Basic R QMMA. Emanuele Taufer. 2/19/2018 Basic R (1)

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1 Basic R QMMA Emanuele Taufer file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 1/21

2 Preliminary R is case sensitive: a is not the same as A. Commands are separated either by a semi-colon ( ; ), or by a newline. When you use the R program it issues a prompt when it expects input commands. The default prompt is >. If a command is not complete at the end of a line, R will give a different prompt, by default +. Comments can be put almost anywhere, starting with a hashmark ( # ). file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 2/21

3 Objects The entities that R creates and manipulates are known as objects. During an R session, objects are created and stored by name The R command objects() can be used to display the names of (most of) the objects which are currently stored. To remove objects the function rm() is available: rm(x, y, z, ink, junk, temp, foo, bar) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 3/21

4 Objects - 2 R has five basic or atomic classes of objects: character numeric (real numbers) integer complex logical (True/False) Vectors and lists Vector: can only contain objects of the same class List: represented as a vector but can contain objects of different classes file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 4/21

5 Entering Input At the R prompt we type expressions. The <- symbol is the assignment operator (= can be used as well - with some exceptions) x<- 3 x # assigns value 1 to an object (vector) x # prints x on the screen ## [1] 3 The [1] indicates that x is a vector and 3 is the first element text<- "Hello" text ## [1] "Hello" file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 5/21

6 Creating vectors The : operator is used to create integer sequences. x<- 1:10 x ## [1] The c() function can be used to create vectors of objects. x<-c(1,3,5) x # numeric ## [1] x<-c("a","c", "f") x<-c(true,false) #character # Logical, also T,F file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 6/21

7 Explicit Coercion Objects can be explicitly coerced from one class to another using the as.* functions, if available. x<- 1:6 class(x) ## [1] "integer" y=as.numeric(x) y ## [1] class(y) ## [1] "numeric" file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 7/21

8 as.character(x) ## [1] "1" "2" "3" "4" "5" "6" class(x) ## [1] "integer" y<-as.character(x) class(y) ## [1] "character" z<-as.factor(x) class(z) ## [1] "factor" str(z) ## Factor w/ 6 levels "1","2","3","4",..: file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 8/21

9 Factors Factors are used to represent categorical data. Factors can be unordered or ordered. One can think of a factor as an integer vector where each integer has a label. Factors are treated specially by modelling functions like lm() and glm() Using factors with labels is better than using integers because factors are self-describing; having a variable that has values Male and Female is better than a variable that has values 1 and 2. file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 9/21

10 Factors -example x<-c(0,0,0,1,1,0,1) class(x) ## [1] "numeric" y<-factor(x,levels=c(0,1),labels=c("female","male")) class(y) ## [1] "factor" y ## [1] Female Female Female Male Male Female Male ## Levels: Female Male file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 10/21

11 Data Frames Data frames are used to store tabular data -They are represented as a special type of list where every element of the list has to have the same length -Each element of the list can be thought of as a column and the length of each element of the list is the number of rows -Unlike matrices, data frames can store different classes of objects in each column (just like lists); matrices must have every element be the same class -Data frames also have a special attribute called row.names -Data frames are created by using data.frame() or by calling data(), read.table() or read.csv(), -Can be converted to a matrix by calling data.matrix() file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 11/21

12 Using data.frame() Define the variables x<-seq(1:10) y<-c(1,2,4,7,8,3,4,5,3,2) z<-c("a","b","b","b","a","a","c","c","c","a") Build the data frame df<-data.frame("y"=y,"x"=x,"group"=z) df ## y x Group ## a ## b ## b ## b ## a ## a ## c ## c ## c ## a Analyze the structure str(df) ## 'data.frame': 10 obs. of 3 variables: ## $ y : num ## $ x : int ## $ Group: Factor w/ 3 levels "a","b","c": file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 12/21

13 Uploading data - data() function Used to upload data-sets already available in R. Type data() to see the available data-sets. data(mtcars) head(mtcars) #show the first rows of the data-set ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX ## Mazda RX4 Wag ## Datsun ## Hornet 4 Drive ## Hornet Sportabout ## Valiant Type help(mtcars) to get info on the variables help(mtcars) file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 13/21

