Using R — A Script Introduction to R

This entry is part 3 of 21 in the series Using R

To many people, R is like the Everglades. They’ve heard of it, they know it’s big and has amazing treasures deep inside. Articles in the media can make it look irresistible. But after a personal or even second hand experience people also learn that R can be a big swamp where you are all but guaranteed to get soggy boots and mosquito bites before you’re done. And there is always the distinct possibility of getting lost and falling into a ‘gator hole’. Indeed, if you go in without a guide, hoping to get in and out quickly you probably won’t enjoy it much. This post contains a script that shows you some of the sights without letting you fall in.  If you like to learn by example, read on.

The rest of this post is the verbatim script with graphics embedded in the appropriate places. You can also download the script and run it yourself.  The comments in this script capture a session of working with and thinking about a dataset.  This script doesn’t try to cover everything. On the contrary, it pedantically reuses as few techniques as possible to show that you can do a lot with a little.

This script also demonstrates how to be systematic with respect to commenting, variable naming, setting graphical parameters, etc.  One of the keys to working successfully with R is writing scripts that explain what they are doing and contain consistent, readable, verging-on-predictable code.

I look forward to any suggestions for corrections/improvements.

# Everglades example by Jonathan Callahan @

# This flag determines whether images are saved as png files

saveAsPng = TRUE

# You can use the '=' sign for assignment but please get used to the
# R convention '<-'. Some tools depend on consistent use '<-' to parse
# the R code you write.

saveAsPng <- TRUE

# A quick google on "Everglades data csv" and you can find this dataset
# of High Accuracy Elevation Data (HEAD) for the Everglades:


# We will pick the bigger of the CSV files and read it in as a 'dataframe'.

# ------- READING IN DATA FROM A URL -------------------------------------------

cat("Reading in large Everglades file ...")
con <- url('')
everglades_df<- read.csv(con)

# ------- QUICK LOOK AT A DATAFRAME --------------------------------------------

[1] 57395     8

# Nice! 57,395 records with 8 variables each.
# Now let's get an overview of the structure of this dataframe.

'data.frame':	57395 obs. of  8 variables:
 $ SUR_METHOD: Factor w/ 2 levels "AHF","airboat": 1 1 1 1 1 1 1 1 1 1 ...
 $ SUR_FILE  : Factor w/ 80 levels "ab020509.cor",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ SUR_INFO  : Factor w/ 7708 levels "1 NODATA","10",..: 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433 ...
 $ ELEV_M    : num  1.72 1.75 1.78 1.86 1.96 2.04 2.07 1.76 1.82 1.92 ...
 $ VEG_FS    : Factor w/ 19 levels "","Alligator Hole",..: 13 13 13 13 4 4 4 4 4 13 ...
 $ X_UTM     : num  555229 554814 554418 554023 553418 ...
 $ Y_UTM     : num  2889791 2889807 2889805 2889801 2889802 ...
 $ Quad_Name : Factor w/ 81 levels "","Alligator Bay",..: 3 3 3 3 3 3 3 3 3 3 ...

# We have some 'factors' (aka 'categorical variables'):
# And we have some 'numerics':

# Let's pull out and rename the variables that are likely to be interesting.

# ------- DEFINING VARIABLES ---------------------------------------------------

surveyMethod <- everglades_df$SUR_METHOD
elevation <- everglades_df$ELEV_M
habitat <- everglades_df$VEG_FS             # VEG_FS ?= "VEGetation Field Survey"
x <- everglades_df$X_UTM
y <- everglades_df$Y_UTM
quad <- everglades_df$Quad_Name

# Now let's check out each of the factors 

# ------- QUICK SUMMARY OF VARIABLES -------------------------------------------

# factors
summary(surveyMethod)                       # 9277 airboat records
    AHF airboat 
  48118    9277 

summary(habitat)                            # nice but Ouch! -- 40725 records with ""
                       Alligator Hole Broadleaf Emergent            Cattail 
             40725                  3                120               2015 
           Cypress  Floating Emergent           Hardwood           Lygodium 
              2654                630                 26                 33 
         Melaleuca         Open Water           Palmetto               Pine 
               142                109                  6                522 
          Sawgrass              Shrub             Slough        Tree Island 
              6446                568                690                289 
       Wet Prairie Wet Prairie/Slough       Willow Shrub 
              1869                 22                526 

summary(quad)                               # nice list of quads with only one ""
                                        Alligator Bay 
                         1                        996 
                  Andytown               Big Boy Lake 
                       984                       1081 
                  Big Lake           Big Lostmans Bay 
                      1065                        943 

