markdown typos (#3142)

Author: musvaage

CONTRIBUTING.md

@@ -144,7 +144,7 @@ It is recommended to add the header `# Examples` above the doctests.
   commit that might confuse GitHub when displaying the diff of your PR, which makes your changes more 
   difficult to review. Alternatively use conflict resolution tool available at GitHub;
 * Please try to use descriptive commit messages to simplify the review process;
-* Using `git add -p` or `git add -i` can be useful to avoid accidently committing unrelated changes;
+* Using `git add -p` or `git add -i` can be useful to avoid accidentally committing unrelated changes;
 * Maintainers get notified of all changes made on GitHub. However, what is useful is writing a short
   message after a sequence of changes is made summarizing what has changed and that the PR is ready
   for a review;

NEWS.md

@@ -494,7 +494,7 @@
   old `name => function` order is now deprecated
   ([#2401](https://github.com/JuliaData/DataFrames.jl/pull/2401))
 * in joins passing `NaN` or real or imaginary `-0.0` in `on` column now throws an
-  error; passing `missing` thows an error unless `matchmissing=:equal` keyword argument
+  error; passing `missing` throws an error unless `matchmissing=:equal` keyword argument
   is passed ([#2504](https://github.com/JuliaData/DataFrames.jl/pull/2504))
 * `unstack` now produces row and column keys in the order of their first appearance
    and has two new keyword arguments `allowmissing` and `allowduplicates`
@@ -533,7 +533,7 @@
   as a suffix in automatically generated column names
   ([#2397](https://github.com/JuliaData/DataFrames.jl/pull/2397))
 * `filter`, `sort`, `dropmissing`, and `unique` now support a `view` keyword argument
-  which if set to `true` makes them retun a `SubDataFrame` view into the passed
+  which if set to `true` makes them return a `SubDataFrame` view into the passed
   data frame.
 * add `only` method for `AbstractDataFrame` ([#2449](https://github.com/JuliaData/DataFrames.jl/pull/2449))
 * passing empty sets of columns in `filter`/`filter!` and in `select`/`transform`/`combine`

docs/src/index.md

@@ -67,7 +67,7 @@ integrated they are with DataFrames.jl.
     - [GLM.jl](https://juliastats.org/GLM.jl/stable/manual/): Tools for estimating
       linear and generalized linear models. Tightly integrated with DataFrames.jl.
     - [StatsModels.jl](https://juliastats.org/StatsModels.jl/stable/):
-      For converting heterogeneous `DataFrame` into homogenous matrices for use
+      For converting heterogeneous `DataFrame` into homogeneous matrices for use
       with linear algebra libraries or machine learning applications that don't
       directly support `DataFrame`s. Will do things like convert categorical
       variables into indicators/one-hot-encodings, create interaction terms, etc.
@@ -138,7 +138,7 @@ integrated they are with DataFrames.jl.
 While not all of these libraries are tightly integrated with DataFrames.jl,
 because `DataFrame`s are essentially collections of aligned Julia vectors, so it
 is easy to (a) pull out a vector for use with a non-DataFrames-integrated
-library, or (b) convert your table into a homogenously-typed matrix using the
+library, or (b) convert your table into a homogeneously-typed matrix using the
 `Matrix` constructor or StatsModels.jl.
 
 ### Other Julia Tabular Libraries

docs/src/man/basics.md

@@ -279,7 +279,7 @@ german_ref = CSV.read(joinpath(dirname(pathof(DataFrames)),
 - first we split the directory part from it using `dirname`;
 - then from this directory we need to move to the directory where the `german.csv` file is stored; we use
   `joinpath` as this is a recommended way to compose paths to resources stored on disk in an operating
-  system independent way (remember that Widnows and Unix differ as they use either `/` or `\` as path
+  system independent way (remember that Windows and Unix differ as they use either `/` or `\` as path
   separator; the `joinpath` function ensures we are not running into issues with this);
 - then we read the CSV file; the second argument to `CSV.read` is `DataFrame` to indicate that we want to
   read in the file into a `DataFrame` (as `CSV.read` allows for many different target formats of data it
@@ -556,7 +556,7 @@ julia> describe(german)
                                                                 1 column omitted
 ```
 
-To limit the columns processed by `desribe` use `cols` keyword argument, e.g.:
+To limit the columns processed by `describe` use `cols` keyword argument, e.g.:
 
 ```jldoctest dataframe
 julia> describe(german, cols=1:3)
@@ -824,7 +824,7 @@ julia> german[!, :Sex]
  "male"
 ```
 
-As it was explained earler in this tutorial the difference between using `!` and `:`
+As it was explained earlier in this tutorial the difference between using `!` and `:`
 when passing a row index is that `!` does not perform a copy of columns, while `:` does.
 Therefore `german[!, [:Sex]]` data frame stores the same vector as the source `german` data frame,
 while `german[:, [:Sex]]` stores its copy.

docs/src/man/reshaping_and_pivoting.md

@@ -393,7 +393,7 @@ julia> permutedims(df1, 1)
 Note that the column indexed by `src_colnames` in the original `df`
 becomes the column names in the permuted result,
 and the column names of the original become a new column.
-Typically, this would be used on columns with homogenous element types,
+Typically, this would be used on columns with homogeneous element types,
 since the element types of the other columns
 are the result of `promote_type` on _all_ the permuted columns.
 Note also that, by default, the new column created from the column names