Package index • rtemis

All functions

any_constant(): Check for constant columns

available_supervised() available_clustering() available_decomposition(): Available Algorithms

available_draw(): Available Draw Functions

available_themes(): Print available rtemis themes

binmat2vec(): Binary matrix times character vector

calibrate.Classification(): Calibrate Binary Classification Models

calibrate.ClassificationRes: Calibrate Cross-validated Binary Classification Models

catrange(): Print range of continuous variable

catsize(): Print Size

check_data(): Check Data

choose_theme(): Select an rtemis theme

class_imbalance(): Class Imbalance

classification_metrics(): Classification Metrics

clean_colnames(): Clean column names

clean_names(): Clean names

cluster(): Perform Clustering

col2grayscale(): Color to Grayscale

col2hex(): Convert R color to hexadecimal code

color_adjust(): Adjust HSV Color

color_fade(): Fade color towards target

color_invertRGB(): Invert Color in RGB space

color_mix(): Create an alternating sequence of graded colors

color_op(): Simple Color Operations

colorgrad(): Color Gradient

crules(): Combine rules

ddSci(): Format Numbers for Printing

ddb_collect(): Collect a lazy-read duckdb table

ddb_data(): Read CSV using DuckDB

decomp(): Perform Data Decomposition

desaturate(): Pastelify a color (make a color more pastel)

describe(): Describe rtemis object

df_movecolumn(): Move data frame column

drange(): Set Dynamic Range

draw_3Dscatter(): Interactive 3D Scatter Plots

draw_bar(): Interactive Barplots

draw_box(): Interactive Boxplots & Violin plots

draw_calibration(): Draw calibration plot

draw_confusion(): Plot confusion matrix

draw_dist(): Draw Distributions using Histograms and Density Plots

draw_fit(): True vs. Predicted Plot

draw_graphD3(): Plot graph using networkD3

draw_graphjs(): Plot network using threejs::graphjs

draw_heat(): Heatmap with plotly

draw_heatmap(): Interactive Heatmaps

draw_leaflet(): Plot interactive choropleth map using leaflet

draw_pie(): Interactive Pie Chart

draw_protein(): Plot an amino acid sequence with annotations

draw_pvals(): Barplot p-values using draw_bar

draw_roc(): Draw ROC curve

draw_scatter(): Interactive Scatter Plots

draw_spectrogram(): Interactive Spectrogram

draw_table(): Simple HTML table

draw_ts(): Interactive Timeseries Plots

draw_varimp(): Interactive Variable Importance Plot

draw_volcano(): Volcano Plot

draw_xt(): Plot timeseries data

dt_describe(): Describe data.table

dt_get_column_attr(): Tabulate column attributes

dt_index_attr(): Index columns by attribute name & value

dt_inspect_type(): Inspect column types

dt_keybin_reshape(): Long to wide key-value reshaping

dt_merge(): Merge data.tables

dt_names_by_attr(): List column names by attribute

dt_names_by_class(): List column names by class

dt_nunique_perfeat(): Number of unique values per feature

dt_pctmatch(): Get N and percent match of values between two columns of two data.tables

dt_pctmissing(): Get percent of missing values from every column

dt_set_autotypes(): Set column types automatically

dt_set_clean_all(): Clean column names and factor levels in-place

dt_set_cleanfactorlevels(): Clean factor levels of data.table in-place

dt_set_logical2factor(): Convert data.table logical columns to factors

dt_set_one_hot(): Convert data.table's factor to one-hot encoding in-place

explain(): Explain Supervised

factor_NA2missing(): Factor NA to "missing" level

factorize(): Factor Analysis

fct_describe(): Describe factor

features(): Get features (all columns except the last one)

fwhm2sigma(): FWHM to Sigma

getfactornames(): Get factor/numeric/logical/character names from data.frame/data.table

get_factor_levels(): Get factor levels from data.frame or similar

get_loaded_pkg_version(): Get version of all loaded packages (namespaces)

get_mode(): Get the mode of a factor or integer

get_vars_from_rules(): Extract variable names from rules

getnames() getnumericnames() getlogicalnames() getcharacternames() getdatenames(): Get names by string matching

getnamesandtypes(): Get data.frame names and types

ggtheme_dark(): rtemis ggplot2 dark theme

ggtheme_light(): rtemis ggplot2 light theme

gmean(): Geometric mean

`%BC%`: Binary matrix times character vector

graph_node_metrics(): Node-wise (i.e. vertex-wise) graph metrics

gt_table(): Greater-than Table

init_project_dir(): Initialize Project Directory

inspect_type(): Inspect character and factor vector

is_constant(): Check if vector is constant

is_discrete(): Check if variable is discrete (factor or integer)

