basic graphics with r
DESCRIPTION
TRANSCRIPT
Advanced Data Analytics: Basic Graphics in R
Jeffrey Stanton
School of Information Studies
Syracuse University
Movie Data Set from JSE
• From McClaren and DePaolo’s article in the Journal of Statistics Education
• Daily per theater box office receipts in dollars for 49 movies• A variable number of entries for each movie depending
upon how long it ran• About 2500 observations altogether• DAILY_PER_THEATER: Amount in Dollars• DATE: mm/dd/yyyy of when the observation was made• DAY_NUM: Which day in the run, number from 1 up• MOVIE: The title of the movie• NUMBER: Index number of the movie
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Movie Dataset from JSE
http://www.amstat.org/publications/jse/datasets/moviedaily.dat
http://www.amstat.org/publications/jse/datasets/moviedaily.txt
> moviedaily <- read.delim("Z:/DataScience/AdvancedAnalytics/moviedaily.dat")
> view(moviedaily) # Display data in R-Studio in a separate pane> attach(moviedaily) # Make the new data the active data frame> class(moviedaily) # Make sure the dataset is a dataframe[1] "data.frame“> ls(moviedaily) # Show the variable names in the dataframe[1] "DAILY_PER_THEATER" "DATE" "DAY_NUM" [4] "MOVIE" "NUMBER" > hist(DAY_NUM)
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Histogram of DAY_NUM
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Histogram of DAY_NUM
DAY_NUM
Fre
quen
cy
0 50 100 150 200
020
040
060
0
About Histograms
• Basic type of diagnostic display shows how frequently each value occurs in the data set
• In R, works on numeric data only; getting counts on other modes of data requires another approach
• Works fine with continuous data (e.g., 3.1, 3.2, 3.25, etc.) because it can cluster together nearby values and count them in a single frequency category (representing a range)
• Try hist(NUMBER) and hist(DAILY_PER_THEATER)• Even though these look like numeric variables, in the data
importing process, R has made them into “factors” – factors are stored as integers with “category labels” and are used in various procedures to divide the data into groups
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Convert a factor into numbers
• Recall that a factor is stored as integers with character labels: It is the labels that we want to convert into numbers (we can’t control how R assigned the integers, so we don’t know exactly what they contain, only that they are unique)
• Try as.character(DAILY_PER_THEATER) – See how we get lots of numbers in quotes, plus some occasional other stuff that is not numbers
• Then try: as.numeric(as.character(DAILY_PER_THEATER))• Note the warning messages: “Warning message: NAs introduced by coercion” –
This is exactly what we want: “NA” is R’s way of coding missing data; all of the unusable string values (like: "No daily data“) have been turned into NAs because they are missing values
> detach(moviedaily)> moviedaily$dailyper<-
as.numeric(as.character(DAILY_PER_THEATER))> attach(moviedaily)> class(dailyper)# Adds a new numeric variable converted from the factor
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On most days, movies make a few $100
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Histogram of dailyper
dailyper
Fre
quen
cy
0 5000 10000 15000 20000
050
010
0020
00
Which Movie Made the Most $$$
• First, we need to aggregate the data, by summing the daily takes for each movie:
aggdata <- aggregate(dailyper,by=list(MOVIE),FUN=sum, na.rm=TRUE)# Aggregates by MOVIE, which is a factor with the movie names# Uses the sum function on the variable dailyper
• Next, lets organize the data in descending order:sortdata<-aggdata[order(-aggdata$x),]# The minus sign means decreasing order
• Remove the items that had no data (the sums ended up as zero):
sortdata<-sortdata[sortdata$x>1,]# Takes the subset of rows where the agg $ value > 1
• Finally, create a barplot showing the totals for each movie:barplot(sortdata$x,names.arg=as.character(sortdata$Group.1),las=2)
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Barplot of Movie Total Daily Take
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Let’s Do the Same Thing With Rcmdr
• The input data file has some anomalies that we had to clear up: Rcmdr data loader is not as forgiving as R-Studio
[3] ERROR: line 423 did not have 5 elements[4] ERROR: line 1990 did not have 5 elementsmoviedaily <-
read.table("Z:/DataScience/AdvancedAnalytics/moviedaily.dat",
header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
[5] NOTE: The dataset moviedaily has 2378 rows and 5 columns.
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Obviously We Need to Tweak It
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121 16 212 29 379 509 65 81 99
DAILY_PER_THEATER
Fre
quen
cy
02
46
810
We Still Need to Coerce
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as.numeric(as.character(DAILY_PER_THEATER))
Aggregate is Under the Menu: Data -> Active Data Set
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Remove Cases with Missing DataSubset the Data for Nonzero Values
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Rcmdr has no Sort Function…And the Barplot is Troubled
• We can use the sorting capability we learned before:aggdata<-aggdata[order(-aggdata$dailyper),]
• The Barplot menu choice in Rcmdr produces this code:barplot(table(aggdata$MOVIE), xlab="MOVIE", ylab="Frequency")– This creates a frequency table based on MOVIE, which is not really what
we want– The resulting chart is a histogram rather than a barchart with heights
based on dailyper
• We can run our own barchart command using the Rcmdr data:barplot(aggdata$dailyper,names.arg=as.character(aggdata$MOVIE),las=2)
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But Some Things are Still Messed Up!
• It has not discarded the zeroes as we asked• There are too many entries – there should be 49 or fewer –
looks like the aggregation did not work correctly
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Tita
nic
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Pha
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0
50000
100000
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Demonstrating Mastery
• Locate a data set in a CSV or Tab-Delimited file and read it into R
• Check the data to ensure that the process of reading in the data worked properly
• Run a histogram on any numeric variable• Aggregate the data based on any grouping variable; use a
sum function, a mean function, or some other function as appropriate
• Display the aggregated data in a barchart or another type of graph as appropriate
• Describe the difference between a histogram and a barchart
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