AST 230
ALL SLIDE
slide 01 (Introduction to R)
n<-("welcome")
print(n)## [1] "welcome"1+2+3+4+5## [1] 15r <-(10^2)*3
print(r)## [1] 300x <- rnorm(5)
x## [1] -1.09331727 0.72008413 0.18097488 0.36768992 -0.06341894Vector
x<-c(0,1,1,2,3,5,6,13,21,34,55)
print(x)## [1] 0 1 1 2 3 5 6 13 21 34 55Matrix
testdata <- matrix(c(1,1,1,2,2,3,3,3,10,11,12,13,
14,15,16,17,28,30,25,40,50,47,62,23), ncol=3,
byrow=FALSE)
testdata## [,1] [,2] [,3]
## [1,] 1 10 28
## [2,] 1 11 30
## [3,] 1 12 25
## [4,] 2 13 40
## [5,] 2 14 50
## [6,] 3 15 47
## [7,] 3 16 62
## [8,] 3 17 23age <- c(1, 3, 5, 2, 11, 9, 3, 9, 12, 3)
weight <- c(4.4, 5.3, 7.2, 5.2, 8.5, 7.3, 6.0, 10.4, 10.2, 6.1)
mean(weight)## [1] 7.06sd(weight)## [1] 2.077498cor(age, weight)## [1] 0.9075655plot(age, weight)
help
help.start()## starting httpd help server ... done## If nothing happens, you should open
## 'http://127.0.0.1:10844/doc/html/index.html' yourselfexample(glm)##
## glm> ## Dobson (1990) Page 93: Randomized Controlled Trial :
## glm> counts <- c(18,17,15,20,10,20,25,13,12)
##
## glm> outcome <- gl(3,1,9)
##
## glm> treatment <- gl(3,3)
##
## glm> data.frame(treatment, outcome, counts) # showing data
## treatment outcome counts
## 1 1 1 18
## 2 1 2 17
## 3 1 3 15
## 4 2 1 20
## 5 2 2 10
## 6 2 3 20
## 7 3 1 25
## 8 3 2 13
## 9 3 3 12
##
## glm> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
##
## glm> anova(glm.D93)
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: counts
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev
## NULL 8 10.5814
## outcome 2 5.4523 6 5.1291
## treatment 2 0.0000 4 5.1291
##
## glm> ## No test:
## glm> ##D summary(glm.D93)
## glm> ## End(No test)
## glm> ## Computing AIC [in many ways]:
## glm> (A0 <- AIC(glm.D93))
## [1] 56.76132
##
## glm> (ll <- logLik(glm.D93))
## 'log Lik.' -23.38066 (df=5)
##
## glm> A1 <- -2*c(ll) + 2*attr(ll, "df")
##
## glm> A2 <- glm.D93$family$aic(counts, mu=fitted(glm.D93), wt=1) +
## glm+ 2 * length(coef(glm.D93))
##
## glm> stopifnot(exprs = {
## glm+ all.equal(A0, A1)
## glm+ all.equal(A1, A2)
## glm+ all.equal(A1, glm.D93$aic)
## glm+ })
##
## glm> ## No test:
## glm> ##D ## an example with offsets from Venables & Ripley (2002, p.189)
## glm> ##D utils::data(anorexia, package = "MASS")
## glm> ##D
## glm> ##D anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
## glm> ##D family = gaussian, data = anorexia)
## glm> ##D summary(anorex.1)
## glm> ## End(No test)
## glm>
## glm> # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
## glm> clotting <- data.frame(
## glm+ u = c(5,10,15,20,30,40,60,80,100),
## glm+ lot1 = c(118,58,42,35,27,25,21,19,18),
## glm+ lot2 = c(69,35,26,21,18,16,13,12,12))
##
## glm> summary(glm(lot1 ~ log(u), data = clotting, family = Gamma))
##
## Call:
## glm(formula = lot1 ~ log(u), family = Gamma, data = clotting)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.04008 -0.03756 -0.02637 0.02905 0.08641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0165544 0.0009275 -17.85 4.28e-07 ***
## log(u) 0.0153431 0.0004150 36.98 2.75e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.002446059)
##
## Null deviance: 3.51283 on 8 degrees of freedom
## Residual deviance: 0.01673 on 7 degrees of freedom
## AIC: 37.99
##
## Number of Fisher Scoring iterations: 3
##
##
## glm> summary(glm(lot2 ~ log(u), data = clotting, family = Gamma))
##
## Call:
## glm(formula = lot2 ~ log(u), family = Gamma, data = clotting)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.05574 -0.02925 0.01030 0.01714 0.06371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0239085 0.0013265 -18.02 4.00e-07 ***
## log(u) 0.0235992 0.0005768 40.91 1.36e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.001813354)
##
## Null deviance: 3.118557 on 8 degrees of freedom
## Residual deviance: 0.012672 on 7 degrees of freedom
## AIC: 27.032
##
## Number of Fisher Scoring iterations: 3
##
##
## glm> ## Aliased ("S"ingular) -> 1 NA coefficient
## glm> (fS <- glm(lot2 ~ log(u) + log(u^2), data = clotting, family = Gamma))
##
## Call: glm(formula = lot2 ~ log(u) + log(u^2), family = Gamma, data = clotting)
##
## Coefficients:
## (Intercept) log(u) log(u^2)
## -0.02391 0.02360 NA
##
## Degrees of Freedom: 8 Total (i.e. Null); 7 Residual
## Null Deviance: 3.119
## Residual Deviance: 0.01267 AIC: 27.03
##
## glm> tools::assertError(update(fS, singular.ok=FALSE), verbose=interactive())
##
## glm> ## -> .. "singular fit encountered"
## glm>
## glm> ## Not run:
## glm> ##D ## for an example of the use of a terms object as a formula
## glm> ##D demo(glm.vr)
## glm> ## End(Not run)
## glm>
## glm>example("regression")## Warning in example("regression"): no help found for 'regression'example(lm)##
## lm> require(graphics)
##
## lm> ## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## lm> ## Page 9: Plant Weight Data.
## lm> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
##
## lm> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
##
## lm> group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
##
## lm> weight <- c(ctl, trt)
##
## lm> lm.D9 <- lm(weight ~ group)
##
## lm> lm.D90 <- lm(weight ~ group - 1) # omitting intercept
##
## lm> ## No test:
## lm> ##D anova(lm.D9)
## lm> ##D summary(lm.D90)
## lm> ## End(No test)
## lm> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
##
## lm> plot(lm.D9, las = 1) # Residuals, Fitted, ...
##
## lm> par(opar)
##
## lm> ## Don't show:
## lm> ## model frame :
## lm> stopifnot(identical(lm(weight ~ group, method = "model.frame"),
## lm+ model.frame(lm.D9)))
##
## lm> ## End(Don't show)
## lm> ### less simple examples in "See Also" above
## lm>
## lm>
## lm>data()
data(cars)
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 85data(Orange)
Orange## Tree age circumference
## 1 1 118 30
## 2 1 484 58
## 3 1 664 87
## 4 1 1004 115
## 5 1 1231 120
## 6 1 1372 142
## 7 1 1582 145
## 8 2 118 33
## 9 2 484 69
## 10 2 664 111
## 11 2 1004 156
## 12 2 1231 172
## 13 2 1372 203
## 14 2 1582 203
## 15 3 118 30
## 16 3 484 51
## 17 3 664 75
## 18 3 1004 108
## 19 3 1231 115
## 20 3 1372 139
## 21 3 1582 140
## 22 4 118 32
## 23 4 484 62
## 24 4 664 112
## 25 4 1004 167
## 26 4 1231 179
## 27 4 1372 209
## 28 4 1582 214
## 29 5 118 30
## 30 5 484 49
## 31 5 664 81
## 32 5 1004 125
## 33 5 1231 142
## 34 5 1372 174
## 35 5 1582 177help(lm)
RSiteSearch("lm")## A search query has been submitted to https://search.r-project.org
## The results page should open in your browser shortly??regressionsave image
An example of commands used to manage the R workspace
setwd("D:\\2nd Year\\AST 230")
x <- runif(20)
summary(x)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.009998 0.281972 0.441006 0.490320 0.707990 0.991474hist(x)
#savehistory()
save.image()Installing a package
#install.packages("vcd")
help(package="vcd")
library(vcd)## Warning: package 'vcd' was built under R version 4.2.2## Loading required package: gridhelp(Arthritis)
Arthritis## ID Treatment Sex Age Improved
## 1 57 Treated Male 27 Some
## 2 46 Treated Male 29 None
## 3 77 Treated Male 30 None
## 4 17 Treated Male 32 Marked
## 5 36 Treated Male 46 Marked
## 6 23 Treated Male 58 Marked
## 7 75 Treated Male 59 None
## 8 39 Treated Male 59 Marked
## 9 33 Treated Male 63 None
## 10 55 Treated Male 63 None
## 11 30 Treated Male 64 None
## 12 5 Treated Male 64 Some
## 13 63 Treated Male 69 None
## 14 83 Treated Male 70 Marked
## 15 66 Treated Female 23 None
## 16 40 Treated Female 32 None
## 17 6 Treated Female 37 Some
## 18 7 Treated Female 41 None
## 19 72 Treated Female 41 Marked
## 20 37 Treated Female 48 None
## 21 82 Treated Female 48 Marked
## 22 53 Treated Female 55 Marked
## 23 79 Treated Female 55 Marked
## 24 26 Treated Female 56 Marked
## 25 28 Treated Female 57 Marked
## 26 60 Treated Female 57 Marked
## 27 22 Treated Female 57 Marked
## 28 27 Treated Female 58 None
## 29 2 Treated Female 59 Marked
## 30 59 Treated Female 59 Marked
## 31 62 Treated Female 60 Marked
## 32 84 Treated Female 61 Marked
## 33 64 Treated Female 62 Some
## 34 34 Treated Female 62 Marked
## 35 58 Treated Female 66 Marked
## 36 13 Treated Female 67 Marked
## 37 61 Treated Female 68 Some
## 38 65 Treated Female 68 Marked
## 39 11 Treated Female 69 None
## 40 56 Treated Female 69 Some
## 41 43 Treated Female 70 Some
## 42 9 Placebo Male 37 None
## 43 14 Placebo Male 44 None
## 44 73 Placebo Male 50 None
## 45 74 Placebo Male 51 None
## 46 25 Placebo Male 52 None
## 47 18 Placebo Male 53 None
## 48 21 Placebo Male 59 None
## 49 52 Placebo Male 59 None
## 50 45 Placebo Male 62 None
## 51 41 Placebo Male 62 None
## 52 8 Placebo Male 63 Marked
## 53 80 Placebo Female 23 None
## 54 12 Placebo Female 30 None
## 55 29 Placebo Female 30 None
## 56 50 Placebo Female 31 Some
## 57 38 Placebo Female 32 None
## 58 35 Placebo Female 33 Marked
## 59 51 Placebo Female 37 None
## 60 54 Placebo Female 44 None
## 61 76 Placebo Female 45 None
## 62 16 Placebo Female 46 None
## 63 69 Placebo Female 48 None
## 64 31 Placebo Female 49 None
## 65 20 Placebo Female 51 None
## 66 68 Placebo Female 53 None
## 67 81 Placebo Female 54 None
## 68 4 Placebo Female 54 None
## 69 78 Placebo Female 54 Marked
## 70 70 Placebo Female 55 Marked
## 71 49 Placebo Female 57 None
## 72 10 Placebo Female 57 Some
## 73 47 Placebo Female 58 Some
## 74 44 Placebo Female 59 Some
## 75 24 Placebo Female 59 Marked
## 76 48 Placebo Female 61 None
## 77 19 Placebo Female 63 Some
## 78 3 Placebo Female 64 None
## 79 67 Placebo Female 65 Marked
## 80 32 Placebo Female 66 None
## 81 42 Placebo Female 66 None
## 82 15 Placebo Female 66 Some
## 83 71 Placebo Female 68 Some
## 84 1 Placebo Female 74 Markedexample(Arthritis)##
## Arthrt> data("Arthritis")
##
## Arthrt> art <- xtabs(~ Treatment + Improved, data = Arthritis, subset = Sex == "Female")
##
## Arthrt> art
## Improved
## Treatment None Some Marked
## Placebo 19 7 6
## Treated 6 5 16
##
## Arthrt> mosaic(art, gp = shading_Friendly)
##
## Arthrt> mosaic(art, gp = shading_max)
slide 02 (Creating Dataset)
vectors
a<-c(1,2,5,3,6,-2,4)
a[3]## [1] 5a[c(1,3,5)]## [1] 1 5 6a[2:6]## [1] 2 5 3 6 -2b<-c("one","two","three")
c<-c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE)
b## [1] "one" "two" "three"c## [1] TRUE TRUE TRUE FALSE TRUE FALSEmatrices
mymatrix <- matrix(vector, nrow=number_of_rows,ncol=number_of_columns, byrow=logical_value,dimnames=list(char_vector_rownames, char_vector_colnames))
y<-matrix(1:20,nrow=5,ncol=4)
y## [,1] [,2] [,3] [,4]
## [1,] 1 6 11 16
## [2,] 2 7 12 17
## [3,] 3 8 13 18
## [4,] 4 9 14 19
## [5,] 5 10 15 20cells<-c(1,26,24,68)
rnames<-c("R1","R2")
cnames <- c("C1", "C2")
mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames))
mymatrix## C1 C2
## R1 1 26
## R2 24 68Array
myarray <- array(vector, dimensions, dimnames)
dim1 <- c("A1", "A2")
dim2 <- c("B1", "B2", "B3")
dim3 <- c("C1", "C2", "C3", "C4")
z <- array(1:48, c(2, 3, 4), dimnames=list(dim1, dim2, dim3))
z## , , C1
##
## B1 B2 B3
## A1 1 3 5
## A2 2 4 6
##
## , , C2
##
## B1 B2 B3
## A1 7 9 11
## A2 8 10 12
##
## , , C3
##
## B1 B2 B3
## A1 13 15 17
## A2 14 16 18
##
## , , C4
##
## B1 B2 B3
## A1 19 21 23
## A2 20 22 24Creating a data frame
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
patientdata <- data.frame(patientID, age, diabetes, status)
patientdata## patientID age diabetes status
## 1 1 25 Type1 Poor
## 2 2 34 Type2 Improved
## 3 3 28 Type1 Excellent
## 4 4 52 Type1 Poorpatientdata[1:2]## patientID age
## 1 1 25
## 2 2 34
## 3 3 28
## 4 4 52patientdata[c("diabetes", "status")]## diabetes status
## 1 Type1 Poor
## 2 Type2 Improved
## 3 Type1 Excellent
## 4 Type1 Poorpatientdata$age## [1] 25 34 28 52without ATTACH, DETACH
mtcars## 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
## 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
## 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
## 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
## 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 2summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90plot(mtcars$mpg, mtcars$disp)
plot(mtcars$mpg, mtcars$wt)
ATTACH, DETACH
attach(mtcars)
summary(mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90plot(mpg, disp)
plot(mpg, wt)
detach(mtcars)more than one object can have the same name
mpg <- c(25, 36, 47) attach(mtcars) plot(mpg, wt) Error in xy.coords(x, y, xlabel, ylabel, log) : ‘x’ and ‘y’ lengths differ > mpg [1] 25 36 47
WITH function
An alternative approach is to use the with() function.