# numeric
summary(elevation)                          # The Everglades are flat!
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  -3.43    0.08    1.41    1.38    2.36    5.19 

# OK, but we can learn so much more if we inspect things visually. 

# ------- FIRST DATA VISUALIZATION:  HISTOGRAM & BOXPLOT -----------------------

# First we'll save the default graphical parameters so we can get back to them

old_par <- par()

# We probably want to work interactively at first but eventually we will want
# to write out image files.  Having this behavior controlled by a flag allows
# us to work in both worlds as we tweak graphical parameters to get things
# "just right".  Here we send graphics output to a .png file if the flag is set.

if (saveAsPng) {  png(file="Everglades_elevation.png",width=500,height=500) }

# We'll plot a histogram of elevation values.
# Then we will add a boxplot that crudely summarizes the histogram.
# We start by creating a layout for two plots with 70% of the vertical space
# going to the upper plot and 30% to the lower.


# Note that the exact settings of various graphical parameters is determined 
# by a lot of trial and error.  Part of why it's nice to have things scripted.

# Here are the rest of the plotting commands.

par(mar=c(0,4.1,4.1,2.1))                        # reduce size of lower margin
hist(elevation, breaks=100, axes=FALSE,          # plot a histogram with 100 bins
     main="Everglades Elevations",               # use this title
     xlab="", ylab="")                           # no axis labels
par(mar=c(5.1,4.1,0,2.1), mgp=c(3,0.5,0.0))      # adjust margins and axis location
boxplot(elevation, horizontal=TRUE, axes=FALSE)  # add a boxplot underneath
axis(1)                                          # add the x-axis 
mtext("elevation (m)", side=1,line=2.5, font=2)  # add the x-axis label
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# The boxplot is basically a 1-dimensional represtantation of the histogram.

# ------- BOXPLOTS BY CATEGORY -------------------------------------------------

# Now that we know what boxplots are, let's break up the data by factor.
# Several functions understand the notation "data ~ factor" and do something smart.

if (saveAsPng) {  png(file="Everglades_elevationByHabitat.png",width=500,height=500) }

par(mar=c(3.1,12.1,4.1,2.1), las=1)              # big left margin, horizontal labels
boxplot(elevation ~ habitat, horizontal=TRUE)    # add a multi-category boxplot
title("Distribution of Elevation by Habitat Type")
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# What if we tried this with all of the Quads?

if (saveAsPng) {  png(file="Everglades_elevationByQuad1.png",width=500,height=500) }

par(mar=c(3.1,12.1,4.1,2.1), las=1)              # big left margin, horizontal labels
boxplot(elevation ~ quad, horizontal=TRUE)
title("Distribution of Elevation by Quad Name")
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# OK, that's ugly.

# What if we leave off the outliers and shrink the size of the labels
# and make the plot 750 pixels high?

if (saveAsPng) {  png(file="Everglades_elevationByQuad2.png",width=500,height=750) }

par(mar=c(3.1,12.1,4.1,2.1), las=1)              # big left margin, horizontal labels
boxplot(elevation ~ quad, horizontal=TRUE, outline=FALSE, cex.axis=0.7)
title("Distribution of Elevation by Quad Name")
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# It's almost readable!

# ------- MASKS AND INDICES ----------------------------------------------------

# You can subset variables by using VAR[...].
# If ... contains a vector of integers, VAR[...] will interpret ... as indices and
# will return the values of VAR found at those indices.
# If ... contains a vector of logicals of the same length as VAR, VAR[...] will
# be used to 'mask' VAR, returning the values of VAR where ... is TRUE.

