Intro to R Day 2
Load your day 1 workspace data:
load("day1.RData")Lists
A list is an ordered collection of objects, which can be any type of R objects (vectors, matrices, data frames, even lists).
A list is constructed using the function list().
my_list <- list(1:5, "a", c(TRUE, FALSE, FALSE), c(3.2, 103.0, 82.3))
my_list## [[1]]
## [1] 1 2 3 4 5
##
## [[2]]
## [1] "a"
##
## [[3]]
## [1] TRUE FALSE FALSE
##
## [[4]]
## [1] 3.2 103.0 82.3str(my_list)## List of 4
## $ : int [1:5] 1 2 3 4 5
## $ : chr "a"
## $ : logi [1:3] TRUE FALSE FALSE
## $ : num [1:3] 3.2 103 82.3One could construct a list by giving names to elements.
my_list <- list(Ranking=1:5, ID="a", Test=c(TRUE, FALSE, FALSE), Score=c(3.2, 103.0, 82.3))
# display the names of elements in the list using the function *names*, or *str*. Compare the output of *str* with the above results to see the difference.
names(my_list)## [1] "Ranking" "ID" "Test" "Score"str(my_list)## List of 4
## $ Ranking: int [1:5] 1 2 3 4 5
## $ ID : chr "a"
## $ Test : logi [1:3] TRUE FALSE FALSE
## $ Score : num [1:3] 3.2 103 82.3# number of elements in the list
length(my_list)## [1] 4Subsetting data
Subsetting allows one to access the piece of data of interest. When combinded with assignment, subsetting can modify selected pieces of data. The operators that can be used to subset data are: [, $, and [[.
First, we are going to talk about subsetting data using [, which is the most commonly used operator. We will start by looking at vectors and talk about four ways to subset a vector.
- Positive integers return elements at the specified positions
# first to recall what are stored in gene_names
gene_names## [1] "ESR1" "p53" "PI3K" "BRCA1" "EGFR"# obtain the first and the third elements
gene_names[c(1,3)]## [1] "ESR1" "PI3K"R uses 1 based indexing, meaning the first element is at the position 1, not at position 0.
- Negative integers omit elements at the specified positions
gene_names[-c(1,3)]## [1] "p53" "BRCA1" "EGFR"One may not mixed positive and negative integers in one single subset operation.
# The following command will produce an error.
gene_names[c(-1, 2)]## Error in gene_names[c(-1, 2)]: only 0's may be mixed with negative subscripts- Logical vectors select elements where the corresponding logical value is TRUE, This is very useful because one may write the expression that creates the logical vector.
gene_names[c(TRUE, FALSE, TRUE, FALSE, FALSE)]## [1] "ESR1" "PI3K"Recall that we have created one vector called gene_expression. Let’s assume that gene_expression stores the expression values correspond to the genes in gene_names. Then we may subset the genes based on expression values.
gene_expression## ESR1 p53 PI3K BRCA1 EGFR
## 0 100 50 200 80gene_names[gene_expression > 50]## [1] "p53" "BRCA1" "EGFR"If the logical vector is shorter in length than the data vector that we want to subset, then it will be recycled to be the same length as the data vector.
gene_names[c(TRUE, FALSE)]## [1] "ESR1" "PI3K" "EGFR"If the logical vector has “NA” in it, the corresponding value will be “NA” in the output. “NA” in R is a symbol for missing value.
gene_names[c(TRUE, NA, FALSE, TRUE, NA)]## [1] "ESR1" NA "BRCA1" NA- Character vectors return elements with matching names, when the vector is named.
gene_expression## ESR1 p53 PI3K BRCA1 EGFR
## 0 100 50 200 80gene_expression[c("ESR1", "p53")]## ESR1 p53
## 0 100- Nothing returns the original vector, This is more useful for matrices, data frames than for vectors.
gene_names[]## [1] "ESR1" "p53" "PI3K" "BRCA1" "EGFR"Subsetting a list works in the same way as subsetting an atomic vector. Using [ will always return a list.
my_list[1]## $Ranking
## [1] 1 2 3 4 5Subsetting a matrix can be done by simply generalizing the one dimension subsetting: one may supply a one dimension index for each dimension of the matrix. Blank/Nothing subsetting is now useful in keeping all rows or all columns.
my_matrix[c(TRUE, FALSE), ]## col1 col2 col3
## row1 1 2 8
## row3 8 27 267Subsetting a data frame can be done similarly as subsetting a matrix. In addition, one may supply only one 1-dimensional index to subset a data frame. In this case, R will treat the data frame as a list with each column is an element in the list.
