typos (#1119)

docs/src/examples.md

@@ -229,7 +229,7 @@ h,i,j
 # note this isn't required, but can be convenient in certain cases
 file = CSV.File(IOBuffer(data); normalizenames=true)
 
-# we can acces the first column like
+# we can access the first column like
 file._1
 
 # another example where we may want to normalize is column names with spaces in them
@@ -491,7 +491,7 @@ using CSV
 # In this data, we have a few "quoted" fields, which means the field's value starts and ends with `quotechar` (or
 # `openquotechar` and `closequotechar`, respectively). Quoted fields allow the field to contain characters that would otherwise
 # be significant to parsing, such as delimiters or newline characters. When quoted, parsing will ignore these otherwise
-# signficant characters until the closing quote character is found. For quoted fields that need to also include the quote
+# significant characters until the closing quote character is found. For quoted fields that need to also include the quote
 # character itself, an escape character is provided to tell parsing to ignore the next character when looking for a close quote
 # character. In the syntax examples, the keyword arguments are passed explicitly, but these also happen to be the default
 # values, so just doing `CSV.File(IOBuffer(data))` would result in successful parsing.

docs/src/reading.md

@@ -23,7 +23,7 @@ Any delimited input is ultimately converted to a byte buffer (`Vector{UInt8}`) f
 
 ## [`header`](@id header)
 
-The `header` keyword argument controls how column names are treated when processing files. By default, it is assumed that the column names are the first row/line of the input, i.e. `header=1`. Alternative valid aguments for `header` include:
+The `header` keyword argument controls how column names are treated when processing files. By default, it is assumed that the column names are the first row/line of the input, i.e. `header=1`. Alternative valid augments for `header` include:
   * `Integer`, e.g. `header=2`: provide the row number as an `Integer` where the column names can be found
   * `Bool`, e.g. `header=false`: no column names exist in the data; column names will be auto-generated depending on the # of columns, like `Column1`, `Column2`, etc.
   * `Vector{String}` or `Vector{Symbol}`: manually provide column names as strings or symbols; should match the # of columns in the data. A copy of the `Vector` will be made and converted to `Vector{Symbol}`
@@ -79,7 +79,7 @@ This argument specifies whether "empty rows", where consecutive [newlines](@ref
 
 ## [`select` / `drop`](@id select)
 
-Arguments that control which columns from the input data will actually be parsed and available after processing. `select` controls which columns _will_ be accessible after parsing while `drop` controls which columns to _ignore_. Either argument can be provided as a vector of `Integer`, `String`, or `Symbol`, specifing the column numbers or names to include/exclude. A vector of `Bool` matching the number of columns in the input data can also be provided, where each element specifies whether the corresponding column should be included/excluded. Finally, these arguments can also be given as boolean functions, of the form `(i, name) -> Bool`, where each column number and name will be given as arguments and the result of the function will determine if the column will be included/excluded.
+Arguments that control which columns from the input data will actually be parsed and available after processing. `select` controls which columns _will_ be accessible after parsing while `drop` controls which columns to _ignore_. Either argument can be provided as a vector of `Integer`, `String`, or `Symbol`, specifying the column numbers or names to include/exclude. A vector of `Bool` matching the number of columns in the input data can also be provided, where each element specifies whether the corresponding column should be included/excluded. Finally, these arguments can also be given as boolean functions, of the form `(i, name) -> Bool`, where each column number and name will be given as arguments and the result of the function will determine if the column will be included/excluded.
 
 ### Examples
   * [Including/excluding columns](@ref select_example)

src/file.jl

@@ -566,7 +566,7 @@ function parsefilechunk!(ctx::Context, pos, len, rowsguess, rowoffset, columns,
         end
         if !ctx.threaded && ctx.ntasks > 1 && !ctx.silencewarnings
             # !ctx.threaded && ctx.ntasks > 1 indicate that multithreaded parsing failed.
-            # Thes messages echo the corresponding debug statement in the definition of ctx
+            # These messages echo the corresponding debug statement in the definition of ctx
             if numwarnings[] > 0
                 @warn "Multithreaded parsing failed and fell back to single-threaded parsing, check previous warnings for possible reasons."
             else

test/write.jl

@@ -342,7 +342,7 @@ end
     CSV.write(io, Tuple[(1,), (2,)], header=false)
     @test String(take!(io)) == "1\n2\n"
 
-    # parition writing
+    # partition writing
     io = IOBuffer()
     io2 = IOBuffer()
     CSV.write([io, io2], Tables.partitioner((default_table, default_table)); partition=true)