BIO5312: R Session 1 An Introduction to R and Descriptive Statistics

Transcription

1 BIO5312: R Session 1 An Introduction to R and Descriptive Statistics Yujin Chung August 30th, 2016 Fall, 2016 Yujin Chung R Session 1 Fall, /24

2 Introduction to R R software R is both open source and open development. You can look at the source code and you can propose changes You can write R functions and publish them. R is available for many platforms: Unix of many flavors, including Linux, Solaris, FreeBSD, AIX Windows 95 and later Mac OS X Binaries and source code are available from The R Console Basic interaction with R is by typing in the console, a.k.a. terminal or command-line You type in commands, R gives back answers (or errors) Yujin Chung R Session 1 Fall, /24

3 RStudio RStudio allows the user to run R in a more user-friendly environment. It is open-source (i.e. free) and available at R console R script/editor Environment/Workspace: all the active object History: a list of commands used so far Files: shows all the files and folders in your default working directory changing working directory: More Set As Working Directory Plots, Packages, Help Yujin Chung R Session 1 Fall, /24

4 Quick start Mathematical operators/functions > log(64) # natural logarithm [1] > sqrt(2) # square root [1] Q) What are the R outputs of the followings? 7+5, 7/5, 7*5, 7-2, 7^2, 7%%5, 7%/%5 Comparisons are also binary operators: they take two objects, like numbers, and give a Boolean > 7 == 5 # 7 is equal to 5 [1] FALSE 7 > 5 # 7 is larger than 5 [1] TRUE 7!= 5 # 7 is not equal to 5 [1] TRUE Yujin Chung R Session 1 Fall, /24

5 Quick start II Boolean operators: & (and), (or) Q) What are the R outputs of the followings? (5>7) & (6*7 == 42) (5>7) (6*7 == 42)!(5>7) (6*7 == 42) R help > help(log) # or >?log Yujin Chung R Session 1 Fall, /24

6 Operators Arithmetic operators Operator Description + addition - subtraction * multiplication / division ˆ or ** exponentiation x %% y remainder x %/% y quotient Logical Operators 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 x y x OR y x & y x AND y istrue(x) test if X is TRUE Yujin Chung R Session 1 Fall, /24

7 Data type We can give names to data objects; these give us variables! Variables are created with the assignment operator: = or <- (arrow) Numeric: numbers, either floating point or integer > x=5 # or x <- 5 character : a character string > x = "I like chocolate ice cream" logical : TRUE or FALSE > x = (1 > 2) built-in variables. E.g. TRUE (or T), FALSE (or F) Yujin Chung R Session 1 Fall, /24

8 Data structures Group of data values into one object of type including vector data frame list matrix factors tables Some R packages have their own data structure Yujin Chung R Session 1 Fall, /24

9 Data structures: vectors Vectors: a sequence of values of numerical, character or logical. Function c() returns a vector containing all its arguments in order. numeric vector > x = c(1,2,3) > x [1] > length(x) # the length of x [1] 3 character > x=c("a","b") > x [1] "a" "b" > length(x) [1] 2 Yujin Chung R Session 1 Fall, /24

10 Data structures: vectors II Sequence generators > x = seq(from=1, to=3, by=1) # or seq(1,3,1) > x [1] > x = 1:3 # same as seq(1,3,1) Extracting sub-vectors > x[2] # return the 2nd elements [1] 2 > x[2:3] # extracting subset from the 2nd to 3rd elements [1] 2 3 > x[c(2,3)] # same as x[2:3] > x[-2] # drop off the 2nd elementsx=c("a","b") [1] 1 3 Yujin Chung R Session 1 Fall, /24

11 Data structures: vectors III Element-wise arithmetic > x = 1:5 > x+1 [1] > x <= 3 [1] TRUE TRUE TRUE FALSE FALSE Pairwise arithmetic > x = 1:5 > y = c(-1, 0, 3:5) > x+y [1] > x == y [1] FALSE FALSE TRUE TRUE TRUE Yujin Chung R Session 1 Fall, /24

12 Data structure: data frames Data frames: a data set that can be represented as a set of observations (rows) on several variables (columns). Example: Fishers or Andersons iris data set (built-in) > iris Sepal.Length Sepal.Width Petal.Length Petal.Width S ## Basic information of data frame > names(iris) # variable names [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Wi > dim(iris) # the numbers of rows and columns [1] > nrow(iris) # the number of rows [1] 150 > ncol(iris) # the number of columns Yujin Chung R Session 1 Fall, /24

13 Data structure: data frames II Extracting subset > iris$sepal.length # extracting variable (1st column) [1] [19] > iris[,1] # extracting the 1st column [1] [19] > iris[2,] # extracting the 2nd row Sepal.Length Sepal.Width Petal.Length Petal.Width Specie setos > iris[1,1] # the element of the 1st column & the 1st row [1] 5.1 Yujin Chung R Session 1 Fall, /24

14 Data structure: data frames III Each of columns or rows is a vector > sepal = iris$sepal.length # extracting variable (1st col > length(sepal) [1] 150 > temp = iris[1:10,2] > length(temp) [1] 10 Yujin Chung R Session 1 Fall, /24

15 Writing data Writing a data set in a text or CSV file write.table(x,file,quote,sep,row.names,col.names,...) x: data object to save, file: file name quote: if TRUE, elements surrounded by double quotes. If FALSE, nothing is quoted sep: the field separator string. Values within each row are separated by this string row.names: a logical value indicating whether the row (or column) names of x are to be written along with x col.names: a logical value indicating whether the column names of x are to be written along with x > write.table(iris, file = "iris.txt", quote=f, sep = " ", row.names = F, col.names=t) Yujin Chung R Session 1 Fall, /24

16 Reading data Reading a data set in a text or CSV file read.table(file, header, sep =,...) file: data file to read header: a logical value indicating whether the file contains the names of the variables as its first line sep: the field separator string. Values within each row are separated by this string > iris2 = read.table("iris.txt", header =T, sep=" ",) > lead = read.table("lead.dat.txt",header=t) Yujin Chung R Session 1 Fall, /24

17 Functions Built-in functions: write.table, read.table, read.cross, etc > names(lead) ## variable names of "lead" [1] "id" "area" "ageyrs" "sex" "iqv_inf" [7] "iqv_ar" "iqv_ds" "iqv_raw" "iqp_pc" "iqp_bd" [13] "iqp_cod" "iqp_raw" "hh_index" "iqv" "iqp" [19] "iq_type" "lead_grp" "Group" "ld72" "ld73" [25] "totyrs" "pica" "colic" "clumsi" "irrit" [31] "X_2plat_r" "X_2plar_l" "visrea_r" "visrea_l" "audrea_r [37] "fwt_r" "fwt_l" "hyperact" "maxfwt" > dim(lead) [1] Yujin Chung R Session 1 Fall, /24

18 min(), max() and range() min(x): the minimum of the argument x max(x): the maximum of the argument x range(): the minimum and maximum of the argument x > fwt = lead$maxfwt # extracting "maxfwt" and creating a new v > fwt [1] [26] > min(fwt) [1] 13 > max(fwt) [1] 99 > range(fwt) [1] > diff(range(fwt)) # the "range" of fwt [1] 86 Yujin Chung R Session 1 Fall, /24

19 mean() mean(): the arithmetic mean of the argument sum(): the sum of the argument > mean(fwt) # arithmetic mean of fwt [1] > sum(fwt)/length(fwt) [1] > colmeans(lead) # the mean of each column id area ageyrs sex iqv_inf iqv_c cf) colmeans(), rowmeans(), colsums(), rowsums() Yujin Chung R Session 1 Fall, /24

20 median() and quantile() median(x): the median of the argument x quantile(x): returns the minimum, Q1, Q2 (median), Q3, maximum of x IQR(x): the IQR of x > mean(fwt) # the median of fwt [1] 56 > quantile(fwt) 0% 25% 50% 75% 100% > IQR(fwt) [1] 24 > quantile(fwt,probs=.25) # Q1, the 25th percentile 25% 48 > quantile(fwt,probs=.75) - quantile(fwt,probs=.25) # IQR 75% 24 Yujin Chung R Session 1 Fall, /24

21 var() and sd() var(x): the variance of the argument x sd(x): the standard deviation of the argument x > var(fwt) [1] > sd(fwt) [1] > n = length(fwt) > n [1] 124 > sum( (fwt - mean(fwt))^2 )/ (n-1) # variance [1] > sqrt( sum( (fwt - mean(fwt))^2 )/(n-1) ) # standard deviatio [1] Yujin Chung R Session 1 Fall, /24

22 summary() summary(x): the minimum, Q1, median, mean, Q3 and maximum of the argument x > summary(fwt) Min. 1st Qu. Median Mean 3rd Qu. Max Yujin Chung R Session 1 Fall, /24

23 Writing and calling functions The structure of a function <function name> = function(arg1, arg2,... ){ statements return(object) } We write another summary function, called mysummary, that returns the mean and standard deviation of an argument variable after removing missing values. > mysummary = function(dat){ # define a function res = c(mean(dat), sd(dat)) return(res) } > mysummary(fwt) # calling a function [1] Yujin Chung R Session 1 Fall, /24

24 More intro Some R resources The official intro, An Introduction to R, available online in Norman Matloff, The Art of R Programming: A Tour of Statistical Software Design Phil Spector, Data Manipulation with R Paul Teetor, The R Cookbook Yujin Chung R Session 1 Fall, /24