# recall a data frame created from above: *meta.data*
meta.data## patients_name disease_stage Family_history patients_age BRCA
## 1 Patient1 Stage1 Y 31 YES
## 2 Patient2 Stage2 N 40 NO
## 3 Patient3 Stage2 Y 39 YES
## 4 Patient4 Stage3 N 50 YES
## 5 Patient5 Stage1 Y 45 YES
## 6 Patient6 Stage4 Y 65 NO# subset the data frame similarly to a matrix
meta.data[c(TRUE, FALSE, FALSE, TRUE),]## patients_name disease_stage Family_history patients_age BRCA
## 1 Patient1 Stage1 Y 31 YES
## 4 Patient4 Stage3 N 50 YES
## 5 Patient5 Stage1 Y 45 YES# subset the data frame using one vector
meta.data[c("patients_age", "disease_stage")]## patients_age disease_stage
## 1 31 Stage1
## 2 40 Stage2
## 3 39 Stage2
## 4 50 Stage3
## 5 45 Stage1
## 6 65 Stage4Subsetting operators: [[ and $
[[ is similar to [, except that it returns the content of the element.
# recall my_list
my_list## $Ranking
## [1] 1 2 3 4 5
##
## $ID
## [1] "a"
##
## $Test
## [1] TRUE FALSE FALSE
##
## $Score
## [1] 3.2 103.0 82.3# comparing [[ with [ in subsetting a list
my_list[[1]]## [1] 1 2 3 4 5my_list[1]## $Ranking
## [1] 1 2 3 4 5[[ is very useful when working with a list. Because when [ is applied to a list, it always returns a list. While [[ returns the contents of the list. [[ can only extrac/return one element, so it only accept one integer/string as input.
Because data frames are implemented as lists of columns, one may use [[ to extract a column from data frames.
meta.data[["disease_stage"]]## [1] Stage1 Stage2 Stage2 Stage3 Stage1 Stage4
## Levels: Stage2 Stage1 Stage3 Stage4$ is a shorthand for [[ combined with character subsetting.
# subsetting a list using $
my_list$Score## [1] 3.2 103.0 82.3# subsetting a data frame using
meta.data$disease_stage## [1] Stage1 Stage2 Stage2 Stage3 Stage1 Stage4
## Levels: Stage2 Stage1 Stage3 Stage4Simplifying vs. preserving subsetting
We have seen some examples of simplying vs. preserving subsetting, for example:
# simplifying subsetting
my_list[[1]]## [1] 1 2 3 4 5# preserving subsetting
my_list[1]## $Ranking
## [1] 1 2 3 4 5Basically, simplying subsetting returns the simplest possible data structure that can represent the output. While preserving subsetting keeps the structure of the output as the same as the input. In the above example, [[ simplifies the output to a vector, while [ keeps the output as a list.
Because the syntax of carrying out simplifying and preserving subsetting differs depending on the data structure, the table below provides the information for the most basic data structure.
| Simplifying | Preserving | |
|---|---|---|
| Vector | x[[1]] | x[1] |
| List | x[[1]] | x[1] |
| Factor | x[1:3, drop=T] | x[1:3] |
| Data frame | x[, 1] or x[[1]] | x[, 1, drop=F] or x[1] |
CHALLENGES
Using the built-in dataset iris, first subset the dataframe keeping only those rows where the sepal length is greater than 6. Then find the total number for each Species in that subset.
Using iris, remove the width columns and then create a new dataframe with the Species and the sum of the rows.
Topic 3. Import and export data in R
R base function read.table() is a general funciton that can be used to read a file in table format. The data will be imported as a data frame.
# There is a very convenient way to read files from the internet.
data1 <- read.table(file="https://github.com/ucdavis-bioinformatics-training/courses/raw/master/Intro2R/raw_counts.txt", sep="\t", header=T, stringsAsFactors=F)
# To read a local file. If you have downloaded the raw_counts.txt file to your local machine, you may use the following command to read it in, by providing the full path for the file location. The way to specify the full path is the same as taught in the command line session.
download.file("https://github.com/ucdavis-bioinformatics-training/courses/raw/master/Intro2R/raw_counts.txt", "./raw_counts.txt")
data1 <- read.table(file="./raw_counts.txt", sep="\t", header=T, stringsAsFactors=F)To check what type of object data1 is in and take a look at the beginning part of the data.
