Subsetting of data frame in R with row selection

Question

I have two data frames. One named "discovery" is like

probeID         symbol  is.TF   entrezID
ILMN_1814092    AACSP1  FALSE   729522
ILMN_1668851    AADACL4 FALSE   343066
ILMN_1805104    ABAT    FALSE   18
ILMN_2070570    ABCA10  FALSE   10349
ILMN_2232084    ABCA11P FALSE   79963
ILMN_1704579    ABCA13  FALSE   154664
ILMN_1722286    ABCA5   FALSE   23461
ILMN_1701551    ABCA6   FALSE   23460
ILMN_1743205    ABCA7   FALSE   10347

Another one named "values" is like

probeID         value   
ILMN_1814092    1.0 
ILMN_1668851    1.9 
ILMN_1805104    1.8 
ILMN_2070570    1.8 
ILMN_2232084    1.5 
ILMN_1704579    2.3 
ILMN_1722286    2.6 
ILMN_1701551    0.1 
ILMN_1743205    5.5 

Two data frames overlap in terms of row "probeID"

How can I select from "discovery" where the "probeID" appeared in "values" ?

overlap <- discovery[values$probeID,]

It gives me a data frame with all values are NA

Answer 1

If you need a dataset with columns values , merge is the better way. But if you just need to get subset of discovery where probeID appears. The following works.

overlap <- discovery[discovery$probeID %in% values$probeID,]

%in% operator is based on match, so here I am selecting only rows where discovery$probeID matches any of values$probeID

Answer 2

Just do merge(discovery, values, by.x = "probeID", by.y = "probeID")

Brief explanations, see http://stat.ethz.ch/R-manual/R-devel/library/base/html/merge.html .

merge(x, y, by = intersect(names(x), names(y)),
     by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all,
     sort = TRUE, suffixes = c(".x",".y"),
     incomparables = NULL, ...)

merge x and y , two data frame together. If you want, you can specify the columns by which the two dataframes should be merged, using by.x and by.y respectively. If the column name is shared between x and y , then just by= is enough. If by= is not specified, then it is defaulted to the intersection of names(x) and names(y) , or the columns that are shared between the two dataframes.