DATA FILE: Data file should be in a table format as follows:

GENERAL FORM OF R COMMAND:

barplot(dataframename$Count,
names.arg=dataframename$CategoricalLevels)

EXAMPLE:

Dataset: oscar.csv

The table gives age (years) of the best actress and actor when Oscar was won. Click on the file to download it and move it into RStudio. We will produce the barplot for the actresses.

barplot(oscar$ActressCount, names.arg=oscar$Age)

Note that the video assumes that you have the original data not the frequency table.

DATA FILE: Data file should be in a table format as follows:

GENERAL FORM OF R COMMAND:

pie(dataframename$Count,
labels=dataframename$CategoricalLevels)

EXAMPLE:

Dataset: oscar.csv

The table gives age (years) of the best actress and actor when Oscar was won. Click on the file to download it and move it into RStudio. We will produce the barplot for the actresses.

pie(oscar$ActressCount, labels=oscar$Age)

Note that the video assumes that you have the original data not the frequency table.

DATA FILE: Each individual has been categorized to one of the levels.

GENERAL FORM OF R COMMAND:

variablename.freq = table(variablename)

EXAMPLE:

Dataset: painters (Located in library MASS)

The data frame is a compilation of technical information of a few eighteen century classical painters. The data set belongs to the MASS package, and has to be pre-loaded into the R workspace prior to its use.

library(MASS)

We will construct the frequency distribution of the school variable

school.freq = table(painters$School)

school.freq

EXERCISE: Produce a barplot and pie chart for the school categorical variable.

Note that the video assumes that you have the original data not the frequency table.

GENERAL FORM OF R COMMAND:

stem(dataframename$variablename)

EXAMPLE:

Dataset: faithful

There are two observation variables in the data set. The first one, called eruptions, is the duration of the geyser eruptions. The second one, called waiting, is the length of waiting period until the next eruption. We will create a stem-and-leaf plot of the eruptions variable.

stem(faithful$eruptions)

The decimal point is 1 digit(s) to the left of the |

16 | 070355555588
18 | 000022233333335577777777888822335777888
20 | 00002223378800035778
22 | 0002335578023578
24 | 00228
26 | 23
28 | 080
30 | 7
32 | 2337
34 | 250077
36 | 0000823577
38 | 2333335582225577
40 | 0000003357788888002233555577778
42 | 03335555778800233333555577778
44 | 02222335557780000000023333357778888
46 | 0000233357700000023578
48 | 00000022335800333
50 | 0370

GENERAL FORM OF R COMMAND:

hist(dataframename$variablename)

EXAMPLE:

Dataset: faithful

There are two observation variables in the data set. The first one, called eruptions, is the duration of the geyser eruptions. The second one, called waiting, is the length of waiting period until the next eruption. We will create a histogram of the eruptions variable.

hist(faithful$eruptions)

GENERAL FORM OF R COMMAND:

boxplot(dataframename$variablename)
boxplot(dataframename$variablename, horizontal=TRUE)

EXAMPLE:

Dataset: faithful

There are two observation variables in the data set. The first one, called eruptions, is the duration of the geyser eruptions. The second one, called waiting, is the length of waiting period until the next eruption. We will create a boxplot of the eruptions variable.

boxplot(faithful$eruptions)

boxplot(faithful$eruptions, horizontal=TRUE)

For side-by-side boxplot (separate columns)

GENERAL FORM OF R COMMAND:

boxplot(dataframename$variablename1, dataframename$variablename2, ...)

boxplot(dataframename$variablename1, dataframename$variablename2, ..., horizontal=TRUE)

For side-by-side boxplot (grouping variable)

GENERAL FORM OF R COMMAND:

boxplot(dataframename$variablename~groupingvariablename)

boxplot(dataframename$variablename~groupingvariablename, horizontal=TRUE)

GENERAL FORM OF R COMMAND:

plot(ExplanatoryVariable, ResponseVariable, main="The Title of the Plot", xlab="Definition of the Explanatory Variable", ylab="Definition of the Response Variable")

EXAMPLE:

Dataset: faithful

There are two observation variables in the data set. The first one, called eruptions, is the duration of the geyser eruptions. The second one, called waiting, is the length of waiting period until the next eruption. We will create a scatter plot of the waiting intervals (response) versus eruption durations (explanatory)

plot(faithful$eruptions, faithful$waiting)

To add a title and label the axes

plot(faithful$eruptions, faithful$waiting,
main="Scatterplot of Time Waited versus Eruption Duration",
xlab="Eruption duration",
ylab="Time waited")

To add a least squares regression line

abline(lm(faithful$waiting~faithful$eruptions))

To add any line with intecept a and slope b you can use

abline(a,b)

Therefore, if you would like to plot y=x, use

abline(0,1)

DATA FILE: Data file should have a time column and variable.