14 Analyze the structure and other elements str(mtcars) ## 'data.frame': 32 obs. of 11 variables: ## $ mpg : num ## $ cyl : num ## $ disp: num ## $ hp : num ## $ drat: num ## $ wt : num ## $ qsec: num ## $ vs : num ## $ am : num ## $ gear: num ## $ carb: num names(mtcars) ## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" ## [11] "carb" attributes(mtcars) ## $names ## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" ## [11] "carb" ## ## $row.names ## [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" ## [4] "Hornet 4 Drive" "Hornet Sportabout" "Valiant" ## [7] "Duster 360" "Merc 240D" "Merc 230" ## [10] "Merc 280" "Merc 280C" "Merc 450SE" ## [13] "Merc 450SL" "Merc 450SLC" "Cadillac Fleetwood" ## [16] "Lincoln Continental" "Chrysler Imperial" "Fiat 128" ## [19] "Honda Civic" "Toyota Corolla" "Toyota Corona" ## [22] "Dodge Challenger" "AMC Javelin" "Camaro Z28" ## [25] "Pontiac Firebird" "Fiat X1-9" "Porsche 914-2" ## [28] "Lotus Europa" "Ford Pantera L" "Ferrari Dino" ## [31] "Maserati Bora" "Volvo 142E" ## ## $class ## [1] "data.frame" file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 14/21

15 Summary statistics summary(mtcars) ## mpg cyl disp hp ## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 ## 1st Qu.: st Qu.: st Qu.: st Qu.: 96.5 ## Median :19.20 Median :6.000 Median :196.3 Median :123.0 ## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 ## 3rd Qu.: rd Qu.: rd Qu.: rd Qu.:180.0 ## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 ## drat wt qsec vs ## Min. :2.760 Min. :1.513 Min. :14.50 Min. : ## 1st Qu.: st Qu.: st Qu.: st Qu.: ## Median :3.695 Median :3.325 Median :17.71 Median : ## Mean :3.597 Mean :3.217 Mean :17.85 Mean : ## 3rd Qu.: rd Qu.: rd Qu.: rd Qu.: ## Max. :4.930 Max. :5.424 Max. :22.90 Max. : ## am gear carb ## Min. : Min. :3.000 Min. :1.000 ## 1st Qu.: st Qu.: st Qu.:2.000 ## Median : Median :4.000 Median :2.000 ## Mean : Mean :3.688 Mean :2.812 ## 3rd Qu.: rd Qu.: rd Qu.:4.000 ## Max. : Max. :5.000 Max. :8.000 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 15/21

16 Uploading data - read.table() Generic function for reading data set from external sources. From the help(): read.table(file, # file (with path if necessary) header = FALSE, # T,F sep = "", # "," for csv files (";" or "") row.names, # can provide a vector of row names col.names, # can provide a vector of col names na.strings = "NA") Upload the file Auto.data from a webpage data<-read.table(" header = TRUE, sep="") head(data) ## mpg cylinders displacement horsepower weight acceleration year origin ## ## ## ## ## ## ## name ## 1 chevrolet chevelle malibu ## 2 buick skylark 320 ## 3 plymouth satellite ## 4 amc rebel sst ## 5 ford torino ## 6 ford galaxie 500 file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 16/21

17 Everything seems fine, however, let us read the structure of the data frame str(data) ## 'data.frame': 397 obs. of 9 variables: ## $ mpg : num ## $ cylinders : int ## $ displacement: num ## $ horsepower : Factor w/ 94 levels "?","100.0","102.0",..: ## $ weight : num ## $ acceleration: num ## $ year : int ## $ origin : int ## $ name : Factor w/ 304 levels "amc ambassador brougham",..: file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 17/21

18 Missing Values Missing values are denoted by NA or NaN for undefined mathematical operations. is.na() is used to test objects if they are NA is.nan() is used to test for NaN NA values have a class also, so there are integer NA, character NA, etc. A NaN value is also NA but the converse is not true file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 18/21

19 Back to the Auto.data In the Auto.data, the missing values are indicated with?. We need to give this information to R in order to read the data correctly. data<-read.table(" header = TRUE, sep="", na.strings="?") str(data) ## 'data.frame': 397 obs. of 9 variables: ## $ mpg : num ## $ cylinders : int ## $ displacement: num ## $ horsepower : num ## $ weight : num ## $ acceleration: num ## $ year : int ## $ origin : int ## $ name : Factor w/ 304 levels "amc ambassador brougham",..: file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 19/21

20 How many missing values? We can try to check how many missing values there are in the variable horsepower is.na(data$horsepower) ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE ## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [100] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [122] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE ## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [155] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [166] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [188] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [199] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [210] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [232] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [243] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [254] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [276] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [298] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [320] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [331] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE ## [342] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [353] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [364] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [375] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [386] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## [397] FALSE file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 20/21

21 The vector above is defined a logical vector If we want to avoid checking the vector element by element, we can simply compute its sum (automatically a 1 will be assigned to TRUE and 0 otherwise) sum(is.na(data$horsepower)) ## [1] 5 If I want to check all the data set I can use colsums(is.na(data)) ## mpg cylinders displacement horsepower weight ## ## acceleration year origin name ## file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20classes/1-3_basic_r.html#(1) 21/21

Introduction for heatmap3 package

Introduction for heatmap3 package Shilin Zhao April 6, 2015 Contents 1 Example 1 2 Highlights 4 3 Usage 5 1 Example Simulate a gene expression data set with 40 probes and 25 samples. These samples are