Objectives
- Be able to subset/retrieve values from a data frame
- Be able to retrieve (subset), name, or replace, values from a vector
- Be able to use logical operators in a subsetting operation
- Be able to manipulate a factor, including subsetting and reordering
Creating and subsetting vectors
Let’s create a few more vectors to play around with:
# Some interesting human SNPs
# while accuracy is important, typos in the data won't hurt you here
snps <- c('rs53576', 'rs1815739', 'rs6152', 'rs1799971')
snp_chromosomes <- c('3', '11', 'X', '6')
snp_positions <- c(8762685, 66560624, 67545785, 154039662)
Once we have vectors, one thing we may want to do is specifically retrieve one or more values from our vector. To do so, we use bracket notation. We type the name of the vector followed by square brackets. In those square brackets we place the index (e.g. a number) in that bracket as follows:
# get the 3rd value in the snp_genes vector
snp_genes[3]
In R, every item your vector is indexed, starting from the first item (1) through to the final number of items in your vector. You can also retrieve a range of numbers:
# get the 1st through 3rd value in the snp_genes vector
snp_genes[1:3]
If you want to retrieve several (but not necessarily sequential) items from a vector, you pass a vector of indices; a vector that has the numbered positions you wish to retrieve.
# get the 1st, 3rd, and 4th value in the snp_genes vector
snp_genes[c(1, 3, 4)]
There are additional (and perhaps less commonly used) ways of subsetting a vector (see these examples). Also, several of these subsetting expressions can be combined:
# get the 1st through the 3rd value, and 4th value in the snp_genes vector
# yes, this is a little silly in a vector of only 4 values.
snp_genes[c(1:3,4)]
Adding to, removing, or replacing values in existing vectors
Once you have an existing vector, you may want to add a new item to it. To do
so, you can use the c() function again to add your new value:
# add the gene 'CYP1A1' and 'APOA5' to our list of snp genes
# this overwrites our existing vector
snp_genes <- c(snp_genes, "CYP1A1", "APOA5")
We can verify that “snp_genes” contains the new gene entry
snp_genes
Using a negative index will return a version of a vector with the index’s value removed:
snp_genes[-6]
We can remove an index value from our vector by overwriting it with this expression:
snp_genes <- snp_genes[-6]
snp_genes
We can also explicitly rename or add a value to our index using double bracket notation:
snp_genes[[7]]<- "APOA5"
snp_genes
(Don’t worry about the all concepts in the URL above for this lesson)
The important thing to notice in the operation above is that R inserts
an NA value to extend our vector so that the gene “APOA5”
is at index 7. This may be a good or not-so-good thing depending
on how you use this.
Logical Subsetting
There is one last set of cool subsetting capabilities we want to introduce. It is possible within R to retrieve items in a vector based on a logical evaluation or numerical comparison. For example, let’s say we wanted get all of the SNPs in our vector of SNP positions that were greater than 100,000,000. We could index using the ‘>’ (greater than) logical operator:
snp_positions[snp_positions > 100000000]
In the square brackets you place the name of the vector followed by the comparison operator and (in this case) a numeric value. Some of the most common logical operators you will use in R are:
| Operator | Description |
|---|---|
| < | less than |
| <= | less than or equal to |
| > | greater than |
| >= | greater than or equal to |
| == | exactly equal to |
| != | not equal to |
| !x | not x |
| a | b | a or b |
| a & b | a and b |
Subsetting data frames
Next, we are going to talk about how you can get specific values from data frames, and where necessary, change the mode of a column of values.
The first thing to remember is that a data frame is two-dimensional (rows and
columns). Therefore, to select a specific value we will once again use
[] (bracket) notation, and can specify more than one value (we also
can specify a range).
