Day 1: Introduction to the CCR, and to R • RPG520

Connecting to the CCR

We’ll use the UB CCR computer cluster for the workshop. This (hopefully) simplifies initial steps in ‘getting going’, while introducing the main resource available when your desktop is not sufficient. See the appendix if you would like to install R and RStudio on your own laptop.

Visit and sign in, using your UB id, from a Roswell Park or UB network: https://ondemand.ccr.buffalo.edu

The next screen will ask for your UB password, and two-factor authentication.

From CCR to RStudio

Use your browser to navigate to https://ondemand.ccr.buffalo.edu/. Look under the “GUIs” section, and choose ‘RStudio Server’.
Provide details on the next screen, then click ‘Launch’

‘Cluster’: choose “Faculty - privately owned nodes” (these are ‘Roswell’ nodes)
- ‘Slurm account’: choose your user name.
- ‘Partition’: choose ‘rpci’.
- ‘Quality of Service (QOS)’: choose ‘rpci’
- ‘Number of hours requested’: choose 2; you’ll be unceremoniously kicked off after this number of hours, so save any work-in-progress.

Clicking the ‘Launch’ button puts your request for compute resources into a queue. You have to wait. Not too long. Hopefully.
Eventually the CCR finds resources for your job, and the queue status changes to ‘Running’, with a link to ‘Connect to RStudio Server’. Click on the link to get to your own session of RStudio.

RStudio

We’ll use RStudio extensively. To get going, copy and paste the following line into the ‘Console’

source('/projects/rpci/rplect/Rpg520/Rsetdirs.R')

Finally, tell RStudio about the changes made by the previous command by navigating to the lower-right panel, choosing the ‘gear’ widget’, and selecting ‘Go To Working Directory’, as in the following figure

OK, we are ready to go!

Introduction to R

Essential R

A simple calculator

1 + 1
## [1] 2

‘Vectors’ as building blocks

c(1, 2, 3)
## [1] 1 2 3
c("January", "February", "March")
## [1] "January"  "February" "March"
c(TRUE, FALSE)
## [1]  TRUE FALSE

Variables, missing values and ‘factors’

age <- c(27, NA, 32, 29)
gender <- factor(
    c("Female", "Male", "Non-binary", NA),
    levels = c("Female", "Male", "Non-binary")
)

Data structures to coordinate related vectors – the data.frame

df <- data.frame(
    age = c(27, NA, 32, 29),
    gender = gender
)
df
##   age     gender
## 1  27     Female
## 2  NA       Male
## 3  32 Non-binary
## 4  29       <NA>

Key operations on data.frame

df[1:3, c("gender", "age")] – subset on rows and columns
df[["age"]], df$age – select columns

Functions

rnorm(5)        # 5 random normal deviates
## [1] -2.4923741  0.9173004  0.4857963  0.8885560  0.4324102
x <- rnorm(100) # 100 random normal deviates
hist(x)         # histogram, approximately normal

plot(density(x)) # a little more sophisticated?

‘Vectorized’ operations, e.g., element-wise addition without an explicit ‘for’ loop. The following creates a vector y by element-wise addition of the 100 elements of vector x with 100 random normal deviates.

y <- x + rnorm(100)

x and y are related, so place them as columns in a data.frame(). There are 100 rows in the data.frame(), head() shows the first 6 rows.

df <- data.frame(x, y)
nrow(df)
## [1] 100
head(df)
##            x           y
## 1 -1.8175809 -1.68495792
## 2  0.2875356  0.25773796
## 3 -1.5832023 -1.65202212
## 4 -1.6701220 -0.13451413
## 5 -0.5075023  0.01547938
## 6 -1.0523105  1.67789046

There is a statistical relationship between y and x, specifically, y is equal to x plus some random noise. Visualize this using plot().

plot(y ~ x, df)

