If you've started a new session since last time:

library(tidyverse)

• Missing values might be given a value (e.g. 9999, "NA")
• Column names might be missing
• File may have comments
• Irrelevant columns
• May be structural issues in the file (Different column numbers)
• White-space in cells

• Make sure the file transport.csv is in your data folder
• Navigate to the file in your Files pane
• Click on the file and choose View File

What problems do we face here?

What problems do we face here?

1. A comment in the first line
2. No column names
3. Missing data encoded as "-" in the 4^th line
4. A redundant column

Let's try writing code for this instead of using the GUI

This will fail

transport <- read_csv("data/transport.csv")

• R uses the first row to guess how many columns there are
  • The comment is indicating 1 column

• We can tell R to ignore any lines beginning with #
• Set the comment argument using comment = "#"

transport <- read_csv("data/transport.csv", comment = "#")
transport

Now R is guessing the correct number of columns \(\implies\) the file will load

What does all that red (or blue) stuff mean?

• R has assumed the first row contains column names
• We can tell R to ignore these using: col_names = FALSE

transport <- read_csv("data/transport.csv", 
                      comment = "#", 
                      col_names = FALSE)
transport

What has R used for column names?

What impact has the missing data in X5 had?

• We can correctly assign missing data as NA (na = "-")

transport <- read_csv("data/transport.csv", 
                      comment = "#", 
                      col_names = FALSE, 
                      na = "-")
transport

• We can also use the code - to skip a column
• We can leave R to guess any remaining columns using ?
• Use the col_types argument: col_types = "-?????"

transport <- read_csv("data/transport.csv",
                      comment = "#", 
                      col_names = FALSE, 
                      na = "-", 
                      col_types = "-?????")
transport

• Or we can specify the exact type of data in each column
• Numeric columns can be specified as n
• Text or character columns can be specified as c

transport <- read_csv("data/transport.csv", 
                      comment = "#", 
                      col_names = FALSE, 
                      na = "-", 
                      col_types = "-ccnnc")
transport

• Let's accidentally get the final column wrong (use n instead of c)

transport <- read_csv("data/transport.csv",
                      comment = "#", 
                      col_names = FALSE, 
                      na = "-",
                      col_types = "-ccnnn")
transport

NB: No warning will be given if a numeric column contains non-numeric characters

Let's change that back to the correct code:

transport <- read_csv("data/transport.csv", 
                      comment = "#", 
                      col_names = FALSE, 
                      na = "-", 
                      col_types = "-ccnnc")
transport

• My fix is to supply a vector of names

myNames <- c("gender", "name", "weight", "height", "method")
transport <- read_csv("data/transport.csv", 
                      comment = "#", 
                      col_names = myNames, 
                      na = "-", 
                      col_types = "-ccnnc")
transport

The most common function in R is c()

• This stands for combine
• Combines all values into a single R object, or vector
• If left empty, it is equivalent to NULL

c()

• I have used this to create a vector of column names
• Used the assignment operator <- to assign this vector

What would happen if I gave too many or too few names?

• We need to be careful here…

After we've edited a file, we might also wish to export it

?write_csv

• This is a wrapper for write_delim()
• Can export .csv, .txt, .tsv etc.
• Individual R objects can be exported using write_rds()

The best way to export this is:

write_csv(transport, "data/transport_clean.csv")

Download the file geneCounts.out (output from featureCounts)

1. Try to import this using the GUI
2. Try to import by using read_delim()
   • Set this to skip the Chr, Start, End and Strand Columns
   • Try to modify the column names using the function basename()
   • Remove _Fem_hisat2_sorted.bam from the end of the column names