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(color = "brown") +
  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

Coding Templates

Code templates can be used to avoid typing the same code over and over again.

• These are templates that we are using to automate things like plot appearance and documentation:
  • .Rmd Template
  • .R Script Template

R Resources

• 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

Workshop survey

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

Upcoming Workshops

Introduction to scATAC-seq Data Analysis
November 14 - November 15, 2024 1:00-4:00pm PST

Introduction to Linear Mixed Effects Models
November 18-November 19, 2024 1:00-3:00pm PST

scATAC-seq and scRNA-seq Data Integration
November 22, 2024 1:00-4:00pm PST

• Check this link at for the full schedule