# Chapter 3 Creating scripts and importing data

Our analysis is of an example data set of observations for 7702 proteins from cells in three control experiments and three treatment experiments. The observations are signal intensity measurements from the mass spectrometer. These intensities relate the concentration of protein observed in each experiment and under each condition.

We consider raw data as the data as we receive it. This doesn’t mean it hasn’t be processed in some way, it just means it hasn’t been processed by us. Generally speaking we don’t change the raw data file, what we do is import it and create an object in R which we then transform.

So let’s understand how to import some data.

## 3.1 Some definitions

• Importing means getting data into our R environment by creating an object that we can then manipulate. The raw data file remains unchanged.
• Inspecting means looking at the dataset to understand what it contains.
• Tidying refers to getting data into a consistent format that makes it easy to use in later steps.

### 3.1.1 Rectangular data and flat formats

Two further things to note:

1. Here we are only considering rectangular data, the sort that comes in rows and columns such as in a spreadsheet. Lots of our data types exist, such as images, but can also be handled by R. As mentioned in 3.5.1 genomic data in particular has led to a project called Bioconductor for the development of analysis tools primarily in R, many of which deal with non-rectangular data, but this is beyond the scope here.

2. Flat formats are files that only contain plain text, with each line representing a set of observations and the variables separated by delimiters such as tabs, commas or spaces. Therefore there aren’t multiple tables such as we’d get in an Excel file, or meta-data such as the colour highlighting of a cell in an Excel file. The advantages of flat files is that they can be opened and used by many different computing languages or programs. So unless there is a good reason not to use a flat format, and there are good reasons, they are the best way to store data in many situations.

## 3.2 Using scripts

Using the console is useful, but as we build up a workflow, that is to say, writing code to:

• explore the data
• and output some results

Then it’s much more useful to contain this in a script: a document of our code.

Why? When we write and save our code in scripts, we can re-use it, share it or edit it. But most importantly a script is a record.

Cmd/Ctrl + Shift + N will open a new script file up and you should see something like Figure 3.1 with the script editor pane open:

## 3.3 Running code

We can run a highlighted portion of code in your script if you click the Run button at the top of the scripts pane as shown in Figure 3.2.

You can run the entire script by clicking the Source button.

Or we can run chunks of code if we split our script into sections, see below.

## 3.4 Creating a R script

We first need to create a script that will form the basis of our analysis.

Go to the file menu and select New Files > R script. This should open the script editor pane.

Now let’s save the script, by going to File > Save and we should find ourselves prompted to save the script in our Project Directory.

Following the advice about naming things we can create a new R script called 01-bspr-workshop-july-2018.

This name is machine readable (no spaces or special characters), human readable, and works well with default ordering by beginning with 01.

## 3.5 Setting up our environment

At the head of our script it’s common to put a title, the name of the author and the date, and any other useful information. This is created as comments using the # at the start of each line.

It’s then usual to follow this by code to load the packages we need into our our R environment using the library() function and providing the name of the package we wish to load. Packages are collections of R functions.

Often we break the code up into regions by adding dashes (or equals symbols) to the comment line. This enables us to run chunks of the script separately from running the whole script when using our code.

Here is a typical head for a script:

# My workshop script
# 7th July 2018
# Alistair Bailey

library(plyr)
library(tidyverse)
library(gplots)
library(pheatmap)
library(gridExtra)
library(VennDiagram)
library(ggseqlogo)

### 3.5.1 Bioconductor

As an aside there are many proteomics specific R packages, these are generally found through Bioconductor which is a project that was initiated in 2001 to create tools for the analysis of high-throughput genomic data, but also includes other ’omics data tools (Gentleman et al. 2004, @huber2015).

Exploring Bioconductor is beyond our scope here, but well worth exploring for manipulation and analysis of raw data formats such as mzxml files.

## 3.6 Importing data

Assuming our data is in a flat format, we can import it into our environment using the tidyverse readr package.

If our data was an excel file, we can use the tidyverse readxl package to import the data, but it will remove any meta-data and each table in the excel file will become a separate R object as per tidy data principles.

For the purposes of this workshop we have a csv (comma separated variable) file.

If you haven’t done so already Click here to download the example data and save it to our project directory. Check the Files pane to see it’s there.