is.data.frame(data1)## [1] TRUEhead(data1)## C61 C62 C63 C64 C91 C92 C93 C94 I561 I562 I563 I564 I591 I592
## AT1G01010 322 346 256 396 372 506 361 342 638 488 440 479 770 430
## AT1G01020 149 87 162 144 189 169 147 108 163 141 119 147 182 156
## AT1G01030 15 32 35 22 24 33 21 35 18 8 54 35 23 8
## AT1G01040 687 469 568 651 885 978 794 862 799 769 725 715 811 567
## AT1G01046 1 1 5 4 5 3 0 2 4 3 1 0 2 8
## AT1G01050 1447 1032 1083 1204 1413 1484 1138 938 1247 1516 984 1044 1374 1355
## I593 I594 I861 I862 I863 I864 I891 I892 I893 I894
## AT1G01010 656 467 143 453 429 206 567 458 520 474
## AT1G01020 153 177 43 144 114 50 161 195 157 144
## AT1G01030 16 24 42 17 22 39 26 28 39 30
## AT1G01040 831 694 345 575 605 404 735 651 725 591
## AT1G01046 8 1 0 4 0 3 5 7 0 5
## AT1G01050 1437 1577 412 1338 1051 621 1434 1552 1248 1186Depending on the format of the file, several variants of read.table() are available to make reading a file easier.
read.csv(): for reading “comma separated value” files (.csv).
read.csv2(): variant used in countries that use a comma “,” as decimal point and a semicolon “;” as field separators.
read.delim(): for reading “tab separated value” files (“.txt”). By default, point(“.”) is used as decimal point.
read.delim2(): for reading “tab separated value” files (“.txt”). By default, comma (“,”) is used as decimal point.
# We are going to read a file over the internet by providing the url of the file.
data2 <- read.csv(file="https://github.com/ucdavis-bioinformatics-training/courses/raw/master/Intro2R/raw_counts.csv", stringsAsFactors=F)
# To look at the file:
head(data2)## C61 C62 C63 C64 C91 C92 C93 C94 I561 I562 I563 I564 I591 I592
## AT1G01010 322 346 256 396 372 506 361 342 638 488 440 479 770 430
## AT1G01020 149 87 162 144 189 169 147 108 163 141 119 147 182 156
## AT1G01030 15 32 35 22 24 33 21 35 18 8 54 35 23 8
## AT1G01040 687 469 568 651 885 978 794 862 799 769 725 715 811 567
## AT1G01046 1 1 5 4 5 3 0 2 4 3 1 0 2 8
## AT1G01050 1447 1032 1083 1204 1413 1484 1138 938 1247 1516 984 1044 1374 1355
## I593 I594 I861 I862 I863 I864 I891 I892 I893 I894
## AT1G01010 656 467 143 453 429 206 567 458 520 474
## AT1G01020 153 177 43 144 114 50 161 195 157 144
## AT1G01030 16 24 42 17 22 39 26 28 39 30
## AT1G01040 831 694 345 575 605 404 735 651 725 591
## AT1G01046 8 1 0 4 0 3 5 7 0 5
## AT1G01050 1437 1577 412 1338 1051 621 1434 1552 1248 1186R base function write.table() can be used to export data to a file.
# To write to a file called "output.txt" in your current working directory.
write.table(data2[1:20,], file="output.txt", sep="\t", quote=F, row.names=T, col.names=T)It is also possible to export data to a csv file.
write.csv()
write.csv2()
Quiz 4
Topic 4. R markdown and R notebooks
Markdown is a system that allow easy incorporation of annotations/comments together with computing code. Both the raw source of markdown file and the rendered output are easy to read. R markdown allows both interactive mode with R and producing a reproducible document. An R notebook is an R markdown document with code chunks that can be executed independently and interactively, with output visible immediately beneath the input. In RStudio, by default, all R markdown documents are run in R notebook mode. Under the R notebook mode, when executing a chunk, the code is sent to the console to be run one line at a time. This allows execution to stop if a line raises an error.
In RStudio, creating an R notebook can be done by going to the menu command ** File -> New File -> R Notebook **.
An example of an R notebook looks like:
The way to run the R code inside the code chunk is to use the green arrow located at the top right corner of each of the code chunk, or use ** Ctrl + Shift + Enter ** on Windows, or ** Cmd + Shift + Enter ** on Mac to run the current code chunk. To run each individual code line, one uses ** Ctrl + Enter ** on Windows, or ** Cmd + Enter ** on Mac.
To render R notebook to html/pdf/word documents can be done using the Preview menu.
