This presentation educates you about R - Operators, Types of Operators, Arithmetic Operators, Logical Operators, Relational Operators, Assignment Operators and Miscellaneous Operators.
For more topics stay tuned with Learnbay
Chart and graphs in R programming language CHANDAN KUMAR
This slide contains basics of charts and graphs in R programming language. I also focused on practical knowledge so I tried to give maximum example to understand the concepts.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
This presentation educates you about R - Operators, Types of Operators, Arithmetic Operators, Logical Operators, Relational Operators, Assignment Operators and Miscellaneous Operators.
For more topics stay tuned with Learnbay
Chart and graphs in R programming language CHANDAN KUMAR
This slide contains basics of charts and graphs in R programming language. I also focused on practical knowledge so I tried to give maximum example to understand the concepts.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
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It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
How to use Map() Filter() and Reduce() functions in Python | EdurekaEdureka!
Youtube Link: https://youtu.be/QxpbE5hDPws
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course**
This Edureka PPT on 'map, filter, and reduce functions in Python' is to educate you about these very important built-in functions in Python. Below are the topics covered in this PPT:
Introduction to map filter reduce
The map() function
The filter() function
The reduce() function
Using map(),filter() and reduce() functions together
filter() within map()
map() within filter()
map() and filter() within reduce()
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Data Manipulation with Numpy and Pandas in PythonStarting with NOllieShoresna
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
This presentation educates you about R - data types in detail with data type syntax, the data types are - Vectors, Lists, Matrices, Arrays, Factors, Data Frames.
For more topics stay tuned with Learnbay.
Python Session - 3
Escape Sequence
Data Types
Conversion between data types
Operators
Python Numbers
Python List
Python Tuple
Python Strings
Python Set
Python Dictionary
A comprehensive introduction to handling date and time data in R. Get an introduction to date and time manipulation in R. Learn to create, transform, extract and operate on date/time objects.
Learn the grammar of data manipulation using dplyr. You will work through a case study to explore the dplyr verbs such as filter, select, mutate, arrange, summarize, group_by etc.
Learn to write readable code with pipes using the magrittr package. You will learn about the forward operator (%>%), exposition operator (%$%) and the assignment operator (%<>%).
tibbles are an alternative for dataframes. You will learn how tibbles are different from dataframes, why you should use them, how to create and modify them.
Learn how to install & update R packages from CRAN, GitHub, Bioconductor etc. You wlll also learn to install specific versions of a package from CRAN or GitHub.
A brief introduction to the R ecosystem for absolute beginners. You will learn about the history and capabilities of R as a modern language for data science.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
In this tutorial, we learn to create dynamic documents using R Markdown. It enables us to create beautiful reports and presentations that are fully reproducible.
In this tutorial, we learn to create univariate bar plots using the Graphics package in R. We also learn to modify graphical parameters associated with the bar plot.
In this tutorial, we explore the most basic data structure in R, the vector. We cover everything from creating vectors to subsetting them in different ways.
In this tutorial, we learn to create variables in R. Followed by that, we explore the different data types including numeric, integer, character, logical and date/time.
Data Visualization With R: Learn To Combine Multiple GraphsRsquared Academy
In this tutorial, we learn to combine multiple graphs into a single frame using the par() and layout() functions. We also compare the differences between the two functions.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Introduction
In this module, we will learn about variables & data types. Specifically:
what is a variable?
how to create variables in R?
how to use variables created?
components of a variable
naming conventions for a variable
data types
numeric/double
integer
logical
character
date/time
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4. What is a variable?
variables are the fundamental elements of any programming language
they are used to represent values that are likely to change
they reference memory locations that store information/data
Let us use a simple case study to understand variables. Suppose you are computing the area of a circle whose
radius is 3. In R, you can do this straight away as shown below:
But you cannot reuse the radius or the area computed in any other analysis or computation. Let us see how
variables can change the above scenario and help us in reusing values and computations.
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3.14 * 3 * 3
## [1] 28.26
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5. Creating Variables
A variable consists of 3 components:
variable name
assignment operator
variable value
We can store the value of the radius by creating a variable and assigning it the value. In this case, we create a
variable called radius and assign it the value 3 using the assignment operator <-.
Now that we have learnt to create variables, let us see how we can use them for other computations. For our
case study, we will use the radius variable to compute the area of a circle.
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radius <- 3
radius
## [1] 3
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6. Using Variables
We will create two variables, radius and pi, and use them to compute the area of a circle and store it in
another variable area.
# assign value 3 to variable radius
radius <- 3
# assign value 3.14 to variable pi
pi <- 3.14
# compute area of circle
area <- pi * radius * radius
# call radius
radius
## [1] 3
# call area
area
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8. Naming Conventions
Name must begin with a letter. Do not use numbers, dollar sign ($) or underscore (_).
