# Chapter 6. Data Visualization in Julia

```julia
import Pkg;
Pkg.add(["Tidier", "TidierData", "TidierText", "TidierStrings", "TidierPlots", "DataFrames", "CSV", "Pipelines", "Makie", "StatsPlots", "RDatasets"]);
using Tidier, TidierData, TidierText, TidierStrings, TidierPlots, DataFrames, CSV, Pipelines, Makie, StatsPlots, RDatasets
import Makie.IntervalsBetween, Makie.Attributes

TidierPlots_set("plot_log", false) # I don't not want to see many logs
TidierPlots_set("plot_show", false) # and repetitive plots in VSCode
```

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





false
```

## Load Data

```julia
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  |

## Basic Plots

### 1. Scatter Plot

```julia
ggplot(data=df) +
geom_point(@aes(x = PetalLength, y = PetalWidth)) +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()
```

![](/files/e2wlYI4Y0ghMjUlm91Kn)

### 2. Bar Plot

```julia
ggplot(data=df) +
geom_bar(@aes(x = PetalWidth)) +
labs(x="Petal Width") +
theme_minimal()
```

![](/files/fvAkEBOOTZAd9K3V2ibE)

### 3. Line Plot

```julia
ggplot(data=df, @aes(x = PetalLength, y = PetalWidth)) +
geom_line() +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()

```

![](/files/nHKHcd6iWiQRjIecEKuT)

### 4. Histogram

```julia
ggplot(data=df, @aes(x = PetalWidth)) +
geom_histogram() +
labs(x="Petal Width") +
theme_minimal()

```

![](/files/AwXdlQUvEZf3rYaMljU1)

## Color and themes

### 1. Color

```julia
ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()
```

![](/files/rqbtm28GmOrDkpJcBO1i)

### 2. Themes

```julia
ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
labs(x="Petal Length", y="Petal Width") +
theme_latexfonts()
```

![](/files/34CjfvtxzDvRYqVRMFXY)

## Facet

```julia
ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
facet_wrap("Species") +
labs(x="Petal Length", y="Petal Width") +
theme_latexfonts()
```

![](/files/JjbSQCforXYGHA0aiUjo)

However, the supports of ggplot-like visualization cannot be identical to that of `ggplot2` in `R`.

If you want to use R packages or Python packages for certain purposes, we can also simply [use them in Julia](https://github.com/reycn/data-analytics-in-julia/blob/main/gitbook/7.r.and.python.in.julia.jl.ipynb)


---

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