This article describes replicating the calculation of Components/Dimensions values derived from the Principal Component Analysis analysis in Displayr. The final output matches the values created by Displayr when following the steps outlined here: How to Save Components/Dimensions from a Dimension Reduction Output.
Requirements
Please note these steps require a Displayr license.
 A dimension reduction output created in Displayr such as Principal Component Analysis, Correspondence Analysis, Multidimensional Scaling or tSNE.
See: How to Do Principal Component Analysis in Displayr.
Method
The below steps assume that the Principal Component Analysis is weighted. If you don't require a weight, you can create a weight of 1 for all respondents. This will return the same outputs as an unweighted analysis. To create a weight of 1 for all respondents:

 Hover over the Data Sources tree and select + > Custom Code > JavaScript  Numeric.
 Input a value of 1 into the JavaScript Code window.
 Click Calculate.
 Select Usable as a weight.
 Select the PCA output, go to Data > Filters & Weight, and select the weight from the dropdown.
The first step of the process requires us to obtain the respondent data and weights from the PCA analysis and compute the weighted mean and standard deviation.

 Select Calculation > Custom Code from the toolbar and draw an output on the page.
 Go to General > R Code and input the below code:
#Define your input mode name
Note: the first of the code assumes the PCA output is called
input.model = dim.reduce
#Obtain the respondent level data and weights
respondent.data = input.model$data.used$subset.data
weights = input.model$data.used$subset.weights
#Function to calculate the weighted response level data and standard devition
weightedMeanAndSD < function(x, weights)
{
complete.cases < !is.na(x) & weights > 0
wx < x * weights
weighted.mean < Sum(wx[complete.cases], remove.missing = FALSE) / Sum(weights[complete.cases], remove.missing = FALSE)
sx < x  weighted.mean
wsx2 < weights * sx * sx
weighted.variance < Sum(wsx2[complete.cases], remove.missing = FALSE) / (Sum(weights[complete.cases], remove.missing = FALSE)  1)
return(list(weighted.mean = weighted.mean, weighted.sd = sqrt(weighted.variance)))
}
weighted.mean.and.sd = lapply(respondent.data, FUN = function(x, weights) { unlist(weightedMeanAndSD(x, weights)) }, weights = weights)
#Return the table wihh the outputs
weighted.mean.and.sd = do.call(rbind, weighted.mean.and.sd)dim.reduce
.  The code will return an output like the one below. Select the output and select Copy > Copy Data from the Tools menu in the upper left corner.
 Paste the data into Excel.
Next, we will need to obtain the score weights from the PCA.

 Select Calculation > Custom Code from the toolbar and draw an output on the page.
 Go to Properties > R Code and input the below code:
#Define your input mode name
input.model = dim.reduce
#Obtain the weights
score.weights = input.model$score.weights  The code will return an output like the one below. Select the output and select Copy > Copy Data from the toolbar.
 Paste the data into Excel.
In the next step, we will obtain the respondentlevel data used in the PCA.

 Select Calculation > Custom Code from the toolbar and draw an output on the page.
 Go to Properties > R Code and input the below code:
dim.reduce$data.used
.  Select the output and select Copy > Copy Data from the toolbar.
 Paste the data into Excel.
For each component, do the sumproduct of the scaled respondent data and the component scores for that component (see Excel file with Calculations).
 To check the values match those obtained using Save Variable(s) > Components/ Dimensions, select
Scores from dim.reduce
variable set from the Data Sources tree, rightclick > View in Data Editor.
And compare it to the outputs calculated in Excel:
Next
How to Do Principal Component Analysis in Displayr
How to Create a Principal Component Analysis Biplot
How to Create a Dimension Reduction Scatterplot
How to Create a Component Plot from a Principal Component Analysis
How to Create a Goodness of Fit Plot from a Dimension Reduction Output
How to Create a Scree Plot from a Principal Component Analysis
How to Do Multidimensional Scaling