SAS 11/01
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The SAS program creates a data set with the student names and scores, uses PROC MEANS to calculate the mean and sum for each student, and outputs the results in two tables. Data _null_; input Names $ Subject Score; cards; John Math 90 John English 96 John French 87 John Physics 45 John History 77 Mary Math 80 Mary English 60 Mary French 87 Mary Physics 65 Mary History 66 Dick Math 78 Dick English 78 Dick French 56 Dick Physics 34 Dick History 88 Lucy Math 74 Luc
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# Filename: ProgrammingBasics.R
# ---Simple Calculations---
2 + 3
x <- 2
y <- 3
x + y
x * y
# ---Data Structures---
# Vectors
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
print(workshop)
workshop
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
q1 <- c(1, 2, 2, 3, 4, 5, 5, 4)
q2 <- c(1, 1, 2, 1, 5, 4, 3, 5)
q3 <- c(5, 4, 4,NA, 2, 5, 4, 5)
q4 <- c(1, 1, 3, 3, 4, 5, 4, 5)
# Selecting Elements of Vectors
q1[5]
q1[ c(5, 6, 7, 8) ]
q1[5:8]
q1[gender == \"m\"]
mean( q1[ gender == \"m\" ], na.rm = TRUE)
# ---Factors---
# Numeric Factors
# First, as a vector
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop
table(workshop)
mean(workshop)
gender[workshop == 2]
# Now as a factor
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(workshop)
workshop
table(workshop)
mean(workshop) #generates error now.
gender[workshop == 2]
gender[workshop == \"2\"]
# Recreate workshop, making it a factor
# including levels that don\'t yet exist.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1, 2, 3, 4),
labels = c(\"R\", \"SAS\", \"SPSS\", \"Stata\")
)
# Recreate it with just the levels it
# curently has.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1, 2),
labels = c(\"R\",\"SAS\")
)
workshop
table(workshop)
gender[workshop == 2]
gender[workshop == \"2\"]
gender[workshop == \"SAS\"]
# Character factors
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
gender <- factor(
gender,
levels = c(\"m\", \"f\"),
labels = c(\"Male\", \"Female\")
)
gender
table(gender)
workshop[gender == \"m\"]
workshop[gender == \"Male\"]
# Recreate gender and make it a factor,
# keeping simpler m and f as labels.
gender <- c(\"f\", \"f\", \"f\", NA, \"m\", \"m\", \"m\", \"m\")
gender <- factor(gender)
gender
# Data Frames
mydata <- data.frame(workshop, gender, q1, q2, q3, q4)
mydata
names(mydata)
row.names(mydata)
# Selecting components by index number
mydata[8, 6] #8th obs, 6th var
mydata[ , 6] #All obs, 6th var
mydata[ , 6][5:8] #6th var, obs 5:8
# Selecting components by name
mydata$q1
mydata$q1[5:8]
# Example renaming gender to sex while
# creating a data frame (left as a comment)
#
# mydata <- data.frame(workshop, sex = gender,
# q1, q2, q3, q4)
# Matrices
# Creating from vectors
mymatrix <- cbind(q1, q2, q3, q4)
mymatrix
dim(mymatrix)
# Creating from matrix function
# left as a comment so we keep
# version with names q1, q2...
#
# mymatrix <- matrix(
# c(1, 1, 5, 1,
# 2, 1, 4, 1,
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Functions.ppt
1. A function is a relation where each input is mapped to exactly one output. It is not a function if one input has more than one output.
2. For a relation to be a function, every input must have an output and two different inputs cannot have the same output. One input can only have one output.
3. The vertical line test can be used to visually check if a relation is a function - if any vertical line intersects the graph at more than one point, it is not a function.
R code for data manipulation
R code can be used for various data manipulation tasks such as creating, recoding, and renaming variables; sorting and merging datasets; aggregating and reshaping data; and subsetting datasets. Specific R functions and operations allow users to efficiently manipulate data frames through actions like transposing data, calculating summary statistics, and selecting subsets of observations and variables.
