R TSplots - Revision history

Rb: /* Loading the data */

2016-07-26T08:25:49Z

‎Loading the data

2016-07-26T08:18:56Z

‎Loading the data

2016-07-26T08:18:43Z

‎Loading the data

2016-07-26T08:18:08Z

‎Loading the data

2016-07-22T15:53:34Z

‎Some more resources

2016-07-19T17:02:36Z

‎Creating a multiple plot

2016-07-19T17:01:23Z

‎Manipulating the plot

2016-07-19T16:40:10Z

‎Manipulating the plot

2016-07-19T16:38:22Z

‎A simple first plot

2016-07-19T16:38:01Z

‎A simple first plot

@@ Line 13: / Line 13: @@
 = Loading the data =
-We shall use an example in which we have annual inflation rates for 13 different countries (1970 to 2013). In fact we have four different types of inflation rates, aggregate, core, energy and food inflation. Each of these are saved in separate <code>csv</code> files. Here we upload the files:
+We shall use an example in which we have annual inflation rates for 13 different countries (1970 to 2013). In fact we have four different types of inflation rates, aggregate, core, energy and food inflation. Each of these are saved in separate <code>csv</code> files. Here we upload the files (you can download the files from here [http://eclr.humanities.manchester.ac.uk/index.php/R#Intermediate_Techniques]:
 <pre class="r"> agg <- read.csv("AggInfl.csv",na.strings = "")

@@ Line 15: / Line 15: @@
 We shall use an example in which we have annual inflation rates for 13 different countries (1970 to 2013). In fact we have four different types of inflation rates, aggregate, core, energy and food inflation. Each of these are saved in separate <code>csv</code> files. Here we upload the files:
-<pre class="r">
+<pre class="r"> agg <- read.csv("AggInfl.csv",na.strings = "")
 core <- read.csv("CoreInfl.csv",na.strings = "")
 ener <- read.csv("EnergInfl.csv",na.strings = "")

@@ Line 20: / Line 20: @@
 ener <- read.csv("EnergInfl.csv",na.strings = "")
 food <- read.csv("FoodInfl.csv",na.strings = "")
-<\pre>
+</pre>
 Let's have a look at what these data look like:

@@ Line 13: / Line 13: @@
 = Loading the data =
-We shall use an example in which we have annual inflation rates for 13 different countries (1970 to 2013). In fact we have four different types of inflation rates, aggregate, core, energy and food inflation. Each of these are saved in separate <code>csv</code> files.
+We shall use an example in which we have annual inflation rates for 13 different countries (1970 to 2013). In fact we have four different types of inflation rates, aggregate, core, energy and food inflation. Each of these are saved in separate <code>csv</code> files. Here we upload the files:
 Let's have a look at what these data look like:
@@ Line 32: / Line 39: @@
 ## 5 23.176230  9.591417 9.911724 9.767415 16.043990 11.054800
 ## 6 11.778410 10.217480 9.779878 6.696594 24.207290  9.143147</pre>
 = Transforming into xts format =

← Older revision		Revision as of 15:53, 22 July 2016
Line 248:		Line 248:
	* A [https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf cheat sheet] for ggplot; only use once you have a basic understanding of how to use it.		* A [https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf cheat sheet] for ggplot; only use once you have a basic understanding of how to use it.
	* For some fine-tuning of figures you can find good advice on [http://www.noamross.net/blog/2013/11/20/formatting-plots-for-pubs.html noamross.net]		* For some fine-tuning of figures you can find good advice on [http://www.noamross.net/blog/2013/11/20/formatting-plots-for-pubs.html noamross.net]
		+	* Some great worked examples from [http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization sthda.com]

@@ Line 233: / Line 233: @@
 <pre>## Warning: Removed 3 rows containing missing values (geom_path).</pre>
 <pre>## Warning: Removed 1 rows containing missing values (geom_path).</pre>
-[[File:figure/unnamed-chunk-18-1.png|frame|none|alt=|caption plot of chunk unnamed-chunk-18]]
+[[File:FinalResult.jpeg|frame|none|alt=|Here is the final result]]
 The use of this function is really straighforward, The first inputs are the figure objects we want to use and <code>nrow=2, ncol=2</code> indicate that we want two rows and two clumns of figures.

@@ Line 146: / Line 146: @@
 <pre class="r">p1</pre>
-[[File:figure/ManipulatedPlot.jpeg|frame|none|alt=|The manipulated ggplot]]
+[[File:ManipulatedPlot.jpeg|frame|none|alt=|The manipulated ggplot]]
 == Creating a multiple plot ==

@@ Line 100: / Line 100: @@
 <pre class="r">ggplot(temp,aes(x =Year, y=Infl, color=Country)) + geom_line()</pre>
-[[File:FirstPlot.jpeg|frame|none|alt=|caption Initial ggplot figure]]
+[[File:FirstPlot.jpeg|frame|none|alt=|Initial ggplot figure]]
 We start with <code>ggplot(NAMEofDF, aes(HERE we define what goes on the axis))</code>. The <code>aes</code> option stands for aesthetics; I am not sure that this is the best choice of names, but hej, let's not worry about this. So here we have specified that the the dates should go on the x axis (<code>Year</code>), inflation should go on the y axis (<code>Infl</code>) and that the values for different contries should be shown in different colours.