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  • How to make GhostScript PS2PDF stop subsetting fonts

    - by gavin-softyolk
    I am using the ps2pdf14 utility that ships with GhostScript, and I am having a problem with fonts. It does not seem to matter what instructions I pass to the command, it insists on subsetting any fonts it finds in the source document. e.g -dPDFSETTINGS#/prepress -dEmbedAllFonts#true -dSubsetFonts#false -dMaxSubsetPct#0 Note that the # is because the command is running on windows, it is the same as =. If anyone has any idea how to tell ps2pdf not to subset fonts, I would be very greatful. Thanks --------------------------Notes ------------------------------------------ The source file is a pdf containing embedded fonts, so it is the fonts already embedded in the source file, that I need to prevent being subset in the destination file. Currently all source file embedded fonts are subset, in some cases this is not apparent from the font name, i.e it contains no hash, and appears at first glance to be the full font, however the widths array has been subset in all cases.

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  • Subsetting a data frame in a function using another data frame as parameter

    - by lecodesportif
    I would like to submit a data frame to a function and use it to subset another data frame. This is the basic data frame: foo <- data.frame(var1= c('1', '1', '1', '2', '2', '3'), var2=c('A', 'A', 'B', 'B', 'C', 'C')) I use the following function to find out the frequencies of var2 for specified values of var1. foobar <- function(x, y, z){ a <- subset(x, (x$var1 == y)) b <- subset(a, (a$var2 == z)) n=nrow(b) return(n) } Examples: foobar(foo, 1, "A") # returns 2 foobar(foo, 1, "B") # returns 1 foobar(foo, 3, "C") # returns 1 This works. But now I want to submit a data frame of values to foobar. Instead of the above examples, I would like to submit df to foobar and get the same results as above (2, 1, 1) df <- data.frame(var1=c('1','1','3'), var2=c("A", "B", "C")) When I change foobar to accept two arguments like foobar(foo, df) and use y[, c(var1)] and y[, c(var2)] instead of the two parameters x and y it still doesn't work. Which way is there to do this?

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  • R - Subsetting XTS via Time and Which

    - by user2844947
    Currently, I have a XTS, called Data, which contains a Date, and two value columns which are numbers. I would like to get a single number as output and which would be the mean of Value1 in the time period from a point where Value2 < mean(Value2) and going forward 14 data points, weeks in this particular data set. In order to get the dates where Value2 < mean(Value2), I wrote the below code Data[which(Data$Value2 < mean(Data$Value2)),"Date"] However, I am not sure how to get the mean of Value1 in the period, going 14 days forward from each of the resultant dates from the above code.

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  • Plotting andrews curves of subsets of a data frame on the same plot

    - by user2976477
    I have a data frame of 12 columns and I want to plot andrews curves in R of this data, basing the color of the curves on the 12th columns. Below are a few samples from the data (sorry the columns are not aligned with the numbers) Teacher_explaining Teacher_enthusiastic Teacher_material_interesting Material_stimulating Material_useful Clear_marking Marking_fair Feedback_prompt Feedback_clarifies Detailed_comments Notes Year 80 80 80 80 85 85 80 80 80 80 70 3 70 60 30 40 70 60 30 40 70 0 30 3 100 90 90 80 80 100 100 90 100 100 100 MSc 85 85 85 90 90 70 90 50 70 80 100 MSc 90 50 90 90 90 70 100 50 80 100 100 4 100 80 80 75 90 80 80 50 80 80 90 3 From this data I tried to plot andrews curves using the code below: install.packages("andrews") library(andrews) col <- as.numeric(factor(course[,12])) andrews(course[,1:12], clr = 12) However, the 12th column has three groups (3 types of responses) and I want to group two of them and then plot the andrews curve of the data, without editing my data frame in Excel. x <- subset(course, Year == "MSc" & "4") y <- subset(course, Year == "3") I tried the above code, but my argument for x don't work. "MSc", "3" and "4" are the groups in the 12th column, and I want to group MSc and 4 so that their Andrews curves have the same color. If you have any idea how to do this, please let me know.

