rebased version for #2327 add filter subsetting to docs

docs/src/man/getting_started.md

@@ -645,6 +645,42 @@ Equivalently, the `in` function can be called with a single argument to create
 a function object that tests whether each value belongs to the subset
 (partial application of `in`): `df[in([1, 5, 601]).(df.A), :]`.
 
+#### Selecting rows with `filter`
+
+We have seen above how to subset a `DataFrame` to several criteria, involving multiple columns, by supplying a logical vector to the first dimension. For instance, in the following we want to subset to all rows where `x > 2` and where `a == 'c'`:
+
+```jldoctest dataframe
+julia> df = DataFrame(:x => 1:4, :y => rand(4), :a => 'a':'d')
+4×3 DataFrame
+ Row │ x      y         a    
+     │ Int64  Float64   Char 
+─────┼───────────────────────
+   1 │     1  0.830964  a
+   2 │     2  0.478479  b
+   3 │     3  0.939474  c
+   4 │     4  0.742664  d
+
+julia> df[(df.x .> 2) .& (df.a .== 'c'), : ]
+1×3 DataFrame
+ Row │ x      y         a    
+     │ Int64  Float64   Char 
+─────┼───────────────────────
+   1 │     3  0.939474  c
+
+```
+
+An alternative formulation, which notably saves on the need to use
+broadcasting syntax via `.` prefixes, uses [`filter`](@ref) or [`filter!`](@ref):
+
+```jldoctest dataframe
+julia> filter([:x, :a] => (x1, x2) -> x1 > 2 && x2 == 'c', df)
+1×3 DataFrame
+ Row │ x      y         a    
+     │ Int64  Float64   Char 
+─────┼───────────────────────
+   1 │     3  0.939474  c
+```
+
 !!! note
 
     As with matrices, subsetting from a data frame will usually return a copy of