README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# clinicalfd (Clinical Fake Data)

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The goal of clinicalfd is to create Fake SDTM/ADaM data for generating TFL.This data is 
available at  (https://github.com/phuse-org/phuse-scripts) for free to download for public use see the licence here (https://github.com/phuse-org/phuse-scripts/blob/master/LICENSE.md)

## Installation

You can install the released version of clinicalfd (clinical fake data) from [Github] (https://github.com/sas2r/clinical_fd)

General Instructions:

Make sure you have lateset version of R-language , the way to check is type in  
session() in R console .

If it <= 0.4.7 then the Package may not be loaded, you need to have >= 0.4.7.

To update the R-version 

If R studio is on your Personnel drive then do
 Install.packages ("rlang") ,
 then library (rlang) ,
 check sessionInfo ()

NOTE: If you are using Rstudio on cloud this would not work , On R-cloud simply go to right most corner of the window and change the R version to either 4.0 or 4.1. 

Once you have updated the R-languge then 

<B>NOTE: devtools and clinical_fd would install couple of standard packages and would take some time 

install.packages("devtools")   wait for the dev tools to be installed 
 then call the library(devtools). Once the devtools is installed then you can call install_github("sas2r/clinical_fd")  to 
install the package clinical_fd  (all lower case , with the underscore in it  ) then call library(clincalfd) (without the underscore)

During the process of Installation ( May take around 10 min depending on the System) you will see couple of packages either been downloaded or updated. 

NOTE: Make sure you type in exactly as shown below without # for install packages part
</B>

```{r example}
#install.packages("devtools") 

library(devtools)

#install_github("sas2r/clinical_fd")

library(clinicalfd)
## basic example code
```
(Please see dataguide in man folder for more information )
Where to Find Key Data

Demographics and Populations

  The ADSL (Subject Level Analysis Data) dataset contains all subject-level variables for
  demographics, subject characteristics, and population flags.
  Safety
  Key safety data are found in the datasets ADAE (Adverse Events Analysis Data), ADLBC
  (Laboratory Results Chemistry Analysis Data), ADLBH (Laboratory Results Hematology
  Analysis Data), ADLBHY (Laboratory Results Hy’s Rule Analysis Data), and ADVS (Vital
  Signs Analysis Dataset). In addition, ADTTE is specifically for safety analyses of the time to the
  first dermatologic AE. Dermatologic AEs are considered an adverse event of special interest.
  
Efficacy
  Study CDISCPilot01 has two primary endpoints, the Alzheimer’s Disease Assessment Scale -
  Cognitive Subscale, total of 11 items [ADAS-Cog (11)] at Week 24 and the Video-referenced
  Clinician’s Interview-based Impression of Change (CIBIC+) at Week 24. All ADAS-Cog data,
  including the first primary endpoint, can be found in the dataset ADQSADAS. All CIBIC+ data,
  including the second primary endpoint, can be found in the dataset ADQSCIBC.
  Mean Revised Neuropsychiatric Inventory (NPI-X) data are considered secondary. These data
  can be found in the dataset ADQSNPIX.

List of ADaM/SDTM Data sets available in ‘clinicalfd’ package are:

ADaM: 
    adadas         
    adae           
    adcibc         
    adlbc          
    adlbcpv        
    adlbh          
    adlbhpv        
    adlbhy         
    adnpix         
    adsl           
    adtte          
    advs 

SDTM: 
    ae               
    cm               
    dm               
    ds              
    ex              
    lbch            
    lbhe            
    mh              
    qsco            
    qsda            
    qsgi            
    qshi            
    relrec          
    sc              
    se              
    suppae          
    suppdm          
    suppds          
    supplbch        
    supplbur        
    sv              
    ta              
    te              
    ti              
    ts              
    tv              
    vs  
    
## Example

This is a basic example which shows you how to solve a common problem:

```{r cars , echo = TRUE }
#from SDTM
summary(dplyr::select(dm, age, sex , race, ethnic))
#from ADaM
summary(dplyr::select(adsl, age, sex , race, ethnic))
```

You can also embed plots, for example:

```{r gender, echo = TRUE}
plot(dplyr::select(dm , sex, age) )
```

What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:

You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date.

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub!