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Hands on R session from Machine learning workshop
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| ### Hands on R session by Harshad Saykhedkar from today's Machine | |
| ### learning workshop (Fifth elephant Mumbai run-up event) | |
| --- | |
| R version 3.1.0 (2014-04-10) -- "Spring Dance" | |
| Copyright (C) 2014 The R Foundation for Statistical Computing | |
| Platform: i686-pc-linux-gnu (32-bit) | |
| R is free software and comes with ABSOLUTELY NO WARRANTY. | |
| You are welcome to redistribute it under certain conditions. | |
| Type 'license()' or 'licence()' for distribution details. | |
| Natural language support but running in an English locale | |
| R is a collaborative project with many contributors. | |
| Type 'contributors()' for more information and | |
| 'citation()' on how to cite R or R packages in publications. | |
| Type 'demo()' for some demos, 'help()' for on-line help, or | |
| 'help.start()' for an HTML browser interface to help. | |
| Type 'q()' to quit R. | |
| > options(STERM='iESS', str.dendrogram.last="'", editor='emacsclient', show.error.locations=TRUE) | |
| > require('tm') | |
| Loading required package: tm | |
| > typeof(1) | |
| [1] "double" | |
| > typeof("c") | |
| [1] "character" | |
| > typeof("car") | |
| [1] "character" | |
| > typeof(TRUE) | |
| [1] "logical" | |
| > typeof(FALSE) | |
| [1] "logical" | |
| > as.factor(c("a", "b")) | |
| [1] a b | |
| Levels: a b | |
| > c("a", "b") | |
| [1] "a" "b" | |
| > typeof(c("a", "b")) | |
| [1] "character" | |
| > is.vector(c("a", "b")) | |
| [1] TRUE | |
| > class(c("a", "b")) | |
| [1] "character" | |
| > | |
| > list(c("a", "b"), 1) | |
| [[1]] | |
| [1] "a" "b" | |
| [[2]] | |
| [1] 1 | |
| > library(datasets) | |
| > library(help = "datasets") | |
| Information on package ‘datasets’ | |
| Description: | |
| Package: datasets | |
| Version: 3.1.0 | |
| Priority: base | |
| Title: The R Datasets Package | |
| Author: R Core Team and contributors worldwide | |
| Maintainer: R Core Team <R-core@r-project.org> | |
| Description: Base R datasets | |
| License: Part of R 3.1.0 | |
| Built: R 3.1.0; ; 2014-04-10 20:21:40 UTC; unix | |
| Index: | |
| AirPassengers Monthly Airline Passenger Numbers 1949-1960 | |
| BJsales Sales Data with Leading Indicator | |
| BOD Biochemical Oxygen Demand | |
| CO2 Carbon Dioxide Uptake in Grass Plants | |
| ChickWeight Weight versus age of chicks on different diets | |
| DNase Elisa assay of DNase | |
| EuStockMarkets Daily Closing Prices of Major European Stock | |
| Indices, 1991-1998 | |
| Formaldehyde Determination of Formaldehyde | |
| HairEyeColor Hair and Eye Color of Statistics Students | |
| Harman23.cor Harman Example 2.3 | |
| Harman74.cor Harman Example 7.4 | |
| Indometh Pharmacokinetics of Indomethacin | |
| InsectSprays Effectiveness of Insect Sprays | |
| JohnsonJohnson Quarterly Earnings per Johnson & Johnson Share | |
| LakeHuron Level of Lake Huron 1875-1972 | |
| LifeCycleSavings Intercountry Life-Cycle Savings Data | |
| Loblolly Growth of Loblolly pine trees | |
| Nile Flow of the River Nile | |
| Orange Growth of Orange Trees | |
| OrchardSprays Potency of Orchard Sprays | |
| PlantGrowth Results from an Experiment on Plant Growth | |
| Puromycin Reaction Velocity of an Enzymatic Reaction | |
| Theoph Pharmacokinetics of Theophylline | |
| Titanic Survival of passengers on the Titanic | |
| ToothGrowth The Effect of Vitamin C on Tooth Growth in | |
| Guinea Pigs | |
| UCBAdmissions Student Admissions at UC Berkeley | |