We then import data and assign it to an object we’ll call data like so:

# Import example data ----------------------------------------------------------
dat <- readr::read_csv("data/070718-proteomics-example-data.csv")
## Parsed with column specification:
## cols(
##   protein_accession = col_character(),
##   protein_description = col_character(),
##   control_1 = col_double(),
##   control_2 = col_double(),
##   control_3 = col_double(),
##   treatment_1 = col_double(),
##   treatment_2 = col_double(),
##   treatment_3 = col_double()
## )

## 3.7 Exploring the data

### 3.7.1glimpse, head and str

The first thing to do with any data set is to actually look at it. Here are four ways to have look at the data in the Console: calling the object directly, glimpse, head and str.

1. We can just call the object and return it to the Console, which may or may not be useful depending on the size and type of object we call.

2 .glimpse is a tidyverse function that tries to show us as much data in a data.frame or tibble as possible, telling us the atomic types of data in the table, the number of observations and the number of variables, and importantly shows all the column variable names by transposing the table.

1. head is a base function that shows us the 6 lines of a R object by default.

2. str is a base function that show the structure of a R object, so it provides a lot of information, but is not so easy to read.

The outputs for these four functions is shown below:

# call object
dat
## # A tibble: 7,702 x 8
##    protein_accessi~ protein_descrip~ control_1 control_2 control_3
##    <chr>            <chr>                <dbl>     <dbl>     <dbl>
##  1 VATA_HUMAN_P386~ V-type proton A~     0.811     0.858     1.04
##  2 RL35A_HUMAN_P18~ 60S ribosomal p~     0.367     0.385     0.409
##  3 MYH10_HUMAN_P35~ Myosin-10 OS=Ho~     2.98      4.62      2.87
##  4 RHOG_HUMAN_P840~ Rho-related GTP~     0.142     0.224     0.128
##  5 PSA1_HUMAN_P257~ Proteasome subu~     1.07      0.945     0.803
##  6 PRDX5_HUMAN_P30~ Peroxiredoxin-5~     0.566     0.540     0.488
##  7 ACLY_HUMAN_P533~ ATP-citrate syn~     5.00      4.22      5.03
##  8 VDAC2_HUMAN_P45~ Voltage-depende~     1.35      1.33      1.14
##  9 LRC47_HUMAN_Q8N~ Leucine-rich re~     0.927     0.770     1.17
## 10 CH60_HUMAN_P108~ 60 kDa heat sho~     9.45      8.41     10.4
## # ... with 7,692 more rows, and 3 more variables: treatment_1 <dbl>,
## #   treatment_2 <dbl>, treatment_3 <dbl>
# tidyverse glimpse function
glimpse(dat)
## Observations: 7,702
## Variables: 8
## $protein_accession <chr> "VATA_HUMAN_P38606", "RL35A_HUMAN_P18077",... ##$ protein_description <chr> "V-type proton ATPase catalytic subunit A ...
## $control_1 <dbl> 0.8114, 0.3672, 2.9815, 0.1424, 1.0748, 0.... ##$ control_2           <dbl> 0.8575, 0.3853, 4.6176, 0.2238, 0.9451, 0....
## $control_3 <dbl> 1.0381, 0.4091, 2.8709, 0.1281, 0.8032, 0.... ##$ treatment_1         <dbl> 0.6448, 0.4109, 7.1670, 0.1643, 0.7884, 0....
## $treatment_2 <dbl> 0.7190, 0.4634, 2.0052, 0.2466, 0.8798, 1.... ##$ treatment_3         <dbl> 0.4805, 0.3561, 0.8995, 0.1268, 0.7631, 0....
# head function
head(dat)
## # A tibble: 6 x 8
##   protein_accessi~ protein_descrip~ control_1 control_2 control_3
##   <chr>            <chr>                <dbl>     <dbl>     <dbl>
## 1 VATA_HUMAN_P386~ V-type proton A~     0.811     0.858     1.04
## 2 RL35A_HUMAN_P18~ 60S ribosomal p~     0.367     0.385     0.409
## 3 MYH10_HUMAN_P35~ Myosin-10 OS=Ho~     2.98      4.62      2.87
## 4 RHOG_HUMAN_P840~ Rho-related GTP~     0.142     0.224     0.128
## 5 PSA1_HUMAN_P257~ Proteasome subu~     1.07      0.945     0.803
## 6 PRDX5_HUMAN_P30~ Peroxiredoxin-5~     0.566     0.540     0.488
## # ... with 3 more variables: treatment_1 <dbl>, treatment_2 <dbl>,
## #   treatment_3 <dbl>
# str function
str(dat)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 7702 obs. of  8 variables:
##  $protein_accession : chr "VATA_HUMAN_P38606" "RL35A_HUMAN_P18077" "MYH10_HUMAN_P35580" "RHOG_HUMAN_P84095" ... ##$ protein_description: chr  "V-type proton ATPase catalytic subunit A OS=Homo sapiens GN=ATP6V1A PE=1 SV=2" "60S ribosomal protein L35a OS=Homo sapiens GN=RPL35A PE=1 SV=2" "Myosin-10 OS=Homo sapiens GN=MYH10 PE=1 SV=3" "Rho-related GTP-binding protein RhoG OS=Homo sapiens GN=RHOG PE=1 SV=1" ...
##  $control_1 : num 0.811 0.367 2.982 0.142 1.075 ... ##$ control_2          : num  0.858 0.385 4.618 0.224 0.945 ...
##  $control_3 : num 1.038 0.409 2.871 0.128 0.803 ... ##$ treatment_1        : num  0.645 0.411 7.167 0.164 0.788 ...
##  $treatment_2 : num 0.719 0.463 2.005 0.247 0.88 ... ##$ treatment_3        : num  0.48 0.356 0.899 0.127 0.763 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   protein_accession = col_character(),
##   ..   protein_description = col_character(),
##   ..   control_1 = col_double(),
##   ..   control_2 = col_double(),
##   ..   control_3 = col_double(),
##   ..   treatment_1 = col_double(),
##   ..   treatment_2 = col_double(),
##   ..   treatment_3 = col_double()
##   .. )

