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sirius | The SIRIUS Framework made by scireum

 by   andyHa Java Version: Current License: No License

 by   andyHa Java Version: Current License: No License

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kandi X-RAY | sirius Summary

sirius is a Java library typically used in Framework applications. sirius has no bugs and it has high support. However sirius has 1 vulnerabilities and it build file is not available. You can download it from GitHub.
DEPRECATED: Visit: https://github.com/scireum - Sirius is now split up into several repositories matching their outcoming maven files.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • sirius has a highly active ecosystem.
  • It has 24 star(s) with 11 fork(s). There are 10 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 1 have been closed. On average issues are closed in 3 days. There are no pull requests.
  • It has a positive sentiment in the developer community.
  • The latest version of sirius is current.
sirius Support
Best in #Java
Average in #Java
sirius Support
Best in #Java
Average in #Java

quality kandi Quality

  • sirius has 0 bugs and 0 code smells.
sirius Quality
Best in #Java
Average in #Java
sirius Quality
Best in #Java
Average in #Java

securitySecurity

  • sirius has 1 vulnerability issues reported (0 critical, 0 high, 1 medium, 0 low).
  • sirius code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
sirius Security
Best in #Java
Average in #Java
sirius Security
Best in #Java
Average in #Java

license License

  • sirius 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.
sirius License
Best in #Java
Average in #Java
sirius License
Best in #Java
Average in #Java

buildReuse

  • sirius releases are not available. You will need to build from source code and install.
  • sirius has no build file. You will be need to create the build yourself to build the component from source.
  • It has 41909 lines of code, 3163 functions and 419 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
sirius Reuse
Best in #Java
Average in #Java
sirius Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Check whether the given key is reserved .
    • Attempt to tunnel a GET request .
      • Runs the metrics for the current thread .
        • Returns the output stream .
          • Handles a request .
            • Parses the next token .
              • Re - index a given prefix .
                • Route a route .
                  • Iterate over the specified entity .
                    • Checks the consistency of the field .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      sirius Key Features

                      The SIRIUS Framework made by scireum

                      Fill Columns with TRUE or FALSE based on other Dataframe

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                      library(tidyverse)
                      
                      x <- data.frame(animals = c("Mice", "Mice", "Mice", "Cats", "Cats", "Cats", "Dogs", "Dogs", "Dogs" ,"Birds", "Birds", "Birds"),
                                  names = c("Jerry", "Speedy", "Feivel", "Tom", "Garfield", "Pumpkin", "Snoopy", "Pluto", "Sirius", "Tweety", "Donald", "Hedwig"),
                                  rank = rep(c(1,2,3),4))
                      
                      y <- data.frame(animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                                  names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety"))
                      
                      y %>% 
                        left_join(x %>% filter(rank == 1) %>% rename(rank1 = rank)) %>% 
                        left_join(x %>% filter(rank == 2) %>% rename(rank2 = rank)) %>% 
                        left_join(x %>% filter(rank == 3) %>% rename(rank3 = rank)) %>% 
                        mutate(across(starts_with("rank"), ~if_else(is.na(.x), F, T)))
                      #>   animals    names rank1 rank2 rank3
                      #> 1    Mice      Pam FALSE FALSE FALSE
                      #> 2    Cats  Michael FALSE FALSE FALSE
                      #> 3    Cats Garfield FALSE  TRUE FALSE
                      #> 4    Dogs   Sirius FALSE FALSE  TRUE
                      #> 5   Birds   Tweety  TRUE FALSE FALSE
                      
                      library(tidyverse)
                      
                      x <- data.frame(
                        animals = c("Mice", "Mice", "Mice", "Cats", "Cats", "Cats", "Dogs", "Dogs", "Dogs", "Birds", "Birds", "Birds"),
                        names = c("Jerry", "Speedy", "Feivel", "Tom", "Garfield", "Pumpkin", "Snoopy", "Pluto", "Sirius", "Tweety", "Donald", "Hedwig"),
                        rank = rep(c(1, 2, 3), 4)
                      )
                      
