CODEWITHSUNDEEP
rfiltering
kikibobo
kikibobo

Reputation: 45

Remove all rows which contain at least one negative value in R

I don't want to remove all negative elements, that I can do. But, I want to remove all rows even if it contains only one negative value in my data frame.

> head(kosoyCorrected)
       BER1_EW    BER2_EW      BER3_EW    BER4_EW      BER5_EW     BER6_EW
1  7.087613184 7.09928796  7.087194381 6.96315939  7.086734346  7.09934523
2  4.599450934 3.89325300  4.160360141 4.81419817  4.090161726  4.34070903
3  0.100477184 0.02351617 -0.001589346 0.01072809  0.023073244 -0.06953596
4  0.132531627 0.09994992  0.123564389 0.13849246  0.217604484  0.09164854
5 -0.005220038 0.07117798  0.133075865 0.05525490 -0.003944601  0.10597363
6  0.107204375 0.11755171  0.060868101 0.14361525  0.109494893  0.13081894

Here, for example, I want to remove entire Row 3 and Row 5, not just the 4 negative values in these rows.

Thanks in advance!

Upvotes: 3

Views: 1828

Answers (3)

akrun
akrun

Reputation: 887088

We can use rowSums to create a logical vector for subsetting the rows

subset(kosoyCorrected, !rowSums(kosoyCorrected < 0))
#  BER1_EW    BER2_EW   BER3_EW   BER4_EW   BER5_EW    BER6_EW
#1 7.0876132 7.09928796 7.0871944 6.9631594 7.0867343 7.09934523
#2 4.5994509 3.89325300 4.1603601 4.8141982 4.0901617 4.34070903
#4 0.1325316 0.09994992 0.1235644 0.1384925 0.2176045 0.09164854
#6 0.1072044 0.11755171 0.0608681 0.1436152 0.1094949 0.13081894

Or another option is Reduce

subset(kosoyCorrected, Reduce(`&`, lapply(kosoyCorrected, `>`, 0)))

Or a vectorized option in dplyr with filter_all

library(dplyr)
kosoyCorrected %>%
       filter_all( all_vars(. > 0))
#    BER1_EW    BER2_EW   BER3_EW   BER4_EW   BER5_EW    BER6_EW
#1 7.0876132 7.09928796 7.0871944 6.9631594 7.0867343 7.09934523
#2 4.5994509 3.89325300 4.1603601 4.8141982 4.0901617 4.34070903
#4 0.1325316 0.09994992 0.1235644 0.1384925 0.2176045 0.09164854
#6 0.1072044 0.11755171 0.0608681 0.1436152 0.1094949 0.13081894

Or in the newer version with across

kosoyCorrected %>% 
        filter(across(everything(), ~ . > 0))

data

kosoyCorrected <- structure(list(BER1_EW = c(7.087613184, 4.599450934, 0.100477184, 
0.132531627, -0.005220038, 0.107204375), BER2_EW = c(7.09928796, 
3.893253, 0.02351617, 0.09994992, 0.07117798, 0.11755171), BER3_EW = c(7.087194381, 
4.160360141, -0.001589346, 0.123564389, 0.133075865, 0.060868101
), BER4_EW = c(6.96315939, 4.81419817, 0.01072809, 0.13849246, 
0.0552549, 0.14361525), BER5_EW = c(7.086734346, 4.090161726, 
0.023073244, 0.217604484, -0.003944601, 0.109494893), BER6_EW = c(7.09934523, 
4.34070903, -0.06953596, 0.09164854, 0.10597363, 0.13081894)), 
class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

Upvotes: 4

ThomasIsCoding
ThomasIsCoding

Reputation: 101327

Another base R option using rowSums + sign

subset(kosoyCorrected,rowSums(sign(kosoyCorrected))==ncol(kosoyCorrected))

giving

    BER1_EW    BER2_EW   BER3_EW   BER4_EW   BER5_EW    BER6_EW
1 7.0876132 7.09928796 7.0871944 6.9631594 7.0867343 7.09934523
2 4.5994509 3.89325300 4.1603601 4.8141982 4.0901617 4.34070903
4 0.1325316 0.09994992 0.1235644 0.1384925 0.2176045 0.09164854
6 0.1072044 0.11755171 0.0608681 0.1436152 0.1094949 0.13081894

Upvotes: 1

tmfmnk
tmfmnk

Reputation: 39858

One dplyr option could be:

df %>%
 rowwise() %>%
 filter(all(c_across(everything()) >= 0))

  BER1_EW BER2_EW BER3_EW BER4_EW BER5_EW BER6_EW
    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1   7.09   7.10    7.09     6.96    7.09   7.10  
2   4.60   3.89    4.16     4.81    4.09   4.34  
3   0.133  0.0999  0.124    0.138   0.218  0.0916
4   0.107  0.118   0.0609   0.144   0.109  0.131

Or:

df %>%
 rowwise() %>%
 filter(min(c_across(everything())) >= 0)

Upvotes: 2

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