Chapter 2. Loading Data in Julia

1. Load common datasets

Firstly, we need to load some sample data, so we can install a common package for convenience:

using Pkg
Pkg.add("RDatasets")

   Resolving package versions...
  No Changes to `~/.julia/environments/v1.11/Project.toml`
  No Changes to `~/.julia/environments/v1.11/Manifest.toml`

using RDatasets
df = dataset("datasets", "iris")
first(df, 5)

5×5 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

Here, we are using first() to see the first several rows of the dataframe.

2. Load *.csv files locally

Pkg.add("CSV")
using CSV

df = CSV.read("./res/data/iris.csv", DataFrame)
first(df, 3)

   Resolving package versions...
  No Changes to `~/.julia/environments/v1.11/Project.toml`
  No Changes to `~/.julia/environments/v1.11/Manifest.toml`

3×5 DataFrame

Row	sepal_length	sepal_width	petal_length	petal_width	species
	Float64	Float64	Float64	Float64	String15
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

3. Load datasets online

Pkg.add("HTTP")
using HTTP

url = "https://github.com/mwaskom/seaborn-data/raw/master/iris.csv"
response = HTTP.get(url)
df = CSV.read(IOBuffer(response.body), DataFrame)
first(df, 3)

   Resolving package versions...
  No Changes to `~/.julia/environments/v1.11/Project.toml`
  No Changes to `~/.julia/environments/v1.11/Manifest.toml`

3×5 DataFrame

Row	sepal_length	sepal_width	petal_length	petal_width	species
	Float64	Float64	Float64	Float64	String15
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. Creating a data frame from scratch:

Pkg.add("DataFrames")
using DataFrames

df2 = DataFrame(
  title = ["A", "B", "C"],
  published = [1, 2, 3], 
  author = "Rongxin"
)
first(df2, 3)

   Resolving package versions...
  No Changes to `~/.julia/environments/v1.11/Project.toml`
  No Changes to `~/.julia/environments/v1.11/Manifest.toml`

3×3 DataFrame

Row	title	published	author
	String	Int64	String
1	A	1	Rongxin
2	B	2	Rongxin
3	C	3	Rongxin

Selecting Data in Julia

1. Indexing a subset

We can select a subset using a pair of row-column indexes. For example, if we want to select the first row to the second row, with all columns, we can:

df[1:2, :]

2×5 DataFrame

Row	sepal_length	sepal_width	petal_length	petal_width	species
	Float64	Float64	Float64	Float64	String15
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa

2. Select by column names

df[:, [:sepal_width, :petal_length]]

150×2 DataFrame125 rows omitted

Row	sepal_width	petal_length
	Float64	Float64
1	3.5	1.4
2	3.0	1.4
3	3.2	1.3
4	3.1	1.5
5	3.6	1.4
6	3.9	1.7
7	3.4	1.4
8	3.4	1.5
9	2.9	1.4
10	3.1	1.5
11	3.7	1.5
12	3.4	1.6
13	3.0	1.4
⋮	⋮	⋮
139	3.0	4.8
140	3.1	5.4
141	3.1	5.6
142	3.1	5.1
143	2.7	5.1
144	3.2	5.9
145	3.3	5.7
146	3.0	5.2
147	2.5	5.0
148	3.0	5.2
149	3.4	5.4
150	3.0	5.1

And the powerful part of it is, we can directly using regex to select columns!

For instance, if we only care about the columns ended with length, we can:

df[:, r".*length$"]

150×2 DataFrame125 rows omitted

Row	sepal_length	petal_length
	Float64	Float64
1	5.1	1.4
2	4.9	1.4
3	4.7	1.3
4	4.6	1.5
5	5.0	1.4
6	5.4	1.7
7	4.6	1.4
8	5.0	1.5
9	4.4	1.4
10	4.9	1.5
11	5.4	1.5
12	4.8	1.6
13	4.8	1.4
⋮	⋮	⋮
139	6.0	4.8
140	6.9	5.4
141	6.7	5.6
142	6.9	5.1
143	5.8	5.1
144	6.8	5.9
145	6.7	5.7
146	6.7	5.2
147	6.3	5.0
148	6.5	5.2
149	6.2	5.4
150	5.9	5.1

3. Conditional filtering

It's common in data analysis that we want to subset a dataframe according to a condition.

In this case, we can define a condition, e.g., find out the rows whose species is virginica, as the following lines:

img

condition = df.species .== "virginica"
df[condition, :]

50×5 DataFrame25 rows omitted

Row	sepal_length	sepal_width	petal_length	petal_width	species
	Float64	Float64	Float64	Float64	String15
1	6.3	3.3	6.0	2.5	virginica
2	5.8	2.7	5.1	1.9	virginica
3	7.1	3.0	5.9	2.1	virginica
4	6.3	2.9	5.6	1.8	virginica
5	6.5	3.0	5.8	2.2	virginica
6	7.6	3.0	6.6	2.1	virginica
7	4.9	2.5	4.5	1.7	virginica
8	7.3	2.9	6.3	1.8	virginica
9	6.7	2.5	5.8	1.8	virginica
10	7.2	3.6	6.1	2.5	virginica
11	6.5	3.2	5.1	2.0	virginica
12	6.4	2.7	5.3	1.9	virginica
13	6.8	3.0	5.5	2.1	virginica
⋮	⋮	⋮	⋮	⋮	⋮
39	6.0	3.0	4.8	1.8	virginica
40	6.9	3.1	5.4	2.1	virginica
41	6.7	3.1	5.6	2.4	virginica
42	6.9	3.1	5.1	2.3	virginica
43	5.8	2.7	5.1	1.9	virginica
44	6.8	3.2	5.9	2.3	virginica
45	6.7	3.3	5.7	2.5	virginica
46	6.7	3.0	5.2	2.3	virginica
47	6.3	2.5	5.0	1.9	virginica
48	6.5	3.0	5.2	2.0	virginica
49	6.2	3.4	5.4	2.3	virginica
50	5.9	3.0	5.1	1.8	virginica

Now, you know how to load and select dataframes upon your interests, it's time to know how to transform your data and calculate your variables