                      
                      y <- data.frame(
                        animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                        names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety")
                      )
                      
                      y %>%
                        inner_join(x) %>%
                        # dummy encoding
                        mutate(present = TRUE) %>%
                        pivot_wider(
                          names_from = rank, values_from = present, names_prefix = "rank",  
                          values_fill = list(present = FALSE)
                        )
                      #> Joining, by = c("animals", "names")
                      #> # A tibble: 3 × 5
                      #>   animals names    rank2 rank3 rank1
                      #>   <chr>   <chr>    <lgl> <lgl> <lgl>
                      #> 1 Cats    Garfield TRUE  FALSE FALSE
                      #> 2 Dogs    Sirius   FALSE TRUE  FALSE
                      #> 3 Birds   Tweety   FALSE FALSE TRUE
                      
                      library(dplyr)
                      
                      x <- data.frame(animals = c("Mice", "Mice", "Mice", "Cats", "Cats", "Cats", "Dogs", "Dogs", "Dogs" ,"Birds", "Birds", "Birds"),
                                      names = c("Jerry", "Speedy", "Feivel", "Tom", "Garfield", "Pumpkin", "Snoopy", "Pluto", "Sirius", "Tweety", "Donald", "Hedwig"),
                                      rank = rep(c(1,2,3),4))
                      
                      y <- data.frame(animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                                      names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety"))
                      
                      z = left_join(y, x) %>% 
                        mutate(rank1 = rank == 1,
                               rank2 = rank == 2,
                               rank3 = rank == 3) %>% 
                        select(-rank) %>% # removing the rank column
                        print()
                      #Joining, by = c("animals", "names")
                      #  animals    names rank1 rank2 rank3
                      #1    Mice      Pam    NA    NA    NA
                      #2    Cats  Michael    NA    NA    NA
                      #3    Cats Garfield FALSE  TRUE FALSE
                      #4    Dogs   Sirius FALSE FALSE  TRUE
                      #5   Birds   Tweety  TRUE FALSE FALSE
                      
                      library(tidyverse)
                      
                      left_join(y, x, by = c("animals", "names")) %>% 
                        pivot_wider(names_from = "rank", values_from = "rank", names_prefix = "rank", names_sort = T) %>% 
                        mutate(across(starts_with("rank"), ~ifelse(is.na(.x), F, T))) %>% 
                        select(-rankNA)
                      
                      # A tibble: 5 × 5
                        animals names    rank1 rank2 rank3
                        <chr>   <chr>    <lgl> <lgl> <lgl>
                      1 Mice    Pam      FALSE FALSE FALSE
                      2 Cats    Michael  FALSE FALSE FALSE
                      3 Cats    Garfield FALSE TRUE  FALSE
                      4 Dogs    Sirius   FALSE FALSE TRUE 
                      5 Birds   Tweety   TRUE  FALSE FALSE
                      

                      Maven jar dependency not found at runtime by OSGi environment

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                      <dependency>
                          <groupId>org.glassfish.hk2</groupId>
                          <artifactId>osgi-resource-locator</artifactId>
                          <version>2.5.0-b42</version>
                          <type>jar</type>
                      </dependency>
                      
                      <dependency>
                          <groupId>org.eclipse.parsson</groupId>
                          <artifactId>jakarta.json</artifactId>
                          <version>1.0.0</version>
                          <type>jar</type>
                      </dependency>
                      <dependency>
                          <groupId>org.eclipse.parsson</groupId>
                          <artifactId>parsson</artifactId>
                          <version>1.0.0</version>
                          <type>jar</type>
                      </dependency>
                      
                      <dependency>
                          <groupId>org.glassfish.hk2</groupId>
                          <artifactId>osgi-resource-locator</artifactId>
                          <version>2.5.0-b42</version>
                          <type>jar</type>
                      </dependency>
                      
