inst/doc/introduction.R

In volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

## ----include=FALSE------------------------------------------------------------
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  echo = TRUE,
  message = FALSE,
  knitr.table.format = "html"
)

options(
  vlkr.fig.settings=list(
    html = list(
      dpi = 96, scale = 1, width = 910, pxperline = 12
    )
  )
)

## ----warning=FALSE------------------------------------------------------------
# Load the package
library(volker)

# Set the basic plot theme
theme_set(theme_vlkr())

# Load an example dataset ds from the package
ds <- volker::chatgpt


## ----eval=FALSE---------------------------------------------------------------
#  # A single variable
#  report_counts(ds, use_private)

## ----eval=FALSE---------------------------------------------------------------
#  # A list of variables
#  report_counts(ds, c(use_private, use_work))

## ----eval=FALSE---------------------------------------------------------------
#  # Variables matched by a pattern
#  report_counts(ds, starts_with("use_"))

## ----eval=FALSE---------------------------------------------------------------
#  # One metric variable
#  tab_metrics(ds, sd_age)

## ----eval=FALSE---------------------------------------------------------------
#  # Multiple metric items
#  tab_metrics(ds, starts_with("cg_adoption_"))
#  

## ----eval=FALSE---------------------------------------------------------------
#  report_counts(ds, adopter, sd_gender)

## ----eval=FALSE---------------------------------------------------------------
#  report_metrics(ds, sd_age, sd_gender, ci = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  tab_metrics(ds, sd_age, use_work, metric = TRUE, ci = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    filter(sd_gender != "diverse") |>
#    report_counts(adopter, sd_gender, prop="rows", numbers= "n")

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    filter(sd_gender != "diverse") |>
#    effect_counts(adopter, sd_gender)

## -----------------------------------------------------------------------------
ds %>% 
  filter(sd_gender != "diverse") %>% 
  report_metrics(starts_with("cg_adoption_"), sd_gender, box=TRUE, ci=TRUE)

## -----------------------------------------------------------------------------
#> ### Adoption types
#> 
#> ```{r echo=FALSE}
#> ds %>% 
#>   filter(sd_gender != "diverse") %>% 
#>   report_counts(adopter, sd_gender, prop="rows", title=FALSE, close=FALSE)
#> ```
#>
#> ##### Method
#> Basis: Only male and female respondents.
#> 
#> #### {-}


## -----------------------------------------------------------------------------
theme_set(theme_vlkr(
  base_fill = c("#F0983A","#3ABEF0","#95EF39","#E35FF5","#7A9B59"),
  base_gradient = c("#FAE2C4","#F0983A")
))


## -----------------------------------------------------------------------------
codebook(ds)

## ----eval = FALSE-------------------------------------------------------------
#  ds %>%
#    labs_apply(
#      items = list(
#        "cg_adoption_advantage_01" = "Allgemeine Vorteile",
#        "cg_adoption_advantage_02" = "Finanzielle Vorteile",
#        "cg_adoption_advantage_03" = "Vorteile bei der Arbeit",
#        "cg_adoption_advantage_04" = "Macht mehr Spaß"
#      )
#    ) %>%
#    report_metrics(starts_with("cg_adoption_advantage_"))
#  

## ----eval=FALSE---------------------------------------------------------------
#  
#  ds %>%
#    labs_apply(
#      cols=starts_with("cg_adoption"),
#      values = list(
#        "1" = "Stimme überhaupt nicht zu",
#        "2" = "Stimme nicht zu",
#        "3" = "Unentschieden",
#        "4" = "Stimme zu",
#        "5" =  "Stimme voll und ganz zu"
#      )
#    ) %>%
#    report_metrics(starts_with("cg_adoption"))
#  

## ----eval=FALSE---------------------------------------------------------------
#  
#  library(readxl)
#  library(writexl)
#  
#  # Save codebook to a file
#  codes <- codebook(ds)
#  write_xlsx(codes,"codebook.xlsx")
#  
#  # Load and apply a codebook from a file
#  codes <- read_xlsx("codebook_revised.xlsx")
#  ds <- labs_apply(ds, codebook)
#  

## ----eval=FALSE---------------------------------------------------------------
#  ds %>%
#    labs_store() %>%
#    mutate(sd_age = 2024 - sd_age) %>%
#    labs_restore() %>%
#  
#    report_metrics(sd_age)

## ----eval=FALSE---------------------------------------------------------------
#  ds %>%
#    add_index(starts_with("cg_adoption_"), newcol = "idx_cg_adoption") %>%
#    report_metrics(idx_cg_adoption)

## ----eval=FALSE---------------------------------------------------------------
#  ds %>%
#    add_index(starts_with("cg_adoption_"), newcol = "idx_cg_adoption") %>%
#    report_metrics(idx_cg_adoption, adopter)

## ----eval=FALSE---------------------------------------------------------------
#  ds %>%
#    add_index(starts_with("cg_adoption_")) %>%
#    add_index(starts_with("cg_adoption_advantage")) %>%
#    add_index(starts_with("cg_adoption_fearofuse")) %>%
#    add_index(starts_with("cg_adoption_social")) %>%
#    tab_metrics(starts_with("idx_cg_adoption"))

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    report_metrics(starts_with("cg_adoption"), factors = TRUE, clusters = TRUE)
#  

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    add_factors(starts_with("cg_adoption"), k = 3)  |>
#    report_metrics(fct_cg_adoption_1, fct_cg_adoption_2, metric = TRUE)
#  

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    add_factors(starts_with("cg_adoption"), k = NULL) |>
#    factor_tab(starts_with("fct_cg_adoption"))

## ----eval=FALSE---------------------------------------------------------------
#  ds |>
#    add_clusters(starts_with("cg_adoption"), k = 3) |>
#    report_counts(sd_gender, cls_cg_adoption, prop = "cols")