add .Rmd and focused on the EA data

myFirstRMarkdown.Rmd

+---
+title: "myFirstRMarkdown"
+author: "Ben Anderson (b.anderson@soton.ac.uk `@dataknut`)"
+date: 'Last run at: `r Sys.time()`'
+output: 
+  html_document:
+    fig_caption: yes
+    keep_md: yes
+    number_sections: yes
+    self_contained: no
+    toc: yes
+    toc_float: yes
+---
+
+```{r Setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+
+# Libraries ----
+library(data.table)
+library(ggplot2)
+```
+
+# Introduction
+
+This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+
+When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. 
+
+# Load data
+
+You can embed an R code chunk like this:
+
+```{r load the generation data over the net}
+generationDTorig <- fread("https://www.emi.ea.govt.nz/Wholesale/Datasets/Generation/Generation_MD/201712_Generation_MD.csv")
+
+# reshape the data as it comes in a rather unhelpful form
+generationDT <- melt(generationDTorig, 
+                     id.vars=c("Site_Code","POC_Code","Nwk_Code", "Gen_Code", "Fuel_Code", "Tech_Code","Trading_date"),
+                     variable.name = "Time_Period", # converts TP1-48
+                     value.name = "MW" # Megawatts
+)
+
+generationDT <- generationDT[, rDate := as.Date(Trading_date)] # fix the dates so R knows what they are```
+```
+
+# Summarise the data
+
+You can also embed tables, for example:
+
+```{r table1, caption = "Summary"}
+summary(generationDT)
+```
+
+Or you can format the table nicely using the kable function from the knitr package:
+
+```{r table2}
+# note the chunk names needs to be unique
+t <- summary(generationDT)
+library(knitr)
+kable(t, caption = "Summary table")
+```
+
+
+# Plot the data
+
+Use ggplot to embed a chart in the document.
+
+```{r plot1, fig.cap="Plot generation by date and time period", echo=FALSE}
+# See how we turned off printing the code into the report
+
+# create an aggregated tabe summing generation by fuel, date and time period. This is what data.table is really good at
+plotDT <- generationDT[, 
+                       .(totalMW = sum(MW),
+                         nObs = .N),
+                       keyby = .(Time_Period, rDate, Fuel_Code)]
+
+# > Use the new data to draw a chart of all generation in the data ----
+myCaption <- "Source: January 2018 wholesale generation data via EMI (NZ Electricity Authority)"
+ggplot(plotDT, 
+       aes(x = Time_Period, y = totalMW, colour = as.factor(rDate), group = rDate)) +
+  geom_point() +
+  facet_grid(Fuel_Code ~ .) + 
+  labs(caption = myCaption)
+
+```
+
+Now draw a plot of hydro as a % of all.
+
+```{r, fig.cap="Plot hydro as a % of all generation by date and time period"}
+# > hydro as a % of all ----
+plotDT <- plotDT[, sumAllMW := sum(totalMW), keyby = .(Time_Period, rDate)] # calculate total
+plotDT <- plotDT[, pcTotalMW := 100*(totalMW/sumAllMW)] # calculate %
+
+# > Use the new data to draw a chart of hydro as a % of all during Christmas week ----
+ggplot(plotDT[Fuel_Code == "Hydro"], 
+       aes(x = Time_Period, y = pcTotalMW, colour = as.factor(rDate), group = rDate)) +
+  geom_line()
+```
+
+# Discuss your results
+here
+
+# Conclusions
+go here
+
+# References