                      <dependency>
                          <groupId>org.eclipse.parsson</groupId>
                          <artifactId>jakarta.json</artifactId>
                          <version>1.0.0</version>
                          <type>jar</type>
                      </dependency>
                      <dependency>
                          <groupId>org.eclipse.parsson</groupId>
                          <artifactId>parsson</artifactId>
                          <version>1.0.0</version>
                          <type>jar</type>
                      </dependency>
                      

                      How to force chrony to sync NOW?

                      copy iconCopydownload iconDownload
                      ExecStartPre=-+systemctl restart chronyd
                      ExecStartPre=/bin/sleep 5
                      ExecStart=/usr/local/bin/cockroach start ...
                      
                      initstepslew 0.5 pool.ntp.org
                      makestep 0.5 -1
                      
                      ExecStartPre=-+systemctl restart chronyd
                      ExecStartPre=/bin/sleep 5
                      ExecStart=/usr/local/bin/cockroach start ...
                      
                      initstepslew 0.5 pool.ntp.org
                      makestep 0.5 -1
                      

                      Flutter Parsing Nested Array Json value using conditional statements

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                      var array = [
                        {
                          "Type": "Planet",
                          "Name": "Jupiter",
                          "Charcs": "Largest",
                          "Color": "blue",
                          "distance": "near"
                        },
                        {
                          "Type": "Planet",
                          "Name": "Earth",
                          "Charcs": "Inhabited",
                          "Color": "green",
                          "distance": "nearby"
                        },
                        {
                          "Type": "Planet",
                          "Name": "Mars",
                          "Charcs": "Red Planet",
                          "Color": "red",
                          "distance": "very near"
                        },
                        {
                          "Type": "Star",
                          "Name": "Polaris",
                          "Charcs": "abcddw",
                          "Color": "casc",
                          "distance": "far"
                        },
                        {
                          "Type": "Star",
                          "Name": "Sirius",
                          "Charcs": "qwqwd",
                          "Color": "asfaf",
                          "distance": "very far"
                        },
                        {
                          "Type": "Star",
                          "Name": "Betelgeuse",
                          "Charcs": "qwdwqeq",
                          "Color": "asfasfa",
                          "distance": "far far away"
                        }
                      ]
                      
                      var json = {}
                      array.forEach(e => {
                        json[e.Type] ||= {}
                        json[e.Type][e.Name] = {Charcs: e.Charcs,Color: e.Color,distance:e.distance}
                      })
                      console.log(json)

                      How to use a for loop for webscraping in r

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                      library(magrittr)
                      library(jsonlite)
                      library(purrr)
                      
                      get_parent_id <- function(child_id, url){
                        json_data <- jsonlite::fromJSON(url)
                        return(json_data$projects[[child_id]]$parentprojid)
                      }
                      
                      
                      df <- structure(list(id = c("P173165", "P175875", "P175841", "P175730"
                      ), url = c("https://search.worldbank.org/api/v2/projects?format=json&fl=*&id=P173165&apilang=en", 
                                 "https://search.worldbank.org/api/v2/projects?format=json&fl=*&id=P175875&apilang=en", 
                                 "https://search.worldbank.org/api/v2/projects?format=json&fl=*&id=P175841&apilang=en", 
                                 "https://search.worldbank.org/api/v2/projects?format=json&fl=*&id=P175730&apilang=en"
                      )), row.names = c(NA, -4L), class = c("data.table", "data.frame"))
                      
                      
                      df$par_proj_id <- purrr::map2(df$id, df$url, get_parent_id)
                      