labelify(): Format text for label printing

list2csv(): Write list elements to CSV files

lotri2edgeList(): Connectivity Matrix to Edge List

make_path(): Expand, normalize, concatenate, clean path

matchcases(): Match cases by covariates

mgetnames(): Get names by string matching multiple patterns

multigplot(): Multipanel ggplot2 plots

nCr(): n Choose r

one_hot2factor(): Convert one-hot encoded matrix to factor

outcome(): Get last column as a vector

permute(): Create permutations

pfread(): Read delimited file in parts

plot(<Classification>): Plot Classification

plot(<Regression>): Plot Regression

plot(<Supervised>): Plot Supervised

plot(<SupervisedRes>): Plot SupervisedRes

plot_roc(): Plot ROC curve

plot_true_pred.Classification(): Plot True vs. Predicted for Classification

plot_true_pred.ClassificationRes(): Plot True vs. Predicted for ClassificationRes

plot_true_pred.Regression(): Plot True vs. Predicted for Regression

plot_true_pred.RegressionRes(): Plot True vs. Predicted for RegressionRes

plot_varimp(): Plot Variable Importance

predict(<nullmod>): rtemis internal: predict for an object of class nullmod

preprocess(): Preprocess Data

present(): Present rtemis object

present.list(): Present list of Supervised or SupervisedRes objects

previewcolor(): Preview color

psd(): Population Standard Deviation

qstat(): SGE qstat

read(): Read tabular data from a variety of formats

recycle(): Recycle values of vector to match length of target

regression_metrics(): Regression Metrics

resample(): Resample data

rnormmat(): Random Normal Matrix

rt_reactable(): View table using reactable

rtemis rtemis-package: rtemis: Machine Learning and Visualization

rtpalette(): Color Palettes

rule_dist(): Rule distance

rules2medmod(): Convert rules from cutoffs to median/mode and range

runifmat(): Random Uniform Matrix

set_outcome(): Move outcome to last column

setdiffsym(): Symmetric Set Difference

setup_CART(): Setup CART Hyperparameters

setup_CMeans(): Setup CMeansParameters

setup_GAM(): Setup GAM Hyperparameters

setup_GLM(): Setup GLM Hyperparameters

setup_GLMNET(): Setup GLMNET Hyperparameters

setup_GridSearch(): Setup Grid Search Parameters

setup_HardCL(): Setup HardCLParameters

setup_ICA(): setup_ICA

setup_Isotonic(): Setup Isotonic Hyperparameters

setup_KMeans(): Setup KMmeansParameters

setup_LightCART(): Setup LightCART Hyperparameters

setup_LightGBM(): Setup LightGBM Hyperparameters

setup_LightRF(): Setup LightRF Hyperparameters

setup_LightRuleFit(): Setup LightRuleFit Hyperparameters

setup_NMF(): Setup NMF parameters.

setup_NeuralGas(): Setup NeuralGasParameters

setup_PCA(): Setup PCA parameters.

setup_Preprocessor(): Setup PreprocessorParameters

setup_RadialSVM(): Setup RadialSVM Hyperparameters

setup_Resampler(): Setup Resampler

setup_TabNet(): Setup TabNet Hyperparameters

setup_UMAP(): Setup UMAP parameters.

setup_tSNE(): Setup tSNE parameters.

sge_submit(): Submit expression to SGE grid

size(): Size of matrix or vector

sparsernorm(): Sparse rnorm

summarize(): Summarize numeric variables

synth_multimodal(): Create "Multimodal" Synthetic Data

synth_reg_data(): Synthesize Simple Regression Data

table1(): Table 1

theme_black() theme_blackgrid() theme_blackigrid() theme_darkgray() theme_darkgraygrid() theme_darkgrayigrid() theme_white() theme_whitegrid() theme_whiteigrid() theme_lightgraygrid() theme_mediumgraygrid(): Themes for draw_* functions

train(): Train Supervised Learning Models

uci_heart_failure: UCI Heart Failure Data

uniprot_get(): Get protein sequence from UniProt

uniquevalsperfeat(): Unique values per feature

vec2df(): Vector to data.frame

winsorize(): Winsorize vector

xlsx2list(): Read all sheets of an XLSX file into a list

xtdescribe(): Describe longitudinal dataset

zip2longlat(): Get Longitude and Lattitude for zip code(s)

zipdist(): Get distance between pairs of zip codes