timesaveR <img src='man/figures/logo.png' align="right" height="139" />

This package aims to speed up research projects in social psychology (and related fields). For that, it primarily includes some functions that help lay the groundwork and others that facilitate the reporting of results.

Among others, the package can help with the following:

Creating scales, including reverse-coding and reporting their internal consistency,
Creating correlation tables in APA style, including descriptive statistics and confidence intervals, and an option to use survey weights, multiple imputation or full-information maximum-likelihood estimation
Creating regression tables comparing standardised and non-standardised regression coefficients, and comparing the F-change between two nested models
Formatting numbers for statistical reporting, including rounding with trailing zeros, or displaying numbers as p-values or as confidence intervals
Data entry and transfer, for quick interactive use. This includes splitting clipboard content into vectors, converting tibbles to tribble code, or obtaining nicely formatted code and results to paste into another application

Why use this package?

There are many packages that support data analysis and reporting. For instance, the psych package offers functions to create scales, while the modelsummary package offers options to create customisable tables in a wide variety of output format. They power many of the functions offered here ‘under the hood.’

apa and papaja are two packages that directly support the reporting of results in APA style - they can complement this package well. However, none of the existing offered quite what we needed. This package

takes an end-to-end view of the data analysis process, streamlining time-consuming steps at various stages
offers analysis templates that make it easy to get started, particularly for R novices,
prioritises publication-readiness and good reporting practices over customisability in creating tables and charts
integrates with the tidyverse by supporting tidy evaluation and returning tibbles where possible

Installation

You can install timesaveR from GitHub with the command below. If you do not have the remotes-package installed, run install.packages("remotes") first.

remotes::install_github('lukaswallrich/timesaveR')

Get started

There are many functions in the package, and we will create vignettes detailing various use cases. However, the following can give you an initial flavor. The examples use data from the European Social Survey Wave 7 (2014). Here, I ignore survey weights. However, the package offers similar functions for analysing weighted survey data, which are explained in the survey data vignette.

Load the package

(I also load dplyr since that is the recommended usage - of course, there are base R alternatives for all of the steps.)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.

Create scales

Let’s create scales for health behaviours and depressive symptoms, each including some reverse coding.

scales <- list(
  depression = c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl", 
                 "enjlf", "fltsd", "cldgng"),
  healthy_eating = c("etfruit", "eatveg")
  )
  
scales_reverse <- list(
  depression = c("wrhpp", "enjlf"),
  healthy_eating = c("etfruit", "eatveg")
)

scales <- make_scales(ess_health, items = scales, reversed = scales_reverse)
#> The following scales will be calculated with specified reverse coding: depression, healthy_eating


#Check descriptives, including reliability
scales$descriptives
#> # A tibble: 2 × 10
#>   Scale      n_items reliability reliability_method  mean    SD reversed rev_min
#>   <chr>        <int>       <dbl> <chr>              <dbl> <dbl> <chr>      <dbl>
#> 1 depression       8       0.802 cronbachs_alpha     1.67 0.484 wrhpp e…       1
#> 2 healthy_e…       2       0.658 spearman_brown      4.97 1.11  etfruit…       1
#> # ℹ 2 more variables: rev_max <dbl>, text <chr>

#Add scale scores to dataset
ess_health <- bind_cols(ess_health, scales$scores)

Report correlations and descriptive statistics

Next, we are often interested in descriptive statistics, variable distributions and correlations.

ess_health %>% select(agea, health, depression, healthy_eating) %>% 
    cor_matrix() %>% report_cor_table()

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TimesaveR

Install / Use

README