Built on MASS, vegan, ggplot2, and scico, this package is meant to provide user friendly functions to quickly and efficiently perform various common statistical operations and generate beautiful, colorblind-accessible plots. Examples of some such operations include: - removing outliers and extremes - generating probability density and cumulative distribution function graphs - running one-sample Kolmogorov-Smirnov tests against multiple distributions at once - generating prediction plots for linear and generalized linear models - scaling data then performing and plotting principal component analyses
Installation
To install from CRAN {r} install.packages("ztils") To install the development version: {r} remotes::install_github("zachpeagler/ztils")
Bug reporting
If you find any bugs, please report them at https://github.com/zachpeagler/ztils/issues.
Functions
no_outliers()
Description
This function works by keeping only rows in the dataframe containing variable values within the quartiles +- 1.5 times the interquartile range.
Usage
This function has no defaults, as it is entirely dependent on the user input. ```{r} no_outliers(data, var )
### Arguments
- **data**: the dataframe to remove rows containing outliers of the target variable
- **var**: the variable to calculate outliers against
### Returns
Returns the specified dataframe **data** minus the rows containing outliers in the **var** variable.
### Examples:
```{r}
no_outliers(iris, Sepal.Length)This isn’t a great example because the iris dataset does not contain any statistical outliers.
no_extremes()
Description
This function works by keeping only rows in the dataframe containing variable values within the quartiles +- 3.0 times the interquartile range.
Usage
This function has no defaults, as it is entirely dependent on the user input. ```{r} no_extremes(data, var )
### Arguments
- **data**: the dataframe to remove rows containing outliers of the target variable
- **var**: the variable to calculate outliers against
### Returns
Returns the specified dataframe **data** minus the rows containing extremes in the **var** variable.
### Examples:
```{r}
no_extremes(iris, Sepal.Length)This isn’t a great example because the iris dataset does not contain any statistical outliers.
multipdf_cont()
Description
This function gets the probability density function (PDF) for selected distributions against continuous variables. Possible distributions include any combination of “normal”, “lognormal”, “gamma”, “exponential”, and “all” (which just uses all of the prior distributions).
Note that only non-negative numbers are supported by the lognormal and gamma distributions. Feeding this function a negative number with those distributions selected will result in an error.
Returns
This function returns a dataframe with row number equal to seq_length containing the real density and the probability density function of var for selected distributions.
Arguments
-
var: the variable of which to get the PDF
- no default
-
seq_length: the length to fit the distribution against
- default 50
-
distributions: the distributions to fit var against
- default “all”
Examples
``` {r} multipdf_cont(iris$Petal.Length)
multipdf_cont(iris$Sepal.Length, 100, c(“normal”, “lognormal”))
## multipdf_plot()
### Description
This function extends **multiPDF_cont** and gets the probability density functions (PDFs) for selected distributions against **continuous**, **non-negative** numbers. Possible distributions include any combination of "normal", "lognormal", "gamma", "exponential", and "all" (which just uses all of the prior distributions). It then plots this using **ggplot2** and a **scico** palette, using **var_name** for the plot labeling, if specified. If not specified, it will use **var** instead.
### Usage
```{r}
multipdf_plot(var,
seq_length = 50,
distributions = "all",
palette = "oslo",
var_name = NULL
)Returns
A plot showing the PDF of the selected variable against the selected distributions over the selected sequence length.
Arguments
- var: the variable of which to get the PDF
- seq_length: the length to fit the distribution against
- distributions: the distributions to fit var against
- palette: A scico palette to use on the graph, with each distribution corresponding to a color. For all possible palettes, call scico_palette_names().
- var_name: A name to use in the title and x axis label of the plot.
Examples
```{r}
multipdf_plot(iris$Sepal.Length)
multipdf_plot(iris$Sepal.Length, seq_length = 100, distributions = c(“normal”, “lognormal”, “gamma”), palette = “bilbao”, var_name = “Sepal Length (cm)” )
<br>
### multicdf_cont()
### Description
This function gets the cumulative distribution function (CDF) for selected distributions against **continuous** variables. Possible distributions include any combination of "normal", "lognormal", "gamma", "exponential", and "all" (which just uses all of the prior distributions).
> Note that only **non-negative** numbers are supported by the lognormal and gamma distributions. Feeding this function a negative number with those distributions selected will result in an error.
### Usage:
```{r}
multicdf_cont(var,
seq_length = 50,
distributions = "all"
)Returns
This function returns a dataframe with row number equal to seq_length containing the real density and the probability density function of var for selected distributions.
Arguments
-
var: the variable of which to get the PDF
- no default
-
seq_length: the length to fit the distribution against
- default 50
-
distributions: the distributions to fit var against
- default “all”
Examples
``` {r} multicdf_cont(iris$Petal.Length)
multicdf_cont(iris$Sepal.Length, 100, c(“normal”, “lognormal”) )
## multicdf_plot()
### Description
This function extends **multiCDF_cont** and gets the cumulative distribution functions (CDFs) for selected distributions against **continuous**, **non-negative** numbers. Possible distributions include any combination of "normal", "lognormal", "gamma", "exponential", and "all" (which just uses all of the prior distributions). It then plots this using **ggplot2** and a **scico** palette, using **var_name** for the plot labeling, if specified. If not specified, it will use **var** instead.
