I am trying to implement {tidyverse}, including {dplyr}, APIs in Julia. This is not intended to be a sustained effort and is meant to be a fun exercise to learn {tidyverse} and teach Julia programming. So prolonged maintenance is purely accidental!
TidyStanza.AcrossandTidyStanza.acrossare synonyms and have the same API asdplyr::acrossTidyStanza.WhereandTidyStanza.whereare synonyms and have the same API asdplyr::across(where(...), ...)
By default, they are NOT exported, and the recommended way is to use TidyStanza.across and TidyStanza.where
to refer to them.
import TidyStanza
const tidy = TidyStanza
tidy.across
tidy.wherewhere (generic function with 1 method)
However, in the examples below, for brevity, I have imported across and where
directly into the namespace.
using TidyStanza: across, where
### load some helper packages
using DataFrames
using Statistics # for using mean
using Pipe: @pipe # for @pipe macro
using RDatasets # for iris dataset
iris = dataset("datasets", "iris");
# a glimpse of the data
first(iris, 8)8×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 Cat…
─────┼───────────────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2 setosa
2 │ 4.9 3.0 1.4 0.2 setosa
3 │ 4.7 3.2 1.3 0.2 setosa
4 │ 4.6 3.1 1.5 0.2 setosa
5 │ 5.0 3.6 1.4 0.2 setosa
6 │ 5.4 3.9 1.7 0.4 setosa
7 │ 4.6 3.4 1.4 0.3 setosa
8 │ 5.0 3.4 1.5 0.2 setosa
# R"""
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), mean))
# """
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), mean))3×3 DataFrame
Row │ Species SepalLength SepalWidth
│ Cat… Float64 Float64
─────┼─────────────────────────────────────
1 │ setosa 5.006 3.428
2 │ versicolor 5.936 2.77
3 │ virginica 6.588 2.974
using CategoricalArrays: CategoricalArray
# R"""
# iris %>%
# as_tibble() %>%
# mutate(across(where(is.factor), as.character))
# """
# define a convenience function for checking if column is categorical
iscatarray(arr) = typeof(arr) <: CategoricalArray
@pipe iris |>
transform(_, across(where(iscatarray), Vector{String})) |>
first(_, 8)
@pipe iris |>
transform(_, across(where(iscatarray), col->string.(col))) |>
first(_, 8)8×5 DataFrame
Row │ SepalLength SepalWidth PetalLength PetalWidth Species
│ Float64 Float64 Float64 Float64 String
─────┼───────────────────────────────────────────────────────────
1 │ 5.1 3.5 1.4 0.2 setosa
2 │ 4.9 3.0 1.4 0.2 setosa
3 │ 4.7 3.2 1.3 0.2 setosa
4 │ 4.6 3.1 1.5 0.2 setosa
5 │ 5.0 3.6 1.4 0.2 setosa
6 │ 5.4 3.9 1.7 0.4 setosa
7 │ 4.6 3.4 1.4 0.3 setosa
8 │ 5.0 3.4 1.5 0.2 setosa
# A purrr-style formula
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), ~mean(.x, na.rm = TRUE)))
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), x->mean(x |> skipmissing)))3×3 DataFrame
Row │ Species SepalLength SepalWidth
│ Cat… Float64 Float64
─────┼─────────────────────────────────────
1 │ setosa 5.006 3.428
2 │ versicolor 5.936 2.77
3 │ virginica 6.588 2.974
# A named list of functions
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), (mean, std)))3×5 DataFrame
Row │ Species SepalLength_mean SepalWidth_mean SepalLength_std Sepa
lWi ⋯
│ Cat… Float64 Float64 Float64 Floa
t64 ⋯
─────┼─────────────────────────────────────────────────────────────────────
─────
1 │ setosa 5.006 3.428 0.35249
0 ⋯
2 │ versicolor 5.936 2.77 0.516171
0
3 │ virginica 6.588 2.974 0.63588
0
1 column om
itted
# Use the .names argument to control the output names
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), mean, .names = "mean_{col}"))
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), mean; names = "mean_{col}"))3×3 DataFrame
Row │ Species mean_SepalLength mean_SepalWidth
│ Cat… Float64 Float64
─────┼───────────────────────────────────────────────
1 │ setosa 5.006 3.428
2 │ versicolor 5.936 2.77
3 │ virginica 6.588 2.974
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd), .names = "{col}_{fn}"))
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), (mean = mean, std = std); names = "{col}_{fn}"))3×5 DataFrame
Row │ Species SepalLength_mean SepalWidth_mean SepalLength_std Sepa
lWi ⋯