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  • R: Are there any alternatives to loops for subsetting from an optimization standpoint?

    - by Adam
    A recurring analysis paradigm I encounter in my research is the need to subset based on all different group id values, performing statistical analysis on each group in turn, and putting the results in an output matrix for further processing/summarizing. How I typically do this in R is something like the following: data.mat <- read.csv("...") groupids <- unique(data.mat$ID) #Assume there are then 100 unique groups results <- matrix(rep("NA",300),ncol=3,nrow=100) for(i in 1:100) { tempmat <- subset(data.mat,ID==groupids[i]) #Run various stats on tempmat (correlations, regressions, etc), checking to #make sure this specific group doesn't have NAs in the variables I'm using #and assign results to x, y, and z, for example. results[i,1] <- x results[i,2] <- y results[i,3] <- z } This ends up working for me, but depending on the size of the data and the number of groups I'm working with, this can take up to three days. Besides branching out into parallel processing, is there any "trick" for making something like this run faster? For instance, converting the loops into something else (something like an apply with a function containing the stats I want to run inside the loop), or eliminating the need to actually assign the subset of data to a variable?

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  • Deterministic/Consistent Unique Masking

    - by Dinesh Rajasekharan-Oracle
    One of the key requirements while masking data in large databases or multi database environment is to consistently mask some columns, i.e. for a given input the output should always be the same. At the same time the masked output should not be predictable. Deterministic masking also eliminates the need to spend enormous amount of time spent in identifying data relationships, i.e. parent and child relationships among columns defined in the application tables. In this blog post I will explain different ways of consistently masking the data across databases using Oracle Data Masking and Subsetting The readers of post should have minimal knowledge on Oracle Enterprise Manager 12c, Application Data Modeling, Data Masking concepts. For more information on these concepts, please refer to Oracle Data Masking and Subsetting document Oracle Data Masking and Subsetting 12c provides four methods using which users can consistently yet irreversibly mask their inputs. 1. Substitute 2. SQL Expression 3. Encrypt 4. User Defined Function SUBSTITUTE The substitute masking format replaces the original value with a value from a pre-created database table. As the method uses a hash based algorithm in the back end the mappings are consistent. For example consider DEPARTMENT_ID in EMPLOYEES table is replaced with FAKE_DEPARTMENT_ID from FAKE_TABLE. The substitute masking transformation that all occurrences of DEPARTMENT_ID say ‘101’ will be replaced with ‘502’ provided same substitution table and column is used , i.e. FAKE_TABLE.FAKE_DEPARTMENT_ID. The following screen shot shows the usage of the Substitute masking format with in a masking definition: Note that the uniqueness of the masked value depends on the number of columns being used in the substitution table i.e. if the original table contains 50000 unique values, then for the masked output to be unique and deterministic the substitution column should also contain 50000 unique values without which only consistency is maintained but not uniqueness. SQL EXPRESSION SQL Expression replaces an existing value with the output of a specified SQL Expression. For example while masking an EMPLOYEES table the EMAIL_ID of an employee has to be in the format EMPLOYEE’s [email protected] while FIRST_NAME and LAST_NAME are the actual column names of the EMPLOYEES table then the corresponding SQL Expression will look like %FIRST_NAME%||’.’||%LAST_NAME%||’@COMPANY.COM’. The advantage of this technique is that if you are masking FIRST_NAME and LAST_NAME of the EMPLOYEES table than the corresponding EMAIL ID will be replaced accordingly by the masking scripts. One of the interesting aspect’s of a SQL Expressions is that you can use sub SQL expressions, which means that you can write a nested SQL and use it as SQL Expression to address a complex masking business use cases. SQL Expression can also be used to consistently replace value with hashed value using Oracle’s PL/SQL function ORA_HASH. The following SQL Expression will help in the previous example for replacing the DEPARTMENT_IDs with a hashed number ORA_HASH (%DEPARTMENT_ID%, 1000) The following screen shot shows the usage of encrypt masking format with in the masking definition: ORA_HASH takes three arguments: 1. Expression which can be of any data type except LONG, LOB, User Defined Type [nested table type is allowed]. In the above example I used the Original value as expression. 