Plain text *italics* and _italics_ **bold** and __bold__ superscript^2^ subscript~2~ ~~strikethrough~~ [link](www.rstudio.com) # Header 1 ## Header 2 ### Header 3 #### Header 4 ##### Header 5 ###### Header 6 endash: -- emdash: --- ellipsis: ... inline equation: $A = \pi*r^{2}$ image: ![](https://www.rstudio.com/wp-content/uploads/2014/03/blue-125.png) horizontal rule (or slide break): *** > block quote * unordered list * item 2 + sub-item 1 + sub-item 2 1. ordered list 2. item 2 + sub-item 1 + sub-item 2 you can write `inline` code with a back-tick ``` code blocks display with fixed-width fond ``` Table Header | Second Header ------------ | -------------- Cell 1 | Cell 2 Cell 3 | Cell 4 ```{r} summary(1:10) ``` You can also embed plots, for example: ```{r plot, echo=FALSE} boxplot(1:10) ``` Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot. Add more R code chunk arguments: ```{r histogram, echo=TRUE, eval=TRUE, fig.width=5, fig.height=4, fig.align='center'} hist(1:10) ``` Inline code is underappreciated: Last night, I saw `r 3 + 4` shooting stars. --- title: "test_rmd" output: html_document author: "someone"" date: "sometime" --- Now include analysis on inflammation data: ```{r} analyze <- function(filename) { # Plots the average, min, and max inflammation over time. # Input is character string of a csv file. dat <- read.csv(file = filename, header = FALSE) avg_day_inflammation <- apply(dat, 2, mean) plot(avg_day_inflammation, main=filename) max_day_inflammation <- apply(dat, 2, max) plot(max_day_inflammation, main=filename) min_day_inflammation <- apply(dat, 2, min) plot(min_day_inflammation, main=filename) } analyze_all <- function(pattern) { # Runs the function analyze for each file in the current working directory # that contains the given pattern. filenames <- list.files(path = "~/Desktop/swc-r-day1/data", pattern = pattern, full.names = TRUE) for (f in filenames) { analyze(f) } } analyze_all("inflammation") ```