Here are a couple examples:
variants[1:4,1]
[1] SRR2584863 SRR2584863 SRR2584863 SRR2584863
Levels: SRR2584863 SRR2584866 SRR2589044
variants[1:10,c("REF","ALT")]
REF ALT
1 T G
2 G T
3 G T
4 CTTTTTTT CTTTTTTTT
5 CCGC CCGCGC
6 C T
7 C A
8 G A
9 ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG AGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG
10 AT ATT
>
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:
# 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)
[1] 25 29
# get a summary of the data frame
summary(SRR2584863_variants)
sample_id CHROM POS ID
SRR2584863:25 CP000819.1:25 Min. : 9972 Mode:logical
SRR2584866: 0 1st Qu.:1331794 NA's:25
SRR2589044: 0 Median :2618472
Mean :2464989
3rd Qu.:3488669
Max. :4616538
REF
A :10
G : 6
C : 3
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG: 1
AT : 1
CCGC : 1
(Other) : 3
ALT
T :7
C :6
A :4
G :3
AC :1
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG:1
(Other) :3
QUAL FILTER INDEL IDV
Min. : 31.89 Mode:logical Mode :logical Min. : 2.00
1st Qu.:104.00 NA's:25 FALSE:19 1st Qu.: 3.25
Median :211.00 TRUE :6 Median : 8.00
Mean :172.97 Mean : 7.00
3rd Qu.:225.00 3rd Qu.: 9.75
Max. :228.00 Max. :12.00
NA's :19
IMF DP VDB RPB
Min. :0.6667 Min. : 2.0 Min. :0.01627 Min. :0.9008
1st Qu.:0.9250 1st Qu.: 9.0 1st Qu.:0.07140 1st Qu.:0.9275
Median :1.0000 Median :10.0 Median :0.37674 Median :0.9542
Mean :0.9278 Mean :10.4 Mean :0.40429 Mean :0.9517
3rd Qu.:1.0000 3rd Qu.:12.0 3rd Qu.:0.65951 3rd Qu.:0.9771
Max. :1.0000 Max. :20.0 Max. :0.99604 Max. :1.0000
NA's :19 NA's :22
MQB BQB MQSB
Min. :0.04979 Min. :0.7507 Min. :0.5000
1st Qu.:0.09996 1st Qu.:0.7627 1st Qu.:0.9599
Median :0.15013 Median :0.7748 Median :0.9962
Mean :0.39997 Mean :0.8418 Mean :0.9442
3rd Qu.:0.57507 3rd Qu.:0.8874 3rd Qu.:1.0000
Max. :1.00000 Max. :1.0000 Max. :1.0128
NA's :22 NA's :22 NA's :3
SGB MQ0F ICB HOB
Min. :-0.6904 Min. :0.00000 Mode:logical Mode:logical
1st Qu.:-0.6762 1st Qu.:0.00000 NA's:25 NA's:25
Median :-0.6620 Median :0.00000
Mean :-0.6341 Mean :0.04667
3rd Qu.:-0.6168 3rd Qu.:0.00000
Max. :-0.4536 Max. :0.66667
AC AN DP4 MQ
Min. :1 Min. :1 0,0,4,5 : 3 Min. :10.00
1st Qu.:1 1st Qu.:1 0,0,4,6 : 2 1st Qu.:60.00
Median :1 Median :1 0,0,0,4 : 1 Median :60.00
Mean :1 Mean :1 0,0,10,6: 1 Mean :55.52
3rd Qu.:1 3rd Qu.:1 0,0,12,4: 1 3rd Qu.:60.00
Max. :1 Max. :1 0,0,12,5: 1 Max. :60.00
(Other) :16
Indiv
/home/dcuser/dc_workshop/results/bam/SRR2584863.aligned.sorted.bam:25
/home/dcuser/dc_workshop/results/bam/SRR2584866.aligned.sorted.bam: 0
/home/dcuser/dc_workshop/results/bam/SRR2589044.aligned.sorted.bam: 0
gt_PL gt_GT
255,0 :11 Min. :1
111,28 : 1 1st Qu.:1
112,0 : 1 Median :1
121,0 : 1 Mean :1
131,0 : 1 3rd Qu.:1
194,0 : 1 Max. :1
(Other): 9
gt_GT_alleles
T :7
C :6
A :4
G :3
AC :1
ACAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAGCCAG:1
(Other) :3