1  Operations on rows

In this chapter we will see operations that deal with rows, be it ordering or throwing some rows away.

The following is necessary to run all examples:

using DataFrames, PalmerPenguins
using Tidier
import DataFramesMeta as DFM

penguins = PalmerPenguins.load() |> DataFrame;
@slice_head(penguins, n = 10)

10×7 DataFrame

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing

1.1 Filtering (or: throwing rows away)

To filter a dataframe means keeping only the rows that satisfy a certain criteria (ie. a boolean condition).

To filter in Tidier, we use the macro @filter. You can use it in the form

@filter(penguins, species == "Adelie")

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

or without parentesis as in

@filter penguins species == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

Notice that the columns are typed as if they were variables on the Julia environment. This is inspired by the tidyverse behaviour of data-masking: inside a tidyverse verb, the columns are taken as “statistical variables” that exist inside the dataframe as columns.

In DataFramesMeta, we have two macros for filtering: @subset and @rsubset. Use the first when you have some criteria that uses a whole column, for example:

DFM.@subset penguins :body_mass_g .>= mean(skipmissing(:body_mass_g))

149×7 DataFrame

124 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.2	19.6	195	4675	male
2	Adelie	Torgersen	42.0	20.2	190	4250	missing
3	Adelie	Torgersen	34.6	21.1	198	4400	male
4	Adelie	Torgersen	42.5	20.7	197	4500	male
5	Adelie	Dream	39.8	19.1	184	4650	male
6	Adelie	Dream	44.1	19.7	196	4400	male
7	Adelie	Dream	39.6	18.8	190	4600	male
8	Adelie	Biscoe	40.1	18.9	188	4300	male
9	Adelie	Biscoe	41.3	21.1	195	4400	male
10	Adelie	Torgersen	41.8	19.4	198	4450	male
11	Adelie	Torgersen	42.8	18.5	195	4250	male
12	Adelie	Torgersen	42.9	17.6	196	4700	male
13	Adelie	Dream	41.1	18.1	205	4300	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
138	Gentoo	Biscoe	47.2	13.7	214	4925	female
139	Gentoo	Biscoe	46.8	14.3	215	4850	female
140	Gentoo	Biscoe	50.4	15.7	222	5750	male
141	Gentoo	Biscoe	45.2	14.8	212	5200	female
142	Gentoo	Biscoe	49.9	16.1	213	5400	male
143	Chinstrap	Dream	49.2	18.2	195	4400	male
144	Chinstrap	Dream	52.8	20.0	205	4550	male
145	Chinstrap	Dream	54.2	20.8	201	4300	male
146	Chinstrap	Dream	52.0	20.7	210	4800	male
147	Chinstrap	Dream	53.5	19.9	205	4500	male
148	Chinstrap	Dream	50.8	18.5	201	4450	male
149	Chinstrap	Dream	49.0	19.6	212	4300	male

Notice the broadcast on >=. We need it because each variable is interpreted as a vector (the whole column). Also, notice that we refer to columns as symbols (i.e. we append : to it).

In the above example, we needed the whole column body_mass_g to take the mean and then filter the rows based on that. If, however, your filtering criteria only uses information about each row (without needing to see it in context of the whole column), then @rsubset (row subset) is easier to use: it interprets each columns as a value (not an array), so no broadcasting is needed:

DFM.@rsubset penguins :species == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

In both Tidier and DataFramesMeta, only the rows to which the criteria is true are returned. This means that false and missing are thrown away.

In pure DataFrames, we use the subset function, and the criteria is passed with the notation

subset(penguins, :column => boolean_function)

where boolean_function is a boolean (with possibly missing values) function on 1 variable (the :column you passed). Add the kwarg skipmissing=true if you want to get rid of missing values.

1.1.1 Filtering with one criteria

Problem: Filtering all the rows with species == “Adelie”.

Tidier

@filter penguins species == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFramesMeta

DFM.@rsubset penguins :species == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFrames

subset(penguins, :species => x -> x .== "Adelie", skipmissing=true)

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

1.1.2 Filtering with several criteria

Problem: Filtering all the rows with species == “Adelie”, sex == “male” and body_mass_g > 4000.

