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# turn the data into a correlation matrix
cor(mydata)

# perform the PCA, using `principal`

pca.fit <- principal(mydata, 
            nfactors = ncol(mydata), # the number of factors = the number of columns in my data
            rotate = "varimax", # I want to perform varimax rotation on the factors
            residuals = TRUE, # report the residuals
            scores = TRUE)  # find the component scores

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mi:::.create.missing
function (data, pct.mis = 10) 
{
    n <- nrow(data)
    J <- ncol(data)
    if (length(pct.mis) == 1) {
        n.mis <- rep((n * (pct.mis/100)), J)
    }
    else {
        if (length(pct.mis) < J) 

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```{r global_options, include=FALSE, cache=FALSE}
library(knitr)
  # Set basic options. You usually do not want your code, messages, warnings    etc.
  # to show in your actual manuscript however for the first run or two these will 
  # be set on.
opts_chunk$set(echo=FALSE, 
               warning=FALSE, 
               message=FALSE,
               cache = TRUE,
               include = FALSE,

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# assuming data has columns named something like: "lat", "long", and "timestamp"

library(ggmaps)
library(ggplot2)

ggplot(data = data,
       aes(x = lat,
           y = long)) + 
      geom_point() + 

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layout	title
post	Another test post

OK so here is my idea about How these post snippets could work. could work.

What problem are you addressing.

In this snippet we address the problem: ...

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#' meteo_distance
#'
#' @description
#'
#' @param data a dataframe. Expects col headers with names latName and longName
#' @param lat Latitude to centre search at
#' @param long Longitude to centre search at
#' @param latName Name of latitude header name in data, Default = 'latitude'
#' @param longName Name of longitude header name in data. Default = 'longitude'
#' @param units Units of the latitude and longitude values: degrees 'deg', radians 'rad', d/m/s 'dms'. Default = 'deg'

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context("Test names with spaces")

messy_names <- data_frame(`Sepal Width` = iris$Sepal.Width,
                          `Sepal Length` = iris$Sepal.Length,
                          `Petal Length` = iris$Petal.Length,
                          `Species Names` = iris$Species)

test_that("vis_dat works on dataframes with irregular variable names", {
  expect_success(
    vis_dat(messy_names)

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# calculate if there has been a smoker in the past
 
# "smoked_previously"

data_prac <- data_frame(ID = c(1, 1, 1, 1,
                               201, 201,
                               342, 342, 342,
                               613, 613, 613, 613, 613),
                        time = c(1, 2, 3, 4,
                                 1, 2,

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library(microbenchmark)
library(wakefield)
library(visdat)

mb1 <-
microbenchmark(vis_dat(airquality),
               table_heat(airquality))

boxplot(mb1)