| UKDriverDeaths Road Casualties in Great Britain 1969-84 | |
| UKLungDeaths Monthly Deaths from Lung Diseases in the UK | |
| UKgas UK Quarterly Gas Consumption | |
| USAccDeaths Accidental Deaths in the US 1973-1978 | |
| USArrests Violent Crime Rates by US State | |
| USJudgeRatings Lawyers' Ratings of State Judges in the US | |
| Superior Court | |
| USPersonalExpenditure Personal Expenditure Data | |
| VADeaths Death Rates in Virginia (1940) | |
| WWWusage Internet Usage per Minute | |
| WorldPhones The World's Telephones | |
| ability.cov Ability and Intelligence Tests | |
| airmiles Passenger Miles on Commercial US Airlines, | |
| 1937-1960 | |
| airquality New York Air Quality Measurements | |
| anscombe Anscombe's Quartet of 'Identical' Simple Linear | |
| Regressions | |
| attenu The Joyner-Boore Attenuation Data | |
| attitude The Chatterjee-Price Attitude Data | |
| austres Quarterly Time Series of the Number of | |
| Australian Residents | |
| beavers Body Temperature Series of Two Beavers | |
| cars Speed and Stopping Distances of Cars | |
| chickwts Chicken Weights by Feed Type | |
| co2 Mauna Loa Atmospheric CO2 Concentration | |
| crimtab Student's 3000 Criminals Data | |
| datasets-package The R Datasets Package | |
| discoveries Yearly Numbers of Important Discoveries | |
| esoph Smoking, Alcohol and (O)esophageal Cancer | |
| euro Conversion Rates of Euro Currencies | |
| eurodist Distances Between European Cities | |
| faithful Old Faithful Geyser Data | |
| freeny Freeny's Revenue Data | |
| infert Infertility after Spontaneous and Induced | |
| Abortion | |
| iris Edgar Anderson's Iris Data | |
| islands Areas of the World's Major Landmasses | |
| lh Luteinizing Hormone in Blood Samples | |
| longley Longley's Economic Regression Data | |
| lynx Annual Canadian Lynx trappings 1821-1934 | |
| morley Michelson Speed of Light Data | |
| mtcars Motor Trend Car Road Tests | |
| nhtemp Average Yearly Temperatures in New Haven | |
| nottem Average Monthly Temperatures at Nottingham, | |
| 1920-1939 | |
| npk Classical N, P, K Factorial Experiment | |
| occupationalStatus Occupational Status of Fathers and their Sons | |
| precip Annual Precipitation in US Cities | |
| presidents Quarterly Approval Ratings of US Presidents | |
| pressure Vapor Pressure of Mercury as a Function of | |
| Temperature | |
| quakes Locations of Earthquakes off Fiji | |
| randu Random Numbers from Congruential Generator | |
| RANDU | |
| rivers Lengths of Major North American Rivers | |
| rock Measurements on Petroleum Rock Samples | |
| sleep Student's Sleep Data | |
| stackloss Brownlee's Stack Loss Plant Data | |
| state US State Facts and Figures | |
| sunspot.month Monthly Sunspot Data, from 1749 to "Present" | |
| sunspot.year Yearly Sunspot Data, 1700-1988 | |
| sunspots Monthly Sunspot Numbers, 1749-1983 | |
| swiss Swiss Fertility and Socioeconomic Indicators | |
| (1888) Data | |
| treering Yearly Treering Data, -6000-1979 | |
| trees Girth, Height and Volume for Black Cherry Trees | |
| uspop Populations Recorded by the US Census | |
| volcano Topographic Information on Auckland's Maunga | |
| Whau Volcano | |
| warpbreaks The Number of Breaks in Yarn during Weaving | |
| women Average Heights and Weights for American Women | |
| > ?cars | |
| > head(cars) | |
| speed dist | |
| 1 4 2 | |
| 2 4 10 | |
| 3 7 4 | |
| 4 7 22 | |
| 5 8 16 | |
| 6 9 10 | |
| > print(cars) | |
| speed dist | |
| 1 4 2 | |
| 2 4 10 | |
| 3 7 4 | |
| 4 7 22 | |
| 5 8 16 | |
| 6 9 10 | |
| 7 10 18 | |
| 8 10 26 | |
| 9 10 34 | |
| 10 11 17 | |
| 11 11 28 | |
