/ Recent content on Jung-Jin Lee's blog Hugo -- gohugo.io en-us Fri, 01 Jan 2021 00:00:00 +0000 /2021/01/01/drexel-lectures-spring-2021/ Fri, 01 Jan 2021 00:00:00 +0000 /2021/01/01/drexel-lectures-spring-2021/ Lecture 1 Homework 1 Lecture 2 Homework 2 Lecture 3 Homework 3 Lecture 4 Homework 4 Lecture 5 Homework 5 Lecture 6 Homework 6 Lecture 7 Homework 7 Lecture 8 Homework 8 Lecture 9 Homework 9 Lecture 10 Homework 10 Lecture 11 Homework 11 Lecture 12 /2020/07/24/r-exercises/ Fri, 24 Jul 2020 00:00:00 +0000 /2020/07/24/r-exercises/ Sum of squares \(\blacksquare \square \square \square \square\) Number of digits \(\blacksquare \square \square \square \square\) Vector filtering \(\blacksquare \square \square \square \square\) Maximum sepal length \(\blacksquare \blacksquare \square \square \square\) Central polygonal numbers \(\blacksquare \blacksquare \blacksquare \square \square\) Taxi fare \(\blacksquare \blacksquare \square \square \square\) Collatz sequence \(\blacksquare \blacksquare \blacksquare \square \square\) Iris scatter plot \(\blacksquare \blacksquare \square \square \square\) Tallest human character in Starwars \(\blacksquare \blacksquare \square \square \square\) /2020/04/25/fdr-adjusted-p-values/ Sat, 25 Apr 2020 00:00:00 +0000 /2020/04/25/fdr-adjusted-p-values/ In this post, we look into the so-called FDR-adjusted \(p\)-values implemented by p.adjust() with method = "BH". To this end, we create a list of \(n=20\) nominal \(p\)-values and rank them in increasing order: library(tidyverse) library(ggrepel) library(ggsci) nom_pval <- c(0.01600000, 0.05865014, 0.08350898, 0.08783160, 0.06777848, 0.08206850, 0.01800000, 0.09725057, 0.00200000, 0.00010000, 0.04862445, 0.00300000, 0.01500000, 0.07574901, 0.05959104, 0.01900000, 0.01700000, 0.00400000, 0.02000000, 0.06827007) n <- length(nom_pval) d <- tibble(sorted_pval = sort(nom_pval), rank = 1:n) Set the significance level \(\alpha=0. /2020/01/04/drexel-lectures-spring-2020/ Sat, 04 Jan 2020 00:00:00 +0000 /2020/01/04/drexel-lectures-spring-2020/ Lecture 1 Lecture 2 Homework 1 Lecture 3 Lecture 4 Homework 2 Lecture 5 Lecture 6 Homework 3 Lecture 7 Lecture 8 Homework 4 Lecture 9 Lecture 10 Homework 5 Lecture 11 Lecture 12 Homework 6 Lecture 13 /2019/10/25/distance-between-groups/ Fri, 25 Oct 2019 00:00:00 +0000 /2019/10/25/distance-between-groups/ library(usedist) library(tidyverse) library(ggforce) library(ggsci) library(gt) library(ggrepel) library(ggdendro) First, we make a fake data set: make_fake <- function(group, n) { tibble(x = rnorm(n, sample(10, 1), sample(2, 1)), y = rnorm(n, sample(10, 1), sample(2, 1)), group = group) } set.seed(2019) df <- map(LETTERS[1:5], make_fake, n = 6) %>% bind_rows() %>% group_by(group) %>% mutate(SampleID = paste0(group, 1:n())) %>% ungroup() Visualization of the data: ggplot(df, aes(x, y)) + geom_point(aes(shape = group), show.legend = FALSE) + geom_mark_hull(aes(label = group, fill = group), show. /2019/10/20/exploring-rstan/ Sun, 20 Oct 2019 00:00:00 +0000 /2019/10/20/exploring-rstan/ Unit 1 /2019/09/28/compositional-bar-plots/ Sat, 28 Sep 2019 00:00:00 +0000 /2019/09/28/compositional-bar-plots/ We don’t make mistakes, just happy little accidents. First of all, download a publicly available dataset from kaggle https://www.kaggle.com/bbhatt001/human-microbiome-project library(tidyverse) d0 <- read_csv("project_catalog.csv") dim(d0) ## [1] 2915 17 names(d0) ## [1] "HMP ID" "GOLD ID" ## [3] "Organism Name" "Domain" ## [5] "NCBI Superkingdom" "HMP Isolation Body Site" ## [7] "Project Status" "Current Finishing Level" ## [9] "NCBI Submission Status" "NCBI Project ID" ## [11] "Genbank ID" "Gene Count" ## [13] "IMG/HMP ID" "HOMD ID" ## [15] "Sequencing Center" "Funding Source" ## [17] "Strain Repository ID" table(d0$`HMP Isolation Body Site`) ## ## airways blood bone ## 236 75 27 ## ear eye gastrointestinal_tract ## 4 8 745 ## heart liver lymph_nodes ## 2 1 1 ## nose oral other ## 2 347 13 ## skin unknown urogenital_tract ## 267 654 529 ## wound ## 4 We subset the data to include only body sites with sufficient data: /2019/03/17/anova-and-anova/ Sun, 17 Mar 2019 00:00:00 +0000 /2019/03/17/anova-and-anova/ After spending hours of despair and confusion, I came across an excellent illustration