datasauRus

https://jumpingrivers.github.io/datasauRus/
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datasauRus is an R package that wraps the Datasaurus Dozen datasets to demonstrate the importance of...
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### Install development version from GitHub

Source: https://jumpingrivers.github.io/datasauRus/index.html

Use the devtools package to install the latest development version of the package directly from the GitHub repository.

```R
devtools::install_github("jumpingrivers/datasauRus")
```

--------------------------------

### Install stable version from CRAN

Source: https://jumpingrivers.github.io/datasauRus/index.html

Use this command to install the latest stable release of the datasauRus package from CRAN.

```R
install.packages("datasauRus")
```

--------------------------------

### Load twelve_from_slant_alternate_long dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_alternate_long.html

Usage example showing how to access the dataset object in R.

```R
twelve_from_slant_alternate_long
```

--------------------------------

### Plot all datasets in twelve_from_slant_alternate_wide

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_alternate_wide.html

This example saves the current graphics parameters, sets up a 4x3 plotting grid with narrow margins, loops over the column pairs to plot each dataset with its name as the title, and restores the original parameters. It assumes the datasauRus package is loaded and the dataset is available.

```R
#save current settings
state = par("mar", "mfrow")

# plot
par(mfrow = c(4, 3), mar = c(1, 3, 3, 1))

nms = names(twelve_from_slant_alternate_wide)
for (i in seq(1, 23, by = 2)) {
  nm = substr(nms[i], 1, nchar(nms[i]) - 2)
  plot(twelve_from_slant_alternate_wide[[nms[i]]],
       twelve_from_slant_alternate_wide[[nms[i + 1]]],
       xlab = "", ylab = "", main = nm)
}


#reset settings
par(state)
```

--------------------------------

### Visualizing the Datasaurus Dozen with Base R

Source: https://jumpingrivers.github.io/datasauRus/reference/datasaurus_dozen_wide.html

This example iterates through the wide-format dataset to plot each of the 12 shapes in a 5x3 grid, resetting the graphical parameters afterwards.

```R
# Save current settings
state = par("mar", "mfrow")

# Base R Plots
par(mfrow = c(5, 3), mar = c(1, 3, 3, 1))

nms = names(datasaurus_dozen_wide)
for (i in seq(1, 25, by = 2)) {
  nm = substr(nms[i], 1, nchar(nms[i]) - 2)
  plot(datasaurus_dozen_wide[[nms[i]]],
       datasaurus_dozen_wide[[nms[i + 1]]],
       xlab = "", ylab = "", main = nm)
}

#reset settings
par(state)
```

--------------------------------

### Visualize Datasaurus Dozen with ggplot2

Source: https://jumpingrivers.github.io/datasauRus/index.html

This example demonstrates how to use ggplot2 to create a faceted plot of the Datasaurus Dozen datasets.

```R
library("ggplot2")
library("datasauRus")
ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset))+
  geom_point() +
  theme_void() +
  theme(legend.position = "none")+
  facet_wrap(~dataset, ncol = 3)
```

--------------------------------

### Base R plots for twelve_from_slant_alternate_long

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_alternate_long.html

Base R plotting example that creates a 4x3 grid of scatter plots for each dataset, saving and restoring graphical parameters.

```R
# Base R Plots
state = par("mfrow", "mar")

par(mfrow = c(4, 3), mar = c(2, 2, 2, 2))

sets = sort(unique(twelve_from_slant_alternate_long$dataset))

for (s in sets) {
  df = twelve_from_slant_alternate_long[twelve_from_slant_alternate_long$dataset == s, ]
  plot(df$x, df$y, pch = 16, xlab = "", ylab = "")
  title(s)
}


par(state)
```

--------------------------------

### Plot twelve_from_slant_alternate_long with ggplot2

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_alternate_long.html

Example using ggplot2 to create a faceted scatter plot of the dataset, with points colored by dataset and no legend.

```R
if (require(ggplot2)) {
  ggplot(twelve_from_slant_alternate_long, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme_void() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}
```