with(mtcars, {
summary(mpg,disp,wt)
plot(mpg, disp)
plot(mpg, wt)
})
local environment
with(mtcars, {
stats <- summary(mpg)
stats
})## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90stats Error: object ‘stats’ not found
global environment
save the object to the global environment outside ofthe with() call by using <<-
with(mtcars, {
nokeepstats <- summary(mpg)
keepstats <<- summary(mpg)
})
keepstats## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90nokeepstats Error: object ‘nokeepstats’ not found
Lists
g <- "My First List"
h <- c(25, 26, 18, 39)
j <- matrix(1:10, nrow=5)
k <- c("one", "two", "three")
mylist <- list(title=g, ages=h, j, k)
mylist## $title
## [1] "My First List"
##
## $ages
## [1] 25 26 18 39
##
## [[3]]
## [,1] [,2]
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10
##
## [[4]]
## [1] "one" "two" "three"#individually
mylist[[1]]## [1] "My First List"mylist[[2]]## [1] 25 26 18 39mylist[[3]]## [,1] [,2]
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10mylist[[4]]## [1] "one" "two" "three"create a data frame
create a data frame named mydata with three variables: age (numeric) , gender (character) , and weight (numeric)
mydata <- data.frame(age=numeric(0),
gender=character(0), weight=numeric(0))edit data
mydata <- edit(mydata)read text file
mydataframe <- read.table(file,header=logical_value,sep=“delimiter”, row.names=“name”)
income <- read.table("D://2nd Year//AST 230//EXCELL PRACTICE2.txt", header=T)
View(income)read.xlsx(file,n)
library(xlsx) mydataframe <- read.xlsx(“D://2nd Year//AST 230//EXCELL PRACTICE2.xlsx”, 1)
Importing data from SPSS
1st way
#install.packages(“Hmisc”)
library(Hmisc) mydataframe <- spss.get(“mydata.sav”, use.value.labels=TRUE)
2nd way
library( foreign) testdata=read.spss(“C:/Users/ SNA.sav”, use.value.labels=TRUE, to.data.frame=TRUE)
Importing data from Stata
library(foreign) mydataframe <- read.dta(“mydata.dta”)
#after importing data from stata,sas,etc
example
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
gender<-c(1,1,2,1)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
patientdata <- data.frame(patientID, age, gender, diabetes, status)
patientdata## patientID age gender diabetes status
## 1 1 25 1 Type1 Poor
## 2 2 34 1 Type2 Improved
## 3 3 28 2 Type1 Excellent
## 4 4 52 1 Type1 PoorRename coloum title (age)
names(patientdata)[2] <- "Age at hospitalization (in
years)"
patientdata## patientID Age at hospitalization (in\nyears) gender diabetes status
## 1 1 25 1 Type1 Poor
## 2 2 34 1 Type2 Improved
## 3 3 28 2 Type1 Excellent
## 4 4 52 1 Type1 Poorvalue lables(coding)
patientdata## patientID Age at hospitalization (in\nyears) gender diabetes status
## 1 1 25 1 Type1 Poor
## 2 2 34 1 Type2 Improved
## 3 3 28 2 Type1 Excellent
## 4 4 52 1 Type1 Poorpatientdata$gender <- factor(patientdata$gender,
levels = c(1,2),
labels = c("male", "female"))
patientdata## patientID Age at hospitalization (in\nyears) gender diabetes status
## 1 1 25 male Type1 Poor
## 2 2 34 male Type2 Improved
## 3 3 28 female Type1 Excellent
## 4 4 52 male Type1 Poorslide 03 (working with graphs)
Regression line
rm(list = ls())
show(mtcars)## 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
## 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
## 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
## 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
## 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 2attach(mtcars)
plot(wt, mpg)
lm(mpg~wt)##
## Call:
## lm(formula = mpg ~ wt)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344abline(lm(mpg~wt))
title("Regression of MPG on Weight")
detach(mtcars)save pdf
pdf("mygraph11.pdf")
attach(mtcars)
plot(wt, mpg)
abline(lm(mpg~wt))
title("Regression of MPG on Weight")
detach(mtcars)
dev.off()## png
## 2# Finally, close the file using the dev.off function.Example
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
plot(dose, drugA, type="b")
resulting graph
opar <- par(no.readonly=TRUE)
par(lty=2, pch=17)
plot(dose, drugA, type="b")
par(opar)#For example, all graphs created after the statement par(font.lab=3, cex.lab=1.5, font.main=4, cex.main=2) will have italic axis labels that are 1.5 times the default text size, and bold italic titles that are twice the default text size.
#The code par(pin=c(4,3), mai=c(1,.5, 1, .2)) produces graphs that are 4 inches wide by 3 inches tall, with a 1-inch margin on the bottom and top, a 0.5-inch margin on the left, and a 0.2-inch margin on the right.
using graping parameter
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
#2 inches wide by 3 inches tall(pin)
par(pin=c(2, 3))
#The line width(lwd)
#symbols width (cex)
par(lwd=2, cex=1.5)
#axis annotation(15,20,25...) size (cex.axis)
#axis annotation(15,20,25...) font (font.axis)
par(cex.axis=.75, font.axis=2)
plot(dose, drugA, type="b", pch=19, lty=2, col="red")
#bg>background
plot(dose, drugB, type="b", pch=23, lty=6, col="blue",
bg="green")
par(opar)Comparing graps
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(lwd=2, cex=1.5, font.lab=2)
plot(dose, drugA, type="b",
pch=15, lty=1, col="red", ylim=c(0, 60),
main="Drug A vs. Drug B",
xlab="Drug Dosage", ylab="Drug Response")
lines(dose, drugB, type="b",
pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
library(Hmisc)## Warning: package 'Hmisc' was built under R version 4.2.2## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Warning: package 'Formula' was built under R version 4.2.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.2.2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, unitsminor.tick(nx=1, ny=1, tick.ratio=0.5)
legend("topleft", inset=.01, title="Drug Type", c("A","B"),
lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
par(opar)slide 04 (Basic Data Management)
Creating new variables
mydata<-data.frame(x1 = c(2, 2, 6, 4),
x2 = c(3, 4, 2, 8))
mydata## x1 x2
## 1 2 3
## 2 2 4
## 3 6 2
## 4 4 81st way
mydata$sumx <- mydata$x1 + mydata$x2
mydata$meanx <- (mydata$x1 + mydata$x2)/2
mydata## x1 x2 sumx meanx
## 1 2 3 5 2.5
## 2 2 4 6 3.0
## 3 6 2 8 4.0
## 4 4 8 12 6.02nd way
attach(mydata)
mydata$sumx <- x1 + x2
mydata$meanx <- (x1 + x2)/2
detach(mydata)
mydata## x1 x2 sumx meanx
## 1 2 3 5 2.5
## 2 2 4 6 3.0
## 3 6 2 8 4.0
## 4 4 8 12 6.03rd way
mydata2<- transform(mydata,
sumx = x1 + x2,
meanx = (x1 + x2)/2)
mydata2## x1 x2 sumx meanx
## 1 2 3 5 2.5
## 2 2 4 6 3.0
## 3 6 2 8 4.0
## 4 4 8 12 6.0Recoding variables
manager <- c(1, 2, 3, 4, 5)
date <- c("10/24/08", "10/28/08", "10/1/08", "10/12/08", "5/1/09")
country <- c("US", "US", "UK", "UK", "UK")
gender <- c("M", "F", "F", "M", "F")
age <- c(32, 45, 25, 39, 99)
q1 <- c(5, 3, 3, 3, 2)
q2 <- c(4, 5, 5, 3, 2)
q3 <- c(5, 2, 5, 4, 1)
q4 <- c(5, 5, 5, NA, 2)
q5 <- c(5, 5, 2, NA, 1)
leadership <<- data.frame(manager, date, country, gender, age,q1, q2,
q3, q4, q5, stringsAsFactors=FALSE)
leadership## manager date country gender age q1 q2 q3 q4 q5
## 1 1 10/24/08 US M 32 5 4 5 5 5
## 2 2 10/28/08 US F 45 3 5 2 5 5
## 3 3 10/1/08 UK F 25 3 5 5 5 2
## 4 4 10/12/08 UK M 39 3 3 4 NA NA
## 5 5 5/1/09 UK F 99 2 2 1 2 11st way
leadership$agecat[leadership$age == 99] <- NA
leadership$agecat[leadership$age > 75] <- "Elder"
leadership$agecat[leadership$age >= 55 & leadership$age <= 75] <- "Middle Aged"
leadership$agecat[leadership$age < 55] <- "Young"
leadership## manager date country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder2nd way
leadership <- within(leadership,{
agecat <- NA
agecat[age > 75] <- "Elder"
agecat[age >= 55 & age <= 75] <- "Middle Aged"
agecat[age < 55] <- "Young" })
leadership## manager date country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 ElderRenaming variables
#Install.packages("reshape")
library(reshape)## Warning: package 'reshape' was built under R version 4.2.2leadership <- rename(leadership,
c(manager="managerID", date="testDate"))
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder#leadership$age[leadership$age == 99] <- NAremoves missing values
x <- c(1, 2, NA, 3)
x## [1] 1 2 NA 3y <- x[1] + x[2] + x[3] + x[4]
y## [1] NAz <- sum(x)
z## [1] NAy1=sum(x,na.rm = T)
y1## [1] 6## remove any observation with missing data by using the na.omit() functionType conversions
a <- c(1,2,3)
a## [1] 1 2 3is.numeric(a)## [1] TRUEis.vector(a)## [1] TRUEis.matrix(a)## [1] FALSEis.complex(a)## [1] FALSEis.data.frame(a)## [1] FALSEis.logical(a)## [1] FALSEa <- as.character(a)
a## [1] "1" "2" "3"is.numeric(a)## [1] FALSEis.vector(a)## [1] TRUEis.character(a)## [1] TRUESorting data set
1st way
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldernewdata <- leadership[order( leadership$age),]
newdata## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldernewdata1 <- leadership[order( gender,age),]
newdata1## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young2nd way using attach
attach(leadership)## The following objects are masked _by_ .GlobalEnv:
##
## age, country, gender, q1, q2, q3, q4, q5newdata <- leadership[order(gender, age),]
newdata## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Youngdetach(leadership)attach(leadership)## The following objects are masked _by_ .GlobalEnv:
##
## age, country, gender, q1, q2, q3, q4, q5newdata1 <-leadership[order(gender, -age),]
newdata1## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 1 1 10/24/08 US M 32 5 4 5 5 5 Youngdetach(leadership)Merging datasets
dataframeA<-data.frame(ID=c(30,31,32,33),A=c(1,2,3,4))
dataframeB<-data.frame(ID=c(30,31,32,33),B=c(3,6,9,8))
dataframeA## ID A
## 1 30 1
## 2 31 2
## 3 32 3
## 4 33 4dataframeB## ID B
## 1 30 3
## 2 31 6
## 3 32 9
## 4 33 8Adding columns
total <- merge(dataframeA, dataframeB, by="ID")
total## ID A B
## 1 30 1 3
## 2 31 2 6
## 3 32 3 9
## 4 33 4 8two matrices or data frame >> cbind
#If you’re joining two matrices or data frames horizontally and don’t need to specify a common key, you can use the cbind() function
A<-c(1,2,3,4)
B<-c(3,6,9,8)
total1<- cbind(A, B)
total1## A B
## [1,] 1 3
## [2,] 2 6
## [3,] 3 9
## [4,] 4 8Adding rows >>rbind
dataframeA1<-data.frame(ID=c(30,31,32,33,56),m=c(1,2,3,4,10))
dataframeA1## ID m
## 1 30 1
## 2 31 2
## 3 32 3
## 4 33 4
## 5 56 10dataframeB1<-data.frame(ID=c(35,90,30),m=c(8,6,07))
dataframeB1## ID m
## 1 35 8
## 2 90 6
## 3 30 7total2 <- rbind(dataframeA1,dataframeB1)
total2## ID m
## 1 30 1
## 2 31 2
## 3 32 3
## 4 33 4
## 5 56 10
## 6 35 8
## 7 90 6
## 8 30 7Subsetting datasets
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldernewdata <- leadership[ ,c(6:10)]
newdata## q1 q2 q3 q4 q5
## 1 5 4 5 5 5
## 2 3 5 2 5 5
## 3 3 5 5 5 2
## 4 3 3 4 NA NA
## 5 2 2 1 2 1Excluding (dropping) variables
#Excluding (dropping) variables/excluding column 8,9
newdata2 <- leadership[c(-8,-9)]
newdata2## managerID testDate country gender age q1 q2 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 Young
## 2 2 10/28/08 US F 45 3 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 NA Young
## 5 5 5/1/09 UK F 99 2 2 1 ElderSelecting observations
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldernewdata <- leadership[1:3,]
newdata## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Youngnewdata <- leadership[which(leadership$gender=="M" &
leadership$age > 30),]
newdata## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Youngalternative
attach(leadership) newdata <- leadership[which(gender==‘M’ & age > 30),] detach(leadership)
The subset() function
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldernewdata <- subset(leadership, age >= 35 | age < 24,
select=c(q1, q2, q3, q4))
newdata## q1 q2 q3 q4
## 2 3 5 2 5
## 4 3 3 4 NA
## 5 2 2 1 2newdata <- subset(leadership, gender=="M" & age > 25,
select=gender:q4)
newdata## gender age q1 q2 q3 q4
## 1 M 32 5 4 5 5
## 4 M 39 3 3 4 NArandom sample
ex-1 row sample
leadership## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 2 2 10/28/08 US F 45 3 5 2 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Young
## 4 4 10/12/08 UK M 39 3 3 4 NA NA Young
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Eldermysample1 <- leadership[sample(1:nrow(leadership), 3,
replace=FALSE),]
mysample1## managerID testDate country gender age q1 q2 q3 q4 q5 agecat
## 5 5 5/1/09 UK F 99 2 2 1 2 1 Elder
## 1 1 10/24/08 US M 32 5 4 5 5 5 Young
## 3 3 10/1/08 UK F 25 3 5 5 5 2 Youngex-2 row sample
CO2## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2
