![r pca column r pca column](https://i.stack.imgur.com/6M4T7.png)
Now we could certainly do correlations, multiple linear regressions, or fit other types of models and would likely gain some useful insights, but instead let’s focus on the PCA. On the other hand, murder arrest rates seem to be unchanged based on urban population. Rape arrest rates follow the linear model much closer than the others but there is still a lot of variability. There appears to be an increase in assault arrests as urban population grows, however, there is a lot of variability around the line of best fit. Simple linear models are fit for each of the different crime types to see if any pattern can be seen in the data. In this figure, each crime rate is plotted against percentage urban population. This figure is a slightly more informative than the last one. Title = "Arrest rate vs percentage urban population") + Ggplot(aes(urbanpop, rate, color = crime)) +įacet_wrap(~crime, scales = "free_y", ncol = 1) + Gather(key = crime, value = rate, c(murder, assault, rape)) %>% You can see that Georgia has the highest murder rate, followed by Mississippi, Louisiana, and Florida Let’s try one more. Title = "Murder rate in each state of the USA") Labs(y = "Murder Arrest Rate per 100,000 people", Theme( = element_text(angle = 90, hjust = 1, vjust = 0.4)) + State = fct_reorder(state, murder) %>% fct_rev()) %>% Let’s take a look at murder arrest rates in each of the stats. # I prefer column names to be all lowercase so I am going to change them here Since tibbles do not support rownames, we will have to convert them to their own column with rownames_to_column. Before we do that, let’s convert the data set to a tibble. Now, that we see how the data set is set up, let’s try to visualize the data as it is.
![r pca column r pca column](https://bookdown.org/brian_nguyen0305/Multivariate_Statistical_Analysis_with_R/_main_files/figure-html/unnamed-chunk-93-1.png)
![r pca column r pca column](https://clauswilke.com/blog/2020/09/07/pca-tidyverse-style/figure-html/pc-scatter-plot-1.png)
Looking at the first 6 rows using the head() function, we can see that each row is a state and and each column is a variable. Another nice walkthough of PCA with this dataset that is online can be found at University of Cincinnati’s R blog.īefore we dive in to the analysis, we want to explore our data set and become familiar with it. In this blog post, my focus will be more on implementing the PCA in the tidyverse framework.
#R PCA COLUMN HOW TO#
This is how I learned how to do PCA and would highly recommend it if you are unfamiliar with the topic. In this book, they work through a PCA and focus on the statistics and explanations behind PCA. For this post, I will be using the USArrests data set that was used in An Introduction to Statistical Thinking by Gareth James et. In my answer, I used the iris data set to demonstrate how PCA can be done in the tidyverse workflow. While there are the same number of principal components created as there are variables (assuming you have more observations than variables-but that is another issue), each principal component explains the maximum possible variation in the data conditional on it being orthogonal, or perpendicular, to the previous principal components. This is a method of unsupervised learning that allows you to better understand the variability in the data set and how different variables are related. Essentially, it allows you to take a data set that has n continuous variables and relate them through n orthogonal dimensions. PCA is a multi-variate statistical technique for dimension reduction. While most questions and answers are good as they are on forum sites, I thought this one might be worth exploring a little more since using the tidyverse framework makes PCA much easier, in my opinion. Conveniently, I had literally just worked through this process the day before and was able to post an answer. The other day, a question was posted on RStudio Community about performing Principal Component Analysis (PCA) in a tidyverse workflow.