Title: Nima Hejazi's R Toolbox
Version: 0.6.2
Description: Miscellaneous R functions developed as collateral damage over the course of work in statistical and scientific computing for research. These include, for example, utilities that supplement existing idiosyncrasies of the R language, extend existing plotting functionality and aesthetics, help prepare data objects for imputation, and extend access to command line tools and systems-level information.
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Depends: R (≥ 3.2.3)
Imports: utils, stats, assertthat, ggplot2, ggthemes, scales, gtools, dplyr, grid, gridExtra,
Suggests: knitr, roxygen2, testthat, tibble, stringr
License: MIT + file LICENSE
URL: https://github.com/nhejazi/nima
BugReports: https://github.com/nhejazi/nima/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-03-06 01:58:19 UTC; nsh
Author: Nima Hejazi ORCID iD [aut, cre, cph]
Repository: CRAN
Date/Publication: 2020-03-06 06:10:03 UTC

Maximum of Absolute Values of Vector

Description

Take the maximum of the absolute values of an input vector.

Usage

absmax(x, na.rm = FALSE)

Arguments

x

A numeric vector or array.

na.rm

A logical indicating whether missing values should be removed.

Value

The maximum of the absolute values of elements of the input vector.

Examples

x <- c(5, 3, -9, -100, 3.14159, 7.5)
absmax(x)

Get Names of Attributes

Description

Get the names of the attributes of an input object.

Usage

attrnames(obj)

Arguments

obj

Any object.

Value

Vector of character strings with the names of the attributes.

Examples

x <- matrix(1:100, ncol = 5)
colnames(x) <- LETTERS[1:5]
attrnames(x)

Clear the Current Screen/Buffer

Description

Clear the screen with a call to system and clear.

Usage

clear()

Details

This function is merely a call to system("clear")

Examples


system("clear")

Add Commas to a Large Number

Description

Convert a number to a string, with commas inserted at every 3rd digit.

Usage

commas(numbers)

Arguments

numbers

Vector of non-negative numbers (will be rounded to integers)

Value

Character string with numbers written like "5,771,009".

Examples

commas(c(2300, 9000, 21456, 987654890, 1256787, 345765, 1432))

Discretize a Vector by Quantiles

Description

Discretizes a non-factor input vector and returns the result as numeric.

Usage

discrete_by_quantile(x, ...)

Arguments

x

A vector containing arbitrary data.

...

Additional arguments passed to quantcut.

Value

A numeric vector with the data re-coded to based on the quantiles.

Examples

x <- rnorm(1000)
discrete_by_quantile(x)

Exit R Without Saving

Description

Exit R without saving workspace, using the ubiquitous UNIX syntax.

Usage

exit()

Details

This function is merely a call to q("no").

Convert a Factor to Numeric

Description

Convert a factor with numeric levels to a non-factor (numeric).

Usage

factor_to_num(x)

Arguments

x

A vector containing a factor with numeric levels.

Value

The input factor made into a numeric vector.

Examples

x <- factor(c(3, 4, 9, 4, 9), levels = c(3, 4, 9))
factor_to_num(x)

View HTML Version of Help Files

Description

View the HTML version of a help file while running R from the terminal.

Usage

hweb(...)

Arguments

...

Help topics.

Details

Calls function help using argument htmlhelp=TRUE.

See Also

help, help.start

Examples


hweb(read.table)

Linear Model Diagnostic Plots

Description

Produce standard diagnostic plots for linear models using ggplot2.

Usage

lm_plot(x, ...)

Arguments

x

A linear model object produced by lm().

...

Extra arguments, currently ignored.

Examples

n <- 100
x1 <- rnorm(n)
y1 <- rnorm(n)
linmod <- lm(y1 ~ x1)
plot(linmod)

Add missingness indicators to existing data object

Description

Add indicator columns to a data.frame showing the pattern of missingness.

Usage

miss_ind(data, prefix = "miss_")

Arguments

data

A numeric vector or array.

prefix

A string used to name the indicator variables..

Value

An augmented data.frame with indicators for missingness patterns.

Examples

data <- data.frame(cbind(rnorm(10), runif(10)))
data[sample(nrow(data), 3), 1] <- NA
data[sample(nrow(data), 4), 2] <- NA
data <- miss_ind(data)

Mean Squared Error

Description

Compute the mean squared error (risk under L2 loss).

Usage

mse(prediction, outcome)

Arguments

prediction

A numeric vector of predictions.

outcome

A numeric vector of outcomes actually observed.

Examples

x <- rnorm(100)
y <- x^2
test_x <- rnorm(100)
test_y <- test_x^2
mod <- glm(y ~ x)
pred <- predict(mod, newx = as.data.frame(test_x))
error <- mse(prediction = pred, outcome = test_y)

Risk for Cross-Entropy Loss

Description

Compute the empirical risk under cross-entropy loss for binary predictions.

Usage

nll(prediction, outcome)

Arguments

prediction

A numeric vector of predicted probabilities.

outcome

A numeric vector of binary outcomes actually observed.

Examples

n_obs <- 100
x <- rnorm(n_obs)
y <- rbinom(n_obs, 1, plogis(x^2))
test_x <- rnorm(n_obs)
test_y <- rbinom(n_obs, 1, plogis(test_x^2))
mod <- glm(y ~ x, family = "binomial")
pred <- predict(mod, newx = as.data.frame(test_x), type = "response")
error <- nll(prediction = unname(pred), outcome = test_y)

Open a File

Description

Open a file using system and open.