## 7 Qn1 Quebec nonchilled 1000 39.7
## 8 Qn2 Quebec nonchilled 95 13.6
## 9 Qn2 Quebec nonchilled 175 27.3
## 10 Qn2 Quebec nonchilled 250 37.1
## 11 Qn2 Quebec nonchilled 350 41.8
## 12 Qn2 Quebec nonchilled 500 40.6
## 13 Qn2 Quebec nonchilled 675 41.4
## 14 Qn2 Quebec nonchilled 1000 44.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 16 Qn3 Quebec nonchilled 175 32.4
## 17 Qn3 Quebec nonchilled 250 40.3
## 18 Qn3 Quebec nonchilled 350 42.1
## 19 Qn3 Quebec nonchilled 500 42.9
## 20 Qn3 Quebec nonchilled 675 43.9
## 21 Qn3 Quebec nonchilled 1000 45.5
## 22 Qc1 Quebec chilled 95 14.2
## 23 Qc1 Quebec chilled 175 24.1
## 24 Qc1 Quebec chilled 250 30.3
## 25 Qc1 Quebec chilled 350 34.6
## 26 Qc1 Quebec chilled 500 32.5
## 27 Qc1 Quebec chilled 675 35.4
## 28 Qc1 Quebec chilled 1000 38.7
## 29 Qc2 Quebec chilled 95 9.3
## 30 Qc2 Quebec chilled 175 27.3
## 31 Qc2 Quebec chilled 250 35.0
## 32 Qc2 Quebec chilled 350 38.8
## 33 Qc2 Quebec chilled 500 38.6
## 34 Qc2 Quebec chilled 675 37.5
## 35 Qc2 Quebec chilled 1000 42.4
## 36 Qc3 Quebec chilled 95 15.1
## 37 Qc3 Quebec chilled 175 21.0
## 38 Qc3 Quebec chilled 250 38.1
## 39 Qc3 Quebec chilled 350 34.0
## 40 Qc3 Quebec chilled 500 38.9
## 41 Qc3 Quebec chilled 675 39.6
## 42 Qc3 Quebec chilled 1000 41.4
## 43 Mn1 Mississippi nonchilled 95 10.6
## 44 Mn1 Mississippi nonchilled 175 19.2
## 45 Mn1 Mississippi nonchilled 250 26.2
## 46 Mn1 Mississippi nonchilled 350 30.0
## 47 Mn1 Mississippi nonchilled 500 30.9
## 48 Mn1 Mississippi nonchilled 675 32.4
## 49 Mn1 Mississippi nonchilled 1000 35.5
## 50 Mn2 Mississippi nonchilled 95 12.0
## 51 Mn2 Mississippi nonchilled 175 22.0
## 52 Mn2 Mississippi nonchilled 250 30.6
## 53 Mn2 Mississippi nonchilled 350 31.8
## 54 Mn2 Mississippi nonchilled 500 32.4
## 55 Mn2 Mississippi nonchilled 675 31.1
## 56 Mn2 Mississippi nonchilled 1000 31.5
## 57 Mn3 Mississippi nonchilled 95 11.3
## 58 Mn3 Mississippi nonchilled 175 19.4
## 59 Mn3 Mississippi nonchilled 250 25.8
## 60 Mn3 Mississippi nonchilled 350 27.9
## 61 Mn3 Mississippi nonchilled 500 28.5
## 62 Mn3 Mississippi nonchilled 675 28.1
## 63 Mn3 Mississippi nonchilled 1000 27.8
## 64 Mc1 Mississippi chilled 95 10.5
## 65 Mc1 Mississippi chilled 175 14.9
## 66 Mc1 Mississippi chilled 250 18.1
## 67 Mc1 Mississippi chilled 350 18.9
## 68 Mc1 Mississippi chilled 500 19.5
## 69 Mc1 Mississippi chilled 675 22.2
## 70 Mc1 Mississippi chilled 1000 21.9
## 71 Mc2 Mississippi chilled 95 7.7
## 72 Mc2 Mississippi chilled 175 11.4
## 73 Mc2 Mississippi chilled 250 12.3
## 74 Mc2 Mississippi chilled 350 13.0
## 75 Mc2 Mississippi chilled 500 12.5
## 76 Mc2 Mississippi chilled 675 13.7
## 77 Mc2 Mississippi chilled 1000 14.4
## 78 Mc3 Mississippi chilled 95 10.6
## 79 Mc3 Mississippi chilled 175 18.0
## 80 Mc3 Mississippi chilled 250 17.9
## 81 Mc3 Mississippi chilled 350 17.9
## 82 Mc3 Mississippi chilled 500 17.9
## 83 Mc3 Mississippi chilled 675 18.9
## 84 Mc3 Mississippi chilled 1000 19.9mysample2 <- CO2[sample(1:nrow(CO2), 10,
replace=FALSE),]
mysample2## Plant Type Treatment conc uptake
## 80 Mc3 Mississippi chilled 250 17.9
## 17 Qn3 Quebec nonchilled 250 40.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 29 Qc2 Quebec chilled 95 9.3
## 70 Mc1 Mississippi chilled 1000 21.9
## 65 Mc1 Mississippi chilled 175 14.9
## 16 Qn3 Quebec nonchilled 175 32.4
## 23 Qc1 Quebec chilled 175 24.1
## 50 Mn2 Mississippi nonchilled 95 12.0
## 60 Mn3 Mississippi nonchilled 350 27.9ex-3 coloum sample
mysample3 <- CO2[,sample(1:ncol(CO2), 2,
replace=FALSE)]
mysample3## Treatment conc
## 1 nonchilled 95
## 2 nonchilled 175
## 3 nonchilled 250
## 4 nonchilled 350
## 5 nonchilled 500
## 6 nonchilled 675
## 7 nonchilled 1000
## 8 nonchilled 95
## 9 nonchilled 175
## 10 nonchilled 250
## 11 nonchilled 350
## 12 nonchilled 500
## 13 nonchilled 675
## 14 nonchilled 1000
## 15 nonchilled 95
## 16 nonchilled 175
## 17 nonchilled 250
## 18 nonchilled 350
## 19 nonchilled 500
## 20 nonchilled 675
## 21 nonchilled 1000
## 22 chilled 95
## 23 chilled 175
## 24 chilled 250
## 25 chilled 350
## 26 chilled 500
## 27 chilled 675
## 28 chilled 1000
## 29 chilled 95
## 30 chilled 175
## 31 chilled 250
## 32 chilled 350
## 33 chilled 500
## 34 chilled 675
## 35 chilled 1000
## 36 chilled 95
## 37 chilled 175
## 38 chilled 250
## 39 chilled 350
## 40 chilled 500
## 41 chilled 675
## 42 chilled 1000
## 43 nonchilled 95
## 44 nonchilled 175
## 45 nonchilled 250
## 46 nonchilled 350
## 47 nonchilled 500
## 48 nonchilled 675
## 49 nonchilled 1000
## 50 nonchilled 95
## 51 nonchilled 175
## 52 nonchilled 250
## 53 nonchilled 350
## 54 nonchilled 500
## 55 nonchilled 675
## 56 nonchilled 1000
## 57 nonchilled 95
## 58 nonchilled 175
## 59 nonchilled 250
## 60 nonchilled 350
## 61 nonchilled 500
## 62 nonchilled 675
## 63 nonchilled 1000
## 64 chilled 95
## 65 chilled 175
## 66 chilled 250
## 67 chilled 350
## 68 chilled 500
## 69 chilled 675
## 70 chilled 1000
## 71 chilled 95
## 72 chilled 175
## 73 chilled 250
## 74 chilled 350
## 75 chilled 500
## 76 chilled 675
## 77 chilled 1000
## 78 chilled 95
## 79 chilled 175
## 80 chilled 250
## 81 chilled 350
## 82 chilled 500
## 83 chilled 675
## 84 chilled 1000slide 05 (Advanced Data Management)
Statistical functions
#z <- mean(x, trim = 0.05, na.rm=TRUE)
ceiling(x = 3.551)## [1] 4floor(x = 3.551)## [1] 3round(3.551,2)## [1] 3.55signif(3.551,2)## [1] 3.6log(3)## [1] 1.098612log10(3)## [1] 0.4771213log(x=3,base = 10)## [1] 0.4771213x=c(1,2,3,3,6,59,09,30,20)
plot(x,sin(x))
x=c(1,2,3,4,5,6,7,8)
mean(x)## [1] 4.5median(x)## [1] 4.5sd(x)## [1] 2.44949var(x)## [1] 6mad(x)## [1] 2.9652quantile(x,c(.3,.84))## 30% 84%
## 3.10 6.88range(x)## [1] 1 8sum(x)## [1] 36diff(x,lag = 1)## [1] 1 1 1 1 1 1 1diff(x,lag = 2)## [1] 2 2 2 2 2 2diff(x,lag = 3)## [1] 3 3 3 3 3min(x)## [1] 1max(x)## [1] 8scale(x,center = TRUE,scale = TRUE)## [,1]
## [1,] -1.4288690
## [2,] -1.0206207
## [3,] -0.6123724
## [4,] -0.2041241
## [5,] 0.2041241
## [6,] 0.6123724
## [7,] 1.0206207
## [8,] 1.4288690
## attr(,"scaled:center")
## [1] 4.5
## attr(,"scaled:scale")
## [1] 2.44949Probability functions
d = density p = distribution function q = quantile function r = random generation (random deviates)
x<-pretty(c(-3,3),30)
x## [1] -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2
## [16] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8
## [31] 3.0y<-dnorm(x)
y## [1] 0.004431848 0.007915452 0.013582969 0.022394530 0.035474593 0.053990967
## [7] 0.078950158 0.110920835 0.149727466 0.194186055 0.241970725 0.289691553
## [13] 0.333224603 0.368270140 0.391042694 0.398942280 0.391042694 0.368270140
## [19] 0.333224603 0.289691553 0.241970725 0.194186055 0.149727466 0.110920835
## [25] 0.078950158 0.053990967 0.035474593 0.022394530 0.013582969 0.007915452
## [31] 0.004431848plot(x,y,type ="l",xlab= "NORMAL DEVIATE",ylab = "DENSITY",yaxs= 'i')
#yaxs The style of axis interval calculation to be used for the
y-axis.
Generating multivariate normal data
#install.packages("MASS")
library(MASS)
options(digits=7)
set.seed(1234)
mean <- c(250.7, 130.5, 5.6)
n <- 3
A <- matrix(runif((n^2),-3,3), ncol=n)
A## [,1] [,2] [,3]
## [1,] -2.3177795 0.7402767 -2.9430255
## [2,] 0.7337964 2.1654923 -1.6046970
## [3,] 0.6556484 0.8418636 0.9965025#generating n by n matrix with values from uniform distribution
sigma<- t(A) %*% A #hypothetical var-cov matrix
sigma## [,1] [,2] [,3]
## [1,] 6.340434 0.425199 6.297119
## [2,] 0.425199 5.946101 -4.814693
## [3,] 6.297119 -4.814693 12.229469mydata <- mvrnorm(400, mean, sigma) #generating 400 values from a
#multivariate normal
mydata <- as.data.frame(mydata)
names(mydata) <- c("y","x1","x2")
dim(mydata)## [1] 400 3head(mydata, n=15)## y x1 x2
## 1 251.0184 130.8935 5.4007886
## 2 251.5151 132.6467 5.4306534
## 3 247.0263 131.7707 0.8881412
## 4 248.9954 132.7586 2.4520034
## 5 250.5811 127.8735 7.7745863
## 6 252.3736 129.7477 8.0861491
## 7 252.4378 130.2279 7.1547260
## 8 254.9901 128.2115 11.2019576
## 9 247.7025 130.1414 2.6478323
## 10 248.0582 133.2701 1.2118612
## 11 254.2280 128.2655 11.3189866
## 12 250.8919 127.6359 7.5363612
## 13 248.1130 127.3608 5.6819667
## 14 249.0960 130.1720 2.7826842
## 15 253.2586 131.2778 7.7792555Pattern Matching and Replacement >>grep
grep("[a-z]", letters) ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26# letters will produce a,b,c,...z and will match a-z letters
letters ## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
## [20] "t" "u" "v" "w" "x" "y" "z"#to see what letters appear
grep("[abc]", letters) ## [1] 1 2 3#match the places where any of 'abc' found in the sequence
grep("[ased]", letters)## [1] 1 4 5 19grep("[ased]", letters,fixed=T)## integer(0)grep("[ased]", letters,fixed=F)## [1] 1 4 5 19grep("[ased]", c(letters,"[ased]"),fixed=T)## [1] 27grep("[ased]", c(letters,"[ased]"),fixed=F)## [1] 1 4 5 19 27a <- 6
sqrt(a)## [1] 2.44949b <- c(1.532, 5.754, 3.99)
round(b)## [1] 2 6 4d <- matrix(runif(16), nrow=4)
d## [,1] [,2] [,3] [,4]
## [1,] 0.9189159 0.2512678 0.9555143 0.5007705
## [2,] 0.8519516 0.6550916 0.7320949 0.2580541
## [3,] 0.7600845 0.1710871 0.9830077 0.5201532
## [4,] 0.7223479 0.5576245 0.1508911 0.1581700log(d)## [,1] [,2] [,3] [,4]
## [1,] -0.0845607 -1.3812359 -0.04550559 -0.6916074
## [2,] -0.1602256 -0.4229802 -0.31184510 -1.3545860
## [3,] -0.2743256 -1.7655828 -0.01713829 -0.6536319
## [4,] -0.3252484 -0.5840695 -1.89119695 -1.8440846mean(d)## [1] 0.5716892Applying functions
mydata <- matrix(rnorm(30), nrow=6)
mydata## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.2779592 -0.2144554 -0.2475043 0.4743173 1.3109869
## [2,] 0.1393738 -0.4587626 1.1982527 -1.0086812 0.4528040
## [3,] 1.6063475 0.2104320 0.8935556 0.5232864 2.1354266
## [4,] -0.9469487 0.3622436 -0.0294763 -0.1711696 0.4319354
## [5,] -1.2656885 -0.9867580 -0.5971864 0.4732156 -0.1613210
## [6,] 1.5545595 1.2751202 0.6129529 0.3315565 -0.3175865#Calculate row means
apply(mydata, 1, mean) ## [1] 0.52026074 0.06459732 1.07380963 -0.07068311 -0.50754765 0.69132053#Calculate column means
apply(mydata, 2, mean) ## [1] 0.3942671 0.0313033 0.3050990 0.1037542 0.6420409apply(mydata, 2, mean, trim=0.2)## [1] 0.5062360 -0.0251356 0.3073820 0.2769800 0.5086013Data management problem
options(digits=3)
Student <- c("Asif Rahman", "Angela Dean", "Heera Das",
"Issac Robin", "Shamsul Huda", "Ashraf Uddin",
"Nazrul Islam", "Shamsur Rahman", "Hemonto Mukherjee",
"Salemun Bahar")
Math <- c(502, 600, 412, 358, 495, 512, 410, 625, 573, 522)
Science <- c(95, 99, 80, 82, 75, 85, 80, 95, 89, 86)
English <- c(25, 22, 18, 15, 20, 28, 15, 30, 27, 18)
roster <- data.frame(Student, Math, Science, English,
stringsAsFactors=FALSE)
roster ## Student Math Science English
## 1 Asif Rahman 502 95 25
## 2 Angela Dean 600 99 22
## 3 Heera Das 412 80 18
## 4 Issac Robin 358 82 15
## 5 Shamsul Huda 495 75 20
## 6 Ashraf Uddin 512 85 28
## 7 Nazrul Islam 410 80 15
## 8 Shamsur Rahman 625 95 30
## 9 Hemonto Mukherjee 573 89 27
## 10 Salemun Bahar 522 86 18#Obtain performance scores
z <- scale(roster[,2:4])
z## Math Science English
## [1,] 0.0127 1.078 0.5869
## [2,] 1.1434 1.591 0.0367
## [3,] -1.0257 -0.847 -0.6969
## [4,] -1.6487 -0.590 -1.2471
## [5,] -0.0681 -1.489 -0.3301
## [6,] 0.1281 -0.205 1.1370
## [7,] -1.0488 -0.847 -1.2471
## [8,] 1.4318 1.078 1.5038
## [9,] 0.8319 0.308 0.9536
## [10,] 0.2434 -0.077 -0.6969
## attr(,"scaled:center")
## Math Science English
## 500.9 86.6 21.8
## attr(,"scaled:scale")
## Math Science English
## 86.67 7.79 5.45#calculates row means i.e.one score from each student after combining math,science, English
score <- apply(z, 1, mean)
score## [1] 0.559 0.924 -0.857 -1.162 -0.629 0.353 -1.048 1.338 0.698 -0.177roster <- cbind(roster, score)
roster## Student Math Science English score
## 1 Asif Rahman 502 95 25 0.559
## 2 Angela Dean 600 99 22 0.924
## 3 Heera Das 412 80 18 -0.857
## 4 Issac Robin 358 82 15 -1.162
## 5 Shamsul Huda 495 75 20 -0.629
## 6 Ashraf Uddin 512 85 28 0.353
## 7 Nazrul Islam 410 80 15 -1.048
## 8 Shamsur Rahman 625 95 30 1.338
## 9 Hemonto Mukherjee 573 89 27 0.698
## 10 Salemun Bahar 522 86 18 -0.177#setting quantiles ofscore vector at 80%, 60%, 40%, 20%
y <- quantile(score, c(.8,.6,.4,.2))