Topic 5. Functions in R
Invoking a function by its name, followed by the parenthesis and zero or more arguments.
# to find out the current working directory
getwd()## [1] "/home/joshi/Desktop/work/workshops/2021-June-Introduction-to-R-for-Bioinformatics/R"# to set a different working directory, use setwd
#setwd("/Users/jli/Desktop")
# to list all objects in the environment
ls()## [1] "a" "b" "brca1_expressed"
## [4] "col1" "col2" "col3"
## [7] "colFmt" "data1" "data2"
## [10] "disease_stage" "expression.data" "Family_history"
## [13] "gene" "gene_expression" "gene_names"
## [16] "hello" "her2_expressed" "her2_expression_level"
## [19] "md2" "meta.data" "my_data"
## [22] "my_list" "my_matrix" "nums"
## [25] "patients_age" "patients_name"# to create a vector from 2 to 3, using increment of 0.1
seq(2, 3, by=0.1)## [1] 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0# to create a vector with repeated elements
rep(1:3, times=3)## [1] 1 2 3 1 2 3 1 2 3rep(1:3, each=3)## [1] 1 1 1 2 2 2 3 3 3# to get help information on a function in R: ?function.name
?seq
?sort
?repOne useful function to find out information on an R object: str(). It compactly display the internal structure of an R object.
str(data2)## 'data.frame': 33602 obs. of 24 variables:
## $ C61 : int 322 149 15 687 1 1447 2667 297 0 74 ...
## $ C62 : int 346 87 32 469 1 1032 2472 226 0 79 ...
## $ C63 : int 256 162 35 568 5 1083 2881 325 0 138 ...
## $ C64 : int 396 144 22 651 4 1204 2632 341 0 85 ...
## $ C91 : int 372 189 24 885 5 1413 5120 199 0 68 ...
## $ C92 : int 506 169 33 978 3 1484 6176 180 0 41 ...
## $ C93 : int 361 147 21 794 0 1138 7088 195 0 110 ...
## $ C94 : int 342 108 35 862 2 938 6810 107 0 81 ...
## $ I561: int 638 163 18 799 4 1247 2258 377 0 72 ...
## $ I562: int 488 141 8 769 3 1516 1808 534 0 76 ...
## $ I563: int 440 119 54 725 1 984 2279 300 0 184 ...
## $ I564: int 479 147 35 715 0 1044 2299 223 0 156 ...
## $ I591: int 770 182 23 811 2 1374 4755 298 0 96 ...
## $ I592: int 430 156 8 567 8 1355 3128 318 0 70 ...
## $ I593: int 656 153 16 831 8 1437 4419 397 0 77 ...
## $ I594: int 467 177 24 694 1 1577 3726 373 0 77 ...
## $ I861: int 143 43 42 345 0 412 1452 86 0 174 ...
## $ I862: int 453 144 17 575 4 1338 1516 266 0 113 ...
## $ I863: int 429 114 22 605 0 1051 1455 281 0 69 ...
## $ I864: int 206 50 39 404 3 621 1429 164 0 176 ...
## $ I891: int 567 161 26 735 5 1434 3867 230 0 69 ...
## $ I892: int 458 195 28 651 7 1552 4718 270 0 80 ...
## $ I893: int 520 157 39 725 0 1248 4580 220 0 81 ...
## $ I894: int 474 144 30 591 5 1186 3575 229 0 62 ...Conditional structure
Decision making is important in programming. This can be achieved using an if…else statement.
The basic structure of an if…else statement is
if (condition statement){
some operation
}
Two examples of if…else statement
Temperature <- 30
if (Temperature < 32) {
print("Very cold")
}## [1] "Very cold"# recall gene_expression, we are going to design a *if...else* statement to decide treatment plans based on gene expression.
if (gene_expression["ESR1"] > 0) {
print("Treatment plan 1")
} else if (gene_expression["BRCA1"] > 0) {
print("Treatment plan 2")
} else if (gene_expression["p53"] > 0) {
print("Treatment plan 3")
} else {
print("Treatment plan 4")
}## [1] "Treatment plan 2"Save your workspace so we can load it for day 3:
save.image("day2.RData")HOMEWORK
Using the state.x77 built-in dataset, find the states whose population (in 1000’s) is greater than 950 AND whose High School Graduation Rate is less than 40%.
Construct a list with three elements:
- A vector of numbers 1 through 15 in increments of 0.2
- A 5x5 matrix using the first 25 letters of the alphabet. (Hint: look at built-in constants)
- The first 10 elements of the built-in data frame “mtcars”.