R Basics continued - factors and data frames¶

Learning outcomes

Key pointsObjectivesQuestions

It is easy to import data into R from tabular formats including Excel. However, you still need to check that R has imported and interpreted your data correctly
There are best practices for organizing your data (keeping it tidy) and R is great for this
Base R has many useful functions for manipulating your data, but all of R's capabilities are greatly enhanced by software packages developed by the community

Understand the basic principle of tidy datasets
Be able to load a tabular dataset using base R functions
Be able to determine the structure of a data frame including its dimensions and the datatypes of variables
Be able to subset/retrieve values from a data frame
Understand how R may coerce data into different modes
Be able to change the mode of an object
Understand that R uses factors to store and manipulate categorical data
Be able to manipulate a factor, including subsetting and reordering
Be able to apply an arithmetic function to a data frame
Be able to coerce the class of an object (including variables in a data frame)
Be able to import data from Excel
Be able to save a data frame as a delimited file

How do I get started with tabular data (e.g., spreadsheets) in R?
What are some best practices for reading data into R?
How do I save tabular data generated in R?

Working with spreadsheets (tabular data)¶

A substantial amount of the data we work with in genomics will be tabular data, this is data arranged in rows and columns - also known as spreadsheets. We could write a whole lesson on how to work with spreadsheets effectively (actually we did). For our purposes, we want to remind you of a few principles before we work with our first set of example data:

1) Keep raw data separate from analyzed data

This is principle number one because if you can't tell which files are the original raw data, you risk making some serious mistakes (e.g., drawing conclusion from data which have been manipulated in some unknown way).

2) Keep spreadsheet data Tidy

The simplest principle of Tidy data is that we have one row in our spreadsheet for each observation or sample, and one column for every variable that we measure or report on. As simple as this sounds, it's very easily violated. Most data scientists agree that significant amounts of their time is spent tidying data for analysis. Read more about data organization in our lesson and in this paper.

3) Trust but verify

Finally, while you don't need to be paranoid about data, you should have a plan for how you will prepare it for analysis. This a focus of this lesson. You probably already have a lot of intuition, expectations, assumptions about your data - the range of values you expect, how many values should have been recorded, etc. Of course, as the data get larger our human ability to keep track will start to fail (and yes, it can fail for small data sets too). R will help you to examine your data so that you can have greater confidence in your analysis and its reproducibility.

Keeping your raw data separate

When you work with data in R, you are not changing the original file you loaded that data from. This is different from (for example) working with a spreadsheet program where changing the value of the cell leaves you one "save"-click away from overwriting the original file. You have to purposely use a writing function (e.g., write.csv()) to save data loaded into R. In that case, be sure to save the manipulated data into a new file. More on this later in the lesson.

Importing tabular data into R¶

There are several ways to import data into R. For our purpose here, we will focus on using the tools every R installation comes with (so called "base" R) to import a comma-delimited file containing the results of our variant calling workflow. We will need to load the sheet using a function called read.csv().

Exercise: Review the arguments of the read.csv() function

Before using the read.csv() function, use R's help feature to answer the following questions.

Hint: Entering '?' before the function name and then running that line will bring up the help documentation. Also, when reading this particular help be careful to pay attention to the 'read.csv' expression under the 'Usage' heading. Other answers will be in the 'Arguments' heading.

A) What is the default parameter for 'header' in the read.csv() function?

B) What argument would you have to change to read a file that was delimited by semicolons (;) rather than commas?

C) What argument would you have to change to read file in which numbers used commas for decimal separation (i.e., 1,00)?

D) What argument would you have to change to read in only the first 10,000 rows of a very large file?

Solution

A) The read.csv() function has the argument 'header' set to TRUE by default. This means the function always assumes the first row is header information, (i.e., column names)

B) The read.csv() function has the argument 'sep' set to ",". This means the function assumes commas are used as delimiters, as you would expect. Changing this parameter (e.g., sep=";") would tell R to interpret semicolons as delimiters.

C) Although it is not listed in the read.csv() usage, read.csv() is a "version" of the function read.table() and accepts all its arguments. If you set dec="," you could change the decimal operator. We'd probably assume the delimiter is some other character.

D) You can set nrow to a numeric value (e.g., nrow=10000) to choose how many rows of a file you read in. This may be useful for very large files where not all the data is needed to test some data cleaning steps you are applying.

Hopefully, this exercise gets you thinking about using the provided help documentation in R. There are many arguments that exist, but which we wont have time to cover.

Now, let's read in the file combined_tidy_vcf.csv which will be located in /home/shared/$USER/R4Genomics/. Call/assign this data variants. The first argument to pass to our read.csv() function is the file path for our data. The file path must be in quotes and now is a good time to remember to use tab autocompletion. If you use tab autocompletion you avoid typos and errors in file paths. Use it!

r

# Read in a CSV file and save it as 'variants'

variants <- read.csv("/home/shared/<USERID>/R4Genomics/combined_tidy_vcf.csv")

One of the first things you should notice is that in the Environment window, you have the variants object, listed as 801 obs. (observations/rows) of 29 variables (columns). Double-clicking on the name of the object will open a view of the data in a new tab.

