install.packages("RSDA", dependencies=TRUE)
::install_github("PROMiDAT/RSDA") devtools
<- read.sym.table(file = 'tsym1.csv', header=TRUE, sep=';',dec='.', row.names=1)
ex3
ex3#> # A tibble: 7 × 7
#> F1 F2 F3 F4 F5 F6 F7
#> <dbl> <symblc_n> <symbl> <dbl> <symblc_> <symblc_n> <symblc_n>
#> 1 2.8 [1.00 : 2.00] <hist> 6 {a,d} [0.00 : 90.00] [9.00 : 24.00]
#> 2 1.4 [3.00 : 9.00] <hist> 8 {b,c,d} [-90.00 : 98.00] [-9.00 : 9.00]
#> 3 3.2 [-1.00 : 4.00] <hist> -7 {a,b} [65.00 : 90.00] [65.00 : 70.00]
#> 4 -2.1 [0.00 : 2.00] <hist> 0 {a,b,c,d} [45.00 : 89.00] [25.00 : 67.00]
#> 5 -3 [-4.00 : -2.00] <hist> -9.5 {b} [20.00 : 40.00] [9.00 : 40.00]
#> 6 0.1 [10.00 : 21.00] <hist> -1 {a,d} [5.00 : 8.00] [5.00 : 8.00]
#> 7 9 [4.00 : 21.00] <hist> 0.5 {a} [3.14 : 6.76] [4.00 : 6.00]
##How to save a Symbolic Table in a CSV file with RSDA?
write.sym.table(ex3, file = 'tsymtemp.csv', sep = ';',dec = '.',
row.names = TRUE, col.names = TRUE)
data(example3)
example3#> # A tibble: 7 × 7
#> F1 F2 F3 F4 F5 F6
#> <dbl> <symblc_n> <symblc_m> <dbl> <symblc_> <symblc_n>
#> 1 2.8 [1.00 : 2.00] M1:0.10 M2:0.70 M3:0.20 6 {e,g,i,k} [0.00 : 90.00]
#> 2 1.4 [3.00 : 9.00] M1:0.60 M2:0.30 M3:0.10 8 {a,b,c,d} [-90.00 : 98.00]
#> 3 3.2 [-1.00 : 4.00] M1:0.20 M2:0.20 M3:0.60 -7 {2,b,1,c} [65.00 : 90.00]
#> 4 -2.1 [0.00 : 2.00] M1:0.90 M2:0.00 M3:0.10 0 {a,3,4,c} [45.00 : 89.00]
#> 5 -3 [-4.00 : -2.00] M1:0.60 M2:0.00 M3:0.40 -9.5 {e,g,i,k} [20.00 : 40.00]
#> 6 0.1 [10.00 : 21.00] M1:0.00 M2:0.70 M3:0.30 -1 {e,1,i} [5.00 : 8.00]
#> 7 9 [4.00 : 21.00] M1:0.20 M2:0.20 M3:0.60 0.5 {e,a,2} [3.14 : 6.76]
#> # ℹ 1 more variable: F7 <symblc_n>
2,]
example3[#> # A tibble: 1 × 7
#> F1 F2 F3 F4 F5 F6
#> <dbl> <symblc_n> <symblc_m> <dbl> <symblc_s> <symblc_n>
#> 1 1.4 [3.00 : 9.00] M1:0.60 M2:0.30 M3:0.10 8 {a,b,c,d} [-90.00 : 98.00]
#> # ℹ 1 more variable: F7 <symblc_n>
3]
example3[,#> # A tibble: 7 × 1
#> F3
#> <symblc_m>
#> 1 M1:0.10 M2:0.70 M3:0.20
#> 2 M1:0.60 M2:0.30 M3:0.10
#> 3 M1:0.20 M2:0.20 M3:0.60
#> 4 M1:0.90 M2:0.00 M3:0.10
#> 5 M1:0.60 M2:0.00 M3:0.40
#> 6 M1:0.00 M2:0.70 M3:0.30
#> 7 M1:0.20 M2:0.20 M3:0.60
2:3,5]
example3[#> # A tibble: 2 × 1
#> F5
#> <symblc_s>
#> 1 {a,b,c,d}
#> 2 {2,b,1,c}
$F1
example3#> [1] 2.8 1.4 3.2 -2.1 -3.0 0.1 9.0
data(ex1_db2so)
ex1_db2so#> state sex county group age
#> 1 Florida M 2 6 3
#> 2 California F 4 3 4
#> 3 Texas M 12 3 4
#> 4 Florida F 2 3 4
#> 5 Texas M 4 6 4
#> 6 Texas F 2 3 3
#> 7 Florida M 6 3 4
#> 8 Florida F 2 6 4
#> 9 California M 2 3 6
#> 10 California F 21 3 4
#> 11 California M 2 3 4
#> 12 California M 2 6 7
#> 13 Texas F 23 3 4
#> 14 Florida M 2 3 4
#> 15 Florida F 12 7 4
#> 16 Texas M 2 3 8
#> 17 California F 3 7 9
#> 18 California M 2 3 11
#> 19 California M 1 3 11
The classic.to.sym
function allows to convert a
traditional table into a symbolic one, to this we must indicate the
following parameters.