                      How to automate webscraping with for loop

                      copy iconCopydownload iconDownload
                      
                      ids <- c("P173165", "P175875", "P175841", "P175730")
                      
                      df <- data.table(
                          id=ids,
                          url = sprintf(
                              "https://search.worldbank.org/api/v2/projects?format=json&fl=*&id=%s&apilang=en",
                              ids
                          )
                      )
                      
                      library(remotes)
                      remotes::install_github("r-lib/async")
                      library(async)
                      
                      async_get <- async(function(url,project) {
                          http_get(url)$
                              then(function(x) { rawToChar(x$content)})$
                              then(function(x) { fromJSON(x)})$
                              then(function(x) { x$projects[[1]]$parentprojid } )
                      })
                      parent.ids <- df$synchronise(async_map(df$url, async_get, .limit=5)) ## 5 is a nice limit not to bombard the site
                      
                      df$par_proj_id <- parent.ids
                      
                      

                      Can you extract defective rows using AssertR in R?

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                      library(tidyverse)
                      library(assertr)
                      #> Warning: package 'assertr' was built under R version 4.0.5
                      
                      test = tribble(
                        ~x,~y,
                        1,4,
                        3,8,
                        1,-2,
                        3,1
                      )
                      
                      my_error_fun <- function( ... ) {
                          
                          args <- list(...)
                      
                          do.call( just_warn, args )
                          
                          bad.data <- args[[1]][[1]]$error_df
                          these.failed.rows <- args$data %>% slice( bad.data$index )
                      
                          if(!exists("my.failed.rows", inherits=TRUE)) {
                              my.failed.rows <- NULL
                          }
                          my.failed.rows <- rbind( my.failed.rows, these.failed.rows )
                          assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
                          
                          good.rows <- args$data %>% slice( -bad.data$index )
                          
                          return( good.rows )
                          
                      }
                      
                      my.result <- test %>%
                           assert(within_bounds(0,100),y, error_fun = my_error_fun ) %>% 
                           group_by(x) %>%
                           summarise(avg = mean(y))
                      
                      print(my.result)
                      
                      print(my.failed.rows)
                      
                      
                      > print(my.result)
                      # A tibble: 2 x 2
                            x   avg
                        <dbl> <dbl>
                      1     1   4  
                      2     3   4.5
                      > print(my.failed.rows)
                      # A tibble: 1 x 2
                            x     y
                        <dbl> <dbl>
                      1     1    -2
                      
                      
                      library(tidyverse)
                      library(assertr)
                      #> Warning: package 'assertr' was built under R version 4.0.5
                      
                      test = tribble(
                        ~x,~y,
                        1,4,
                        3,8,
                        1,-2,
                        3,1
                      )
                      
                      my_error_fun <- function( ... ) {
                          
                          args <- list(...)
                      
                          do.call( just_warn, args )
                          
                          bad.data <- args[[1]][[1]]$error_df
                          these.failed.rows <- args$data %>% slice( bad.data$index )
                      
                          if(!exists("my.failed.rows", inherits=TRUE)) {
                              my.failed.rows <- NULL
                          }
                          my.failed.rows <- rbind( my.failed.rows, these.failed.rows )
                          assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
                          
                          good.rows <- args$data %>% slice( -bad.data$index )
                          
                          return( good.rows )
                          
                      }
                      
                      my.result <- test %>%
                           assert(within_bounds(0,100),y, error_fun = my_error_fun ) %>% 
                           group_by(x) %>%
                           summarise(avg = mean(y))
                      
                      print(my.result)
                      
                      print(my.failed.rows)
                      
                      
                      > print(my.result)
                      # A tibble: 2 x 2
                            x   avg
                        <dbl> <dbl>
                      1     1   4  
                      2     3   4.5
                      > print(my.failed.rows)
                      # A tibble: 1 x 2
                            x     y
                        <dbl> <dbl>
                      1     1    -2
                      

                      What is the M2DOC Eclipse Capella command to maximize an image in a Word page?