given at Stack Exchange. There are various \(p\)-values coming out of lm(), anova(), and Anova(). First of all, car library is loaded for use of Anova(): library(car) # for Anova() The built-in mtcars will be used as an example. attach(mtcars) head(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21. /2019/02/10/arithmetic-sequences-of-prime-numbers/ Sun, 10 Feb 2019 00:00:00 +0000 /2019/02/10/arithmetic-sequences-of-prime-numbers/ Today, I saw a question that goes as follows: If \(\{a,b,c,d,e\}\) is an increasing arithmetic sequence of five prime numbers, what is the least possible value of \(|a-b|\)? Let’s write the sequence as \(\{a, a+k, a+2k, a+3k, a+4k\}\), where \(k \in \mathbb{N}\). It is easy to see that \(a\neq 2\). It is also easy to see that \(k\) cannot be odd. Assume that \(k=2\) and consider the remainders of \(\{a, a+2, a+4, a+6, a+8\}\) when divided by \(3\). /2019/01/10/pcoa-of-points-in-euclidean-space/ Thu, 10 Jan 2019 00:00:00 +0000 /2019/01/10/pcoa-of-points-in-euclidean-space/ knitr::opts_chunk$set( message = F ) library(tidyverse) library(ggrepel) library(ape) # for pcoa() Original points in \(\mathbb{R}^2\) are generated: set.seed(2019) n <- 15 d0 <- data.frame(ID = 1:n, x = rnorm(n), y = rnorm(n)) g <- ggplot(d0, aes(x, y)) + geom_point() + geom_text_repel(aes(label = ID), size = 3, segment.color = "grey") + theme(aspect.ratio = 1) print(g) DistMat <- d0 %>% select(-ID) %>% dist() PCoA_Result <- pcoa(DistMat) d <- PCoA_Result$vectors %>% as.data.frame() %>% mutate(ID = 1:n) g <- ggplot(d, aes(Axis. /2018/12/15/drexel-lectures-spring-2019/ Sat, 15 Dec 2018 00:00:00 +0000 /2018/12/15/drexel-lectures-spring-2019/ Lecture 1 Lecture 2 Homework 1 Lecture 3 Lecture 4 Homework 2 Lecture 5 Lecture 6 Homework 3 Lecture 7 Lecture 8 Homework 4 Lecture 9 Lecture 10 Homework 5 Lecture 11 Lecture 12 /2018/12/12/shannon-diversity-index/ Wed, 12 Dec 2018 00:00:00 +0000 /2018/12/12/shannon-diversity-index/ In ecology, the Shannon diversity of a community is defined by \[H=-\sum_{i}^R p_i\log p_i,\] where \(R\) is the number of distinct species and \(p_i\) is the proportion of species \(i\). The Shannon diversity measures richness (i.e. the number of distinct species in a community) and evenness (i.e. how evenly the proportions of species are spread). In the following illustration, each dot represents a mock community: the horizontal coordinate shows the number of distinct species (richness) and the color shows the various distributions of proportions given the number of distinct species. /2018/12/08/principal-component-analysis/ Sat, 08 Dec 2018 00:00:00 +0000 /2018/12/08/principal-component-analysis/ A mock data set consisting of 100 data points, say \(\mathbf{P}_1, \cdots, \mathbf{P}_{100}\), is generated below. They depict a perturbed and slanted ellipse in \(\mathbb{R}^3\). Let \(X_{raw}\) be the \(100 \times 3\) matrix whose rows consist of \(\mathbf{P}_1, \cdots, \mathbf{P}_{100}\). set.seed(2018) n <- 100 ## Create a perturbed and slanted ellipse in R^3 x_center <- 3; y_center <- 1; z_center <- 2 x_stretch <- 4; y_stretch <- 3; z_stretch <- 1 angle <- pi/3 x_ori <- x_stretch*cos(2*pi*(1:n)/n) + rnorm(n, mean = 0, sd = 0. /2018/11/27/cabrini-lectures-spring-2019/ Tue, 27 Nov 2018 00:00:00 +0000 /2018/11/27/cabrini-lectures-spring-2019/ Lecture 1 Lecture 2 Homework 1 Lecture 3 Lecture 4 Homework 2 Lecture 5 Lecture 6 Homework 3 Lecture 7 Lecture 8 Homework 4 Lecture 9 Lecture 10 Homework 5 /2018/11/17/netlify-blog-workflow/ Sat, 17 Nov 2018 00:00:00 +0000 /2018/11/17/netlify-blog-workflow/ R package blogdown과 Netlify를 이용한 블로그 개설/관리 과정을 여기에 기록해 둠 How to create a blog using blogdown and Netlify Create a new repository: https://github.com/junglee0713/blog2020 Clone the repository, e.g. cd /Users/leej39/Dropbox/GitHub git clone git@github.com:junglee0713/blog2020.git (optional) Create an R project, say /Users/leej39/Dropbox/GitHub/blog2020/blog2020.Rproj. (do only once) Open RStudio and install necessary packages, following instruction in https://bookdown.org/yihui/blogdown/installation.html install.packages("blogdown") blogdown::install_hugo() Make sure that /Users/leej39/Dropbox/GitHub/blog2020 does not include files other than LICENSE, the RStudio project file (*.