--------------------------------

### ggplot2 density plots for box_plots

Source: https://jumpingrivers.github.io/datasauRus/reference/box_plots.html

Generates density plots for each variable in box_plots using ggplot2. Requires the ggplot2 package; if not installed, the code is skipped.

```R
## ggplot
if (require(ggplot2)) {
  ggplot(box_plots, aes(x = left)) +
    geom_density()
  ggplot(box_plots, aes(x = lines)) +
    geom_density()
  ggplot(box_plots, aes(x = normal)) +
    geom_density()
  ggplot(box_plots, aes(x = right)) +
    geom_density()
  ggplot(box_plots, aes(x = split)) +
    geom_density()
}
#> Loading required package: ggplot2
```

--------------------------------

### Load twelve_from_slant_wide dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_wide.html

Access the dataset object directly. It is a data frame with 182 rows and 24 variables, each pair of columns (e.g., bullseye_x and bullseye_y) holding the x- and y-values for one of the 12 datasets.

```r
twelve_from_slant_wide
```

--------------------------------

### Visualize with ggplot2

Source: https://jumpingrivers.github.io/datasauRus/reference/datasaurus_dozen.html

Use ggplot2 to create a faceted scatter plot of all 12 datasets.

```R
if (require(ggplot2)) {
  ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme_void() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}
```

--------------------------------

### Load simpsons_paradox_wide dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/simpsons_paradox_wide.html

Access the built-in dataset object. The dataset is a data frame with 222 rows and 4 variables: simpson_1_x, simpson_1_y, simpson_2_x, and simpson_2_y.

```r
simpsons_paradox_wide
```

--------------------------------

### Visualize datasets with ggplot2

Source: https://jumpingrivers.github.io/datasauRus/articles/Datasaurus.html

Create scatter plots for each sub-dataset using ggplot2 to visualize the differences in distribution.

```R
if (requireNamespace("ggplot2")) {
  library(ggplot2)
  ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme_void() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}
```

```text
## Loading required namespace: ggplot2
```

--------------------------------

### Plot all datasets from twelve_from_slant_wide

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_wide.html

Plots all 12 datasets in a 4x3 grid by iterating over the column pairs. Saves the current par settings first and restores them at the end with par(state).

```r
#save current settings
state = par("mar", "mfrow")

# plot
par(mfrow = c(4, 3), mar = c(1, 3, 3, 1))

nms = names(twelve_from_slant_wide)
for (i in seq(1, 23, by = 2)) {
  nm = substr(nms[i], 1, nchar(nms[i]) - 2)
  plot(twelve_from_slant_wide[[nms[i]]],
       twelve_from_slant_wide[[nms[i + 1]]],
       xlab = "", ylab = "", main = nm)
}


#reset settings
par(state)
```

--------------------------------

### Accessing the simpsons_paradox dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/simpsons_paradox.html

The dataset is accessed by calling its name directly.

```R
simpsons_paradox
```

--------------------------------

### Visualize with Base R plots

Source: https://jumpingrivers.github.io/datasauRus/reference/datasaurus_dozen.html

Iterate through the unique datasets to generate individual scatter plots using Base R graphics.

```R
state = par("mar", "mfrow")

# plot
par(mfrow = c(5, 3), mar = c(1, 2, 2, 1))

sets = sort(unique(datasaurus_dozen$dataset))

for (s in sets) {
  df = datasaurus_dozen[datasaurus_dozen$dataset == s, ]
  plot(df$x, df$y, pch = 16)
  title(s)
}

#reset settings
par(state)
```

--------------------------------

### Plot simpsons_paradox_wide datasets

Source: https://jumpingrivers.github.io/datasauRus/reference/simpsons_paradox_wide.html

