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Statistical Packages

Statistical packages are software titles, such as JMP and GNU Octave, and programming languages, such as MATLAB, R and SAS, that are used to discover, explore and analyze data and suggest useful conclusions, either to learn something unexpected or to confirm a hypothesis. The field includes the design and analysis of techniques to give approximate but accurate solutions to hard problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

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I have the following query on prior help and I added a column 'text1', change the Comp5 heading to Comp15 and I can't get 'text1' and Comp15 values on the result:
        PID INT ,
        TEXT1 VARCHAR(10),
Comp1 VARCHAR(10),
 Comp2 VARCHAR(10),
Comp3 VARCHAR(10),
Comp4 VARCHAR(10),
Comp15 VARCHAR(10)

VALUES ( 11122, '1212', NULL, NULL, NULL, NULL, NULL ) ,
         ( 12345, NULL, NULL, NULL, '123', NULL, NULL ) ,
       ( 23456, NULL, '234', NULL, 'ewr', NULL, NULL ) ,
       ( 34567, NULL, NULL, 'acc', NULL, NULL, 'def' ) ,
       ( 45678, NULL, NULL, NULL, 'jkl', NULL, NULL ) ,
       ( 56789, NULL, NULL, NULL, NULL, NULL, 'we1' ) ,
       ( 23450, NULL, 'abc', 'acc', 'exy', 'ert', 'def' );

WITH Unpivoted
     FROM   @TEMP4 T UNPIVOT(CompValue FOR CompType IN(Comp1, Comp2, Comp3, Comp4, Comp15)) U ) ,
            U.CompValue ,
                                              ORDER BY U.CompType ASC ) AS VARCHAR(255)) AS NewCompType
     FROM   Unpivoted U )
FROM   Reordered R
    PIVOT (   MIN(CompValue)
              FOR NewCompType IN ( Comp1, Comp2, Comp3, Comp4, Comp15 )) P;
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So I have a dataset wherein I have account number and "days past due" with every observation. So for every account number, as soon as the "days past due" column hits a code like "DLQ3" , I want to remove rest of the rows for that account(even if DLQ3 is the first observation for that account).

My dataset looks like :

Observation date Account num   Days past due

2016-09                           200056              DLQ1
2016-09                           200048              DLQ2
2016-09                           389490              NORM
2016-09                           383984              DLQ3.....

So for account 383984, I want to remove all the rows post the date 2016-09 as now its in default.

So in all I want to see when the account hits DLQ3 and when it does I want to remove all the rows post the first DLQ3 observation.
I have 2 java projects to do a replication , a RMIreplication and the publhiser, in the RMIReplication I create an ArrayList of Subjects anda in the publisher I nedd to aceed this ArrayList to do the attach and setstate how can I do, I will put belong the code of the 2 class from diferents projects
public class Replication {

	 //static ArrayList<Subject> theList ;
	static ArrayList<Subject> theList;
	public static void main(String args[]){
				theList =new ArrayList<Subject>();
				Registry r=null;
				Registry r1=null;
				Registry r2=null;
					r = LocateRegistry.createRegistry(2023);
					r1 = LocateRegistry.createRegistry(2024);
					r2 = LocateRegistry.createRegistry(2025);
				}catch(RemoteException a){}
				//System.setSecurityManager(new RMISecurityManager());
					Subject list = new Event();
					Subject list1 = new Event();
					Subject list2 = new Event();
		            	Naming.rebind("//localhost:2023/Subject", (Remote) list );
		            	Naming.rebind("//localhost:2024/Subject1", (Remote) list1 );
		            	Naming.rebind("//localhost:2025/Subject2", (Remote) list2 );
		            	theList.add( list);

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I have a data frame with some names (rows) and I have some positions that I want to use, for instance 35th row and 145th row. How do I get the names of the rows which are in this positions? I uploaded a print screen that may help understanding. Thanks!