# Create some 'masks' to help us subset the data.

# Survey methods:
airboat_mask <- surveyMethod == "airboat"

# Habitats of interest:
missingHabitat_mask <- habitat == ""
alligatorHole_mask <- habitat == "Alligator Hole"

# Lets look at those quads again and list the ones with animal names.

quadNames <- levels(quad)                        # extract the 'factor' levels
 [1] ""                           "Alligator Bay"             
 [3] "Andytown"                   "Big Boy Lake"              
 [5] "Big Lake"                   "Big Lostmans Bay"          

# We can use grep() to find the indices where quadNames matches a string
# and then print out the quadNames at these indices.
# Note that these indices are for the short vector of unique quadNames
# we just created, not the much longer quad variable.

shark_in_quad_name_indices <- grep('shark',quadNames,
[1] "Shark Point"                "Shark River Island"         "Shark Valley Lookout Tower"

# Or we can just do all that in a single line

[1] "Alligator Bay"    "Gator Hook Swamp" "Gator Lake"      
[1] "Plover Key"
[1] "Flamingo"
# Let's create logical masks for the following quads:

flamingoQuad_mask <- quad == "Flamingo"
gatorHookSwampQuad_mask <- quad == "Gator Hook Swamp"
sharkPointQuad_mask <- quad == "Shark Point"

# What else can we do? We want to interrogate this dataset.

# ------- SUBSETTING DATA WITH MASKS -------------------------------------------

# Let's look at habitat around gator holes.

# In one line we will find all of the quads that contain "Alligator Holes". 
# Working from the innermost part of the expression toward the outside:
#   1) mask (aka filter) quads for those with habitat == "Alligator Holes"
#   2) convert the resulting quads from  class 'factor' to class 'character'
#   3) return only the unique values

quads_with_gator_holes <- unique(as.character(quad[alligatorHole_mask]))
[1] "East of Deem City" "Big Lake"          "West of Big Lake" 

# ------- PIE CHART SHOWING CATEGORY ABUNDANCE ---------------------------------

# Now we create a table of gator habitat values:
#   1) create another mask that checks against any of these quad names
#   2) subset the habitat variable with this mask
#   3) create a table of habitats found in those quads
#   4) display the habitats as a pie chart

gatorHoleQuad_mask <- quad %in% quads_with_gator_holes
gatorHabitat <- habitat[gatorHoleQuad_mask]
gatorHabitat_table <- table(gatorHabitat)

if (saveAsPng) {  png(file="Everglades_gatorHabitat1.png",width=500,height=500) }

title("Habitat types in quadrangles with alligator holes")
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# With default parameters, it's ugly as sin.

# ------- PRETTIER PIE CHART:  DONUT CHART -------------------------------------

# After a little trial and error we can clean it up with the following:
#   1) omit any habitats representing < 50 sample sites
#   2) sort from low to high

mainGatorHabitat <- sort(gatorHabitat_table[gatorHabitat_table > 50])
     Willow Shrub       Tree Island             Shrub       Wet Prairie 
               59                69               176               225 
Floating Emergent           Cattail            Slough          Sawgrass 
              257               287               401              1387 

# Before we start plotting, let's review the named colors we have to choose from

  [1] "white"                "aliceblue"            "antiquewhite"        
  [4] "antiquewhite1"        "antiquewhite2"        "antiquewhite3"       
  [7] "antiquewhite4"        "aquamarine"           "aquamarine1"         

# Better yet, just pull up a browser and point it at:

# Now choose colors and plot

habitat_cols <- c("burlywood1","forestgreen","burlywood3","darkolivegreen3",

if (saveAsPng) {  png(file="Everglades_gatorHabitat2.png",width=500,height=500) }

pie(mainGatorHabitat, col=habitat_cols, border='white', cex=1.2)

# Now let's turn it into a 'donut plot' with a legend in the middle.

par(new=TRUE)                                    # add a new plot on top of the old one

# NOTE:  This has to be the most counterintuitive setting ever!!!
# NOTE:  Use "new=TRUE" to keep working on the old plot.
# NOTE:  Use "new=FALSE" to start a new one.

