Tidyverse

• The tidyverse packages work well together because they share common data representations and design principles
  • Rows = observations, columns = variables
• ggplot2, for data visualization.
• dplyr, for data manipulation.
• tidyr, for data tidying.
• readr, for data import.
• purrr, for iteration.
• and more..

dplyr

• Offers a common “grammar” of functions for data manipulation
  • mutate() adds new variables that are functions of existing columns
  • select() picks columns based on their names
  • filter() picks rows based on their values
  • summarise() reduces multiple values down to a single summary
  • arrange() changes the ordering of the rows
  • group_by() allows any operation to be done “by group”

Example Dataframe

• mpg is a dataframe built into the ggplot2 package

head(mpg)

# A tibble: 6 × 11
  manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
  <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…

Select Columns

select(.data = mpg,
       year, cty, hwy, manufacturer)

# A tibble: 234 × 4
    year   cty   hwy manufacturer
   <int> <int> <int> <chr>       
 1  1999    18    29 audi        
 2  1999    21    29 audi        
 3  2008    20    31 audi        
 4  2008    21    30 audi        
 5  1999    16    26 audi        
 6  1999    18    26 audi        
 7  2008    18    27 audi        
 8  1999    18    26 audi        
 9  1999    16    25 audi        
10  2008    20    28 audi        
# ℹ 224 more rows

Filter Rows

filter(.data = mpg,
       year == 2008)

# A tibble: 117 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 audi         a4           2    2008     4 manu… f        20    31 p     comp…
 2 audi         a4           2    2008     4 auto… f        21    30 p     comp…
 3 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
 4 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
 5 audi         a4 quattro   2    2008     4 auto… 4        19    27 p     comp…
 6 audi         a4 quattro   3.1  2008     6 auto… 4        17    25 p     comp…
 7 audi         a4 quattro   3.1  2008     6 manu… 4        15    25 p     comp…
 8 audi         a6 quattro   3.1  2008     6 auto… 4        17    25 p     mids…
 9 audi         a6 quattro   4.2  2008     8 auto… 4        16    23 p     mids…
10 chevrolet    c1500 sub…   5.3  2008     8 auto… r        14    20 r     suv  
# ℹ 107 more rows

Arrange Rows

• desc() is used to arrange rows in descending order, the default is ascending

arrange(.data = mpg,
        desc(cty))

# A tibble: 234 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 volkswagen   new beetle   1.9  1999     4 manu… f        35    44 d     subc…
 2 volkswagen   jetta        1.9  1999     4 manu… f        33    44 d     comp…
 3 volkswagen   new beetle   1.9  1999     4 auto… f        29    41 d     subc…
 4 honda        civic        1.6  1999     4 manu… f        28    33 r     subc…
 5 toyota       corolla      1.8  2008     4 manu… f        28    37 r     comp…
 6 honda        civic        1.8  2008     4 manu… f        26    34 r     subc…
 7 toyota       corolla      1.8  1999     4 manu… f        26    35 r     comp…
 8 toyota       corolla      1.8  2008     4 auto… f        26    35 r     comp…
 9 honda        civic        1.6  1999     4 manu… f        25    32 r     subc…
10 honda        civic        1.8  2008     4 auto… f        25    36 r     subc…
# ℹ 224 more rows

Summarising data

• The dplyr summarise() function computes a table of summaries for a data frame
• group_by() groups the input data frame by the specified variable(s)
• Combining these two allows us to easily create summaries for different categorical groupings

Group and Summarise

• Get the mean and median city mileage within manufacturer

summarise(group_by(.data = mpg,
                   manufacturer),
          mean_cty = mean(cty),
          median_cty = median(cty))

# A tibble: 10 × 3
   manufacturer mean_cty median_cty
   <chr>           <dbl>      <dbl>
 1 audi             17.6       17.5
 2 chevrolet        15         15  
 3 dodge            13.1       13  
 4 ford             14         14  
 5 honda            24.4       24  
 6 hyundai          18.6       18.5
 7 jeep             13.5       14  
 8 land rover       11.5       11.5
 9 lincoln          11.3       11  
10 mercury          13.2       13  

