diff --git a/Project.toml b/Project.toml
index 90dea1d..456198e 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
name = "Tidier"
uuid = "f0413319-3358-4bb0-8e7c-0c83523a93bd"
authors = ["Karandeep Singh"]
-version = "1.2.0"
+version = "1.2.1"
[deps]
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -15,7 +15,7 @@ TidierVest = "969b988e-7aed-4820-b60d-bdec252047c4"
[compat]
Reexport = "0.2, 1"
-TidierData = "0.13, 1"
+TidierData = "0.14, 1"
TidierPlots = "0.5, 1"
TidierCats = "0.1, 1"
TidierDates = "0.2, 1"
diff --git a/README.md b/README.md
index 6e600e4..27764e0 100644
--- a/README.md
+++ b/README.md
@@ -97,6 +97,7 @@ This dataset comes with the Ecdat R package and and is titled OFP. [You can read
```julia
julia> using Tidier, RDatasets
julia> ofp = dataset("Ecdat", "OFP")
+
4406×19 DataFrame
Row │ OFP OFNP OPP OPNP EMR Hosp NumChro ⋯
│ Int32 Int32 Int32 Int32 Int32 Int32 Int32 ⋯
@@ -136,6 +137,7 @@ We can use `@glimpse()` to find out the columns, data types, and peek at the fir
```julia
julia> @glimpse(ofp)
+
Rows: 4406
Columns: 19
.OFP Int32 5, 1, 13, 16, 3, 17, 9, 3, 1, 0, 0, 44, 2, 1, 19,
@@ -168,6 +170,7 @@ To avoid having to keep track of capitalization, data analysts often prefer colu
```julia
julia> ofp = @clean_names(ofp)
julia> @glimpse(ofp)
+
Rows: 4406
Columns: 19
.ofp Int32 5, 1, 13, 16, 3, 17, 9, 3, 1, 0, 0, 44, 2, 1, 19,
@@ -199,6 +202,7 @@ Because age is measured in decades according to the [dataset documentation](http
```julia
julia> @chain ofp @group_by(region) @summarize(mean_age = mean(age * 10))
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -221,6 +225,7 @@ An alternate way of calling `@chain` using the parentheses syntax is as follows.
```julia
julia> @chain(ofp, @group_by(region), @summarize(mean_age = mean(age * 10)))
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -238,6 +243,7 @@ julia> @chain ofp begin
@group_by(region)
@summarize(mean_age = mean(age * 10))
end
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -255,6 +261,7 @@ julia> @chain ofp begin
# @group_by(region)
@summarize(mean_age = mean(age * 10))
end
+
1×1 DataFrame
Row │ mean_age
│ Float64
@@ -297,6 +304,10 @@ Tidier comes with batteries included. If you are using Tidier, you generally won
If you are a package developer, then you definitely should consider depending on one of the smaller packages that make up Tidier.jl rather than Tidier itself. For example, if you want to use the categorical variable functions from Tidier, then you should use rely on only TidierCats.jl as a dependency.
+### Should I update Tidier.jl or the underlying packages (e.g., TidierPlots.jl) individually?
+
+Either approach is okay. For most users, we recommend updating Tidier.jl directly, as this will update the underlying packages up to their latest minor versions (but not necessarily up to their latest patch release). However, if you need access to the latest functionality in the underlying packages, you should feel free to update them directly. We will keep Tidier.jl future-proof to underlying package updates, so this shouldn't cause any problems with Tidier.jl.
+
### Where can I learn more about the underlying packages that make up Tidier.jl?
diff --git a/docs/src/index.md b/docs/src/index.md
index 7d9c00a..36b1936 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -90,6 +90,7 @@ This dataset comes with the Ecdat R package and and is titled OFP. [You can read
```julia
julia> using Tidier, RDatasets
julia> ofp = dataset("Ecdat", "OFP")
+
4406×19 DataFrame
Row │ OFP OFNP OPP OPNP EMR Hosp NumChro ⋯
│ Int32 Int32 Int32 Int32 Int32 Int32 Int32 ⋯
@@ -129,6 +130,7 @@ We can use `@glimpse()` to find out the columns, data types, and peek at the fir
```julia
julia> @glimpse(ofp)
+
Rows: 4406
Columns: 19
.OFP Int32 5, 1, 13, 16, 3, 17, 9, 3, 1, 0, 0, 44, 2, 1, 19,
@@ -161,6 +163,7 @@ To avoid having to keep track of capitalization, data analysts often prefer colu
```julia
julia> ofp = @clean_names(ofp)
julia> @glimpse(ofp)
+
Rows: 4406
Columns: 19
.ofp Int32 5, 1, 13, 16, 3, 17, 9, 3, 1, 0, 0, 44, 2, 1, 19,
@@ -192,6 +195,7 @@ Because age is measured in decades according to the [dataset documentation](http
```julia
julia> @chain ofp @group_by(region) @summarize(mean_age = mean(age * 10))
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -214,6 +218,7 @@ An alternate way of calling `@chain` using the parentheses syntax is as follows.
```julia
julia> @chain(ofp, @group_by(region), @summarize(mean_age = mean(age * 10)))
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -231,6 +236,7 @@ julia> @chain ofp begin
@group_by(region)
@summarize(mean_age = mean(age * 10))
end
+
4×2 DataFrame
Row │ region mean_age
│ Cat… Float64
@@ -248,6 +254,7 @@ julia> @chain ofp begin
# @group_by(region)
@summarize(mean_age = mean(age * 10))
end
+
1×1 DataFrame
Row │ mean_age
│ Float64
@@ -290,6 +297,10 @@ Tidier comes with batteries included. If you are using Tidier, you generally won
If you are a package developer, then you definitely should consider depending on one of the smaller packages that make up Tidier.jl rather than Tidier itself. For example, if you want to use the categorical variable functions from Tidier, then you should use rely on only TidierCats.jl as a dependency.
+### Should I update Tidier.jl or the underlying packages (e.g., TidierPlots.jl) individually?
+
+Either approach is okay. For most users, we recommend updating Tidier.jl directly, as this will update the underlying packages up to their latest minor versions (but not necessarily up to their latest patch release). However, if you need access to the latest functionality in the underlying packages, you should feel free to update them directly. We will keep Tidier.jl future-proof to underlying package updates, so this shouldn't cause any problems with Tidier.jl.
+
### Where can I learn more about the underlying packages that make up Tidier.jl?
