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dvn | Dataverse Network 3.x , distinct from the newer | Machine Learning library

 by   IQSS Java Version: Current License: No License

 by   IQSS Java Version: Current License: No License

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kandi X-RAY | dvn Summary

dvn is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. dvn has low support. However dvn has 691 bugs, it has 10 vulnerabilities and it build file is not available. You can download it from GitHub.
Looking to download Dataverse 4.0? You can find that here: https://github.com/IQSS/dataverse/releases. Dataverse 4.0 code and further development takes place at https://github.com/iqss/dataverse. The Dataverse Network Project is an open source web application for sharing, citing, analyzing, and preserving research data. To learn more, please visit http://dataverse.org.
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Quality
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Security
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kandi-support Support

  • dvn has a low active ecosystem.
  • It has 24 star(s) with 16 fork(s). There are 31 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 5 open issues and 0 have been closed. On average issues are closed in 2137 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of dvn is current.
dvn Support
Best in #Machine Learning
Average in #Machine Learning
dvn Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • dvn has 691 bugs (104 blocker, 22 critical, 394 major, 171 minor) and 12527 code smells.
dvn Quality
Best in #Machine Learning
Average in #Machine Learning
dvn Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • dvn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • dvn code analysis shows 10 unresolved vulnerabilities (10 blocker, 0 critical, 0 major, 0 minor).
  • There are 304 security hotspots that need review.
dvn Security
Best in #Machine Learning
Average in #Machine Learning
dvn Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • dvn does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
dvn License
Best in #Machine Learning
Average in #Machine Learning
dvn License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • dvn releases are not available. You will need to build from source code and install.
  • dvn has no build file. You will be need to create the build yourself to build the component from source.
dvn Reuse
Best in #Machine Learning
Average in #Machine Learning
dvn Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed dvn and discovered the below as its top functions. This is intended to give you an instant insight into dvn implemented functionality, and help decide if they suit your requirements.

  • Performs advStat action on the database .
    • Decode the RecordTypeData from a stream
      • Method to execute a DForeignRJobRequest
        • Adds a document to the database .
          • Decode the SPSS data
            • Imports the study .
              • Gets a merged result .
                • Validate that the data table can be released .
                  • Harvest a record
                    • Generate SDI section .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      dvn Key Features

                      Dataverse Network (DVN) 3.x, distinct from the newer code base at https://github.com/IQSS/dataverse

                      R: Trying to recreate mean-median difference gerrymander tests

                      copy iconCopydownload iconDownload
                      data <- house_2012_reduced 
                      # created with dplyr, contains total and percentage of votes
                      # for Democrats and Republicans.
                      B <- 100000
                      del_districts <- 18 # 18 districts in PA
                      samples_diff <- vector("numeric", B)
                      samples_mean <- vector("numeric", B)
                      samples_median <- vector("numeric", B)
                      
                      for(samp in 1:B) {
                        sample_delegation <- sample_n(data, del_districts)
                        sample_delegation_pct_dem_mean <- weighted.mean(sample_delegation$pct_dem_votes, w = sample_delegation$total_votes)
                        sample_delegation_pct_dem_median <- median(sample_delegation$pct_dem_votes)
                        if(near(mean_dem_pct_PA, sample_delegation_pct_dem_mean, 1)){
                          samples_mean[samp] <- sample_delegation_pct_dem_mean
                          samples_median[samp] <- sample_delegation_pct_dem_median
                          samples_diff[samp] <- (sample_delegation_pct_dem_mean - sample_delegation_pct_dem_median)
                        }
                      }
                      
                      samples <- data.frame(samples_mean,samples_median,samples_diff)
                      samples <- filter_all(samples, any_vars(. != 0))
                      quantile(samples$samples_median, c(0.025,0.975))
                      

                      How to sum over subsets of rows in R

                      copy iconCopydownload iconDownload
                      library(tidyverse)
                      
                      df <- read_csv("~/Desktop/countypres_2000-2020.csv")
                      #> Rows: 72617 Columns: 12
                      #> ── Column specification ────────────────────────────────────────────────────────
                      #> Delimiter: ","
                      #> chr (8): state, state_po, county_name, county_fips, office, candidate, party...
                      #> dbl (4): year, candidatevotes, totalvotes, version
                      #> 
                      #> ℹ Use `spec()` to retrieve the full column specification for this data.
                      #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
                      
