dpastoor

# 200 mg dose ID's overlaid
ggplot(data = CleanedPK200, aes(x = TAD, y = CONC4873)) + 
  geom_point(aes(group = ID, color = ID), size = 3) +
  geom_line(aes(group = ID, color = ID), size = 1) +
  labs(title = "200 mg cohort WCK 4873 CONC-TIME") + 
  ylab("Concentration (ng/mL)") + xlab("Time After Dose (hrs)") +
  theme(axis.title.x = element_text(size=17)) +
  theme(axis.title.y = element_text(size=17)) +
  theme(title = element_text(size=20)) + 
  scale_y_log10() 

dpastoor

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.

dpastoor

library(ggplot2)
dat <- data.frame(group = factor(c("Infliximab","Etanercept","Adalimumab","Golimumab","Certolizumab","Abatacept","Rituximab","Tocilizumab","Total"), 
                           
                           levels=c("Total","Certolizumab","Golimumab","Adalimumab","Etanercept","Infliximab","Tocilizumab","Rituximab","Abatacept")),
                  cen = c(1.88,2.67,2.35,2.14,5.08,1.68,2.11,2.01,2.16),
                  low = c(1.01,1.44,1.52,1.59,3.46,1.47,1.64,1.57,1.83),
                  high = c(3.51,4.94,3.65,2.89,7.48,1.90,2.72,2.57,2.55))

dpastoor

theme_ind <- function(...) {
  theme(legend.text = element_text(size = 18),
            legend.title = element_text(size = 20),
            axis.title.x = element_text(size = 22, face = "bold"),
            axis.title.y = element_text(size = 22, face = "bold"),
            axis.text.x = element_text(color = "black", size = 18),
            axis.text.y = element_text(color = "black", size = 18),
            strip.text.x = element_text(color = "black", size = 16, face = "bold"))
}

dpastoor

Notes:

• I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.

• Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.

Programming R curriculum

Data structures

dpastoor

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

dpastoor

libraries <- c(
  "bear",
  "deSolve",
  "devtools",
  "dplyr",
  "formatR",
  "ggplot2",
  "gridExtra",
  "installr",
  "knitr",

dpastoor

#'Saves a ggplot graphic to a file and creates the code to include it in a
#'LaTeX document.
#'
#'Saves a ggplot graphic to a file and creates the code to include it in a
#'LaTeX document. The code is inspired by xtable() from the xtable-package.
#'
#'
#'@param ...  arguments passed to the (\code{\link{ggsave}}) function
#'@param caption The caption. Default to NULL, indicating no caption.
#'@param label The label. Default to NULL, indicating no label.

dpastoor

library(shiny)

shinyServer(function(input,output){
  
  output$distPlot<-reactivePlot(function(){
    dist<-rnorm(input$obs)
    p<-qplot(dist,binwidth=0.1)+geom_vline(xintercept=mean(dist))+theme_dpi()
    p<-p+coord_cartesian(xlim=c(-4,4))+geom_vline(xintercept=median(dist),color=I("red"))
    print(p)
  })

dpastoor

# add to .Rmd file and place the a custom.css file with the desired css in the same directory
options(rstudio.markdownToHTML = 
  function(inputFile, outputFile) {      
    require(markdown)
    markdownToHTML(inputFile, outputFile, stylesheet='custom.css')   
  }
)