Chapter 7 Reading and data inspection

7.1 Reading data into R

7.1.1 The basics

There are several function in base R that exist to read data in, and the function in R you use will depend on the file format being read in. Below we have a table with the base R functions that can be used for importing some common text data types (plain text).

Data type Extension Function
Comma separated values csv read.csv()
Tab separated values tsv read.delim
Other delimited formats txt read.table()

For example, if we have text file where the columns are separated by commas (comma-delimited), you could use the function read.csv. However, if the data are separated by a different delimiter in a text file (e.g. “:”, “;”, ” “), you could use the generic read.table function and specify the delimiter (sep = " ") as an argument in the function.

7.1.2 Metadata

When working with large datasets, you will very likely be working with a “metadata” file which contains the information about each sample in your dataset.

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7.1.3 The read.csv() function

Let’s bring in the metadata file in our data folder (mouse_exp_design.csv) using the read.csv function.

First, check the arguments for the function using the ? to ensure that you are entering all the information appropriately:

?read.csv

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The first item on the documentation page is the function Description, which specifies that the output of this set of functions is going to be a data frame.

In usage, all of the arguments listed for read.table() are the default values for all of the family members unless otherwise specified for a given function. Let’s take a look at 3 examples:

  1. The separator

  • for read.table() sep = "" (space or tab)

  • for read.csv() sep = "," (a comma).

  1. The header - This argument refers to the column headers that may (TRUE) or may not (FALSE) exist in the plain text file you are reading in.

  • for read.table() header = FALSE (by default, it assumes you do not have column names)

  • for read.csv() header = TRUE (by default, it assumes that all your columns have names listed).

  1. The row.names - This argument refers to the rownames.

  • for read.table() row.names by default assumes that your rownames are not in the first column.

  • for read.csv() header = TRUE (by default, it assumes that your rownames are in the first column.

Note: this one is tricky because the default isn’t listed as such in the help file.

The take-home from the “Usage” section for read.csv() is that it has one mandatory argument, the path to the file and filename in quotations; in our case that is data/mouse_exp_design.csv

7.1.4 Create a data frame by reading in the file

Let’s read in the mouse_exp_design.csv file and create a new data frame called metadata.

metadata <- read.csv(file="data/mouse_exp_design.csv")

We can see if it has successfully been read in by running:

metadata
##          genotype celltype replicate
## sample1        Wt    typeA         1
## sample2        Wt    typeA         2
## sample3        Wt    typeA         3
## sample4        KO    typeA         1
## sample5        KO    typeA         2
## sample6        KO    typeA         3
## sample7        Wt    typeB         1
## sample8        Wt    typeB         2
## sample9        Wt    typeB         3
## sample10       KO    typeB         1
## sample11       KO    typeB         2
## sample12       KO    typeB         3

Exercise 1

  1. Read “project-summary.txt” in to R using read.table() with the appropriate arguments and store it as the variable proj_summary. To figure out the appropriate arguments to use with read.table(), keep the following in mind:

  • all the columns in the input text file have column name/headers

  • you want the first column of the text file to be used as row names

  • hint: look up the input for the row.names = argument in read.table()

  1. Display the contents of proj_summary in your console

7.2 Inspecting data structures

There are a wide selection of base functions in R that are useful for inspecting your data and summarizing it. Below is a non-exhaustive list of these functions:

The list has been divided into functions that work on all types of objects, some that work only on vectors/factors (1 dimensional objects), and others that work on data frames and matrices (2 dimensional objects).

All data structures - content display:

  • str(): compact display of data contents (similar to what you see in the Global environment)

  • class(): displays the data type for vectors (e.g. character, numeric, etc.) and data structure for dataframes, matrices

  • summary(): detailed display of the contents of a given object, including descriptive statistics, frequencies

  • head(): prints the first 6 entries (elements for 1-D objects, rows for 2-D objects)

  • tail(): prints the last 6 entries (elements for 1-D objects, rows for 2-D objects)

Vector and factor variables:

  • length(): returns the number of elements in a vector or factor

Dataframe and matrix variables:

  • dim(): returns dimensions of the dataset (number_of_rows, number_of_columns) [Note, row numbers will always be displayed before column numbers in R]

  • nrow(): returns the number of rows in the dataset

  • ncol(): returns the number of columns in the dataset

  • rownames(): returns the row names in the dataset

  • colnames(): returns the column names in the dataset

Let’s use the metadata file that we created to test out data inspection functions.

head(metadata)
##         genotype celltype replicate
## sample1       Wt    typeA         1
## sample2       Wt    typeA         2
## sample3       Wt    typeA         3
## sample4       KO    typeA         1
## sample5       KO    typeA         2
## sample6       KO    typeA         3
str(metadata)
## 'data.frame':    12 obs. of  3 variables:
##  $ genotype : chr  "Wt" "Wt" "Wt" "KO" ...
##  $ celltype : chr  "typeA" "typeA" "typeA" "typeA" ...
##  $ replicate: int  1 2 3 1 2 3 1 2 3 1 ...
dim(metadata)
## [1] 12  3
nrow(metadata)
## [1] 12
ncol(metadata)
## [1] 3
class(metadata)
## [1] "data.frame"
colnames(metadata)
## [1] "genotype"  "celltype"  "replicate"

Exercise 2

  • What is the class of each column in metadata (use one command)?

  • What is the median of the replicates in metadata (use one command)?

Exercise 3

  • Use the class() function on glengths and metadata, how does the output differ between the two?
  • Use the summary() function on the proj_summary dataframe, what is the median “rRNA_rate”?
  • How long is the samplegroup factor?
  • What are the dimensions of the proj_summary dataframe?
  • When you use the rownames() function on metadata, what is the data structure of the output?
  • [Optional] How many elements in (how long is) the output of colnames(proj_summary)? Don’t count, but use another function to determine this.