Summarizing and determining the structure of a data frame.¶

A data frame is the standard way to store tabular data in R. A data frame could also be thought of as a collection of vectors, all of which have the same length. Using only two functions, we can learn a lot about out data frame including some summary statistics as well as the "structure" of the data frame. Let's examine what each of these functions can tell us:

r

# Get summary statistics on a data frame

summary(variants)

Output

 sample_id            CHROM                POS             ID              REF           
Length:801         Length:801         Min.   :   1521   Mode:logical   Length:801        
Class :character   Class :character   1st Qu.:1115970   NA's:801       Class :character  
Mode  :character   Mode  :character   Median :2290361                  Mode  :character  
                                      Mean   :2243682                                    
                                      3rd Qu.:3317082                                    
                                      Max.   :4629225                                    

    ALT                 QUAL          FILTER          INDEL              IDV              IMF        
Length:801         Min.   :  4.385   Mode:logical   Mode :logical   Min.   : 2.000   Min.   :0.5714  
Class :character   1st Qu.:139.000   NA's:801       FALSE:700       1st Qu.: 7.000   1st Qu.:0.8824  
Mode  :character   Median :195.000                  TRUE :101       Median : 9.000   Median :1.0000  
                   Mean   :172.276                                  Mean   : 9.396   Mean   :0.9219  
                   3rd Qu.:225.000                                  3rd Qu.:11.000   3rd Qu.:1.0000  
                   Max.   :228.000                                  Max.   :20.000   Max.   :1.0000  
                                                                    NA's   :700      NA's   :700     
      DP             VDB                 RPB              MQB              BQB        
Min.   : 2.00   Min.   :0.0005387   Min.   :0.0000   Min.   :0.0000   Min.   :0.1153  
1st Qu.: 7.00   1st Qu.:0.2180410   1st Qu.:0.3776   1st Qu.:0.1070   1st Qu.:0.6963  
Median :10.00   Median :0.4827410   Median :0.8663   Median :0.2872   Median :0.8615  
Mean   :10.57   Mean   :0.4926291   Mean   :0.6970   Mean   :0.5330   Mean   :0.7784  
3rd Qu.:13.00   3rd Qu.:0.7598940   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
Max.   :79.00   Max.   :0.9997130   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
                                    NA's   :773      NA's   :773      NA's   :773     
     MQSB              SGB               MQ0F           ICB            HOB                AC   
Min.   :0.01348   Min.   :-0.6931   Min.   :0.00000   Mode:logical   Mode:logical   Min.   :1  
1st Qu.:0.95494   1st Qu.:-0.6762   1st Qu.:0.00000   NA's:801       NA's:801       1st Qu.:1  
Median :1.00000   Median :-0.6620   Median :0.00000                                 Median :1  
Mean   :0.96428   Mean   :-0.6444   Mean   :0.01127                                 Mean   :1  
3rd Qu.:1.00000   3rd Qu.:-0.6364   3rd Qu.:0.00000                                 3rd Qu.:1  
Max.   :1.01283   Max.   :-0.4536   Max.   :0.66667                                 Max.   :1  
NA's   :48                                                                                     
      AN        DP4                  MQ           Indiv              gt_PL               gt_GT  
Min.   :1   Length:801         Min.   :10.00   Length:801         Length:801         Min.   :1  
1st Qu.:1   Class :character   1st Qu.:60.00   Class :character   Class :character   1st Qu.:1  
Median :1   Mode  :character   Median :60.00   Mode  :character   Mode  :character   Median :1  
Mean   :1                      Mean   :58.19                                         Mean   :1  
3rd Qu.:1                      3rd Qu.:60.00                                         3rd Qu.:1  
Max.   :1                      Max.   :60.00                                         Max.   :1  

gt_GT_alleles     
Length:801        
Class :character  
Mode  :character

Our data frame has 29 variables, so we get 29 fields that summarize the data. The QUAL, IMF, and VDB variables (and several others) are numerical data and so you get summary statistics on the minimum and maximum values for these columns, as well as mean, median, and 1^st and 3^rd quantile. Many of the other variables (e.g., sample_id) are treated as character data (more on this in a bit). There is a lot to work with, so we will subset the first three columns into a new data frame using the data.frame() function.

r

# Put the first three columns of variants into a new data frame called subset_variants

subset_variants <- variants[, c(1:3, 6)]

Now, let's use the str() (structure) function to look a little more closely at how data frames work:

r

# Get the structure of a data frame

str(subset_variants)

Output

'data.frame':   801 obs. of  4 variables:
 $ sample_id: chr  "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" ...
 $ CHROM    : chr  "CP000819.1" "CP000819.1" "CP000819.1" "CP000819.1" ...
 $ POS      : int  9972 263235 281923 433359 473901 648692 1331794 1733343 2103887 2333538 ...
 $ ALT      : chr  "G" "T" "T" "CTTTTTTTT" ...

Ok, that is a lot up unpack! Some things to notice.

The object type data.frame is displayed in the first row along with its dimensions, in this case 801 observations (rows) and 4 variables (columns).
Each variable (column) has a name (e.g., sample_id). This is followed by the object mode (e.g., chr, int, etc.). Notice that before each variable name there is a $ (this will be important later).

