Small docs fixes (#1077)

I apparently missed these occurrences when removing these functions.
JuliaData · Sep 2, 2017 · 8a3a9b1 · 8a3a9b1
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diff --git a/docs/src/man/getting_started.md b/docs/src/man/getting_started.md
@@ -135,7 +135,7 @@ To see more of the functionality for working with `DataFrame` objects, we need a
 For example, we can access Fisher's iris data set using the following functions:
 
 ```julia
-iris = DataFrames.loadiris()
+iris = readtable(joinpath(Pkg.dir("DataFrames"), "test/data/iris.csv"))
 head(iris)
 ```
 

diff --git a/docs/src/man/pooling.md b/docs/src/man/pooling.md
@@ -30,10 +30,10 @@ By default, a `CategoricalArray` is able to represent 2<sup>32</sup>differents l
 cv = compact(cv)
 ```
 
-Often, you will have factors encoded inside a DataFrame with `Array` or `NullableArray` columns instead of `CategoricalArray` or `NullableCategoricalArray` columns. You can do conversion of a single column using the `categorize` function:
+Often, you will have factors encoded inside a DataFrame with `Array` or `NullableArray` columns instead of `CategoricalArray` or `NullableCategoricalArray` columns. You can do conversion of a single column using the `categorical` function:
 
 ```julia
-cv = categorize(v)
+cv = categorical(v)
 ```
 
 Or you can edit the columns of a `DataFrame` in-place using the `categorical!` function:

diff --git a/docs/src/man/split_apply_combine.md b/docs/src/man/split_apply_combine.md
@@ -8,7 +8,7 @@ We show several examples of the `by` function applied to the `iris` dataset belo
 
 ```julia
 using DataFrames
-iris = DataFrames.loadiris()
+iris = readtable(joinpath(Pkg.dir("DataFrames"), "test/data/iris.csv"))
 
 by(iris, :Species, size)
 by(iris, :Species, df -> mean(dropnull(df[:PetalLength])))