A friendly intro of R
(as friendly as possible)
Michiel Noback
Contents
- Lesson goals
- Why R?
- RStudio
- R as a calculator
GOALS
Goals
The objectives of these two lessons are
- demystify the concept of programming
- show why programming can be better than Excel (or SPSS)
- give you a friendly intro on programming with R
WHY R?
The analytical toolbox
Many tools are used in the life sciences to perform data analysis and visualisation tasks.
The most well-known one is Excel, but you may have encountered SPSS.
Another much-used tool is R, a programming language with embedded statistics and graphics support.
What is R?
Besides its free nature, R is very popular because it
- has an interactive mode (read–evaluate–print loop: REPL)
- makes repeating analyses (with new data) very easy
- supports “literate programming” for creating presentations (such as this one!) and reports
RSTUDIO
The workbench
Panels of the workbench
You work with 4 panels in the workbench:
- Code editor where you write your scripts: text file with code you want to execute more than once
- R console where you execute lines of code one by one
- Workspace and history See what data you have in memory, and what you have done so far
- Plots, Help & Files …
The console vs code editor
Use the console to do basic calculations, try pieces of code.
- Use the code editor to work on
- scripts - analyses you may want to repeat or develop further.
- data files
- analytical notebooks (RMarkdown)
USING THE CONSOLE
Let’s calculate
R, like any programming language, supports all math operations in the way you would expect them:
+ is ‘plus’, as in 2 + 2 = 4
- ditto, subtract, as in 2 - 2 = 0
* multiply
/ divide
^ exponent (identical to: **)
Remember that \(\sqrt{n} = n^{0.5}\)
Use parentheses () for grouping parts of equations.
Precedence
All “operators” adhere to the standard mathematrical precedence rules (PEMDAS):
Parentheses (simplify inside these)
Exponents
Multiplication and Division (from left to right)
Addition and Subtraction (from left to right)–Practice 1–
In the console, calculate the following:
\(31 + 11\)
\(66 - 24\)
\(\frac{126}{3}\)
\(12^2\)
\(\sqrt{256}\)
\(\frac{3*(4+\sqrt{8})}{5^3}\)
Solutions
\(31 + 11 = 42\)
\(66 - 24 = 42\)
\(\frac{126}{3} = 42\)
\(12^2 = 144\)
\(\sqrt{256} = 16\)
\(\frac{3 * (4 + \sqrt{8})}{5^3} = 0.1638823\) – in R: (3 * (4 + 8^0.5))/5^3
DATA TYPES
Four types of data
You have seen that R works with numbers. There are a few more types of data:
numeric: numbers with a decimal part:
- 3.123 & 5000.0 & 4.1E3
integer: numbers without a decimal part:
- 1 & 0 & 2999
logical: also called boolean values:
- true or false
character: text, always between quotes:
- "hello R" or "GATC"
factor: nominal and ordinal scales (visited later)
Note When you type a number in the console it will always be numeric if it is without decimal part.
FUNCTIONS
Definition
Simple mathematics is not the core business of R.
You want to do more complex things and this is where functions come in.
A function is a named piece of functionality that you can execute by typing its name, followed by a pair of parentheses. Within these parentheses, you can pass data for the function to work on. Functions may or may not return a value
It has this general form: \[function\_name(argument, argument, ...)\]
Example: Square root with sqrt()
You have already seen that the square root can be calculated as \(n^{0.5}\).
However, there is also a function for it: sqrt(). It returns the square root of the given number, e.g. sqrt(36)
sqrt(36)[1] 636^0.5[1] 6Another example: paste()
The paste function can take any number of arguments and returns them, combined into a single text. You can also supply a separator using sep=<separator string>:
paste(1, 2, 3, sep="---")[1] "1---2---3"The quotes around the three dashes"---" indicate it is text data.
Getting help on a function
Type ?function_name in the console to get help on a function.
For instance, typing ?sqrt will give the help page of the square root function.
Scroll down in the help page to see example usages of the function.
–Practice 2–
- View the help page for
paste. There are two variants of this function.- Which?
- What is the difference between them?
- Use both variants to generate exactly this message
"welcome to R"from these arguments:"welcome", "to", "R"
- Which?
- What does the
absfunction do?- What is returned by
abs(-20)and what isabs(20)?
