LittleHelpers

Continuously updated collection of little helpers (tm) that facilitates my life in analyzing data (mostly comparative datasets) with R.

Use remotes::install_github("maksimrudnev/LittleHelpers") to install.

Overview

• Multilevel helpers
• Multigroup helpers
• Tools for labelled data and Rstudio viewer
• deprecated - Pipe helpers [see gist]
• deprecated - Values, Schwartz, ESS
• Miscellaneous

Multilevel helpers

Explore multilevel data:

• cor_within prints and plots individual correlations within each group.
• cor_between computes means and shows group-level correlation between two variables.
• scatter_means_ci Computes means by group and plots on scatterplot against each other (shows country-level correlations).
• graph_means_ci Plots means by group.
• stacked_bar Computes proportions cross-table and plots them in a nice way, returns ggplot object, so any further +theme(), +scale_x(), etc. codes can be added.

Recode multilevel data:

• aggr_and_merge helps to create group-level variables from individual-level variables and merge them back to the data.frame on the go.
• grand_center Quick grand-mean centering.
• group_center Quick group-mean centering.

Summarize and visualize multilevel regressions:

• good_table/lmer_table Function that creates customizable coefficients tables using multiple lmer models; outputs in Rstudio viewer.
• potential_interactions Exploratory. If you have no idea what cross-level interactions to look for. Computes pairwise tests of all the possible interactions in the lmer() model, or simply shows correlations between random effects and group-level variables.
• random_interaction Plots cross-level interactions for lmer()-fitted models. Customizable. Can automatically choose real moderator values close to mean+-(2)SD.
• random_plot Plots random effects from lmer()-fitted models.
• plef Quick interaction plot for simple models

Compute extra stats for multilevel regressions:

• explained_variance.merMod Computes psudo-R-square for two-level regressions fitted with lmer().
• vif_mer Compute variance inflation factor for multilevel regressions fitted with lmer().

Multigroup helpers

• lavTestScore_clean Wrapper around lavaan::lavTestScore(), merging parameter labels with parameters and groups names and adding stars. Useful when you decide with between-group contraints might be relaxed.

• mgcfa_diagnose Print comprehensible output to diagnose problems with MGCFA models.

• [[ Moved to MIE package under the name globalMI() ]] mi_test Series of measurement invariance tests, analogous to semTools::measurementInvariance().

• See also Measurement invariance explorer

Tools for labelled data and Rstudio viewer

Know the labels:

• label_book/label_table Creates a codebook for data.frames with labels.

Make use of labels:

• cor_table Prints ready-to-publish correlation tables with significance stars.
• crosstab Simple cross-tabulation with labels.

Get rid of labels and other tidyverse attributes:

• drop_labs Drops labels if you don't need them.
• untibble Get rid of tibble and get clean data.frame.
• lab_to_fac Converts labelled variables to factors.

Make use of Rstudio viewer:

• df_to_viewer Puts any data.frame to RStudio viewer. Also works with models and anything that can be passed through stargazer or kable.

Mplus Automation

• traceplots_mplus Extracts Bayesian data from data Mplus, draws the trace plots and autocorrelation plots, and saves in a single pdf.
• checkMplusModel Checks if the output contains any negative variances and correlations higher than 1. Also can print errors and warnings. Handy when working with many models.
• diffTestMLR_ Computes Likelihood ratio test for estimators like MLR.
• getParamsMplus Extracts parameters from Bayesian models produced by Mplus where MplusAutomation fails.
• partable_mplus Collects parameters from several models and ,erges them into a single table (persuing the idea of stargazer/semTable for Mplus).

Miscellaneous

• reverse Recodes variable in reverse order. Works with labels.
• replace_by_table Useful for recoding when matching tables are alsready specified in a table. Particularly useful for translation.
• mean_se_lower_upper Simply mean, SE, upper and lower 95% CI.
• verb Simply prints its arguments.
• theme_mr Clean theme for ggplot.

Values, Schwartz, ESS

• values list of value labels.
• download_ess Download European Social Survey data

• ess_values Computes 2, 4, or 10 value indices as they are measured in ESS.