y## 80% 60% 40% 20%
## 0.743 0.436 -0.358 -0.895#will give natural quantile at 0%, 25%,50%, 75%, 100% points.
t=quantile(score)
t## 0% 25% 50% 75% 100%
## -1.1620 -0.7997 0.0882 0.6632 1.3379roster$grade[score >= y[1]] <- "A"
roster$grade[score < y[1] & score >= y[2]] <- "B"
roster$grade[score < y[2] & score >= y[3]] <- "C"
roster$grade[score < y[3] & score >= y[4]] <- "D"
roster$grade[score < y[4]] <- "F"
roster## Student Math Science English score grade
## 1 Asif Rahman 502 95 25 0.559 B
## 2 Angela Dean 600 99 22 0.924 A
## 3 Heera Das 412 80 18 -0.857 D
## 4 Issac Robin 358 82 15 -1.162 F
## 5 Shamsul Huda 495 75 20 -0.629 D
## 6 Ashraf Uddin 512 85 28 0.353 C
## 7 Nazrul Islam 410 80 15 -1.048 F
## 8 Shamsur Rahman 625 95 30 1.338 A
## 9 Hemonto Mukherjee 573 89 27 0.698 B
## 10 Salemun Bahar 522 86 18 -0.177 Cname <- strsplit((roster$Student), " ")
name## [[1]]
## [1] "Asif" "Rahman"
##
## [[2]]
## [1] "Angela" "Dean"
##
## [[3]]
## [1] "Heera" "Das"
##
## [[4]]
## [1] "Issac" "Robin"
##
## [[5]]
## [1] "Shamsul" "Huda"
##
## [[6]]
## [1] "Ashraf" "Uddin"
##
## [[7]]
## [1] "Nazrul" "Islam"
##
## [[8]]
## [1] "Shamsur" "Rahman"
##
## [[9]]
## [1] "Hemonto" "Mukherjee"
##
## [[10]]
## [1] "Salemun" "Bahar"#extracting last name
lastname <- sapply(name, "[", 2)
lastname## [1] "Rahman" "Dean" "Das" "Robin" "Huda" "Uddin"
## [7] "Islam" "Rahman" "Mukherjee" "Bahar"firstname <- sapply(name, "[", 1)
firstname ## [1] "Asif" "Angela" "Heera" "Issac" "Shamsul" "Ashraf" "Nazrul"
## [8] "Shamsur" "Hemonto" "Salemun"roster <- cbind(firstname,lastname, roster[,-1])
roster## firstname lastname Math Science English score grade
## 1 Asif Rahman 502 95 25 0.559 B
## 2 Angela Dean 600 99 22 0.924 A
## 3 Heera Das 412 80 18 -0.857 D
## 4 Issac Robin 358 82 15 -1.162 F
## 5 Shamsul Huda 495 75 20 -0.629 D
## 6 Ashraf Uddin 512 85 28 0.353 C
## 7 Nazrul Islam 410 80 15 -1.048 F
## 8 Shamsur Rahman 625 95 30 1.338 A
## 9 Hemonto Mukherjee 573 89 27 0.698 B
## 10 Salemun Bahar 522 86 18 -0.177 COrdering
#orders according to frist name and then by last name
roster <- roster[order(firstname,lastname),]
roster## firstname lastname Math Science English score grade
## 2 Angela Dean 600 99 22 0.924 A
## 6 Ashraf Uddin 512 85 28 0.353 C
## 1 Asif Rahman 502 95 25 0.559 B
## 3 Heera Das 412 80 18 -0.857 D
## 9 Hemonto Mukherjee 573 89 27 0.698 B
## 4 Issac Robin 358 82 15 -1.162 F
## 7 Nazrul Islam 410 80 15 -1.048 F
## 10 Salemun Bahar 522 86 18 -0.177 C
## 5 Shamsul Huda 495 75 20 -0.629 D
## 8 Shamsur Rahman 625 95 30 1.338 A#orders according to last name and then by frist name
roster <- roster[order(lastname,firstname),]
roster## firstname lastname Math Science English score grade
## 8 Shamsur Rahman 625 95 30 1.338 A
## 1 Asif Rahman 502 95 25 0.559 B
## 6 Ashraf Uddin 512 85 28 0.353 C
## 9 Hemonto Mukherjee 573 89 27 0.698 B
## 7 Nazrul Islam 410 80 15 -1.048 F
## 5 Shamsul Huda 495 75 20 -0.629 D
## 2 Angela Dean 600 99 22 0.924 A
## 10 Salemun Bahar 522 86 18 -0.177 C
## 3 Heera Das 412 80 18 -0.857 D
## 4 Issac Robin 358 82 15 -1.162 F##orders according grade…….. roster
roster <-roster[order(roster$grade,firstname,lastname),]
roster## firstname lastname Math Science English score grade
## 8 Shamsur Rahman 625 95 30 1.338 A
## 2 Angela Dean 600 99 22 0.924 A
## 1 Asif Rahman 502 95 25 0.559 B
## 9 Hemonto Mukherjee 573 89 27 0.698 B
## 6 Ashraf Uddin 512 85 28 0.353 C
## 10 Salemun Bahar 522 86 18 -0.177 C
## 5 Shamsul Huda 495 75 20 -0.629 D
## 3 Heera Das 412 80 18 -0.857 D
## 4 Issac Robin 358 82 15 -1.162 F
## 7 Nazrul Islam 410 80 15 -1.048 FRepetition and looping
For
for (i in 1:5)
print("Welcome")## [1] "Welcome"
## [1] "Welcome"
## [1] "Welcome"
## [1] "Welcome"
## [1] "Welcome"while
i <- 10
while (i > 0) {
print("2nd year");
i <- i - 1
}## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"
## [1] "2nd year"IFELSE
score<-runif(50, 0,100)
score## [1] 15.14 42.96 36.86 61.42 83.11 8.97 35.30 73.66 6.31 58.19 69.58 73.87
## [13] 31.07 34.07 17.26 95.40 47.65 44.44 1.50 54.82 29.61 45.79 15.10 80.13
## [25] 77.78 22.11 1.98 57.98 1.07 71.72 44.10 10.65 93.54 59.01 29.83 27.63
## [37] 23.30 92.35 57.21 75.96 14.76 4.23 33.95 82.10 91.53 58.62 54.44 28.73
## [49] 65.59 36.11outcome <- ifelse (score > 40.0, "Passed", "Failed")
outcome## [1] "Failed" "Passed" "Failed" "Passed" "Passed" "Failed" "Failed" "Passed"
## [9] "Failed" "Passed" "Passed" "Passed" "Failed" "Failed" "Failed" "Passed"
## [17] "Passed" "Passed" "Failed" "Passed" "Failed" "Passed" "Failed" "Passed"
## [25] "Passed" "Failed" "Failed" "Passed" "Failed" "Passed" "Passed" "Failed"
## [33] "Passed" "Passed" "Failed" "Failed" "Failed" "Passed" "Passed" "Passed"
## [41] "Failed" "Failed" "Failed" "Passed" "Passed" "Passed" "Passed" "Failed"
## [49] "Passed" "Failed"table(outcome)## outcome
## Failed Passed
## 23 27Transpose
cars <- mtcars[1:8,1:5]
cars## mpg cyl disp hp drat
## Mazda RX4 21.0 6 160 110 3.90
## Mazda RX4 Wag 21.0 6 160 110 3.90
## Datsun 710 22.8 4 108 93 3.85
## Hornet 4 Drive 21.4 6 258 110 3.08
## Hornet Sportabout 18.7 8 360 175 3.15
## Valiant 18.1 6 225 105 2.76
## Duster 360 14.3 8 360 245 3.21
## Merc 240D 24.4 4 147 62 3.69t(cars)## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout
## mpg 21.0 21.0 22.80 21.40 18.70
## cyl 6.0 6.0 4.00 6.00 8.00
## disp 160.0 160.0 108.00 258.00 360.00
## hp 110.0 110.0 93.00 110.00 175.00
## drat 3.9 3.9 3.85 3.08 3.15
## Valiant Duster 360 Merc 240D
## mpg 18.10 14.30 24.40
## cyl 6.00 8.00 4.00
## disp 225.00 360.00 146.70
## hp 105.00 245.00 62.00
## drat 2.76 3.21 3.69Aggregation and restructuring
Aggregate function
#takes upto 4 digits for each variable
options(digits=4)
attach(mtcars)## The following object is masked from package:ggplot2:
##
## mpg#Aggregates mtcars by varaiables cyl, and gear.
aggcars<-aggregate(mtcars,by=list(cyl,gear),FUN=mean, na.rm=TRUE)
aggcars## Group.1 Group.2 mpg cyl disp hp drat wt qsec vs am gear carb
## 1 4 3 21.50 4 120.1 97.0 3.700 2.465 20.01 1.0 0.00 3 1.000
## 2 6 3 19.75 6 241.5 107.5 2.920 3.337 19.83 1.0 0.00 3 1.000
## 3 8 3 15.05 8 357.6 194.2 3.121 4.104 17.14 0.0 0.00 3 3.083
## 4 4 4 26.93 4 102.6 76.0 4.110 2.378 19.61 1.0 0.75 4 1.500
## 5 6 4 19.75 6 163.8 116.5 3.910 3.094 17.67 0.5 0.50 4 4.000
## 6 4 5 28.20 4 107.7 102.0 4.100 1.827 16.80 0.5 1.00 5 2.000
## 7 6 5 19.70 6 145.0 175.0 3.620 2.770 15.50 0.0 1.00 5 6.000
## 8 8 5 15.40 8 326.0 299.5 3.880 3.370 14.55 0.0 1.00 5 6.000Reshaping data (Use of melt() and cast() functions)
#install.packages("reshape")
library(reshape)
options(digits=2)
ID<-rep(1:5, each = 2)
Semester<-rep(1:2,times=5)
Math <- c(502, 560, 412, 358, 590, 595, 410, 423, 573, 522)
Science <- c(95, 99, 80, 82, 75, 85, 80, 95, 89, 86)
English <- c(25, 22, 18, 15, 25, 28, 15, 18, 27, 28)
examdata <- data.frame(ID,Semester, Math, Science, English,row.names = NULL,check.rows = F,check.names = T)
examdata## ID Semester Math Science English
## 1 1 1 502 95 25
## 2 1 2 560 99 22
## 3 2 1 412 80 18
## 4 2 2 358 82 15
## 5 3 1 590 75 25
## 6 3 2 595 85 28
## 7 4 1 410 80 15
## 8 4 2 423 95 18
## 9 5 1 573 89 27
## 10 5 2 522 86 28melt() functions
library(reshape)
# for restructure
md <- melt(examdata, id=c("ID", "Semester"))
md## ID Semester variable value
## 1 1 1 Math 502
## 2 1 2 Math 560
## 3 2 1 Math 412
## 4 2 2 Math 358
## 5 3 1 Math 590
## 6 3 2 Math 595
## 7 4 1 Math 410
## 8 4 2 Math 423
## 9 5 1 Math 573
## 10 5 2 Math 522
## 11 1 1 Science 95
## 12 1 2 Science 99
## 13 2 1 Science 80
## 14 2 2 Science 82
## 15 3 1 Science 75
## 16 3 2 Science 85
## 17 4 1 Science 80
## 18 4 2 Science 95
## 19 5 1 Science 89
## 20 5 2 Science 86
## 21 1 1 English 25
## 22 1 2 English 22
## 23 2 1 English 18
## 24 2 2 English 15
## 25 3 1 English 25
## 26 3 2 English 28
## 27 4 1 English 15
## 28 4 2 English 18
## 29 5 1 English 27
## 30 5 2 English 28cast() functions *** problem
Aggregation
library(reshape)
#Aggregation
#cast(md, ID~variable, mean)
#cast(md, Semester~variable, mean)
#cast(md, ID~Semester, mean)#average of 3 module scores in each semester
Without Aggregation
#Reshaping data (Use of melt() and cast() functions)
md <- melt(examdata, id=(c("ID", "Semester")))
md## ID Semester variable value
## 1 1 1 Math 502
## 2 1 2 Math 560
## 3 2 1 Math 412
## 4 2 2 Math 358
## 5 3 1 Math 590
## 6 3 2 Math 595
## 7 4 1 Math 410
## 8 4 2 Math 423
## 9 5 1 Math 573
## 10 5 2 Math 522
## 11 1 1 Science 95
## 12 1 2 Science 99
## 13 2 1 Science 80
## 14 2 2 Science 82
## 15 3 1 Science 75
## 16 3 2 Science 85
## 17 4 1 Science 80
## 18 4 2 Science 95
## 19 5 1 Science 89
## 20 5 2 Science 86
## 21 1 1 English 25
## 22 1 2 English 22
## 23 2 1 English 18
## 24 2 2 English 15
## 25 3 1 English 25
## 26 3 2 English 28
## 27 4 1 English 15
## 28 4 2 English 18
## 29 5 1 English 27
## 30 5 2 English 28#Without Aggregation
cast(md, ID+Semester~variable)## ID Semester Math Science English
## 1 1 1 502 95 25
## 2 1 2 560 99 22
## 3 2 1 412 80 18
## 4 2 2 358 82 15
## 5 3 1 590 75 25
## 6 3 2 595 85 28
## 7 4 1 410 80 15
## 8 4 2 423 95 18
## 9 5 1 573 89 27
## 10 5 2 522 86 28cast(md, ID+variable~Semester)## ID variable 1 2
## 1 1 Math 502 560
## 2 1 Science 95 99
## 3 1 English 25 22
## 4 2 Math 412 358
## 5 2 Science 80 82
## 6 2 English 18 15
## 7 3 Math 590 595
## 8 3 Science 75 85
## 9 3 English 25 28
## 10 4 Math 410 423
## 11 4 Science 80 95
## 12 4 English 15 18
## 13 5 Math 573 522
## 14 5 Science 89 86
## 15 5 English 27 28cast(md, ID~variable+Semester)## ID Math_1 Math_2 Science_1 Science_2 English_1 English_2
## 1 1 502 560 95 99 25 22
## 2 2 412 358 80 82 18 15
## 3 3 590 595 75 85 25 28
## 4 4 410 423 80 95 15 18
## 5 5 573 522 89 86 27 28slide 06 (Functions, Loops, Simulation)
Example 1
h<- function(number) {
for(i in seq_len(number)) {
cat("Hello, can you listen me?\n")
} }
h(5)## Hello, can you listen me?
