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fix documentation examples #1946

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Sep 8, 2019
Merged

fix documentation examples #1946

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abf43ff
fix documentation examples
bkamins Sep 5, 2019
256f35e
change style
bkamins Sep 6, 2019
7d4a1ec
Change CSV.read to CSV.File
bkamins Sep 6, 2019
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4 changes: 2 additions & 2 deletions docs/src/man/getting_started.md
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Expand Up @@ -612,7 +612,7 @@ using CSV

A dataset can now be read from a CSV file at path `input` using
```julia
CSV.read(input)
DataFrame(CSV.File(input))
```

A `DataFrame` can be written to a CSV file at path `output` using
Expand All @@ -621,4 +621,4 @@ df = DataFrame(x = 1, y = 2)
CSV.write(output, df)
```

The behavior of CSV functions can be adapted via keyword arguments. For more information, see `?CSV.read` and `?CSV.write`, or checkout the online [CSV.jl documentation](https://juliadata.github.io/CSV.jl/stable/).
The behavior of CSV functions can be adapted via keyword arguments. For more information, see `?CSV.File`, `?CSV.read` and `?CSV.write`, or checkout the online [CSV.jl documentation](https://juliadata.github.io/CSV.jl/stable/).
4 changes: 3 additions & 1 deletion docs/src/man/reshaping_and_pivoting.md
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Expand Up @@ -5,7 +5,7 @@ Reshape data from wide to long format using the `stack` function:
```jldoctest reshape
julia> using DataFrames, CSV

julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv"));
julia> iris = DataFrame(CSV.File(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv")));

julia> first(iris, 6)
6×5 DataFrame
Expand Down Expand Up @@ -305,6 +305,8 @@ This repeats the original columns N times where N is the number of columns stack
None of these reshaping functions perform any aggregation. To do aggregation, use the split-apply-combine functions in combination with reshaping. Here is an example:

```jldoctest reshape
julia> using Statistics

julia> d = melt(iris, :Species);

julia> first(d, 6)
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2 changes: 1 addition & 1 deletion docs/src/man/sorting.md
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Expand Up @@ -5,7 +5,7 @@ Sorting is a fundamental component of data analysis. Basic sorting is trivial: j
```jldoctest sort
julia> using DataFrames, CSV

julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv"));
julia> iris = DataFrame(CSV.File(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv")));

julia> first(iris, 4)
4×5 DataFrame
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2 changes: 1 addition & 1 deletion docs/src/man/split_apply_combine.md
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Expand Up @@ -27,7 +27,7 @@ We show several examples of the `by` function applied to the `iris` dataset belo
```jldoctest sac
julia> using DataFrames, CSV, Statistics

julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv"));
julia> iris = DataFrame(CSV.File(joinpath(dirname(pathof(DataFrames)), "../docs/src/assets/iris.csv")));

julia> first(iris, 6)
6×5 DataFrame
Expand Down