Step Charts: R is Easier Than Excel

In this post, I show how to make a Step Chart with R.  The chart also includes a lowess smoother and annotation.  Readers can visit my ProcessTrends.com site to see how to make a step chart in Excel.

R’s Step Chart Capability

Here’s  an R step chart that I made using R’s built-in step chart capabilities. It shows  global temperature anomalies from 1880 to 2007, as calculated by NASA – GISS.

 

Why Use a Step Chart? 

Line charts connect two data points with a single straight line, while step charts connect these same two points with a horizontal and a vertical line, in a stair step fashion. Since our temperature chart shows annual average global temperature anomalies, a step chart is more appropriate than a line chart because the calculated Y values are constant for one year spans.

Here’s a side by side comparison of a line chart and a step chart for our temperature data. The line chart is quite spiky. To me, the step chart is both more realistic and more attractive.

Many time series reflect step changes rather than gradual changes implied by a line chart. Postage stamp costs, for example, are constant until an increase on a specific date. Using a line chart for postal rates implies a gradual change in postal rates over time when they are actually step changes at specific dates.

Excel does not provide a step chart option, so users need to use a workaround like the one I show on my site. As a long time  Excel user, I accepted Excel’s limitations and used workarounds like my step chart technique. As I look back, I was like the woodworker who got by with hand tools when I really needed a power tool. As I got better and better with my workarounds, I spent more time working on neat techniques than I did on my actual goal, producing an effective chart.

Overview of R Script 

The script image is shown below, text files of the script and data are available here.

Let’s walk through the script to see how R handles step charting as well as chart annotation and adding a lowess smoother. The script is set up in the 4 steps I have described before. I have deliberately arranged the script to highlight the arguments for each function to help me be able to reuse the script from chart to chart and help both you and I understand the options for each function. 

Step 1 – Setup 

## STEP 1: SETUP
setwd(‘C:/R_Home/Charts & Graphs Blog/GISS_Temp_Anom/’)
link =  c(“GISS_Temp.txt”)

These 2 lines of actual code (remember # designates a comment line) establish the working directory and establish the variable link that I use to specify the source data file. 

Step 2 – Read Data 

# STEP 2: READ DATA

my_Data <- read.table(link,
             skip = 0, sep = “”, dec=”.”,
             row.names = NULL, header = FALSE,
             colClasses <- rep(“numeric”,3),
             comment.char = “#”, na.strings = c(“*”, “-”,-99.9, -999.9),
             col.names <- c(“Yr”, “Ann_Anomaly”, “5_Yr_Mean”) )

This single line of script reads the data in the file defined by link. In this case, the skip argument is 0, the na.strings include (*, -, -99.9, -999.9) and the column names are specified as Yr, etc.

The nice thing about this script is that I can reuse it over and over, without worrying about file names. I just have to tweak the col.names and colClasses and adjust the na.strings arguments if I find an unusual missing character situation.

Step 3 – Data Manipulation 

# STEP 3: MANIPULATE DATA
  # Construct chart title – use 2 lines w/ \n
  Title <- paste(“Annual Surface Air Temperature Anomaly  \n Based on Meterologic Stations (1880 – 2007)” )

In this example, the only data manipulation is to construct the chart title.

Step 4 – Make Step Chart

## STEP 4: CREATE PLOT
   par(las = 1)       # Set Y axis label orientation to vertical, set text font sizes
   par(cex.main=0.8); par(cex.sub=0.7); par(cex.lab = 0.8); par(cex.axis =.75)
plot(Ann_Anomaly ~ Yr, my_Data,
         ylim = c(-1,1),
         type=c(“s”),
         col = “dark grey”,
         xlab = “”,
         asp = “full”,
         ylab = expression(paste(“Annual Temperature Anomaly – “,degree, “C (Baseline: 1951-1980)”)),
         main = Title,
         sub = “Source: NASA – GISS @ http://data.giss.nasa.gov/gistemp/graphs/Fig.A.txt”)
lines(lowess(my_Data$Ann_Anomaly ~ my_Data$Yr, f=0.15),col = “blue” )
arrows(1951,-.5, 1980, -.5, code = 3, angle = 20, col = “dark grey”)
abline(h=0, col = “grey”)          # o.o horizontal line
text(1965, -.495, “Baseline\n Period\n1951-1980″, cex = 0.6, pos=3)
text(1910, -0.95, “Lowess smoother fit, f = 0.15″, cex =0.65, pos = 1 )
points(c(1881,1890), c(-1,-1), col=”blue”,  type = “l”)

This script establishes the chart text sizes, makes the step chart, adds a lowess smoother line, adds an arrow line to designate the 1951-1980 baseline period, adds text notes for baseline period and lowess smoother and adds a short line for lowess line legend.

How did I make my step chart? By using “s” in the type argument! That’s right, one letter changes the chart type. Incredibly simple and powerful.

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