2. Number of hash buckets which can be number between 0 and 4294967295. The default value is 4294967295. You can also co-relate the number of hash buckets to a range of numbers. In the above example above the bucket value is specified as 1000, so the end result will be a hashed number in between 0 and 1000. 3. Seed, can be any number which decides the consistency, i.e. for a given seed value the output will always be same. The default seed is 0. In the above SQL Expression a seed in not specified, so it to 0. If you have to use a non default seed then the function will look like. ORA_HASH (%DEPARTMENT_ID%, 1000, 1234 The uniqueness depends on the input and the number of hash buckets used. However as ORA_HASH uses a 32 bit algorithm, considering birthday paradox or pigeonhole principle there is a 0.5 probability of collision after 232-1 unique values. ENCRYPT Encrypt masking format uses a blend of 3DES encryption algorithm, hashing, and regular expression to produce a deterministic and unique masked output. The format of the masked output corresponds to the specified regular expression. As this technique uses a key [string] to encrypt the data, the same string can be used to decrypt the data. The key also acts as seed to maintain consistent outputs for a given input. The following screen shot shows the usage of encrypt masking format with in the masking definition: Regular Expressions may look complex for the first time users but you will soon realize that it’s a simple language. There are many resources in internet, oracle documentation, oracle learning library, my oracle support on writing a Regular Expressions, out of all the following My Oracle Support document helped me to get started with Regular Expressions: Oracle SQL Support for Regular Expressions[Video](Doc ID 1369668.1) USER DEFINED FUNCTION [UDF] User Defined Function or UDF provides flexibility for the users to code their own masking logic in PL/SQL, which can be called from masking Defintion. The standard format of an UDF in Oracle Data Masking and Subsetting is: Function udf_func (rowid varchar2, column_name varchar2, original_value varchar2) returns varchar2; Where • rowid is the row identifier of the column that needs to be masked • column_name is the name of the column that needs to be masked • original_value is the column value that needs to be masked You can achieve deterministic masking by using Oracle’s built in hash functions like, ORA_HASH, DBMS_CRYPTO.MD4, DBMS_CRYPTO.MD5, DBMS_UTILITY. GET_HASH_VALUE.Please refers to the Oracle Database Documentation for more information on the Oracle Hash functions. For example the following masking UDF generate deterministic unique hexadecimal values for a given string input: CREATE OR REPLACE FUNCTION RD_DUX (rid varchar2, column_name varchar2, orig_val VARCHAR2) RETURN VARCHAR2 DETERMINISTIC PARALLEL_ENABLE IS stext varchar2 (26); no_of_characters number(2); BEGIN no_of_characters:=6; stext:=substr(RAWTOHEX(DBMS_CRYPTO.HASH(UTL_RAW.CAST_TO_RAW(text),1)),0,no_of_characters); RETURN stext; END; The uniqueness depends on the input and length of the string and number of bits used by hash algorithm. In the above function MD4 hash is used [denoted by argument 1 in the DBMS_CRYPTO.HASH function which is a 128 bit algorithm which produces 2^128-1 unique hashed values , however this is limited by the length of the input string which is 6, so only 6^6 unique values will be generated. Also do not forget about the birthday paradox/pigeonhole principle mentioned earlier in this post. An another example is to consistently replace characters or numbers preserving the length and special characters as shown below: CREATE OR REPLACE FUNCTION RD_DUS(rid varchar2,column_name varchar2,orig_val VARCHAR2) RETURN VARCHAR2 DETERMINISTIC PARALLEL_ENABLE IS stext varchar2(26); BEGIN DBMS_RANDOM.SEED(orig_val); stext:=TRANSLATE(orig_val,'ABCDEFGHILKLMNOPQRSTUVWXYZ',DBMS_RANDOM.STRING('U',26)); stext:=TRANSLATE(stext,'abcdefghijklmnopqrstuvwxyz',DBMS_RANDOM.STRING('L',26)); stext:=TRANSLATE(stext,'0123456789',to_char(DBMS_RANDOM.VALUE(1,9))); stext:=REPLACE(stext,'.','0'); RETURN stext; END; The following screen shot shows the usage of an UDF with in a masking definition: To summarize, Oracle Data Masking and Subsetting helps you to consistently mask data across databases using one or all of the methods described in this post. It saves the hassle of identifying the parent-child relationships defined in the application table. Happy Masking

