMs into columns") +proc_transpose(race_comb, id = BY, var = CNTPCT, + copy = VAR, by = LABEL) -> race_trans + +put("Clean up") + +datastep(race_trans, drop = NAME, + where = expression(del == FALSE), + { + LABEL <- fapply(LABEL, fc$RACE) + LABEL <- factor(LABEL, levels = levels(fc$RACE)) + + }) -> race_cnts + +put("Sort by label") +proc_sort(race_cnts, by = LABEL) -> race_cnts + +put("Get race chisq") +proc_freq(adsl, tables = RACE * ARM, + options = v(chisq, notable)) -> race_chisq + +put("Combine chisq statistics") +datastep(race_chisq, + format = fc, + keep = c("PVALUE"), + { + + PVALUE = fapply2(CHISQ, CHISQ.P) + }) -> race_chisq_comb + +put("Append chisq") +datastep(race_cnts, merge = race_chisq_comb, {}) -> race_block + + +# Age Group Block ---------------------------------------------------------- + +sep("Create frequency counts for Age Group") + + +put("Get age group frequency counts") +proc_freq(adsl, + table = AGECAT, + by = ARM, + options = nonobs) -> ageg_freq + +put("Combine counts and percents and assign age group factor for sorting") +datastep(ageg_freq, + format = fc, + keep = v(VAR, LABEL, BY, CNTPCT), + { + CNTPCT <- fapply2(CNT, PCT) + LABEL <- factor(CAT, levels = levels(fc$AGECAT)) + }) -> ageg_comb + + +put("Sort by age group factor") +proc_sort(ageg_comb, by = v(BY, LABEL)) -> ageg_sort + +put("Tranpose age group block") +proc_transpose(ageg_sort, + var = CNTPCT, + copy = VAR, + id = BY, + by = LABEL) -> ageg_trans + +put("Some clean up") +datastep(ageg_trans, + drop = NAME, + {}) -> ageg_cnts + +put("Get ageg chisq") +proc_freq(adsl, tables = AGECAT * ARM, + options = v(chisq, notable)) -> ageg_chisq + +put("Combine chisq statistics") +datastep(ageg_chisq, + format = fc, + keep = c("PVALUE"), + { + PVALUE = fapply2(CHISQ, CHISQ.P) + }) -> ageg_chisq_comb + +put("Append chisq") +datastep(ageg_cnts, merge = ageg_chisq_comb, + {}) -> ageg_block + + +put("Combine blocks into final data frame") +datastep(age_block, + set = list(ageg_block, sex_block, race_block), + {}) -> final + +# Report ------------------------------------------------------------------ + + +sep("Create and print report") + +var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race") + +plbl <- "Tests of Association{supsc('1')}\n Value (P-Value)" + +# Create Table +tbl <- create_table(final, first_row_blank = TRUE) |> + column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |> + stub(vars = c("VAR", "LABEL"), "Variable", width = 2.5) |> + define(VAR, blank_after = TRUE, dedupe = TRUE, label = "Variable", + format = var_fmt,label_row = TRUE) |> + define(LABEL, indent = .25, label = "Demographic Category") |> + define(`ARM A`, label = "Placebo", n = arm_pop["ARM A"]) |> + define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |> + define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |> + define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |> + define(PVALUE, label = plbl, width = 2, dedupe = TRUE, align = "center") |> + titles("Table 1.0", "Analysis of Demographic Characteristics", + "Safety Population", bold = TRUE) |> + footnotes("Program: DM_Table.R", + "NOTE: Denominator based on number of non-missing responses.", + "{supsc('1')}Pearson's Chi-Square tests will be used for " + %p% "Categorical variables and ANOVA tests for continuous variables.") + +rpt <- create_report(file.path(tmp, "example2.rtf"), + output_type = "RTF", + font = "Arial") |> + page_header("Sponsor: Company", "Study: ABC") |> + set_margins(top = 1, bottom = 1) |> + add_content(tbl) |> + page_footer("Date Produced: {Sys.Date()}", right = "Page [pg] of [tpg]") + +put("Write out the report") +res <- write_report(rpt) + +# Clean Up ---------------------------------------------------------------- +sep("Clean Up") + +put("Close log") +log_close() + + +# Uncomment to view report +# file.show(res$modified_path) + +# Uncomment to view log +# file.show(lf)