How to remove rows with any zero value

Question

I have a problem to solve how to remove rows with a Zero value in R  In others hand  I can use na omit   to delete all the NA values or use complete cases   to delete rows that contains NA values   Is there anyone know how to remove rows with a Zero Values in R   For example    Before       DateTime        Mac1    Mac2    Mac3    Mac4    ----------------------------------------------------   2011-04-02 06 00   20      0       20      20          2011-04-02 06 05   21      21      21      21          2011-04-02 06 10   22      22      22      22          2011-04-02 06 15   23      23      0       23          2011-04-02 06 20   24      24      24      24         2011-04-02 06 25   0       25      25      0          After       DateTime        Mac1    Mac2    Mac3    Mac4    ----------------------------------------------------   2011-04-02 06 05   21      21      21      21          2011-04-02 06 10   22      22      22      22          2011-04-02 06 20   24      24      24      24

User · Answer

Well  you could swap your 0 s for NA and then use one of those solutions  but for sake of a difference  you could notice that a number will only have a finite logarithm if it is greater than 0  so that rowSums of the log will only be finite if there are no zeros in a row   dfr is finite rowSums log dfr -1

User · Answer

I prefer a simple adaptation of csgillespie s method  foregoing the need of a function definition   d apply d  0  1  all      where d is your data frame

User · Answer

Using tidyverse dplyr  you can also remove rows with any zero value in a subset of variables     variables starting with Mac must be non-zero filter at df  vars starts with  Mac     all vars        0      variables x  y  and z must be non-zero filter at df  vars x  y  z   all vars        0      all numeric variables must be non-zero filter if df  is numeric  all vars        0

User · Answer

In base R  we can select the columns which we want to test using grep  compare the data with 0  use rowSums to select rows which has all non-zero values   cols  lt - grep   Mac   names df   df rowSums df cols     0     length cols                 DateTime Mac1 Mac2 Mac3 Mac4  1 2011-04-02 06 05   21   21   21   21  2 2011-04-02 06 10   22   22   22   22  3 2011-04-02 06 20   24   24   24   24   Doing this with inverted logic but giving the same output  df rowSums df cols     0     0        In dplyr  we can use filter at to test for specific columns and use all vars to select rows where all the values are not equal to 0    library dplyr  df   gt    filter at vars starts with  Mac     all vars      0     data  df  lt - structure list DateTime   structure 1 6   Label   c  2011-04-02 06 00     2011-04-02 06 05    2011-04-02 06 10    2011-04-02 06 15    2011-04-02 06 20     2011-04-02 06 25    class    factor    Mac1   c 20L  21L  22L   23L  24L  0L   Mac2   c 0L  21L  22L  23L  24L  25L   Mac3   c 20L   21L  22L  0L  24L  25L   Mac4   c 20L  21L  22L  23L  24L  0L     class    data frame   row names   c NA  -6L

User · Answer

You can use filter from dplyr package   Let s call your data frame df   library dplyr  df1  lt - filter df  Mac1  gt  0  Mac2  gt  0  Mac3  gt  0  Mac4  gt  0   df1 will have only rows with entries above zero  Hope this helps

User · Answer

There are a few different ways of doing this  I prefer using apply  since it s easily extendable     Generate some data dd   data frame a   1 4  b  1 0  c 0 3     Go through each row and determine if a value is zero row sub   apply dd  1  function row  all row   0      Subset as usual dd row sub

User · Answer

I would probably go with Joran s suggestion of replacing 0 s with NAs and then using the built in functions you mentioned  If you can t don t want to do that  one approach is to use any   to find rows that contain 0 s and subset those out   set seed 42   Fake data x  lt - data frame a   sample 0 2  5  TRUE   b   sample 0 2  5  TRUE    gt  x   a b 1 2 1 2 2 2 3 0 0 4 2 1 5 1 2  Subset out any rows with a 0 in them  Note the negation with   around the apply function x   apply x  1  function y  any y    0        a b 1 2 1 2 2 2 4 2 1 5 1 2   To implement Joran s method  something like this should get you started   x x  0   lt - NA

User · Answer

As dplyr 1 0 0 deprecated the scoped variants which  Feng Mai nicely showed  here is an update with the new syntax  This might be useful because in this case  across   doesn t work  and it took me some time to figure out the solution as follows  The goal was to extract all rows that contain at least one 0 in a column  df   gt      rowwise     gt      filter any c across everything        0    with the data df  lt - data frame a   1 4  b  1 0  c 0 3  df  lt - rbind df  c 0 0 0   df  lt - rbind df  c 9 9 9      A tibble  4 x 3   Rowwise         a     b     c    lt dbl gt   lt dbl gt   lt dbl gt  1     1     1     0 2     2     0     1 3     4     0     3 4     0     0     0  So it correctly doesn t return the last row containing all 9s

User · Answer

I would do the following   Set the zero to NA    data data  0   lt - NA  data   Delete the rows associated with NA    data2 lt -data complete cases data

[r] How to remove rows with any zero value

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