I have a data looks like this
df<- structure(list(V1 = structure(c(6L, 2L, 3L, 7L, 5L, 4L, 8L, 1L
), .Label = c(\"A0A0G2JDV6\", \"P01901\", \"P13745\", \"Q03
To get the desired output, you can use a combination of order
and rowSums
. Using:
df2 <- df[order(rowSums(df[,-1]), decreasing = TRUE),]
gives:
> df2
V1 V2 V3 V4 V5 V6
8 A0A0G2JDV6 2.4361702 2.6861702 2.2872340 2.5797872 2.5585106
3 P13745 1.5213675 1.5584046 1.7150997 2.0683761 1.7920228
1 Q3UCW4 1.2345483 1.1854200 1.0681458 1.5356577 1.6053883
2 P01901 0.9829689 1.1312027 1.0899916 1.2586207 1.2802775
6 Q03141 1.0357143 0.8972303 0.9278426 1.0466472 1.1836735
4 Q8CBE6 1.0062305 0.7757009 0.9439252 1.0124611 0.8753894
5 Q3TMK4 0.8681063 0.7458057 0.7746279 0.8283145 0.8283576
7 Q8VCQ8 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
To get only the rows in which all the values are higher than 1.1
, you can use:
df2[rowSums(df2[,-1] > 1.1) == 5, ]
which gives:
V1 V2 V3 V4 V5 V6
8 A0A0G2JDV6 2.436170 2.686170 2.287234 2.579787 2.558511
3 P13745 1.521368 1.558405 1.715100 2.068376 1.792023