Tidier

@filter penguins species == "Adelie" sex == "male" body_mass_g > 4000

34×7 DataFrame

9 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.2	19.6	195	4675	male
2	Adelie	Torgersen	34.6	21.1	198	4400	male
3	Adelie	Torgersen	42.5	20.7	197	4500	male
4	Adelie	Torgersen	46.0	21.5	194	4200	male
5	Adelie	Dream	39.2	21.1	196	4150	male
6	Adelie	Dream	39.8	19.1	184	4650	male
7	Adelie	Dream	44.1	19.7	196	4400	male
8	Adelie	Dream	39.6	18.8	190	4600	male
9	Adelie	Dream	42.3	21.2	191	4150	male
10	Adelie	Biscoe	40.1	18.9	188	4300	male
11	Adelie	Biscoe	42.0	19.5	200	4050	male
12	Adelie	Biscoe	41.3	21.1	195	4400	male
13	Adelie	Biscoe	41.1	18.2	192	4050	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
23	Adelie	Dream	40.3	18.5	196	4350	male
24	Adelie	Dream	43.2	18.5	192	4100	male
25	Adelie	Biscoe	41.0	20.0	203	4725	male
26	Adelie	Biscoe	37.8	20.0	190	4250	male
27	Adelie	Biscoe	43.2	19.0	197	4775	male
28	Adelie	Biscoe	45.6	20.3	191	4600	male
29	Adelie	Biscoe	42.2	19.5	197	4275	male
30	Adelie	Biscoe	42.7	18.3	196	4075	male
31	Adelie	Torgersen	41.5	18.3	195	4300	male
32	Adelie	Dream	37.5	18.5	199	4475	male
33	Adelie	Dream	39.7	17.9	193	4250	male
34	Adelie	Dream	39.2	18.6	190	4250	male

DataFramesMeta

DFM.@rsubset penguins :species == "Adelie" :sex == "male" :body_mass_g > 4000

34×7 DataFrame

9 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.2	19.6	195	4675	male
2	Adelie	Torgersen	34.6	21.1	198	4400	male
3	Adelie	Torgersen	42.5	20.7	197	4500	male
4	Adelie	Torgersen	46.0	21.5	194	4200	male
5	Adelie	Dream	39.2	21.1	196	4150	male
6	Adelie	Dream	39.8	19.1	184	4650	male
7	Adelie	Dream	44.1	19.7	196	4400	male
8	Adelie	Dream	39.6	18.8	190	4600	male
9	Adelie	Dream	42.3	21.2	191	4150	male
10	Adelie	Biscoe	40.1	18.9	188	4300	male
11	Adelie	Biscoe	42.0	19.5	200	4050	male
12	Adelie	Biscoe	41.3	21.1	195	4400	male
13	Adelie	Biscoe	41.1	18.2	192	4050	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
23	Adelie	Dream	40.3	18.5	196	4350	male
24	Adelie	Dream	43.2	18.5	192	4100	male
25	Adelie	Biscoe	41.0	20.0	203	4725	male
26	Adelie	Biscoe	37.8	20.0	190	4250	male
27	Adelie	Biscoe	43.2	19.0	197	4775	male
28	Adelie	Biscoe	45.6	20.3	191	4600	male
29	Adelie	Biscoe	42.2	19.5	197	4275	male
30	Adelie	Biscoe	42.7	18.3	196	4075	male
31	Adelie	Torgersen	41.5	18.3	195	4300	male
32	Adelie	Dream	37.5	18.5	199	4475	male
33	Adelie	Dream	39.7	17.9	193	4250	male
34	Adelie	Dream	39.2	18.6	190	4250	male

DataFrames

subset(
    penguins
    , [:species, :sex, :body_mass_g] => 
    (x, y, z) -> (x .== "Adelie") .& (y .== "male") .& (z .> 4000)
    ,skipmissing=true
)