## Hello, can you listen me?
## Hello, can you listen me?
## Hello, can you listen me?
## Hello, can you listen me?Example 2
sumdice=function(n){
k=sample(1:6, size=n, replace=TRUE)
#return(k)
return(sum(k))
}
sumdice(2)## [1] 10sumdice(3)## [1] 7Example 3
dice=function(n){
k=sample(1:6, size=n, replace=TRUE)
return(k)
#return(k)
}
dice(2)## [1] 2 4dice(3)## [1] 5 3 2Example 4
sumdice1=function(m, n){
k=sample(1:m, size=n, replace=TRUE)
return(k)
#return(sum(k))
}
sumdice1(7,2)## [1] 7 2sumdice1(7,3)## [1] 4 7 1sumdice1(500,30)## [1] 330 416 329 3 121 263 322 106 445 99 104 368 217 52 13 372 362 188 213
## [20] 75 249 262 88 90 253 22 342 327 180 290Example 5
poisson2=function(lambda, x){
p=(exp(-lambda)*(lambda)^x)/factorial(x)
return(p)
}
poisson2(lambda=2, x=3)## [1] 0.18poisson2(lambda=(50/60), x=5)## [1] 0.0015slide 07 (Exploratory Data Analysis)
base plotting system
ex:1
data(airquality)
with(airquality, {
plot(Temp, Ozone)
lines(loess.smooth(Temp, Ozone))
})
ex:2
data(cars)
## Create the plot / draw canvas
with(cars, plot(speed, dist))
## Add annotation
title("Speed vs. Stopping distance")
The Lattice System
#install lattice packages
library(lattice)
state <- data.frame(state.x77, region = state.region)
xyplot(Life.Exp ~ Income | region, data = state, layout =
c(4, 1))
The ggplot2 System
#install.packages("ggplot2")
library(ggplot2)
data(mpg)
qplot(displ, hwy, data = mpg)## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
slide 08 (The Base Plotting System)
Histogram
##The Base Plotting System
library(datasets)
hist(airquality$Ozone)
Boxplot
airquality <- transform(airquality,
Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab
= "Month", ylab = "Ozone (ppb)")
slide 09 (The ggplot2 Plotting System)
Ex 1
with(airquality,{
plot(Temp,Ozone)
lines(loess.smooth(Temp,Ozone))
})
Ex 2
#install.packages("ggplot2")
library(ggplot2)
ggplot(airquality,aes(Temp,Ozone))+
geom_point()+
geom_smooth(method = "loess",se = F)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 37 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 37 rows containing missing values (`geom_point()`).
Ex 3
library(ggplot2)
str(mpg)## tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
## $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
## $ model : chr [1:234] "a4" "a4" "a4" "a4" ...
## $ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr [1:234] "f" "f" "f" "f" ...
## $ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr [1:234] "p" "p" "p" "p" ...
## $ class : chr [1:234] "compact" "compact" "compact" "compact" ...qplot(displ,hwy,data =mpg)
qplot(cty,cyl,data=mpg)
qplot(displ,cyl,data=mpg)
qplot(cyl,displ,data=mpg)
qplot(cty,displ,data=mpg)
qplot(displ,hwy,data=mpg,color=drv)
qplot(displ,hwy,data=mpg,color=fl)
qplot(displ,hwy,data=mpg,color=class)
View(mpg)
qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
slide 12 (R packages: Tidyverse)
Aesthetic Mappings
ggplot(data = ) +
#install.packages("tidyverse")
#install.packages(c("nycflights13", "gapminder", "Lahman"))
library(ggplot2)
#to know about mpg data set
ggplot(data = mpg) +
geom_point (mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = class))## Warning: Using size for a discrete variable is not advised.
#to get rid of warnings in the previous plot:
ggplot(data = mpg) +
geom_point(mapping =aes(x = displ, y = hwy, size = displ))
#adding color "blue" instead of default color "black"
ggplot(data = mpg) + geom_point(mapping =
aes(x = displ, y = hwy, size =
displ),color="blue")
### Facets
ggplot(data = mpg) +
geom_point(mapping =aes(x = displ, y = hwy)) +
facet_wrap(~class, nrow = 2)
Geometric Objects
# left
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
# right
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth (mapping = aes(x = displ,y = hwy, linetype = drv))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy, color=drv))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy, color = drv),show.legend = F)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Statistical Transformations
ggplot(data = diamonds) +
stat_count(mapping = aes(x = cut))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, y =..prop.., group = 1))## Warning: The dot-dot notation (`..prop..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(prop)` instead.
ggplot(data = diamonds) +
stat_summary(
mapping = aes(x = cut, y = depth),
fun.ymin = min,
fun.ymax = max,
fun.y = median
)## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.## Warning: The `fun.ymin` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun.min` argument instead.## Warning: The `fun.ymax` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun.max` argument instead.
ggplot(data = diamonds) +
stat_summary(
mapping = aes(x = cut, y = price),
fun.ymin = min,
fun.ymax = max,
fun.y = median
)
Position Adjustments
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, color =
cut))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill =
cut))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill =
clarity))
ggplot(data = diamonds,
mapping = aes(x = cut, fill = clarity)
) +
geom_bar(alpha = 1/5, position = "identity")
ggplot( data = diamonds,
mapping = aes(x = cut, color = clarity)
) +
geom_bar(fill = NA, position = "identity")
INCOURSE SOLVE
1st incourse
2019
Q1
set.seed(100)
x<-rnorm(n = 100,mean = 15,sd = 4)
mean(x)## [1] 15var(x)## [1] 17min(x)## [1] 5.9max(x)## [1] 25hist(x)
Q2
set.seed(100)
x<-runif(n=10,min=10,20)2a
c<-matrix(x,2,5,byrow =T)
c## [,1] [,2] [,3] [,4] [,5]
## [1,] 13 13 16 11 15
## [2,] 15 18 14 15 122b
d<-matrix(x,nrow = 5,ncol=2,byrow = F)
d## [,1] [,2]
## [1,] 13 15
## [2,] 13 18
## [3,] 16 14
## [4,] 11 15
## [5,] 15 12Q3
a<-matrix(data = 1:24,nrow = 6,ncol = 4,byrow = T)
rownames(a)<-c("R1","R2","R3","R4","R5","R6")
colnames(a)<-c("C1","C2","C3","C4")
a## C1 C2 C3 C4
## R1 1 2 3 4
## R2 5 6 7 8
## R3 9 10 11 12
## R4 13 14 15 16
## R5 17 18 19 20
## R6 21 22 23 24or
a<-c(1:24)
rnam<-c("R1","R2","R3","R4","R5","R6")
cnam<-c("C1","C2","C3","C4")
x<-matrix(a,nrow=6,ncol=4,byrow = T,dimnames = list(rnam,cnam))
x## C1 C2 C3 C4
## R1 1 2 3 4
## R2 5 6 7 8
## R3 9 10 11 12
## R4 13 14 15 16
## R5 17 18 19 20
## R6 21 22 23 24Q5
age<-c(10,5,3,3,12,8,6,7,7,4,15,7,13,10,12)
weight<-c(30,20,15,11,35,28,16,19,22,20,55,27,46,35,39)
mean(age)## [1] 8.1mean(weight)## [1] 28sd(age)## [1] 3.7sd(weight)## [1] 12cor(age,weight)## [1] 0.95plot(age,weight)
abline(lm(weight~age))
#create scatter plot of x vs. y plot(x, y)
#add line of best fit to scatter plot abline(lm(y ~ x))
Q6
x<-c(1:120)
rnames<-c(1:10)
cnames<-c("a","b","c","d","e","f","g","h","i","j","k","l")
y<-matrix(x,nrow=10,ncol=12,byrow = T,dimnames = list(rnames,cnames))
y## a b c d e f g h i j k l
## 1 1 2 3 4 5 6 7 8 9 10 11 12
## 2 13 14 15 16 17 18 19 20 21 22 23 24
## 3 25 26 27 28 29 30 31 32 33 34 35 36
## 4 37 38 39 40 41 42 43 44 45 46 47 48
## 5 49 50 51 52 53 54 55 56 57 58 59 60
## 6 61 62 63 64 65 66 67 68 69 70 71 72
## 7 73 74 75 76 77 78 79 80 81 82 83 84
## 8 85 86 87 88 89 90 91 92 93 94 95 96
## 9 97 98 99 100 101 102 103 104 105 106 107 108
## 10 109 110 111 112 113 114 115 116 117 118 119 120sm<-y[,c(2,6,8)]
sm## b f h
## 1 2 6 8
## 2 14 18 20
## 3 26 30 32
## 4 38 42 44
## 5 50 54 56
## 6 62 66 68
## 7 74 78 80
## 8 86 90 92
## 9 98 102 104
## 10 110 114 116Q7
dose<-c(10,15,20,30,45,50,60,65,70,80)
drugA<-c(16,18,24,32,40,48,50,65,65,80)
drugB<-c(15,16,20,25,35,42,47,55,56,70)
plot(x = dose,y = drugA, type="b",
pch=15, lty=1, col="red", ylim=c(0, 80),
main="Drug A vs. Drug B",
xlab="Drug Dosage", ylab="Drug Response")
lines(x = dose,y = drugB, type="b",
pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
legend("topleft", inset=.05, title="Drug Type", c("A","B"),
lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
Q8
rm(list = ls())
library(ggplot2)
attach(mtcars)## The following objects are masked from mtcars (pos = 3):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt## The following object is masked from package:ggplot2:
##
## mpgplot(wt,mpg)
plot(mpg,disp)
hist(mpg)
boxplot(wt)
detach(mtcars)2020
Q.1
1a
sample<-(rpois(n = 500,lambda = 2))
mean(sample)## [1] 2var(sample)## [1] 2min(sample)## [1] 0max(sample)## [1] 71b
hist(sample)
Q.2
sam<-rnorm(n = 16,mean = 5,sd = sqrt(2))
sam## [1] 4.4 2.3 7.7 3.2 4.3 5.3 5.6 3.3 5.4 6.3 5.7 6.2 3.7 4.8 6.6 5.7a
matrix(data = sam,nrow = 2)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 4.4 7.7 4.3 5.6 5.4 5.7 3.7 6.6
## [2,] 2.3 3.2 5.3 3.3 6.3 6.2 4.8 5.7b
matrix(data = sam,ncol = 2,byrow = FALSE)## [,1] [,2]
## [1,] 4.4 5.4
## [2,] 2.3 6.3
## [3,] 7.7 5.7
## [4,] 3.2 6.2
## [5,] 4.3 3.7
## [6,] 5.3 4.8
## [7,] 5.6 6.6
## [8,] 3.3 5.7Q.3
The attach() function adds the data frame to R search path attach (mtcars)
The detach() function removes the data frame from the search path
detach(mtcars)
The with()function evaliate an expression in R environment
with(data = mtcars,summary(mpg))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 15 19 20 23 34Q.4
id<-c(1:16)
iq<-c(180,150,140,110,122,181,186,170,117,142,115,175,103,90,101,135)
math<-c(85,60,70,71,55,78,90,89,75,65,55,80,46,40,39,60)
data.frame(id,iq,math)## id iq math
## 1 1 180 85
## 2 2 150 60
## 3 3 140 70
## 4 4 110 71
## 5 5 122 55
## 6 6 181 78
## 7 7 186 90
## 8 8 170 89
## 9 9 117 75
## 10 10 142 65
## 11 11 115 55
## 12 12 175 80
## 13 13 103 46
## 14 14 90 40
## 15 15 101 39
## 16 16 135 60#ans
mean(iq)## [1] 139sd(iq)## [1] 32mean(math)## [1] 66sd(math)## [1] 16cor(iq,math)## [1] 0.85cor.test(iq,math)##
## Pearson's product-moment correlation
##
## data: iq and math
## t = 6, df = 14, p-value = 3e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.61 0.95
## sample estimates:
## cor
## 0.85plot(iq,math)
abline(lm(math~iq))
#### Q.5
d=matrix(data = 1:110,nrow = 10)
#for column label
colnames(d )<-c("a","b","c","d","e","f","g","h","i","j","k")
d## a b c d e f g h i j k
## [1,] 1 11 21 31 41 51 61 71 81 91 101
## [2,] 2 12 22 32 42 52 62 72 82 92 102
## [3,] 3 13 23 33 43 53 63 73 83 93 103
## [4,] 4 14 24 34 44 54 64 74 84 94 104
## [5,] 5 15 25 35 45 55 65 75 85 95 105
## [6,] 6 16 26 36 46 56 66 76 86 96 106
## [7,] 7 17 27 37 47 57 67 77 87 97 107
## [8,] 8 18 28 38 48 58 68 78 88 98 108
## [9,] 9 19 29 39 49 59 69 79 89 99 109
## [10,] 10 20 30 40 50 60 70 80 90 100 110#for row label
rownames(d )<-c("a","b","c","d","e","f","g","h","i","j")
d## a b c d e f g h i j k
## a 1 11 21 31 41 51 61 71 81 91 101
## b 2 12 22 32 42 52 62 72 82 92 102
## c 3 13 23 33 43 53 63 73 83 93 103
## d 4 14 24 34 44 54 64 74 84 94 104
## e 5 15 25 35 45 55 65 75 85 95 105
## f 6 16 26 36 46 56 66 76 86 96 106
## g 7 17 27 37 47 57 67 77 87 97 107
## h 8 18 28 38 48 58 68 78 88 98 108
## i 9 19 29 39 49 59 69 79 89 99 109
## j 10 20 30 40 50 60 70 80 90 100 110#for 2nd 4th 8th coloums
d[,c(2,4,8)]## b d h
## a 11 31 71
## b 12 32 72
## c 13 33 73
## d 14 34 74
## e 15 35 75
## f 16 36 76
## g 17 37 77
## h 18 38 78
## i 19 39 79
## j 20 40 80#for 2nd 4th 8th rows
d[c(2,4,8),]## a b c d e f g h i j k
## b 2 12 22 32 42 52 62 72 82 92 102
## d 4 14 24 34 44 54 64 74 84 94 104
## h 8 18 28 38 48 58 68 78 88 98 108alternative
colnames<-c("a","b","c","d","e","f","g","h","i","j","k")
rownames<-c("a","b","c","d","e","f","g","h","i","j")
matrix(data = 1:110,nrow = 10,dimnames = list(rownames,colnames))## a b c d e f g h i j k
## a 1 11 21 31 41 51 61 71 81 91 101
## b 2 12 22 32 42 52 62 72 82 92 102
## c 3 13 23 33 43 53 63 73 83 93 103
## d 4 14 24 34 44 54 64 74 84 94 104
## e 5 15 25 35 45 55 65 75 85 95 105
## f 6 16 26 36 46 56 66 76 86 96 106
## g 7 17 27 37 47 57 67 77 87 97 107
## h 8 18 28 38 48 58 68 78 88 98 108
## i 9 19 29 39 49 59 69 79 89 99 109
## j 10 20 30 40 50 60 70 80 90 100 110Q.6
doses<-c(10,15,20,30,45,50,60,65,70,80,85,90)
