I have a sample (rows) by species (columns) dataframe. And a column in another dataframe that codes the samples into groups. I want to select all of the columns where all of the samples in any of the groups have a nonzero value.
species frame:
structure(list(Otu000132 = c(0L, 56L, 30L, 52L, 1L, 4L, 31L, 4L, 17L, 9L, 4L),
Otu000144 = c(191L, 14L, 58L, 137L, 127L, 222L, 26L, 175L, 133L, 107L, 43L),
Otu000146 = c(0L, 0L, 0L, 0L, 16L, 62L, 41L, 16L, 60L, 32L, 0L),
Otu000147 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Otu000151 = c(2L, 9L, 4L, 1L, 0L, 4L, 4L, 2L, 3L, 0L, 0L),
Otu000162 = c(2L, 1L, 0L, 0L, 1L, 1L, 0L, 2L, 1L, 0L, 0L),
Otu000164 = c(2L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Otu000174 = c(0L, 0L, 3L, 1L, 0L, 2L, 0L, 1L, 2L, 1L, 0L),
Otu000176 = c(1L, 9L, 0L, 1L, 2L, 5L, 3L, 3L, 8L, 2L, 2L),
Otu000186 = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L),
Otu000190 = c(1L, 1L, 1L, 0L, 0L, 5L, 1L, 2L, 7L, 0L, 0L)),
.Names = c("Otu000132", "Otu000144", "Otu000146", "Otu000147",
"Otu000151", "Otu000162", "Otu000164", "Otu000174",
"Otu000176", "Otu000186", "Otu000190"),
row.names = 30:40, class = "data.frame")
grouping frame:
structure(c(30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
.Dim = c(11L, 2L))
desired output:
structure(list(Otu000132 = c(0L, 56L, 30L, 52L, 1L, 4L, 31L, 4L, 17L, 9L, 4L),
Otu000144 = c(191L, 14L, 58L, 137L, 127L, 222L, 26L, 175L, 133L, 107L, 43L),
Otu000151 = c(2L, 9L, 4L, 1L, 0L, 4L, 4L, 2L, 3L, 0L, 0L),
Otu000176 = c(1L, 9L, 0L, 1L, 2L, 5L, 3L, 3L, 8L, 2L, 2L),
Otu000190 = c(1L, 1L, 1L, 0L, 0L, 5L, 1L, 2L, 7L, 0L, 0L)),
.Names = c("Otu000132", "Otu000144", "Otu000151",
"Otu000176", "Otu000190"),
row.names = 30:40, class = "data.frame")
I feel like this should be something that I could do with dplyr select, but I can't figure it out. Anyone have suggestions for starting me on a path?
This can indeed be done with dplyr, and in a fairly straightforward way. As others have pointed out, "Otu000146" does not meet your described criteria and would not be included in the final column selection.
library(dplyr)
library(tidyr)
df.species <- cbind(species, group = grouping[,2]) %>% # merge the grouping variable into the main data set
gather(variable, value, -group) %>% # gather the columns into 'long' format
group_by(variable, group) %>% # group by column name and group
summarize(keep = all(value != 0)) %>% # variables and groups where all values are non-zero
ungroup %>% group_by(variable) %>% # reset grouping
summarize(keep = any(keep)) %>% # variables where at least 1 group met the aforementioned criterion
dplyr::filter(keep) # final list
variable keep
<chr> <lgl>
1 Otu000132 TRUE
2 Otu000144 TRUE
3 Otu000151 TRUE
4 Otu000176 TRUE
5 Otu000190 TRUE
# retrieve only the matching columns
df.desired <- species[df.species$variable]
Otu000132 Otu000144 Otu000151 Otu000176 Otu000190
30 0 191 2 1 1
31 56 14 9 9 1
32 30 58 4 0 1
33 52 137 1 1 0
34 1 127 0 2 0
35 4 222 4 5 5
36 31 26 4 3 1
37 4 175 2 3 2
38 17 133 3 8 7
39 9 107 0 2 0
40 4 43 0 2 0