│ Cat… Float64 Float64 Float64 Floa
t64 ⋯
─────┼─────────────────────────────────────────────────────────────────────
─────
1 │ setosa 5.006 3.428 0.35249
0 ⋯
2 │ versicolor 5.936 2.77 0.516171
0
3 │ virginica 6.588 2.974 0.63588
0
1 column om
itted
# iris %>%
# group_by(Species) %>%
# summarise(across(starts_with("Sepal"), list(mean, sd), .names = "{col}.fn{fn}"))
@pipe iris |>
groupby(_, :Species) |>
combine(_, across(startswith("Sepal"), (mean, std); names = "{col}_fn{fn}"))3×5 DataFrame
Row │ Species SepalLength_fn1 SepalWidth_fn1 SepalLength_fn2 SepalW
idt ⋯
│ Cat… Float64 Float64 Float64 Float6
4 ⋯
─────┼─────────────────────────────────────────────────────────────────────
─────
1 │ setosa 5.006 3.428 0.35249
0.3 ⋯
2 │ versicolor 5.936 2.77 0.516171
0.3
3 │ virginica 6.588 2.974 0.63588
0.3
1 column om
itted
df = DataFrame(x = repeat(1:3,inner = 2,outer = 2),
a = repeat(4:6,inner = 2,outer = 2),
b = repeat(7:9,inner = 2,outer = 2),
val1 = ["ce_val1_1","cf_val1_1","ce_val1_2","cf_val1_2","ce_val1_3","cf_val1_3","de_val1_1",
"df_val1_1","de_val1_2","df_val1_2","de_val1_3","df_val1_3"],
val2 = ["ce_val2_1","cf_val2_1","ce_val2_2","cf_val2_2","ce_val2_3","cf_val2_3","de_val2_1",
"df_val2_1","de_val2_2","df_val2_2","de_val2_3","df_val2_3"],
cname1 = repeat(["c", "d"], inner = 6),
cname2 = repeat(["e", "f"], 6)
)12×7 DataFrame
Row │ x a b val1 val2 cname1 cname2
│ Int64 Int64 Int64 String String String String
─────┼───────────────────────────────────────────────────────────
1 │ 1 4 7 ce_val1_1 ce_val2_1 c e
2 │ 1 4 7 cf_val1_1 cf_val2_1 c f
3 │ 2 5 8 ce_val1_2 ce_val2_2 c e
4 │ 2 5 8 cf_val1_2 cf_val2_2 c f
5 │ 3 6 9 ce_val1_3 ce_val2_3 c e
6 │ 3 6 9 cf_val1_3 cf_val2_3 c f
7 │ 1 4 7 de_val1_1 de_val2_1 d e
8 │ 1 4 7 df_val1_1 df_val2_1 d f
9 │ 2 5 8 de_val1_2 de_val2_2 d e
10 │ 2 5 8 df_val1_2 df_val2_2 d f
11 │ 3 6 9 de_val1_3 de_val2_3 d e
12 │ 3 6 9 df_val1_3 df_val2_3 d f
using TidyStanza: pivot_wider
pivot_wider(df; names_from = [:cname1, :cname2], values_from = [:val1, :val2])3×11 DataFrame
Row │ x a b val1_c_e val1_c_f val1_d_e val1_d_f val
2_c ⋯
│ Int64 Int64 Int64 String? String? String? String? Str
ing ⋯
─────┼─────────────────────────────────────────────────────────────────────
─────
1 │ 1 4 7 ce_val1_1 cf_val1_1 de_val1_1 df_val1_1 ce_
val ⋯
2 │ 2 5 8 ce_val1_2 cf_val1_2 de_val1_2 df_val1_2 ce_
val
3 │ 3 6 9 ce_val1_3 cf_val1_3 de_val1_3 df_val1_3 ce_
val
4 columns om
itted
This is for relocating columns and implements a replica of dplyr::relocate
using DataFrames
using DataConvenience: @>
using TidyStanza: relocate, any_of, last_col
# df <- tibble(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
df = DataFrame(a = 1, b = 1, c = 1, d = "a", e = "a", f = "a")
# df %>% relocate(f)
@> df relocate(:f)
# df %>% relocate(a, .after = c)
@> df relocate(:a, after = :c)
# df %>% relocate(f, .before = b)
@> df relocate(:f, before = :b)
# df %>% relocate(a, .after = last_col())
@> df relocate(:a, after = names(df)[end])
@> df relocate(:a, after = last_col())
middle_col() = df->names(df)[end ÷ 2]
@> df relocate(:a, after = middle_col())
using TidyStanza: where
# df %>% relocate(where(is.character))
isstring(x) = eltype(x) <: AbstractString
@> df relocate(where(isstring))
@> df relocate(where(x->eltype(x) <: AbstractString))
# df %>% relocate(where(is.numeric), .after = last_col())
isnumeric(x) = eltype(x) <: Number
@> df relocate(where(isnumeric), after = last_col())
# df %>% relocate(any_of(c("a", "e", "i", "o", "u")))
@> df relocate(intersect(["a", "e", "i", "o", "u"], names(df)))
@> df relocate(any_of(["a", "e", "i", "o", "u"]))
#df2 <- tibble(a = 1, b = "a", c = 1, d = "a")
df2 = DataFrame(a = 1, b = "a", c = 1, d = "a")
#df2 %>% relocate(where(is.numeric), .after = where(is.character))
@> df2 relocate(where(isnumeric), after = where(isstring))
#df2 %>% relocate(where(is.numeric), .before = where(is.character))
@> df2 relocate(where(isnumeric), before = where(isstring))
The verse in tidyverse is referring to the universe, but "verse" is a technical term in poetry, so is stanza.