| 12 12 14 | |
| 13 12 20 | |
| 14 12 24 | |
| 15 12 28 | |
| 16 13 26 | |
| 17 13 34 | |
| 18 13 34 | |
| 19 13 46 | |
| 20 14 26 | |
| 21 14 36 | |
| 22 14 60 | |
| 23 14 80 | |
| 24 15 20 | |
| 25 15 26 | |
| 26 15 54 | |
| 27 16 32 | |
| 28 16 40 | |
| 29 17 32 | |
| 30 17 40 | |
| 31 17 50 | |
| 32 18 42 | |
| 33 18 56 | |
| 34 18 76 | |
| 35 18 84 | |
| 36 19 36 | |
| 37 19 46 | |
| 38 19 68 | |
| 39 20 32 | |
| 40 20 48 | |
| 41 20 52 | |
| 42 20 56 | |
| 43 20 64 | |
| 44 22 66 | |
| 45 23 54 | |
| 46 24 70 | |
| 47 24 92 | |
| 48 24 93 | |
| 49 24 120 | |
| 50 25 85 | |
| > head(cars) | |
| speed dist | |
| 1 4 2 | |
| 2 4 10 | |
| 3 7 4 | |
| 4 7 22 | |
| 5 8 16 | |
| 6 9 10 | |
| > cars[1, ] | |
| speed dist | |
| 1 4 2 | |
| > seq(1, 10) | |
| [1] 1 2 3 4 5 6 7 8 9 10 | |
| > cars[seq(1, 10), ] | |
| speed dist | |
| 1 4 2 | |
| 2 4 10 | |
| 3 7 4 | |
| 4 7 22 | |
| 5 8 16 | |
| 6 9 10 | |
| 7 10 18 | |
| 8 10 26 | |
| 9 10 34 | |
| 10 11 17 | |
| > names(cars) | |
| [1] "speed" "dist" | |
| > cars$speed | |
| [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 | |
| [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25 | |
| > cars$dist | |
| [1] 2 10 4 22 16 10 18 26 34 17 28 14 20 24 28 26 34 34 46 | |
| [20] 26 36 60 80 20 26 54 32 40 32 40 50 42 56 76 84 36 46 68 | |
| [39] 32 48 52 56 64 66 54 70 92 93 120 85 | |
| > cars[["speed"]] | |
| [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 | |
| [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25 | |
| > typeof(cars$dist) | |
| [1] "double" | |
| > head(mtcars) | |
| mpg cyl disp hp drat wt qsec vs am gear carb | |
| Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 | |
| Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 | |
| Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 | |
| Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 | |
| Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 | |
| Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 | |
| > mtcars[, c("mpg", "cyl", "wt")] | |
| mpg cyl wt | |
| Mazda RX4 21.0 6 2.620 | |
| Mazda RX4 Wag 21.0 6 2.875 | |
| Datsun 710 22.8 4 2.320 | |
| Hornet 4 Drive 21.4 6 3.215 | |
| Hornet Sportabout 18.7 8 3.440 | |
| Valiant 18.1 6 3.460 | |
| Duster 360 14.3 8 3.570 | |
| Merc 240D 24.4 4 3.190 | |
| Merc 230 22.8 4 3.150 | |
| Merc 280 19.2 6 3.440 | |
| Merc 280C 17.8 6 3.440 | |
| Merc 450SE 16.4 8 4.070 | |
| Merc 450SL 17.3 8 3.730 | |
| Merc 450SLC 15.2 8 3.780 | |
| Cadillac Fleetwood 10.4 8 5.250 | |
| Lincoln Continental 10.4 8 5.424 | |
| Chrysler Imperial 14.7 8 5.345 | |
| Fiat 128 32.4 4 2.200 | |
| Honda Civic 30.4 4 1.615 | |
| Toyota Corolla 33.9 4 1.835 | |
| Toyota Corona 21.5 4 2.465 | |
| Dodge Challenger 15.5 8 3.520 | |
| AMC Javelin 15.2 8 3.435 | |
| Camaro Z28 13.3 8 3.840 | |
| Pontiac Firebird 19.2 8 3.845 | |
| Fiat X1-9 27.3 4 1.935 | |
| Porsche 914-2 26.0 4 2.140 | |
| Lotus Europa 30.4 4 1.513 | |
| Ford Pantera L 15.8 8 3.170 | |
| Ferrari Dino 19.7 6 2.770 | |
| Maserati Bora 15.0 8 3.570 | |
| Volvo 142E 21.4 4 2.780 | |
| > mtcars[1:5, c("mpg", "cyl", "wt")] | |
| mpg cyl wt | |
| Mazda RX4 21.0 6 2.620 | |
| Mazda RX4 Wag 21.0 6 2.875 | |
| Datsun 710 22.8 4 2.320 | |
| Hornet 4 Drive 21.4 6 3.215 | |
| Hornet Sportabout 18.7 8 3.440 | |
| > mtcars[1:5, c(1, 2, 3)] | |
| mpg cyl disp | |
| Mazda RX4 21.0 6 160 | |
| Mazda RX4 Wag 21.0 6 160 | |
| Datsun 710 22.8 4 108 | |
| Hornet 4 Drive 21.4 6 258 | |
| Hornet Sportabout 18.7 8 360 | |
| > mtcars[1:5, -c(1, 2)] | |
| disp hp drat wt qsec vs am gear carb | |
| Mazda RX4 160 110 3.90 2.620 16.46 0 1 4 4 | |