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  • OpenWorld 2012—Is Almost Here!

    - by Scott McNeil
    With OpenWorld fast approaching, I thought I would take this opportunity to look at some of the “must see” database manageability activities and sessions happening this year. Here's a quick run down: Oracle Database Manageability: Download all the details for sessions, hands-on-labs, and demos (PDF) Keynotes: Sunday, September 30 Hardware and Software, Engineered to Work Together: Why It’s A Different Approach Larry Ellison, CEO, Oracle Monday, October 1 Shift Complexity Hosted by Mark Hurd, President, Oracle Andrew Mendelsohn, Senior Vice President, Database Server Technologies, Oracle IOUG SIG Sunday: Database Performance Tuning: Getting the Best out of Oracle Enterprise Manager Cloud Control 12c (session ID# CON6511) Oracle DEMOgrounds: Floor plan – Moscone South Automatic Application and SQL Tuning Automatic Performance Diagnostics Complete Database Lifecycle Management Data Masking and Data Subsetting Database Testing with Oracle Real Application Testing Oracle Enterprise Manager Cloud Control 12c Overview Oracle Exadata Management Hands-on-Labs: Database Performance Testing, Data Masking, and Subsetting (session ID# HOL10720) Database Performance Tuning Hands-on Lab (session ID# HOL10393) Sessions: What’s Next for Oracle Database? (session ID# GEN8259) Building and Managing a Private Oracle Database Cloud (session ID# GEN11421) Using Oracle Enterprise Manager to Manage Your Own Private Cloud (session ID# GEN11423) Extreme Database Management with the Latest Generation of Database Technology (session ID# CON9547) Oracle OpenWorld Music Festival New this year is Oracle’s first annual Oracle OpenWorld Musical Festival, featuring some of today's breakthrough musicians from around the country and the world. It's five nights of back-to-back performances in the heart of San Francisco—free to registered attendees. See the lineup Not Heading to OpenWorld—Watch it Live! Stay Connected: Twitter | Facebook | YouTube | Linkedin | Newsletter Download the Oracle Enterprise Manager Cloud Control12c Mobile app

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  • OpenWorld - Database Security Demonstrations in Moscone South Left

    - by Troy Kitch
    All this week, Oracle security experts will be giving live product demos of Oracle Database Security solutions in Moscone South Left, in the Oracle DEMOgrounds for "database." Demonstrations include Oracle Database Defense-in-Depth Security, Database Application Data Redaction, Transparent Data Encryption, Oracle Audit Vault and Database Firewall, Data Masking and Data Subsetting. Don't miss it!

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  • Today at Oracle OpenWorld 2012

