Make a script with all analyses

make_script(
  study,
  path = NULL,
  data_path = "data",
  data_format = "tsv",
  show_codebook = TRUE,
  use_rmarkdown = TRUE,
  header_lvl = 2,
  header = TRUE
)

Arguments

study: A study list object with class scivrs_study
path: Path to write script to (outputs text if NULL)
data_path: Where to load data from external files (you may need to creates data files with `make_data or edit the script for your data paths); loads from the script if set to NULL
data_format: The data format to save with (defaults to tsv)
show_codebook: Include codebook for each dataset in comments
use_rmarkdown: Creates an Rmarkdown file if TRUE, a .R file if FALSE
header_lvl: The starting header level for the section (defaults to 2)
header: Whether the rmarkdown version should have a header

Value

Text of the script if path is NULL

Examples

s <- study() %>%
  add_data("my_cars", mtcars) %>%
  add_analysis("A1", cor.test(my_cars$mpg, my_cars$wt)) %>%
  study_analyse()
#> mpg set to dataType float
#> cyl set to dataType int
#> disp set to dataType float
#> hp set to dataType int
#> drat set to dataType float
#> wt set to dataType float
#> qsec set to dataType float
#> vs set to dataType int
#> am set to dataType int
#> gear set to dataType int
#> carb set to dataType int
#> The study has no hypotheses.

# get Rmd text output
make_script(s) %>% cat()
#> ---
#> title: "Demo Study"
#> author: ""
#> date: "05/05/2024"
#> output:
#>   html_document:
#>     toc: true
#>     toc_float: true
#> ---
#> 
#> 
#> 
#> ## Data
#> 
#> ### my_cars
#> 
#> 
#> * mpg (float)
#> * cyl (int)
#> * disp (float)
#> * hp (int)
#> * drat (float)
#> * wt (float)
#> * qsec (float)
#> * vs (int)
#> * am (int)
#> * gear (int)
#> * carb (int)
#> 
#> 
#> ```{r}
#> my_cars <- read.csv('data/my_cars_data.tsv', sep='\t')
#> ```
#> 
#> 
#> 
#> ## Analysis 1: A1 {#analysis_1}
#> 
#> ```{r}
#> cor.test(my_cars$mpg, my_cars$wt)
#> ```
#> 
#> ### Stored Results
#> 
#> * statistic: 
#>     * t: `-9.55904414697211`
#> * parameter: 
#>     * df: `30`
#> * p.value: `1.29395870135052e-10`
#> * estimate: 
#>     * cor: `-0.867659376517228`
#> * null.value: 
#>     * correlation: `0`
#> * alternative: `two.sided`
#> * method: `Pearson's product-moment correlation`
#> * data.name: `my_cars$mpg and my_cars$wt`
#> * conf.int: 
#>     1. `-0.933826413284994`
#>     2. `-0.744087196460113`
#> 
# get R text output
make_script(s, use_rmarkdown = FALSE) %>% cat()
#> # Code for Demo Study
#> # Authors: 
#> # Created 05/05/2024
#> 
#> 
#> 
#> ## Data
#> 
#> ### my_cars
#> 
#> 
#> # * mpg (float)
#> # * cyl (int)
#> # * disp (float)
#> # * hp (int)
#> # * drat (float)
#> # * wt (float)
#> # * qsec (float)
#> # * vs (int)
#> # * am (int)
#> # * gear (int)
#> # * carb (int)
#> 
#> my_cars <- read.csv('data/my_cars_data.tsv', sep='\t')
#> 
#> 
#> 
#> ## Analysis 1: A1
#> 
#> cor.test(my_cars$mpg, my_cars$wt)
#> 
#> ### Stored Results
#> 
#> # * statistic: 
#> #     * t: -9.55904414697211
#> # * parameter: 
#> #     * df: 30
#> # * p.value: 1.29395870135052e-10
#> # * estimate: 
#> #     * cor: -0.867659376517228
#> # * null.value: 
#> #     * correlation: 0
#> # * alternative: two.sided
#> # * method: Pearson's product-moment correlation
#> # * data.name: my_cars$mpg and my_cars$wt
#> # * conf.int: 
#> #     1. -0.933826413284994
#> #     2. -0.744087196460113
#>