Description
A Toolkit for Behavioral Scientists.
Description
A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.
README.md
kim: A Toolkit for Behavioral Scientists
This package contains various functions that simplify and expedite analyses of experimental data. Examples include a function that plots sample means of groups in a factorial experimental design, a function that conducts robust regressions with bootstrapped samples, and a function that conducts robust two-way analysis of variance.
Installation
You can install the released version of kim from CRAN with:
install.packages("kim")
You can also install the development version from kim on GitHub with:
install.packages("remotes")
remotes::install_github("jinkim3/kim")
If you run into errors while using the package, try updating the package to the most recent version available on kim on GitHub with:
update_kim()
Example
Here are some examples of using this package.
library(kim)
# (Optional) install all dependencies for all functions in Package 'kim'
install_all_dependencies()
# update the package 'kim', clear the console and environment,
# set up working directory to location of the active document,
# and load the two default packages ('data.table' and 'ggplot2')
start_kim()
# create a scatter plot
scatterplot(data = mtcars, x_var_name = "wt", y_var_name = "mpg")
# get descriptive statistics by group
desc_stats_by_group(
data = mtcars, var_for_stats = "mpg", grouping_vars = c("vs", "am"))
# plot histograms by group
histogram_by_group(data = mtcars, iv_name = "cyl", dv_name = "mpg")
# plot sample means of groups in a factorial experimental design
plot_group_means(data = mtcars, dv_name = "mpg", iv_name = "gear")
# conduct a two-way ANOVA
two_way_anova(
data = mtcars, dv_name = "mpg", iv_1_name = "vs", iv_2_name = "am")
# conduct a multiple regression analysis
multiple_regression(data = mtcars, formula = mpg ~ gear * cyl)
# conduct a robust regression analysis using bootstrapped samples
robust_regression(data = mtcars, formula = mpg ~ cyl * hp)
# conduct a mediation analysis
mediation_analysis(
data = mtcars, iv_name = "cyl", mediator_name = "disp", dv_name = "mpg")
# conduct a floodlight analysis for a 2 x continuous design
floodlight_2_by_continuous(
data = mtcars, iv_name = "am", dv_name = "mpg", mod_name = "qsec")