vignettes/introduction.R

In josie-athens/pubh: A Toolbox for Public Health and Epidemiology

## ----eval=FALSE---------------------------------------------------------------
#  y ~ x, data = my_data

## ----eval=FALSE---------------------------------------------------------------
#  y ~ x|z, data = my_data

## ----eval=FALSE---------------------------------------------------------------
#  data %>%
#    f1(...) %>%
#    f2(...) %>%
#    f3(...)

## ----message=FALSE, results='hide'--------------------------------------------
rm(list = ls())
library(tidyverse)
library(rstatix)
library(jtools)
library(pubh)
library(sjlabelled)
library(sjPlot)

theme_set(sjPlot::theme_sjplot2(base_size = 10))
theme_update(legend.position = "top")
options('huxtable.knit_print_df' = FALSE)
options('huxtable.autoformat_number_format' = list(numeric = "%5.2f"))
knitr::opts_chunk$set(comment = NA)

## -----------------------------------------------------------------------------
data(Oncho)
Oncho %>% head()

## -----------------------------------------------------------------------------
Oncho %>%
  mutate(
    mf = relevel(mf, ref = "Infected")
  ) %>%
  copy_labels(Oncho) %>%
  select(mf, area) %>% 
  cross_tbl(by = "area") %>%
  theme_pubh(2)

## -----------------------------------------------------------------------------
Oncho %>%
  select(- c(id, mfload)) %>%
  mutate(
    mf = relevel(mf, ref = "Infected")
  ) %>%
  copy_labels(Oncho) %>%
  cross_tbl(by = "area") %>%
  theme_pubh(2)

## -----------------------------------------------------------------------------
data(Hodgkin)
Hodgkin <- Hodgkin %>%
  mutate(Ratio = CD4/CD8) %>%
  var_labels(
    Ratio = "CD4+ / CD8+ T-cells ratio"
    )

Hodgkin %>% head()

## -----------------------------------------------------------------------------
Hodgkin %>%
  estat(~ CD4) %>%
  as_hux() %>% theme_pubh()

## -----------------------------------------------------------------------------
Hodgkin %>%
  estat(~ Ratio|Group) %>%
  as_hux() %>% theme_pubh()

## -----------------------------------------------------------------------------
Hodgkin %>%
  mutate(
    Group = relevel(Group, ref = "Hodgkin")
  ) %>%
  copy_labels(Hodgkin) %>%
  cross_tbl(by = "Group", bold = FALSE) %>%
  theme_pubh(2) %>%
  add_footnote("Median (IQR)", font_size = 9)

## -----------------------------------------------------------------------------
var.test(Ratio ~ Group, data = Hodgkin)

## -----------------------------------------------------------------------------
Hodgkin %>%
  qq_plot(~ Ratio|Group) 

## -----------------------------------------------------------------------------
wilcox.test(Ratio ~ Group, data = Hodgkin)

## -----------------------------------------------------------------------------
Hodgkin %>%
  strip_error(Ratio ~ Group)

## -----------------------------------------------------------------------------
Hodgkin %>%
  strip_error(Ratio ~ Group) %>%
  gf_star(x1 = 1, y1 = 4, x2 = 2, y2 = 4.05, y3 = 4.1, "**")

## -----------------------------------------------------------------------------
data(birthwt, package = "MASS")
birthwt <- birthwt %>%
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    Race = factor(race > 1, labels = c("White", "Non-white")),
    race = factor(race, labels = c("White", "Afican American", "Other"))
    ) %>%
  var_labels(
    bwt = 'Birth weight (g)',
    smoke = 'Smoking status',
    race = 'Race',
  )

## -----------------------------------------------------------------------------
birthwt %>%
  bar_error(bwt ~ smoke)

## -----------------------------------------------------------------------------
birthwt %>%
  qq_plot(~ bwt|smoke)

## -----------------------------------------------------------------------------
birthwt %>% 
  t_test(bwt ~ smoke, detailed = TRUE) %>% 
  as.data.frame()

## -----------------------------------------------------------------------------
birthwt %>%
  bar_error(bwt ~ smoke) %>%
  gf_star(x1 = 1, x2 = 2, y1 = 3400, y2 = 3500, y3 = 3550, "**")

## -----------------------------------------------------------------------------
birthwt %>%
  bar_error(bwt ~ smoke, fill = ~ Race) %>%
  gf_refine(ggsci::scale_fill_jama()) 

## -----------------------------------------------------------------------------
birthwt %>%
  bar_error(bwt ~ smoke|Race)

## -----------------------------------------------------------------------------
birthwt %>%
  strip_error(bwt ~ smoke, pch = ~ Race, col = ~ Race) %>%
  gf_refine(ggsci::scale_color_jama())

## -----------------------------------------------------------------------------
model_bwt <- lm(bwt ~ smoke + race, data = birthwt)

## -----------------------------------------------------------------------------
model_bwt %>% 
  tbl_regression() %>% 
  cosm_reg() %>% theme_pubh() %>% 
  add_footnote(get_r2(model_bwt), font_size = 9)

## -----------------------------------------------------------------------------
model_bwt %>%
  glm_coef(labels = model_labels(model_bwt)) %>%
  as_hux() %>% theme_pubh() %>% 
  set_align(everywhere, 2:3, "right") %>%
  add_footnote(get_r2(model_bwt), font_size = 9)

## -----------------------------------------------------------------------------
multiple(model_bwt, ~ race)$df

## -----------------------------------------------------------------------------
multiple(model_bwt, ~ race)$fig_ci %>%
  gf_labs(x = "Difference in birth weights (g)")

## -----------------------------------------------------------------------------
multiple(model_bwt, ~ race)$fig_pval %>%
  gf_labs(y = " ")

## -----------------------------------------------------------------------------
data(Bernard)
Bernard %>% head()

## -----------------------------------------------------------------------------
Bernard %>%
  mutate(
    fate = relevel(fate, ref = "Dead"),
    treat = relevel(treat, ref = "Ibuprofen")
  ) %>%
  copy_labels(Bernard) %>%
  select(fate, treat) %>% 
  cross_tbl(by = "treat") %>%
  theme_pubh(2)

## -----------------------------------------------------------------------------
dat <- matrix(c(84, 140 , 92, 139), nrow = 2, byrow = TRUE)
epiR::epi.2by2(as.table(dat))

## -----------------------------------------------------------------------------
Bernard %>%
  contingency(fate ~ treat) 

## ----eval=FALSE---------------------------------------------------------------
#  outcome ~ stratum/exposure, data = my_data

## -----------------------------------------------------------------------------
data(oswego, package = "epitools")

oswego <- oswego %>%
  mutate(
    ill = factor(ill, labels = c("No", "Yes")),
    sex = factor(sex, labels = c("Female", "Male")),
    chocolate.ice.cream = factor(chocolate.ice.cream, labels = c("No", "Yes"))
  ) %>%
  var_labels(
    ill = "Developed illness",
    sex = "Sex",
    chocolate.ice.cream = "Consumed chocolate ice cream"
  )

## -----------------------------------------------------------------------------
oswego %>%
  mhor(ill ~ sex/chocolate.ice.cream)

## -----------------------------------------------------------------------------
Freq <- c(1739, 8, 51, 22)
BCG <- gl(2, 1, 4, labels=c("Negative", "Positive"))
Xray <- gl(2, 2, labels=c("Negative", "Positive"))
tb <- data.frame(Freq, BCG, Xray)
tb <- expand_df(tb)

diag_test(BCG ~ Xray, data = tb)

## -----------------------------------------------------------------------------
diag_test2(22, 51, 8, 1739)