doses## [1] 10 15 20 30 45 50 60 65 70 80 85 90drugc<-c(16,18,24,32,40,48,50,65,65,80,82,87)
drugc## [1] 16 18 24 32 40 48 50 65 65 80 82 87drugd<-c(15,16,20,25,35,42,47,55,56,70,75,79)
drugd## [1] 15 16 20 25 35 42 47 55 56 70 75 79#two different way combining data
data.frame(doses,drugc,drugd)## doses drugc drugd
## 1 10 16 15
## 2 15 18 16
## 3 20 24 20
## 4 30 32 25
## 5 45 40 35
## 6 50 48 42
## 7 60 50 47
## 8 65 65 55
## 9 70 65 56
## 10 80 80 70
## 11 85 82 75
## 12 90 87 79cbind(doses,drugc,drugd)## doses drugc drugd
## [1,] 10 16 15
## [2,] 15 18 16
## [3,] 20 24 20
## [4,] 30 32 25
## [5,] 45 40 35
## [6,] 50 48 42
## [7,] 60 50 47
## [8,] 65 65 55
## [9,] 70 65 56
## [10,] 80 80 70
## [11,] 85 82 75
## [12,] 90 87 79##main part
plot(x = doses,y = drugc,type = "b",main = "Drug c vs Drug d",xlab ="Drug Doses", ylab = "Response to Drug",col="red",pch="c")
lines(x = doses,y = drugd,type = "b",col="blue",pch="d")
legend("topleft",title = "Durg types",c("C","D"),fill=c("red",'blue'))
Q.7
attach(mtcars)## The following objects are masked from mtcars (pos = 3):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt## The following object is masked from package:ggplot2:
##
## mpg7a
plot(wt,hp)
7b
plot(mpg,disp)
7c
hist(disp)
7d
boxplot(mpg)
detach(mtcars)2021(same 19)
Q1
set.seed(100)
x<-rnorm(n = 100,mean = 15,sd = 4)
mean(x)## [1] 15var(x)## [1] 17min(x)## [1] 5.9max(x)## [1] 25hist(x)
Q2
set.seed(13)
x<-runif(n=25,min=5,10)2a
c<-matrix(x,nrow=2,byrow =T)## Warning in matrix(x, nrow = 2, byrow = T): data length [25] is not a
## sub-multiple or multiple of the number of rows [2]c## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 8.6 6.2 6.9 5.5 9.8 5.1 7.9 8.8 9.4 5.2 8.3 9.4 9.5
## [2,] 7.8 8.0 6.8 6.8 8.0 9.3 8.4 5.7 7.7 8.4 7.6 5.4 8.62b
d<-matrix(x,ncol=2,byrow = F)## Warning in matrix(x, ncol = 2, byrow = F): data length [25] is not a
## sub-multiple or multiple of the number of rows [13]d## [,1] [,2]
## [1,] 8.6 7.8
## [2,] 6.2 8.0
## [3,] 6.9 6.8
## [4,] 5.5 6.8
## [5,] 9.8 8.0
## [6,] 5.1 9.3
## [7,] 7.9 8.4
## [8,] 8.8 5.7
## [9,] 9.4 7.7
## [10,] 5.2 8.4
## [11,] 8.3 7.6
## [12,] 9.4 5.4
## [13,] 9.5 8.6Q3
a<-matrix(data = 1:24,nrow = 6,ncol = 4,byrow = T)
rownames(a)<-c("R1","R2","R3","R4","R5","R6")
colnames(a)<-c("C1","C2","C3","C4")
a## C1 C2 C3 C4
## R1 1 2 3 4
## R2 5 6 7 8
## R3 9 10 11 12
## R4 13 14 15 16
## R5 17 18 19 20
## R6 21 22 23 24or
a<-c(1:24)
rnam<-c("R1","R2","R3","R4","R5","R6")
cnam<-c("C1","C2","C3","C4")
x<-matrix(a,nrow=6,ncol=4,byrow = T,dimnames = list(rnam,cnam))
x## C1 C2 C3 C4
## R1 1 2 3 4
## R2 5 6 7 8
## R3 9 10 11 12
## R4 13 14 15 16
## R5 17 18 19 20
## R6 21 22 23 24Q5
age<-c(10,5,3,3,12,8,6,7,7,4,15,7,13,10,12)
weight<-c(30,20,15,11,35,28,16,19,22,20,55,27,46,35,39)
mean(age)## [1] 8.1mean(weight)## [1] 28sd(age)## [1] 3.7sd(weight)## [1] 12cor(age,weight)## [1] 0.95plot(age,weight)
abline(lm(weight~age))
#create scatter plot of x vs. y plot(x, y)
#add line of best fit to scatter plot abline(lm(y ~ x))
Q6
x<-c(1:120)
rnames<-c(1:10)
cnames<-c("a","b","c","d","e","f","g","h","i","j","k","l")
y<-matrix(x,nrow=10,ncol=12,byrow = T,dimnames = list(rnames,cnames))
y## a b c d e f g h i j k l
## 1 1 2 3 4 5 6 7 8 9 10 11 12
## 2 13 14 15 16 17 18 19 20 21 22 23 24
## 3 25 26 27 28 29 30 31 32 33 34 35 36
## 4 37 38 39 40 41 42 43 44 45 46 47 48
## 5 49 50 51 52 53 54 55 56 57 58 59 60
## 6 61 62 63 64 65 66 67 68 69 70 71 72
## 7 73 74 75 76 77 78 79 80 81 82 83 84
## 8 85 86 87 88 89 90 91 92 93 94 95 96
## 9 97 98 99 100 101 102 103 104 105 106 107 108
## 10 109 110 111 112 113 114 115 116 117 118 119 120sm<-y[,c(2,6,8)]
sm## b f h
## 1 2 6 8
## 2 14 18 20
## 3 26 30 32
## 4 38 42 44
## 5 50 54 56
## 6 62 66 68
## 7 74 78 80
## 8 86 90 92
## 9 98 102 104
## 10 110 114 116Q7
dose<-c(10,15,20,30,45,50,60,65,70,80)
drugA<-c(16,18,24,32,40,48,50,65,65,80)
drugB<-c(15,16,20,25,35,42,47,55,56,70)
plot(x = dose,y = drugA, type="b",
pch=15, lty=1, col="red", ylim=c(0, 80),
main="Drug A vs. Drug B",
xlab="Drug Dosage", ylab="Drug Response")
lines(x = dose,y = drugB, type="b",
pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
legend("topleft", inset=.05, title="Drug Type", c("A","B"),
lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
Q8
attach(mtcars)## The following objects are masked from mtcars (pos = 3):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt## The following object is masked from package:ggplot2:
##
## mpgplot(wt,mpg)
plot(mpg,disp)
hist(mpg)
boxplot(wt)
2022
Q1
1a
*how can you trace a working directory in r ?
To trace the current working directory in R, you can use the getwd() function. This function returns the current working directory as a character string.
To use this function, simply type getwd() into the R console and press enter. The output will be the current working directory.
Alternatively, you can also use the dir() function to list the files and directories in the current working directory. This function takes an optional argument that specifies the directory to list, so you can use dir(getwd()) to list the contents of the current working directory.
You can also change the current working directory using the setwd() function. This function takes a character string argument specifying the path to the new working directory. For example, to change the working directory to a folder named “data” located in the current working directory, you could use setwd(“data”).
It’s important to note that when working with file paths in R, you should use forward slashes (/) instead of backslashes () on Windows systems. For example, to specify a file path in the “data” folder located in the current working directory on a Windows system, you would use “./data” instead of “.”.
1b
What is necessity of changing working directory in r?
Changing the working directory in R is important when you are working with files or data that are located in a different directory than the one where R is currently running.
When you are working with files in R, you often need to specify the path to the file. If the file is located in a different directory than the one where R is currently running, you need to provide the full path to the file. Changing the working directory to the directory where the file is located allows you to simply use the file name instead of the full path, which can save time and reduce the chance of errors.
In addition, changing the working directory can make it easier to organize your files and keep your code organized. For example, you might keep your data files in a separate directory from your R scripts. By changing the working directory to the data directory, you can more easily access and work with the data files in your R scripts.
Finally, some R packages, such as shiny, require that the working directory be set correctly in order to work properly. For example, if you are building a shiny app that reads in data from a file, you need to set the working directory to the location of the file in order for the app to work correctly.
Overall, changing the working directory in R can make it easier to work with files, keep your code organized, and ensure that your code works correctly with R packages that require the working directory to be set properly.
Q2
2a
data("rivers")
rivers## [1] 735 320 325 392 524 450 1459 135 465 600 330 336 280 315 870
## [16] 906 202 329 290 1000 600 505 1450 840 1243 890 350 407 286 280
## [31] 525 720 390 250 327 230 265 850 210 630 260 230 360 730 600
## [46] 306 390 420 291 710 340 217 281 352 259 250 470 680 570 350
## [61] 300 560 900 625 332 2348 1171 3710 2315 2533 780 280 410 460 260
## [76] 255 431 350 760 618 338 981 1306 500 696 605 250 411 1054 735
## [91] 233 435 490 310 460 383 375 1270 545 445 1885 380 300 380 377
## [106] 425 276 210 800 420 350 360 538 1100 1205 314 237 610 360 540
## [121] 1038 424 310 300 444 301 268 620 215 652 900 525 246 360 529
## [136] 500 720 270 430 671 1770summary(rivers)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 135 310 425 591 680 37102b
hist(rivers)
boxplot(rivers)
Q3
3a
data(stackloss)
stackloss## Air.Flow Water.Temp Acid.Conc. stack.loss
## 1 80 27 89 42
## 2 80 27 88 37
## 3 75 25 90 37
## 4 62 24 87 28
## 5 62 22 87 18
## 6 62 23 87 18
## 7 62 24 93 19
## 8 62 24 93 20
## 9 58 23 87 15
## 10 58 18 80 14
## 11 58 18 89 14
## 12 58 17 88 13
## 13 58 18 82 11
## 14 58 19 93 12
## 15 50 18 89 8
## 16 50 18 86 7
## 17 50 19 72 8
## 18 50 19 79 8
## 19 50 20 80 9
## 20 56 20 82 15
## 21 70 20 91 15plot(stackloss$Air.Flow,stackloss$stack.loss)
abline(lm(stackloss$stack.loss~stackloss$Air.Flow))
plot(stackloss$Water.Temp,stackloss$stack.loss)
abline(lm(stackloss$stack.loss~stackloss$Water.Temp))
plot(stackloss$Acid.Conc.,stackloss$stack.loss)
abline(lm(stackloss$stack.loss~stackloss$Acid.Conc.))
3b
cor(stackloss$Air.Flow,stackloss$stack.loss)## [1] 0.92cor(stackloss$Water.Temp,stackloss$stack.loss)## [1] 0.88cor(stackloss$Acid.Conc.,stackloss$stack.loss)## [1] 0.43c
lm(data = stackloss,stack.loss~Air.Flow+Water.Temp+Acid.Conc.)##
## Call:
## lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.,
## data = stackloss)
##
## Coefficients:
## (Intercept) Air.Flow Water.Temp Acid.Conc.
## -39.920 0.716 1.295 -0.152Q4
x<-c(1,2,3,4,5,6,7,8,9,10,11,12)
a<-c(73.3, 75.9, 89.2, 88.3, 90.0, 100.0, 85.4, 103.0, 91.2, 65.7, 63.3, 75.4)
b<-c(67.3, 59.5, 74.7, 58.3, 72.0, 48.3, 66.0, 75.6, 61.3, 50.6, 59.7, 61.0)
c<-c(104.0, 142.5, 80.1, 51.0, 70.1, 83.3, 109.8, 126.3, 104.4, 103.6, 132.2, 102.3)
plot(x = x,y = a,main="sunpot",type = "b",col="red" )
lines(x=x,y=b,col="blue")
lines(x=x,y=c)
Q5
data(attitude)
attitude## rating complaints privileges learning raises critical advance
## 1 43 51 30 39 61 92 45
## 2 63 64 51 54 63 73 47
## 3 71 70 68 69 76 86 48
## 4 61 63 45 47 54 84 35
## 5 81 78 56 66 71 83 47
## 6 43 55 49 44 54 49 34
## 7 58 67 42 56 66 68 35
## 8 71 75 50 55 70 66 41
## 9 72 82 72 67 71 83 31
## 10 67 61 45 47 62 80 41
## 11 64 53 53 58 58 67 34
## 12 67 60 47 39 59 74 41
## 13 69 62 57 42 55 63 25
## 14 68 83 83 45 59 77 35
## 15 77 77 54 72 79 77 46
## 16 81 90 50 72 60 54 36
## 17 74 85 64 69 79 79 63
## 18 65 60 65 75 55 80 60
## 19 65 70 46 57 75 85 46
## 20 50 58 68 54 64 78 52
## 21 50 40 33 34 43 64 33
## 22 64 61 52 62 66 80 41
## 23 53 66 52 50 63 80 37
## 24 40 37 42 58 50 57 49
## 25 63 54 42 48 66 75 33
## 26 66 77 66 63 88 76 72
## 27 78 75 58 74 80 78 49
## 28 48 57 44 45 51 83 38
## 29 85 85 71 71 77 74 55
## 30 82 82 39 59 64 78 39#a
plot(attitude$rating ,attitude$complaints )
#b
abline(lm(attitude$complaints~attitude$rating))
5c
attitude$learning## [1] 39 54 69 47 66 44 56 55 67 47 58 39 42 45 72 72 69 75 57 54 34 62 50 58 48
## [26] 63 74 45 71 59w<-cut(x = attitude$learning,breaks = c(30,45,60,75))
w## [1] (30,45] (45,60] (60,75] (45,60] (60,75] (30,45] (45,60] (45,60] (60,75]
## [10] (45,60] (45,60] (30,45] (30,45] (30,45] (60,75] (60,75] (60,75] (60,75]
## [19] (45,60] (45,60] (30,45] (60,75] (45,60] (45,60] (45,60] (60,75] (60,75]
## [28] (30,45] (60,75] (45,60]
## Levels: (30,45] (45,60] (60,75]table(w)## w
## (30,45] (45,60] (60,75]
## 7 12 112nd incourse
2019
Question no. 01
The ls() code lists all of the objects in your workspace.
The rm() code removes objects in your workspace. You can begin your code with the rm() function to clear all of the objects from your workspace to start with a clean environment. This way the workspace is empty and everything you create is clearly visible.
rm(list=ls())
attach(trees)
par(mfrow = c(2,2))
plot(Girth, Height)
plot(Girth, Volume)
hist(Volume)
boxplot(Volume)
detach(trees)Question no. 02
The swiss data set is built in R as a built in resource.