34×7 DataFrame

9 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.2	19.6	195	4675	male
2	Adelie	Torgersen	34.6	21.1	198	4400	male
3	Adelie	Torgersen	42.5	20.7	197	4500	male
4	Adelie	Torgersen	46.0	21.5	194	4200	male
5	Adelie	Dream	39.2	21.1	196	4150	male
6	Adelie	Dream	39.8	19.1	184	4650	male
7	Adelie	Dream	44.1	19.7	196	4400	male
8	Adelie	Dream	39.6	18.8	190	4600	male
9	Adelie	Dream	42.3	21.2	191	4150	male
10	Adelie	Biscoe	40.1	18.9	188	4300	male
11	Adelie	Biscoe	42.0	19.5	200	4050	male
12	Adelie	Biscoe	41.3	21.1	195	4400	male
13	Adelie	Biscoe	41.1	18.2	192	4050	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
23	Adelie	Dream	40.3	18.5	196	4350	male
24	Adelie	Dream	43.2	18.5	192	4100	male
25	Adelie	Biscoe	41.0	20.0	203	4725	male
26	Adelie	Biscoe	37.8	20.0	190	4250	male
27	Adelie	Biscoe	43.2	19.0	197	4775	male
28	Adelie	Biscoe	45.6	20.3	191	4600	male
29	Adelie	Biscoe	42.2	19.5	197	4275	male
30	Adelie	Biscoe	42.7	18.3	196	4075	male
31	Adelie	Torgersen	41.5	18.3	195	4300	male
32	Adelie	Dream	37.5	18.5	199	4475	male
33	Adelie	Dream	39.7	17.9	193	4250	male
34	Adelie	Dream	39.2	18.6	190	4250	male

Problem: Filtering all the rows with species == “Adelie” OR sex == “male”.

Tidier

@filter penguins (species == "Adelie") | (sex == "male")

247×7 DataFrame

222 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
236	Chinstrap	Dream	50.8	18.5	201	4450	male
237	Chinstrap	Dream	49.0	19.6	212	4300	male
238	Chinstrap	Dream	51.5	18.7	187	3250	male
239	Chinstrap	Dream	51.4	19.0	201	3950	male
240	Chinstrap	Dream	50.7	19.7	203	4050	male
241	Chinstrap	Dream	52.2	18.8	197	3450	male
242	Chinstrap	Dream	49.3	19.9	203	4050	male
243	Chinstrap	Dream	50.2	18.8	202	3800	male
244	Chinstrap	Dream	51.9	19.5	206	3950	male
245	Chinstrap	Dream	55.8	19.8	207	4000	male
246	Chinstrap	Dream	49.6	18.2	193	3775	male
247	Chinstrap	Dream	50.8	19.0	210	4100	male

DataFramesMeta

DFM.@rsubset penguins (:species == "Adelie") | (:sex == "male")

247×7 DataFrame

222 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
236	Chinstrap	Dream	50.8	18.5	201	4450	male
237	Chinstrap	Dream	49.0	19.6	212	4300	male
238	Chinstrap	Dream	51.5	18.7	187	3250	male
239	Chinstrap	Dream	51.4	19.0	201	3950	male
240	Chinstrap	Dream	50.7	19.7	203	4050	male
241	Chinstrap	Dream	52.2	18.8	197	3450	male
242	Chinstrap	Dream	49.3	19.9	203	4050	male
243	Chinstrap	Dream	50.2	18.8	202	3800	male
244	Chinstrap	Dream	51.9	19.5	206	3950	male
245	Chinstrap	Dream	55.8	19.8	207	4000	male
246	Chinstrap	Dream	49.6	18.2	193	3775	male
247	Chinstrap	Dream	50.8	19.0	210	4100	male

DataFrames

subset(penguins, [:species, :sex] => (x, y) -> (x .== "Adelie") .| (y .== "male"), skipmissing=true)

247×7 DataFrame

222 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
236	Chinstrap	Dream	50.8	18.5	201	4450	male
237	Chinstrap	Dream	49.0	19.6	212	4300	male
238	Chinstrap	Dream	51.5	18.7	187	3250	male
239	Chinstrap	Dream	51.4	19.0	201	3950	male
240	Chinstrap	Dream	50.7	19.7	203	4050	male
241	Chinstrap	Dream	52.2	18.8	197	3450	male
242	Chinstrap	Dream	49.3	19.9	203	4050	male
243	Chinstrap	Dream	50.2	18.8	202	3800	male
244	Chinstrap	Dream	51.9	19.5	206	3950	male
245	Chinstrap	Dream	55.8	19.8	207	4000	male
246	Chinstrap	Dream	49.6	18.2	193	3775	male
247	Chinstrap	Dream	50.8	19.0	210	4100	male

1.1.3 Filtering with metadata

By metadata here we mean data that is inside the dataframe, as the mean/max/min of a column.