### Usage
```{r}
multicdf_plot(var,
seq_length = 50,
distributions = "all",
palette = "oslo",
var_name = NULL
)Returns
A plot showing the CDF of the selected variable against the selected distributions over the selected sequence length.
Arguments
- var: the variable of which to get the CDF
- seq_length: the length to fit the distribution against
- distributions: the distributions to fit var against
- palette: A scico palette to use on the graph, with each distribution corresponding to a color. For all possible palettes, call scico_palette_names().
- var_name: A name to use in the title and x axis label of the plot.
Examples
```{r}
multicdf_plot(iris$Sepal.Length)
multicdf_plot(iris$Sepal.Length, seq_length = 100, distributions = c(“normal”, “lognormal”, “gamma”), palette = “bilbao”, var_name = “Sepal Length (cm)” )
<br>
## multiks_cont()
### Description
This function gets the distance and p-value from a one-sample Kolmogorov-Smirnov (KS) test for selected distributions against a continous input variable. Possible distributions include "normal", "lognormal", "gamma", "exponential", and "all".
### Usage
```{r}
multiks_cont(var,
distributions = "all"
)Note: If using “lognormal” or “gamma” distributions, the target variable must be non-negative.
Arguments
- var: The variable to perform one-sample KS tests on
- distributions: The distributions to test against
Returns
Returns a dataframe with the distance and p-value for each performed KS test. The distance is a relative metric of similarity. A p-value of > 0.05 indicates that the target variable’s distribution is not significantly different from the specified distribution.
Examples
```{r} multiks_cont(iris$Sepal.Length)
multiks_cont(iris$Sepal.Length, c(“normal”, “lognormal”))
<br>
## gml_pseudor2
### Description
This function calculates the pseudo R^2 (proportion of variance explained by the model) for a general linear model (glm). glms don't have real R^2 due to the intrinsic difference between a linear model and a generalized linear model, but we can still calculate an approximiation of the R^2 as (1 - (deviance/null deviance)).
### Usage
```{r}
glm_pseudor2(mod)pca_plot()
Description
This function performs a principal component analysis (PCA) for the selected pcavars with the option to automatically scale the variables. It then graphs PC1 on the x axis and PC2 on the y-axis using ggplot2, coloring the graph with a scico palette over the specified groups. This is similar to the biplot command from the stats package, but performs all the steps required in graphing a PCA for you.
Arguments
- group: The group column, used for assigning colors.
- pcavars: The variables (columns) to perform a principal component analysis on. Should be explanatory variables and not response variables.
- scaled: A boolean (TRUE or FALSE) indicated if the pcavars have already been scaled or if they should be scaled in the function.
- palette: A scico palette used to color the graph. For all possible palettes, call scico_palette_names(). If non-scico palettes are desired, the palette can be overridden with scale_color and scale_fill functions.
Returns
A ggplot object showing PC1 on the x axis and PC2 on the y axis, colored by group with vectors and labels showing the individual pca variables.
Examples
```{r} pca_plot(iris$Species, iris[,c(1:4)])
pca_plot(iris$Species, iris[,c(1:4)], FALSE, “bilbao”)
<br>
## pca_data()
### Description
This function performs a principal component analysis (PCA) on the specified variables, **pcavars** and attaches the resulting principal components to the specified dataframe, **data**, with optional variable scaling.
### Usage
```{r}
pca_data(data,
pcavars,
scaled = FALSE
)predict_plot()
Description
This function performs a prediction based on the supplied model, then graphs it using ggplot2. Options are available for predicting based on the confidence or prediction interval, as well as for applying corrections, such as exponential and logistic.
I would like to alter this function to reduce the number of required inputs, as all the information should be available from the model call, but that’s a work in progress. ### Usage ```{r} predict_plot(mod, data, rvar, pvar, group = NULL, length = 50, interval = “confidence”, correction = “normal”, palette = “oslo” )
### Arguments
- **mod**: A univariate linear model to base predictions on.
- **data**: The dataframe used in the model. Will be used to pull variables for plotting.
- **rvar**: The response variable (y-axis), must be the same as the one in the model
- **pvar**: The predictor variable (x-axis), must be the same as the one in the model.
- **group**: An optional grouping variable. If a group is present, separate predictions will be made for each group.
- **length**: The length to predict over. A longer length will result in more precision.
- **interval**: Tells the function to predict over either the confidence interval or the prediction interval.
- "confidence" or "prediction"
- **correction**: If you log transform or logit transform the variables in the model, you can choose to apply a correction to the predicted output to reverse that transformation.
- "normal", "exponential", or "logit"
- **palette**: A *scico* palette used to color the graph. For all possible palettes, call **scico_palette_names()**. If non-scico palettes are desired, the palette can be overridden with scale_color and scale_fill functions.
### Returns
Returns a plot with the observed (real) data plotted as points and the prediction plotted as lines, with a 95% confidence or prediction interval.
> This function has a known issue with the colors on ungrouped predictions being kind of funky, as the function uses the predictor variable (x-axis) for the color, which works for the actual data (points), but doesn't translate well to the predicted lines and ribbon.
### Examples
```{r}
mod1 <- lm(Sepal.Length ~ Petal.Length + Species, data = iris)
predict_plot(mod1, iris, Sepal.Length, Petal.Length, Species)