Subsetting data frames¶

Next, we are going to talk about how you can get specific values from data frames.

The first thing to remember is that a data frame is two-dimensional (rows and columns). Therefore, to select a specific value we will will once again use [] (bracket) notation, but we will specify more than one value (except in some cases where we are taking a range).

Exercise: Subsetting a data frame

Try the following indices and functions and try to figure out what they return

a. variants[1, 1]

b. variants[2, 4]

c. variants[801, 29]

d. variants[2,]

e. variants[-1,]

f. variants[1:4, 1]

g. variants[1:10, c("REF","ALT")]

h. variants[, c("sample_id")]

i. head(variants)

j. tail(variants)

k. variants$sample_id

l. variants[variants$REF == "A",]

Output

a.

[1] "SRR2584863"

b.

[1] NA

c.

[1] "T"

d.

    sample_id      CHROM    POS ID REF ALT QUAL FILTER INDEL IDV IMF DP      VDB RPB MQB BQB
2  SRR2584863 CP000819.1 263235 NA   G   T   85     NA FALSE  NA  NA  6 0.096133   1   1   1
    MQSB       SGB     MQ0F ICB HOB AC AN     DP4 MQ
2     NA -0.590765 0.166667  NA  NA  1  1 0,1,0,5 33
                                                               Indiv gt_PL gt_GT gt_GT_alleles
2 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 112,0     1             T

e.

   sample_id      CHROM     POS ID      REF       ALT QUAL FILTER INDEL IDV IMF
2 SRR2584863 CP000819.1  263235 NA        G         T   85     NA FALSE  NA  NA
3 SRR2584863 CP000819.1  281923 NA        G         T  217     NA FALSE  NA  NA
4 SRR2584863 CP000819.1  433359 NA CTTTTTTT CTTTTTTTT   64     NA  TRUE  12 1.0
5 SRR2584863 CP000819.1  473901 NA     CCGC    CCGCGC  228     NA  TRUE   9 0.9
6 SRR2584863 CP000819.1  648692 NA        C         T  210     NA FALSE  NA  NA
7 SRR2584863 CP000819.1 1331794 NA        C         A  178     NA FALSE  NA  NA
  DP      VDB RPB MQB BQB     MQSB       SGB     MQ0F ICB HOB AC AN     DP4 MQ
2  6 0.096133   1   1   1       NA -0.590765 0.166667  NA  NA  1  1 0,1,0,5 33
3 10 0.774083  NA  NA  NA 0.974597 -0.662043 0.000000  NA  NA  1  1 0,0,4,5 60
4 12 0.477704  NA  NA  NA 1.000000 -0.676189 0.000000  NA  NA  1  1 0,1,3,8 60
5 10 0.659505  NA  NA  NA 0.916482 -0.662043 0.000000  NA  NA  1  1 1,0,2,7 60
6 10 0.268014  NA  NA  NA 0.916482 -0.670168 0.000000  NA  NA  1  1 0,0,7,3 60
7  8 0.624078  NA  NA  NA 0.900802 -0.651104 0.000000  NA  NA  1  1 0,0,3,5 60
                                                               Indiv gt_PL
2 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 112,0
3 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 247,0
4 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam  91,0
5 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 255,0
6 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 240,0
7 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 208,0
  gt_GT gt_GT_alleles
2     1             T
3     1             T
4     1     CTTTTTTTT
5     1        CCGCGC
6     1             T
7     1             A

f.

[1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"

g.

1 2 3 4 5 6 7 8 9 10  1 2 3 4 5 6 7 8 9 10

href="#__codelineno-12-1"> REF T G G CTTTTTTT CCGC C C G ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG AT ALT G T T CTTTTTTTT CCGCGC T A A ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG ATT

h.

[1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"
[6] "SRR2584863"

i.

   sample_id      CHROM    POS ID      REF       ALT QUAL FILTER INDEL IDV IMF DP       VDB RPB
1 SRR2584863 CP000819.1   9972 NA        T         G   91     NA FALSE  NA  NA  4 0.0257451  NA
2 SRR2584863 CP000819.1 263235 NA        G         T   85     NA FALSE  NA  NA  6 0.0961330   1
3 SRR2584863 CP000819.1 281923 NA        G         T  217     NA FALSE  NA  NA 10 0.7740830  NA
4 SRR2584863 CP000819.1 433359 NA CTTTTTTT CTTTTTTTT   64     NA  TRUE  12 1.0 12 0.4777040  NA
5 SRR2584863 CP000819.1 473901 NA     CCGC    CCGCGC  228     NA  TRUE   9 0.9 10 0.6595050  NA
6 SRR2584863 CP000819.1 648692 NA        C         T  210     NA FALSE  NA  NA 10 0.2680140  NA
  MQB BQB     MQSB       SGB     MQ0F ICB HOB AC AN     DP4 MQ
1  NA  NA       NA -0.556411 0.000000  NA  NA  1  1 0,0,0,4 60
2   1   1       NA -0.590765 0.166667  NA  NA  1  1 0,1,0,5 33
3  NA  NA 0.974597 -0.662043 0.000000  NA  NA  1  1 0,0,4,5 60
4  NA  NA 1.000000 -0.676189 0.000000  NA  NA  1  1 0,1,3,8 60
5  NA  NA 0.916482 -0.662043 0.000000  NA  NA  1  1 1,0,2,7 60
6  NA  NA 0.916482 -0.670168 0.000000  NA  NA  1  1 0,0,7,3 60
                                                               Indiv gt_PL gt_GT gt_GT_alleles
1 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 121,0     1             G
2 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 112,0     1             T
3 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 247,0     1             T
4 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam  91,0     1     CTTTTTTTT
5 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 255,0     1        CCGCGC
6 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 240,0     1             T