- What is returned by
- What does the
cfunction do?- What is the difference in its result when you combine
1,3and"a"as arguments, or1,2and3?
- What is the difference in its result when you combine
VARIABLES
What are variables?
- Variables are used to name and reuse pieces of data
- E.g.,
x <- 42is used to create a variablexwith a value of42. - Variables are really variable - their value can change!
- You define a variable using
<-in R, sox <- 42is the same asx = 42 - The next chunk creates a variable called dna and then uses it in
paste():
dna <- 'GATC'
paste("The DNA letters are:", dna)[1] "The DNA letters are: GATC"–Practice 3–
Create these three variabless: x=20, y=10 and z=3.
Next, calculate the following with these variables:
- \(x+y\)
- \(x^z\)
- \(q = x \times y \times z\)
- \(\sqrt{q}\)
- \(\frac{q}{\pi}\) (pi is just
piin R) - \(\log_{10}{(x \times y)}\)
VECTORS AND INDEXING
The function c()
The function c() generates a vector from the passed arguments.
The data type will be the one that best fits the arguments.
c(1, 2, 5)[1] 1 2 5c(1, "a", 2)[1] "1" "a" "2"c(1, TRUE, FALSE)[1] 1 1 0Vectors and Indexing
- In R, all data lives inside vectors.
- A vector is a series of elements maintained as a single unit.
- Individual elements can be accessed using their index - a number between square brackets:
[]
nucleotides <- c("A", "C", "G", "T")
nucleotides
[1] "A" "C" "G" "T"
nucleotides[3] # fetch the third
[1] "G"
nucleotides[2]
[1] "C"Note the use of # to put regular text within code: this is code comment and is ignored by R.
More on indexing
- You can use any order and combination of numbers to select elements from a vector.
nucleotides <- c("A", "C", "G", "T")
nucleotides[c(3,1,4)][1] "G" "A" "T"or use a series of indices with a:b – a through b
nucleotides[1:3] [1] "A" "C" "G"–Practice 4–
Given the letters of the alphabet, available as the variable letters in R, make a selection that gives you this output:
- “b”
- “z”
- “c” “d” “e” “f”
- “d” “n” “a”
- “dna” (Look at the help for
pasteand use thecollapseargument):
Indexing on conditions
One of the nice things about indexing is that you can use logical indexing to select elements you are interested in.
q <- c(2, 1, 4)
q[c(TRUE, FALSE, TRUE)] #select with a logical[1] 2 4q > 2 #which values in q are higher than 2?[1] FALSE FALSE TRUEq[q > 2] #select those[1] 4q <- c(2, 1, 4)
q == 1
q[q == 1][1] FALSE TRUE FALSE
[1] 1q <= 3
q[q <= 3][1] TRUE TRUE FALSE
[1] 2 1q == 1 | q == 4 # using logical "OR"
q[q == 1 | q == 4][1] FALSE TRUE TRUE
[1] 1 4Overview logical operators
- Compare
>(greater then)>=(greater then or equal)<(less then)<=(less then or equal)==(equal)
- Combine
&(AND)|(OR)
- Negate
!(NOT)
Here is a named vector to demonstrate logical indexing further:
grades <- c(3.4, 5.6, 8.3, 2.9, 6.8)
# Attach names to the vector for readable display
names(grades) <- c("Ian", "Mark", "Lara", "Rowan", "Iris")
grades Ian Mark Lara Rowan Iris
3.4 5.6 8.3 2.9 6.8 grades Ian Mark Lara Rowan Iris
3.4 5.6 8.3 2.9 6.8 pass_test <- grades >= 5.5
pass_test Ian Mark Lara Rowan Iris
FALSE TRUE TRUE FALSE TRUE grades[pass_test] Mark Lara Iris
5.6 8.3 6.8 highest grade and average students
grades[grades == max(grades)]Lara
8.3 grades[grades >= 5.5 & grades < 7] Mark Iris
5.6 6.8 –Practice 5 (intro)–
Given these vectors, representing a hypothetical controlled drug test experiment:
participant_ids <- c("P01", "P02", "P03", "P04", "P05", "P06")
placebo_given <- c(FALSE, TRUE, TRUE, FALSE, TRUE, FALSE)
patient_responses <- c(76, 44, 38, 92, 28, 81)
names(placebo_given) <- participant_ids
names(patient_responses) <- participant_ids
placebo_given P01 P02 P03 P04 P05 P06
FALSE TRUE TRUE FALSE TRUE FALSE patient_responsesP01 P02 P03 P04 P05 P06
76 44 38 92 28 81 –Practice 5–
Copy the code from the previous slide and, using only logical selections, select
- those participant_ids for which a placebo was given
- those participant_ids for which NO placebo was given
- the responses for which a placebo was given
- the responses for which a placebo was given and calculate the mean of this group (using
mean()) - the highest value (using
max()) of the patients who were given a placebo - (challenge) the patient responses with a response higher than the mean of all responses
REAL DATA
Protein analysis results
The data is the analysis result of a set of proteins encoded on the Staphylococcus aureus genome.