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                      rep.asImage().getHeight()
                      
                      {m:for rep | lc.representationByDescriptionName('Logical Architecture Blank')}
                        {m:let image = rep.asImage()}
                          {m:if image.getHeight() > image.getWidth()}
                            {m:image.setHeight(600)}
                          {m:else}
                            {m:image.setWidth(400)}
                          {m:endif}
                        {m:endlet}
                      {m:endfor}
                      
                      rep.asImage().getHeight()
                      
                      {m:for rep | lc.representationByDescriptionName('Logical Architecture Blank')}
                        {m:let image = rep.asImage()}
                          {m:if image.getHeight() > image.getWidth()}
                            {m:image.setHeight(600)}
                          {m:else}
                            {m:image.setWidth(400)}
                          {m:endif}
                        {m:endlet}
                      {m:endfor}
                      

                      R Fill backwards with flexible window based on number of rows in a separate column

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                      set.seed(1)
                      test <- data.frame(x = sample(1:15,replace = F), y = c(NA,NA,NA,1,NA,NA,3,1,2,1,1,NA,NA,NA,2))
                      
                      desired_out <- test
                      desired_out$out <- c(NA,NA,NA,1,11,11,11,8,8,12,5,NA,NA,14,14)
                      desired_out
                      
                      test %<>% mutate( id = seq(n()) ) %>%
                          filter( !is.na(y) & y != 0  ) %>%
                          group_by(id) %>%
                          slice( rep(1,y) ) %>%
                          mutate( id = rev( max(id)+1-1:n() ) ) %>%
                          group_by(id) %>%
                          summarize( out = as.numeric(last(x)) ) %>%
                          right_join( test %>% mutate( id=seq(n()) ) ) %>%
                          arrange( id ) %>% select( -id ) %>% relocate( x, y, out )
                      
                      identical( as.data.frame(test), desired_out ) ## TRUE
                      
                      test
                      
                      
                      
                      > test
                      # A tibble: 15 x 3
                             x     y   out
                         <int> <dbl> <dbl>
                       1     9    NA    NA
                       2     4    NA    NA
                       3     7    NA    NA
                       4     1     1     1
                       5     2    NA    11
                       6    13    NA    11
                       7    11     3    11
                       8     3     1     8
                       9     8     2     8
                      10    12     1    12
                      11     5     1     5
                      12     6    NA    NA
                      13    15    NA    NA
                      14    10    NA    14
                      15    14     2    14
                      
                      
                      set.seed(1)
                      test <- data.frame(x = sample(1:15,replace = F), y = c(NA,NA,NA,1,NA,NA,3,1,2,1,1,NA,NA,NA,2))
                      
                      desired_out <- test
                      desired_out$out <- c(NA,NA,NA,1,11,11,11,8,8,12,5,NA,NA,14,14)
                      desired_out
                      
                      test %<>% mutate( id = seq(n()) ) %>%
                          filter( !is.na(y) & y != 0  ) %>%
                          group_by(id) %>%
                          slice( rep(1,y) ) %>%
                          mutate( id = rev( max(id)+1-1:n() ) ) %>%
                          group_by(id) %>%
                          summarize( out = as.numeric(last(x)) ) %>%
                          right_join( test %>% mutate( id=seq(n()) ) ) %>%
                          arrange( id ) %>% select( -id ) %>% relocate( x, y, out )
                      
                      identical( as.data.frame(test), desired_out ) ## TRUE
                      
                      test
                      
                      
                      
                      > test
                      # A tibble: 15 x 3
                             x     y   out
                         <int> <dbl> <dbl>
                       1     9    NA    NA
                       2     4    NA    NA
                       3     7    NA    NA
                       4     1     1     1
                       5     2    NA    11
                       6    13    NA    11
                       7    11     3    11
                       8     3     1     8
                       9     8     2     8
                      10    12     1    12
                      11     5     1     5
                      12     6    NA    NA
                      13    15    NA    NA
                      14    10    NA    14
                      15    14     2    14
                      
                      test$rn <- seq_len(nrow(test))
                      src     <- with(test[!is.na(test$y),],
                                      list(val = rep(x,y), idx = rep(rn,y) - sequence(y) + 1) )
                      test$out[src$idx] <- src$val
                      test$rn <- NULL
                      
