dashboardPT.Rmd

---
title: "Candidate Analysis"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    theme: spacelab    
---

```{r setup, include=TRUE}
library(data.table)
library(dplyr)
library(DT)
library(flexdashboard)
library(formattable)
library(pdftools)
library(plotly)
library(qdap) 
library(qdapDictionaries) 
library(qdapRegex) 
library(qdapTools)
library(quanteda)
library(treemap)

x<-data.table(load("candidates.RData"))

if (!require("pacman")) install.packages("pacman")
pacman::p_load(syllable, readability)


```

Summary {data-navmenu="Readability"}
=====================================

Row {}
-----------------------------------------------------------------------
### Applicants

```{r echo=FALSE, include=TRUE, Applicants}
tbl_candidate<-datatable((candidate), options = list(
  searching = FALSE,
  paging = FALSE,
  lengthMenu = c(5, 10, 15, 20)
))
tbl_candidate
```

Score {data-navmenu="Readability"}
=====================================

Row {}
-----------------------------------------------------------------------

### Applicant Readability & Grade Level

```{r  width=700, echo=FALSE, Applicant-Readability-Grade-Level}
plot(candidate)
```

Formality {data-navmenu="Readability"}
=====================================  

Column {}
-------------------------------------
    
### Formality
    
```{r echo=FALSE, formality}
 plot(formality)
```

ARI {data-navmenu="Readability"}
=====================================  
    
Row {data-height=25}
-------------------------------------

### About Automated Readability Index

The Automated Readability Index (ARI) is a readability test designed to assess the understandability of a text. Like other popular readability formulas, the ARI formula outputs a number which approximates the grade level needed to comprehend the text. For example, if the ARI outputs the number 10, this equates to a high school student, ages 15-16 years old; a number 3 means students in 3rd grade (ages 8-9 yrs. old) should be able to comprehend the text. 

Column {}
-------------------------------------

### Automated Readability Index
    
```{r echo=FALSE, Automated-Readability-Index}
plot(ari)
```

Coleman Liau {data-navmenu="Readability"}
=====================================

Row {data-height=25}
-------------------------------------

### About Coleman Liau

The majority of classic readability measures such as Flesch-Kincaid and Gunning Fog involve counting the number of syllables per word and per sentence. Yet, a distinctive feature of the Coleman-Liau Index is that the formula does not involve any counting of syllables.

By basing calculations on the number of letters in a word and number of words in a sentence, readability scores measured by Coleman-Liau Index may be seen as more accurate than those measured by formula that relies on syllable counting.

Row
-----------------------------------------------------------------------

### Coleman Liau

```{r echo=FALSE, coleman-liau}
cl <- readRDS("cl.rds")
plot(cl)
```

Linsear Write {data-navmenu="Readability"}
=====================================

Row {data-height=25}
-------------------------------------

### About Linsear Write

It is one of many such readability metrics, but is specifically designed to calculate the United States grade level of a text sample based on sentence length and the number of words used that have three or more syllables.

Row {}
-----------------------------------------------------------------------

### Linsear Write

```{r echo=FALSE, Linsear-Write}
 plot(scores(lw))
```


Charts {data-navmenu="College Detail"}  {.hidden}
=================================================     

Column {data-width=350}
-----------------------------------------------------------------------

### Cost of Attendance-Graduate Resident

```{r echo=FALSE, COA-Grad-Resident2}

```

### Cost of Attendance-Graduate Non Resident

```{r}

```

Tables {data-navmenu="College Detail"}  {.hidden}
=================================================    

Row {data-width=450}
-------------------------------------
    
### Top 5 Candidates

```{r echo=FALSE, Top-10-Candidates}


```
    
### Bottom 5 Candidates

```{r echo=FALSE, Bottom-10-Institutions}

```

Row
-------------------------------------
    
### Institutional Ranking
    
```{r}

```

Page 1 {data-navmenu="Fin Aid Awd"}  {.hidden}
=====================================    

Row
-------------------------------------
    
### Financial Aid Award-Undergraduate Resident

```{r echo=FALSE, Fin-Aid-Awd-Undergrad-Resident}

```
    
### Financial Aid Award Non Resident

```{r echo=FALSE, Fin-Aid-Awd-NonResident}

```

Column
-------------------------------------
    
### Financial Aid Award
    
```{r}

```





Page 2 {data-navmenu="Fin Aid Awd"}  {.hidden}
=====================================    

Column 
-------------------------------------
    
### Chart 1
    
```{r}

```
   
Column {.tabset}
-------------------------------------
   
### Chart 2

```{r echo=FALSE, Fin-Aid-Awd2-Undergrad-Resident}

```   
 
### Chart 3
    
```{r echo=FALSE, Fin-Aid-Awd2-NonResident}

```