I tried something like

names1 <- row.names(which(size_96 < median(size_96, na.rm= T)))
Hi, I have one data frame (df1) with 20 observations (one for each year) and 597 variables (each one is one stock). The values are a ratio called book-to-market ratio. I need to build two portfolios for each year which consists of the stocks with values lower than the median and stocks with values above the median. The names of the stocks are the columns from df1. So I need to check if each value from each row (each year) is below or above the median and identify each stock name (columns in df1). Then I need to match it with the columns from another data frame (df2) which has data from the return of each stock in each year (20x597). The end result would be a vector with 20 entries, which are the differences of average returns between the two portfolios. I hope it was clear enough, thanks for the answer and I`m here for any explanation.

I've logged into a Microsoft R Server using mrsdeploy::remoteLogin()

Test with session:

REMOTE> result <- system("gpg --yes --batch -r [e-mail] --passphrase=[youPassphrase] --armor --utf8-strings --decrypt youFile", intern = TRUE)

REMOTE> result
[1] 2

REMOTE> exit
>Logout from remote R session complete

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Test without session:

result <- system("gpg --yes --batch -r [e-mail] --passphrase= youPassphrase] --armor --utf8-strings --decrypt youFile", intern = TRUE)

gpg: encrypted with 2048-bit RSA key, ID XXXXXXX, created 2017-11-20 "name<e-mail>"

[1] "Esta es la frase\r"
[2] "que he encriptado\r"

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I need that it work based on remote session because this way works on service.

Thanks for your reply.

Kind regards,
I have written the function below. It works, but is slow. On my windows 7 R installation, what should I do to get this function working with the parallel library? Or is there some other obvious performance improvement I could do?

I followed the answer which led me to try vectorising but the improvement is minimal. Given I have another 23 cores and 50GB of RAM available I suspect the biggest improvement would be parallel processing. Albeit tricky to do on my Windows OS and using my newly learnt R skills.


# Build the encoding function

  encode <- function(dataframe, columnName, code_key){
    asc <- function(x) { strtoi(charToRaw(x),16L) }
    chr <- function(n) { rawToChar(as.raw(n)) } 
    encoded <- c()
    for (j in 1:length(dataframe[[columnName]])) {
      asc1<- NULL
      asc1 <- c()
      if((j%%(1E4)) == 0) {print(paste0(j," of ",length(dataframe[[columnName]]), " records processed"))}
      for (i in 1:nchar(dataframe[[columnName]][j])) {
        asc1[i] <- chr(asc(substr(dataframe[[columnName]][j], i, i ))  + i + code_key)  
        encoded[j] <- paste(asc1, collapse='')}} 
    encName <- paste0(columnName, "_Encoded")
    dataframe[[encName]] <- encoded

# Example data set to work function on

  df1 <- as.data.frame(rep(iris$Species, 10000))
  colnames(df1) <- "Species"
  df1$Species <- 

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I recently procured Visual Studio 2017 professional and trying to hands on with R tools.
I created a new R project and created a custom (user defined) function.  The function generates
4 sub-graphs with the function par(mfrow=c(2,2)) in one main graph
My function is working well with regular R software version 3.4.
When I trying the same function in R tools in Visual Studio 2017, I am getting an error
Error in plot.new() : figure margins too large
What could be the problem. Any solutions for rectification?
I am building a 2-tier Microsoft PKI infrastructure.
I have 1 off-line root CA and 2 issuing CA r running Windows server 2012 R2.  I want to have 1 active issuing CA an and the 2nd CA as a standby in a disaster recovery site.
How should I configure the CDP and AIA LOCATION?  Do I need a shared location where both CA’s can access the CRL information or can  I make the CDP and AIA  location local to the  issuing CA and rely on  a backup/restore  if I need to activate the 2nd  CA in DR.

Hello All Experts,
I am a student enthusiast in learning "Data Analytics" , which is the best platform to learn for FREE?
I want to Learn 'Data Science (Statistics)' & 'SAS/R' from scratch?
Any videos? Any websites? Any Blogs?