It has been run through the sequence analysis tools SignalP, LipoP and TMHMM.
How it looks in Excel
Export to simple text file
I modified the Excel sheet a bit and then exported the data as a plain text file. The data is now in a tab-delimited file called protein_processing_pred.csv.
When opened in a simple text editor (e.g. Notepad) it looks like this.
Loading into R using read.table()
Here is how you load data files in R
No mouse clicks!
protein_data <- read.table(file = "data/protein_processing_pred.csv",
header = TRUE,
sep = ";",
dec = ",",
as.is = c(1, 2, 3, 21)) Don’t worry - you do not need to do this for the test. The next slide explains what happens.
protein_data <- read.table(file = "data/protein_processing_pred.csv",
header = TRUE,
sep = ";",
dec = ",",
as.is = c(1, 2, 3, 21))read.tableis the function you use to load data from a file. It accepts many optional arguments and only one mandatory - the file name.- the key = value pairs between the parentheses such as
sep = ";"andheader=TRUEare the function arguments that specify where the file is and how it should be loaded.
- the data are assigned to a variable called
protein_data.
- type
?read.tableif you are interested in the details.
Inspect the data: column names
names(protein_data) [1] "FASTA_Header" "Sequence" "SignalP_name" "SignalP_Cmax"
[5] "SignalP_Cmax_pos" "SignalP_Ymax" "SignalP_Ymax_pos" "SignalP_Smax"
[9] "SignalP_Smax_pos" "SignalP_Smean" "SignalP_D" "SignalP_YesNo"
[13] "SignalP_Dmaxcut" "SignalP_Networks_used" "LipoP_Localisation" "LipoP_Score"
[17] "TMHMM_Length" "TMHMM_ExpAA" "TMHMM_First60" "TMHMM_PredHel"
[21] "TMHMM_Topology" Inspect the data: the first few entries
head(protein_data) FASTA_Header
1 gi_15925706_ref_NP_373239.1_ chromosomal replication initiator protein [Staphylococcus aureus subsp. aureus N315]
2 gi_15925707_ref_NP_373240.1_ DNA polymerase III, beta chain [Staphylococcus aureus subsp. aureus N315]
3 gi_15925708_ref_NP_373241.1_ conserved hypothetical protein [Staphylococcus aureus subsp. aureus N315]
4 gi_15925709_ref_NP_373242.1_ DNA repair and genetic recombination protein [Staphylococcus aureus subsp. aureus N315]
5 gi_15925710_ref_NP_373243.1_ DNA gyrase subunit B [Staphylococcus aureus subsp. aureus N315]
6 gi_15925711_ref_NP_373244.1_ DNA gyrase subunit A [Staphylococcus aureus subsp. aureus N315]
Sequence
1 MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNV
2 MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTY
3 MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQ
4 MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIK
5 MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDF
6 MAELPQSRINERNITSEMRESFLDYAMSVIVARALPDVRDGLKPVHRRILYGLNEQGMTPDKSYKKSARIVGDVMGKYHPHGDSSIYEAMVRMAQDFSYRYPLVDGQGNFGSMDGDGAAAMRYTEARMTKITLELLRDINKDTIDFIDNYDGNEREPSVLPARFPNLLANGASGIAVGMATNIPPHNLTELINGVLSLSKNPDISIAELMEDIEGPDFPTAGLILGKSGIRRAYETGRGSIQMRSRAVIEERGGGRQRIVVTEIPFQVNKARMIEKIAELVRDKKIDGITDLRDETSLRTGVRVVIDVRKDANASVILNNLYKQTPLQTSFGVNMIALVNGRPKLINLKEALVHYLEHQKTVVRRRTQYNLRKAKDRAHILEGLRIALDHIDEIISTIRESDTDKVAMESLQQRFKLSEKQAQAILDMRLRRLTGLERDKIEAEYNELLNYISELETILADEEVLLQLVRDELTEIRDRFGDDRRTEIQLGGFEDLEDEDLIPEEQIVITLSHNNYIKRLPVSTYRAQNRGGRGVQGMNTLEEDFVSQLVTLSTHDHVLFFTNKGRVYKLKGYEVPELSRQSKGIPVVNAIELENDEVISTMIAVKDLESEDNFLVFATKRGVVKRSALSNFSRINRNGKIAISFREDDELIAVRLTSGQEDILIGTSHASLIRFPESTLRPLGRTATGVKGITLREGDEVVGLDVAHANSVDEVLVVTENGYGKRTPVNDYRLSNRGGKGIKTATITERNGNVVCITTVTGEEDLMIVTNAGVIIRLDVADISQNGRAAQGVRLIRLGDDQFVSTVAKVKEDAEDETNEDEQSTSTVSEDGTEQQREAVVNDETPGNAIHTEVIDSEENDEDGRIEVRQDFMDRVEEDIQQSSDEDEE
SignalP_name SignalP_Cmax SignalP_Cmax_pos SignalP_Ymax SignalP_Ymax_pos
1 gi_15925706_ref_NP_373239.1_ 0.111 59 0.101 41
2 gi_15925707_ref_NP_373240.1_ 0.168 39 0.179 39
3 gi_15925708_ref_NP_373241.1_ 0.109 13 0.098 33
4 gi_15925709_ref_NP_373242.1_ 0.131 36 0.133 15
5 gi_15925710_ref_NP_373243.1_ 0.124 16 0.096 25
6 gi_15925711_ref_NP_373244.1_ 0.115 35 0.104 29
SignalP_Smax SignalP_Smax_pos SignalP_Smean SignalP_D SignalP_YesNo SignalP_Dmaxcut
1 0.132 37 0.069 0.089 N 0.45
2 0.372 36 0.125 0.158 N 0.45
3 0.121 30 0.085 0.093 N 0.45
4 0.226 7 0.167 0.147 N 0.45
5 0.107 29 0.076 0.088 N 0.45
6 0.165 20 0.100 0.102 N 0.45
SignalP_Networks_used LipoP_Localisation LipoP_Score TMHMM_Length TMHMM_ExpAA TMHMM_First60
1 SignalP-TM CYT -0.200913 453 0.03 0.00
2 SignalP-TM CYT -0.200913 377 0.00 0.00
3 SignalP-TM CYT -0.200913 81 0.00 0.00
4 SignalP-TM CYT -0.200913 370 0.00 0.00
5 SignalP-TM CYT -0.200913 644 0.06 0.00
6 SignalP-TM CYT -0.200913 889 0.02 0.01
TMHMM_PredHel TMHMM_Topology
1 0 o
2 0 o
3 0 o
4 0 o
5 0 o
6 0 oInspect the data: View like Excel
If you want to have a look at the data “spreadsheet-style”, you can type View(prot_data) in the Console. The Viewer will show the data associated with the variable in the editor panel.
DATAFRAMES
What is a dataframe
- The dataset
protein_datais what is called a dataframe in R. - In a dataframe, data is organized in rows and columns.
- Columns contain measurements of a single variable; they are formed by vectors.
- Rows contain observations (here: different measurements on a protein).
- A dataframe is an ordered list of vectors of the same length.
