Check if two values within consecutive dates are identical

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• Problems plotting log-likelihood-function with ggplot2

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               AD1_2= rpois(4,9),
               AD2_1= rpois(4,10),
               AD2_2= rpois(4,9),
               AD3_1= rpois(4,10),
               AD3_2= rpois(4,9),
               AD4_1= rpois(4,10),
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               AD5_1= rpois(4,10),
               AD5_2= rpois(4,9),
               AD6_1= rpois(4,10),
               AD6_2= rpois(4,9))
  

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  Here is my data:

  dat <- read.table(text = "id    val1    val2    vt
  1   14  12  19
  2   13  13  12
  3   12  12  13
  4   12  13  13
  5   12  14  22
  6   12  12  14
  7   12  13  14
  8   12  14  12
  9   13  13  14
  10  13  14  14
  11  14  14  14
  12  13  14  17
  13  13  14  31
  14  13  13  14
  15  13  14  13
  16  13  14  23
                  
  ", header = TRUE)
  

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  Here is the output top25%

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  13  13  14  31
  16  13  14  23
  5   12  14  22
  1   14  12  19
  

  and the top 45% is

  id  val1    val2    vt
  7   12  13  14
  9   13  13  14
  10  13  14  14
  11  14  14  14
  14  13  13  14
  3   12  12  13
  4   12  13  13
  15  13  14  13
  2   13  13  12
  8   12  14  12
  

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• Why does `filter` crash with an input length error in my shiny app?

  i am pretty new to programmring but i have to make a shiny app for a university course.

  As you can see i webscraped a data table thats presents different bike geometries and i wanted to create a shiny app, where i can compare the geometries with each other. I am quite happy with my progress, but now i got the problem that it always shows me the error: "Error in : Problem with filter() input ..1. x Input ..1 must be of size 19 or 1, not size 0. i Input ..1 is !=.... 161: "

  I want that its possible in the app to choose one bike and it automatically compares the bike and shows me the 10 most similar bikes.

  
  #table
  
  Canyon <- read_html("https://enduro-mtb.com/canyon-strive-cfr-9-0-ltd-test-2020/")
  Rose <- read_html("https://enduro-mtb.com/rose-root-miller-2020-test/")
  Ghost <- read_html("https://enduro-mtb.com/ghost-riot-enduro-2021-erster-test/")
  Cube <- read_html("https://enduro-mtb.com/cube-stereo-170-sl-29-test-2020/")
  
  
  Comparison <- tibble(
    Geometry = Canyon %>%
      html_nodes(".geometry strong") %>%
      html_text()%>%
      str_trim(),
    CanyonStrive = Canyon %>%
      html_nodes("td:nth-child(3)") %>%
      html_text()%>%
      str_trim(),
    GhostRiot = Ghost %>%
      html_nodes("td:nth-child(3)") %>%
      html_text()%>%
      str_trim(),
     CubeStereo = Cube %>%
      html_nodes("td:nth-child(3)") %>%
      html_text()%>%
      str_trim(),
     RoseRootMiller = Rose %>%
      html_nodes("td:nth-child(3)") %>%
      html_text()%>%
      str_trim(),
  )
  
  ComparisonTable <- Comparison %>%
    mutate_all(~gsub("mm|°|-.*|/.*|\\.", "", .)) %>%
    mutate_all(~gsub(",", ".", .)) %>%
    mutate_all(type.convert, as.is=TRUE) %>% 
    gather("Bikes", "value", 2:ncol(Comparison)) %>% 
    spread(Geometry,value)
  
  
  Art <- c("Enduro", "Enduro", "AllMountain", "Enduro")
  
  
  ComparisonTableHallo <- ComparisonTable
  
  ComparisonTableHallo$Art <- Art
  
  # server
  
  server <- function(input, output, session) {
   selectedData1 <- reactive({
        ComparisonTableHallo %>%
        filter(ComparisonTableHallo$Bikes != gsub("[[:space:]]*$","",gsub("- .*",'',input$Bikes))) 
      })
      
       
      
      selectedData2 <- reactive({
        selectedData1() %>%
          select(1:12) %>%
        filter(selectedData1()$Art %in% input$Art) 
      })
      
      selectedData3 <- reactive({
        ComparisonTableHallo  %>%
          select(1:12) %>%
          filter(ComparisonTableHallo$Bikes == gsub("[[:space:]]*$","",gsub("- .*",'',input$Bikes))) 
        
      })
      
      selectedData4 <- reactive({
        rbind(selectedData3(),selectedData2())
        