    - by Scott McNeil
    We have another full day of great Oracle OpenWorld keynotes, sessions, demos and customer presentations in the Seen and Be Heard threater. Here's a quick run down of what's happening today with Oracle Enterprise Manager 12c: Download the Oracle Enterprise Manager 12c OpenWorld schedule (PDF) Oracle Enterprise Manager Cloud Control 12c (and Private Cloud) General Session Tues 2 Oct, 2012 Time Title Location 11:45 AM - 12:45 PM General Session: Using Oracle Enterprise Manager to Manage Your Own Private Cloud Moscone South - 103* 1:15 PM - 2:15 PM General Session: Breakthrough Efficiency in Private Cloud Infrastructure Moscone West - 3014 Conference Session Tues 2 Oct, 2012 Time Title Location 10:15 AM - 11:15 AM Oracle Exadata/Oracle Enterprise Manager 12c: Journey into Oracle Database Cloud Moscone West - 3018 10:15 AM - 11:15 AM Bulletproof Your Application Upgrades with Secure Data Masking and Subsetting Moscone West - 3020 10:15 AM - 11:15 AM Oracle Enterprise Manager 12c: Architecture Deep Dive, Tips, and Techniques Moscone South - 303 11:45 AM - 12:45 PM RDBMS Forensics: Troubleshooting with Active Session History Moscone West - 3018 11:45 AM - 12:45 PM Building and Operationalizing Your Data Center Environment with Oracle Exalogic Moscone South - 309 11:45 AM - 12:45 PM Securely Building a National Electronic Health Record: Singapore Case Study Westin San Francisco - Concordia 1:15 PM - 2:15 PM Managing Heterogeneous Environments with Oracle Enterprise Manager Moscone West - 3018 1:15 PM - 2:15 PM Complete Oracle WebLogic Server Management with Oracle Enterprise Manager 12c Moscone South - 309 1:15 PM - 2:15 PM Database Lifecycle Management with Oracle Enterprise Manager 12c Moscone West - 3020 1:15 PM - 2:15 PM Best Practices, Key Features, Tips, Techniques for Oracle Enterprise Manager 12c Upgrade Moscone South - 307 1:15 PM - 2:15 PM Enterprise Cloud with CSC’s Foundation Services for Oracle and Oracle Enterprise Manager 12c Moscone South - 236 5:00 PM - 6:00 PM Deep Dive 3-D on Oracle Exadata Management: From Discovery to Deployment to Diagnostics Moscone West - 3018 5:00 PM - 6:00 PM Everything You Need to Know About Monitoring and Troubleshooting Oracle GoldenGate Moscone West - 3005 5:00 PM - 6:00 PM Oracle Enterprise Manager 12c: The Nerve Center of Oracle Cloud Moscone West - 3020 5:00 PM - 6:00 PM Advanced Management of Oracle E-Business Suite with Oracle Enterprise Manager Moscone West - 2016 5:00 PM - 6:00 PM Oracle Enterprise Manager 12c Cloud Control Performance Pages: Falling in Love Again Moscone West - 3014 Hands-on Labs Tues 2 Oct, 2012 Time Title Location 10:15 AM - 12:45 PM Managing the Cloud with Oracle Enterprise Manager 12c Marriott Marquis - Salon 5/6 1:15 PM - 2:15 PM Database Performance Tuning Hands-on Lab Marriott Marquis - Salon 5/6 Scene and Be Heard Theater Session Tues 2 Oct, 2012 Time Title Location 10:30 AM - 10:50 AM Start Small, Grow Big: Hands-On Oracle Private Cloud—A Step-by-Step Guide Moscone South Exhibition Hall - Booth 2407 12:30 PM - 12:50 PM Blue Medora’s Oracle Enterprise Manager Plug-in for VMware vSphere Monitoring Moscone South Exhibition Hall - Booth 2407 Demos Demo Location Application and Infrastructure Testing Moscone West - W-092 Automatic Application and SQL Tuning Moscone South, Left - S-042 Automatic Fault Diagnostics Moscone South, Left - S-036 Automatic Performance Diagnostics Moscone South, Left - S-033 Complete Care for Oracle Using My Oracle Support Moscone South, Left - S-031 Complete Cloud Lifecycle Management Moscone North, Upper Lobby - N-019 Complete Database Lifecycle Management Moscone South, Left - S-030 Comprehensive Infrastructure as a Service via Oracle Enterprise Manager Moscone South, Left - S-045 Data Masking and Data Subsetting Moscone South, Left - S-034 Database Testing with Oracle Real Application Testing Moscone South, Left - S-041 Identity Management Monitoring with Oracle Enterprise Manager Moscone South, Right - S-212 Mission-Critical, SPARC-Powered Infrastructure as a Service Moscone South, Center - S-157 Oracle E-Business Suite, Siebel, JD Edwards, and PeopleSoft Management Moscone West - W-084 Oracle Enterprise Manager Cloud Control 12c Overview Moscone South, Left - S-039 Oracle Enterprise Manager: Complete Data Center Management Moscone South, Left - S-040 Oracle Exadata Management Moscone South, Center - Oracle Exalogic Management Moscone South, Center - Oracle Fusion Applications Management Moscone West - W-018 Oracle Real User Experience Insight Moscone South, Right - S-226 Oracle WebLogic Server Management and Java Diagnostics Moscone South, Right - S-206 Platform as a Service Using Oracle Enterprise Manager Moscone North, Upper Lobby - N-020 SOA Management Moscone South, Right - S-225 Self-Service Application Testing on Private and Public Clouds Moscone West - W-110 Oracle OpenWorld Music Festival New this year is Oracle’s first annual Oracle OpenWorld Musical Festival, featuring some of today's breakthrough musicians from around the country and the world. It's five nights of back-to-back performances in the heart of San Francisco—free to registered attendees. See the lineup Not Heading to OpenWorld—Watch it Live! Stay Connected: Twitter | Facebook | YouTube | Linkedin | Newsletter Download the Oracle Enterprise Manager Cloud Control12c Mobile app