                      #    x  y out
                      #1   9 NA  NA
                      #2   4 NA  NA
                      #3   7 NA  NA
                      #4   1  1   1
                      #5   2 NA  11
                      #6  13 NA  11
                      #7  11  3  11
                      #8   3  1   8
                      #9   8  2   8
                      #10 12  1  12
                      #11  5  1   5
                      #12  6 NA  NA
                      #13 15 NA  NA
                      #14 10 NA  14
                      #15 14  2  14
                      
                      test <- test[rep(1:15, 3e5),]
                      
                      system.time({
                        test$rn <- seq_len(nrow(test))
                        src <- with(test[!is.na(test$y),],
                                    list(val = rep(x,y), idx = rep(rn,y) - sequence(y) + 1) )
                        test$out[src$idx] <- src$val
                        test$rn <- NULL
                      })
                      # user  system elapsed 
                      # 0.28    0.00    0.28 
                      
                      test$rn <- seq_len(nrow(test))
                      src     <- with(test[!is.na(test$y),],
                                      list(val = rep(x,y), idx = rep(rn,y) - sequence(y) + 1) )
                      test$out[src$idx] <- src$val
                      test$rn <- NULL
                      
                      #    x  y out
                      #1   9 NA  NA
                      #2   4 NA  NA
                      #3   7 NA  NA
                      #4   1  1   1
                      #5   2 NA  11
                      #6  13 NA  11
                      #7  11  3  11
                      #8   3  1   8
                      #9   8  2   8
                      #10 12  1  12
                      #11  5  1   5
                      #12  6 NA  NA
                      #13 15 NA  NA
                      #14 10 NA  14
                      #15 14  2  14
                      
                      test <- test[rep(1:15, 3e5),]
                      
                      system.time({
                        test$rn <- seq_len(nrow(test))
                        src <- with(test[!is.na(test$y),],
                                    list(val = rep(x,y), idx = rep(rn,y) - sequence(y) + 1) )
                        test$out[src$idx] <- src$val
                        test$rn <- NULL
                      })
                      # user  system elapsed 
                      # 0.28    0.00    0.28 
                      

                      MongodDB with Java driver: How to find nested atributes and how to use &quot;and&quot; operator

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                      FindIterable<Document> aliveStudents = harrypotter.find(and(eq("alive", true), eq("hogwartsStudent", true)));
                      System.out.println("---------- ALIVE STUDENTS ----------");
                      for (Object character : aliveStudents) {System.out.println(((Document) character).toJson());}
                      
                      FindIterable<Document> wand= harrypotter.find(eq("wand.wood", "holly"));
                      System.out.println("---------- CHARACTERS WITH HOLLY WAND ----------");
                      for (Object character: wand) {System.out.println(((Document) character).toJson());}
                      
                      FindIterable<Document> aliveStudents = harrypotter.find(and(eq("alive", true), eq("hogwartsStudent", true)));
                      System.out.println("---------- ALIVE STUDENTS ----------");
                      for (Object character : aliveStudents) {System.out.println(((Document) character).toJson());}
                      
                      FindIterable<Document> wand= harrypotter.find(eq("wand.wood", "holly"));
                      System.out.println("---------- CHARACTERS WITH HOLLY WAND ----------");
                      for (Object character: wand) {System.out.println(((Document) character).toJson());}
                      

                      Community Discussions

                      Trending Discussions on sirius
                      • Fill Columns with TRUE or FALSE based on other Dataframe
                      • Maven jar dependency not found at runtime by OSGi environment
                      • How to force chrony to sync NOW?
                      • Flutter Parsing Nested Array Json value using conditional statements
                      • How to use a for loop for webscraping in r
                      • How to automate webscraping with for loop
                      • Can you extract defective rows using AssertR in R?
                      • What is the M2DOC Eclipse Capella command to maximize an image in a Word page?
                      • R Fill backwards with flexible window based on number of rows in a separate column
                      • How to get GTK Cairo to plot multiple times without a triggering event
                      Trending Discussions on sirius

                      QUESTION

                      Fill Columns with TRUE or FALSE based on other Dataframe

                      Asked 2022-Apr-07 at 07:44

                      Unfortunately I have a problem and would have to ask for your support.