R code for data manipulation
R code can be used for various data manipulation tasks such as creating, recoding, and renaming variables; sorting and merging datasets; aggregating and reshaping data; and subsetting datasets. Specific R functions and operations allow users to efficiently manipulate data frames through actions like transposing data, calculating summary statistics, and selecting subsets of observations and variables.
Ggplot2 work
This document introduces examples of using R and the ggplot2 library to create plots from data. It discusses loading data, examining data structures, extracting variables, and creating bar plots of categorical variables. In particular, it shows how to create a bar plot of students' academic level and discusses reordering the levels using the reorder() function to put them in a more logical order from freshman to senior.
R and data mining
This document summarizes R and data mining. It introduces R language features including vectors, factors, arrays, matrices, data frames, lists, and functions. It also discusses R text mining frameworks like the 'tm' package, and preprocessing text data in R using packages like rmmseg4j, openNLP, Rstem, and Snowball. Finally, it briefly mentions high performance computing in R, network analysis in R, and statistical graphics.
INTRODUCTION TO MATLAB presentation.pptx
Here are the steps to solve this problem numerically in MATLAB:
1. Define the 2nd order ODE for the pendulum as two first order equations:
y1' = y2
y2' = -sin(y1)
2. Create an M-file function pendulum.m that returns the right hand side:
function dy = pendulum(t,y)
dy = [y(2); -sin(y(1))];
end
3. Use an ODE solver like ode45 to integrate from t=0 to t=6pi with initial conditions y1(0)=pi, y2(0)=0:
[t,y] = ode45
R programming
About R programming, it's data type, Key, Basic computation in R,
Control Structure, If Condition, Import Export Data.
Matlab Workshop Presentation
MATLAB is a programming language for technical computing and data analysis. It allows users to perform arithmetic operations, use mathematical functions, manipulate data, create plots and graphs, write programs using conditional statements and loops. The document provides an overview of MATLAB's interface and important terms, and demonstrates how to perform common tasks like creating arrays and matrices, plotting data, writing for and while loops, and defining functions. MATLAB is relevant for tasks in many engineering fields like signal processing, optimization, finite element analysis, and statistical analysis.
INTRODUCTION TO STATA.pptx
This document provides an introduction and overview of Stata, a statistical software package. It discusses what Stata is, the different versions of Stata, the Stata interface and how to navigate it. It also summarizes key functions for working with data in Stata, including importing and exporting data, exploring and managing variables, and conducting statistical analysis. The document is intended as a starting point for learning to use Stata.
20100528
The document provides examples of using MATLAB to perform calculations and functions. It demonstrates operations like matrix multiplication, taking the inverse of a matrix, reshaping arrays, and using functions like sort, sin, and cos. It also shows accessing elements of matrices and arrays, defining vectors and matrices, and returning errors when inputs are invalid.
20100528
The document provides examples of using MATLAB to perform calculations and functions. It demonstrates operations like matrix multiplication, taking the inverse of a matrix, reshaping arrays, and using functions like sort, sin, and cos. It also shows accessing elements of matrices and arrays, defining vectors and matrices, and returning errors when inputs are invalid.
Matlab ppt
MATLAB is a numerical computing environment and programming language. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, and interfacing with programs in other languages. MATLAB can be used for applications like signal processing, image processing, control systems, and computational finance. It offers advantages like ease of use, platform independence, and predefined functions. However, it can sometimes be slow and is commercial software. The MATLAB interface includes a command window, current directory, workspace, and command history. Arrays are fundamental data types in MATLAB and can be vectors, matrices, or multidimensional. Variables are used to store information in the workspace and can represent different data types. Common operations include arithmetic, functions, and following the
Introduction To Lisp
Introduction to Lisp. A survey of lisp's history, current incarnations and advanced features such as list comprehensions, macros and domain-specific-language [DSL] support.
Adobe
The document describes several Adobe interview test papers that the author took. It provides examples of questions asked in sections on coding (C/Java), data structures, algorithms, quantitative aptitude, and logical reasoning. Some example questions include finding the fourth smallest element in a binary search tree, reversing a linked list, checking if all computers are connected in a network, and problems involving arithmetic, triangles, and pie charts. The tests focused on fundamental concepts in coding, data structures, algorithms, and math.