Recode the variable Infant.Mortality into four categories- the values upto first quartile as Good, first quartile until median as Normal, median until third quartile as Weaker and above third quartile as Poor.
a
#rm(list = ls())
#library(tidyverse) # needed for tibble & ggplot
#library(knitr) # needed for kable function
#tibble(swiss) # original data set
swiss## Fertility Agriculture Examination Education Catholic
## Courtelary 80 17.0 15 12 10.0
## Delemont 83 45.1 6 9 84.8
## Franches-Mnt 92 39.7 5 5 93.4
## Moutier 86 36.5 12 7 33.8
## Neuveville 77 43.5 17 15 5.2
## Porrentruy 76 35.3 9 7 90.6
## Broye 84 70.2 16 7 92.8
## Glane 92 67.8 14 8 97.2
## Gruyere 82 53.3 12 7 97.7
## Sarine 83 45.2 16 13 91.4
## Veveyse 87 64.5 14 6 98.6
## Aigle 64 62.0 21 12 8.5
## Aubonne 67 67.5 14 7 2.3
## Avenches 69 60.7 19 12 4.4
## Cossonay 62 69.3 22 5 2.8
## Echallens 68 72.6 18 2 24.2
## Grandson 72 34.0 17 8 3.3
## Lausanne 56 19.4 26 28 12.1
## La Vallee 54 15.2 31 20 2.1
## Lavaux 65 73.0 19 9 2.8
## Morges 66 59.8 22 10 5.2
## Moudon 65 55.1 14 3 4.5
## Nyone 57 50.9 22 12 15.1
## Orbe 57 54.1 20 6 4.2
## Oron 72 71.2 12 1 2.4
## Payerne 74 58.1 14 8 5.2
## Paysd'enhaut 72 63.5 6 3 2.6
## Rolle 60 60.8 16 10 7.7
## Vevey 58 26.8 25 19 18.5
## Yverdon 65 49.5 15 8 6.1
## Conthey 76 85.9 3 2 99.7
## Entremont 69 84.9 7 6 99.7
## Herens 77 89.7 5 2 100.0
## Martigwy 70 78.2 12 6 99.0
## Monthey 79 64.9 7 3 98.2
## St Maurice 65 75.9 9 9 99.1
## Sierre 92 84.6 3 3 99.5
## Sion 79 63.1 13 13 96.8
## Boudry 70 38.4 26 12 5.6
## La Chauxdfnd 66 7.7 29 11 13.8
## Le Locle 73 16.7 22 13 11.2
## Neuchatel 64 17.6 35 32 16.9
## Val de Ruz 78 37.6 15 7 5.0
## ValdeTravers 68 18.7 25 7 8.6
## V. De Geneve 35 1.2 37 53 42.3
## Rive Droite 45 46.6 16 29 50.4
## Rive Gauche 43 27.7 22 29 58.3
## Infant.Mortality
## Courtelary 22
## Delemont 22
## Franches-Mnt 20
## Moutier 20
## Neuveville 21
## Porrentruy 27
## Broye 24
## Glane 25
## Gruyere 21
## Sarine 24
## Veveyse 24
## Aigle 16
## Aubonne 19
## Avenches 23
## Cossonay 19
## Echallens 21
## Grandson 20
## Lausanne 20
## La Vallee 11
## Lavaux 20
## Morges 18
## Moudon 22
## Nyone 17
## Orbe 15
## Oron 21
## Payerne 24
## Paysd'enhaut 18
## Rolle 16
## Vevey 21
## Yverdon 22
## Conthey 15
## Entremont 20
## Herens 18
## Martigwy 19
## Monthey 20
## St Maurice 18
## Sierre 16
## Sion 18
## Boudry 20
## La Chauxdfnd 20
## Le Locle 19
## Neuchatel 23
## Val de Ruz 20
## ValdeTravers 20
## V. De Geneve 18
## Rive Droite 18
## Rive Gauche 19# ans
quantile(swiss$Infant.Mortality)## 0% 25% 50% 75% 100%
## 11 18 20 22 27swiss <- within(data = swiss, expr = {
status <- NA
status[Infant.Mortality <= 18.15] <- "Good"
status[Infant.Mortality > 18.15 & Infant.Mortality <= 20] <- "Normal"
status[Infant.Mortality > 20 & Infant.Mortality <= 21.70] <- "Weaker"
status[Infant.Mortality > 21.70] <- "Poor"
})
swiss## Fertility Agriculture Examination Education Catholic
## Courtelary 80 17.0 15 12 10.0
## Delemont 83 45.1 6 9 84.8
## Franches-Mnt 92 39.7 5 5 93.4
## Moutier 86 36.5 12 7 33.8
## Neuveville 77 43.5 17 15 5.2
## Porrentruy 76 35.3 9 7 90.6
## Broye 84 70.2 16 7 92.8
## Glane 92 67.8 14 8 97.2
## Gruyere 82 53.3 12 7 97.7
## Sarine 83 45.2 16 13 91.4
## Veveyse 87 64.5 14 6 98.6
## Aigle 64 62.0 21 12 8.5
## Aubonne 67 67.5 14 7 2.3
## Avenches 69 60.7 19 12 4.4
## Cossonay 62 69.3 22 5 2.8
## Echallens 68 72.6 18 2 24.2
## Grandson 72 34.0 17 8 3.3
## Lausanne 56 19.4 26 28 12.1
## La Vallee 54 15.2 31 20 2.1
## Lavaux 65 73.0 19 9 2.8
## Morges 66 59.8 22 10 5.2
## Moudon 65 55.1 14 3 4.5
## Nyone 57 50.9 22 12 15.1
## Orbe 57 54.1 20 6 4.2
## Oron 72 71.2 12 1 2.4
## Payerne 74 58.1 14 8 5.2
## Paysd'enhaut 72 63.5 6 3 2.6
## Rolle 60 60.8 16 10 7.7
## Vevey 58 26.8 25 19 18.5
## Yverdon 65 49.5 15 8 6.1
## Conthey 76 85.9 3 2 99.7
## Entremont 69 84.9 7 6 99.7
## Herens 77 89.7 5 2 100.0
## Martigwy 70 78.2 12 6 99.0
## Monthey 79 64.9 7 3 98.2
## St Maurice 65 75.9 9 9 99.1
## Sierre 92 84.6 3 3 99.5
## Sion 79 63.1 13 13 96.8
## Boudry 70 38.4 26 12 5.6
## La Chauxdfnd 66 7.7 29 11 13.8
## Le Locle 73 16.7 22 13 11.2
## Neuchatel 64 17.6 35 32 16.9
## Val de Ruz 78 37.6 15 7 5.0
## ValdeTravers 68 18.7 25 7 8.6
## V. De Geneve 35 1.2 37 53 42.3
## Rive Droite 45 46.6 16 29 50.4
## Rive Gauche 43 27.7 22 29 58.3
## Infant.Mortality status
## Courtelary 22 Poor
## Delemont 22 Poor
## Franches-Mnt 20 Weaker
## Moutier 20 Weaker
## Neuveville 21 Weaker
## Porrentruy 27 Poor
## Broye 24 Poor
## Glane 25 Poor
## Gruyere 21 Weaker
## Sarine 24 Poor
## Veveyse 24 Poor
## Aigle 16 Good
## Aubonne 19 Normal
## Avenches 23 Poor
## Cossonay 19 Normal
## Echallens 21 Weaker
## Grandson 20 Normal
## Lausanne 20 Weaker
## La Vallee 11 Good
## Lavaux 20 Normal
## Morges 18 Good
## Moudon 22 Poor
## Nyone 17 Good
## Orbe 15 Good
## Oron 21 Weaker
## Payerne 24 Poor
## Paysd'enhaut 18 Good
## Rolle 16 Good
## Vevey 21 Weaker
## Yverdon 22 Poor
## Conthey 15 Good
## Entremont 20 Normal
## Herens 18 Normal
## Martigwy 19 Normal
## Monthey 20 Weaker
## St Maurice 18 Good
## Sierre 16 Good
## Sion 18 Good
## Boudry 20 Weaker
## La Chauxdfnd 20 Weaker
## Le Locle 19 Normal
## Neuchatel 23 Poor
## Val de Ruz 20 Normal
## ValdeTravers 20 Normal
## V. De Geneve 18 Good
## Rive Droite 18 Normal
## Rive Gauche 19 Normalb
library(tidyverse) # needed for tibble & ggplot## Warning: package 'tidyverse' was built under R version 4.2.2## Warning: package 'tidyr' was built under R version 4.2.2## Warning: package 'readr' was built under R version 4.2.2## Warning: package 'purrr' was built under R version 4.2.2## Warning: package 'dplyr' was built under R version 4.2.2## Warning: package 'stringr' was built under R version 4.2.2## Warning: package 'forcats' was built under R version 4.2.2## Warning: package 'lubridate' was built under R version 4.2.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.1.8
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks reshape::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::rename() masks reshape::rename()
## ✖ dplyr::select() masks MASS::select()
## ✖ dplyr::src() masks Hmisc::src()
## ✖ lubridate::stamp() masks reshape::stamp()
## ✖ dplyr::summarize() masks Hmisc::summarize()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorsggplot(data = swiss)+
geom_bar(mapping = aes(x = status), fill = "blue")
c
#Draw a random sample of size 10 from the swiss data set and calculate summary statistics for the variables Fertility and Education
set.seed(30)
new_swiss <- swiss[sample(x = nrow(swiss),size = 10),]
# don't forget that comma at the end!!
new_swiss## Fertility Agriculture Examination Education Catholic
## Sarine 83 45 16 13 91.4
## Rive Droite 45 47 16 29 50.4
## Aubonne 67 68 14 7 2.3
## Aigle 64 62 21 12 8.5
## Rive Gauche 43 28 22 29 58.3
## Val de Ruz 78 38 15 7 5.0
## Vevey 58 27 25 19 18.5
## Moutier 86 36 12 7 33.8
## Franches-Mnt 92 40 5 5 93.4
## Entremont 69 85 7 6 99.7
## Infant.Mortality status
## Sarine 24 Poor
## Rive Droite 18 Normal
## Aubonne 19 Normal
## Aigle 16 Good
## Rive Gauche 19 Normal
## Val de Ruz 20 Normal
## Vevey 21 Weaker
## Moutier 20 Weaker
## Franches-Mnt 20 Weaker
## Entremont 20 Normalsummary(new_swiss$Fertility)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43 60 68 68 82 92summary(new_swiss$Education)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.0 7.0 9.5 13.4 17.5 29.0Question no. 03
Distinguish between FOR loop and WHILE loop by giving R executable examples.
For loop executes a statement repetitively till the variable’s value is contained in the sequence.The syntax is for(variable in sequence) statement Example:
for (i in 1:5) print("Hello world!")## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"While loop executes a statement repetitively till the condition is true The syntax is while(condition) statement Example:
i <-5
while(i > 0){
print("Hello world!");
i <- i-1
}## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"
## [1] "Hello world!"Question no. 04
Aggregates warpbreaks data for breaks by the variables wool and tension. Ignore the warnings and find the mean number of breaks corresponding to wool=A and tension=M
#rm(list = ls())
aggregate(warpbreaks, by = list(wool=warpbreaks$wool,tension=warpbreaks$tension), FUN=mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## wool tension breaks wool tension
## 1 A L 45 NA NA
## 2 B L 28 NA NA
## 3 A M 24 NA NA
## 4 B M 29 NA NA
## 5 A H 25 NA NA
## 6 B H 19 NA NAaggregate(warpbreaks,by=list(warpbreaks$wool,warpbreaks$tension),mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Group.1 Group.2 breaks wool tension
## 1 A L 45 NA NA
## 2 B L 28 NA NA
## 3 A M 24 NA NA
## 4 B M 29 NA NA
## 5 A H 25 NA NA
## 6 B H 19 NA NAlibrary(reshape)
m<-melt(data = warpbreaks,id= c('wool' ,'tension'))
m## wool tension variable value
## 1 A L breaks 26
## 2 A L breaks 30
## 3 A L breaks 54
## 4 A L breaks 25
## 5 A L breaks 70
## 6 A L breaks 52
## 7 A L breaks 51
## 8 A L breaks 26
## 9 A L breaks 67
## 10 A M breaks 18
## 11 A M breaks 21
## 12 A M breaks 29
## 13 A M breaks 17
## 14 A M breaks 12
## 15 A M breaks 18
## 16 A M breaks 35
## 17 A M breaks 30
## 18 A M breaks 36
## 19 A H breaks 36
## 20 A H breaks 21
## 21 A H breaks 24
## 22 A H breaks 18
## 23 A H breaks 10
## 24 A H breaks 43
## 25 A H breaks 28
## 26 A H breaks 15
## 27 A H breaks 26
## 28 B L breaks 27
## 29 B L breaks 14
## 30 B L breaks 29
## 31 B L breaks 19
## 32 B L breaks 29
## 33 B L breaks 31
## 34 B L breaks 41
## 35 B L breaks 20
## 36 B L breaks 44
## 37 B M breaks 42
## 38 B M breaks 26
## 39 B M breaks 19
## 40 B M breaks 16
## 41 B M breaks 39
## 42 B M breaks 28
## 43 B M breaks 21
## 44 B M breaks 39
## 45 B M breaks 29
## 46 B H breaks 20
## 47 B H breaks 21
## 48 B H breaks 24
## 49 B H breaks 17
## 50 B H breaks 13
## 51 B H breaks 15
## 52 B H breaks 15
## 53 B H breaks 16
## 54 B H breaks 28cast(data = m,wool+tension ~ variable,mean)## wool tension breaks
## 1 A L 45
## 2 A M 24
## 3 A H 25
## 4 B L 28
## 5 B M 29
## 6 B H 19Question no. 05
rm(list = ls())
p_weibull <- function(x, a, lambda){
if (! x >= 0){
0
} else {
if (! a > 0){
"a must be greater than zero"
} else {
if (! lambda > 0){
"lambda must be greater than zero"
} else {
1-exp((-1*(lambda*x))**a)
}
}
}
}
p_weibull(5,3,1) # function we created## [1] 1pweibull(5,3,1) # fuction built in 'stats' package## [1] 1p_weibull(1.0,3,2)## [1] 1pweibull(1.0,3,.5)## [1] 1p_weibull(1.5,3,5)## [1] 1pweibull(1.5,3,5^-1)## [1] 1p_weibull(-5,3,1)## [1] 0pweibull(-5,3,1)## [1] 0p_weibull(5,-3,1)## [1] "a must be greater than zero"pweibull(5,-3,1)## Warning in pweibull(5, -3, 1): NaNs produced## [1] NaNp_weibull(5,3,-1)## [1] "lambda must be greater than zero"pweibull(5,3,-1)## Warning in pweibull(5, 3, -1): NaNs produced## [1] NaN2020
Question no. 01
rm(list = ls())
breaks<-c(26, 30, 54, 47, 26, 48, 58, 76, 21, 56, 96, 30, 14, 45, 65, 32, 14, 85, 45)
wool<-c('A','A','A', 'B', 'B', 'B', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'E', 'E', 'E', 'E')
tension<-c('L','L', 'L', 'L', 'L', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'N', 'N', 'N','N', 'N', 'N')
data<-data.frame(breaks,wool,tension)
data## breaks wool tension
## 1 26 A L
## 2 30 A L
## 3 54 A L
## 4 47 B L
## 5 26 B L
## 6 48 B M
## 7 58 C M
## 8 76 C M
## 9 21 C M
## 10 56 D M
## 11 96 D M
## 12 30 D M
## 13 14 D M
## 14 45 D N
## 15 65 D N
## 16 32 E N
## 17 14 E N
## 18 85 E N
## 19 45 E N1a
quantile(breaks)## 0% 25% 50% 75% 100%
## 14 28 45 57 96status<-NA
status[breaks<=28]<-"Good"
status[breaks>28 & breaks <=45 ]<-"Normal"
status[breaks>45 & breaks <=57 ]<-"Weaker"
status[breaks>57 ]<-"Poor"
status## [1] "Good" "Normal" "Weaker" "Weaker" "Good" "Weaker" "Poor" "Poor"
## [9] "Good" "Weaker" "Poor" "Normal" "Good" "Normal" "Poor" "Normal"
## [17] "Good" "Poor" "Normal"data$status<-status
data## breaks wool tension status
## 1 26 A L Good
## 2 30 A L Normal
## 3 54 A L Weaker
## 4 47 B L Weaker
## 5 26 B L Good
## 6 48 B M Weaker
## 7 58 C M Poor
## 8 76 C M Poor
## 9 21 C M Good
## 10 56 D M Weaker
## 11 96 D M Poor
## 12 30 D M Normal
## 13 14 D M Good
## 14 45 D N Normal
## 15 65 D N Poor
## 16 32 E N Normal
## 17 14 E N Good
## 18 85 E N Poor
## 19 45 E N Normalalternative
data<- within(data = data,expr = {
quantile(breaks)
status1<-NULL
status1[breaks<=28]<-"Good"
status1[breaks>28 & breaks <=45 ]<-"Normal"
status1[breaks>45 & breaks <=57 ]<-"Weaker"
status1[breaks>57 ]<-"Poor"
status1
})
data## breaks wool tension status status1
## 1 26 A L Good Good
## 2 30 A L Normal Normal
## 3 54 A L Weaker Weaker
## 4 47 B L Weaker Weaker
## 5 26 B L Good Good
## 6 48 B M Weaker Weaker
## 7 58 C M Poor Poor
## 8 76 C M Poor Poor
## 9 21 C M Good Good
## 10 56 D M Weaker Weaker
## 11 96 D M Poor Poor
## 12 30 D M Normal Normal
## 13 14 D M Good Good
## 14 45 D N Normal Normal
## 15 65 D N Poor Poor
## 16 32 E N Normal Normal
## 17 14 E N Good Good
## 18 85 E N Poor Poor
## 19 45 E N Normal Normal1b
library(tidyverse)
ggplot(data = data)+
geom_bar(aes(status))
1c
set.seed(13)
sample<-data[sample(x = nrow(data),size = 5),]
sample## breaks wool tension status status1
## 3 54 A L Weaker Weaker
## 5 26 B L Good Good
## 10 56 D M Weaker Weaker
## 13 14 D M Good Good
## 6 48 B M Weaker Weakersummary(wool)## Length Class Mode
## 19 character charactersummary(tension)## Length Class Mode
## 19 character characterQuestion no. 03
3a
Detect the errors from the following codes & explain why they appear to be errors. (I) qplot(x1, x2, data=(), color=x3) data argument should be assigned to a valid data frame in which object x1, x2, x3 must exist.
