This produces two lines of different colours.
#Text file time series in r series#
The term colour is misleading we use colour=Series to distinguish between the types of value specified in the Series column (in this case, index values and centred averages). The first part in the ggplot brackets says where the data is found. We use colClasses to make sure R reads the values in the Date column of my data frame as dates. Then the data from the CSV file is read as a data frame (a table of data, the top and bottom few entries of which are shown below).
Ggplot(df, aes(Date, Values, colour=Series)) + geom_line() + ggtitle("UKRPI")įirst of all, the ggplot2 package is imported.
library(ggplot2)ĭf <-read.csv(file="UK_CentredAvgsJava.csv", sep=",", head=TRUE, colClasses=c("Date"="Date")) The R script for this graph is as follows. This is the graph I produced for the UK Retail Price Index, which I will use throughout this blog post. There was another column called “Series” in which I specified which values were the original values from the inflation index and which were the centred averages. The file was in long format that is, instead of having one column for index values and one for centred averages, I had one column with index values and centred averages. I used centred moving averages to find the trend, then wrote a CSV file with the index values and centred averages. Basic Graphsįirst of all, I wanted to draw a graph showing the inflation index values and the overall trend. Then I will show my edited ones to demonstrate how simple graphs can be improved and how much control R gives the user. In this post, I will present my first attempts at graphs in R and the scripts that produce them to show just how straightforward they are to make. It wasn’t long before I was able to produce graphs which showed the trends in the data effectively, but with a bit more time and effort I could change them to make them look exactly as I wanted.
#Text file time series in r software#
The best way to see the overall trend and seasonality in this data is to use graphs, so I turned to the statistical software R (namely, its “ggplot2” package). Recently, I have been looking at inflation indices and studying their seasonality. Simple graphs can be refined for stronger visual impact.Graphs can be created simply and quickly in R.Certain types of graph help identify seasonality.
Inflation index values are decomposed into trend, seasonality and noise.
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