Summarize a numeric variable

Usage

hts_summary_num(
  prepped_dt,
  summarize_var = NULL,
  summarize_by = NULL,
  weighted = TRUE,
  se = FALSE,
  wtname = NULL,
  strataname = NULL
)

Arguments

prepped_dt: A prepared dataset in data.table format with the variable to summarize, the variable to summarize by, and the weights, if used.
summarize_var: Name of the variable to summarize. Default is NULL.
summarize_by: Name of the variable to summarize the summarize_var by. Default is NULL.
weighted: Whether the data is weighted. Default is TRUE.
se: Whether to calculate standard error. Default is FALSE. Will be set to FALSE if weighted is FALSE.
wtname: Name of the weight column to use. Default is NULL. Must be specified when weighted = TRUE.
strataname: Name of strata name to bring in. Default is NULL.

Value

List of unweighted and weighted numeric summaries including count, min, max, mean, se, and median.

Examples


require(data.table)
require(stringr)
require(dplyr)
require(srvyr)
DT = hts_prep_variable(
  summarize_var = "speed_mph",
  variables_dt = variable_list,
  data = list(
    "hh" = hh,
    "person" = person,
    "day" = day,
    "trip" = trip,
    "vehicle" = vehicle
  )
)$num
#> Warning: 378 outliers were removed based on the threshold of 0.975.
hts_summary_num(
  prepped_dt = DT,
  summarize_var = "speed_mph",
  wtname = "trip_weight"
)
#> $unwtd
#>    count min      max     mean   median
#> 1: 14722   0 112.5371 12.27017 8.914392
#> 
#> $wtd
#>    count min      max     mean   median
#> 1: 14722   0 112.5371 12.18441 8.877364
#> 
#> $weight_name
#> [1] "trip_weight"
#> 
DT = hts_prep_variable(
  summarize_var = "speed_mph",
  summarize_by = "age",
  variables_dt = variable_list,
  data = list(
    "hh" = hh,
    "person" = person,
    "day" = day,
    "trip" = trip,
    "vehicle" = vehicle
  )
)$num
#> Warning: 378 outliers were removed based on the threshold of 0.975.
hts_summary_num(
  prepped_dt = DT,
  summarize_var = "speed_mph",
  summarize_by = "age",
  wtname = "trip_weight"
)
#> $unwtd
#>     age count         min       max     mean   median
#>  1:   1  1330 0.052703535  99.66345 12.77219 9.043035
#>  2:   2  1034 0.013607390 101.97575 11.79746 8.642971
#>  3:   3  1175 0.000000000 106.33820 11.80088 8.762973
#>  4:   4  1322 0.000000000 100.93678 12.17496 8.960005
#>  5:   5  1342 0.020070228 108.02176 11.98645 8.380280
#>  6:   6  1220 0.008117147 108.05151 11.61808 8.787320
#>  7:   7  1310 0.000000000 105.12236 12.13324 9.233571
#>  8:   8  1164 0.000000000  88.06935 12.65997 8.917070
#>  9:   9  1095 0.000000000 111.79519 12.08160 8.305877
#> 10:  10  1190 0.108733750 112.53715 12.67014 9.687523
#> 11:  11  1283 0.000000000 110.65782 12.52175 8.745631
#> 12:  12  1257 0.000000000 109.20438 12.91303 9.876124
#> 
#> $wtd
#>     age count         min       max     mean   median
#>  1:   1  1330 0.052703535  99.66345 12.45417 8.955092
#>  2:   2  1034 0.013607390 101.97575 11.86040 8.807183
#>  3:   3  1175 0.000000000 106.33820 11.63899 8.625382
#>  4:   4  1322 0.000000000 100.93678 12.17959 8.890321
#>  5:   5  1342 0.020070228 108.02176 11.50444 8.031314
#>  6:   6  1220 0.008117147 108.05151 11.48691 8.788419
#>  7:   7  1310 0.000000000 105.12236 12.57005 9.480196
#>  8:   8  1164 0.000000000  88.06935 12.72844 8.925704
#>  9:   9  1095 0.000000000 111.79519 11.78338 7.904890
#> 10:  10  1190 0.108733750 112.53715 12.82312 9.744795
#> 11:  11  1283 0.000000000 110.65782 12.22370 8.451265
#> 12:  12  1257 0.000000000 109.20438 12.83858 9.863703
#> 
#> $weight_name
#> [1] "trip_weight"
#>