Find global index for first and last NA value by group

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I have a data set of the form

#create data.frame
df <- data.frame(id    = rep(1:3,each=10),
                 value = rnorm(30))

#throw in some NAs
df[c(1:5, 25:30),2] <- NA 

df[1:10,]
   id      value
1   1         NA
2   1         NA
3   1         NA
4   1         NA
5   1         NA
6   1 -1.0763008
7   1 -0.4026228
8   1  1.6110506
9   1 -1.0626593
10  1 -0.4058101

I would like to find the first and last non-NA value by group. I tried to code up a function that does that and it works fine if there's no grouping:

first.last.non.na = function(x){
  return(c(min(which(!is.na(x))),max(which(!is.na(x)))))
}

When I try to use this in combination with aggregate, it unfortunately only returns the indices of the first and last non-NA value within groups (as is to be expected):

aggregate(df[,2], by = list(df[,1]), FUN = first.last.non.na)
  Group.1 x.1 x.2
1       1   6  10
2       2   1  10
3       3   1   4

My desired output are the ''global'' indices of first and last non-NA values, i.e.

  Group.1 x.1 x.2
1       1   6  10
2       2   11 20
3       3   21 24

Any solutions that would also work with extremely large data sets?

The main idea is to create a variable based on the row numbers before grouping. Using dplyr,

library(dplyr)

df %>% 
 mutate(rn = row_number()) %>% 
 group_by(id) %>% 
 summarise(v1 = first(rn[!is.na(value)]), 
           v2 = last(rn[!is.na(value)]))

which gives,

# A tibble: 3 x 3
     id    v1    v2
  <int> <int> <int>
1     1     6    10
2     2    11    20
3     3    21    24

Find global index for first and last NA value by group, Find global index for first and last NA value by group. 发布于 2020-04-20 11:15: 46. I have a data set of the form #create data.frame df <- data.frame(id = rep(1:3� set.seed(234) x <- sample(c(rep(NA,3),1:5)) x [1] 3 5 NA 1 4 NA NA 2 For each NA, I want the index (or value) of the last preceeding non-NA value. That is, for the first NA, the last previous non-NA has the index 2. For the next two NA, their last previous non-NA has index 5: [1] NA NA 2 NA NA 5 5 NA Base R or tidyverse would be ok.

Same idea as @Sotos in data.table:

library(data.table)

setDT(df)[!is.na(value), .(x.1 = .I[1], x.2 = .I[.N]), by = id]

   id x.1 x.2
1:  1   6  10
2:  2  11  20
3:  3  21  24

Whereby we first filter for non-missing values of your df (in the value column) and then we extract the global row numbers (.I) for both first ([1]) and last ([.N]) value per each id.

Swiss Re Group, Publication Global resilience 2020. The pandemic is putting the world economy's resilience to the test. Discover our latest resilience index. (first|last)_valid_index isn't defined on DataFrames, but you can apply them on each column using apply. # first valid index for each column df.apply(pd.Series.first_valid_index) A 1 B 0 dtype: int64 # last valid index for each column df.apply(pd.Series.last_valid_index) A 3 B 0 dtype: int64 As before, you can also use notna and idxmax

Here is a base R solution using aggregate

res <- aggregate(value~id, df, function(x) range(which(!is.na(x))),na.action = NULL)
res$value[-1,1] <- res$value[-1,1] + cumsum(res$value[-nrow(res$value),2])
res$value[,2] <- cumsum(res$value[,2])

such that

> res
  id value.1 value.2
1  1       6      10
2  2      11      20
3  3      21      24

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17 Optimizer Hints, Hints that specify an index can use either a simple index name or a parenthesized values, such as allocated storage for such tables, to estimate the missing The FIRST_ROWS hint, which optimizes for the best plan to return the first single For a description of the tablespec syntax, see "Specifying Global Table Hints". Compute first of group values. Parameters numeric_only bool, default False. Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. min_count int, default -1. The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be

the "first_value(LastValTest.UserName) over (order by LastValTest.Modified desc)" is a WINDOW aggregate expression and is evaluated after the GROUP BY groups are evaluated, and as such the GROUP BY must contain all expressions that are not contained within a GROUP BY aggregate function and includes any references within any WINDOW functions.