Problem: Filtering all the rows where the flipper_length_mm is greater than the mean.

Tidier

@filter penguins flipper_length_mm > mean(skipmissing(flipper_length_mm))

148×7 DataFrame

123 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Dream	35.7	18.0	202	3550	female
2	Adelie	Dream	41.1	18.1	205	4300	male
3	Adelie	Dream	40.8	18.9	208	4300	male
4	Adelie	Biscoe	41.0	20.0	203	4725	male
5	Adelie	Torgersen	41.4	18.5	202	3875	male
6	Adelie	Torgersen	44.1	18.0	210	4000	male
7	Adelie	Dream	41.5	18.5	201	4000	male
8	Gentoo	Biscoe	46.1	13.2	211	4500	female
9	Gentoo	Biscoe	50.0	16.3	230	5700	male
10	Gentoo	Biscoe	48.7	14.1	210	4450	female
11	Gentoo	Biscoe	50.0	15.2	218	5700	male
12	Gentoo	Biscoe	47.6	14.5	215	5400	male
13	Gentoo	Biscoe	46.5	13.5	210	4550	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
137	Chinstrap	Dream	53.5	19.9	205	4500	male
138	Chinstrap	Dream	49.0	19.5	210	3950	male
139	Chinstrap	Dream	50.8	18.5	201	4450	male
140	Chinstrap	Dream	49.0	19.6	212	4300	male
141	Chinstrap	Dream	51.4	19.0	201	3950	male
142	Chinstrap	Dream	50.7	19.7	203	4050	male
143	Chinstrap	Dream	49.3	19.9	203	4050	male
144	Chinstrap	Dream	50.2	18.8	202	3800	male
145	Chinstrap	Dream	51.9	19.5	206	3950	male
146	Chinstrap	Dream	55.8	19.8	207	4000	male
147	Chinstrap	Dream	43.5	18.1	202	3400	female
148	Chinstrap	Dream	50.8	19.0	210	4100	male

DataFramesMeta

DFM.@subset penguins :flipper_length_mm .>= mean(skipmissing(:flipper_length_mm))

148×7 DataFrame

123 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Dream	35.7	18.0	202	3550	female
2	Adelie	Dream	41.1	18.1	205	4300	male
3	Adelie	Dream	40.8	18.9	208	4300	male
4	Adelie	Biscoe	41.0	20.0	203	4725	male
5	Adelie	Torgersen	41.4	18.5	202	3875	male
6	Adelie	Torgersen	44.1	18.0	210	4000	male
7	Adelie	Dream	41.5	18.5	201	4000	male
8	Gentoo	Biscoe	46.1	13.2	211	4500	female
9	Gentoo	Biscoe	50.0	16.3	230	5700	male
10	Gentoo	Biscoe	48.7	14.1	210	4450	female
11	Gentoo	Biscoe	50.0	15.2	218	5700	male
12	Gentoo	Biscoe	47.6	14.5	215	5400	male
13	Gentoo	Biscoe	46.5	13.5	210	4550	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
137	Chinstrap	Dream	53.5	19.9	205	4500	male
138	Chinstrap	Dream	49.0	19.5	210	3950	male
139	Chinstrap	Dream	50.8	18.5	201	4450	male
140	Chinstrap	Dream	49.0	19.6	212	4300	male
141	Chinstrap	Dream	51.4	19.0	201	3950	male
142	Chinstrap	Dream	50.7	19.7	203	4050	male
143	Chinstrap	Dream	49.3	19.9	203	4050	male
144	Chinstrap	Dream	50.2	18.8	202	3800	male
145	Chinstrap	Dream	51.9	19.5	206	3950	male
146	Chinstrap	Dream	55.8	19.8	207	4000	male
147	Chinstrap	Dream	43.5	18.1	202	3400	female
148	Chinstrap	Dream	50.8	19.0	210	4100	male

DataFrames

subset(penguins, :flipper_length_mm => x -> x .> mean(skipmissing(x)), skipmissing=true)