      })
      
      selectedData5 <- reactive({
        selectedData4() %>%
        select(3:11)
      })
      
      selectedData6 <- reactive({
      as.numeric(knnx.index(selectedData5(), selectedData5()[1, , drop=FALSE], k=2))
      })
      
      selectedData7 <- reactive({
        selectedData4()[selectedData6(),]
      })
      
      selectedData8 <- reactive({
        selectedData7() %>%
          select(3:11)
      })
  
  
      # Combine the selected variables into a new data frame
    output$plot1 <- renderPlotly({
      
      validate(
        need(dim(selectedData2())[1]>=2, "Sorry, no ten similar bikes were found. 
             Please change the input filters."
        )
        )
  
      plot_ly(
        type = 'scatterpolar',
        mode = "closest",
        fill = 'toself'
      ) %>%
        add_trace(
          r = as.matrix(selectedData8()[1,]),
          theta = c("Kettenstrebe", "Lenkwinkel","Oberrohr","Radstand","Reach","Sattelrohr","Sitzwinkel","Stack","Steuerrohr",
                    "Tretlagerabsenkung"),
          showlegend = TRUE,
          mode = "markers",
          name = selectedData7()[1,1]
        ) %>%
        add_trace(
          r = as.matrix(selectedData8()[2,]),
          theta = c("Kettenstrebe","Lenkwinkel","Oberrohr","Radstand","Reach","Sattelrohr","Sitzwinkel","Stack","Steuerrohr",
                    "Tretlagerabsenkung"),
          showlegend = TRUE,
          mode = "markers",
          visible="legendonly",
          name = selectedData7()[2,1]
        ) %>% 
        layout(
          polar = list(
            radialaxis = list(
              visible = T,
              range = c(0,100)
            )
         ),
       
       showlegend=TRUE
        
          
        )
      
    })
    
  }
  
  #shiny app
  
  ui <- fluidPage(navbarPage("Bike Comparison",
             tabPanel("Graphic",fluidPage(theme = shinytheme("flatly")),
  tags$head(
    tags$style(HTML(".shiny-output-error-validation{color: red;}"))),
  pageWithSidebar(
    headerPanel('Apply filters'),
    sidebarPanel(width = 4,
      selectInput('Bike', 'Choose a Bike:',paste(ComparisonTableHallo$Bikes)),
        checkboxGroupInput(inputId = "Art",
                         label = 'Art:', choices = c("Enduro" = "Enduro", "AllMountain" = "AllMountain"
                                                          ), 
                         selected = c("Enduro" = "Enduro","AllMountain" = "AllMountain"),inline=TRUE),
     submitButton("Update filters")
    ),
    mainPanel(
      column(8, plotlyOutput("plot1", width = 800, height=700),
             p("To visualize the graph of the player, click the icon at side of names 
               in the graphic legend. It is worth noting that graphics will be overlapped.",
               style = "font-size:25px")
      
    )
    )
  )))
  )
  
  shinyApp(ui = ui, server = server)
  
  
• Simulate multiple dates between two dates

  library(lubridate)
  date1<-ymd("2021/01/01")
  
  date2<-ymd("2021/01/31")
  

  How can I simulate multiple dates between "2021-01-01" and "2021-01-31", for example ten dates like this:

  [1] "2021-01-21" "2021-01-07" "2021-01-09" "2021-01-18" "2021-01-02" "2021-01-13" "2021-01-24" "2021-01-30" "2021-01-11" "2021-01-25"
  
• Is there a function (preferably in lubridate) to get the number of weeks in any given year?

  I can probably hard code a lookup table and reference from that but I am wondering if there is a handy function in any R package that can return the number of weeks in a given year. ISO 8601 calendar is needed.

  lubridate has the function isoweek which returns the week number for a given date

  lubridate::isoweek("2020-12-31")

  lubridate::isoweek("2021-12-31")

  However, what I need is something like

  isoweeks(2020) which would return 53

  isoweeks(2021) which would return 52
• How do I calculate the shortest and longest time intervals in R?

  I have the following dataset called phone :

  TIMESTAMP	time	date
  2021-01-12 10:42:50.221	10:42:50	2021-01-12
  2021-01-12 10:46:01.826	10:46:01	2021-01-12
  2021-01-12 10:50:10.063	10:50:10	2021-01-12
  2021-01-12 10:53:10.715	10:53:10	2021-01-12
  2021-01-12 10:53:14.329	10:53:14	2021-01-12
  2021-01-12 10:54:19.792	10:54:19	2021-01-12
  2021-01-12 11:01:43.044	11:01:43	2021-01-12
  2021-01-12 11:04:36.202	11:04:36	2021-01-12

  I would like to calculate the time intervals between two consecutive time values for the entire dataset so that I can find the shortest and longest time intervals of the day. I've tried the following code, trying to calculate the time differences but it gives me negative values and I don't exactly know how accurate this is. I've tried changing the unit to minutes as well, but the output doesn't make sense to me.