j.

     sample_id      CHROM     POS ID REF ALT QUAL FILTER INDEL IDV IMF DP
796 SRR2589044 CP000819.1 3444175 NA   G   T  184     NA FALSE  NA  NA  9
797 SRR2589044 CP000819.1 3481820 NA   A   G  225     NA FALSE  NA  NA 12
798 SRR2589044 CP000819.1 3893550 NA  AG AGG  101     NA  TRUE   4   1  4
799 SRR2589044 CP000819.1 3901455 NA   A  AC   70     NA  TRUE   3   1  3
800 SRR2589044 CP000819.1 4100183 NA   A   G  177     NA FALSE  NA  NA  8
801 SRR2589044 CP000819.1 4431393 NA TGG   T  225     NA  TRUE  10   1 10
          VDB RPB MQB BQB     MQSB       SGB MQ0F ICB HOB AC AN     DP4 MQ
796 0.4714620  NA  NA  NA 0.992367 -0.651104    0  NA  NA  1  1 0,0,4,4 60
797 0.8707240  NA  NA  NA 1.000000 -0.680642    0  NA  NA  1  1 0,0,4,8 60
798 0.9182970  NA  NA  NA 1.000000 -0.556411    0  NA  NA  1  1 0,0,3,1 52
799 0.0221621  NA  NA  NA       NA -0.511536    0  NA  NA  1  1 0,0,3,0 60
800 0.9272700  NA  NA  NA 0.900802 -0.651104    0  NA  NA  1  1 0,0,3,5 60
801 0.7488140  NA  NA  NA 1.007750 -0.670168    0  NA  NA  1  1 0,0,4,6 60
                                                                 Indiv gt_PL
796 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 214,0
797 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 255,0
798 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 131,0
799 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 100,0
800 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 207,0
801 /home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam 255,0
    gt_GT gt_GT_alleles
796     1             T
797     1             G
798     1           AGG
799     1            AC
800     1             G
801     1             T

k.

[1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"
[6] "SRR2584863"

l.

    sample_id      CHROM     POS ID REF ALT QUAL FILTER INDEL IDV IMF DP
11 SRR2584863 CP000819.1 2407766 NA   A   C  104     NA FALSE  NA  NA  9
12 SRR2584863 CP000819.1 2446984 NA   A   C  225     NA FALSE  NA  NA 20
14 SRR2584863 CP000819.1 2665639 NA   A   T  225     NA FALSE  NA  NA 19
16 SRR2584863 CP000819.1 3339313 NA   A   C  211     NA FALSE  NA  NA 10
18 SRR2584863 CP000819.1 3481820 NA   A   G  200     NA FALSE  NA  NA  9
19 SRR2584863 CP000819.1 3488669 NA   A   C  225     NA FALSE  NA  NA 13
         VDB      RPB      MQB      BQB     MQSB       SGB     MQ0F ICB HOB AC
11 0.0230738 0.900802 0.150134 0.750668 0.500000 -0.590765 0.333333  NA  NA  1
12 0.0714027       NA       NA       NA 1.000000 -0.689466 0.000000  NA  NA  1
14 0.9960390       NA       NA       NA 1.000000 -0.690438 0.000000  NA  NA  1
16 0.4059360       NA       NA       NA 1.007750 -0.670168 0.000000  NA  NA  1
18 0.1070810       NA       NA       NA 0.974597 -0.662043 0.000000  NA  NA  1
19 0.0162706       NA       NA       NA 1.000000 -0.680642 0.000000  NA  NA  1
   AN      DP4 MQ
11  1  3,0,3,2 25
12  1 0,0,10,6 60
14  1 0,0,12,5 60
16  1  0,0,4,6 60
18  1  0,0,4,5 60
19  1  0,0,8,4 60
                                                                Indiv gt_PL
11 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 131,0
12 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 255,0
14 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 255,0
16 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 241,0
18 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 230,0
19 /home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam 255,0
   gt_GT gt_GT_alleles
11     1             C
12     1             C
14     1             T
16     1             C
18     1             G
19     1             C

The subsetting notation is very similar to what we learned for vectors. The key differences include:

Typically provide two values separated by commas: data.frame[row, column]
In cases where you are taking a continuous range of numbers use a colon between the numbers (start:stop, inclusive)
For a non continuous set of numbers, pass a vector using c()
Index using the name of a column(s) by passing them as vectors using c()