Usage

openfile(file)

Arguments

file

File name (as character string).

Details

Open files from R by using the default operating system program.

Examples

## Not run: 
openfile("myplot.pdf")

## End(Not run)

Quantile-Quantile Plots

Description

Produce standard quantile-quantile plots for modeling using ggplot2.

Usage

qq_plot(
  x,
  distribution = "norm",
  ...,
  line.estimate = NULL,
  conf = 0.95,
  labels = names(x)
)

Arguments

x

A numeric vector of residuals from a generalized linear model.

distribution

The reference probability distribution for residuals.

...

Any additional parameters to be passed to distribution functions.

line.estimate

Should quantiles be estimated, if so which quantiles?

conf

The confidence level to be used with confidence intervals.

labels

The names to be used when identifying points on the Q-Q plot.

Examples

n <- 100
x1 <- rnorm(n)
y1 <- rnorm(n)
linmod <- lm(y1 ~ x1)
x <- linmod$residuals
qq_plot(x)

Nima's ggplot2 theme - supplement: scale_color

Description

Nima's ggplot2 theme scale_color supplement: colors optimized via ColorBrewer

Usage

scale_color_nima(...)

Arguments

...

Passed to ggplot

Nima's ggplot2 theme - supplement: scale_fill

Description

Nima's ggplot2 theme scale_fill supplement: colors optimized via ColorBrewer

Usage

scale_fill_nima(...)

Arguments

...

Passed to ggplot

Visualize Summaries of Simulation Results

Description

Visualize Summaries of Simulation Results

Usage

sim_plot(x, ..., sample_sizes, stat = c("bias", "mc_var", "mse"))

Arguments

x

A list of several simulation summary objects, of class simulation_stats.

...

Extra arguments currently ignored.

sample_sizes

A numeric vector giving the sample sizes at which each of the simulations in the input x was performed. There should be one unique sample size corresponding to each element of x.

stat

A character indicating which of three simulation summary statistics for which to generate a plot. Options are currently limited to bias ("bias"), variance ("mc_var"), and mean-squared error ("mse").

Examples

n_sim <- 100
n_obs <- c(100, 10000)
mu <- 2
sim_results <- lapply(n_obs, function(sample_size) {
  estimator_sim <- lapply(seq_len(n_sim), function(iter) {
    y_obs <- rnorm(sample_size, mu)
    est_param <- mean(y_obs)
    est_var <- var(y_obs)
    estimate <- tibble::as_tibble(list(
      param_est = est_param,
      param_var = est_var
    ))
    return(estimate)
  })
  estimates <- do.call(rbind, estimator_sim)
  return(estimates)
})
sim_summary <- lapply(sim_results, summarize_sim, truth = mu)
p_sim_summary <- sim_plot(sim_summary, sample_sizes = n_obs, stat = "mse")
p_sim_summary

Summarize Simulations Results

Description

Summarize Simulations Results

Usage

summarize_sim(simulation_results, truth, ci_level = 0.95)

Arguments

simulation_results

A data.frame, tibble or similar with exactly two columns named "param_est" and "param_var" giving the estimate of a parameter of interest and estimate of its variance (based on a valid variance estimator specific to that parameter).Each row of this data structure corresponds to the parameter estimate and variance for a single iteration of several simulations.

truth

A numeric value giving the true value of the parameter of interest in the simulation setting.

ci_level

A numeric value giving the level of the confidence intervals to be generated around the parameter estimates and statistics computed to summarize the simulation.

Examples

n_sim <- 1000
n_obs <- c(100, 10000)
mu <- 2
sim_results <- lapply(n_obs, function(sample_size) {
  estimator_sim <- lapply(seq_len(n_sim), function(iter) {
    y_obs <- rnorm(sample_size, mu)
    est_param <- mean(y_obs)
    est_var <- var(y_obs) / sample_size
    estimate <- tibble::as_tibble(list(
      param_est = est_param,
      param_var = est_var
    ))
    return(estimate)
  })
  estimates <- do.call(rbind, estimator_sim)
  return(estimates)
})
sim_summary <- lapply(sim_results, summarize_sim, truth = mu)

A jet black theme with inverted colors

Description

A jet black theme with inverted colors

Usage

theme_jetblack(base_size = 12, base_family = "")

Arguments

base_size

Base font size

base_family

Base font family

Value

An object as returned by theme

See Also

theme

Examples

library(ggplot2)
p <- ggplot(mtcars, aes(y = mpg, x = disp, color = factor(cyl)))
p <- p + geom_point() + theme_jetblack()
p

Nima's plotting theme

Description

Nima's ggplot2 theme: white background, colors optimized

Usage

theme_nima(base_size = 14, base_family = "Helvetica")

nima_theme(base_size = 14, base_family = "Helvetica")

Arguments

base_size

Base font size

base_family

Base font family

Value

An object as returned by theme

See Also

theme

Examples

library(ggplot2)
p <- ggplot(mtcars, aes(y = mpg, x = disp, color = factor(cyl)))
p <- p + geom_point() + scale_fill_nima() + scale_color_nima()
p <- p + theme_nima()
p

Find Number of Unique Values

Description

Get the number of unique values in an input vector.

Usage

uniqlen(vec, na.rm = TRUE)

Arguments

vec

A vector of any type.

na.rm

If TRUE, remove missing values.

Value

Number of unique values.

Examples

x <- c(1, 3, 1, 1, NA, 2, 2, 3, NA, NA, 1, 3, 1)
uniqlen(x)
uniqlen(x, na.rm = FALSE)