148×7 DataFrame

123 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Dream	35.7	18.0	202	3550	female
2	Adelie	Dream	41.1	18.1	205	4300	male
3	Adelie	Dream	40.8	18.9	208	4300	male
4	Adelie	Biscoe	41.0	20.0	203	4725	male
5	Adelie	Torgersen	41.4	18.5	202	3875	male
6	Adelie	Torgersen	44.1	18.0	210	4000	male
7	Adelie	Dream	41.5	18.5	201	4000	male
8	Gentoo	Biscoe	46.1	13.2	211	4500	female
9	Gentoo	Biscoe	50.0	16.3	230	5700	male
10	Gentoo	Biscoe	48.7	14.1	210	4450	female
11	Gentoo	Biscoe	50.0	15.2	218	5700	male
12	Gentoo	Biscoe	47.6	14.5	215	5400	male
13	Gentoo	Biscoe	46.5	13.5	210	4550	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
137	Chinstrap	Dream	53.5	19.9	205	4500	male
138	Chinstrap	Dream	49.0	19.5	210	3950	male
139	Chinstrap	Dream	50.8	18.5	201	4450	male
140	Chinstrap	Dream	49.0	19.6	212	4300	male
141	Chinstrap	Dream	51.4	19.0	201	3950	male
142	Chinstrap	Dream	50.7	19.7	203	4050	male
143	Chinstrap	Dream	49.3	19.9	203	4050	male
144	Chinstrap	Dream	50.2	18.8	202	3800	male
145	Chinstrap	Dream	51.9	19.5	206	3950	male
146	Chinstrap	Dream	55.8	19.8	207	4000	male
147	Chinstrap	Dream	43.5	18.1	202	3400	female
148	Chinstrap	Dream	50.8	19.0	210	4100	male

1.1.4 Filtering with a variable column name

Suppose the column you want to filter is a variable, let’s say a symbol

my_column = :species;

Problem: Filtering all the rows where the column stored in my_column is “Adelie”.

Tidier

@eval @filter penguins $my_column == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFramesMeta

DFM.@rsubset penguins $my_column == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFrames

subset(penguins, my_column => x -> x .== "Adelie")

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

In case the column is a string

my_column_string = "species";

instead of a symbol, we can write in the same way, just taking care in Tidier to convert it to a symbol

Tidier

@eval @filter penguins $(Symbol(my_column_string)) == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFramesMeta

DFM.@rsubset penguins $(my_column_string) == "Adelie"

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

DataFrames

subset(penguins, my_column_string => x -> x .== "Adelie")

152×7 DataFrame

127 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	missing	missing	missing	missing	missing
5	Adelie	Torgersen	36.7	19.3	193	3450	female
6	Adelie	Torgersen	39.3	20.6	190	3650	male
7	Adelie	Torgersen	38.9	17.8	181	3625	female
8	Adelie	Torgersen	39.2	19.6	195	4675	male
9	Adelie	Torgersen	34.1	18.1	193	3475	missing
10	Adelie	Torgersen	42.0	20.2	190	4250	missing
11	Adelie	Torgersen	37.8	17.1	186	3300	missing
12	Adelie	Torgersen	37.8	17.3	180	3700	missing
13	Adelie	Torgersen	41.1	17.6	182	3200	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
141	Adelie	Dream	40.2	17.1	193	3400	female
142	Adelie	Dream	40.6	17.2	187	3475	male
143	Adelie	Dream	32.1	15.5	188	3050	female
144	Adelie	Dream	40.7	17.0	190	3725	male
145	Adelie	Dream	37.3	16.8	192	3000	female
146	Adelie	Dream	39.0	18.7	185	3650	male
147	Adelie	Dream	39.2	18.6	190	4250	male
148	Adelie	Dream	36.6	18.4	184	3475	female
149	Adelie	Dream	36.0	17.8	195	3450	female
150	Adelie	Dream	37.8	18.1	193	3750	male
151	Adelie	Dream	36.0	17.1	187	3700	female
152	Adelie	Dream	41.5	18.5	201	4000	male

1.2 Arranging

To arrange a dataframe means to reorder the rows according to the order of some columns. The rows are first arranged by the first column, then by the second (if any), and so on. In Tidier, when we want to invert the ordering, just put the column name inside a desc() call.