The pipe operator |>

• Allows “chaining” of function calls to make code more readable

mpg |>
  group_by(manufacturer) |>
  summarise(mean_cty = mean(cty),
            median_cty = median(cty)) |>
  head(5)

# A tibble: 5 × 3
  manufacturer mean_cty median_cty
  <chr>           <dbl>      <dbl>
1 audi             17.6       17.5
2 chevrolet        15         15  
3 dodge            13.1       13  
4 ford             14         14  
5 honda            24.4       24  

ggplot2

• The most popular tidyverse package
• Create publication quality, highly customizable plots
  • See the R graph gallery for examples
• ggplots use “layers” to build, modify and overlap visualizations
  • Layers are added using the + symbol and can be added to an existing ggplot
• Many popular packages output ggplots which can then be easily modified by adding layers

Creating ggplots

Plot Example

ggplot(data = mpg,                         # Input dataframe
       mapping = aes(x = cty, y = hwy)) +  # Aesthetic mapping
  geom_point()                             # Point graph

Adding and Modifying Layers

ggplot(data = mpg,                         
       mapping = aes(x = cty, y = hwy)) +  
  geom_point() +
  geom_smooth(formula = y ~ x, method = "lm")

Dataset Description

• PanTHERIA
  • A global species-level data set of key traits of all known extant and recently extinct mammals compiled from literature
  • Used in macroecological and macroevolutionary research projects
  • Data is organized by taxonomic rank

Taxonomic Rank

Data Preview

Hands-on Analysis

• We will read in the data and explore if the trophic level has a significant impact on the adult body mass of mammals

Steps:
1. Combine and clean the data
2. Visualize adult body mass by trophic level
3. Check for overrepresented groups
4. Fit a simple linear model

Hands-on Analysis

• Open part_2.Rmd
• If you just want to follow along and not run code, open part2_filled_out.html

General Tips

• Follow any relevant institutional guidelines on using LLMs
• Always confirm ChatGPT’s outputs are correct
• Provide as much detail as possible about the problem in the 1st prompt
• Use separate chats for separate tasks/projects
• Try the ‘Custom Instructions’ function

Code Tips

• Commented R code yields better responses
• Provide the code and error message in the same prompt
• ChatGPT can work well to convert syntax and improve your code:
  • “Turn this loop into a function : [your code]”
  • “Is there a better way to do this : [your code]”
• Check out the file: example_code/1_convert_syntax_example.R for an example use case

Bioinformatics Questions

For any bioinformatics specific questions feel free to reach out to the Gladstone Bioinformatics Core.

• Email
  • bioinformatics@gladstone.ucsf.edu
• Slack channel #questions-about-bioinformatics
  • Contact us at the email above to be added to the channel

Debugging Errors

• Try searching the web by pasting the error message and any relevant keywords (package or function name)
• Websites like Stack Overflow and Posit Community Forum should have the most relevant answers
• If the problem is package specific, check the documentation and reach out to the authors using their preferred method

R

• R for Data Science
• Top 10 R Errors and How to Fix them
• R Markdown: The Definitive Guide
• ggplot2: elegant graphics for data analysis
• Advanced R

Statistics

• Data Analysis in R : This book has more statistics details than R for Data Science
• Generalized Linear Models
  
• Random Effects

RNA-seq Analysis

• RNA-seqlopedia : Everything you need to know about RNA-seq experiments
• RNA-seq Expression Units : Blog post on understanding common units
• Introduction to Single-Cell Analysis with Bioconductor : Covers the basics of scRNA-seq analysis in R

Dimensional Reduction

• Tutorial on PCA : PCA explained with R code examples
• Understanding UMAP : Short explanation with great visualizations, mainly useful for scRNA-seq analysis

Workshop survey

• Please fill out our workshop survey so we can continue to improve these workshops

Upcoming Workshops

Single Cell ATAC-Seq Data Analysis Part 2

• Check this link at the end of the summer for out fall workshop schedule

• Gladstone Bioinformatics Workshops - workshop wiki page for all of the workshops we offer