initial import of first version of the data types lecture

Author: lrjohn

VB_IntroData.qmd

@@ -0,0 +1,154 @@
+---
+title: "VectorByte Methods Training"
+subtitle: "Introduction to Data Types and Best Practices"
+author: "The VectorByte Team (Leah R. Johnson, Virginia Tech)"
+title-slide-attributes:
+  data-background-image: graphics/VectorByte-logo_lg.png
+  data-background-size: contain
+  data-background-opacity: "0.2"
+format: 
+  revealjs:
+    auto-stretch: false
+---
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(cache = FALSE, 
+                      echo = FALSE, 
+                      message = FALSE, 
+                      warning = FALSE,
+                      #fig.height=6, 
+                      #fig.width = 1.777777*6,
+                      tidy = FALSE, 
+                      comment = NA, 
+                      highlight = TRUE, 
+                      prompt = FALSE, 
+                      crop = TRUE,
+                      comment = "#>",
+                      collapse = TRUE)
+library(knitr)
+library(kableExtra)
+library(xtable)
+library(viridis)
+
+options(stringsAsFactors=FALSE)
+knit_hooks$set(no.main = function(before, options, envir) {
+    if (before) par(mar = c(4.1, 4.1, 1.1, 1.1))  # smaller margin on top
+})
+knitr::opts_chunk$set(echo = FALSE)
+knitr::opts_knit$set(width = 60)
+source("my_knitter.R")
+#library(tidyverse)
+#library(reshape2)
+#theme_set(theme_light(base_size = 16))
+make_latex_decorator <- function(output, otherwise) {
+  function() {
+      if (knitr:::is_latex_output()) output else otherwise
+  }
+}
+insert_pause <- make_latex_decorator(". . .", "\n")
+insert_slide_break <- make_latex_decorator("----", "\n")
+insert_inc_bullet <- make_latex_decorator("> *", "*")
+insert_html_math <- make_latex_decorator("", "$$")
+## classoption: aspectratio=169
+```
+
+
+## Why is Data/Code Curation and Management Important?
+
+In order for analyses to be repeatable, data and code first must:
+
+- properly organized and documented
+- accessibly stored and findable, and
+- ideally, made available to others.
+
+Data obtained with support from public funds (such as NSF or NIH) are usually ***required*** to be made available to other scientists and the public.
+
+
+## Steps to Data Management
+
+There are many steps to obtaining and effectively managing data (right, below). Today we talk about important components that fit in areas (ii) to (v).
+
+
+::: columns
+::: {.column width="45%"}
+
+<br>
+
+1. Manage Raw Data
+1. Check Data
+1. Store and Curate Data
+
+
+:::
+
+::: {.column width="55%"}
+
+<center>
+![](graphics/datamanagement.jpg){width="70%"}
+</center>
+
+
+:::
+:::
+
+## 1. Manage Raw Data
+
+So you’ve got some "raw" data:
+
+- handwritten notes
+- automatic data logger (this includes sequencing machines, temperature monitors, etc.)
+- output from simulation
+
+These data should be transferred to an organized electronic format and checked as soon as possible after collection.
+
+## What Electronic Formats?
+
+Often easiest to input as a table into a spreadsheet. 
+
+But don’t leave it simply as a spreadsheet – save it to a non-proprietary format, like a comma-delimited file (csv).
+
+<br>
+
+## How should data be recorded
+
+<center>
+**`r myred("Input it in the least compact form that you can – you don’t want to lose information!")`**
+</center>
+
+<br>
+
+Usually this means that you want your data to be in a "long" format -- but what does this mean?
+
+## Long vs. Wide
+
+## Metadata
+
+## What is in a "row" of data?
+
+- units separate from measured values
+- dates
+- individual measurements when possible
+- separate columns for all covariates with units and settings recorded separately. 
+
+## Examples from VectorByte
+
+
+## Check Data
+
+Almost always errors are made when data are being collected or inputted.
+
+- Decimal points moved
+- Digits switched
+- Missing data are not properly encoded
+- Instrument errors
+- Skip some data
+
+As (and after) you input, do some "sanity checks" 
+
+- count/sum across rows and columns 
+- check for empty fields
+- visualize your data and look for outliers.
+
+
+## FAIR Data Practices
+