| Mazda RX4 Wag 160 110 3.90 2.875 17.02 0 1 4 4 | |
| Datsun 710 108 93 3.85 2.320 18.61 1 1 4 1 | |
| Hornet 4 Drive 258 110 3.08 3.215 19.44 1 0 3 1 | |
| Hornet Sportabout 360 175 3.15 3.440 17.02 0 0 3 2 | |
| > dim(mtcars) | |
| [1] 32 11 | |
| > rownames(mtcars) | |
| [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" | |
| > names(mtcars) | |
| [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" | |
| [11] "carb" | |
| > plot(mtcars$disp) | |
| > plot(mtcars$cyl, mtcars$mpg) | |
| > ?pairs | |
| > pairs(cyl ~ mpg + hp, data=mtcars) | |
| > mtcars$mpg | |
| [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 | |
| [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 | |
| [31] 15.0 21.4 | |
| > mtcars$mpg > 10 | |
| [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE | |
| [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE | |
| [31] TRUE TRUE | |
| > mtcars[mtcars$mpg > 10] | |
| Error in `[.data.frame`(mtcars, mtcars$mpg > 10) : | |
| undefined columns selected | |
| > mtcars$mpg > 20 | |
| [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE | |
| [13] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE | |
| [25] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE | |
| > mtcars[mtcars$mpg > 20,] | |
| mpg cyl disp hp drat wt qsec vs am gear carb | |
| Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 | |
| Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 | |
| Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 | |
| Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 | |
| Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 | |
| Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 | |
| Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 | |
| Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 | |
| Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 | |
| Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 | |
| Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 | |
| Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 | |
| Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 | |
| Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 | |
| > sqrt(mtcars$mpg) | |
| [1] 4.582576 4.582576 4.774935 4.626013 4.324350 4.254409 3.781534 4.939636 | |
| [9] 4.774935 4.381780 4.219005 4.049691 4.159327 3.898718 3.224903 3.224903 | |
| [17] 3.834058 5.692100 5.513620 5.822371 4.636809 3.937004 3.898718 3.646917 | |
| [25] 4.381780 5.224940 5.099020 5.513620 3.974921 4.438468 3.872983 4.626013 | |
| > mtcars[sqrt(mtcars$mpg) < 5,] | |
| mpg cyl disp hp drat wt qsec vs am gear carb | |
| Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 | |
| Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 | |
| Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 | |
| Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 | |
| Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 | |
| Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 | |
| Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 | |
| Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 | |
| Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 | |
| Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 | |
| Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 | |
| Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 | |
| Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 | |
| Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 | |
| Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 | |
| Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 | |
| Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 | |
| Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 | |
| Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 | |
| AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 | |
| Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 | |
| Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 | |
| Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 | |
| Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 | |
| Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 | |
| Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 | |
| > mtcars$newvar <- sqrt(mtcars$mpg) | |
| > head(mtcars) | |
| mpg cyl disp hp drat wt qsec vs am gear carb newvar | |
| Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 4.582576 | |
| Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 4.582576 | |
| Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 4.774935 | |
| Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 4.626013 | |
| Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 4.324350 | |
| Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 4.254409 | |
| > mean(mtcars$mpg) | |
| [1] 20.09062 | |
| > summary(mtcars$mpg) | |
| Min. 1st Qu. Median Mean 3rd Qu. Max. | |
| 10.40 15.42 19.20 20.09 22.80 33.90 | |
| > quantile(mtcars$mpg, seq(0, 1, 0.1)) | |
| 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% | |
| 10.40 14.34 15.20 15.98 17.92 19.20 21.00 21.47 24.08 30.09 33.90 | |
| > sapply(mtcars, summary) | |
| mpg cyl disp hp drat wt qsec vs am gear carb | |
| Min. 10.40 4.000 71.1 52.0 2.760 1.513 14.50 0.0000 0.0000 3.000 1.000 | |
| 1st Qu. 15.42 4.000 120.8 96.5 3.080 2.581 16.89 0.0000 0.0000 3.000 2.000 | |
| Median 19.20 6.000 196.3 123.0 3.695 3.325 17.71 0.0000 0.0000 4.000 2.000 | |
| Mean 20.09 6.188 230.7 146.7 3.597 3.217 17.85 0.4375 0.4062 3.688 2.812 | |
| 3rd Qu. 22.80 8.000 326.0 180.0 3.920 3.610 18.90 1.0000 1.0000 4.000 4.000 | |
| Max. 33.90 8.000 472.0 335.0 4.930 5.424 22.90 1.0000 1.0000 5.000 8.000 | |
| newvar | |
| Min. 3.225 | |
| 1st Qu. 3.927 | |
| Median 4.382 | |
| Mean 4.435 | |
| 3rd Qu. 4.775 | |
| Max. 5.822 | |
| > hist(mtcars$mpg) | |
| > density(mtcars$mpg) | |
| Call: | |
| density.default(x = mtcars$mpg) | |
| Data: mtcars$mpg (32 obs.); Bandwidth 'bw' = 2.477 | |
| x y | |
| Min. : 2.97 Min. :6.481e-05 | |
| 1st Qu.:12.56 1st Qu.:5.461e-03 | |
| Median :22.15 Median :1.926e-02 | |
| Mean :22.15 Mean :2.604e-02 | |
| 3rd Qu.:31.74 3rd Qu.:4.530e-02 | |
| Max. :41.33 Max. :6.795e-02 | |
| > plot(density(mtcars$mpg)) | |
| > quantile(mtcars, seq(0.9, 1, 0.005)) | |
| Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing = decreasing)) : | |
| undefined columns selected | |
| > quantile(mtcars$mpg, seq(0.9, 1, 0.005)) | |
| 90% 90.5% 91% 91.5% 92% 92.5% 93% 93.5% 94% 94.5% | |
| 30.0900 30.4000 30.4000 30.4000 30.4000 30.4000 30.4000 30.4000 30.6800 30.9900 | |
| 95% 95.5% 96% 96.5% 97% 97.5% 98% 98.5% 99% 99.5% | |
| 31.3000 31.6100 31.9200 32.2300 32.5050 32.7375 32.9700 33.2025 33.4350 33.6675 | |
| 100% | |
| 33.9000 | |
| > ?cars | |
| > linregres <- lm(dist ~ speed, data = cars) | |
| > linregres | |
| Call: | |
| lm(formula = dist ~ speed, data = cars) | |
| Coefficients: | |
| (Intercept) speed | |
| -17.579 3.932 | |
| > | |
| [1] "linrre" | |
| > summary(linregres) | |
| Call: | |
| lm(formula = dist ~ speed, data = cars) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -29.069 -9.525 -2.272 9.215 43.201 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| (Intercept) -17.5791 6.7584 -2.601 0.0123 * | |
