R-projects
projects to get started with R programming.
STEPS:
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- Download R from CRAN https://cran.cnr.berkeley.edu/STEP
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- Download R Studio. from https://www.rstudio.com/products/rstudio/download3/
setup working directory.
Open R -> Misc -> Change Working Directory. {Create and set your working directory, default for these projects.} Inside R Console check the directory set by typing
$ getwd()
## to set directory manually
$ setwd('path to directory')Basic usage in R ##Exercies is meant for the user to run
## This is a comment
x <- 5 ## Assignment
x ## Prints x ; Exercise
y <- 1:26 ## creates a sequence from 1 through 26 Creates a VECTOR
y ## Exercies.
Objects in R Atomic Classes : (character, numeric -real number, integer, complex, logical(T/F)) vectors(elts of same class), Lists(can have elts of diff. classes) factors(categorical data, ordered/unordered data) missing values data frames(store tabular data, each col. is different class) names (all objs can have name, for descriptio.)
## create a vector (contains same type), exception list (this can contain multiple types)
vector()
## Number, Default behaviour is two precision.
x <- 5 ##( x is 5.00)
## Integer
z <- 7L ##(z is 7)
## Infinity - (this is a special number)
Inf
## NaN - is a special number 'Not a Number'
0/0
############### VECTOR ####################
##1. c() function to create vector
x <- c(T, F) ## boolean
x <- c("sandeep","svara", "aadya", "rajni", "kandekar") ## char
x <- 7:29 ## integer
x <- c(1+0i 2+3i) ## complex number. i is immaginary part of complex num.
##2. vector() function to create vector
x <- vector(numeric, length = 10)
x ##Exercise initialized with 0s
## be careful of coersion when creating mixed object vector.
## LIST
x <- list(35, "sandeep", FALSE, 89+7i)
x ## Exercise
############## MATRIX #####################
## Matrix is a special kind of Vector, which has dimension.
m <- matrix(1:6, nrow = 2, ncol = 3) ##creates column wise.
m ## Exercise
m <- 1:10
dim(m) <- c(2,5) ## take teh vector m, and transform it into a matrix with 2 rows and 5 columns
m ## Exercise
c-bind
x <- 3:5
y <- 13:15
cbind(x,y) ## creates matrix with 3,4,5 in rows
rbind(x,y) ## creates matrix with 3,4,5 in column
##NaN is Na but converse is not true.
x<- c(1,2,NaN,NA,4)
is.na(x) ##FALSE FALSE TRUE TRUE FALSE
is.nan(x) ##FALSE FALSE TRUE FALSE FALSE
##################################################################
##Data Frames - used to store tabular data.
read.table() or read.csv() | data.matrix() converts to matrix.
x <- data.frame(foo = 1:4, bar = c(T,T,F,F))
x ## Exercise.
################R Objects can have NAME ##############################
x <- 1:3
names(X) ## NULL
names(x) <- c("midge", "cameroon", "zelda")
x ## Exercies
names(x) ## Exercise
matrices can have names to ## Exercise check it.
Reading Data to a table
Make sure you try to specify columns, seperator, etc. if not all data is loaded in RAM and u system will choke
=========== Calculate the memory requirements =====================
1.5 mil rows, with 120 columns (say all are numeric)
1,500,000 * 120 * 8bytes/numeric = 1440000000 bytes ==> 14400000000/2^20 bytes/MB = 1,373.29 MB ==> 1.34 GB
## so you need twice than above, like 2.68 GB RAM memory.