                      I have a list of the most popular pet names. From the dataframe x, it appears that "Jerry" is the most popular mouse name, "Garfield" is the second most popular cat name, and so on.

                      x <- data.frame(animals = c("Mice", "Mice", "Mice", "Cats", "Cats", "Cats", "Dogs", "Dogs", "Dogs" ,"Birds", "Birds", "Birds"),
                                  names = c("Jerry", "Speedy", "Feivel", "Tom", "Garfield", "Pumpkin", "Snoopy", "Pluto", "Sirius", "Tweety", "Donald", "Hedwig"),
                                  rank = rep(c(1,2,3),4))
                      

                      Dataframe y also contains pets and their names:

                      y <- data.frame(animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                                  names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety"))
                      

                      I would now like to find out if the names of the animals in dataframe y are among the most popular animal names and if so, what the rank of that name would be. Thus, three columns should be created for each of the three ranks. If there is then a match with the most popular pet names a TRUE should be entered in the column with the corresponding rank. For example, "Pam" is not one of the most popular mouse names, so FALSE is entered in each of the three columns (or NA, I wouldn't care about that). On the other hand, Garfield is one of the most popular cat names, so the value TRUE should be entered in the rank2 column. This result should be stored in dataframe z.

                      z <-  data.frame(animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                                   names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety"),
                                   rank1 = c(FALSE, FALSE, FALSE, FALSE, TRUE),
                                   rank2 = c(FALSE, FALSE, TRUE, FALSE, FALSE),
                                   rank3 = c(FALSE, FALSE, FALSE, TRUE, FALSE))
                      

                      I already thought about using the functions spread() or pivot_wider() from the tidyr package, but unfortunately I can't get the right result.

                      I would be very happy about your help and thank you very much in advance!

                      ANSWER

                      Answered 2022-Apr-07 at 07:36

                      One option is to serially left_join() x onto y and then convert the NA into logical at the end.

                      library(tidyverse)
                      
                      x <- data.frame(animals = c("Mice", "Mice", "Mice", "Cats", "Cats", "Cats", "Dogs", "Dogs", "Dogs" ,"Birds", "Birds", "Birds"),
                                  names = c("Jerry", "Speedy", "Feivel", "Tom", "Garfield", "Pumpkin", "Snoopy", "Pluto", "Sirius", "Tweety", "Donald", "Hedwig"),
                                  rank = rep(c(1,2,3),4))
                      
                      y <- data.frame(animals = c("Mice", "Cats", "Cats", "Dogs", "Birds"),
                                  names = c("Pam", "Michael", "Garfield", "Sirius", "Tweety"))
                      
                      y %>% 
                        left_join(x %>% filter(rank == 1) %>% rename(rank1 = rank)) %>% 
                        left_join(x %>% filter(rank == 2) %>% rename(rank2 = rank)) %>% 
                        left_join(x %>% filter(rank == 3) %>% rename(rank3 = rank)) %>% 
                        mutate(across(starts_with("rank"), ~if_else(is.na(.x), F, T)))
                      #>   animals    names rank1 rank2 rank3
                      #> 1    Mice      Pam FALSE FALSE FALSE
                      #> 2    Cats  Michael FALSE FALSE FALSE
                      #> 3    Cats Garfield FALSE  TRUE FALSE
                      #> 4    Dogs   Sirius FALSE FALSE  TRUE
                      #> 5   Birds   Tweety  TRUE FALSE FALSE
                      

                      Created on 2022-04-07 by the reprex package (v2.0.1)

                      Source https://stackoverflow.com/questions/71777628

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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