2. What is R?
• A language and environment for statistical computing
and graphics
• Wide variety of statistical & graphical techniques built in
• Free and Open Source software
• Compiles and runs on a wide variety of UNIX platforms,
Windows and MacOS
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3. R Environment
• Most functionality through built in functions
• Basic functions available by default
• Other functions contained in packages that can be
attached
• All datasets created in the session are in Memory
• Output can be used as input to other functions
• R commands are Case Sensitive
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4. R-CRAN
• The Comprehensive R Archive Network
• A network of global web servers storing identical, up-to-
date, versions of code and documentation for R
• Use the CRAN mirror nearest to you to download R
setup at a faster speed
• http://cran.r-project.org/
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6. The R-UI
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The R console
-- Type in the command
-- Press enter
-- See the output
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7. Create Your Data Set
x<-c(12,23,45)
y<-c(13,21,6) Create vectors x, y, z on R Console
z<-c("a","b","c")
data1<-data.frame(x,y,z) Combine them in a table
data1 Type data name for output
Note that R is Case-Sensitive
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8. Your data in Table mode
edit(data1)
Edit your table, add new values & close the window to save your
updates
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9. Deriving & Removing Variables
Add new variables using mathematical operators
data1$z<-data1$x+data1$y
Delete an unwanted column from your table by using one of the
following methods
data1$x<-NULL
data1<-subset(data1, select = -y)
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10. Export your updated table
write.csv (data1,file.choose())
Select the path, name your file with .csv extension, click open and Yes
to export your table
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11. Data Set types
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Vector 1 dimension
All elements have the same data
types
Numeric
Character
Logic
Factor
Matrix 2 dimensions
Array
2 or more
dimensions
Data frame 2 dimensions
Table-like data object allowing
different data types for different
columns
List
Collection of data objects, each element of a list is a data
object
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12. Useful In-Built Functions
min(data1$x) Use $ sign after data set name to use specific columns
max(data1$y) length(data1$x)
mean(data1$y)
levels(data1$z) Gives a list of unique categories in the data
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13. Save Your Script
Open a new script, write commands, execute using F5 key. Save the file at a desired
folder. Helps to save the script for a later use.
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14. R Workspace
• Objects that you create during an R session are hold
in memory, the collection of objects that you
currently have is called the workspace
• This workspace is not saved on disk unless you tell R
to do so
• This means that your objects are lost when you close
R and not save the objects, or worse when R or your
system crashes on you during a session
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15. R Workspace
• If you have saved a workspace image and you start R
the next time, it will restore the workspace
• So all your previously saved objects are available
again
• You can also explicitly load a saved workspace i.e.,
that could be the workspace image of someone else
• Go the `File' menu and select `Load workspace'
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16. R Workspace
Display all previous commands
history()
Display last 25 commands
history(max.show=25)
Save your command history to a file. Default is ".Rhistory"
savehistory(file="myfile")
Recall your command history. Default is ".Rhistory"
loadhistory(file="myfile")
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17. Types of Variables
• Type: Logical, integer, double, complex, character,
factor
• Type conversion functions have the naming
convention
as.xxx
• For example, as.integer(3.2) returns the integer 3,
and as.character(3.2) returns the string "3.2".
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18. Factors
• Tell R that a variable is nominal by making it a
factor
• The factor stores the nominal values as a vector of
integers in the range [ 1... k ] (where k is the number
of unique values in the nominal variable), and an
internal vector of character strings (the original
values) mapped to these integers
• An ordered factor is used to represent an ordinal
variable
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19. Factors
• R will treat factors as nominal variables and ordered
factors as ordinal variables in statistical procedures
and graphical analyses
• You can use options in the factor( ) and ordered( )
functions to control the mapping of integers to
strings (overriding the alphabetical ordering)
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20. Help in R
• If you know the topic but not the exact function
help.search(“topic”)
• If you know the exact function
help(function name) OR
?functionname
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21. In-Memory Computing
• Storage of information in the main RAM of
dedicated servers rather than in complicated
relational databases operating on comparatively
slow disk drives
• Helps business customers, including retailers,
banks and utilities, to quickly detect patterns,
analyze massive data volumes on the fly, and
perform their operations quickly
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22. Import .csv Data File
basic_salary<-read.table(“C:/Users/Documents/R/BASIC
SALARY DATA.csv", header=TRUE, sep=",")
• Command requires the file path separated by a /
• header=TRUE if there are row labels
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24. Importing .txt File
Importing a text file having delimiter = “blank/space”
sample1<-read.table(file.choose(),header=T)
sample1
Importing a comma separated text file
sample2<-read.table(file.choose(), header=T, sep=",")
sample2
Various special characters can act as separators. Check them in the
file before importing.
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25. Data Imported
Use Head function to get an idea about how your data looks like
Note that it displays the first 6 rows
head(basic_salary)
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29. Sub-setting using Indices
• Row level: display some selected rows
• By setting condition on observations
• Column level: display some selected columns
• By setting condition on variable names
• Display selected rows for selected columns
• By setting conditions on variables & observations
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60. Merging Cases (append)
• Appending two datasets using rbind function requires
both the datasets with exactly the same number of
variables with exactly the same names
• If datasets do not have the same number of variables,
variables can be either dropped or created so both match
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61. R-String Functions
Extract or replaces substrings in a character vector
General syntax: substr(column name, start, stop)
basic_salary$Location<-substr(basic_salary$Location,1,1)
head(basic_salary)
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62. Replace Function
Try for yourself
x<-c(“green” , “red”, “yellow”); x
x<-replace(x, 1,”good”); x
x<-replace(x, c(2,3), c(“apple”, “banana”)); x
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63. Extracting Complete Cases
Create a dataset
x<-c(12, NA, 13, 12)
y<-c(25, 48, NA, NA)
complete_case<-data.frame(x, y); complete_case
Extract complete cases using any one of the three commnds
case1<-na.omit(complete_case); case1
case2<-complete_case[complete.cases(complete_case),]
case2
case3<-na.exclude(complete_case); case3
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64. FIRST MILESTONE REACHED!
NOW THAT YOU HAVE MASTERED FIRST STEP, IT’S TIME FOR
R GET STARTED II
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