R Get Started II

1. R GET STARTED II
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2. Summarizing Data
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3. Summarizing Data
data<-read.csv(file.choose(),header=T)
summary(data) Variables summarized based on their type
Command effectively handles missing observations in the data
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4. Variable Summary Statistics
summary(data$ba) Specify the variable
summary(data$Grade) Note the type of variable
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5. Inclusion & Exclusion
summary(data[ ,c(-1,-2,-3,-4,-5)])
Excludes specific variables
summary(data[,c("ba","ms")])
Includes specific variables
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6. Missing Observations
nmiss<-sum(is.na(data$ba))
nmiss
Number of missing observations
mean(data$ba )
NA output due to missing observations
mean(data$ba,na.rm=TRUE)
Handles the issue very well
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7. More Statistics
sd(data$ba,na.rm=TRUE)
Standard Deviation of a variable
var(data$ms,na.rm=TRUE)
Variance of a variable
sqrt(var(data$ba,na.rm=TRUE)/length(na.omit(data$ba)))
Standard error of the mean
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8. More Statistics
trimmed_mean<-mean(data$ms,0.10,na.rm=TRUE)
trimmed_mean Trim ‘P’ percent observations from both ends
length(data$ba) Number of observations
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9. tapply Function
A<-tapply( data$ba,data$Location,mean, na.rm=TRUE)
A Break up vector into groups by factors and compute functions
B<- tapply( data$ms,list(data$Location,data$Grade),
mean,na.rm=T); B
Mean value for ‘ms’ grouped by categorical variables Location and
Grade of the same data set
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10. Using Aggregation Function
• Single variable, single factor, single function
• Single variable, single factor, multiple functions
• Multiple variables, single factor, multiple functions
• Single variable, multiple factors, multiple functions
• Multiple variables, multiple factors, multiple
functions
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11. Single Variable, Single Factor, Single Function
A<-aggregate(ba ~ Location,data=data, FUN = mean )
A
To calculate mean for variable ‘ba’ by Location variable
Aggregate function by default ignores the missing data values
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12. Single Variable, Single Factor, Multiple Functions
f<-function(x) c( mean=mean(x), median=median(x),
sd=sd(x))
B<-aggregate(ba ~ Location,data=data, FUN = f ); B
To calculate mean, median & S.D for variable ‘ba’ by Location
variable
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13. Multiple Variables, Single Factor, Multiple Functions
f<-function(x) c( mean=round(mean(x,0)),
median=round(median(x,0)), sd=round(sd(x,0)))
C<-aggregate(cbind(ba,ms) ~ Location,data=data, FUN=f )
C
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14. Single Variable, Multiple Factors, Multiple Functions
f<-function(x) c( mean=round(mean(x,0)),
median=round(median(x,0)), sd=round(sd(x,0)))
D<-aggregate(ba ~ Location+Grade+Function,data=data,
FUN = f ); D
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15. Multiple Variables, Multiple Factors, Multiple Functions
f<-function(x) c(mean=round(mean(x),0),
sd=round(sd(x),0))
E<-aggregate (cbind(ba,ms) ~ Location+Grade+Function,
data=data, FUN = f ); E
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16. ddply Function
install.packages(“plyr”)
library(plyr)
ddply(data,.(Location), summarize, mean.ba=
mean(ba,na.rm=TRUE))
‘.’ allows use of factors without quoting
ddply(data, .(Location), summarize, mean.ba = mean(ba,
na.rm=TRUE), sd.ms = sd(ms,na.rm=TRUE), max.ms =
max(ms,na.rm=TRUE))
multiple functions in a single cell
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17. ddply Function
ddply(data, .(Location, Grade), summarize, avg.ba =
mean(ba,na.rm=TRUE), sd.ms = sd(ms,na.rm=TRUE),
max.ba = max(ba,na.rm=TRUE))
Summarize by combination of variables and factors
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18. Do you know?
ddply can take one tenth of time to process a data than the
aggregate function
Read more about our research on efficient processing in R at
www.Sanaitics.com/research-paper.html
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19. Generating Frequency Tables
table<-table(data$Location, data$Grade)
2-way frequency table
prop.table(table)
Table to cell proportions
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20. Generating Frequency Tables
prop.table(table, 1)
Row proportions
prop.table(table, 2)
Column proportions
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21. Generating Frequency Tables
table1 <- table(data$Location,data$Grade,data$Function)
ftable(table1)
Table ignores missing values. To include NA as a category in
counts, include the table option exclude=NULL
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22. Generating Frequency Tables
table2 <- xtabs(~Location+Grade+Function,data=data)
ftable(table2)
Allows formula style input
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23. Cross Tables
install.packages(“gmodels”)
library(gmodels)
CrossTable(data$Grade,data$Location)
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24. Cross Tables
library(gmodels)
CrossTable(data$Grade,data$Location, prop.r=FALSE,
prop.c=FALSE)
Remove row & column
proportions
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25. Visualizing Data
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26. Bar Plot- Median salary for two grades
A<-aggregate(ba ~ Grade,data=data, FUN = median )
barplot(A$ba, names = A$Grade, col="pink",xlab = "GRADE",
ylab = "median_salary ", main = "SALARY DATA OF
EMPLOYEES")
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GR1 GR2
SALARY DATA OF EMPLOYEES
GRADE
median_salary
050001000015000
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27. Standard Box-Whiskers Plot
boxplot(data$ba,range=0)
• Range determines how far the plot whiskers extend out from the box
• If range is positive, the whiskers extend to the most extreme data
point which is no more than range times the interquartile range
from the box
• A value of zero causes the whiskers to extend to the data extremes.