                      df %>%
                        filter(year == 2020) %>%
                        group_by(candidate, county_fips) %>%
                        summarise(
                          county_name,
                          total_votes_per_candidate_per_county = sum(candidatevotes)
                          ) %>%
                        relocate(candidate, .before = 4) %>%
                        distinct() %>%
                        arrange(county_fips)
                      #> `summarise()` has grouped output by 'candidate', 'county_fips'. You can override using the `.groups` argument.
                      #> # A tibble: 11,902 × 4
                      #> # Groups:   candidate, county_fips [11,898]
                      #>    county_fips county_name candidate         total_votes_per_candidate_per_coun…
                      #>    <chr>       <chr>       <chr>                                           <dbl>
                      #>  1 01001       AUTAUGA     DONALD J TRUMP                                  19838
                      #>  2 01001       AUTAUGA     JOSEPH R BIDEN JR                                7503
                      #>  3 01001       AUTAUGA     OTHER                                             429
                      #>  4 01003       BALDWIN     DONALD J TRUMP                                  83544
                      #>  5 01003       BALDWIN     JOSEPH R BIDEN JR                               24578
                      #>  6 01003       BALDWIN     OTHER                                            1557
                      #>  7 01005       BARBOUR     DONALD J TRUMP                                   5622
                      #>  8 01005       BARBOUR     JOSEPH R BIDEN JR                                4816
                      #>  9 01005       BARBOUR     OTHER                                              80
                      #> 10 01007       BIBB        DONALD J TRUMP                                   7525
                      #> # … with 11,892 more rows
                      

                      SortExpression not working in ASP.NET If Header Text is passed dynamically through VB code

                      copy iconCopydownload iconDownload
                      Dim text As String
                      If iOrderType = 4 Then
                          text = "DVN date" 
                      ElseIf iOrderType = 11 Then
                          text = "Lab Order Date" 
                      Else
                          text = "Order Date" 
                      End If
                      
                      Dim cell As TableCell = gvOrders.HeaderRow.Cells(6)
                      Dim button As IButtonControl = If(cell.HasControls(), TryCast(cell.Controls(0), IButtonControl), Nothing)
                      If button Is Nothing Then
                          cell.Text = text
                      Else
                          button.Text = text
                      End If
                      

                      Is there a way to apply Spacy en_core_web_sm to data in chunks?

                      copy iconCopydownload iconDownload
                      for text in texts:
                          doc = nlp(text)
                          ... do something with the doc ...
                      

                      Python yFInance api how to get close share price instead of adjusted close share price?

                      copy iconCopydownload iconDownload
                      import yfinance as yf
                      data = yf.download("DVN", start='2021-09-01', end='2021-09-11')
                      
                      data
                          Open    High    Low     Close   Adj Close   Volume
                      Date                        
                      2021-08-31  29.660000   30.320000   29.420000   29.549999   29.041054   11534800
                      2021-09-01  29.400000   29.590000   27.670000   28.240000   27.753616   20559000
                      2021-09-02  28.600000   29.969999   28.600000   29.320000   28.815016   11711300
                      2021-09-03  29.209999   29.799999   29.000000   29.170000   28.667601   7305300
                      2021-09-07  28.900000   29.330000   28.730000   29.059999   28.559494   5939700
                      2021-09-08  29.250000   29.620001   28.030001   28.230000   27.743790   8874000
                      2021-09-09  28.010000   29.080000   27.730000   28.450001   27.960001   9205700
                      2021-09-10  28.700001   29.030001   28.020000   28.070000   28.070000   6581800
                      

                      Invalid host/bind variable name ORA-01745

                      copy iconCopydownload iconDownload
                       EXECUTE IMMEDIATE 
                      'INSERT INTO TPILVALEUR (idValeur, idTableauDeBord, codeLigne, codeColonne, valeur)
                       SELECT 
                              tab.idTableauDeBord || '''||cSep||''' || tmpLfi.codeDdaf || '''||cSep||''' || '''||cCodeColTheorie||''',  
                              tab.idTableauDeBord,
                              tmpLfi.codeDdaf,
                              '''||cCodeColTheorie||''',
                              COUNT(DISTINCT tmpLfi.numeroPacage)
                         FROM
                              TPILTABLEAUDEBORD tab
                         JOIN
                              TPILTMPLFIICHN'||pCampagne||' tmpLfi 
                           ON tmpLfi.codeDdaf = 
                             CASE 
                             WHEN tab.codeTypeGeoTableauDeBord = '''||cCodeGeoTableauDep||''' THEN tab.codeDepartement
                             WHEN tab.codetypegeotableaudebord = '''||cCodeGeoTableauReg||''' AND EXISTS(SELECT 1 FROM trefdepartement dept WHERE dept.code = tmpLfi.codeDdaf AND dept.codeRegion = tab.codeRegion) THEN tmpLfi.codeDdaf       
                             WHEN tab.codetypegeotableaudebord = '''||cCodeGeoTableauNat||''' THEN tmpLfi.codeDdaf
                              END     
                        WHERE tab.campagne = '''||pCampagne||'''
                          AND tmpLfi.campagne = '''||pCampagne||'''
                          AND tab.codeTypeTableauDeBord = '''||cCodeTypeTableauCourant||'''
                        GROUP BY tab.idTableauDeBord, tmpLfi.codeDdaf';
                      

