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Filtering a dataset and making a ggplot

By : user2174477
Date : October 17 2020, 08:10 PM
Any of those help I'm trying to filter a dataset within ggplot(), and display a set of line charts for various countries with a population under 5 million. , Few changes in the code and it works for me after loading the data.
code :

countriesbyFood %>%
   mutate(POP_EST = as.numeric(as.character(POP_EST))) %>%
   filter(Item == "Wheat and products" & POP_EST < 5000000) %>%
   ggplot() + 
   geom_line(aes(x = Year, y=Amount, color=Element)) + 

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How to return a dataset from a dataset filtering value

By : Adam
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , You can filter Rows, by DataTable.Select function
code :
private void GetRowsByFilter(){
   DataTable myTable;
   myTable = DataSet1.Tables["Orders"];
   // Presuming the DataTable has a column named Date.
   string strExpr;
   strExpr = "Date > '1/1/00'";
   DataRow[] foundRows;
   // Use the Select method to find all rows matching the filter.
   foundRows = myTable.Select(strExpr);
   // Print column 0 of each returned row.
   for(int i = 0; i < foundRows.Length; i ++){
ds.Tables[<table name>].DefaultView.RowFilter = "ProductId=5"

SSAS (MDX) - Filtering Dataset based on another Dataset

By : xbook
Date : March 29 2020, 07:55 AM
hope this fix your issue If you can change the second query, then this could return the list of allowed members as a set definition string instead of a list of members, using e. g. the SetToStr function. This could then be used in the first query as a parameter replacing the text [Dim Division].[Hierarchy].ALLMEMBERS for the rows by StrToSet(@AllowedDivisionSet, CONSTRAINED).

ggplot error: Making line graphs of three variables, then making a facet plot of those

By : GJ Farmer
Date : March 29 2020, 07:55 AM
I wish this helpful for you As Richard pointed out, deleting the group=countrySpellId will eliminate the error, because you are trying to apply a group twice on two different variables (the linetype argument essentially does the same thing as group, it just means that that the different lines will also have different linetypes). The grouping on country will happen later in the facet_wrap.
Just doing that will get you three different linetypes that ggplot automatically chooses for you, but since you're particular about how those lines should look, you'll want to use scale_linetype_manual, which allows you to specify what linetype ggplot assigns to each factor level. You were on the right tract with scale_linetype_discrete!
code :
ggplot(data = oirfsFacetPlot2, aes(x = step, y = vals, stat = "identity"))  +
    geom_line(aes(linetype = indicator)) +
    xlab("Month") + ylab("Percent change") +
    theme_bw() + scale_x_continuous(breaks = seq(0,3,1)) +
    scale_linetype_manual(name = "indicator", values = c(2,1,2))  +
    facet_wrap( ~ country, scales = "free_y", nrow = 3 )  

Why does filtering to a sample of a dataset inside ggplot() return an incorrect sample?

By : sfabor
Date : March 29 2020, 07:55 AM
will be helpful for those in need TLDR: The filter expression gets evaluated multiple times, so you should not use a non-deterministic expression.
Not sure if this is good enough for an answer, but if you try to run your example with different seeds, you'll notice that the number of charts changes with each seed. This suggests that the number of ids we are filtering the data frame changes with each seed, suggesting that sample is actually called multiple times. We can confirm this by creating a function that takes the place of sample:
code :
sample_out <- function(data, n) {
  print("running sample_out ")
  return (sample(data, n))
  data = df %>% filter(id %in% sample_out(unique(df$id), 3)), 
  mapping = aes(x = date, y = value, colour = band)
filter.tbl_df <- function(.data, ..., .preserve = TRUE) {
  // elided
  out <- filter_impl(.data, quo)
template <typename SlicedTibble>
SEXP filter_template(const SlicedTibble& gdf, const NamedQuosure& quo) {
  // elided
  Proxy call_proxy(quo.expr(), gdf, quo.env()) ;
  // elided
  int ngroups = gdf.ngroups() ;    
  // elided    
  for (int i = 0; i < ngroups; i++, ++git) {
    // elided
    LogicalVector g_test = check_result_lgl_type(call_proxy.get(indices));
    // elided
  // elided

Filtering a dataset based on another dataset

By : user2307739
Date : March 29 2020, 07:55 AM
I wish this help you Extending on @jake2389 idea, there are several tricks you can do. What you can really do greatly depends on how big your dataset is and how many times can you fit it in your memory (or your database). The obvious way to improve performance is to do some caching. Assume you have a method getRecordsForColors(colors) that does the real filtering (or real query to the DB). Some very naive approach would go like this (note I didn't try this code so there might be a lot of tiny mistakes):
code :
cache = dict()

def getRecordsCached(colors):
    global cache
    if colors not in cache:
       records = getRecordsForColors(colors)
       cache[colors] = records
       return records
       return cache[colors]
cache = dict()

def getRecordsCached(colors):
    global cache
    if colors not in cache:
       records = getRecordsForColors(colors)
       if len(colors) < threshold:
          cache[colors] = records
       return records
       return cache[colors]
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