RAMU
January 26, 2025
object-oriented programming language (like Python,JavaScript).object?Important
Objects are like boxes in which we can put things: data, functions, and even other objects. Ben Skinner
R import those data files into R Objectstypeof() to check the type of datatypeof() returns the Data type or data structureDoubles are numbers like 2.0, 2.2, 2.999R by default all numbers are DoubleIntegers are natural numbersR by default treated as DoubleComplex like x+yiLogical: a variable of logical type data has values like TRUE or FALSECharacters represents a string values in Rdouble quotesRFactor represents categorical dataas.Date is used to create Date and Time objectshelp(as.Date)RR missing data is represented by NA meaning Not AvailableR is NaN meaning Not a NumberNULL in R represent an object with ZERO length-Inf and Inf represents negative and positive infinityRR there are certain structures followed by imported data.
c(value1,value2,...)vectors also| Functions | Explanations |
|---|---|
numeric(n) |
vector with n zeros |
rep(x,n) |
Vector with n equal elements of x |
seq(x)/seq(1:x) |
Sequence from 1 to x |
seq(f,x)/seq(f:x) |
Sequence from 1 to x |
seq(f,x,s) |
Sequence from 1 to x in steps s |
| Functions | Explanations |
|---|---|
length(v) |
Number of elements in vector v |
max(v) |
Largest Number in vector v |
min(v) |
Smallest number in vector v |
sum(v) |
Sum of the elements in vector v |
prod(v) |
Product of elements in vector v |
sort(v) |
Sorting of the elements of vector v |
matrix is used to create matrix [,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 5 6dim, cbind and rbind [,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12 [,1] [,2] [,3]
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12 [,1] [,2] [,3]
r1 1 2 3
r2 4 5 6 Eco Hist Pol
SI 1 10 20
SII 2 9 5
SIII 3 8 15m1 with 3R a list is a generic collection of objectsmylist <- list(name1=component1, name2=component2, ...)$A
[1] 10 15 20 25 30
$student
[1] "aadil"
$idm
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1[1] "A" "student" "idm" data.frame or as.data.frame to transform into data frame GDP INV
1991 200 100
1992 250 150
1993 300 100
1994 320 120
1995 400 200[1] 200 250 300 320 400# Generating a new variable in the data frame
macro$lnGDP <- log(macro$GDP)
# Using `with` function
macro$lnINV <- with(macro,log(INV))
#Using `attach()` function
attach(macro)
macro$total <- GDP+INV
detach(macro)
# Results
macro GDP INV lnGDP lnINV total
1991 200 100 5.298317 4.605170 300
1992 250 150 5.521461 5.010635 400
1993 300 100 5.703782 4.605170 400
1994 320 120 5.768321 4.787492 440
1995 400 200 5.991465 5.298317 600 GDP INV lnGDP lnINV total
1991 200 100 5.298317 4.605170 300
1992 250 150 5.521461 5.010635 400
1993 300 100 5.703782 4.605170 400
1994 320 120 5.768321 4.787492 440
1995 400 200 5.991465 5.298317 600 GDP INV lnGDP lnINV total
1994 320 120 5.768321 4.787492 440
1995 400 200 5.991465 5.298317 600THANKS