Accessing a column of a dataframe
- Use the dollar sign
$to get hold of a single columnm
protein_data$SignalP_YesNo [1] N N N N N N N N N N N N N N N Y N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N
[48] N N N N N N N N N N N N N N N N N N N N N N N N N Y N N N Y Y N N N N N N Y N N Y N N N N N N
[95] N N N N N N N N N N N N N N N Y Y N N N N N N Y Y N Y Y N Y N N Y N Y Y Y N Y N N Y N Y Y N N
[142] Y Y Y Y Y Y Y Y Y Y Y Y N Y Y N Y Y Y Y N Y N N N N N N N Y N Y N N Y N N N Y N N Y N N N Y N
[189] Y N N N N Y Y N Y Y Y Y Y Y N N N N Y N N N Y N N Y N N Y Y Y Y Y N N Y N Y N N N Y Y Y Y Y Y
[236] Y N Y Y Y N N N N N N N N N Y N N N Y Y Y N N Y N N N N N N N N N N N N N N Y Y Y Y N Y Y Y Y
[283] Y N Y Y N N Y N N N N N Y Y N N N Y Y Y Y Y N N N Y Y Y Y Y N N N N N N N N Y N N N N Y Y N N
[330] N Y N Y Y Y N N N N N Y N N Y Y N Y N Y Y Y Y N N N N N N Y N Y Y N N Y N N N N Y N N N Y Y Y
[377] Y Y Y Y N Y Y Y N N N
Levels: N Y- Here is a table summary of column “SignalP_YesNo”, showing that 134 out of 387 proteins have a putative signal sequence:
table(protein_data$SignalP_YesNo)
N Y
253 134 Since it is a vector, you can use indexing on a column:
protein_data$SignalP_YesNo[330:345] [1] N Y N Y Y Y N N N N N Y N N Y Y
Levels: N YThe number of rows and columns
Get the dimensions of the dataset:
dim(protein_data)[1] 387 21This is a vector of two integers. Which one is the number of rows?
Indexing on dataframes
- Indexing on whole dataframes is like using a coordinate system:
dataframe[rows, columns] - Leave empty if you want all values of the row or column
- The next few slides use this example dataframe called
students
students <- data.frame(sid=paste0("S0", 1:5),
name=c("Mark", "Lynn", "Lianne", "Peter", "Rose"),
sex=factor(c("m", "f", "f", "m", "f"),
labels = c("female", "male")),
biology=c(5.6, 6.2, 7.9, 4.4, 9.1),
statistics=c(6.1, 5.1, 8.0, 4.7, 7.3),
informatics=c(6.3, 6.1, 7.7, 5.4, 9.5),
stringsAsFactors = F)
students sid name sex biology statistics informatics
1 S01 Mark male 5.6 6.1 6.3
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
4 S04 Peter male 4.4 4.7 5.4
5 S05 Rose female 9.1 7.3 9.5 sid name sex biology statistics informatics
1 S01 Mark male 5.6 6.1 6.3
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
4 S04 Peter male 4.4 4.7 5.4
5 S05 Rose female 9.1 7.3 9.5students[1, 2] # row 1, second value[1] "Mark"students[2, 4:6] # all grades of Lynn biology statistics informatics
2 6.2 5.1 6.1 sid name sex biology statistics informatics
1 S01 Mark male 5.6 6.1 6.3
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
4 S04 Peter male 4.4 4.7 5.4
5 S05 Rose female 9.1 7.3 9.5students[, 2] # all student names - same as students$name[1] "Mark" "Lynn" "Lianne" "Peter" "Rose" students[2, ] # row 2 sid name sex biology statistics informatics
2 S02 Lynn female 6.2 5.1 6.1Using logical selections
sid name sex biology statistics informatics
1 S01 Mark male 5.6 6.1 6.3
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
4 S04 Peter male 4.4 4.7 5.4
5 S05 Rose female 9.1 7.3 9.5All grades for Lynn:
students[students$name == "Lynn", 4:6] biology statistics informatics
2 6.2 5.1 6.1 sid name sex biology statistics informatics
1 S01 Mark male 5.6 6.1 6.3
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
4 S04 Peter male 4.4 4.7 5.4
5 S05 Rose female 9.1 7.3 9.5All grades for girls:
students[students$sex == "female", ] sid name sex biology statistics informatics
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7
5 S05 Rose female 9.1 7.3 9.5–Practice 6–
To get the student data into your session, type source("https://git.io/fjfMW") in the console. It is this file: https://raw.githubusercontent.com/MichielNoback/intro_R_lessons/gh-pages/data/intro_lesson_data.R
Type students or View(students) in the console to verify you have it
- select all
informaticsgrades - select the whole third and fourth rows
- select the
statisticsgrade for Peter - select the biology and statistics grades for the female students
- select the student names where the biology grade is below 6
–Practice 6 Solutions–
1: select all informatics grades: two alternatives
students$informatics[1] 6.3 6.1 7.7 5.4 9.5students[, 5][1] 6.1 5.1 8.0 4.7 7.32: select the third and fourth row entirely: two alternatives
students[2:3, ] sid name sex biology statistics informatics
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.7students[c(2, 3), ] sid name sex biology statistics informatics
2 S02 Lynn female 6.2 5.1 6.1
3 S03 Lianne female 7.9 8.0 7.73: select the statistics grade for Peter: three alternatives
students[4, 5] # simple[1] 4.7students$statistics[4] # also OK[1] 4.7students[students$name == "Peter", 5] # better[1] 4.74: select the biology and statistics grades for the female students
students[students$sex == "female", c(4, 5)] biology statistics
2 6.2 5.1
3 7.9 8.0
5 9.1 7.35: select the student names where the biology grade is below 6
students[students$biology < 6, 2][1] "Mark" "Peter"CREATING FIGURES
Load the data yourself.