| speed 3.9324 0.4155 9.464 1.49e-12 *** | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 15.38 on 48 degrees of freedom | |
| Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438 | |
| F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12 | |
| > linregres <- lm(dist ~ 0 + speed, data = cars) | |
| > summary(linregres) | |
| Call: | |
| lm(formula = dist ~ 0 + speed, data = cars) | |
| Residuals: | |
| Min 1Q Median 3Q Max | |
| -26.183 -12.637 -5.455 4.590 50.181 | |
| Coefficients: | |
| Estimate Std. Error t value Pr(>|t|) | |
| speed 2.9091 0.1414 20.58 <2e-16 *** | |
| --- | |
| Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |
| Residual standard error: 16.26 on 49 degrees of freedom | |
| Multiple R-squared: 0.8963, Adjusted R-squared: 0.8942 | |
| F-statistic: 423.5 on 1 and 49 DF, p-value: < 2.2e-16 | |
| > plot(linregres) | |
| Hit <Return> to see next plot: | |
| Error in plot.new() : attempt to plot on null device | |
| > plot(linregres) | |
| Hit <Return> to see next plot: | |
| Hit <Return> to see next plot: | |
| Hit <Return> to see next plot: | |
| Hit <Return> to see next plot: | |
| > predict(linregres) | |
| 1 2 3 4 5 6 7 8 | |
| 11.63653 11.63653 20.36393 20.36393 23.27306 26.18219 29.09132 29.09132 | |
| 9 10 11 12 13 14 15 16 | |
| 29.09132 32.00045 32.00045 34.90959 34.90959 34.90959 34.90959 37.81872 | |
| 17 18 19 20 21 22 23 24 | |
| 37.81872 37.81872 37.81872 40.72785 40.72785 40.72785 40.72785 43.63698 | |
| 25 26 27 28 29 30 31 32 | |
| 43.63698 43.63698 46.54611 46.54611 49.45525 49.45525 49.45525 52.36438 | |
| 33 34 35 36 37 38 39 40 | |
| 52.36438 52.36438 52.36438 55.27351 55.27351 55.27351 58.18264 58.18264 | |
| 41 42 43 44 45 46 47 48 | |
| 58.18264 58.18264 58.18264 64.00091 66.91004 69.81917 69.81917 69.81917 | |
| 49 50 | |
| 69.81917 72.72830 | |
| > predict(linregres, data.frame(speed = c(0,3))) | |
| 1 2 | |
| 0.000000 8.727396 | |
| > ?eurodist | |
| > # hierarchical clustering | |
| > temp <- as.matrix(eurodist) | |
| > rownames(temp) | |
| [1] "Athens" "Barcelona" "Brussels" "Calais" | |
| [5] "Cherbourg" "Cologne" "Copenhagen" "Geneva" | |
| [9] "Gibraltar" "Hamburg" "Hook of Holland" "Lisbon" | |
| [13] "Lyons" "Madrid" "Marseilles" "Milan" | |
| [17] "Munich" "Paris" "Rome" "Stockholm" | |
| [21] "Vienna" | |
| > dim(temp) | |
| [1] 21 21 | |
| > head(temp) | |
| Athens Barcelona Brussels Calais Cherbourg Cologne Copenhagen Geneva | |
| Athens 0 3313 2963 3175 3339 2762 3276 2610 | |
| Barcelona 3313 0 1318 1326 1294 1498 2218 803 | |
| Brussels 2963 1318 0 204 583 206 966 677 | |
| Calais 3175 1326 204 0 460 409 1136 747 | |
| Cherbourg 3339 1294 583 460 0 785 1545 853 | |
| Cologne 2762 1498 206 409 785 0 760 1662 | |
| Gibraltar Hamburg Hook of Holland Lisbon Lyons Madrid Marseilles | |
| Athens 4485 2977 3030 4532 2753 3949 2865 | |
| Barcelona 1172 2018 1490 1305 645 636 521 | |
| Brussels 2256 597 172 2084 690 1558 1011 | |
| Calais 2224 714 330 2052 739 1550 1059 | |
| Cherbourg 2047 1115 731 1827 789 1347 1101 | |
| Cologne 2436 460 269 2290 714 1764 1035 | |
| Milan Munich Paris Rome Stockholm Vienna | |
| Athens 2282 2179 3000 817 3927 1991 | |
| Barcelona 1014 1365 1033 1460 2868 1802 | |
| Brussels 925 747 285 1511 1616 1175 | |
| Calais 1077 977 280 1662 1786 1381 | |
| Cherbourg 1209 1160 340 1794 2196 1588 | |
| Cologne 911 583 465 1497 1403 937 | |
| > ?hclust | |
| > hclustres <- hclust(eurodist) | |
| > plot(hclustres) | |
| > head(cars) | |
| speed dist | |
| 1 4 2 | |
| 2 4 10 | |
| 3 7 4 | |
| 4 7 22 | |
| 5 8 16 | |
| 6 9 10 | |
| > |
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