  v2 <- ymd_hms(phone$TIMESTAMP)
  v1 <- difftime(v2[-length(v2)], v2[-1], unit = "hour")
  

  Output of v1:

  x
  -0.0532236 hours
  -0.0689547 hours
  -0.0501811 hours
  -0.0010039 hours
  -0.0181842 hours

  How can I calculate the intervals and arrange them in descending order of the length?

  dput of my data is as follows:

  structure(list(TIMESTAMP = c("2021-01-12 10:42:50.221", "2021-01-12 10:46:01.826", 
  "2021-01-12 10:50:10.063", "2021-01-12 10:53:10.715", "2021-01-12 10:53:14.329", 
  "2021-01-12 10:54:19.792", "2021-01-12 11:01:43.044", "2021-01-12 11:04:36.202", 
  "2021-01-12 11:07:36.636", "2021-01-12 11:18:59.169", "2021-01-12 11:25:44.954", 
  "2021-01-12 11:25:54.263", "2021-01-12 11:26:25.414", "2021-01-12 11:28:05.471", 
  "2021-01-12 11:30:24.349"), time = c("10:42:50", "10:46:01", 
  "10:50:10", "10:53:10", "10:53:14", "10:54:19", "11:01:43", "11:04:36", 
  "11:07:36", "11:18:59", "11:25:44", "11:25:54", "11:26:25", "11:28:05", 
  "11:30:24"), date = structure(c(18639, 18639, 18639, 18639, 18639, 
  18639, 18639, 18639, 18639, 18639, 18639, 18639, 18639, 18639, 
  18639), class = "Date")), row.names = c(NA, 15L), class = "data.frame")
  
• Using tidyverse to "unnest" a data.frame column inside a tibble

  I'm working with some data which is returned from a www call which jsonlite and as_tibble somehow convert into a data.frame column.

  This result data has an Id integer column and an ActionCode data.frame column with two internal columns. these show in the console as:

  > result
  # A tibble: 117 x 2
    Id    ActionCode$Code $Name 
    <int> <chr>           <chr>
    1     A1              First Code
    2     A2              Second Code
    3     A3              Third Code
    4     A4              Fourth Code
    ...
  

  and this can be inspected with str() as:

  > result %>% str()
  tibble [117 x 2] (S3: tbl_df/tbl/data.frame)
   $ Id : int [1:117] 1 2 3 4 ...
   $ ActionCode:'data.frame': 117 obs. of  2 variables:
    ..$ Code: chr [1:117] "A1" "A2" "A3" "A4" ...
    ..$ Name: chr [1:117] "First Code" "Second Code" "Third Code" "Fourth Code" ...
  

  I've seen from e.g. https://tibble.tidyverse.org/articles/types.html that this sort of data.frame column is perfectly legal, but I'm struggling to work out how to access the data in this column from tidy dplyr pipelines - e.g. I can't select(ActionCode$Code)

  Is there a way to work with these columns in dplyr pipelines? Or is there a way to somehow flatten these columns similar to how unnest can be used on list columns (although I realise here that I'm not creating extra rows - I'm just flattening the column hierarchy).

  i.e. I'm trying to find a function foo which can output:

  > result %>% foo() %>% str()
  tibble [117 x 2] (S3: tbl_df/tbl/data.frame)
   $ Id : int [1:117] 1 2 3 4 ...
   $ Code: chr [1:117] "A1" "A2" "A3" "A4" ...
   $ Name: chr [1:117] "First Code" "Second Code" "Third Code" "Fourth Code" ...
  

  ---

  I can't provide the www call as a sample, but as a working example I think the sort of data I am presented with is something like:

  sample_data <- tibble(
    Id = 1:10,
    ActionCode = tibble(
      Code = paste0("Id", 1:10),
      Name = paste0("Name ", 1:10),
    )
  )
  
• Using case_when, how to mutate a new list-column that nests a vector within?

  I'm trying to use dplyr's case_when() to mutate a new column based on conditions in other columns. However, I want the new column to be nesting a vector.

  Example

  Consider the following toy data. Based on it, I want to summarize the geographical territory of the UK.

  library(tibble)
  
  set.seed(1)
  my_mat <- matrix(sample(c(TRUE, FALSE), size = 40, replace = TRUE), nrow = 10, ncol = 4) 
  colnames(my_mat) <- c("England", "Wales", "Scotland", "Northern_Ireland")
  my_df <- as_tibble(my_mat)
  
  > my_df
  
  ## # A tibble: 10 x 4
  ##    England Wales Scotland Northern_Ireland
  ##    <lgl>   <lgl> <lgl>    <lgl>           
  ##  1 TRUE    TRUE  TRUE     FALSE           
  ##  2 FALSE   TRUE  TRUE     FALSE           
  ##  3 TRUE    TRUE  TRUE     TRUE            
  ##  4 TRUE    TRUE  TRUE     FALSE           
  ##  5 FALSE   TRUE  TRUE     TRUE            
  ##  6 TRUE    FALSE TRUE     TRUE            
  ##  7 TRUE    FALSE FALSE    FALSE           
  ##  8 TRUE    FALSE TRUE     TRUE            
  ##  9 FALSE   FALSE TRUE     FALSE           
  ## 10 FALSE   TRUE  FALSE    FALSE  
  

  I want to mutate a new collective_geo_territory column.

