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description_data_rshiny.Rmd
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description_data_rshiny.Rmd
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---
title: "CLARITY: Documentation of data sets"
author: "N. Melzer, D. Wittenburg"
date: October 19, 2022
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo =TRUE)
library(DT)
path.input <- "input_rshiny"
```
## Input data format required for CLARITY app
### 1. geneticMap.Rdata
data.frame *geneticMap*
```{r table1, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
load(file.path(path.input, "geneticMap.Rdata"))
df=cbind(1:ncol(geneticMap),colnames(geneticMap))
Class=c()
for(i in 1:ncol(geneticMap))Class[i]=class(geneticMap[,i])
df=cbind(df,Class)
colnames(df)<-c("Column no.","Column name","Class")
# output the table in a format suitable for HTML/PDF/docx conversion
datatable(df,rownames=F,options=list(searching=F,dom=""))
```
### 2. genetic_map_summary.Rdata
data.frame *genetic_map_summary*
```{r table2, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
load(file.path(path.input, "genetic_map_summary.Rdata"))
df=cbind(1:ncol(genetic_map_summary),colnames(genetic_map_summary))
Class=c()
for(i in 1:ncol(genetic_map_summary))Class[i]=class(genetic_map_summary[,i])
df=cbind(df,Class)
colnames(df)<-c("Column no.","Column name","Class")
# output the table in a format suitable for HTML/PDF/docx conversion
datatable(df,rownames=F,options=list(searching=F,dom=""))
```
### 3. adjacentRecRate.Rdata
data.frame *adjacentRecRate*
```{r table3, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
load(file.path(path.input, "adjacentRecRate.Rdata"))
df=cbind(1:ncol(adjacentRecRate),colnames(adjacentRecRate))
Class=c()
for(i in 1:ncol(adjacentRecRate))Class[i]=class(adjacentRecRate[,i])
df=cbind(df,Class)
colnames(df)<-c("Column no.","Column name","Class")
# output the table in a format suitable for HTML/PDF/docx conversion
datatable(df,rownames=F,options=list(searching=F,dom=""))
```
### 4. bestmapfun.Rdata
matrix *out*
```{r table4, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
load(file.path(path.input, "bestmapfun.Rdata"))
df=cbind(1:ncol(out),colnames(out))
Class=c()
for(i in 1:ncol(out))Class[i]=class(out[,i])
df=cbind(df,Class)
colnames(df)<-c("Column no.","Column name","Class")
# output the table in a format suitable for HTML/PDF/docx conversion
datatable(df,rownames=F,options=list(searching=F,dom=""))
```
### 5. curve-short-\<chr\>.Rdata
$\text{<chr>}=1,\ldots,29$; list *store* with four elements:
1. matrix: dist_M and theta
2. matrix: x-values for Haldane scaled, Rao, Felsenstein, and Liberman & Karlin
3. matrix: y-values for Haldane scaled, Rao, Felsenstein, and Liberman & Karlin
4. numeric value: the percentage of marker pairs involved in scatter plot of dist_M and theta