IST 3108 Data Analysis and Graphics Using R. Summarizing Data Data Import-Export

Transcription

1 IST 3108 Data Analysis and Graphics Using R Summarizing Data Data Import-Export Engin YILDIZTEPE, PhD Working with Vectors and Logical Subscripts >x<-1:20 >sum(x) how many of the values were less than 10: >sum(x<10) sum of the values of x that are less than 10: >sum(x[x<10]) 2 1

2 Working with Vectors and Logical Subscripts sort, rev >x<-c(3,5,12,6,7,10,7,2,4,9,3,2) >rev(x) [1] >sort(x) [1] How can you sort the values in decreasing order? > sort(x,decreasing=true) [1] > rev(sort(x)) [1] Working with Vectors and Logical Subscripts sort, rev >x<-c(3,5,12,6,7,10,7,2,4,9,3,2) What is the sum of the three largest values in x? > sum(sort(x,true)[1:3]) [1]

3 order() function order() returns a permutation which rearranges its first argument into ascending or descending order. (you need to sort a series of variables according to the values of some other variables) Example: Sort mtcars values sorted by mpg > order(mtcars$mpg) [1] > mtcars[order(mtcars$mpg),] > mtcars[rev(order(mtcars$mpg)),] > mtcars[order(mtcars$mpg,mtcars$qsec),] 5 Working with Vectors and Logical Subscripts To extract every 25th value in a 1000-long vector of normal random numbers with mean value 0 and std. dev. 1 >x<-rnorm(1000,0,1) >x[seq(25,length(x),25)] 6 3

4 Summarizing Data Name mean() median() summary() min(), max() quantile() var(), sd() cov(), cor() Operation arithmetic mean sample median generic summary function for data smallest/largest values calculate sample quantiles (percentiles) sample variance, sample std. dev. sample covariance/correlation 7 Summarizing Data summary() data(mtcars) # load in dataset attach(mtcars) # add mtcars to search path?mtcars mtcars >summary(mpg) Min. 1st Qu. Median Mean 3rd Qu. Max

5 Summarizing Data quantile() mean(hp) [1] var(mpg) [1] #qsec :1/4 mile (402 m) time in seconds quantile(qsec) 0% 25% 50% 75% 100% Summarizing Data quantile() >quantile(qsec, probs = c(0.20, 0.80)) # 20th and 80th percentiles 20% 80% How can we get this result? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% > per<-seq(0,1,0.1) > per [1]

6 Summarizing Data quantile() > quantile(qsec,probs=per) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% > quantile(qsec,probs=per,type=1) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% > length(qsec) [1] 32 > sort(qsec) Summarizing Data quantile() Can we compute the median with quantile()? > median(qsec) [1] > quantile(qsec, probs= 0.5) 50% > quantile(qsec, probs= 0.5,type=1) 50%

7 > cor(wt,mpg) [1] Summarizing Data Example > cor(hp,disp) [1] Summarizing Data table() For the discrete variables, we can get summary counts; > table(mtcars$cyl) > table(mtcars$cyl,mtcars$gear)

8 Data Export write.table(x, file = "", append = FALSE, quote = TRUE, sep= " ", eol = "\n", na = "NA", dec = ".", row.names=true, col.names = TRUE, qmethod=c("escape", "double"), fileencoding = "") write.table prints its required argument x (after converting it to a data frame if it is not one nor a matrix) to a file 15 Data Export - Example >x<-rnorm(20) >write.table(x, file="s:\\x1.txt") or >write.table(x, file="s:/x1.txt") 16 8

9 Data Export - Examples >m <- matrix(1:20,ncol=5) >m >write.table(m,file= s:\\m1.txt") >write.table(m,file= s:\\m2.txt", sep = "\t") >write.table(m,file= s:\\m3.txt", sep = "\n") >write.table(m,file= s:\\m4.txt", sep = ";", row.names=f, col.names = F) 17 Data Export - Examples >x<-rnorm(20) >y<-rnorm(20) >df<-data.frame(x,y) >write.table(df,file=" s:\\df1.txt", sep = "\t", row.names=f, col.names = F) >write.table(df,file=" s:\\df2.txt", sep = "\t",dec=',, row.names=f, col.names = F) 18 9

10 Data Export - Examples Export mtcars data.frame to a tab delimated text file. >write.table(mtcars,file="s:\\carsdata.txt", sep="\t", dec=',', row.names=t, col.names = T) 19 Data Export - Examples Export mtcars data.frame to a tab delimated text file. >write.table(mtcars,file=file.choose(), sep="\t", dec=',', row.names=t, col.names = T) 20 10

11 Data Import read.table(file, header = FALSE, sep = "", quote = "\"'", dec = ".", row.names, col.names, as.is =!stringsasfactors, na.strings = "NA", colclasses = NA, nrows = -1, Reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file. 21 Data Import - Examples new.df1<-read.table(file="s:/df1.txt", header=f, sep="\t", dec=".") new.df2<-read.table(file="s:/df2.txt", header=f, sep="\t", dec=",") 22 11

12 Data Import - Examples >cars<-read.table(file=file.choose(),header=t, sep="\t", dec=',') >cars 23 Data Import - Examples >m <- matrix(1:20,ncol=5) >write.table(m,file="m5.txt", sep = " ",row.names=f, col.names = T) >new.m<-read.table(file= m5.txt", header=t, sep=" ", dec=".") >new.m >class(new.m) 24 12

13 Variants of read.table Data Import read.csv(file, header=t, sep = ",", dec=".",...) read.csv2(file, header=t, sep=";", dec=",",...) The former assumes that fields are seperated by comma, and the latter assumes that they are seperated by semicolons but use a comma as the decimal point (the format in Turkey and European) 25 Data Import Further variants of read.table for tab delimited files read.delim (file, header=t, sep = "\t", dec=".",...) read.delim2(file, header=t, sep = "\t", dec=",",...) The former assumes that decimal point is period, and the latter assumes that the decimal point is comma. Example: air<-read.delim2(file="data1.txt") 26 13