- boxplot(nitrogen=Month, airquality, xlab=Month, ylab=Nitrogen) Nitrogen object does not exist in airquality data frame. We should replace it with Ozone. Also the formula is incorrect it should be written with ~ sign. Also x- and y-axis annotations should be characters not object.
3b
Write down the executable R codes to generate 30 random numbers from the following distributions:
#rm(list = ls())
set.seed(30)
# log normal
rlnorm(30)## [1] 0.28 0.71 0.59 3.57 6.20 0.22 1.12 0.47 0.51 1.32 0.36 0.16 0.51 0.94 2.41
## [16] 1.31 0.98 0.59 0.24 0.16 0.85 2.13 0.40 2.23 4.44 0.33 0.59 0.24 0.29 1.26# geometric
rgeom(30, .4)## [1] 2 1 1 4 0 0 0 0 2 0 0 5 2 0 4 0 0 7 1 2 0 1 1 0 0 1 0 2 0 0# gamma
round(rgamma(30, shape = 5), 2)## [1] 8.7 7.2 3.1 4.7 2.5 5.6 3.1 2.6 4.1 8.8 4.3 3.6 5.7 6.6 7.6 6.9 2.5 5.8 4.0
## [20] 6.8 2.7 5.1 4.7 5.1 5.0 2.0 3.5 6.0 3.4 6.4Question no. 04
Interpret the following R codes (a) apply(data5, 2, median, trim=.12)
Calculate trimmed (based on middle 76 percent) column medians of the data object data5
- marks< − rnorm(50, 30, 50)
Create a numeric vector named marks by generating 50 random numbers that follow normal disrtibution with mean 30 and stadanrd deviation 50.
** results< − ifelse(marks>= 50, ”Passed”, ”Failed”)
Create a character vector named results of size 50, which include “Passed” if the marks obtained is 50 or more, and include “Failed” if marks obtained in less than 50.
- boxplot(oxygen∼year, weather, xlab=”year”, ylab=”oxygen”)
Create a boxplot by splitting oxygen (which must be a numeric vector) into year from the data object weather putting oxygen data in the y-axis and year in the x-axis.
Question no. 05
Write a fuction to calculate 1−e−λx where x≥0, λ is the rate parameter
#rm(list = ls())
p_exp <- function(x, lambda){
if (! x >= 0){
0
} else {
1-exp(-1*lambda*x)
}
}
p_exp(1,4) # function we created ## [1] 0.98pexp(1,4) # function built in 'stats' package## [1] 0.98p_exp(-1,4) ## [1] 0pexp(-1,4)## [1] 0d_exp <- function(x, lambda){
if (! x >= 0){
0
} else {
exp(-1*lambda*x)*lambda
}
}
d_exp(1,4) # function we created ## [1] 0.073dexp(x = 1,4) # function built in 'stats' package## [1] 0.073d_exp(-1,4) ## [1] 0dexp(-1,4)## [1] 02021 (same 19)
Q1
Base plotting system, lattice system, and ggplot2 are all R packages that can be used for data visualization. Each has its own advantages and disadvantages, and the choice of which to use may depend on the specific needs of the user.
Base Plotting System: Advantages:
Base plotting system is the default plotting system in R, so it requires no additional packages or installation. It provides a lot of control over the appearance of plots, allowing users to customize almost every aspect of the plot. It has a very shallow learning curve, and is easy to use for simple plots. Disadvantages:
It can be difficult to use for more complex plots. The code required to produce even simple plots can be quite verbose and difficult to read. It produces static plots that cannot be easily updated or modified. Lattice System: Advantages:
The lattice system provides a wide range of statistical graphics with many advanced features, such as multi-panel displays and conditioning plots. It provides a simple syntax for producing complex graphics. It is well-suited for creating plots that show relationships between multiple variables. Disadvantages:
It can be difficult to customize plots to the degree that the base plotting system allows. It can be slower than other plotting systems when creating large numbers of plots. ggplot2: Advantages:
ggplot2 is a powerful and flexible plotting system, capable of creating complex and beautiful visualizations. It has a more intuitive syntax than the base plotting system, making it easier to use for complex plots. It is highly customizable, and allows users to create plots that are tailored to their needs. Disadvantages:
ggplot2 can have a steep learning curve for users unfamiliar with its syntax and structure. It may require additional packages to produce certain types of plots, adding to the complexity of the process. It can be slower than other plotting systems when creating large numbers of plots. In summary, the base plotting system is easy to use and highly customizable, but can be difficult to use for complex plots. The lattice system is well-suited for creating plots with multiple variables, but can be difficult to customize. ggplot2 is highly flexible and customizable, but may require additional packages and can have a steep learning curve. The choice of which to use may depend on the specific needs of the user, and the type of plot they are looking to create.
2022
Q1
1a
library(Hmisc) world95 summary(world95\(babymort) cor(world95\)lifeexpm,world95\(literacy, na.rm= T) abline(lm(world95\)literacy~world95$lifeexpm),)
Q3
3a
library(reshape)
ChickWeight## Grouped Data: weight ~ Time | Chick
## weight Time Chick Diet
## 1 42 0 1 1
## 2 51 2 1 1
## 3 59 4 1 1
## 4 64 6 1 1
## 5 76 8 1 1
## 6 93 10 1 1
## 7 106 12 1 1
## 8 125 14 1 1
## 9 149 16 1 1
## 10 171 18 1 1
## 11 199 20 1 1
## 12 205 21 1 1
## 13 40 0 2 1
## 14 49 2 2 1
## 15 58 4 2 1
## 16 72 6 2 1
## 17 84 8 2 1
## 18 103 10 2 1
## 19 122 12 2 1
## 20 138 14 2 1
## 21 162 16 2 1
## 22 187 18 2 1
## 23 209 20 2 1
## 24 215 21 2 1
## 25 43 0 3 1
## 26 39 2 3 1
## 27 55 4 3 1
## 28 67 6 3 1
## 29 84 8 3 1
## 30 99 10 3 1
## 31 115 12 3 1
## 32 138 14 3 1
## 33 163 16 3 1
## 34 187 18 3 1
## 35 198 20 3 1
## 36 202 21 3 1
## 37 42 0 4 1
## 38 49 2 4 1
## 39 56 4 4 1
## 40 67 6 4 1
## 41 74 8 4 1
## 42 87 10 4 1
## 43 102 12 4 1
## 44 108 14 4 1
## 45 136 16 4 1
## 46 154 18 4 1
## 47 160 20 4 1
## 48 157 21 4 1
## 49 41 0 5 1
## 50 42 2 5 1
## 51 48 4 5 1
## 52 60 6 5 1
## 53 79 8 5 1
## 54 106 10 5 1
## 55 141 12 5 1
## 56 164 14 5 1
## 57 197 16 5 1
## 58 199 18 5 1
## 59 220 20 5 1
## 60 223 21 5 1
## 61 41 0 6 1
## 62 49 2 6 1
## 63 59 4 6 1
## 64 74 6 6 1
## 65 97 8 6 1
## 66 124 10 6 1
## 67 141 12 6 1
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## 84 305 21 7 1
## 85 42 0 8 1
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## 95 125 20 8 1
## 96 42 0 9 1
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## 341 42 0 31 3
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## 348 138 14 31 3
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## 352 256 21 31 3
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## 363 291 20 32 3
## 364 305 21 32 3
## 365 39 0 33 3
## 366 50 2 33 3
## 367 63 4 33 3
## 368 77 6 33 3
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## 370 111 10 33 3
## 371 137 12 33 3
## 372 144 14 33 3
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## 375 156 20 33 3
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## 577 264 20 50 4
## 578 264 21 50 4x<-melt(data =ChickWeight,id=c("Diet"))
x## Diet variable value
## 1 1 weight 42
## 2 1 weight 51
## 3 1 weight 59
## 4 1 weight 64
## 5 1 weight 76
## 6 1 weight 93
## 7 1 weight 106
## 8 1 weight 125
## 9 1 weight 149
## 10 1 weight 171
## 11 1 weight 199
## 12 1 weight 205
## 13 1 weight 40
## 14 1 weight 49
## 15 1 weight 58
## 16 1 weight 72
## 17 1 weight 84
## 18 1 weight 103
## 19 1 weight 122
## 20 1 weight 138
## 21 1 weight 162
## 22 1 weight 187
## 23 1 weight 209
## 24 1 weight 215
## 25 1 weight 43
## 26 1 weight 39
## 27 1 weight 55
## 28 1 weight 67
## 29 1 weight 84
## 30 1 weight 99
## 31 1 weight 115
## 32 1 weight 138
## 33 1 weight 163
## 34 1 weight 187
## 35 1 weight 198
## 36 1 weight 202
## 37 1 weight 42
## 38 1 weight 49
## 39 1 weight 56
## 40 1 weight 67
## 41 1 weight 74
## 42 1 weight 87
## 43 1 weight 102
## 44 1 weight 108
## 45 1 weight 136
## 46 1 weight 154
## 47 1 weight 160
## 48 1 weight 157
## 49 1 weight 41
## 50 1 weight 42
## 51 1 weight 48
## 52 1 weight 60
## 53 1 weight 79
## 54 1 weight 106
## 55 1 weight 141
## 56 1 weight 164
## 57 1 weight 197
## 58 1 weight 199
## 59 1 weight 220
## 60 1 weight 223
## 61 1 weight 41
## 62 1 weight 49
## 63 1 weight 59
## 64 1 weight 74
## 65 1 weight 97
## 66 1 weight 124
## 67 1 weight 141
## 68 1 weight 148
## 69 1 weight 155
## 70 1 weight 160
## 71 1 weight 160
## 72 1 weight 157
## 73 1 weight 41
## 74 1 weight 49
## 75 1 weight 57
## 76 1 weight 71
## 77 1 weight 89
## 78 1 weight 112
## 79 1 weight 146
## 80 1 weight 174
## 81 1 weight 218
## 82 1 weight 250
## 83 1 weight 288
## 84 1 weight 305
## 85 1 weight 42
## 86 1 weight 50
## 87 1 weight 61
## 88 1 weight 71
## 89 1 weight 84
## 90 1 weight 93
## 91 1 weight 110
## 92 1 weight 116
## 93 1 weight 126
## 94 1 weight 134
## 95 1 weight 125
## 96 1 weight 42
## 97 1 weight 51
## 98 1 weight 59
## 99 1 weight 68
## 100 1 weight 85
## 101 1 weight 96
## 102 1 weight 90
## 103 1 weight 92
## 104 1 weight 93
## 105 1 weight 100
## 106 1 weight 100
## 107 1 weight 98
## 108 1 weight 41
## 109 1 weight 44
## 110 1 weight 52
## 111 1 weight 63
## 112 1 weight 74
## 113 1 weight 81
## 114 1 weight 89
## 115 1 weight 96
## 116 1 weight 101
## 117 1 weight 112
## 118 1 weight 120
## 119 1 weight 124
## 120 1 weight 43
## 121 1 weight 51
## 122 1 weight 63
## 123 1 weight 84
## 124 1 weight 112
## 125 1 weight 139
## 126 1 weight 168
## 127 1 weight 177
## 128 1 weight 182
## 129 1 weight 184
## 130 1 weight 181
## 131 1 weight 175
## 132 1 weight 41
## 133 1 weight 49
## 134 1 weight 56
## 135 1 weight 62
## 136 1 weight 72
## 137 1 weight 88
## 138 1 weight 119
## 139 1 weight 135
## 140 1 weight 162
## 141 1 weight 185
## 142 1 weight 195
## 143 1 weight 205
## 144 1 weight 41
## 145 1 weight 48
## 146 1 weight 53
## 147 1 weight 60
## 148 1 weight 65
## 149 1 weight 67
## 150 1 weight 71
## 151 1 weight 70
## 152 1 weight 71
## 153 1 weight 81
## 154 1 weight 91
## 155 1 weight 96
## 156 1 weight 41
## 157 1 weight 49
## 158 1 weight 62
## 159 1 weight 79
## 160 1 weight 101
## 161 1 weight 128
## 162 1 weight 164
## 163 1 weight 192
## 164 1 weight 227
## 165 1 weight 248
## 166 1 weight 259
## 167 1 weight 266
## 168 1 weight 41
## 169 1 weight 49
## 170 1 weight 56
## 171 1 weight 64
## 172 1 weight 68
## 173 1 weight 68
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## 1734 4 Chick 50#cast(data = x,Diet~variable,mean)
cast(x, Diet~variable, mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Diet weight Time Chick
## 1 1 NA NA NA
## 2 2 NA NA NA
## 3 3 NA NA NA
## 4 4 NA NA NAalternative 1 (eassiest)
# Load the ChickWeight dataset
data(ChickWeight)
# Load the reshape2 package
library(reshape2)## Warning: package 'reshape2' was built under R version 4.2.2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths## The following objects are masked from 'package:reshape':
##
## colsplit, melt, recast# Cast the data to calculate the mean weight for each diet type
avg_weight <- cast(ChickWeight, Diet ~ ., mean, value = "weight")
# Print the average weight for each diet type
print(avg_weight)## Diet (all)
## 1 1 103
## 2 2 123
## 3 3 143
## 4 4 135alternative 2
# Load the ChickWeight dataset
data(ChickWeight)
# Group the data by diet type and calculate the mean weight for each group
avg_weight <- aggregate(weight ~ Diet, data = ChickWeight, mean)
# Print the average weight for each diet type
print(avg_weight)## Diet weight
## 1 1 103
## 2 2 123
## 3 3 143
## 4 4 1353b
cast(data = ChickWeight,Time~.,value = "weight")## Aggregation requires fun.aggregate: length used as default## Time (all)
## 1 0 50
## 2 2 50
## 3 4 49
## 4 6 49
## 5 8 49
## 6 10 49
## 7 12 49
## 8 14 48
## 9 16 47
## 10 18 47
## 11 20 46
## 12 21 453c
boxplot(weight~ Diet,ChickWeight)
Q4
cdf<-function(x){
m<-function(x){exp(-x)}
return(integrate(f = m,lower = 0,upper = x))
#return(exp(-x))
}
cdf(6)## 1 with absolute error < 1.1e-14