Finally, in all of the subsetting exercises above, we printed values to the screen. You can create a new data frame object by assigning them to a new object name:

r

# Create a new data frame containing only observations from "SRR2584863"
SRR2584863_variants <- variants[variants$sample_id == "SRR2584863", ]

# Check the dimension of the data frame
dim(SRR2584863_variants)

# Get a summary of the data frame
summary(SRR2584863_variants)

Output

[1] 25 29

  sample_id            CHROM                POS             ID         
 Length:25          Length:25          Min.   :   9972   Mode:logical  
 Class :character   Class :character   1st Qu.:1331794   NA's:25       
 Mode  :character   Mode  :character   Median :2618472                 
                                       Mean   :2464989                 
                                       3rd Qu.:3488669                 
                                       Max.   :4616538                 

     REF                ALT                 QUAL         FILTER       
 Length:25          Length:25          Min.   : 31.89   Mode:logical  
 Class :character   Class :character   1st Qu.:104.00   NA's:25       
 Mode  :character   Mode  :character   Median :211.00                 
                                       Mean   :172.97                 
                                       3rd Qu.:225.00                 
                                       Max.   :228.00                 

   INDEL              IDV             IMF               DP      
 Mode :logical   Min.   : 2.00   Min.   :0.6667   Min.   : 2.0  
 FALSE:19        1st Qu.: 3.25   1st Qu.:0.9250   1st Qu.: 9.0  
 TRUE :6         Median : 8.00   Median :1.0000   Median :10.0  
                 Mean   : 7.00   Mean   :0.9278   Mean   :10.4  
                 3rd Qu.: 9.75   3rd Qu.:1.0000   3rd Qu.:12.0  
                 Max.   :12.00   Max.   :1.0000   Max.   :20.0  
                 NA's   :19      NA's   :19                     
      VDB               RPB              MQB               BQB        
 Min.   :0.01627   Min.   :0.9008   Min.   :0.04979   Min.   :0.7507  
 1st Qu.:0.07140   1st Qu.:0.9275   1st Qu.:0.09996   1st Qu.:0.7627  
 Median :0.37674   Median :0.9542   Median :0.15013   Median :0.7748  
 Mean   :0.40429   Mean   :0.9517   Mean   :0.39997   Mean   :0.8418  
 3rd Qu.:0.65951   3rd Qu.:0.9771   3rd Qu.:0.57507   3rd Qu.:0.8874  
 Max.   :0.99604   Max.   :1.0000   Max.   :1.00000   Max.   :1.0000  
                   NA's   :22       NA's   :22        NA's   :22      
      MQSB             SGB               MQ0F           ICB         
 Min.   :0.5000   Min.   :-0.6904   Min.   :0.00000   Mode:logical  
 1st Qu.:0.9599   1st Qu.:-0.6762   1st Qu.:0.00000   NA's:25       
 Median :0.9962   Median :-0.6620   Median :0.00000                 
 Mean   :0.9442   Mean   :-0.6341   Mean   :0.04667                 
 3rd Qu.:1.0000   3rd Qu.:-0.6168   3rd Qu.:0.00000                 
 Max.   :1.0128   Max.   :-0.4536   Max.   :0.66667                 
 NA's   :3                                                          
   HOB                AC          AN        DP4                  MQ       
 Mode:logical   Min.   :1   Min.   :1   Length:25          Min.   :10.00  
 NA's:25        1st Qu.:1   1st Qu.:1   Class :character   1st Qu.:60.00  
                Median :1   Median :1   Mode  :character   Median :60.00  
                Mean   :1   Mean   :1                      Mean   :55.52  
                3rd Qu.:1   3rd Qu.:1                      3rd Qu.:60.00  
                Max.   :1   Max.   :1                      Max.   :60.00  

    Indiv              gt_PL               gt_GT   gt_GT_alleles     
 Length:25          Length:25          Min.   :1   Length:25         
 Class :character   Class :character   1st Qu.:1   Class :character  
 Mode  :character   Mode  :character   Median :1   Mode  :character  
                                       Mean   :1                     
                                       3rd Qu.:1                     
                                       Max.   :1

Introducing factors¶

Factors are the final major data structure we will introduce in our Introduction to R lessons. Factors can be thought of as vectors which are specialized for categorical data. Given R's specialization for statistics, this make sense since categorial and continuous variables are usually treated differently. Sometimes you may want to have data treated as a factor, but in other cases, this may be undesirable. Let's see the value of treating some of which are categorical in nature as factors. Let's take a look at just the alternate alleles.

r

# Extract the "ALT" column to a new object

alt_alleles <- subset_variants$ALT

Let's look at the first few items in our factor using head():

r

head(alt_alleles)

Output

[1] "G"         "T"         "T"         "CTTTTTTTT" "CCGCGC"    "T"

There are 801 alleles (one for each row). To simplify, lets look at just the single-nucleotide alleles (SNPs). We can use some of the vector indexing skills from the last episode.

r

alt_snps <- c(alt_alleles[alt_alleles=="A"],
              alt_alleles[alt_alleles=="T"],
              alt_alleles[alt_alleles=="G"],
              alt_alleles[alt_alleles=="C"])