The name can contain numbers or underscore. Do not use dash (-) or period (.).
Do not use the names of keywords and avoid using the names of built in functions.
Variables are case sensitive; average and Average would be different variables.
Use names that are descriptive. Generally, variable names should be nouns.
If the name is made of more than one word, use underscore to separate the words.
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10. Numeric
In R, numbers are represented by the data type numeric. We will first create a variable and assign it a value.
Next we will learn a few methods of checking the type of the variable.
# create two variables
number1 <- 3.5
number2 <- 3
# check data type
class(number1)
## [1] "numeric"
class(number2)
## [1] "numeric"
# check if data type is numeric
is.numeric(number1)
## [1] TRUE
is.numeric(number2)
## [1] TRUE
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11. Numeric
If you carefully observe, integers are also treated as numeric/double. We will learn to create integers in a
while. In the meanwhile, we have introduced two new functions in the above example:
class(): returns the class or type
is.numeric(): tests whether the variable is of type numeric
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12. Integer
Unless specified otherwise, integers are treated as numeric or double. In this section, we will learn to create
variables of the type integer and to convert other data types to integer.
create a variable number1 and assign it the value 3
check the data type of number1 using class
create a second variable number2 using as.integer() and assign it the value 3
check the data type of number2 using class
finally use is.integer() to check the data type of both number1 and number2
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13. Integer
# create a variable and assign it an integer value
number1 <- 3
# create another variable using as.integer
number2 <- as.integer(3)
# check the data type
class(number1)
## [1] "numeric"
class(number2)
## [1] "integer"
# use is.integer to check data type
is.integer(number1)
## [1] FALSE
is.integer(number2)
## [1] TRUE
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14. Character
Letters, words and group of words are represented by the data type character. All data of type character
must be enclosed in single or double quotation marks. In fact any value enclosed in quotes will be treated as
character. Let us create two variables to store the first and last name of a some random guy.
# first name
first_name <- "jovial"
# last name
last_name <- 'mann'
# check data type
class(first_name)
## [1] "character"
class(last_name)
## [1] "character"
# use is.charactert to check data type
is.character(first_name)
## [1] TRUE
is.character(last_name)
## [1] TRUE
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15. Integer
You can coerce any data type to character using as.character().
# create variable of different data types
age <- as.integer(30) # integer
score <- 9.8 # numeric/double
opt_course <- TRUE # logical
today <- Sys.time() # date time
as.character(age)
## [1] "30"
as.character(score)
## [1] "9.8"
as.character(opt_course)
## [1] "TRUE"
as.character(today)
## [1] "2019-03-03 09:24:28"
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16. Logical
Logical data types take only 2 values. Either TRUE or FALSE. Such data types are created when we compare
two objects in R using comparison or logical operators.
create two variables x and y
assign them the values TRUE and FALSE respectively
use is.logical() to check data type
use as.logical() to coerce other data types to logical
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# create variables x and y
x <- TRUE
y <- FALSE
# check data type
class(x)
## [1] "logical"
is.logical(y)
## [1] TRUE
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17. Logical
The outcome of comparison operators is always logical. In the below example, we compare two numbers to
see the outcome.
# create two numeric variables
x <- 3
y <- 4
# compare x and y
x > y
## [1] FALSE
x < y
## [1] TRUE
# store the result
z <- x > y
class(z)
## [1] "logical"
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18. Logical
TRUE is represented by all numbers except 0. FALSE is represented only by 0 and no other numbers.
# TRUE and FALSE are represented by 1 and 0
as.logical(1)
## [1] TRUE
as.logical(0)
## [1] FALSE
# using numbers
as.numeric(TRUE)
## [1] 1
as.numeric(FALSE)
## [1] 0
# using different numbers
as.logical(-2, -1.5, -1, 0, 1, 2)
## [1] TRUE
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19. Logical
Use as.logical() to coerce other data types to logical.
# create variable of different data types
age <- as.integer(30) # integer
score <- 9.8 # numeric/double
opt_course <- TRUE # logical
today <- Sys.time() # date time
as.logical(age)
## [1] TRUE
as.logical(score)
## [1] TRUE
as.logical(opt_course)
## [1] TRUE
as.logical(today)
## [1] TRUE
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20. Summary
Variables are the building blocks of a programming language
Variables reference memory locations that store information/data
An assignment operator <- assigns values to a variable
A variable has the following components:
name
memory address
value
data type
Certain rules must be followed while naming variables
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21. Summary
numeric, integer, character, logical and date are the basic data types in R
class() or typeof() return the data type
is.data_type checks whether the data is of the specified data type
is.numeric()
is.integer()
is.character()
is.logical()
is.date()
as.data_type will coerce objects to the specified data type
as.numeric()
as.integer()
as.character()
as.logical()
as.date()
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