It looks like a linear relationship; can we demonstrate this statistically? Use the lm() function to fit a linear model to the relationship between y and x:

fit <- lm(y ~ x, df)
fit           # an R 'object' containing information about the
## 
## Call:
## lm(formula = y ~ x, data = df)
## 
## Coefficients:
## (Intercept)            x  
##     0.07458      1.04282
              # regression of y on x
summary(fit)  # statistical summary of linear regression
## 
## Call:
## lm(formula = y ~ x, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.20765 -0.65162 -0.08072  0.57617  2.70068 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07458    0.10273   0.726     0.47    
## x            1.04282    0.10479   9.951   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.018 on 98 degrees of freedom
## Multiple R-squared:  0.5026, Adjusted R-squared:  0.4975 
## F-statistic: 99.03 on 1 and 98 DF,  p-value: < 2.2e-16

What about adding the regression line to the scatter plot? Do this by plotting the points, and then adding abline() to add the straight line implied by the linear model fit.

plot(y ~ x, df)
abline(lm(y ~ x, df))

A little more advanced…

Write your own functions

hello <- function(who) {
    paste("hello", who, "with", nchar(who), "letters in your name")
}
hello("Martin")
## [1] "hello Martin with 6 letters in your name"

Iterate, usually with lapply() although for() is available

names <- c("Martin", "Thomas")
lapply(names, hello)
## [[1]]
## [1] "hello Martin with 6 letters in your name"
## 
## [[2]]
## [1] "hello Thomas with 6 letters in your name"

Packages

Packages extend functionality of base R. Can be part of the ‘base’ distribution (lm() is from the ‘stats’ package)…

stats::lm(y ~ x)
## 
## Call:
## stats::lm(formula = y ~ x)
## 
## Coefficients:
## (Intercept)            x  
##     0.07458      1.04282

… or a package contributed by users to the Comprehensive R Archive Network (CRAN), or to Bioconductor or other repositories.

CRAN – more than 20,000 packages (!) https://CRAN.R-project.org
- Use install.packages() to install CRAN packages
Bioconductor – 2266 packages for high-througput genomic data analysis https://bioconductor.org
- First install BiocManager from CRAN, then use BiocManager::install() to install Bioconductor packages.

A particularly common suite of packages is the ‘tidyverse’. To use the dplyr contributed CRAN package, load the package

library(dplyr)

A dplyr ‘tibble’ is like a ‘data.frame’, but more user-friendly

tbl <- tibble(
    x = rnorm(100),
    y = x + rnorm(100)
)

tbl # e.g., only displays the first 10 rows
## # A tibble: 100 × 2
##         x      y
##     <dbl>  <dbl>
##  1  0.456  2.08 
##  2  1.07   0.761
##  3 -1.05  -1.10 
##  4 -0.314  0.126
##  5  0.234  0.924
##  6  0.929  0.777
##  7 -1.12  -1.57 
##  8 -0.742 -0.886
##  9  2.55   0.673
## 10 -0.465  1.13 
## # ℹ 90 more rows

The ggplot2 package is often used for visualizing data

library(ggplot2)

ggplot2 uses a ‘grammar of graphics’ to assemble a plot in layers

ggplot(tbl) +                    # use 'tbl' as a source of data for the plot
    aes(x, y) +                  # use columns 'x' and 'y' for axes
    geom_point() +               # add x and y coordinates as points
    geom_smooth(method = "lm")   # add a regression line, with confidence bands
## `geom_smooth()` using formula = 'y ~ x'

How to Learn R

Help

Every function has a help page, e.g.,
```
?data.frame
```
- Sections with ‘Description’, ‘Usage’, ‘Arguments’ (often with defaults), ‘Value’, ‘Examples’
- Can be quite difficult to follow, but are usually quite helpful.