pie(c(1), labels=NA, border='white', radius=0.4) # a blank white circle
text(0,0,labels="Gator\nHabitat", cex=2, font=2) # text in the middle
par(old_par)                                     # restore the original parameters

if (saveAsPng) { }                     # end plotting, write file

# Awesome!

# Now we're going to create our Everglades vacation map and highlight areas of interest. 

# ------- PLOTTING POINTS:  GRAPHICAL TWEAKS -----------------------------------

# We'll create masks for the 'gator habitat' we discovered
treeIsland_mask <- habitat == "Tree Island"
willowShrub_mask <- habitat == "Willow Shrub"
treeIsland_mask <- habitat == "Tree Island"
shrub_mask <- habitat == "Shrub"
wetPrarie_mask <- habitat == "Wet Prarie"
floatingEmergent_mask <- habitat == "Floating Emergent"
cattail_mask <- habitat == "Cattail"
slough_mask <- habitat == "Slough"

# This plot will be a little bigger to accommodate everything.
# A lot of 'cex' tweaking may be needed for different combinations of
# image size & system fonts.

standard_definition_ratio <- 4/3
height <- 550
width <- height * standard_definition_ratio
if (saveAsPng) {  png(file="Everglades_poster.png",width=width,height=height) }

# First, we plot all of the locations
# Then we overlay the locations of "AHF" and "airboat" surveys.

plot(x, y, pch=15, axes=FALSE, xlab="", ylab="", cex=0.1, col='antiquewhite2', asp=1)
points(x[!missingHabitat_mask], y[!missingHabitat_mask], pch=15, cex=0.2, col="grey90")
points(x[airboat_mask], y[airboat_mask], pch=15, cex=0.2, col="grey85")

# ------- SIMPLE 'FOR' LOOPS ---------------------------------------------------

# Now add overlays for our habitats of interest.
# This will require a little programming (but not much).
# Remember:  we already created the "habitat_cols" for the pie plot.

habitatMask_list <- list(treeIsland_mask, willowShrub_mask, treeIsland_mask,
                         shrub_mask, wetPrarie_mask, floatingEmergent_mask,
                         cattail_mask, slough_mask)
i <- 0
for (mask in habitatMask_list) {
  i <- i + 1
  points(x[mask], y[mask], pch=15, cex=0.3, col=habitat_cols[i])

# Now put those nasty gator holes on top with a big, bold letter 'G'.

points(x[alligatorHole_mask], y[alligatorHole_mask], pch=16, cex=2.5, col="white")
points(x[alligatorHole_mask], y[alligatorHole_mask], pch='G', cex=0.9, col="red", font=2)

# Let's draw boxes around our quads of interest with a little more programming

quadMask_list <- list(flamingoQuad_mask, gatorHookSwampQuad_mask, sharkPointQuad_mask)
quad_cols <- c('black','black','black')
quad_labels <- c('Flamingo','Gator Hook Swamp','Shark Point')
i <- 0
for (mask in quadMask_list) {
  i <- i + 1
  w <- min(x[mask])
  e <- max(x[mask])
  s <- min(y[mask])
  n <- max(y[mask])
  rect(e,s,w,n, lwd=2, border=quad_cols[i])
  text_x <- w
  text_y <- (s+n)/2
  text(text_x, text_y, labels=quad_labels[i], col=quad_cols[i], pos=2)
par(old_par)                                     # restore the original parameters

# ------- PASTING MORE PLOTS ON TOP --------------------------------------------

# Now let's add elevation information for these three quads.

# Note that the primitive graphics functions like points(), rect() and text()
# draw on top of existing plots.  When we use higher level plotting functions
# like hist() we need to set "par(new=TRUE)" to continue in 'overplot' mode.
par(new=TRUE,fig=c(0.05,0.30,0.40,0.47), mar=c(0,0,0,0), cex=0.5, xpd=NA)
hist(elevation[gatorHookSwampQuad_mask], breaks=seq(-3,3,.2), 
     axes=FALSE,  main="",xlab="", ylab="", xpd=NA)

par(new=TRUE,fig=c(0.08,0.33,0.26,0.33), mar=c(0,0,0,0), cex=0.5, xpd=NA)
hist(elevation[sharkPointQuad_mask], breaks=seq(-3,3,.2), 
     axes=FALSE,  main="",xlab="", ylab="", xpd=NA)

par(new=TRUE,fig=c(0.15,0.40,0.15,0.21), mar=c(0,0,0,0), cex=0.5, xpd=NA)
hist(elevation[flamingoQuad_mask], breaks=seq(-3,3,.2), 
     axes=FALSE, main="",xlab="", ylab="")
mtext("elevation (m)", side=1, line=4, xpd=NA)

par(old_par)                                     # restore the original parameters