So here in this case no outliers will be returned
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28. Modified Box-Whiskers Plot
• Constructed to highlight outliers where Standard Boxplot fails
• Default in R, requires no special parameters
• The "dots" at the end of the boxplot represent outliers
• There are a number of different rules for determining if a point is
an outlier, but the method that R and ggplot use is the "1.5 rule“
If a data point is either:
1. less than Q1 - 1.5*IQR
2. greater than Q3 + 1.5*IQR
then that point is classed as an "outlier". The whisker line goes to
the first data point before the "1.5" cut-off.
Note: IQR = Q3 - Q1
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29. Box Plot – Single Variable
boxplot(data$ba,col="coral1",main="boxplot for variable
ba " , ylab=" basic allowance range " , xlab="ba")
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30. Box Plot – Single Variable, Two Factors
boxplot(data$ba~data$Location+data$Grade,
col=c("orange"),main="boxplot" , ylab="basic allowance
range”, xlab="ba" )
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31. Box Plot – Single Variable, Multiple Factors
boxplot(data$ms~data$Location+data$Grade+data$Funct
ion, col=“gold”, main=“boxplot”,ylab=“MS range”,
xlab=“ms” )
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32. Pie Chart
pie(table(data$Location),main="pie chart of employee
location",col=rainbow(8))
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33. Histogram
hist(data$ba,include.lowest=TRUE,labels=TRUE,
col="orange",main="Histogram_BA" , xlab=" class intervals" ,
ylab="frequency",border="blue")
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Histogram_BA
class intervals
frequency
10000 15000 20000 25000 30000
0246810
2
10
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6
5
3
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34. Histogram
install.packages(“lattice”)
library(lattice)
histogram(~ms+ba, layout=c(1,2), data=data, col=“pink”,
xlab=“ba/ms”, ylab=“frequency”, main=“multiple histogram in same
panel”
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35. Bivariate Analysis
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36. Scatter Plot
Upload the Job Data provided to you into R Console
plot(job_data$Aptitude,job_data$JOB_Prof, main=“ simple
Scatter plot", xlab=“Aptitude Score", ylab="Job proficiency" )
Shows positive correlation
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60 80 100 120 140
60708090100110120
simple Scatter plot
Aptitude Score
Jobproficiency
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37. Scatter Plot Matrix
pairs(~.,data=job_data[,-1],main=“Simple Scatterplot Matrix”)
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38. Pearson Correlation
Cor1<-round(cor(job_data[,c(1,5)],
use="pairwise.complete.obs", method="pearson"), 2)
Cor1
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39. Pearson Correlation
Cor2<-round(cor(job_data[,-1], use="pairwise.complete.obs",
method="pearson"),2)
Cor2
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40. Spearman Correlation
machine_performance<-c(5, 7, 9, 9, 8, 6, 4, 8, 7, 7)
operator_satisfaction<-c(6, 7, 4, 4, 8, 7, 3, 9, 5, 8)
Newdata<-
data.frame(machine_performance,operator_satisfaction)
Newdata
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41. Spearman Correlation
Cor3<-round(cor(Newdata, use="pairwise.complete.obs",
method="spearman"),2)
Cor3
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42. Save Output to HTML format
install.packages(“R2HTML”)
library(R2HTML)
HTMLStart(outdir="C:/Users/Desktop",file="myreport",
extension="html",echo=TRUE,HTMLframe=TRUE)
HTML.title("myreport",HR=1)
summary(data)
HTML.title("histogram_BA",HR=3)
hist(data$ba,include.lowest=TRUE, col="orange",main="Histogram_BA" ,
xlab="class intervals" , ylab="frequency",border="blue")
HTMLplot()
HTMLStop()
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43. Save Output to HTML format
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44. Save Output to HTML format
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45. NOW YOU ARE AN EXPERT!
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Become a SANKHYA CERTIFIED R PROGRAMMER
Enroll at www.Sanaitics.com/certification.asp