# 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: ```julia 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` ``` ```julia 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 ```julia 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 ```julia 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: ```julia 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 ![](/files/mwN3c8rdp2A2FkxHfV9E) 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: ```julia 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 ![](/files/8nISdAcjFLsF4oZ8EmfZ) ```julia 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: ![](/files/f4C1j9hElYnyLETTvGhj) ```julia 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](/files/SAEgD3xxI1fQfMsJ67i2) ```julia 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](https://data-julia.rongxin.me/data-analysis-in-julia/3.transform.calculate.jl) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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