The goal of the this package is to provide users with a fast and easy method of conducting Heartbeat Analysis on Numeric Likert data with a single function. Heartbeat Analysis itself is a method of gauging sentiment of respondents at the item level by using within-group methods to highlight when an individual’s response is significantly higher or lower than their own typical baseline. The analysis was developed and presented at SIOP 2021 by Christopher Patton and Justin Purl.
A more in-depth explanation of arguments used within the package as well as the math behind the analysis and how results can be used is included in the vignette “introduction.rmd” or online: Click here
Installation
Installation of the package can be done by copying the line of code below.
devtools::install_github("PatrickGreen93/Heartbeat")Example
Below is a basic example of how the code works and a reproducible data set.
library(Heartbeat)
# Sample data set, set 30 items as it is a good amount to set a theoretical "baseline".
set.seed(43)
n <- 30
y <- 100
example <- data.frame()
for (i in 1:n){
x <- data.frame(sample(1:5, y, replace = TRUE, prob = c(0.1, 0.2, 0.4, 0.2, 0.1)))
colnames(x) <- paste0("Q",i)
example[1:y,i] <- x
}
example[1:5,1:5]
#> Q1 Q2 Q3 Q4 Q5
#> 1 2 4 3 4 3
#> 2 1 4 2 1 3
#> 3 3 4 2 4 4
#> 4 4 2 2 4 3
#> 5 3 2 3 3 3
example <- heartbeat(example, type = "counts", threshold = 1, props = FALSE, controversial = TRUE)
example[1:5,]
#> Item Down_Vote Neutral Up_Vote Controversial
#> 1 Q1 14 75 11 0.2933333
#> 2 Q2 18 68 14 0.4117647
#> 3 Q3 22 59 19 0.6440678
#> 4 Q4 16 68 16 0.4705882
#> 5 Q5 18 70 12 0.3428571