Plots the two datasets side by side to visualize Simpson's Paradox. Saves the current graphical parameters, sets a 1x2 layout, creates scatter plots for simpson_1 and simpson_2, then restores the original settings.

```r
#save current settings
state = par("mar", "mfrow")

par(mfrow = c(1, 2))

plot(simpsons_paradox_wide[["simpson_1_x"]],
     simpsons_paradox_wide[["simpson_1_y"]],
     xlab = "x", ylab = "y", main = "Simpson's Paradox 1")

plot(simpsons_paradox_wide[["simpson_2_x"]],
     simpsons_paradox_wide[["simpson_2_y"]],
     xlab = "x", ylab = "y", main = "Simpson's Paradox 2")


#reset settings
par(state)
```

--------------------------------

### Visualizing simpsons_paradox with Base R

Source: https://jumpingrivers.github.io/datasauRus/reference/simpsons_paradox.html

Uses base R plotting functions to iterate through the datasets and plot them side-by-side.

```R
state = par("mfrow")

par(mfrow = c(1, 2))

sets = unique(datasaurus_dozen$dataset)

for (i in 1:2) {
  df = simpsons_paradox[simpsons_paradox$dataset == paste0("simpson_", i), ]
  plot(df$x, df$y, pch = 16, xlab = "", ylab = "")
  title(paste0("Simpson\'s Paradox ", i))
}


par(state)
#> Warning: argument 1 does not name a graphical parameter
#> NULL
```

--------------------------------

### Visualizing simpsons_paradox with ggplot2

Source: https://jumpingrivers.github.io/datasauRus/reference/simpsons_paradox.html

Uses ggplot2 to facet the two datasets and display them as scatter plots.

```R
if (require(ggplot2)) {
  ggplot(simpsons_paradox, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}
```

--------------------------------

### Accessing the datasaurus_dozen_wide dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/datasaurus_dozen_wide.html

The dataset can be accessed directly by its name.

```R
datasaurus_dozen_wide
```

--------------------------------

### Access the twelve_from_slant_alternate_wide dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_alternate_wide.html

The dataset is provided as an R object named twelve_from_slant_alternate_wide. It is a data frame with 182 rows and 24 variables, each pair representing x and y coordinates for one of the 12 datasets.

```R
twelve_from_slant_alternate_wide
```

--------------------------------

### Plot twelve_from_slant_long with ggplot2 and base R

Source: https://jumpingrivers.github.io/datasauRus/reference/twelve_from_slant_long.html

Two plotting approaches for the twelve_from_slant_long dataset. The first uses ggplot2 with faceting by dataset; the second uses base R plots with a loop over the unique datasets and restores the original graphics state with par(state).

```r
if (require(ggplot2)) {
  ggplot(twelve_from_slant_long, aes(x = x, y = y, colour = dataset)) +
    geom_point() +
    theme_void() +
    theme(legend.position = "none") +
    facet_wrap(~dataset, ncol = 3)
}


# Base R Plots
state = par("mfrow", "mar")

par(mfrow = c(4, 3), mar = c(3, 2, 2, 2))

sets = sort(unique(twelve_from_slant_long$dataset))

for (s in sets) {
  df = twelve_from_slant_long[twelve_from_slant_long$dataset == s, ]
  plot(df$x, df$y, pch = 16, xlab = "", ylab = "")
  title(s)
}


par(state)

```

--------------------------------

### Access the datasaurus_dozen dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/datasaurus_dozen.html

The dataset is available as a data frame named datasaurus_dozen.

```R
datasaurus_dozen
```

--------------------------------

### Summarize box_plots dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/box_plots.html

Displays summary statistics for each variable in the box_plots dataset. The output shows minimum, quartiles, median, mean, and maximum for left, lines, normal, right, and split.

```R
summary(box_plots)
#>       left              lines               normal          right       
#>  Min.   :-9.76964   Min.   :-9.769575   Min.   :-9.76   Min.   :-9.760  
#>  1st Qu.:-2.68999   1st Qu.:-2.689993   1st Qu.:-2.68   1st Qu.:-2.680  
#>  Median :-0.00999   Median :-0.007132   Median : 0.00   Median : 0.000  
#>  Mean   :-1.17780   Mean   :-0.831733   Mean   : 0.00   Mean   : 1.174  
#>  3rd Qu.: 2.67007   3rd Qu.: 2.670236   3rd Qu.: 2.68   3rd Qu.: 2.680  
#>  Max.   : 9.75025   Max.   : 9.756001   Max.   : 9.76   Max.   : 9.760  
#>      split          
#>  Min.   :-9.769886  
#>  1st Qu.:-2.689989  
#>  Median :-0.003099  
#>  Mean   :-0.003060  
#>  3rd Qu.: 2.680000  
#>  Max.   : 9.760000  
```