1.2.1 Arranging by one column

Problem: Arrange by body_mass_g.

Tidier

@arrange penguins body_mass_g

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing

DataFramesMeta

DFM.@orderby penguins :body_mass_g

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing

DataFrames

sort(penguins, :body_mass_g)

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing

1.2.2 Arranging by two columns, with one reversed

Problem: First arrange by island, then by reversed body_mass_g.

Tidier

@arrange penguins island desc(body_mass_g)

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Gentoo	Biscoe	missing	missing	missing	missing	missing
2	Gentoo	Biscoe	49.2	15.2	221	6300	male
3	Gentoo	Biscoe	59.6	17.0	230	6050	male
4	Gentoo	Biscoe	51.1	16.3	220	6000	male
5	Gentoo	Biscoe	48.8	16.2	222	6000	male
6	Gentoo	Biscoe	45.2	16.4	223	5950	male
7	Gentoo	Biscoe	49.8	15.9	229	5950	male
8	Gentoo	Biscoe	48.4	14.6	213	5850	male
9	Gentoo	Biscoe	49.3	15.7	217	5850	male
10	Gentoo	Biscoe	55.1	16.0	230	5850	male
11	Gentoo	Biscoe	49.5	16.2	229	5800	male
12	Gentoo	Biscoe	48.6	16.0	230	5800	male
13	Gentoo	Biscoe	50.4	15.7	222	5750	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Adelie	Torgersen	41.1	18.6	189	3325	male
334	Adelie	Torgersen	38.5	17.9	190	3325	female
335	Adelie	Torgersen	37.8	17.1	186	3300	missing
336	Adelie	Torgersen	38.8	17.6	191	3275	female
337	Adelie	Torgersen	40.3	18.0	195	3250	female
338	Adelie	Torgersen	41.1	17.6	182	3200	female
339	Adelie	Torgersen	34.6	17.2	189	3200	female
340	Adelie	Torgersen	36.2	17.2	187	3150	female
341	Adelie	Torgersen	35.9	16.6	190	3050	female
342	Adelie	Torgersen	35.2	15.9	186	3050	female
343	Adelie	Torgersen	39.0	17.1	191	3050	female
344	Adelie	Torgersen	38.6	17.0	188	2900	female

DataFramesMeta

# works only when the reversed column is numeric?

DFM.@orderby penguins :island :body_mass_g .* -1

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Gentoo	Biscoe	49.2	15.2	221	6300	male
2	Gentoo	Biscoe	59.6	17.0	230	6050	male
3	Gentoo	Biscoe	51.1	16.3	220	6000	male
4	Gentoo	Biscoe	48.8	16.2	222	6000	male
5	Gentoo	Biscoe	45.2	16.4	223	5950	male
6	Gentoo	Biscoe	49.8	15.9	229	5950	male
7	Gentoo	Biscoe	48.4	14.6	213	5850	male
8	Gentoo	Biscoe	49.3	15.7	217	5850	male
9	Gentoo	Biscoe	55.1	16.0	230	5850	male
10	Gentoo	Biscoe	49.5	16.2	229	5800	male
11	Gentoo	Biscoe	48.6	16.0	230	5800	male
12	Gentoo	Biscoe	50.4	15.7	222	5750	male
13	Gentoo	Biscoe	50.0	16.3	230	5700	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Adelie	Torgersen	38.5	17.9	190	3325	female
334	Adelie	Torgersen	37.8	17.1	186	3300	missing
335	Adelie	Torgersen	38.8	17.6	191	3275	female
336	Adelie	Torgersen	40.3	18.0	195	3250	female
337	Adelie	Torgersen	41.1	17.6	182	3200	female
338	Adelie	Torgersen	34.6	17.2	189	3200	female
339	Adelie	Torgersen	36.2	17.2	187	3150	female
340	Adelie	Torgersen	35.9	16.6	190	3050	female
341	Adelie	Torgersen	35.2	15.9	186	3050	female
342	Adelie	Torgersen	39.0	17.1	191	3050	female
343	Adelie	Torgersen	38.6	17.0	188	2900	female
344	Adelie	Torgersen	missing	missing	missing	missing	missing