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  • Offline web font optimization tool

    - by avok00
    I have a few web fonts on my web site that I want to reduce in size. I tried http://www.fontsquirrel.com/fontface/generator with very good results, but I need an offline professional tool to rely on. Can somebody recommend such a tool? I am not a specialist font creator, so I need something like a wizard that can guide me through font optimization. Any suggestion is much appretiated! EDIT: To make myself more clear, I need a font subsetting tool

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  • Subset and lagging list data structure R

    - by user1234440
    I have a list that is indexed like the following: >list.stuff [[1]] [[1]]$vector ... [[1]]$matrix .... [[1]]$vector [[2]] null [[3]] [[3]]$vector ... [[3]]$matrix .... [[3]]$vector . . . Each segment in the list is indexed according to another vector of indexes: >index.list 1, 3, 5, 10, 15 In list.stuff, only at each of the indexes 1,3,5,10,15 will there be 2 vectors and one matrix; everything else will be null like [[2]]. What I want to do is to lag like the lag.xts function so that whatever is stored in [[1]] will be pushed to [[3]] and the last one drops off. This also requires subsetting the list, if its possible. I was wondering if there exists some functions that handle list manipulation. My thinking is that for xts, a time series can be extracted based on an index you supply: xts.object[index,] #returns the rows 1,3,5,10,15 From here I can lag it with: lag.xts(xts.object[index,]) Any help would be appreciated thanks: EDIT: Here is a reproducible example: list.stuff<-list() vec<-c(1,2,3,4,5,6,7,8,9) vec2<-c(1,2,3,4,5,6,7,8,9) mat<-matrix(c(1,2,3,4,5,6,7,8),4,2) list.vec.mat<-list(vec=vec,mat=mat,vec2=vec2) ind<-c(2,4,6,8,10) for(i in ind){ list.stuff[[i]]<-list.vec.mat }

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  • Workflow for statistical analysis and report writing

