Filter data.frame rows by a logical condition

Question

To select rows according to one 'cell_type' (e.g. 'hesc'), use ==:

expr[expr$cell_type == "hesc", ]

To select rows according to two or more different 'cell_type', (e.g. either 'hesc' or 'bj fibroblast'), use %in%:

expr[expr$cell_type %in% c("hesc", "bj fibroblast"), ]

Use subset (for interactive use)

subset(expr, cell_type == "hesc")
subset(expr, cell_type %in% c("bj fibroblast", "hesc"))

or better dplyr::filter()

filter(expr, cell_type %in% c("bj fibroblast", "hesc"))

The reason expr[expr[2] == 'hesc'] doesn't work is that for a data frame, x[y] selects columns, not rows. If you want to select rows, change to the syntax x[y,] instead:

> expr[expr[2] == 'hesc',]
  expr_value cell_type
4   5.929771      hesc
5   5.873096      hesc
6   5.665857      hesc

You could use the dplyr package:

library(dplyr)
filter(expr, cell_type == "hesc")
filter(expr, cell_type == "hesc" | cell_type == "bj fibroblast")

No one seems to have included the which function. It can also prove useful for filtering.

expr[which(expr$cell == 'hesc'),]

This will also handle NAs and drop them from the resulting dataframe.

Running this on a 9840 by 24 dataframe 50000 times, it seems like the which method has a 60% faster run time than the %in% method.