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Author: DragonflyStats

Rtutorial.md

@@ -97,18 +97,7 @@ library(RODBC) channel <- odbcConnectExcel("bdr.xls") ## list the spreadsheets >
 TABLE_CAT TABLE_SCHEM TABLE_NAME TABLE_TYPE REMARKS 1 C:\\bdr NA Sheet1$ SYSTEM TABLE NA 2 C:\\bdr NA Sheet2$ SYSTEM TABLE NA 3 C:\\bdr NA Sheet3$ SYSTEM TABLE NA 4 C:\\bdr NA Sheet1$Print_Area TABLE NA
 sh1 <- sqlFetch(channel, "Sheet1") sh1 <- sqlQuery(channel, "select * from [Sheet1$]")
 
-#### Reading and Writing Data
 
-Connections
-Connections provide a flexible way for R to read data from a variety of sources, providing more complete control over the nature of the connection than simply specifying a file name as input to functions like read.table and scan.
-file: files on the local file system
-pipe: output from a command
-textConnection: treats text as a file
-gzfile: local gzipped file
-unz: local zip archive (with single file; read-only)
-bzfile: local bzipped file
-url: remote file read via http
-socketConnection: socket for client/server programs
 
 ##### Examples
 Skip last lines of a data file (e.g. last two lines):

Tidyverse/importing_data.Rmd Tidyverse/Segments/importing_data.Rmd

@@ -0,0 +1,44 @@
+---
+title: "R Workshop"
+output: 
+  flexdashboard::flex_dashboard:
+    theme: united
+    css: style.css
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+
+```
+
+Introduction
+==================================
+Column {.tabset}
+--------------------
+
+### Introduction 
+
+
+
+<div style="margin-left: auto;
+            margin-right: auto;
+            width: 80%">
+            
+|  <b> <strong><u>Contents</u> </strong></b>       |   
+|:------------------|
+|   |    
+| * <b> <strong><a href="actuarialexams.html">R for actuarial students</a>  -  CS1B and CS2B </strong></b>|        
+| * <b> <strong><a href="distributions.html">Probability Distributions</a> </strong></b>|        
+|   | 
+</div>
+
+
+
+---------------------------------------
+
+
+```{r child="Segments/intro_dlyr.Rmd"}
+```
+
+```{r child = "Segments/importing_data.Rmd"}
+```

Tidyverse/intro_dplyr.Rmd Tidyverse/Segments/intro_dplyr.Rmd

@@ -1,24 +1,21 @@
 
-%- http://blog.revolutionanalytics.com/2015/04/new-packages-for-reading-data-into-r-fast.html
-
-%==
 
 {Importing Data}
+=======================
 
 
 New packages for reading data into R — fast
 Hadley Wickham and the RStudio team have created some new packages for R, which will be very useful for anyone who needs to read data into R (that is, everyone). The readr package provides functions for reading text data into R, and the readxl package provides functions for reading Excel spreadsheet data into R. Both are much faster than the functions you're probably using now.
 
-%==
-
-{Importing Data}
 
 
 The readr package provides several functions for reading tabular text data into R. This is a task normally accomplished with the read.table family of functions in R, and readr provides a number of replacement functions that provide additional functionality and are much faster.
 
-%==
 
-{Importing Data}
+%- http://blog.revolutionanalytics.com/2015/04/new-packages-for-reading-data-into-r-fast.html
+
+
+
 
 
 First, there's read_table which provides a near-replacement for read.table. Here's a comparison of using both functions on a file with 4 million rows of data (which I created by stacking copies of this file):
@@ -51,19 +48,15 @@ dat2 <- read.fwf("biggerfile.txt", c(3,15,16,12),
 \end{verbatim}
 \end{framed}
 
-%==
-
-{Importing Data}
+----------------------------
 
 
 While readr's read_fwf again accomplished the task in about a second, the standard read.fwf took over 3 minutes — almost 200 times as long.
 
 Other functions in the package include read_csv (and a European-friendly variant read_csv2) for comma-separated data, read_tsv for tab-separated data, and read_lines for line-by-line file extraction (great for complicated post-processing). The package also makes it much easier to read columns of dates in various formats, and sensibly always handles text data as strings (no more strings.as.factors=FALSE). 
 
 
-%==
-
-{Importing Data}
+----------------------------
 
 
 For data in Excel format, there's also the new readxl package. This package provides function to read Excel worksheets in both .xls and .xlsx formats. I haven't benchmarked the read_excel function myself, but like the readr functions it's based on a C++ library so should be quite snappy. And best of all, it has no external dependencies, so you can use it to read Excel data on just about any platform — there's no requirement that Excel itself be installed.

Tidyverse/index.Rmd

@@ -0,0 +1,48 @@
+/* Whole document: */
+body{
+  font-family: Helvetica;
+  font-size: 16pt;
+}
+/* Headers */
+h1,h2,h3{
+  font-size: 24pt;
+}
+h4,h5,h6{
+  font-size: 20pt;
+  font-weight: bold;
+}
+
+pre {
+  background: #ADD8E6;
+  max-width: 100%;
+  overflow-x: scroll;
+}
+
+a { 
+  color: #000082; 
+} 
+
+.container {
+  display: flex;
+  flex-direction: row;
+  justify-content: left;
+  align-items: stretch;
+
+  width: 50%;
+  height: 50%;
+
+  border-radius: 0px;
+  background-color: hsl(0, 0%, 96%);
+}
+
+.box {
+  border-radius: 4px;
+  display: flex;
+}
+
+.box img {
+  width: 60%;
+  object-fit: contain;
+  border-radius: 4px;
+}
+

Tidyverse/readr_R_package.Rmd

@@ -36,6 +36,19 @@ Rsconnect
 Shiny App
 Deployment
 
+Deployment strategies
+Dashboards
+Flexdashboard
+shiny.
+
+Deployment on Rsconnect
+
+
+### Flex Dashboards
+
+Simple in Construction
+Uses RMD files to build a HTML page
+
 --------------------------------
 
 ### Deplying Your Shiny App

Tidyverse/style.css

@@ -1,4 +1,14 @@
-magicK Rpackage
+### magicK Rpackage
 
-The magick package: Advanced Image-Processing in R • magick (ropensci.org)
-Colour palettes
+The magick package: Advanced Image-Processing in R magick (ropensci.org)
+
+
+
+The magick package provide a modern and simple toolkit for image processing in R. It wraps the ***ImageMagick STL*** which is the most comprehensive open-source image processing library available today.
+
+The ImageMagick library has an overwhelming amount of functionality. Magick exposes a decent subset of it, but it is impossible to document everything in detail. This article introduces some basic concepts and examples to get started.
+
+
+### colours
+
+Colour palettes

Working_With_Dashboards/Creating_Dashboards.txt

@@ -0,0 +1,12 @@
+#### Reading and Writing Data
+
+Connections
+Connections provide a flexible way for R to read data from a variety of sources, providing more complete control over the nature of the connection than simply specifying a file name as input to functions like read.table and scan.
+file: files on the local file system
+pipe: output from a command
+textConnection: treats text as a file
+gzfile: local gzipped file
+unz: local zip archive (with single file; read-only)
+bzfile: local bzipped file
+url: remote file read via http
+socketConnection: socket for client/server programs