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Summary Table

Source: R/summary_table.R
summary_table.Rd

Creates stratified summary table with rates.

Usage

summary_table(
  data,
  vars,
  strata = NULL,
  overall = FALSE,
  get_rates = FALSE,
  get_no_events = FALSE,
  event = NULL,
  st = NULL,
  control = list(ctype = "inc.rate", method = "exact")
)

Arguments

data

A data.frame that includes the data that should be summarised.

vars

A character vector specifying the variables that should be summarised. summary_table can only summarise variables of class numeric or factor.

strata

An optional character vector specifying the variables on which the summary table should be stratified on.

overall

If TRUE an additionall summary table will be created including the complete dataset. This can be used in combination with strata.

get_rates

If TRUE the summary tables includes events rates for events specified in the event argument for each level of variables in var. Rates can only be estimated for factor variables in var.

get_no_events

If TRUE the summary tables includes the number of events specified in the event argument for each level of variables in var. This argument is obsolete if get_rates == TRUE as the number of events is automatically added to the table in this case.

event

Name of the event indicator that should be used to compute rates. Needs to be specified if get_rates == TRUE.

st

Name of the varaible that includes the survival time that should be used to comptue rates. Needs to be specified if get_rates == TRUE.

control

A list of control parameters passed to the epiR::epi.conf() call when estimating confidence intervals for rates. This can be used to change the type and method used for estimating confidence intervals. Check out the documentation of epiR::epi.conf() for more information.

Value

A data.frame with the following columns:

  • varname: Including the name of the variable or the name of the category for factor variables

  • mean: The mean of the variable, i.e. the proportion for binary variables

  • p25: The 25th percentile (only for metric variables.)

  • p75: The 75th percentile (only for metric varaibles.)

  • min: Minimum of the variable

  • max: Maximum of the variable

  • n_category: The number of non-NA observations for each variable or cateogry

  • n_total: The number of non-NA observations for the variable

  • n_NA: The number of NA observations for this variable or category

If strata variables were supplied to summary_table the output data.table will include one row for each strata variable with the corresponding value for the specific strata the row belongs to.

Examples

require(rstpm2)
data("brcancer")

# Simple example
summary_table(brcancer,
              vars = c("x1", "x2", "x3"))
#>   varname mean p25 p75 min max n_category n_total n_NA
#> 1      x1   53  46  61  21  80         NA     686    0
#> 2      x2    2   1   2   1   2         NA     686    0
#> 3      x3   25  20  35   3 120         NA     686    0

# Example with stratas
summary_table(brcancer,
              vars = c("x1", "x2", "x3"),
              strata = c("hormon", "x4"))
#>    hormon x4 varname mean  p25  p75 min max n_category n_total n_NA
#> 1       0  2      x1 50.0 45.0 59.0  21  80         NA     281    0
#> 2       0  2      x2  1.0  1.0  2.0   1   2         NA     281    0
#> 3       0  2      x3 25.0 20.0 35.0   5 120         NA     281    0
#> 4       1  2      x1 59.0 51.0 63.0  32  80         NA     163    0
#> 5       1  2      x2  2.0  2.0  2.0   1   2         NA     163    0
#> 6       1  2      x3 25.0 20.0 35.0   7 100         NA     163    0
#> 7       0  3      x1 50.0 43.5 56.5  29  77         NA     111    0
#> 8       0  3      x2  1.0  1.0  2.0   1   2         NA     111    0
#> 9       0  3      x3 30.0 22.0 41.0   3  80         NA     111    0
#> 10      1  3      x1 57.5 47.0 64.0  33  74         NA      50    0
#> 11      1  3      x2  2.0  2.0  2.0   1   2         NA      50    0
#> 12      1  3      x3 28.0 23.0 35.0  12  60         NA      50    0
#> 13      0  1      x1 50.0 46.0 61.2  37  69         NA      48    0
#> 14      0  1      x2  1.0  1.0  2.0   1   2         NA      48    0
#> 15      0  1      x3 25.0 19.0 35.0  10  65         NA      48    0
#> 16      1  1      x1 56.0 49.0 61.0  37  79         NA      33    0
#> 17      1  1      x2  2.0  2.0  2.0   1   2         NA      33    0
#> 18      1  1      x3 25.0 18.0 30.0   4  56         NA      33    0

# Example with strata and rates
brcancer$x4 <- as.factor(brcancer$x4)

summary_table(brcancer,
              vars      = c("x1", "x2", "x3", "x4"),
              strata    = c("hormon"),
              get_rates = TRUE,
              event     = "censrec",
              st        = "rectime")
#> ! Rates will only be estimted for factor variables in `vars`.
#>    hormon varname   mean p25 p75 min max n_category n_total n_NA no_events
#> 1       0      x1 50.000  45  59  21  80         NA     440    0        NA
#> 2       0      x2  1.000   1   2   1   2         NA     440    0        NA
#> 3       0      x3 25.000  20  35   3 120         NA     440    0        NA
#> 4       0    x4 1  0.109  NA  NA   0   1         48     440    0        12
#> 5       0    x4 2  0.639  NA  NA   0   1        281     440    0       137
#> 6       0    x4 3  0.252  NA  NA   0   1        111     440    0        56
#> 7       1      x1 58.000  50  63  32  80         NA     246    0        NA
#> 8       1      x2  2.000   2   2   1   2         NA     246    0        NA
#> 9       1      x3 25.000  20  35   4 100         NA     246    0        NA
#> 10      1    x4 1  0.134  NA  NA   0   1         33     246    0         6
#> 11      1    x4 2  0.663  NA  NA   0   1        163     246    0        65
#> 12      1    x4 3  0.203  NA  NA   0   1         50     246    0        23
#>    t_at_risk   p_events     rate_est   rate_lower   rate_upper
#> 1         NA         NA           NA           NA           NA
#> 2         NA         NA           NA           NA           NA
#> 3         NA         NA           NA           NA           NA
#> 4      62398 0.05853659 0.0001923139 9.937138e-05 0.0003359336
#> 5     299081 0.66829268 0.0004580699 3.845799e-04 0.0005415135
#> 6     104802 0.27317073 0.0005343409 4.036354e-04 0.0006938862
#> 7         NA         NA           NA           NA           NA
#> 8         NA         NA           NA           NA           NA
#> 9         NA         NA           NA           NA           NA
#> 10     45477 0.06382979 0.0001319348 4.841776e-05 0.0002871666
#> 11    210621 0.69148936 0.0003086112 2.381796e-04 0.0003933503
#> 12     49021 0.24468085 0.0004691867 2.974241e-04 0.0007040104