I am to extract data from netCDF file using R for specific loaction the code i've written as showen and I have an error at the end of the code

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• pivot_wider does not keep all the variables

  I would like to keep the variable cat (category) in the output of my function. However, I am not able to keep it. The idea is to apply a similar function to m <- 1 - (1 - se * p2)^df$n based on the category. But in order to perform that step, I need to keep the variable category.

  Here's the code:

  #script3
  
  suppressPackageStartupMessages({
    library(mc2d)
    library(tidyverse)
  })
  
  sim_one <- function() {
  df<-data.frame(id=c(1:30),cat=c(rep("a",12),rep("b",18)),month=c(1:6,1,6,4,1,5,2,3,2,5,4,6,3:6,4:6,1:5,5),n=rpois(30,5))
    nr <- nrow(df)
    df$n[df$n == "0"] <- 3
    se <- rbeta(nr, 96, 6)
    epi.a <- rpert(nr, min = 1.5, mode = 2, max = 3)
    p <- 0.2
    p2 <- epi.a*p
    m <- 1 - (1 - se * p2)^df$n
    results <- data.frame(month = df$month, m, df$cat)
    results %>%
      arrange(month) %>%
      group_by(month) %>%
      mutate(n = row_number(), .groups = "drop") %>%
      pivot_wider(
      id_cols = n,
      names_from = month,
      names_glue = "m_{.name}",
      values_from =m
      ) 
  }
  
  set.seed(99)
  
  iters <- 1000
  sim_list <- replicate(iters, sim_one(), simplify = FALSE)
  sim_list[[1]]
  #> # A tibble: 7 x 7
  #>       n    m_1    m_2    m_3    m_4   m_5    m_6
  #>   <int>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>  <dbl>
  #> 1     1  0.970  0.623  0.905  0.998 0.929  0.980
  #> 2     2  0.912  0.892  0.736  0.830 0.890  0.862
  #> 3     3  0.795  0.932  0.553  0.958 0.931  0.798
  #> 4     4  0.950  0.892  0.732  0.649 0.777  0.743
  #> 5     5 NA     NA     NA      0.657 0.980  0.945
  #> 6     6 NA     NA     NA      0.976 0.836 NA    
  #> 7     7 NA     NA     NA     NA     0.740 NA
  

  ^{Created on 2022-05-07 by the reprex package (v2.0.1)}
• calculate weighted average over several columns with NA

  I have a data frame like this one:

  ID  duration1   duration2   total_duration  quantity1   quantity2
   1     5            2             7             3         1
   2     NA           4             4             3         4
   3     5            NA            5             2         NA
  

  I would like to do a weighted mean for each subject like this:

  df$weighted_mean<-  ((df$duration1*df$quantity1) + (df$duration2*df$quantity2) ) / (df$total_duration)
  

  But as I have NA, this command does not work and it is not very nice....

  The result would be this:

  ID  duration1   duration2   total_duration  quantity1   quantity2   weighted_mean
   1     5            2             7             3         1          2.43
   2     NA           4             4             3         4          4
   3     5            NA            5             2         NA         2
  

  Thanks in advance for the help
• cant read the netcdf file

  I am trying to load netcdf file into R:

  f <- "A20020012002365.L3b_YR_CHL.nc"
  u <- "https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/"
  curl::curl_download(file.path(u, f), f)
  r <- brick("A20020012002365.L3b_YR_CHL.nc")
  

  Error message I get: Error in R_nc4_open: NetCDF: Unknown file format

  Also tried:

  tidync::tidync("A20020012002365.L3b_YR_CHL.nc")
  nc <-  RNetCDF::open.nc("A20020012002365.L3b_YR_CHL.nc")
  RNetCDF::print.nc(nc)
  file <- RNetCDF::open.nc("A20020012002365.L3b_YR_CHL.nc")
  ncdf4::nc_open("A20020012002365.L3b_YR_CHL.nc", verbose=TRUE, readunlim = FALSE, suppress_dimvals = TRUE)
  

  none of those worked
• Making numpy.datetime64 coordinate variable CF-Compliant - cftime?

  I'm unable to find any examples on how to write a CF-compliant NetCDF with a time coordinate variable of type np.datetime64. The timesteps were originally as pd.Timestamp's but got automatically converted. I don't really think in terms of nanoseconds elapsed since the epoch or whatever, so could someone please point me to how I can make the conversion painlessly and produce a CF-compliant .nc?

  Thanks
• ValueError: Resulting object does not have monotonic global indexes along dimension lat when combining netcdf files

  I posted this previous question and found a way to create a (lat,lon,time) as my dimensions. However once I started processing it, I started to get the ValueError: Resulting object does not have monotonic global indexes along dimension lat. I double checked the longitude and latitude values by using .sel() and they indexed the right value. I have multiple daily files of the dataset and I plan to loop and run this code for each file. Here's the code I used.

  import xarray as xr
  
  all_alt = xr.open_dataset('dataset.nc')
  ssha = all_alt.ssha
  ssha = ssha.set_index(time=('lon','lat'))
  rechunked = ssha.chunk(chunks={'time':300}) #divided it into chunks for faster processing
  unstack = rechunked.unstack('time')
  unstack = unstack.to_dataset()
  
  time1 = all_alt.resample(time='1D').mean() #Get the daily value of time and assign that to the whole dataset
  timeval = time1['time'].values
  
  unstack = unstack.expand_dims(time=timeval) #to add back the time dimension
  

  This results to

  <xarray.Dataset>
  Dimensions:  (lon: 56, lat: 56, time: 1)
  Coordinates:
    * time     (time) datetime64[ns] 2002-01-23
    * lon      (lon) float64 121.7 121.7 121.7 121.7 ... 122.8 122.8 122.8 122.8
    * lat      (lat) float64 13.03 13.08 13.13 13.18 ... 15.81 15.86 15.91 15.96
  Data variables:
      ssha     (time, lon, lat) float32 dask.array<chunksize=(1, 56, 56), meta=np.ndarray>
  

  And this is exactly the dataset I want. However, when I try to combine these files through xr.open_mfdataset(combine='by_coords'), I receive the ValueError: Resulting object does not have monotonic global indexes along dimension lat. Is there something wrong with the code? I tried searching around stackoverflow but I can't seem to find a solution.

  Here's the link to one of my data