Satish Kumar G N
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Hi All,
While using REF keyword in my logical file , i get compilation error - "Record name same as name of file being created"

DDS of LF -

*************** Beginning of data *************************************
                R USEREF                                                
                  ACCLVL    R               REFFLD(ACCLEVELID ACCOUNT)  
                  ACCORG    R               REFFLD(ACTORGCOD  ACCOUNT)  
                  ACCNUM    R               REFFLD(ACCOUNTNUM ACCOUNT)  
****************** End of data ****************************************

May i know why is that so ?
Issue is that when I set a different it doesn't update neither my texblock.Text nor my listbox.Items;

Help very appreciated:)

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices.WindowsRuntime;
using Windows.Foundation;
using Windows.Foundation.Collections;
using Windows.UI.Xaml;
using Windows.UI.Xaml.Controls;
using Windows.UI.Xaml.Controls.Primitives;
using Windows.UI.Xaml.Data;
using Windows.UI.Xaml.Input;
using Windows.UI.Xaml.Media;
using Windows.UI.Xaml.Navigation;
using Windows.Services.Maps;
using Windows.Devices.Geolocation;

// The Blank Page item template is documented at https://go.microsoft.com/fwlink/?LinkId=402352&clcid=0x409

namespace New_World_Map
    /// <summary>
    /// An empty page that can be used on its own or navigated to within a Frame.
    /// </summary>
    public sealed partial class MainPage : Page


        List<string> stringlist = new List<string>();

        public MainPage()

            this.RightTapped += MainPage_RightTapped;

            mapscontrol.CenterChanged += Mapscontrol_CenterChanged;

            listbox.DoubleTapped += Listbox_DoubleTapped;

            listview.Items.Add("Zoom In");

            listview.Items.Add("Zoom Out");

            listview.Items.Add("Navigate North");

            listview.Items.Add("Navigate South");

write.csv(df,file="~C:/Users/anitha/Documents/social_media analysis/socialmedia/tweets.csv",row.names=FALSE,append = TRUE)
Error in file(file, ifelse(append, "a", "w")) :
  cannot open the connection

I fairly new in R, I am doing some simple visualization in shiny app, I am trying to flip a bar chart downward using  scale_y_reverse() , it works well when I run my code in R console, but when I run it in shiny it does not flip the bar chart, below is my code in the server part:

output$trendbarPlot <- renderPlotly({
                              mydat <- mydatCopy %>% filter(Country ==input$Country)

attacksbarplot = ggplot(data=mydat,aes(x=as.factor(Year))) + geom_bar() + theme_bw(base_size=35) + xlab("") + ylab("") + theme(axis.text.x = element_blank(), axis.ticks=element_blank(),panel.grid.major=element_blank(),panel.grid.minor=element_blank(),panel.border=element_blank())  + scale_y_reverse()

attacksbarplotnol = ggplot(data=mydat,aes(x=as.factor(Year))) + geom_bar() + theme_bw(base_size=15) + xlab("") + ylab("") + theme(axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks=element_blank(),panel.grid.major=element_blank(),panel.grid.minor=element_blank(),panel.border=element_blank()) +  scale_y_reverse()

attached file has the required flipped bar chart in shiny.

Does anyone knows how can I solve this issue?
My data:

Gage_number Latitude    Longitude   Date    Gage_1  Gage_2  Gage_3

1   35.02   -80.84  1/1/2002    0.23    0   0.7
2   35.03   -81.04  1/2/2002    0   0   0.2
3   35.06   -80.81  1/3/2002    3.2 2.1 0.1
This is just a subset of data. I around 50 gauge stations. I want to find spatial auto correction between my gauge stations for rain fall. Based on distance between them. I have created my distance matrix. But I don’t want to use any library in R. I want to do all steps in a function.

loc <- read.table("rain_data.txt",header=TRUE,fill=TRUE)  
gauge.dists <- as.matrix(dist(cbind(loc$Latitude, loc$Latitude))) #distance matrix
Now since distance between gauges is not uniform. I want to use a certain bin size to decide about distance lags.


