![]() MoreGratiSD <- round(sd(datMeans$MoreGrati, na.rm = FALSE), digits = 2) MoreGratiMean <- round(mean(datMeans$MoreGrati), digits = 2) Satisfiedmax <- round(max(datMeans$Satisfied, na.rm = FALSE), digits = 2)ĭatMeans <- ame(aggregate(MoreGrati ~ PID, dat, mean)) Satisfiedmin <- round(min(datMeans$Satisfied, na.rm = FALSE), digits = 2) SatisfiedSD <- round(sd(datMeans$Satisfied, na.rm = FALSE), digits = 2) SatisfiedMean <- round(mean(datMeans$Satisfied), digits = 2) datMeans <- ame(aggregate(Distract1 ~ PID, dat, mean))ĭistract1Mean <- round(mean(datMeans$Distract1), digits = 2)ĭistract1SD <- round(sd(datMeans$Distract1, na.rm = FALSE), digits = 2)ĭistract1min <- round(min(datMeans$Distract1, na.rm = FALSE), digits = 2)ĭistract1max <- round(max(datMeans$Distract1, na.rm = FALSE), digits = 2)ĭatMeans <- ame(aggregate(ActCon ~ PID, dat, mean))ĪctConMean <- round(mean(datMeans$ActCon), digits = 2)ĪctConSD <- round(sd(datMeans$ActCon, na.rm = FALSE), digits = 2)ĪctConmin <- round(min(datMeans$ActCon, na.rm = FALSE), digits = 2)ĪctConmax <- round(max(datMeans$ActCon, na.rm = FALSE), digits = 2)ĭatMeans <- ame(aggregate(ActEnj ~ PID, dat, mean))ĪctEnjMean <- round(mean(datMeans$ActEnj), digits = 2)ĪctEnjSD <- round(sd(datMeans$ActEnj, na.rm = FALSE), digits = 2)ĪctEnjmin <- round(min(datMeans$ActEnj, na.rm = FALSE), digits = 2)ĪctEnjmax <- round(max(datMeans$ActEnj, na.rm = FALSE), digits = 2)ĭatMeans <- ame(aggregate(Satisfied ~ PID, dat, mean)) In the table above the data expected to update are those in the ‘Mean’, ‘SD’, ‘Min’, and ‘Max’ columns (the ‘Possible Range’ column specifies the possible Likert scale range of each variable), for any changes in the participant inclusion criteria, how the variables are calculated, etc, should lead to changes in variable mean, SD, etc. Your first step to creating a table like this in your reproducible manuscript is to ensure the data that will update within your table (given prior dependent code changes) is saved as an R object. See below for an example of this table from my own research. This table type is arguably the most common in academic manuscripts, often being used to display descriptive data and/or correlations amongst study variables. The table I will show you how to create has one header row for the column names, and one left-oriented column for the row names. ![]() I provide a walkthrough of this alternative method here. ![]() Nevertheless, I stumbled upon an alternative method to creating an APA formatted table with Papaja that wasn’t specified in the formal guide, and which some may find useful in light of its flexible nature. If followed, researchers should quite easily be able to create APA formatted manuscripts that automatically update given prior dependent code changes. The official Papaja guide gives instruction on how to use Papaja within R Markdown. This is very useful as it ensures the LaTeX version of the manuscript automatically generated after knitting the R Markdown document can be used for submission to academic journals that demand APA formatting. ![]() Papaja is an excellent R package that enables reproducible academic manuscripts created in R Markdown (where R code and prose are fused in one place) to align with APA formatting requirements.
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