Storyboard math
1. The document discusses relations and functions, including identifying their domain and range. It provides examples of plotting points on a Cartesian plane and using graphs to represent equations.
2. Functions are defined as relations where each input is mapped to only one output. Several examples are given to demonstrate determining if a relation qualifies as a function using techniques like the vertical line test.
3. The key concepts of domain, range, and using graphs are illustrated through multiple examples of sketching and analyzing relations and functions.
bobok
This document provides an overview of MATLAB, including:
- MATLAB is a software package for numerical computation, originally designed for linear algebra problems using matrices. It has since expanded to include other scientific computations.
- MATLAB treats all variables as matrices and supports various matrix operations like addition, multiplication, element-wise operations, and matrix manipulation functions.
- MATLAB allows plotting of 2D and 3D graphics, importing/exporting of data from files and Excel, and includes flow control statements like if/else, for loops, and while loops to structure code execution.
- Efficient MATLAB programming involves using built-in functions instead of custom functions, preallocating arrays, and avoiding nested loops where possible through matrix operations.
MA3696 Lecture 5
The document discusses more advanced looping techniques in VBA, including looping through userform controls, generalizing loops, nested for-next loops, and one-dimensional arrays. It provides examples of declaring and filling 1D arrays, as well as using for-next loops to output array values to cells or ranges. Exercises are included to practice these skills by rewriting code using arrays and loops.
Similar to SAS 11/01 (20)
R is a very flexible and powerful programming language, as well as a.pdf
R is a very flexible and powerful programming language, as well as a.pdf
SAS 11/01
- 1. PROC STANDARD SAS FUNCTIONS SAS OPERATIONS Canicia Smith caniciasmith@gmail.com Office hour: Wed 13:00-14:00
- 3. What does proc standard do? 1. It standardizes variables in a data set to a given mean and standard deviation. 2. It creates a new data set containing the standardized values http://support.sas.com/documentation/cdl/en/proc/61895/HTM L/default/viewer.htm#a000146748.htm
- 7. SAMPLE STATISTICS Mean =Mean(var1, var2) Sum =Sum(var1, var2) Square root =Sqrt(var) Round =round(var, .1)
- 8. SAMPLE STATISTICS (con.) Max max(argument1, argument2) Min min(argument1, argument2) Mod mod(argument1,argument-2) Eg. 9/3 Argument1- the dividend=9 Argument2-the divisor=3
- 10. PROBABILITY Ex. Poisson probability distribution cdf (‘POISSON’, n, m) N is an integer random variable (range: n=0,1, ..) M is a numeric mean parameter (range: m>0)
- 11. EXPONENTIAL • Exponential x=exp(1.0)=2.718 exp (argument)
- 12. SAS OPERATORS
- 13. SAS OPERATORS (ARITHMETIC) Symbol Definition Example raise to a ** a**3 power * multiplication 2*y / division var/5 + addition num+3 var1- - subtraction var2
- 14. SAS OPERATORS (COMPARISON) Symbo Mnemonic Definition Example l Equivalent = EQ equal to a=3 ^= NE not equal to a ne 3 > GT greater than num>5 < LT less than num<5 greater than or equal >= GE var>=300 to <= LE less than or equal to var<=300 num in (3, 4, IN IN equal to one of a list 5)
- 15. SAS OPERATORS (LOGICAL) Symbol Mnemonic Equivalent Example & AND (a>b) & (c>d) / OR (a>b) / (c>d) ^ NOT not (a>b)
- 16. QUESTIONS~?????
- 17. Possible Exam questions Create a data set to give this output. Which statement should be inserted to produce the group output?
- 18. Create a data set to get this output..
- 19. Proc means What statement would produce the following output?
- 20. What statements do you need to produce the second output?
- 21. 5 students name John (boy), Mary (girl), Dick (boy), Lucy (girl), and James (boy) has the following scores in the following subjects: Names Subject John Mary Dick Lucy James Math 90 80 78 74 70 English 96 60 78 50 74 French 87 87 56 78 95 Physics 45 65 34 77 73 History 77 66 88 56 89 1. Write a sas program to output this data. 2. Find the mean and sum of each student’s grades.