If the distance between guage pair 1-2 is 1 meter then assign a distance lag of 1 and so on So Lag 1=intergage dist=1 meter. So Lag 5=intergage dist=5 meter After creating that matrix I will find autocorrelation between gauge pairs.

so for lag 1 intergage dist=1 for lag 5 intergage dist=5

Gage pair   date    RainA   RainB       Gage pair   date    RainA   RainB

1-2 1/1/2002    0.23    0       1-3 1/1/2002    0.23    0.7
1-2 1/2/2002    0   0       1-3 1/2/2002    0   0.2
1-2 1/3/2002    3.2 2.1     1-3 1/3/2002    3.2 0.1
I have a hard time translating it into loop or a function. Any ideas?
I am bit new to R so I am not sure if this is possible or if its more difficult than I am assuming.

Objective: I want to find the correlation between Diagnosis codes. If patient #1 has condition X what the likelihood he will at some point also have condition Y as well.

Here is what I have:
136,337 Unique patient IDs (74,527 Female, 61,810 Male)
34,442 Unique Diagnosis that exists in my population
7,777,728 Unique observations

So my 2 questions are:
1. How should I layout my Table for R?
Right now I have the table columns as :
ID, SEX, Diagnosis

2. What should my Rscript look like in order to create correlation coefficients between all my diagnosis codes.  

FYI: Yes I also have a time stamp per diagnosis code but adding it now would be to adding more confusion to the confusion I already have.
I have an excel file that I want to add a two new columns to and then group and sum the new and other columns in R Studio and save the output, not entirely sure how to do this.  

Adding two new columns:
if Sec_flag is "Y" then I want to add a new column called Sec_checked and put a 1 as the value
if stu_status is "Ret" i want to add another new column Stu_check and put a 1 as the value

Group & Sum
I would like to group the data by columns Year, Month, Stu_status, Point1, Point2 and Point3 and sum them by the values in stu_fee, stu_return_fee, student_count, Sec_checked and Stu_check.
Overtime I will add new data points to my excel file so I would like to be able to add these in future and get new groupings.

I tried using plyr but i dont know how to add the new columns and group & sum the data.
system("java -version")

mydata <- read.xlsx("stu_d_sample.xlsx", sheetName = "Sample") 

groupColumns = c("year","month", "Stu_status","Point1","Point2","Point3")
dataColumns = c("stu_fee", "stu_return_fee","student_count", "Sec_checked", "stu_check")
res = ddply(baseball, groupColumns, function(x) colSums(x[dataColumns]))

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2 Questions about regression in R
  Question 1:
  Let's say I create a model that correlates the unique words found in a corpus to the number of lines read. Notice that this model compiles the logs of BOTH, the outcome and the predictor.
  x <- lm( log(Words) ~ log(Lines) )
  Does that mean that exp(predict(x,list(Lines=100000))) will give me the number of words for a given number of lines? Or will it give me the LOG of a number of words for a given number of lines?
  Question 2:
  How do I invert this model so that I can input a number of words, and get back a prediction for the number of lines required in order to obtain this quantity of words?
Hello all,
i have a situation where a common value out of available data is to be computed, but the data contains different summary stats, for example:
consider there are apples in different boxes and average size of the apple is to be determined, and the available data consists  of size mean  from one basket, standard deviation of size from other basket,min size and max size from other boxes, is there any way that a general value can be derived to represent size of the apple? 

Statistical Packages

Statistical packages are software titles, such as JMP and GNU Octave, and programming languages, such as MATLAB, R and SAS, that are used to discover, explore and analyze data and suggest useful conclusions, either to learn something unexpected or to confirm a hypothesis. The field includes the design and analysis of techniques to give approximate but accurate solutions to hard problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

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Statistical Packages