01_Session2_excel_and_import.Rmd

---
title: "Biochemistry R training"
subtitle: "Session 1a: Data organisation in Excel"
author: "James Boocock<BR>Department of Biochemistry"
date: "22 February 2016"
output: ioslides_presentation
---

## Data organisation.

Today, we are going to talk about:

  - Good data entry practices
  - How to avoid common formatting mistakes
  - Dates
  - Basic quality control
  - Exporting data from spreadsheets

We are going to follow these lessons developed by data carpentry:
http://www.datacarpentry.org/spreadsheet-ecology-lesson/00-intro.html

## URL for the lesson files.

Follow the link below.

```bash
bit.ly/1oVvND1
```
View raw

Extract the zip file

## R data wrangling

###Benefits

  - Needed for large datasets too big for excel e.g Sequencing data
  - Can automate the quality control procedure.
  - Reproducible.
  
###Downsides

  - Pre-formatting required
  - Excel sometimes better for simple wrangling
  
You will learn more about these possibilities this afternoon!

## Importing data into R.

```{r}
  # read in the data file
  setwd("~/Biochem_R_training/data/")
  dat = read.csv("survey_data_spreadsheet_messy_fixed.csv",header=T)
  # Summarise some columns
  summary(dat$Sex)
  summary(dat$Weight)
```


## Visualise your data

```{r}
  # Hmmm let's see what plot does with weight, not that useful.
  plot(dat$Weight)
```

## Visualise your data

```{r}
  # A histogram is what we want
  hist(dat$Weight, breaks=10)

```

## Plotting variables against each other

```{r}
  # Let's see the weight by species.
  # you can clearly see that DS is much heavier on average.
  plot(dat$Weight ~ dat$Species)
```

## Plotting variables against each other

```{r}
  
  # What about the relationship between plot and weight
  plot(dat$Weight ~ dat$Plot)

```

## Box and whisker

```{r}
  # We can make R give us what we want.
  boxplot(dat$Weight ~ dat$Plot)
```