DataFrames

sort(penguins, [order(:island), order(:body_mass_g, rev=true)])

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Gentoo	Biscoe	missing	missing	missing	missing	missing
2	Gentoo	Biscoe	49.2	15.2	221	6300	male
3	Gentoo	Biscoe	59.6	17.0	230	6050	male
4	Gentoo	Biscoe	51.1	16.3	220	6000	male
5	Gentoo	Biscoe	48.8	16.2	222	6000	male
6	Gentoo	Biscoe	45.2	16.4	223	5950	male
7	Gentoo	Biscoe	49.8	15.9	229	5950	male
8	Gentoo	Biscoe	48.4	14.6	213	5850	male
9	Gentoo	Biscoe	49.3	15.7	217	5850	male
10	Gentoo	Biscoe	55.1	16.0	230	5850	male
11	Gentoo	Biscoe	49.5	16.2	229	5800	male
12	Gentoo	Biscoe	48.6	16.0	230	5800	male
13	Gentoo	Biscoe	50.4	15.7	222	5750	male
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Adelie	Torgersen	41.1	18.6	189	3325	male
334	Adelie	Torgersen	38.5	17.9	190	3325	female
335	Adelie	Torgersen	37.8	17.1	186	3300	missing
336	Adelie	Torgersen	38.8	17.6	191	3275	female
337	Adelie	Torgersen	40.3	18.0	195	3250	female
338	Adelie	Torgersen	41.1	17.6	182	3200	female
339	Adelie	Torgersen	34.6	17.2	189	3200	female
340	Adelie	Torgersen	36.2	17.2	187	3150	female
341	Adelie	Torgersen	35.9	16.6	190	3050	female
342	Adelie	Torgersen	35.2	15.9	186	3050	female
343	Adelie	Torgersen	39.0	17.1	191	3050	female
344	Adelie	Torgersen	38.6	17.0	188	2900	female

1.2.3 Arranging by one variable column

Problem: Arrange by a column stored in a variable my_arrange_column.

my_arrange_column = :body_mass_g;

Tidier

@eval @arrange penguins $my_arrange_column

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing

DataFramesMeta

DFM.@orderby penguins $my_arrange_column

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing

DataFrames

sort(penguins, my_arrange_column)

344×7 DataFrame

319 rows omitted

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String15	String15	Float64?	Float64?	Int64?	Int64?	String7?
1	Chinstrap	Dream	46.9	16.6	192	2700	female
2	Adelie	Biscoe	36.5	16.6	181	2850	female
3	Adelie	Biscoe	36.4	17.1	184	2850	female
4	Adelie	Biscoe	34.5	18.1	187	2900	female
5	Adelie	Dream	33.1	16.1	178	2900	female
6	Adelie	Torgersen	38.6	17.0	188	2900	female
7	Chinstrap	Dream	43.2	16.6	187	2900	female
8	Adelie	Biscoe	37.9	18.6	193	2925	female
9	Adelie	Dream	37.5	18.9	179	2975	missing
10	Adelie	Dream	37.0	16.9	185	3000	female
11	Adelie	Dream	37.3	16.8	192	3000	female
12	Adelie	Torgersen	35.9	16.6	190	3050	female
13	Adelie	Torgersen	35.2	15.9	186	3050	female
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
333	Gentoo	Biscoe	48.6	16.0	230	5800	male
334	Gentoo	Biscoe	48.4	14.6	213	5850	male
335	Gentoo	Biscoe	49.3	15.7	217	5850	male
336	Gentoo	Biscoe	55.1	16.0	230	5850	male
337	Gentoo	Biscoe	45.2	16.4	223	5950	male
338	Gentoo	Biscoe	49.8	15.9	229	5950	male
339	Gentoo	Biscoe	51.1	16.3	220	6000	male
340	Gentoo	Biscoe	48.8	16.2	222	6000	male
341	Gentoo	Biscoe	59.6	17.0	230	6050	male
342	Gentoo	Biscoe	49.2	15.2	221	6300	male
343	Adelie	Torgersen	missing	missing	missing	missing	missing
344	Gentoo	Biscoe	missing	missing	missing	missing	missing