Naive Principal Component Analysis in R

Naive Principal Component Analysis in R

A post from Pablo Bernabeu’s blog.

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set the those yourself, based on whatever prior information or purposes you had. I will tackle the naive method, mainly by following the guidelines in Field, Miles, and Field (2012), with updated code where necessary. This lecture material was also useful.

The ‘naive’ approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables. I ascribe these to the ‘naive’ or formal approach because either or both could potentially be skipped in exceptional circumstances, where the purpose is not scientific, or where enough information exists in advance.

STAGE 1.  Determine whether PCA is appropriate at all, considering the variables

  • Variables should be inter-correlated enough but not too much. Field et al. (2012) provide some thresholds, suggesting that no variable should have many correlations below .30, or any correlation at all above .90. Thus, in the example here, variable Q06 should probably be excluded from the PCA.
  • Bartlett’s test, on the nature of the intercorrelations, should be significant. Significance suggests that the variables are not an ‘identity matrix’ in which correlations are a sampling error.
  • KMO (Kaiser-Meyer-Olkin), a measure of sampling adequacy based on common variance (so similar purpose as Bartlett’s). As Field et al. review, ‘values between .5 and .7 are mediocre, values between .7 and .8 are good, values between .8 and .9 are great and values above .9 are superb’ (p. 761). There’s a general score as well as one per variable. The general one will often be good, whereas the individual scores may more likely fail. Any variable with a score below .5 should probably be removed, and the test should be run again.
  • Determinant: A formula about multicollinearity. The result should preferably fall below .00001.

Note that some of these tests are run on the dataframe and others on a correlation matrix of the data, as distinguished below.

# Necessary libraries

# Select your variables of interest: only those must be used
dataset = mydata[, c(‘select_var1′,’select_var1′,’select_var2’,

# Create matrix: some tests will require it
data_matrix = cor(dataset, use = ‘complete.obs’)
# See intercorrelations
round(data_matrix, 2)

# Bartlett’s


# Determinant

STAGE 2. Identify number of components (aka factors)

In this stage, principal components (formally called ‘factors’ at this stage) are identified among the set of variables.

  • The identification is done through a basic, ‘unrotated’ PCA. The number of components set a priori must equal the number of variables that are being tested.
# Start off with unrotated PCA  
pc1 = psych::principal(dataset, nfactors = length(dataset), rotate="none")

Below, an example result:

## Principal Components Analysis
## Call: psych::principal(r = eng_prop, nfactors = 3, rotate = “none”)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 h2 u2 com
## Aud_eng -0.89 0.13 0.44 1 -2.2e-16 1.5
## Hap_eng 0.64 0.75 0.15 1 1.1e-16 2.0
## Vis_eng 0.81 -0.46 0.36 1 -4.4e-16 2.0
## PC1 PC2 PC3
## SS loadings 1.87 0.79 0.34
## Proportion Var 0.62 0.26 0.11
## Cumulative Var 0.62 0.89 1.00
## Proportion Explained 0.62 0.26 0.11
## Cumulative Proportion 0.62 0.89 1.00
## Mean item complexity = 1.9
## Test of the hypothesis that 3 components are sufficient.
## The root mean square of the residuals (RMSR) is 0
## with the empirical chi square 0 with prob < NA
## Fit based upon off diagonal values = 1

Among the columns, there are first the correlations between variables and components, followed by a column (h2) with the ‘communalities‘. If less factors than variables had been selected, communality values would be below 1. Then there is the uniqueness column (u2): uniqueness is equal to 1 minus the communality. Next is ‘com’, which reflects the complexity with which a variable relates to the principal components. Those components are precisely found below. The first row contains the sums of squared loadings, or eigenvalues, namely, the total variance explained by each linear component. This value corresponds to the number of units explained out of all possible factors (which were three in the above example). The rows below all cut from the same cloth. Proportion var = variance explained over a total of 1. This is the result of dividing the eigenvalue by the number of components. Multiply by 100 and you get the percentage of total variance explained, which becomes useful. In the example, 99% of the variance has been explained. Aside from the meddling maths, we should actually expect 100% there because the number of factors equaled the number of variables. Cumulative var: variance added consecutively up to the last component. Proportion explained: variance explained over what has actually been explained (only when variables = factors is this the same as Proportion var). Cumulative proportion: the actually explained variance added consecutively up to the last component.

Two sources will determine the number of components to select for the next stage:

  • Kaiser’s criterion: components with SS loadings > 1. In our example, only PC1.

A more lenient alternative is Joliffe’s criterion, SS loadings > .7.

  • Scree plot: the number of points after point of inflexion. For this plot, call:
plot(pc1$values, type = 'b')

Imagine a straight line from the first point on the right. Once this line bends considerably, count the points after the bend and up to the last point on the left. The number of points is the number of components to select. The example here is probably the most complicated (two components were finally chosen), but normally it’s not difficult.

Based on both criteria, go ahead and select the definitive number of components.

STAGE 3.  Run definitive PCA

Run a very similar command as you did before, but now with a more advanced method. The first PCA, a heuristic one, worked essentially on the inter-correlations. The definitive PCA, in contrast, will implement a prior shuffling known as ‘rotation’, to ensure that the result is robust enough (just like cards are shuffled). Explained variance is captured better this way. The go-to rotation method is the orthogonal, or ‘varimax’ (though others may be considered too).

