Fix some typos

docs/src/lib/metadata.md

@@ -7,7 +7,7 @@ level. This is supported using the functions defined by the DataAPI.jl interface
 
 * for table-level metadata: [`metadata`](@ref), [`metadatakeys`](@ref),
   [`metadata!`](@ref), [`deletemetadata!`](@ref), [`emptymetadata!`](@ref);
-* for column-level metatadata: [`colmetadata`](@ref), [`colmetadatakeys`](@ref),
+* for column-level metadata: [`colmetadata`](@ref), [`colmetadatakeys`](@ref),
   [`colmetadata!`](@ref), [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 Additionally you might find the
@@ -185,7 +185,7 @@ julia> colmetadatakeys(df)
 
 ## Propagation of `:note`-style metadata
 
-An important design feature of `:note`-style metatada is how it is handled when
+An important design feature of `:note`-style metadata is how it is handled when
 data frames are transformed.
 
 !!! note
@@ -265,7 +265,7 @@ metadata is always dropped). These are:
    metadata.
 * broadcasted assignment does not change target metadata;
   under Julia earlier than 1.7 operation of kind `df.a .= s` does not drop non-`:note`-style
-  metadata; under Julia 1.7 or later this operation perserves only `:note`-style
+  metadata; under Julia 1.7 or later this operation preserves only `:note`-style
   metadata
 * broadcasting propagates table-level metadata if some key is present
   in all passed data frames and value associated with it is identical in all

docs/src/man/reshaping_and_pivoting.md

@@ -88,7 +88,7 @@ rules of `vcat`.
 
 The stacked `DataFrame` that results includes all of the columns not specified
 to be stacked. These are repeated for each stacked column. These are normally
-refered to as identifier (id) columns. In addition to the id columns, two
+referred to as identifier (id) columns. In addition to the id columns, two
 additional columns labeled `:variable` and `:values` contain the column
 identifier and the stacked columns.
 

docs/src/man/split_apply_combine.md

@@ -33,7 +33,7 @@ object from your data frame using the `groupby` function that takes two argument
 
 Operations can then be applied on each group using one of the following functions:
 * `combine`: does not put restrictions on number of rows returned per group;
-  the returned values are vertically concatenaded following order of groups in
+  the returned values are vertically concatenated following order of groups in
   `GroupedDataFrame`; it is typically used to compute summary statistics by group;
   for `GroupedDataFrame` if grouping columns are kept they are put as first columns
   in the result;
@@ -1279,7 +1279,7 @@ two aspects:
 ## Specifying group order in `groupby`
 
 By default order of groups produced by `groupby` is undefined.
-If you want the order of groups to follow the order of first appereance in
+If you want the order of groups to follow the order of first appearance in
 the source data frame of a grouping key then pass the `sort=false` keyword argument
 to `groupby`:
 

src/abstractdataframe/reshape.jl

@@ -667,8 +667,8 @@ RepeatedVector(parent::AbstractVector, inner::Int, outer::Int)
 
 # Arguments
 - `parent` : the AbstractVector that's repeated
-- `inner` : the numer of times each element is repeated
-- `outer` : the numer of times the whole vector is repeated after
+- `inner` : the number of times each element is repeated
+- `outer` : the number of times the whole vector is repeated after
   expanded by `inner`
 
 `inner` and `outer` have the same meaning as similarly named arguments

src/abstractdataframe/selection.jl

@@ -38,7 +38,7 @@ const TRANSFORMATION_COMMON_RULES =
 
     Operations can then be applied on each group using one of the following functions:
     * `combine`: does not put restrictions on number of rows returned per group;
-      the returned values are vertically concatenaded following order of groups in
+      the returned values are vertically concatenated following order of groups in
       `GroupedDataFrame`; it is typically used to compute summary statistics by group;
       for `GroupedDataFrame` if grouping columns are kept they are put as first columns
       in the result;

src/abstractdataframe/sort.jl

@@ -210,7 +210,7 @@ function ordering(df::AbstractDataFrame, cols::AbstractVector, lt::Function,
 
     if length(cols) == 0
         if ncol(df) > 0
-            Base.depwarn("When empty column selector is passed ordering is done on all colums. " *
+            Base.depwarn("When empty column selector is passed ordering is done on all columns. " *
                         "This behavior is deprecated and will change in the future.", :ordering)
         end
         return ordering(df, lt, by, rev, order)

src/groupeddataframe/fastaggregates.jl

@@ -189,7 +189,7 @@ function groupreduce!(res::AbstractVector, f, op, condf, adjust, checkempty::Boo
 
     groupreduce!_helper(res, f, op, condf, adjust, checkempty,
                              incol, groups, counts, batches)
-    # handle the case of an unitialized reduction
+    # handle the case of an uninitialized reduction
     if eltype(res) === Any
         if op === Base.add_sum
             initf = zero

test/reshape.jl

@@ -575,7 +575,7 @@ end
     @test unstack(df, [:id, :id2], :var, :val) == unstack(df, :var, :val) ==
           DataFrame(id=1:3, id2=1:3, a=1:3:7, b=2:3:8, c=3:3:9)
 
-    # make sure we always use order of appereance
+    # make sure we always use order of appearance
     Random.seed!(1234)
     # Use a large value to test several orders of appearance
     for i in 1:16