DDP_week3.Rmd

---
title: "DDP_week3"
author: "Yohann Potier"
date: "7/4/2019"
output: html_document
---

## Week 3 Developer Data product exercise 
<!-- # Week 3 Developer product exercise 
# Create a web page presentation using R Markdown that features a plot created with Plotly. 
# Host your webpage on either GitHub Pages, RPubs, or NeoCities. Your webpage must contain the date that you created the document, 
# and it must contain a plot created with Plotly. 
# We would love to see you show off your creativity!-->


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Melanoma dataset 
```{r, eval=FALSE}
library(plotly)
library(knitr)
load(file="Melanoma.RData")
kable(Melanoma[1:5,], caption = "Melanoma table content")
```

```{r, echo=FALSE}
library(plotly)
library(knitr)
load(file="Melanoma.RData")
kable(Melanoma[1:5,], caption = "Melanoma table content")
```


* time
  + Survival time in days since the operation, possibly censored.
* status
  + The patients status at the end of the study. 1 indicates that they had died from melanoma, 2 indicates that they were still alive and 3 indicates that they had died from causes unrelated to their melanoma.
* sex
  + The patients sex; 1=male, 0=female.
* age
  + Age in years at the time of the operation.
* year
  + Year of operation.
* thickness
  + Tumour thickness in mm.
* ulcer
  + Indicator of ulceration; 1=present, 0=absent.

## build linear model based on data for plotting
```{r, eval=FALSE}
time<-Melanoma$time
status<-Melanoma$status

status<-as.factor(status) 

age<-Melanoma$age
thickness<-Melanoma$thickness
ulcer<-Melanoma$ulcer

model<-glm(ulcer~time+thickness,
           family = binomial(link = "logit"), data = Melanoma)
```

```{r, echo=FALSE}
time<-Melanoma$time
status<-Melanoma$status

status<-as.factor(status) 

age<-Melanoma$age
thickness<-Melanoma$thickness
ulcer<-Melanoma$ulcer

model<-glm(ulcer~time+thickness,
           family = binomial(link = "logit"), data = Melanoma)
```

## Update legend, axis labels and font
```{r, eval=FALSE}
f <- list(
  family = "Arial, monospace",
  size = 18,
  color = "#7f7f7f"
)

x <- list(
  title = "Time",
  titlefont = f
)
y <- list(
  title = "Prediction of ulcer based on time and thickness",
  titlefont = f
)

Melanoma$sex <- factor(Melanoma$sex,
                    levels = c(0,1),
                    labels = c("female", "male"))
```

```{r, echo=FALSE}
f <- list(
  family = "Arial, monospace",
  size = 18,
  color = "#7f7f7f"
)

x <- list(
  title = "Time",
  titlefont = f
)
y <- list(
  title = "Prediction of ulcer based on time and thickness",
  titlefont = f
)

Melanoma$sex <- factor(Melanoma$sex,
                    levels = c(0,1),
                    labels = c("female", "male"))
```
## Plotting linear predictive model of ulcer as a function of time and tumor thickness
```{r, eval=FALSE}
plot_ly(Melanoma, x = ~time, y = ~model, type = "scatter", mode = "lines", color = ~factor(sex)) %>%
  layout(xaxis = x, yaxis = y)
```

```{r, echo=FALSE}
plot_ly(Melanoma, x = ~time, y = ~model, type = "scatter", mode = "lines", color = ~factor(sex)) %>%
  layout(xaxis = x, yaxis = y)
```