This leaves us with a vector of the 701 alternative alleles which were single nucleotides. Right now, they are being treated a characters, but we could treat them as categories of SNP. Doing this will enable some nice features. For example, we can try to generate a plot of this character vector as it is right now:

r

plot(alt_snps)

Output

Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf

Whoops! Though the plot() function will do its best to give us a quick plot, it is unable to do so here. One way to fix this it to tell R to treat the SNPs as categories (i.e., a factor vector); we will create a new object to avoid confusion using the factor() function:

r

factor_snps <- factor(alt_snps)

Let's learn a little more about this new type of vector:

r

str(factor_snps)

Output

 Factor w/ 4 levels "A","C","G","T": 1 1 1 1 1 1 1 1 1 1 ...

What we get back are the categories ("A","C","G","T") in our factor; these are called "levels". Levels are the different categories contained in a factor. By default, R will organize the levels in a factor in alphabetical order. So the first level in this factor is "A".

For the sake of efficiency, R stores the content of a factor as a vector of integers, which an integer is assigned to each of the possible levels. Recall levels are assigned in alphabetical order. In this case, the first item in our factor_snps object is "A", which happens to be the 1^st level of our factor, ordered alphabetically. This explains the sequence of "1"s (Factor w/ 4 levels"A","C","G","T": 1 1 1 1 1 1 1 1 1 1 ...), since "A" is the first level and the first few items in our factor are all "A"s.

We can see how many items in our vector fall into each category:

r

summary(factor_snps)

Output

  A   C   G   T 
211 139 154 203

As you can imagine, this is already useful when you want to generate a tally.

Treating objects as categories without changing their mode

You don't have to make an object a factor to get the benefits of treating an object as a factor. See what happens when you use the as.factor() function on factor_snps. To generate a tally, you can sometimes also use the table() function; though sometimes you may need to combine both (i.e., table(as.factor(object)))

Plotting and ordering factors¶

One of the most common uses for factors will be when you plot categorical values. For example, suppose we want to know how many of our variants had each possible SNP we could generate a plot:

r

plot(factor_snps)

Output

This isn't a particularly pretty example of a plot but it works. We'll be learning much more about creating nice, publication-quality graphics later in this workshop.

If you recall, factors are ordered alphabetically. That might make sense, but categories (e.g., "red", "blue", "green") often do not have an intrinsic order. What if we wanted to order our plot according to the numerical value (i.e., in descending order of SNP frequency)? We can enforce an order on our factors:

r

ordered_factor_snps <- factor(
  factor_snps, 
  levels = names(sort(table(factor_snps)))
)

Let's deconstruct this from the inside out (you can try each of these commands to see why this works):

We create a table of factor_snps to get the frequency of each SNP: table(factor_snps)
We sort this table: sort(table(factor_snps)); use the decreasing = parameter for this function if you wanted to change from the default of FALSE
Using the names() function gives us just the character names of the table sorted by frequencies:names(sort(table(factor_snps)))
The factor function is what allows us to create a factor. We give it the factor_snps object as input, and use the levels= parameter to enforce the ordering of the levels.

Now we see our plot has be reordered:

r

plot(ordered_factor_snps)

Output

Factors come in handy in many places when using R. Even using more sophisticated plotting packages such as ggplot2 will sometimes require you to understand how to manipulate factors.

Packages in R – what are they and why do we use them?

Packages are simply collections of functions and/or data that can be used to extend the capabilities of R beyond the core functionality that comes with it by default. There are useful R packages available that span all types of statistical analysis, data visualization, and more. The main place that R packages are installed from is a website called CRAN (the Comprehensive R Archive Network). Many thousands of R packages are available there, and when you use the built-in R function install.packages(), it will look for a CRAN repository to install from. So, for example, to install tidyverse packages such as dplyr and ggplot2 (which you'll do in the next few lessons), you would use the following command:

r

# Install a package from CRAN
install.packages("ggplot2")

Coercing values¶

Sometimes, it is possible that R will misinterpret the type of data represented in a data frame, or store that data in a mode which prevents you from operating on the data the way you wish. For example, a long list of gene names isn't usually thought of as a categorical variable, the way that your experimental condition (e.g., control, treatment) might be. More importantly, some R packages you use to analyze your data may expect characters as input, not factors. At other times (such as plotting or some statistical analyses) a factor may be more appropriate. Ultimately, you should know how to change the mode of an object.

