In this Article, we
will learn to plot additional parameters to the time-series graph to
understand if additional variables has any impact on predicted (to be
estimated) values.
We will take SBI
closing price on daily basis and will analyze the impact of RBI Repo
Rate changes on that. Below code reads the RBI Repo Rate table and
rename 'Last.Update' column to Date
> Repo <-
read.csv(file = "Repo-Rate.csv", header=TRUE,sep = ",")
>
names(Repo)[names(Repo)== "Last.Update"] <- "Date"
>
typeof(Repo$Date)
[1] "integer"
We have taken the
closing NSE price from NSE website and retained only Date &
Closing price information for our comparison.
> SBI <-
read.csv(file = "03-06-2018-TO-31-05-2019SBINALLN.csv",
header=TRUE,sep = ",")
> SBIEQ <-
SBI[SBI$Series == 'EQ',]
> SBIEQ$Date <-
as.Date(SBIEQ$Date,format = "%d-%b-%Y")
> SBIRepo <-
data.frame(SBIEQ$Date,SBIEQ$Close.Price)
>
names(SBIRepo)[names(SBIRepo) == "SBIEQ.Date"] <- "Date"
Changing the date
format to text so we can combine easily using Merge function in R
> SBIRepo$Date <-
as.character(SBIRepo$Date)
> Repo$Date <-
as.character(Repo$Date)
> PlotG <-
merge.data.frame(SBIRepo,Repo,by= 'Date',all.x=T)
Convert Date back to
original format as ggplot() will not be able to plot time series
graph on text data
> PlotG$Date <-
as.Date(PlotG$Date,format="%Y-%m-%d")
>
ggplot(PlotG,aes(x=Date, y= SBIEQ.Close.Price, color = Rate )) +
geom_point() + scale_x_date()