# Now with varimax rotation, Kaiser-normalized by default:
pc2 = psych::principal(dataset, nfactors=2, rotate = “varimax”,
scores = TRUE)

# Healthcheck

We would want:

  • Less than half of residuals with absolute values > 0.05
  • Model fit > .9
  • All communalities > .7

If any of this fails, consider changing the number of factors. Next, the rotated components that have been ‘extracted’ from the core of the set of variables can be added to the dataset. This would enable the use of these components as new variables that might prove powerful and useful (as in this research).

dataset <- cbind(dataset, pc2$scores) 
summary(dataset$RC1, dataset$RC2)

STAGE 4.  Determine ascription of each variable to components

Check the main summary by just calling pc2, and see how each correlates with the components. This is essential because it reveals how the variable loads on each component, or really, to which component how component each variable belongs. When PCAs work out well, a cut-off point of r = .8 may be applied for considering a variable as part of a component.

STAGE 5.  Enjoy the plot

The plot is perhaps the coolest part about PCA. It really makes an awesome illustration of the power of data analysis.

aes(RC1, RC2, label = as.character(main_eng))) +
aes (x = RC1, y = RC2, by = main_eng) + stat_density2d(color = “gray87”)+
geom_text(size = 7) +
ggtitle (‘English properties’) +
theme_bw() +
plot.background = element_blank()
,panel.grid.major = element_blank()
,panel.grid.minor = element_blank()
,panel.border = element_blank()
) +
theme(axis.line = element_line(color = ‘black’)) +
theme(axis.title.x = element_text(colour = ‘black’, size = 23,
axis.title.y = element_text(colour = ‘black’, size = 23,
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16)) +
labs(x = “”, y = “Varimax-rotated Principal Component 2”) +
theme(plot.title = element_text(hjust = 0.5, size = 32, face = “bold”,

Below is an example combining PCA plots with code similar to the above. The properties in both languages spread the ‘a’, ‘h’, and ‘v’ categories much more clearly than the concepts do (see more).

An example of this code is use is available here (with data here).


Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.

Feel free to comment below or on the original post.

Naive Principal Component Analysis in R

Online data presentation: What does it add?

Online data presentation: What does it add?

Interfaces for online data presentation, such as Tableau or Shiny, enable a programming-free, interactive exploration of data via plots or tables, through a dashboard with drop-down lists or checkboxes. The apps can be useful for both the data analyst and the public.

Data presentation interfaces run on internet browsers: This allows for private consultation of the data as well as online publication. There are two main platforms: R Shiny and Tableau. Shiny has a free starter license with limited use, where free apps can handle a certain amount of data, and up to five apps may be created. Beyond that, RStudio offers a wide range of subscriptions starting at $9/month. For its part, Tableau in principle deals only with subscriptions from $35/month on. However, they offer free 1-year licenses to students, instructors, and NGO volunteers (further comparisons of these platforms are posted online).

Apps may serve a wide range of purposes. For instance, at a time of open sciencewhen publicly archiving data is becoming standard practice among researchers (e.g.,,,, data presentation interfaces can be used to let anyone delve into data sets at once without programming. It’s the right tool for acknowledging all facets of the data (of course, apps do not replace raw data archiving).

Apps can prove valuable even to those doing the first analyses. For instance, mine help me to identify the extent of noise in a section of the data. Instead of running through every section of the data set in a heavy score of code, the drop-down lists of the app let me seamlessly surf the data. So, I ran into this data section that was very noisy—systematically more than the rest—, and eventually I had to discard it from the statistical analyses. Yet, instead of removing it from the app, I maintained it, with a note. This example of use concerned a rather salient trait in the data, but another app might help someone spot patterns such as individual differences or third variables.

Building an app is not difficult if you already code. I will now focus on R Shiny because it is a bit trickier than Tableau. Shiny apps draw on some data presentation code (tables, plots) that you already have. Then you add in a couple of scripts: one for the user interface (named iu.R), one for the process (named server.R), and perhaps another one compiling the commands for deploying the app and checking any errors. On deploying, a folder named ‘rsconnect’ is automatically created. This folder contains a text file which can be used to modify the URL of the app and the title of the webpage.

The steps to start a Shiny app from scratch are:

1: Tutorials. Being open-source software, the best manuals are available through a Google search.

2: User token. Signing up and reading in your private key—just once.

3: GO. Plunge into the ui and server scripts, and deployApp().

4: Bugs and logs. They are not bugs in fact—rather fancies. For instance, some special characters have to get even more special (technically, UTF-8 encoding). For a character such as μ, Shiny prefers Âμ. Cling to your logs by calling:

showLogs(appPath = getwd(), appFile = NULL, appName = NULL, account = NULLentries = 50, streaming = FALSE)

At best, the log output will mention any typos and unaccepted characters, pointing to specific lines in your code. 

It may take a couple of intense days to get a Shiny app running. As usual with programming, it’s easy to run into the traps which are there to spice up the way. Shiny’s been around for years, so tips and tricks abound online. For greater companionship, there are dedicated Google groups, and then StackOverflow etc., where you can post any needs/despair. Post your code, log, and explanation, and you’ll be rescued out in a couple of days. Long live those contributors.

It’s sometimes enough to upload a bare app, but you might then think it can look better.

5 (optional): Advance. Use tabs to combine multiple apps on one webpage, use different widgets, etc. For instance, the function conditionalPanel() is used to modify the app’s sidebar based on which tab is selected. Apps can include any text, such as explanations of any length or web links. Tutorials can take you there, especially those that provide the code, like a good one on YouTube. Use previous scripts as templates.

Last, I’ll just mention a third alternative platform, D3.js (Javascript-based), which allows for lush graphics but is also more demanding.

Feel free to share any ideas, experiences or doubts in a comment below, or on the original blog.

Online data presentation: What does it add?