# Now we'll add our gator habitat plot from before in the space in the upper left.

par(new=TRUE, fig=c(0.0,0.4,0.55,0.95), mar=c(1,1,1,3))
pie(mainGatorHabitat, col=habitat_cols, border='white', cex=1.0, xpd=NA)
pie(c(1), labels=NA, border='white', radius=0.4) # a blank white circle
text(0,0,labels="G", col='mistyrose', cex=4, font=2)
text(0,0,labels="Gator\nHabitat", cex=1.2, font=2) # text in the middle
par(old_par)                                       # restore the original parameters

# ------- TEXT ANNOTATIONS -----------------------------------------------------

# Now for some final annotations 

# Set the graphics parameter for 'full bleed' -- don't cut off at the margins 

# Diagonal, 'serif' text
text(0.48, 0.3, labels="U N C H A R T E D", family='serif',
     col='grey70', cex=1.4, font=2, srt=-45)

# Cheesy shadow effect
nudge <- 0.004
text(0.7+nudge, 1.0-nudge, labels="Visit the Everglades!", 
     pos=3, col='grey80', cex=3, font=4)
text(0.7, 1.0, labels="Visit the Everglades!", pos=3, cex=3, font=4)

text(0.7+nudge, 0.0-nudge, labels="Use R!", 
     pos=4, col='grey80', cex=2.5, font=4, family='serif')
text(0.7, 0.0, labels="Use R!", pos=4, cex=2.5, font=4, family='serif')

# Italic
text(0.70, 0.5, labels="Enjoy 'Gator' habitat.", pos=4, cex=1.2, font=3)
text(0.70, 0.43, labels="Explore uncharted swamps.", pos=4, cex=1.2, font=3)
text(0.70, 0.36, labels="Avoid elevation sickness.", pos=4, cex=1.2, font=3)

par(old_par)                                     # restore the original parameters
if (saveAsPng) { }                     # end plotting, write file

OK, perhaps we shouldn’t expect a job offer from the Florida Department of Tourism any time soon.  But I hope this short tour through the R swamp shed some light on how just a few techniques can help you begin interrogating large datasets and telling interesting  stores.

Happy Exploring!

Series NavigationUsing R — Installing PackagesUsing R — Basic error Handing with tryCatch()
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4 Responses to Using R — A Script Introduction to R

  1. Wei says:

    Recommend to add



    saveAsPng <- TRUE

    Great post!!!

  2. bk says:

    Awesome post. Thanks..

  3. Curlew says:

    Amazing and very detailed post!
    Two comments:
    Why create those lot of mask_variables? All you do is indexing and it is a lot easier in R by using the command “which” as in ‘gator_data[which(gator=”text”)]’. But i guess this is just personal taste.
    Furthermore i didn’t know about the ‘par(new=T)’ command, but you could also add the parameter ‘add=T’ to the plot command.

    • Jonathan Callahan says:

      Thanks for the suggestions.

      Regarding which()

      Yes, we could have created a set of indices to plot “Tree Islands” with the following:

      treeIsland_indices <- which(habitat == "Tree Island")
      plot(x[treeIsland_indices], y[treeIsland_indices], ...)

      I prefer masks because of the possibility of generating SQL type queries from these masks:

      aboveSeaLevel_mask  <- elevation > 0
      mySites_mask <- gatorHoleQuad_mask &
                      aboveSeaLevel_mask &

      This kind of bit-masking is ridiculously fast and, once you’ve defined a bunch of masks, allows you to be very thorough in your interrogation of the data. You can’t do that sort of thing with disjoint sets of indices.

      Regarding add=TRUE

      I believe I’ve encountered cases where this doesn’t work but I’m not sure what they were. Using par(new=TRUE) always seems to work.