trouble excluding rows using crunch package

General
1

Hi All,
I'm trying to use the crunch package to exclude certain rows from my dataset. I found a simple code online and tried to make it work with my data but I'm getting an error message. The original online solution was:

dim(ds)
exclusion(ds) <-ds$perc_skipped > 15
exclusion(ds)
dim(ds)

I tried:

library(crunch)
dim(all_data_merged)
exclusion(all_data_merged) <- all_data_merged$completeness > 1
exclusion(all_data_merged)
dim(all_data_merged)

I get the error message: Error in exclusion(all_data_merged) : is.dataset(x) is not TRUE

I don't know what this means, it recognizes my dataset and executes the dim(all_data_merged) command with no trouble.

My goal is to be able to run summary statistics on my data without the rows that have the value of 1 in the completeness column. I don't want to delete those rows because I will need to run some statistics on them separately.

Would it be simpler to use the dplyr(filter) function?

Any help would be greatly appreciated!

2

Yes, definitely.

If you still need help with this, please provide a minimal REPRoducible EXample (reprex) ilustrating your issue. A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

3

Thanks Andre,
I still need some help. I tried the following code to exclude rows with a value of 1 in the completeness column but I'm not sure it worked. It stated that 3041 rows were omitted but I have 3091 rows with a value of 1 in the completeness column. I'm also unsure of how to manipulate the filtered data after I get it sorted. Thanks for your patience, I'm just getting started with R.

library(dplyr)
filter(all_data_merged,!(completeness >1))

site Weight Completeness termination
4 7.76 2 2
4 8.44 2 3
4 7.67 1 0
4 7.51 2 1
4 10.19 4 5
4 8.47 1 0
4 4.81 1 0
4 4.88 2 5
4 5.46 4 5
4

To get you going with both reprex and dplyr::filter, here is a small example of how it works (there are more examples in documentation):

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

mtcars <- tibble::as_tibble(mtcars)
mtcars
#> # A tibble: 32 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # … with 22 more rows

mtcars %>%
  dplyr::filter(cyl > 4)
#> # A tibble: 21 x 11
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  4  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  5  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  6  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  7  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#>  8  17.8     6  168.   123  3.92  3.44  18.9     1     0     4     4
#>  9  16.4     8  276.   180  3.07  4.07  17.4     0     0     3     3
#> 10  17.3     8  276.   180  3.07  3.73  17.6     0     0     3     3
#> # … with 11 more rows

res <- mtcars %>%
  dplyr::filter(cyl > 4)

Created on 2019-08-12 by the reprex package (v0.3.0)

As you can see, after I've used dplyr::filter, only rows with cyl > 4 are left. However, after applying the transformation, you still need to save it to an object to continue working with it.

Let us know if that helps.

5

This is what I understand from what you are saying, you want to "exclude rows with a value of 1 in the completeness column" and "run summary statistics on my data without the rows that have the value of 1 in the completeness column".

Then this would do the job

# Details about the used libraries
library(dplyr)

# Sample data on a copy/paste friendly format
all_data_merged <- data.frame(
           site = c(4, 4, 4, 4, 4, 4, 4, 4, 4),
         Weight = c(7.76, 8.44, 7.67, 7.51, 10.19, 8.47, 4.81, 4.88, 5.46),
   Completeness = c(2, 2, 1, 2, 4, 1, 1, 2, 4),
    termination = c(2, 3, 0, 1, 5, 0, 0, 5, 5)
)

# Relevant code
all_data_merged %>% 
    filter(Completeness != 1) %>% 
    summary()
#>       site       Weight        Completeness    termination  
#>  Min.   :4   Min.   : 4.880   Min.   :2.000   Min.   :1.00  
#>  1st Qu.:4   1st Qu.: 5.973   1st Qu.:2.000   1st Qu.:2.25  
#>  Median :4   Median : 7.635   Median :2.000   Median :4.00  
#>  Mean   :4   Mean   : 7.373   Mean   :2.667   Mean   :3.50  
#>  3rd Qu.:4   3rd Qu.: 8.270   3rd Qu.:3.500   3rd Qu.:5.00  
#>  Max.   :4   Max.   :10.190   Max.   :4.000   Max.   :5.00

Note: Please notice the way I'm sharing the sample data and code, that would be the proper way of posting a reproducible example.

6

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