  1. if both England, Scotland, Wales, and Northern_Ireland are TRUE, then we say this is United_Kingdom.
  2. otherwise, if only England, Scotland, and Wales are TRUE, then we say this is Great_Britain
  3. any other combination would simply return a vector with the names of countries that are TRUE.

  My attempt

  So far, I know how to address conditions (1) and (2) detailed above, using the following code

  library(dplyr)
  
  my_df %>%
    mutate(collective_geo_territory = case_when(England == TRUE & Wales == TRUE & Scotland == TRUE & Northern_Ireland == TRUE ~ "United_Kingdom",
                                                England == TRUE & Wales == TRUE & Scotland == TRUE ~ "Great_Britain"))
  

  Desired Output

  However, I want to achieve an output with collective_geo_territory column that looks like the following:

  ## # A tibble: 10 x 5
  ##      England Wales Scotland Northern_Ireland collective_geo_territory
  ##      <lgl>   <lgl> <lgl>    <lgl>            <list>                   
  ##   1  TRUE    TRUE  TRUE     FALSE            <chr [1]>   # c("Great_Britain")           
  ##   2  FALSE   TRUE  TRUE     FALSE            <chr [2]>   # c("Wales", "Scotland")                      
  ##   3  TRUE    TRUE  TRUE     TRUE             <chr [1]>   # c("United_Kingdom")        
  ##   4  TRUE    TRUE  TRUE     FALSE            <chr [1]>   # c("Great_Britain")
  ##   5  FALSE   TRUE  TRUE     TRUE             <chr [3]>   # c("Wales", "Scotland", "Northern_Ireland")
  ##   6  TRUE    FALSE TRUE     TRUE             <chr [3]>   # c("England", "Scotland", "Northern_Ireland")
  ##   7  TRUE    FALSE FALSE    FALSE            <chr [1]>   # c("England") 
  ##   8  TRUE    FALSE TRUE     TRUE             <chr [3]>   # c("England", "Scotland", "Northern_Ireland")
  ##   9  FALSE   FALSE TRUE     FALSE            <chr [1]>   # c("Scotland") 
  ##   10 FALSE   TRUE  FALSE    FALSE            <chr [1]>   # c("Wales") 
  
• R: Loop through columns in tibble to find differences between each and create new for each difference

  I have been working on this for a while now, but I can't seem to figure it out. I'm looking for a solution that can: calculate difference between col1 and col2 and create colA based on this; then calculate difference between col2 and col3 and create colB based on this, etc. etc. I have about 70 rows and 42 of these columns so it's not something I want to do by hand (at this point I am almost desperate enough).

  To give a note also, some of the cells in the rows are empty (NA). An emergency solution would be to fill these with zeroes, but I'd rather not.

  Also, the dataframe I use is a tibble, however, I am not bound to this so much that I can't change it to a real dataframe.

  My data looks like this: testdata

  As you can see, the columns have annoyingly long names I did not know how to change also :). I use the column numbers usually, which are 77:119. I hope this is complete enough. Sorry for the noob-ness and possibly unclear explanation, this is my first question on here and I'm not that craftsy in R!

  Finally, to create the 'user/intermittent_answers/n_length' columns I used the following loop, so I thought it'd be possible to reuse this for the calculations that I need now.

  #loop through PARTS of testdata to create _length's
  for(i in names(testdata[34:76]))
    testdata[[paste(i, 'length', sep="_")]] <- str_length(testdata[[i]])
  

  Then I tried something similar which I found here: FOR loop to calculate difference on dates in R

  for (j in 2:length(testdata$`user/intermittant_answers/42_length`))
  + testdata$lag[j] <- as.numeric(difftime(testdata$`user/intermittant_answers/42_length`[j], testdata$`user/intermittant_answers/42_length`[j-1], units=c("difference")), units = "days")
  Error in as.POSIXct.numeric(time1) : 'origin' must be supplied
  

  I figured this was because I am not working with anything time related, but I don't know/don't know how to find another 'diff' related function that is not bound to matrixes like the one from matrixStats package.

  I hope someone can push me in the right direction!

  Thank you!!