Anatomy of an R Chart Script

In this post, I examine an R script to show how to:  1) Read dynamic data from a web site text file, 2) Determine number of data rows in file; 3) Determine date of last measurement, 4) Create dynamic  title; 5) Bank to 45, and 6) Create an XY Plot. Excel users are encouraged to dust off their R program and try this script on your own PC. Links to the source data and script are provided.

Example R Chart and R Script

The web based source data file for this example, available at this link, includes the monthly CO2 measurements at Mana Loa Observatory in Hawaii since March, 1958. The data is updated monthly.

Our goal is to develop a tool to update our chart monthly as new measurements are added to the source file.

The script image is shown below, a text file of the script is available here.

We will walk through the R script to learn how to read web based data files, handle dynamic data ranges, construct dynamic titles,  and produce an XY plot.

Overview of R Script

I see most Excel or R chart tasks as a sequence of 4 steps:

1. Setup the background information
2. Read the source data
3. Manipulate data for chart preparation
4. Make the chart

The R script for this example is set up in these 4 steps.  I’ve deliberately arranged the script to clearly highlight the arguments for each function to help both you and I to understand the options available under each function.

Step 1 – Setup

## STEP 1: SETUP
   setwd(”C:/R_Home/CG”)
   library(lattice)
   link <-url(”ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt”)

These 3 lines (remember # lines are comments) set the working directory, install load the lattice library package and specify the link for the CO2 data text file.

Step 2 – Read Data

## STEP 2: READ DATA
   CO2_Data <- read.table(link,
                    sep = “”,
                    dec=”.”,
                    skip = 68,
                    row.names = NULL,
                    header = FALSE,
                   colClasses = rep(”numeric”,6),
                   na.strings = -99.99,
                   col.names <-  = c(”Yr”, “Mo”, “Mo_Yr”, “CO2″, “Trnd”, “X”) )

Notice the skip argument, this tells R to skip the first 68 lines in the source file becasue this is file documentation information, not data. Also, the na.strings argument tells R that -99.99 is the code for data not available.

We now have a data frame named CO2_Data that is ready for data manipulation and charting.

Step 3: Data Manipulation

## STEP 3: MANIPULATE DATA
   c <- nrow(CO2_Data)                    # Find number of data rows
   mo <- CO2_Data$Mo[c]                   # Find month for last data row
   yr <- CO2_Data$Yr[c]                   # Find year for last data row
   thru <-   paste(”Updated Through:”, mo,”/”,yr)   # Note on last data point
   Title <- expression(paste(”Monthly C”, O[2], ” (ppmv) – Mana Loa, Hawaii”))

To determine the last measurement’s month and year, we determine the row number (c) for the last row, then we determine the mo and yr for row c.

To construct a note that shows the last month, we use the paste() function to construct a concatenated text string of plain text and the mo and yr variables. The title uses the expression() function to allow us to format the 2 in CO2 as a subscript.

Step 4 – Make XY Plot

## STEP 4: CREATE PLOT
   xyplot(CO2 ~ Mo_Yr, CO2_Data,
             ylim = c(300,400), # y axis limits
            xlim = c(1958, 2009), # x axis limits
            type=”l”, # type of line
            col = “red”, # color of line
            xlab = “”, # no x axis label
            ylab = expression(paste(CO[2] – ppmv)), # y axis title w/ subscript for 2
            main = Title, # add main title using Title variable
            sub = thru, # add chart note using variable thru
            par.settings=list(axis.text=list(cex=0.8), 
                                fontsize=list(text=10)), # font sizes
          aspect = “xy”) # banking to 45

This xyplot() function includes arguments for x and y axis limits, line type, line color, x and y axis titles, fontsizes and banking to 45.

Try It For Yourself

The R script file includes the source data file link so you have everything you need to make this chart.

If you have R setup on your computer, you are ready to go. Why put off trying R until a later date, next month or next year?

Try it and let me know how you do.

---

Possibly related posts: (automatically generated)

• No Title