--------------------------------

### Calculate summary statistics with dplyr

Source: https://jumpingrivers.github.io/datasauRus/articles/Datasaurus.html

Use dplyr to group the datasaurus_dozen dataset by the 'dataset' column and calculate the mean, standard deviation, and correlation for each sub-dataset.

```R
library(datasauRus)
if (requireNamespace("dplyr")) {
  suppressPackageStartupMessages(library(dplyr))
  datasaurus_dozen %>%
    group_by(dataset) %>%
    summarize(
      mean_x    = mean(x),
      mean_y    = mean(y),
      std_dev_x = sd(x),
      std_dev_y = sd(y),
      corr_x_y  = cor(x, y)
    )
}
```

```text
## Loading required namespace: dplyr
```

```text
## # A tibble: 13 × 6
##    dataset    mean_x mean_y std_dev_x std_dev_y corr_x_y
##    <chr>       <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
##  1 away         54.3   47.8      16.8      26.9  -0.0641
##  2 bullseye     54.3   47.8      16.8      26.9  -0.0686
##  3 circle       54.3   47.8      16.8      26.9  -0.0683
##  4 dino         54.3   47.8      16.8      26.9  -0.0645
##  5 dots         54.3   47.8      16.8      26.9  -0.0603
##  6 h_lines      54.3   47.8      16.8      26.9  -0.0617
##  7 high_lines   54.3   47.8      16.8      26.9  -0.0685
##  8 slant_down   54.3   47.8      16.8      26.9  -0.0690
##  9 slant_up     54.3   47.8      16.8      26.9  -0.0686
## 10 star         54.3   47.8      16.8      26.9  -0.0630
## 11 v_lines      54.3   47.8      16.8      26.9  -0.0694
## 12 wide_lines   54.3   47.8      16.8      26.9  -0.0666
## 13 x_shape      54.3   47.8      16.8      26.9  -0.0656
```

--------------------------------

### Base plot: histograms and boxplots for box_plots

Source: https://jumpingrivers.github.io/datasauRus/reference/box_plots.html

Creates a 5x2 grid of histograms and horizontal boxplots for each variable in box_plots. Saves and restores graphical parameters (mar, mfrow) to avoid side effects.

```R
## base plot

#save current settings
state = par("mar", "mfrow")

par(mfrow = c(5, 2), mar = c(1, 2, 2, 1))

nms = names(box_plots)

for (i in 1:5) {
  nm = nms[i]
  hist(box_plots[[nms[i]]],
       breaks = 100,
       main = nm)
  boxplot(box_plots[[nms[i]]],
          horizontal = TRUE)
}



#reset settings
par(state)
```

--------------------------------

### BibTeX Citation for datasauRus

Source: https://jumpingrivers.github.io/datasauRus/authors.html

The BibTeX entry for citing the datasauRus package in academic or technical documents.

```bibtex
@Manual{,
  title = {datasauRus: Datasets from the Datasaurus Dozen},
  author = {Colin Gillespie and Steph Locke and Rhian Davies and Lucy {D'Agostino McGowan}},
  year = {2025},
  note = {R package version 0.1.9,
    https://jumpingrivers.github.io/datasauRus/},
  url = {https://github.com/jumpingrivers/datasauRus},
}
```

--------------------------------

### Access the box_plots dataset

Source: https://jumpingrivers.github.io/datasauRus/reference/box_plots.html

Loads the box_plots dataset from the datasauRus package. The dataset is a data frame with 2484 rows and 5 variables.

```R
box_plots
```
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