The packages used in this vignette are:
Variable Class Conversion
rtables requires that split variables to be factors.
When you try and split a variable that isn’t, a warning message will
appear. Here we purposefully convert the SEX variable to character to
demonstrate what happens when we try splitting the rows by this
variable. To fix this, df_explict_na will convert this to a
factor resulting in the table being generated.
adsl <- tern_ex_adsl
adsl$SEX <- as.character(adsl$SEX)
vars <- c("AGE", "SEX", "RACE", "BMRKR1")
var_labels <- c(
"Age (yr)",
"Sex",
"Race",
"Continous Level Biomarker 1"
)
result <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
#> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically
#> converting character variable x to factor, better manually convert to factor to
#> avoid failures
#> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically
#> converting character variable x to factor, better manually convert to factor to
#> avoid failures
#> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically
#> converting character variable x to factor, better manually convert to factor to
#> avoid failures
#> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically
#> converting character variable x to factor, better manually convert to factor to
#> avoid failures
result
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=69) (N=73) (N=58) (N=200)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 69 73 58 200
#> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1)
#> Median 32.8 35.4 36.2 34.8
#> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3
#> Sex
#> n 69 73 58 200
#> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%)
#> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)
#> Race
#> n 69 73 58 200
#> ASIAN 38 (55.1%) 43 (58.9%) 29 (50%) 110 (55%)
#> BLACK OR AFRICAN AMERICAN 15 (21.7%) 13 (17.8%) 12 (20.7%) 40 (20%)
#> WHITE 11 (15.9%) 12 (16.4%) 11 (19%) 34 (17%)
#> AMERICAN INDIAN OR ALASKA NATIVE 4 (5.8%) 3 (4.1%) 6 (10.3%) 13 (6.5%)
#> MULTIPLE 1 (1.4%) 1 (1.4%) 0 2 (1%)
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (1.4%) 0 1 (0.5%)
#> OTHER 0 0 0 0
#> UNKNOWN 0 0 0 0
#> Continous Level Biomarker 1
#> n 69 73 58 200
#> Mean (SD) 6.3 (3.6) 6.7 (3.5) 6.2 (3.3) 6.4 (3.5)
#> Median 5.4 6.3 5.4 5.6
#> Min - Max 0.4 - 17.8 1.0 - 18.5 2.4 - 19.1 0.4 - 19.1Including Missing Values in rtables
Here we purposefully convert all M values to
NA in the SEX variable. After running
df_explicit_na the NA values are encoded as
<Missing> but they are not included in the table. As
well, the missing values are not included in the n count
and they are not included in the denominator value for calculating the
percent values.
adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl)
vars <- c("AGE", "SEX")
var_labels <- c(
"Age (yr)",
"Sex"
)
result <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
result
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=69) (N=73) (N=58) (N=200)
#> ———————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 69 73 58 200
#> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1)
#> Median 32.8 35.4 36.2 34.8
#> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3
#> Sex
#> n 38 40 32 110
#> F 38 (100%) 40 (100%) 32 (100%) 110 (100%)
#> M 0 0 0 0If you want the Na values to be displayed in the table
and included in the n count and as the denominator for
calculating percent values, use the na_level argument.
adsl <- tern_ex_adsl
adsl$SEX[adsl$SEX == "M"] <- NA
adsl <- df_explicit_na(adsl, na_level = "Missing Values")
result <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
result
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=69) (N=73) (N=58) (N=200)
#> ————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 69 73 58 200
#> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1)
#> Median 32.8 35.4 36.2 34.8
#> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3
#> Sex
#> n 69 73 58 200
#> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%)
#> M 0 0 0 0
#> Missing Values 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)Missing Values in Numeric Variables
Numeric variables that have missing values are not altered. This
means that any NA value in a numeric variable will not be
included in the summary statistics, nor will they be included in the
denominator value for calculating the percent values. Here we make any
value less than 30 missing in the AGE variable and only the
valued greater than 30 are included in the table below.
adsl <- tern_ex_adsl
adsl$AGE[adsl$AGE < 30] <- NA
adsl <- df_explicit_na(adsl)
vars <- c("AGE", "SEX")
var_labels <- c(
"Age (yr)",
"Sex"
)
result <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
add_overall_col("All Patients") %>%
analyze_vars(
vars = vars,
var_labels = var_labels
) %>%
build_table(adsl)
result
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=69) (N=73) (N=58) (N=200)
#> ———————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 46 56 44 146
#> Mean (SD) 37.8 (5.2) 38.3 (6.3) 39.1 (5.9) 38.3 (5.8)
#> Median 37.2 37.3 37.5 37.5
#> Min - Max 30.3 - 48.0 30.0 - 57.5 30.5 - 58.3 30.0 - 58.3
#> Sex
#> n 69 73 58 200
#> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%)
#> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)