                      QuantMod error using the for loop . Error in runSum(x, n) : n = 20 is outside valid range: [1, 5]

                      copy iconCopydownload iconDownload
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      
                      Warning message:
                      HWM contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them. 
                      
                      head(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2019-10-07       NA       NA      NA        NA         NA           NA
                      2019-10-08       NA       NA      NA        NA         NA           NA
                      2019-10-09       NA       NA      NA        NA         NA           NA
                      2019-10-10       NA       NA      NA        NA         NA           NA
                      
                      tail(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2020-03-27       NA       NA      NA        NA         NA           NA
                      2020-03-30       NA       NA      NA        NA         NA           NA
                      2020-03-31       NA       NA      NA        NA         NA           NA
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      stocks
                      [1] "HWM"
                      
                      x <- try.xts(x, error = "chartSeries requires an xtsible object")
                      x <- na.omit(x)
                      
                       na.omit(stockEnv[[stocks]])
                                 HWM.Open HWM.High HWM.Low HWM.Close HWM.Volume HWM.Adjusted
                      2019-10-03    24.17    24.28   23.74     24.25    2828500        24.25
                      2019-10-04    24.21    24.50   23.96     24.49    2250500        24.49
                      2020-04-01    15.40    15.40   12.71     13.20    2531000        13.20
                      2020-04-02    12.97    13.71   12.00     12.50    4431900        12.50
                      2020-04-03    12.10    12.69   11.85     12.54    4053100        12.54
                      
                      Error in runSum(x, n) : n = 20 is outside valid range: [1, 5] 
                      

                      Community Discussions

                      Trending Discussions on dvn
                      • R: Trying to recreate mean-median difference gerrymander tests
                      • How to sum over subsets of rows in R
                      • SortExpression not working in ASP.NET If Header Text is passed dynamically through VB code
                      • Is there a way to apply Spacy en_core_web_sm to data in chunks?
                      • Python yFInance api how to get close share price instead of adjusted close share price?
                      • Return Duplicated index of using Pivot function
                      • how to wget or curl from a download button with no attached content URL?
                      • Invalid host/bind variable name ORA-01745
                      • QuantMod error using the for loop . Error in runSum(x, n) : n = 20 is outside valid range: [1, 5]
                      Trending Discussions on dvn

                      QUESTION

                      R: Trying to recreate mean-median difference gerrymander tests

                      Asked 2022-Feb-09 at 23:58

                      I'm trying to recreate the mean-median difference test described here: Archive of NYT article. I've downloaded House data from MIT's Election Lab, and pared it down to the 2012 Pennsylvania race. Using dplyr, I whittled it down to the relevant columns, and it now looks something like this:

                      Rows: 42
                      Columns: 5
                      $ district       <dbl> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 1~
                      $ party          <chr> "REPUBLICAN", "DEMOCRAT", "INDEPENDENT", "REPUBLICAN", "DEMOCRAT", "DEMOCRAT", ~
                      $ candidatevotes <dbl> 41708, 235394, 4829, 33381, 318176, 123933, 165826, 12755, 6210, 181603, 11524,~
                      $ totalvotes     <dbl> 277102, 277102, 356386, 356386, 356386, 302514, 302514, 302514, 303980, 303980,~
                      $ pct_votes      <dbl> 15.051497, 84.948503, 1.354991, 9.366530, 89.278479, 40.967691, 54.815975, 4.21~
                      

                      Each row represents a district candidate. The final column was created using mutate, and represents the percentage of the vote in that district that went to the candidate. Now, I can find the median and mean democratic vote with

                      PA2012_house_dem <- PA2012_house %>% filter(party == "DEMOCRAT") 
                      obs_median <- median(PA2012_house_dem$pct_votes)
                      obs_mean <- mean(PA2012_house_dem$pct_votes)
                      obs_median - obs_mean
                      

                      What's giving me fits is calculating the "zone of chance". What I'd like to do is some kind of Monte Carlo simulation of taking each voter and randomly assigning them to a district, so that the number of voters in each district is unchanged, the number of total votes for each party is unchanged, but the proportion of Republican and Democratic (and other parties) in each district is random, as in a permutation test. The mean Democratic vote should be unchanged, but I can't figure out a good way to carry out this randomization so that I can calculate the median district's Democratic vote percentage.

                      Thanks in advance for your help!