To see the data in a spreadsheet fashion use View(dat), note the capital V and a new tab will open. This can also be launched from the Environment tab by clicking on dat.

Although this provides us with some useful information, such as the number of observations and variables, to understand more plotting the data will be helpful as we’ll see in Section 5.3.

### 3.7.2 Summary statisitics

Another useful way to quickly get a sense of the data is to use the summary function, which will return summary of the spread of the data and importantly if there are missing values. We can see immediately below that the experimental replicates have different distributions, and missing values that we need to deal with in Chapter 5.

summary(dat)
##  protein_accession  protein_description   control_1        control_2
##  Length:7702        Length:7702         Min.   : 0.001   Min.   : 0.000
##  Class :character   Class :character    1st Qu.: 0.143   1st Qu.: 0.132
##  Mode  :character   Mode  :character    Median : 0.345   Median : 0.322
##                                         Mean   : 0.933   Mean   : 0.845
##                                         3rd Qu.: 0.959   3rd Qu.: 0.845
##                                         Max.   :31.944   Max.   :31.697
##                                         NA's   :4888     NA's   :4828
##    control_3       treatment_1      treatment_2      treatment_3
##  Min.   : 0.001   Min.   : 0.000   Min.   : 0.002   Min.   : 0.002
##  1st Qu.: 0.149   1st Qu.: 0.112   1st Qu.: 0.135   1st Qu.: 0.101
##  Median : 0.388   Median : 0.286   Median : 0.319   Median : 0.254
##  Mean   : 0.977   Mean   : 0.795   Mean   : 0.856   Mean   : 0.675
##  3rd Qu.: 0.999   3rd Qu.: 0.780   3rd Qu.: 0.880   3rd Qu.: 0.682
##  Max.   :31.320   Max.   :41.686   Max.   :28.234   Max.   :21.428
##  NA's   :5087     NA's   :4739     NA's   :4902     NA's   :5074

### References

Gentleman, Robert C, Vincent J Carey, Douglas M Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, et al. 2004. “Bioconductor: Open Software Development for Computational Biology and Bioinformatics.” Genome Biology 5 (10): R80. https://doi.org/10.1186/gb-2004-5-10-r80.