read_popu: Read population data (from df) into a riskyr scenario...

In riskyr: Rendering Risk Literacy more Transparent

Read population data (from df) into a riskyr scenario (description).

Description

read_popu reads a data frame df (containing observations of some population that are cross-classified on two binary variables) and returns a scenario of class "riskyr" (i.e., a description of the data).

Usage

read_popu(
  df = popu,
  ix_by_top = 1,
  ix_by_bot = 2,
  ix_sdt = 3,
  hi_lbl = txt$hi_lbl,
  mi_lbl = txt$mi_lbl,
  fa_lbl = txt$fa_lbl,
  cr_lbl = txt$cr_lbl,
  ...
)

Arguments

df	A data frame providing a population popu of individuals, which are identified on at least 2 binary variables and cross-classified into 4 cases in a 3rd variable. Default: df = popu (as data frame).
ix_by_top	Index of variable (column) providing the 1st (X/top) perspective (in df). Default: ix_by_top = 1 (1st column).
ix_by_bot	Index of variable (column) providing the 2nd (Y/bot) perspective (in df). Default: ix_by_bot = 2 (2nd column).
ix_sdt	Index of variable (column) providing a cross-classification into 4 cases (in df). Default: ix_by_bot = 3 (3rd column).
hi_lbl	Label of cases classified as hi (TP).
mi_lbl	Label of cases classified as mi (FN).
fa_lbl	Label of cases classified as fa (FP).
cr_lbl	Label of cases classified as cr (TN).
...	Additional parameters (passed to riskyr).

Details

Note that df needs to be structured (cross-classified) according to the data frame popu, created by comp_popu.

Value

An object of class "riskyr" describing a risk-related scenario.

See Also

comp_popu creates data (as df) from description (frequencies); write_popu creates data (as df) from a riskyr scenario (description); popu for data format; riskyr initializes a riskyr scenario.

Other functions converting data/descriptions: comp_popu(), write_popu()

Examples

# Generating and interpreting different scenario types:

# (A) Diagnostic/screening scenario (using default labels): ------
popu_diag <- comp_popu(hi = 4, mi = 1, fa = 2, cr = 3)
# popu_diag
scen_diag <- read_popu(popu_diag, scen_lbl = "Diagnostics", popu_lbl = "Population tested")
plot(scen_diag, type = "prism", area = "no", f_lbl = "namnum")

# (B) Intervention/treatment scenario: ------
popu_treat <- comp_popu(hi = 80, mi = 20, fa = 45, cr = 55,
                        cond_lbl = "Treatment", cond_true_lbl = "pill", cond_false_lbl = "placebo",
                        dec_lbl = "Health status", dec_pos_lbl = "healthy", dec_neg_lbl = "sick")
# popu_treat
s_treat <- read_popu(popu_treat, scen_lbl = "Treatment", popu_lbl = "Population treated")
plot(s_treat, type = "prism", area = "sq", f_lbl = "namnum", p_lbl = "num")
plot(s_treat, type = "icon", lbl_txt = txt_org, col_pal = pal_org)

# (C) Prevention scenario (e.g., vaccination): ------
popu_vacc <- comp_popu(hi = 960, mi = 40, fa = 880, cr = 120,
                       cond_lbl = "Vaccination", cond_true_lbl = "yes", cond_false_lbl = "no",
                       dec_lbl = "Disease", dec_pos_lbl = "no flu", dec_neg_lbl = "flu")
# popu_vacc
s_vacc <- read_popu(popu_vacc, scen_lbl = "Vaccination effects", popu_lbl = "RCT population")
plot(s_vacc, type = "prism", area = "sq", f_lbl = "namnum", col_pal = pal_rgb, p_lbl = "num")

riskyr documentation built on Aug. 15, 2022, 9:09 a.m.