Frequency counts and percentages for a variable by treatment and/or group.
freq(
df,
denom_df = df,
colvar = NULL,
tablebyvar = NULL,
rowvar = NULL,
rowbyvar = NULL,
statlist = getOption("tidytlg.freq.statlist.default"),
decimal = 1,
nested = FALSE,
cutoff = NULL,
cutoff_stat = "pct",
subset = TRUE,
descending_by = NULL,
display_missing = FALSE,
rowtext = NULL,
row_header = NULL,
.keep = TRUE,
.ord = FALSE,
pad = TRUE,
...
)(required) dataframe containing records to summarize by treatment
(optional) dataframe used for population based denominators (default = df)
(required) treatment variable within df to use to summarize
(optional) repeat entire table by variable within df
(required) character vector of variables to summarize within the dataframe
(optional) repeat rowvar by variable within df
(optional) statlist object of stats to keep of length 1 or 2 specifying list of statistics and format desired (e.g statlist(c("N", "n (x.x\ (x.x)")))
(optional) decimal precision root level default (default = 1)
(optional) INTERNAL USE ONLY. The default should not be changed.
Switch on when this function is called by nested_freq() so we will not include
the by variables as part of the group denominators (default = FALSE)
(optional) percentage cutoff threshold. This can be passed as a numeric cutoff, in that case any rows with greater than or equal to that cutoff will be preserved, others will be dropped. To specify a single column to define the cutoff logic, pass a character value of the form "colName >= value" and only that column will be used.
(optional) The value to cutoff by, n or pct. (default =
'pct'). Can be done with multiple columns by adding & or | ex. col1 >=
val1 & col2 >= val2
(optional) An R expression that will be passed to a
dplyr::filter() function to subset the data.frame. This is performed on
the numerator before any other derivations. Denominators must be
preprocessed and passed through using denom_df.
(optional) The column or columns to sort descending
counts. Can also provide a named list to do ascending order ex.
c("VarName1" = "asc", "VarName2" = "desc") would sort by VarName1 in
ascending order and VarName2 in descending order. In case of a tie in count
or descending_by not provided, the columns will be sorted alphabetically.
(optional) Should the "missing" values be displayed? If missing values are displayed, denominators will include missing values. (default = FALSE)
(optional) A character vector used to rename the label
column. If named, names will give the new level and values will be the
replaced value. If unnamed, and the table has only one row, the rowtext will
rename the label of the row. If the rowtext is unnamed, the table has no
rows, and there is a subset, the table will be populated with zeros and the
label will be the only row.
(optional) A character vector to be added to the table.
(optional) Should the rowbyvar and tablebyvar be output in the
table. If FALSE, rowbyvar will still be output in the label column.
(default = TRUE)
Should the ordering columns be output with the table? This is useful if a table needs to be merged or reordered in any way after build.
(optional) A boolean that controls if levels with zero records should be included in the final table. (default = TRUE)
(optional) Named arguments to be included as columns on the table.
A dataframe of results
By default, a frequency table is sorted based on the factor level of the
rowvar variable. If the rowvar variable isn't a factor, it will be
sorted alphabetically. This behavior can be modified in two ways, the first
is the char2factor() function that offers a interface for releveling a
variable based on a numeric variable, like VISITN. The second is based on
the descending_by argument which will sort based on counts on a variable.