If you want to tag along, download and load the file as follows:
protein_data <- read.table(file = "https://git.io/fjfum",
header = TRUE,
sep = ";",
dec = ",",
as.is = c(1, 2, 3, 21))(if the short URL does not work, use
“https://raw.githubusercontent.com/MichielNoback/intro_R_lessons/gh-pages/data/protein_processing_pred.csv”)
A scatterplot
Let’s investigate the relation Cmax vs Ymax of the SignalP analysis:
head(protein_data$SignalP_Cmax)[1] 0.111 0.168 0.109 0.131 0.124 0.115head(protein_data$SignalP_Ymax)[1] 0.101 0.179 0.098 0.133 0.096 0.104See http://www.cbs.dtu.dk/services/SignalP-4.1/output.php for a description of these analysis results.
Create variables for convenience, and plot:
cmax <- protein_data$SignalP_Cmax
ymax <- protein_data$SignalP_Ymax
plot(x = cmax, y = ymax)
Tweak a little
plot(x = cmax, y = ymax,
xlab = "Cmax value", ylab="Ymax value",
pch=19, cex = 0.8, col=rgb(0, 0, 1, 0.3))
Or, even better, with a log transform:
plot(x = log2(cmax), y = log2(ymax),
xlab = "log2(Cmax value)", ylab="log2(Ymax value)",
pch=19, cex = 0.8, col=rgb(0, 0, 1, 0.3))
Add a regression line
plot(x = log2(cmax), y = log2(ymax),
xlab = "log2(Cmax value)", ylab="log2(Ymax value)",
pch=19, cex = 0.8, col=rgb(0, 0, 1, 0.3))
model <- lm(log2(ymax) ~ log2(cmax))
abline(model, col = "red", lwd=2)
Or with a smoother?
scatter.smooth(x = log2(cmax), y = log2(ymax),
xlab = "log2(Cmax value)", ylab="log2(Ymax value)",
pch=19, cex = 0.8, col=rgb(0, 0, 1, 0.3))
abline(model, col = "red", lwd=2)
Summary
- You have seen R at work over a variety of activities, including basic plotting.
- Creating a thorough analysis is quite some work - as with any analysis platform, but repeating the analysis with new data is as simple as a mouse click.
- Advise: use RMarkdown whenever you are going to do a data analysis project.
On the test
What is expected of you at the test of this course:
You should be be able to
- perform basic mathematic operations
- apply selections on vectors and dataframes
- read documentation for a function
- use existing functions correctly
You will be allowed to use all R documentation as well as this presentation.
FINAL ASSIGNMENT
Final assignment
Download the assignment RMarkdown document from this location:
Long URL:
https://michielnoback.github.io/intro_R_lessons/final_assignment_R_BMR.Rmd
Web-page view:
https://michielnoback.github.io/intro_R_lessons/final_assignment_R_BMR.html
Save the file on your computer, open it in RStudio and then deal with the assignments. Put your solutions where it says
## YOUR CODE HERE.
You can process the document into Word form by klicking “Knit” Submit this Word document.