GENERAL FORM OF R COMMAND:

plot(dataframename$timevariable, dataframename$variable, xlab=" ", ylab=" ", type="l", col="red")

l stands for line plot. See the video if you have a date as the time variable

EXAMPLE:

Dataset: LungCancer.csv

The data consists of Year of diagnosis, incidence rates (out of 100,000 people) for Total popultaion, Males, and Females. Click on the file to download it and move it into RStudio. We will produce the timeplot for the total.

plot(LungCancer$Year, LungCancer$Total, xlab="Year", ylab="Total Lung Cancer Rate", type="l", col="red")

EXERCISE: Produce a timeplot for females and males separately and compare.

GENERAL FORM OF R COMMAND:

5-number Summary

summary(dataframename$variablename)

Mean

mean(dataframename$variablename)

Median

median(dataframename$variablename)

Interquartile Range

IQR(dataframename$variablename)

Standard Deviation

sd(dataframename$variablename)

EXAMPLE:

Dataset: faithful

There are two observation variables in the data set. The first one, called eruptions, is the duration of the geyser eruptions. The second one, called waiting, is the length of waiting period until the next eruption. We will create a histogram of the eruptions variable.

summary(faithful$eruptions)
mean(faithful$eruptions)
median(faithful$eruptions)
IQR(faithful$eruptions)
sd(faithful$eruptions)

DATA FILE: Data file should have

• Identifying variable (e.g. country, region, etc.)
• Time variable (e.g. year)
• At least two variables

PACKAGE: googleVis

GENERAL FORM OF R COMMAND:

library(googleVis)

dataframename <- read.csv(file.choose(), header = T)

attach(dataframename)

namemotion <- gvisMotionChart(dataframename, idvar = 'name of Identifying variable', timevar = 'name of time variable')

plot(namemotion)

EXAMPLE:

Dataset: MinnesotaData.csv

The data gives various population characteristics on Minnesota Counties from 1900 to 2010. Click on the file to download it and move it into RStudio. We will produce a motion chart for this data. To see the detailed instructions click here.

library(googleVis)

county<- read.csv(file.choose(), header=TRUE)

attach(county)

countyMotion<- gvisMotionChart(county, idvar='County', timevar='Year')

plot(countyMotion)

DATA FILE: Data file should have

• Location variable(s) (latitude/longtitude locations, address, country name, region (including states), or US metropolitan area code))
• Numerical Variable ( information that you want to appear on the map)

PACKAGE: googleVis

GENERAL FORM OF R COMMAND:

library(googleVis)

dataframename <- read.csv(file.choose(), header = T)

attach(dataframename)

namegeoMap<- gvisGeoMap(dataframename, locationvar = '', numvar = '', hovervar = '', options = list())

plot(namegeoMap)

EXAMPLE:

Dataset: alcohol.csv

The data gives 2008 alcohol consumption per person for 182 countries. Click on the file to download it and move it into RStudio. We will produce a map this data. To see the detailed instructions click here.

library(googleVis)

alcohol<- read.csv(file.choose(), header=TRUE)

attach(alcohol)

alcoholgeoMap<- gvisGeoMap(alcohol, locationvar = 'Country', numvar = 'Alcohol')

plot(alcoholgeoMap)

DATA FILE: Data file should have

• Location variable(s) (latitude/longtitude locations, address, country name, region (including states), or US metropolitan area code))
• Variable ( information that you want to appear on the map/chart)

PACKAGE: googleVis

GENERAL FORM OF R COMMAND:

library(googleVis)

dataframename <- read.csv(file.choose(), header = T)

attach(dataframename)

namegeoChart<- gvisGeoChart(dataframename, locationvar = "", colorvar = "", sizevar = "", hovervar = "", options = list())

plot(namegeoChart)

EXAMPLE:

Dataset: lifeexpectancy2009.csv

The data gives 2009 life expectancies for the 197 countries. Click on the file to download it and move it into RStudio. We will produce a map/chart of this data. Our location variable is Country and color variable is Life_Expectancy.

library(googleVis)

lifeexpectancy<- read.csv(file.choose(), header=TRUE)

attach(lifeexpectancy)

lifeexpectancyChart<- gvisGeoChart(lifeexpectancy, "Country", "Life_Expectancy")

plot(lifeexpectancyChart)

If you would like to be able to edit the chart use

lifeexpectancyChartedit<- gvisGeoChart(lifeexpectancy, "Country", "Life_Expectancy", options = list(gvis.editor = "Editor"))

plot(lifeexpectancyChartedit)