adsl <- data.frame(
USUBJID = c("DEMO-101", "DEMO-102", "DEMO-103"),
RACE = c("WHITE", "BLACK", "ASIAN"),
SEX = c("F", "M", "F"),
colnbr = factor(c("Placebo", "Low", "High"))
)
# Unique subject count of a single variable
freq(adsl
,colvar = "colnbr"
,rowvar = "RACE"
,statlist = statlist("n"))
#> Column Variables: colnbr
#> Row Variable: RACE
#> Statistic Formatting: n
#> Value Cutoffs: pct by variable pct
#> Descending By: Factored Sorting
#> Denominators By Variables: colnbr
#> # A tibble: 3 × 6
#> label High Low Placebo row_type group_level
#> * <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ASIAN 1 0 0 VALUE 0
#> 2 BLACK 0 1 0 VALUE 0
#> 3 WHITE 0 0 1 VALUE 0
# Unique subject count and percent of a single variable
freq(adsl
,colvar = "colnbr"
,rowvar = "RACE"
,statlist = statlist(c("N", "n (x.x%)")))
#> Column Variables: colnbr
#> Row Variable: RACE
#> Statistic Formatting: c("N", "n (x.x%)")
#> Value Cutoffs: pct by variable pct
#> Descending By: Factored Sorting
#> Denominators By Variables: colnbr
#> # A tibble: 4 × 6
#> label High Low Placebo row_type group_level
#> * <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 N 1 1 1 N 0
#> 2 ASIAN 1 (100.0%) 0 0 VALUE 0
#> 3 BLACK 0 1 (100.0%) 0 VALUE 0
#> 4 WHITE 0 0 1 (100.0%) VALUE 0
# Unique subject count of a variable by another variable
freq(adsl
,colvar = "colnbr"
,rowvar = "RACE"
,rowbyvar = "SEX"
,statlist = statlist("n"))
#> Column Variables: colnbr
#> Row By Variables Variables: SEX
#> Row Variable: RACE
#> Statistic Formatting: n
#> Value Cutoffs: pct by variable pct
#> Descending By: Factored Sorting
#> Denominators By Variables: SEX colnbr
#> # A tibble: 8 × 7
#> label High Low Placebo row_type group_level SEX
#> * <chr> <chr> <chr> <chr> <chr> <dbl> <fct>
#> 1 F "" "" "" BY_HEADER1 0 F
#> 2 ASIAN "1" "-" "0" VALUE 0 F
#> 3 BLACK "0" "-" "0" VALUE 0 F
#> 4 WHITE "0" "-" "1" VALUE 0 F
#> 5 M "" "" "" BY_HEADER1 0 M
#> 6 ASIAN "-" "0" "-" VALUE 0 M
#> 7 BLACK "-" "1" "-" VALUE 0 M
#> 8 WHITE "-" "0" "-" VALUE 0 M
# Unique subject count of a variable by another variable using colvar and
# group to define the denominator
freq(adsl
,colvar = "colnbr"
,rowvar = "RACE"
,rowbyvar = "SEX"
,statlist = statlist("n (x.x%)", denoms_by = c("colnbr", "SEX")))
#> Column Variables: colnbr
#> Row By Variables Variables: SEX
#> Row Variable: RACE
#> Statistic Formatting: n (x.x%) c("colnbr", "SEX")
#> Value Cutoffs: pct by variable pct
#> Descending By: Factored Sorting
#> Denominators By Variables: colnbr SEX
#> # A tibble: 8 × 7
#> label High Low Placebo row_type group_level SEX
#> * <chr> <chr> <chr> <chr> <chr> <dbl> <fct>
#> 1 F "" "" "" BY_HEADER1 0 F
#> 2 ASIAN "1 (100.0%)" "-" "0" VALUE 0 F
#> 3 BLACK "0" "-" "0" VALUE 0 F
#> 4 WHITE "0" "-" "1 (100.0%)" VALUE 0 F
#> 5 M "" "" "" BY_HEADER1 0 M
#> 6 ASIAN "-" "0" "-" VALUE 0 M
#> 7 BLACK "-" "1 (100.0%)" "-" VALUE 0 M
#> 8 WHITE "-" "0" "-" VALUE 0 M
# Cut records where count meets threshold for any column
freq(cdisc_adsl
,rowvar = "ETHNIC"
,colvar = "TRT01P"
,statlist = statlist("n (x.x%)")
,cutoff = "5"
,cutoff_stat = "n")
#> Column Variables: TRT01P
#> Row Variable: ETHNIC
#> Statistic Formatting: n (x.x%)
