This post is a continuation of the blog post i wrote on Jan 14, 2015 - oil . I did not feel like explaining all the R code i used in one place and felt that it would be easier to learn how the plot was generated if it is broken into 3 parts. The first part is the entire image generated on Jan 14, 2015. The second part will elaborate on the R functions plot(), points(), lines() and ablines(). The third part will dig into the generating the custom axis.
So lets get started...
We have already discussed the method to import the data and generate a blank plot using the read.csv() and plot() functions in part one of the series on oil. We construct our plot on top of this blank plot. We would like to add gridlines which can be added to the plot using the abline() function. Since we require the plot to have horizontal and vertical lines we use both the v= and h= as arguments in the abline() function. The lty argument is used to specify the line type, readers can choose between 0 to 6 as lty. We chose 3 to specify ".". To learn more on the abline() function type ?abline in R console window. To learn about all the possible values type ?par in R console window. The lwd argument is the line width.
![](data:image/png;base64,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)
Once we generate the grid we can construct our plot. We have added two additional lines of code - the lines() function and the points() function.R will plot the lines before plotting the points. If we are interested in displaying the line over the points we can just change the order of the two function.
![](data:image/png;base64,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)
So lets get started...
oils II
atmajit
Thursday, January 15, 2015
setwd("")
embargo=read.csv("oils.csv")
par(mar = c(5,7,5,7))
plot(embargo$percapita,embargo$price, type ="n",axes = FALSE,xlab = NA,ylab =NA)
abline(v= c(7000,8000,9000), h = c(25,50,75,100),lty = 3,lwd = 1.5)
Once we generate the grid we can construct our plot. We have added two additional lines of code - the lines() function and the points() function.R will plot the lines before plotting the points. If we are interested in displaying the line over the points we can just change the order of the two function.
par(mar = c(5,7,5,7))
plot(embargo$percapita,embargo$price, type ="n",axes = FALSE,xlab = NA,ylab =NA)
abline(v= c(7000,8000,9000), h = c(25,50,75,100),lty = 3,lwd = 1.5)
lines(embargo$percapita,embargo$price, lwd = 2, col = "#28363F")
points(embargo$percapita,embargo$price, lwd = 2, pch = 21, bg = "white", col ="#28363F", cex = 1.5)
The initial two arguments in the lines() function are the data points to be displayed on the Xaxis and Yaxis.The lines() function will simply join the points with a smooth line. The points() function has exactly the same arguments. The cex argument allows us to control the size of the points displayed.