First, its very important to recognize that coercion happens in R all the time. This can be a good thing when R gets it right, or a bad thing when the result is not what you expect. Consider:

r

snp_chromosomes <- c('3', '11', 'X', '6')
typeof(snp_chromosomes)

Output

[1] "character"

Although there are several numbers in our vector, they are all in quotes, so we have explicitly told R to consider them as characters. However, even if we removed the quotes from the numbers, R would coerce everything into a character:

r

snp_chromosomes_2 <- c(3, 11, 'X', 6)
typeof(snp_chromosomes_2)
snp_chromosomes_2[1]

Output

[1] "character"
[1] "3"

We can use the as. functions to explicitly coerce values from one form into another. Consider the following vector of characters, which all happen to be valid numbers:

r

snp_positions_2 <- c("8762685", "66560624", "67545785", "154039662")
typeof(snp_positions_2)
snp_positions_2[1]

Output

[1] "character"
[1] "8762685"

Now we can coerce snp_positions_2 into a numeric mode using as.numeric():

r

snp_positions_2 <- as.numeric(snp_positions_2)
typeof(snp_positions_2)
snp_positions_2[1]

Output

[1] "double"
[1] 8762685

Sometimes coercion is straight forward, but what would happen if we tried using as.numeric() on snp_chromosomes_2

r

snp_chromosomes_2 <- as.numeric(snp_chromosomes_2)

Output

Warning message:
NAs introduced by coercion

If we check, we will see that an NA value (R's default value for missing data) has been introduced.

r

snp_chromosomes_2

Output

[1]  3 11 NA  6

Trouble can really start when we try to coerce a factor. For example, when we try to coerce the factor_snps into a numeric mode look at the result:

r

as.numeric(factor_snps)

Output

  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [28] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [55] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [82] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[109] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[136] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[163] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[190] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4
[217] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[244] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[271] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[298] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[325] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[352] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[379] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[406] 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[433] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[460] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[487] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[514] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[541] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[568] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[595] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[622] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[649] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[676] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[703] 2 2 2 2 2

Strangely, it works! Almost. Instead of giving an error message, R returns numeric values, which in this case are the integers assigned to the levels in this factor. This kind of behavior can lead to hard-to-find bugs, for example when we do have numbers in a factor, and we get numbers from a coercion. If we don't look carefully, we may not notice a problem.

If you need to coerce an entire column you can overwrite it using an expression like this one:

r

# Make the 'sample_id' column a factor type column
variants$sample_id <- as.factor(variants$sample_id)

# check the structure of the column
str(variants$sample_id)

Output

Factor w/ 3 levels "SRR2584863","SRR2584866",..: 1 1 1 1 1 1 1 1 1 1 ...

Lesson summary: Data coercion¶

Lets summarize this section on coercion with a few take home messages.

When you explicitly coerce one data type into another (this is known as explicit coercion), be careful to check the result. Ideally, you should try to see if its possible to avoid steps in your analysis that force you to coerce.
R will sometimes coerce without you asking for it. This is called (appropriately) implicit coercion. For example when we tried to create a vector with multiple data types, R chose one type through implicit coercion.
Check the structure (str()) of your data before working with them!

StringsAsFactors

Prior to R 4.0 when importing a data frame using any one of the read.table() functions such as read.csv() , the argument StringsAsFactors was by default set to true TRUE. Setting it to FALSE will treat any non-numeric column to a character type. read.csv() documentation, you will also see you can explicitly type your columns using the colClasses argument. Other R packages (such as the Tidyverse readr) don't have this particular conversion issue, but many packages will still try to guess a data type.

Data frame bonus material: math, sorting, renaming¶

Here are a few operations that don't need much explanation, but which are good to know.

There are lots of arithmetic functions you may want to apply to your data frame, covering those would be a course in itself (there is some starting material here).

You can use functions like mean(), min(), max() on an individual column. Let's look at the "DP" or filtered depth. This value shows the number of filtered reads that support each of the reported variants.

r

max(variants$DP)

Output

[1] 79

You can sort a data frame using the order() function:

r

sorted_by_DP <- variants[order(variants$DP), ] 
head(sorted_by_DP$DP)

Output

[1] 2 2 2 2 2 2

Exercise

The order() function lists values in increasing order by default. Look at the documentation for this function and change sorted_by_DP to start with variants with the greatest filtered depth ("DP").

Solution

sorted_by_DP <- variants[order(variants$DP, decreasing = TRUE), ]
head(sorted_by_DP$DP)

Output

[1] 79 46 41 29 29 27

These functions work on vectors too!

When using these functions (i.e., mean(), min(), max(), and order()), replace the input with a vector and it will do the same job! For example:

r

# Minimum position of a SNP
min(snp_positions)

# Decreasing order of SNPs by positions
snp_positions[order(snp_positions, decreasing = TRUE)]

Output

[1] 8762685
[1] 154039662  67545785  66560624   8762685

You can rename columns by logical subsetting or index:

r

# By logical subsetting
colnames(variants)[colnames(variants) == "sample_id"] <- "strain"

# By index
colnames(variants)[2] <- "chromosome"

# Check the column name (hint names are returned as a vector)
colnames(variants)

Output

 [1] "sample_id"     "CHROM"         "POS"          
 [4] "ID"            "REF"           "ALT"          
 [7] "QUAL"          "FILTER"        "INDEL"        
[10] "IDV"           "IMF"           "DP"           
[13] "VDB"           "RPB"           "MQB"          
[16] "BQB"           "MQSB"          "SGB"          
[19] "MQ0F"          "ICB"           "HOB"          
[22] "AC"            "AN"            "DP4"          
[25] "MQ"            "Indiv"         "gt_PL"        
[28] "gt_GT"         "gt_GT_alleles"

Saving your data frame to a file¶

We can save data to a file. We will save our SRR2584863_variants object to a .csv (comma-separated values) file using the write.csv() function:

r

write.csv(SRR2584863_variants, file = "SRR2584863_variants.csv")

The write.csv() function has some additional arguments listed in the help, but at a minimum you need to tell it what data frame to write to file, and give a path to a file name in quotes (if you only provide a file name, the file will be written in the current working directory).