    - by ws
    Does anyone have any wisdom on workflows for data analysis related to custom report writing? The use-case is basically this: Client commissions a report that uses data analysis, e.g. a population estimate and related maps for a water district. The analyst downloads some data, munges the data and saves the result (e.g. adding a column for population per unit, or subsetting the data based on district boundaries). The analyst analyzes the data created in (2), gets close to her goal, but sees that needs more data and so goes back to (1). Rinse repeat until the tables and graphics meet QA/QC and satisfy the client. Write report incorporating tables and graphics. Next year, the happy client comes back and wants an update. This should be as simple as updating the upstream data by a new download (e.g. get the building permits from the last year), and pressing a "RECALCULATE" button, unless specifications change. At the moment, I just start a directory and ad-hoc it the best I can. I would like a more systematic approach, so I am hoping someone has figured this out... I use a mix of spreadsheets, SQL, ARCGIS, R, and Unix tools. Thanks! PS: Below is a basic Makefile that checks for dependencies on various intermediate datasets (w/ ".RData" suffix) and scripts (".R" suffix). Make uses timestamps to check dependencies, so if you 'touch ss07por.csv', it will see that this file is newer than all the files / targets that depend on it, and execute the given scripts in order to update them accordingly. This is still a work in progress, including a step for putting into SQL database, and a step for a templating language like sweave. Note that Make relies on tabs in its syntax, so read the manual before cutting and pasting. Enjoy and give feedback! http://www.gnu.org/software/make/manual/html%5Fnode/index.html#Top R=/home/wsprague/R-2.9.2/bin/R persondata.RData : ImportData.R ../../DATA/ss07por.csv Functions.R $R --slave -f ImportData.R persondata.Munged.RData : MungeData.R persondata.RData Functions.R $R --slave -f MungeData.R report.txt: TabulateAndGraph.R persondata.Munged.RData Functions.R $R --slave -f TabulateAndGraph.R report.txt

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  • How to use an adjacency matrix to determine which rows to 'pass' to a function in r?

    - by dubhousing
    New to R, and I have a long-ish question: I have a shapefile/map, and I'm aiming to calculate a certain index for every polygon in that map, based on attributes of that polygon and each polygon that neighbors it. I have an adjacency matrix -- which I think is the same as a "1st-order queen contiguity weights matrix", although I'm not sure -- that describes which polygons border which other polygons, e.g., POLYID A B C D E A 0 0 1 0 1 B 0 0 1 0 0 C 1 1 0 1 0 D 0 0 1 0 1 E 1 0 0 1 0 The above indicates, for instance, that polygons 'C' and 'E' adjoin polygon 'A'; polygon 'B' adjoins only polygon 'C', etc. The attribute table I have has one polygon per row: POLYID TOT L10K 10_15K 15_20K ... A 500 24 30 77 ... Where TOT, L10K, etc. are the variables I use to calculate an index. There are 525 polygons/rows in my data, so I'd like to use the adjacency matrix to determine which rows' attributes to incorporate into the calculation of the index of interest. For now, I can calculate the index when I subset the rows that correspond to one 'bundle' of neighboring polygons, and then use a loop (if it's of interest, I'm calculating the Centile Gap Index, a measure of local income segregation). E.g., subsetting the 'neighborhood' of the Detroit City Schools: Detroit <- UNSD00[c(142,150,164,221,226,236,295,327,157,177,178,364,233,373,418,424,449,451,487),] Then record the marginal column proportions and a running total: catprops <- vector() for(i in 4:19) { catprops[(i-3)]<-sum(Detroit[,i])/sum(Detroit[,3]) } catprops <- as.data.frame(catprops) catprops[,2]<-cumsum(catprops[,1]) Columns 4:19 are the necessary ones in the attribute table. Then I use the following code to calculate the index -- note that the loop has "i in 1:19" because the Detroit subset has 19 polygons. cgidistsum <- 0 for(i in 1:19) { pranks <- vector() for(j in 4:19) { if (Detroit[i,j]==0) pranks <- append(pranks,0) else if (j == 4) pranks <- append(pranks,seq(0,catprops[1,2],by=catprops[1,2]/Detroit[i,j])) else pranks <- append(pranks,seq(catprops[j-4,2],catprops[j-3,2],by=catprops[j-3,1]/Detroit[i,j])) } distpranks <- vector() distpranks<-abs(pranks-median(pranks)) cgidistsum <- cgidistsum + sum(distpranks) } cgi <- (.25-(cgidistsum/sum(Detroit[,3])))/.25 My apologies if I've provided more information than is necessary. I would really like to exploit the adjacency matrix in order to calculate the CGI for each 'bundle' of these rows. If you happen to know how I could started with this, that would be great. and my apologies for any novice mistakes, I'm new to R!

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