What you will learn over the next 3 lectures

Loading, Transforming, Visualizing Tabular Data using Tidyverse packages

• readr
• ggplot2
• dplyr

Principles of Data Visualization (see book)

Example Datasets

Plate Reader Assay

Flow Cytometry

Raw Flow Cytometry Data

5 cols × 2,720,000 rows

Flow Cytometry Analysis Using Tidyverse

Tidyverse Functions for Working with Tabular Data

Use TSV and CSV file formats for tabular data

Tab-Separated Values:

strain   mean_yfp  mean_rfp  mean_ratio  se_ratio  insert_sequence  kozak_region 
schp674      1270     20316       0.561     0.004  10×AAG           CAAA         
schp675      3687     20438       1.621     0.036  10×AAG           CCGC         
schp676      2657     20223       1.177     0.048  10×AAG           CCAA         
schp677      3967     20604       1.728      0.03  10×AAG           CCAC         

Comma-Separated Values:

strain,mean_yfp,mean_rfp,mean_ratio,se_ratio,insert_sequence,kozak_region
schp674,1270,20316,0.561,0.004,10×AAG,CAAA
schp675,3687,20438,1.621,0.036,10×AAG,CCGC
schp676,2657,20223,1.177,0.048,10×AAG,CCAA
schp677,3967,20604,1.728,0.03,10×AAG,CCAC

Reading tabular data into R

library(tidyverse)

data <- read_tsv("data/example_dataset_1.tsv")

Read tabular data into a DataFrame (tibble)

library(tidyverse)

data <- read_tsv("data/example_dataset_1.tsv")

print(data, n = 5)

# A tibble: 16 x 7
  strain  mean_yfp mean_rfp mean_ratio se_ratio insert_sequence kozak_region
  <chr>      <dbl>    <dbl>      <dbl>    <dbl> <chr>           <chr>       
1 schp688     1748    20754      0.755    0.066 10×AGA          A           
2 schp684     3294    20585      1.44     0.021 10×AGA          B           
3 schp690     3535    20593      1.54     0.018 10×AGA          C           
4 schp687     4658    20860      2.00     0.021 10×AGA          D           
5 schp686     5000    21171      2.12     0.023 10×AGA          E           
# … with 11 more rows

Comment your code

# library to work with tabular data
library(tidyverse)

# read the tsv file into a tibble and 
# assign it to the 'data' variable
data <- read_tsv("data/example_dataset_1.tsv")

# display the contents of 'data' 
print(data, n = 5)

Plotting a point graph

ggplot(data, aes(x = kozak_region,
                 y = mean_ratio)) +
  geom_point()

How do we show multiple experimental parameters?

Plotting a point graph with color

ggplot(data, aes(x = kozak_region,
                 y = mean_ratio,
                 color = insert_sequence)) +
  geom_point()

Plotting a line graph

ggplot(data, aes(x = kozak_region,
                 y = mean_ratio,
                 color = insert_sequence,
                 group = insert_sequence)) +
  geom_line()

Plotting point and line graphs

ggplot(data, aes(x = kozak_region,
                 y = mean_ratio,
                 color = insert_sequence,
                 group = insert_sequence)) +
  geom_line() +
  geom_point()

‘Faceting’ – Plotting in multiple panels

ggplot(data, aes(x = kozak_region,
                 y = mean_ratio,
                 group = insert_sequence)) +
  geom_line() +
  geom_point() +
  facet_grid(~ insert_sequence)

Play time!

• Get used to the RStudio interface.
• Plot data and customize appearance.
• Learn how to "Knit" RMarkdown files.
• Learn more at https://ggplot2.tidyverse.org.