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Rstudio OverView
we have 4 panes
1) script pan – to write and save the programing script
2) Console pane – where all the code will get executed
3) Environment/history pane – displays all the variables created,functions
used with in the current session
4) Helper pane – contains multiple tabs to install/display pacakges,
view visualization plots,
locate files within the workspace
1) script pan – to write and save the programing script
2) Console pane – where all the code will get executed
3) Environment/history pane – displays all the variables created,functions
used with in the current session
4) Helper pane – contains multiple tabs to install/display pacakges,
view visualization plots,
locate files within the workspace
In [1]:
help(mean)
getting and setting workspace
In [2]:
# to display current working directory use getwd() function getwd()
‘C:/Users/Suresh/mlclassscripts’
In [ ]:
# to set up workspace or working directory use setwd() function
#syntax is shown below
setwd("path")
In [6]:
setwd("C:\\Suresh\\R&D\\Projects\\ML classroom training\\sessions")
setwd("C:/Suresh/R&D/Projects/ML classroom training/sessions")
getting help in R
To get help within R environment, we use help() function to get the
documentation
for any of the functions/packages available within R environment.
To see the arguments required for a function, we use args() function.
to see the example of a function, example() function is used.
documentation
for any of the functions/packages available within R environment.
To see the arguments required for a function, we use args() function.
to see the example of a function, example() function is used.
In [ ]:
help("stats")
help("mean")
args("mean")
example("mean")
#getting help documentation for a package
help(package="caret")
online help for R programming
We can get online help on available packages in R from official website of R-Cran
https://cran.r-project.org/web/views/
https://cran.r-project.org/web/views/
We can also get online support for our day to day activities from below websites:
https://stackoverflow.com/
https://stats.stackexchange.com
https://stackoverflow.com/
https://stats.stackexchange.com
Installing Packages
In [ ]:
#install pacakges in R can be done in two ways,
#1) using install.packages() function and from the bottom right pane of Rstudio
install.packages("randomForest")
#loading of installed or downloaded packages can be done using library() function.
#Note that we can only load the package if
# we have installed the package already within our R environment
library(cluster)
In [ ]:
#below code to first verify if the library is installed in the R environment,
#if it is not available
# then the package will get installed.
if(!library(cluster)){
install.pacakges("cluster")
}
basic operations in R
In [ ]:
# Adding two numericals 1+1 #multiplying two numericals 10*2 #dividing two numericals 10/2 #applying modulus operation on two numericals 10%%2
printing results to R console
In [ ]:
#printing the data on the console
print(10*2)
print("data science")
print(pi^2)
Variable declaration and assignment in R
variable assignment: In the below example, we are creating variable named z:
In [8]:
z <- 100
we use left arrow or = symbol for variable assignment. Its always good
practice to use left arrow for assignment.
practice to use left arrow for assignment.
In [9]:
z = 10.009 z <- 10.009
Loading existing or default datasets available in R environment
we can access default datasets avaiable in R using data() function.
data() function will displays all the avaiable datasets within R.
data() function will displays all the avaiable datasets within R.
In [ ]:
data()
In order to load a specific dataset into R, we need to give the dataset name as argument to the data() function
In [ ]:
data(AirPassengers)
Viewing data of R objects
To view first 5 records of a R object (ex:dataframe), we use head() function.
head() function expects the data object as argument and prints the first 5 records on the R console.
head() function expects the data object as argument and prints the first 5 records on the R console.
In [ ]:
head(AirPassengers)
to view all the records in a nice tabular view
In [ ]:
View(AirPassengers)
Getting the decription and structure of R object
use str function to see the descriptions of the data object,
In [ ]:
str(AirPassengers)
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