Importing data from Excel¶

Excel is one of the most common formats, so we need to discuss how to make these files play nicely with R. The simplest way to import data from Excel is to save your Excel file in .csv format. You can then import into R right away. Sometimes you may not be able to do this (imagine you have data in 300 Excel files, are you going to open and export all of them?).

One common R package (a set of code with features you can download and add to your R installation) is the readxl package which can open and import Excel files. Rather than addressing package installation this second (we'll discuss this soon!), we can take advantage of RStudio's import feature which integrates this package.

readxl-RStudio integration

This feature is only available on RStudio version 1.0.44 or later.

First, in the RStudio menu go to File, select Import Dataset, and choose From Excel... (notice there are several other options you can explore).

Next, under File/Url: click the Browse button and navigate to the Ecoli_metadata.xlsx file located at /home/dcuser/dc_sample_data/R. You should now see a preview of the data to be imported:

Notice that you have the option to change the data type of each variable by clicking arrow (drop-down menu) next to each column title. Under Import Options you may also rename the data, choose a different sheet to import, and choose how you will handle headers and skipped rows. Under Code Preview you can see the code that will be used to import this file. We could have written this code and imported the Excel file without the RStudio import function, but now you can choose your preference.

In this exercise, we will leave the name of the data frame as Ecoli_metadata, and there are no other options we need to adjust. Click the Import button to import the data.

Finally, let's check the first few lines of the Ecoli_metadata data frame:

r

head(Ecoli_metadata)

Output

# A tibble: 6 × 7
  sample   generation clade   strain cit     run       genome_size
  <chr>         <dbl> <chr>   <chr>  <chr>   <chr>           <dbl>
1 REL606            0 NA      REL606 unknown NA               4.62
2 REL1166A       2000 unknown REL606 unknown SRR098028        4.63
3 ZDB409         5000 unknown REL606 unknown SRR098281        4.6 
4 ZDB429        10000 UC      REL606 unknown SRR098282        4.59
5 ZDB446        15000 UC      REL606 unknown SRR098283        4.66
6 ZDB458        20000 (C1,C2) REL606 unknown SRR098284        4.63

The type of this object is tibble, a type of data frame we will talk more about in the dplyr section. If you needed a true R data frame you could coerce with as.data.frame().

Exercise: Putting it all together - data frames

Using the Ecoli_metadata data frame created above, answer the following questions

A) What are the dimensions (# rows, # columns) of the data frame?

B) What are categories are there in the cit column? hint: treat column as factor

C) How many of each of the cit categories are there?

D) What is the genome size for the 7^th observation in this data set?

E) What is the median value of the variable genome_size?

F) Rename the column sample to sample_id.

G) Create a new column named genome_size_bp and set it equal to the genome_size multiplied by 1,000,000.

H) Save the edited Ecoli_metadata data frame as "exercise_solution.csv" in your current working directory.

Solution

A)

r

dim(Ecoli_metadata)

Output

[1] 30  7

B)

r

levels(as.factor(Ecoli_metadata$cit))

Output

[1] "minus"   "plus"    "unknown"

C)

r

table(as.factor(Ecoli_metadata$cit))

Output

minus    plus unknown 
    9       9      12

D)

r

Ecoli_metadata[7, 7]

Output

# A tibble: 1 × 1
  genome_size
        <dbl>
1        4.62

E)

r

median(Ecoli_metadata$genome_size)

Output

[1] 4.625

F)

r

colnames(Ecoli_metadata)[colnames(Ecoli_metadata) == "sample"] <- "sample_id"

# Check the column names
colnames(Ecoli_metadata)

Output

[1] "sample_id"   "generation"  "clade"       "strain"     
[5] "cit"         "run"         "genome_size"

G)

r

Ecoli_metadata$genome_size_bp <- Ecoli_metadata$genome_size * 1000000

# Check the first few rows
head(Ecoli_metadata)

Output

# A tibble: 6 × 8
  sample_id generation clade   strain cit     run       genome_size genome_size_bp
  <chr>          <dbl> <chr>   <chr>  <chr>   <chr>           <dbl>          <dbl>
1 REL606             0 NA      REL606 unknown NA               4.62        4620000
2 REL1166A        2000 unknown REL606 unknown SRR098028        4.63        4630000
3 ZDB409          5000 unknown REL606 unknown SRR098281        4.6         4600000
4 ZDB429         10000 UC      REL606 unknown SRR098282        4.59        4590000
5 ZDB446         15000 UC      REL606 unknown SRR098283        4.66        4660000
6 ZDB458         20000 (C1,C2) REL606 unknown SRR098284        4.63        4630000

H)

r

write.csv(Ecoli_metadata, file = "exercise_solution.csv")

Check the Files tab on the bottom-right panel to see if the file is there.