                      Edit for clarification: I'd like to do the randomization, say, 10,000 times, and for each of those trials, calculate the median-mean difference. The result should then, ideally, be a vector or data frame with 10,000 rows, that I can then turn into a histogram or something.

                      EDIT 2 -- PARTIAL SOLUTION:

                      I have some code that runs, but it's not giving me a reasonable answer. Using dplyr, I've filtered out all but the DEMOCRAT votes, so that each row just gives me the Democrat vote share for a single district.

                      Rows: 18
                      Columns: 5
                      $ district       <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
                      $ party          <chr> "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCRAT", "DEMOCR~
                      $ candidatevotes <dbl> 235394, 318176, 123933, 104643, 104725, 143803, 143509, 152859, 105128, 94227, 118231, 163589, 209901, ~
                      $ totalvotes     <dbl> 277102, 356386, 302514, 303980, 282465, 335528, 353451, 352238, 274305, 273790, 285198, 338941, 303819,~
                      $ pct_votes      <dbl> 84.94850, 89.27848, 40.96769, 34.42430, 37.07539, 42.85872, 40.60223, 43.39651, 38.32522, 34.41579, 41.~
                      

                      This is saved as PA2012_reduced_dem.

                      Now, here is my code:

                      require(mosaic) # for the tally() function
                      data <- PA2012_reduced_dem
                      B <- 100
                      samples_diff <- vector("numeric", B)
                      samples_mean <- vector("numeric", B)
                      samples_median <- vector("numeric", B)
                      
                      for(samp in 1:B) {
                      data_w_sample <- mutate(data, sample_vote = tally(sample(district, sum(candidatevotes),replace=T, prob = totalvotes)))
                        data_w_sample <- mutate(data_w_sample, sample_vote_pct = (sample_vote / totalvotes *100))
                        mean_sample <- weighted.mean(data_w_sample$sample_vote_pct, w = data_w_sample$totalvotes)
                        median_sample <- median(data_w_sample$sample_vote_pct)
                        diff_mean_median <- mean_sample - median_sample
                        samples_diff[samp] <- diff_mean_median
                        samples_mean[samp] <- mean_sample
                        samples_median[samp] <- median_sample
                      }
                      
                      samples <- data.frame(samples_mean,samples_median,samples_diff)
                      

                      The idea is that I'm randomly placing each Democrat voter in a district, weighted by the total number of votes per district. Since I have the total vote as a variable, I can compute the share of vote in each district that goes to the Democrat (I'm ignoring independent and other party votes).

                      Obviously, this is slow, because each trial is sampling for every single Democrat vote (roughly 2.8 million), so I'm only running 100 trials right now.

                      However, my Monte Carlo simulations are finding a very small "zone of chance" around the mean, the median is only about 0.05 percent above or below the mean. Again, I'm only running 100 trials, but I was expecting a wider zone of chance.

                      ANSWER

                      Answered 2022-Feb-09 at 23:58

                      I figured it out! Randomly placing voters in each district is not correct, and honestly it was pretty silly of me to do so. Instead, I had to use dplyr to create a data frame with the number of Democrat and Republican votes in each of the 435 House districts, one district per row. Then, I followed the advice on page 12 of this paper. I created samples of 18 districts sampled from this 435-row data frame, rejecting them if the mean vote share was more than 1 percent away from that of PA. The results have a much nicer 95% confidence interval, that matches the results of the original article.

                      data <- house_2012_reduced 
                      # created with dplyr, contains total and percentage of votes
                      # for Democrats and Republicans.
                      B <- 100000
                      del_districts <- 18 # 18 districts in PA
                      samples_diff <- vector("numeric", B)
                      samples_mean <- vector("numeric", B)
                      samples_median <- vector("numeric", B)
                      
                      for(samp in 1:B) {
                        sample_delegation <- sample_n(data, del_districts)
                        sample_delegation_pct_dem_mean <- weighted.mean(sample_delegation$pct_dem_votes, w = sample_delegation$total_votes)
                        sample_delegation_pct_dem_median <- median(sample_delegation$pct_dem_votes)
                        if(near(mean_dem_pct_PA, sample_delegation_pct_dem_mean, 1)){
                          samples_mean[samp] <- sample_delegation_pct_dem_mean
                          samples_median[samp] <- sample_delegation_pct_dem_median
                          samples_diff[samp] <- (sample_delegation_pct_dem_mean - sample_delegation_pct_dem_median)
                        }
                      }
                      
                      samples <- data.frame(samples_mean,samples_median,samples_diff)
                      samples <- filter_all(samples, any_vars(. != 0))
                      quantile(samples$samples_median, c(0.025,0.975))
                      

                      Source https://stackoverflow.com/questions/71026587

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