#> Value Cutoffs: 5 by variable n
#> Descending By: Factored Sorting
#> Denominators By Variables: TRT01P
#> # A tibble: 1 × 6
#> label Placebo `Xanomeline High Dose` `Xanomeline Low Dose` row_type
#> * <chr> <chr> <chr> <chr> <chr>
#> 1 NOT HISPANIC OR… 3 (60.… 5 (100.0%) 5 (100.0%) VALUE
#> # ℹ 1 more variable: group_level <dbl>
# Cut records where count meets threshold for a specific column
freq(cdisc_adsl
,rowvar = "ETHNIC"
,colvar = "TRT01P"
,statlist = statlist("n (x.x%)")
,cutoff = "Placebo >= 3"
,cutoff_stat = "n")
#> Column Variables: TRT01P
#> Row Variable: ETHNIC
#> Statistic Formatting: n (x.x%)
#> Value Cutoffs: Placebo >= 3 by variable n
#> Descending By: Factored Sorting
#> Denominators By Variables: TRT01P
#> # A tibble: 1 × 6
#> label Placebo `Xanomeline High Dose` `Xanomeline Low Dose` row_type
#> * <chr> <chr> <chr> <chr> <chr>
#> 1 NOT HISPANIC OR… 3 (60.… 5 (100.0%) 5 (100.0%) VALUE
#> # ℹ 1 more variable: group_level <dbl>
# Below illustrates how to make the same calls to freq() as above, using
# table and column metadata.
# Unique subject count of a single variable
table_metadata <- tibble::tribble(
~anbr, ~func, ~df, ~rowvar, ~statlist, ~colvar,
1, "freq", "cdisc_adsl", "ETHNIC", statlist("n"), "TRT01PN"
)
generate_results(table_metadata,
column_metadata = column_metadata,
tbltype = "type1")
#> # A tibble: 2 × 11
#> label col1 col2 col3 row_type func anbr indentme roworder newrows newpage
#> * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 HISP… 2 0 0 VALUE freq 1 1 1 0 0
#> 2 NOT … 3 5 5 VALUE freq 1 1 2 0 0
# Unique subject count and percent of a single variable
table_metadata <- tibble::tribble(
~anbr, ~func, ~df, ~rowvar, ~statlist, ~colvar,
"1", "freq", "cdisc_adsl", "ETHNIC", statlist(c("N", "n (x.x%)")),"TRT01PN"
)
generate_results(table_metadata,
column_metadata = column_metadata,
tbltype = "type1")
#> # A tibble: 3 × 11
#> label col1 col2 col3 row_type func anbr indentme roworder newrows newpage
#> * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 N 5 5 5 N freq 1 1 1 0 0
#> 2 HISP… 2 (4… 0 0 VALUE freq 1 2 2 0 0
#> 3 NOT … 3 (6… 5 (1… 5 (1… VALUE freq 1 2 3 0 0
# Cut records where count meets threshold for any column
table_metadata <- tibble::tibble(
anbr= "1", func = "freq", df = "cdisc_adsl", rowvar = "ETHNIC",
statlist = statlist("n (x.x%)"), colvar = "TRT01PN", cutoff = 5,
cutoff_stat = "n")
generate_results(table_metadata,
column_metadata = column_metadata,
tbltype = "type1")
#> # A tibble: 1 × 11
#> label col1 col2 col3 row_type func anbr indentme roworder newrows newpage
#> * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 NOT … 3 (6… 5 (1… 5 (1… VALUE freq 1 1 1 0 0
# Cut records where count meets threshold for a specific column
table_metadata <- tibble::tibble(
anbr= 1, func = "freq", df = "cdisc_adsl", rowvar = "ETHNIC",
statlist = statlist("n (x.x%)"), colvar = "TRT01PN",
cutoff = 'col1 >= 3', cutoff_stat = "n")
generate_results(table_metadata,
column_metadata = column_metadata,
tbltype = "type1")
#> # A tibble: 1 × 11
#> label col1 col2 col3 row_type func anbr indentme roworder newrows newpage
#> * <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 NOT … 3 (6… 5 (1… 5 (1… VALUE freq 1 1 1 0 0