Vignettes

Many packages have ‘vignettes’ that illustrate how the package can be used.

vignette(package = "dplyr")
## Vignettes in package 'dplyr':
##
## colwise                 Column-wise operations (source, html)
## base                    dplyr <-> base R (source, html)
## grouping                Grouped data (source, html)
## dplyr                   Introduction to dplyr (source, html)
## programming             Programming with dplyr (source, html)
## rowwise                 Row-wise operations (source, html)
## two-table               Two-table verbs (source, html)
## in-packages             Using dplyr in packages (source, html)
## window-functions        Window functions (source, html)

vignette(package = "dplyr", "dplyr")

Books

Hands-on Programming with R introduces R programming. The Very Basics provides an elementary starting point.
R for Data Science is a book-length web spanning with both introductory and more advanced topics. One of the authors, Hadley Wickham, is the author of the dplyr and ggplot2 packages, among others.

Web resources

Google can be a great place to find general R resource, e.g., ‘how to do survival analysis in R’
StackOverflow is useful for trouble-shooting particular problems – ‘I try to fit a linear model with lm(mgp ~ cyl, mtcars) but R says …, what am I doing wrong?’.
ChatGPT & friends can also be useful, especially if asking well-focused questions or, e.g., asked to ‘explain the following R code’ to help understand code someone else has written. I have almost always disagreed with the specific code ChatGPT generates; sometimes it simply does not work (e.g., use the package ‘X’, but ‘X’ is not a package), but more dangerous are the situations where code is syntactically correct but provides the wrong answer. Use, but with extreme care!

Grading for this week

The task for this week is to write an R script that performs data management and statistical analysis of a data set of your choice – essentially reproducing selected steps in the work that we will do on Wednesday.

You will provide me (on the CCR server) with a file ‘your_name.R’. I will run the file in a new R session using the command source('your_name.R', echo = TRUE). This will read and evaluate each R command in the file.

Grading will be out of 10, with the following thresholds

7 / 10 if source('your_name.R', echo = TRUE) works without error.
8 / 10 for scripts that implement more extensive analyses, or that present interesting or complicated data.
up to 10 / 10 for work that goes beyond the material we cover this week, e.g., using ‘Rmarkdown’ to present your work, performing additional (and appropriate) data manipulation steps using dplyr or other packages, additional visualizations or statstical analyses.

Note that the 7 / 10 score could be obtained by copying & pasting (a subset of) the commands from Wednesday’s lab into ‘your_name.R’. Please feel free to contact or speak with me if you have problems or ideas.

Session information

For reproducibility, I record the software versions used to create this document

sessionInfo()
## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.5.0 dplyr_1.1.4  
## 
## loaded via a namespace (and not attached):
##  [1] Matrix_1.6-5      gtable_0.3.4      jsonlite_1.8.8    compiler_4.3.3   
##  [5] highr_0.10        tidyselect_1.2.0  stringr_1.5.1     jquerylib_0.1.4  
##  [9] splines_4.3.3     scales_1.3.0      systemfonts_1.0.5 textshaping_0.3.7
## [13] yaml_2.3.8        fastmap_1.1.1     lattice_0.22-5    R6_2.5.1         
## [17] labeling_0.4.3    generics_0.1.3    knitr_1.45        tibble_3.2.1     
## [21] desc_1.4.3        munsell_0.5.0     bslib_0.6.1       pillar_1.9.0     
## [25] rlang_1.1.3       utf8_1.2.4        cachem_1.0.8      stringi_1.8.3    
## [29] xfun_0.42         fs_1.6.3          sass_0.4.8        memoise_2.0.1    
## [33] cli_3.6.2         mgcv_1.9-1        withr_3.0.0       pkgdown_2.0.7    
## [37] magrittr_2.0.3    digest_0.6.34     grid_4.3.3        nlme_3.1-164     
## [41] lifecycle_1.0.4   vctrs_0.6.5       evaluate_0.23     glue_1.7.0       
## [45] farver_2.1.1      ragg_1.2.7        colorspace_2.1-0  fansi_1.0.6      
## [49] rmarkdown_2.25    purrr_1.0.2       tools_4.3.3       pkgconfig_2.0.3  
## [53] htmltools_0.5.7