x
= a data.frameconcept
= variables to be used as a conceptvariables
= variables to be used, conceptible with
tidyselect optionsdefault.numeric
= function that will be used by default
for numerical values (sym.interval)default.categorical
= functions to be used by default
for categorical values (sym.model)<- classic.to.sym(x = ex1_db2so,
result concept = c(state, sex),
variables = c(county, group, age))
result#> # A tibble: 6 × 3
#> county group age
#> <symblc_n> <symblc_n> <symblc_n>
#> 1 [3.00 : 21.00] [3.00 : 7.00] [4.00 : 9.00]
#> 2 [1.00 : 2.00] [3.00 : 6.00] [4.00 : 11.00]
#> 3 [2.00 : 12.00] [3.00 : 7.00] [4.00 : 4.00]
#> 4 [2.00 : 6.00] [3.00 : 6.00] [3.00 : 4.00]
#> 5 [2.00 : 23.00] [3.00 : 3.00] [3.00 : 4.00]
#> 6 [2.00 : 12.00] [3.00 : 6.00] [4.00 : 8.00]
We can add new variables indicating the type we want them to be.
<- classic.to.sym(x = ex1_db2so,
result concept = c("state", "sex"),
variables = c(county, group, age),
age_hist = sym.histogram(age, breaks = pretty(ex1_db2so$age, 5)))
result#> # A tibble: 6 × 4
#> age_hist county group age
#> <symblc_h> <symblc_n> <symblc_n> <symblc_n>
#> 1 <hist> [3.00 : 21.00] [3.00 : 7.00] [4.00 : 9.00]
#> 2 <hist> [1.00 : 2.00] [3.00 : 6.00] [4.00 : 11.00]
#> 3 <hist> [2.00 : 12.00] [3.00 : 7.00] [4.00 : 4.00]
#> 4 <hist> [2.00 : 6.00] [3.00 : 6.00] [3.00 : 4.00]
#> 5 <hist> [2.00 : 23.00] [3.00 : 3.00] [3.00 : 4.00]
#> 6 <hist> [2.00 : 12.00] [3.00 : 6.00] [4.00 : 8.00]
data(USCrime)
head(USCrime)
#> state fold population householdsize racepctblack racePctWhite racePctAsian
#> 1 8 1 0.19 0.33 0.02 0.90 0.12
#> 2 53 1 0.00 0.16 0.12 0.74 0.45
#> 3 24 1 0.00 0.42 0.49 0.56 0.17
#> 4 34 1 0.04 0.77 1.00 0.08 0.12
#> 5 42 1 0.01 0.55 0.02 0.95 0.09
#> 6 6 1 0.02 0.28 0.06 0.54 1.00
#> racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up numbUrban pctUrban
#> 1 0.17 0.34 0.47 0.29 0.32 0.20 1.0
#> 2 0.07 0.26 0.59 0.35 0.27 0.02 1.0
#> 3 0.04 0.39 0.47 0.28 0.32 0.00 0.0
#> 4 0.10 0.51 0.50 0.34 0.21 0.06 1.0
#> 5 0.05 0.38 0.38 0.23 0.36 0.02 0.9
#> 6 0.25 0.31 0.48 0.27 0.37 0.04 1.0
#> medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec pctWPubAsst pctWRetire
#> 1 0.37 0.72 0.34 0.60 0.29 0.15 0.43
#> 2 0.31 0.72 0.11 0.45 0.25 0.29 0.39
#> 3 0.30 0.58 0.19 0.39 0.38 0.40 0.84
#> 4 0.58 0.89 0.21 0.43 0.36 0.20 0.82
#> 5 0.50 0.72 0.16 0.68 0.44 0.11 0.71
#> 6 0.52 0.68 0.20 0.61 0.28 0.15 0.25
#> medFamInc perCapInc whitePerCap blackPerCap indianPerCap AsianPerCap
#> 1 0.39 0.40 0.39 0.32 0.27 0.27
#> 2 0.29 0.37 0.38 0.33 0.16 0.30
#> 3 0.28 0.27 0.29 0.27 0.07 0.29
#> 4 0.51 0.36 0.40 0.39 0.16 0.25
#> 5 0.46 0.43 0.41 0.28 0.00 0.74
#> 6 0.62 0.72 0.76 0.77 0.28 0.52
#> OtherPerCap HispPerCap NumUnderPov PctPopUnderPov PctLess9thGrade
#> 1 0.36 0.41 0.08 0.19 0.10
#> 2 0.22 0.35 0.01 0.24 0.14
#> 3 0.28 0.39 0.01 0.27 0.27
#> 4 0.36 0.44 0.01 0.10 0.09
#> 5 0.51 0.48 0.00 0.06 0.25
#> 6 0.48 0.60 0.01 0.12 0.13
#> PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu PctEmplProfServ
#> 1 0.18 0.48 0.27 0.68 0.23 0.41
#> 2 0.24 0.30 0.27 0.73 0.57 0.15
#> 3 0.43 0.19 0.36 0.58 0.32 0.29
#> 4 0.25 0.31 0.33 0.71 0.36 0.45
#> 5 0.30 0.33 0.12 0.65 0.67 0.38
#> 6 0.12 0.80 0.10 0.65 0.19 0.77
#> PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr FemalePctDiv
#> 1 0.25 0.52 0.68 0.40 0.75
#> 2 0.42 0.36 1.00 0.63 0.91
#> 3 0.49 0.32 0.63 0.41 0.71
#> 4 0.37 0.39 0.34 0.45 0.49
#> 5 0.42 0.46 0.22 0.27 0.20
#> 6 0.06 0.91 0.49 0.57 0.61
#> TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par PctTeen2Par
#> 1 0.75 0.35 0.55 0.59 0.61 0.56
#> 2 1.00 0.29 0.43 0.47 0.60 0.39
#> 3 0.70 0.45 0.42 0.44 0.43 0.43
#> 4 0.44 0.75 0.65 0.54 0.83 0.65
#> 5 0.21 0.51 0.91 0.91 0.89 0.85
#> 6 0.58 0.44 0.62 0.69 0.87 0.53
#> PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig PctImmigRecent
#> 1 0.74 0.76 0.04 0.14 0.03 0.24
#> 2 0.46 0.53 0.00 0.24 0.01 0.52
#> 3 0.71 0.67 0.01 0.46 0.00 0.07
#> 4 0.85 0.86 0.03 0.33 0.02 0.11
#> 5 0.40 0.60 0.00 0.06 0.00 0.03
#> 6 0.30 0.43 0.00 0.11 0.04 0.30
#> PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig PctRecImmig5
#> 1 0.27 0.37 0.39 0.07 0.07
#> 2 0.62 0.64 0.63 0.25 0.27
#> 3 0.06 0.15 0.19 0.02 0.02
#> 4 0.20 0.30 0.31 0.05 0.08
#> 5 0.07 0.20 0.27 0.01 0.02
#> 6 0.35 0.43 0.47 0.50 0.50
#> PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
#> 1 0.08 0.08 0.89 0.06
#> 2 0.25 0.23 0.84 0.10
#> 3 0.04 0.05 0.88 0.04
#> 4 0.11 0.11 0.81 0.08
#> 5 0.04 0.05 0.88 0.05
#> 6 0.56 0.57 0.45 0.28
#> PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
#> 1 0.14 0.13 0.33 0.39
#> 2 0.16 0.10 0.17 0.29
#> 3 0.20 0.20 0.46 0.52
#> 4 0.56 0.62 0.85 0.77
#> 5 0.16 0.19 0.59 0.60
#> 6 0.25 0.19 0.29 0.53
#> PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
#> 1 0.28 0.55 0.09 0.51 0.5
#> 2 0.17 0.26 0.20 0.82 0.0
#> 3 0.43 0.42 0.15 0.51 0.5
#> 4 1.00 0.94 0.12 0.01 0.5
#> 5 0.37 0.89 0.02 0.19 0.5
#> 6 0.18 0.39 0.26 0.73 0.0
#> HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
#> 1 0.21 0.71 0.52 0.05 0.26
#> 2 0.02 0.79 0.24 0.02 0.25
#> 3 0.01 0.86 0.41 0.29 0.30
#> 4 0.01 0.97 0.96 0.60 0.47
#> 5 0.01 0.89 0.87 0.04 0.55
#> 6 0.02 0.84 0.30 0.16 0.28
#> MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
#> 1 0.65 0.14 0.06 0.22 0.19
#> 2 0.65 0.16 0.00 0.21 0.20
#> 3 0.52 0.47 0.45 0.18 0.17
#> 4 0.52 0.11 0.11 0.24 0.21
#> 5 0.73 0.05 0.14 0.31 0.31
#> 6 0.25 0.02 0.05 0.94 1.00
#> OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
#> 1 0.18 0.36 0.35 0.38 0.34 0.38
#> 2 0.21 0.42 0.38 0.40 0.37 0.29
#> 3 0.16 0.27 0.29 0.27 0.31 0.48
#> 4 0.19 0.75 0.70 0.77 0.89 0.63
#> 5 0.30 0.40 0.36 0.38 0.38 0.22
#> 6 1.00 0.67 0.63 0.68 0.62 0.47
#> MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
#> 1 0.46 0.25 0.04 0 0.12
#> 2 0.32 0.18 0.00 0 0.21
#> 3 0.39 0.28 0.00 0 0.14
#> 4 0.51 0.47 0.00 0 0.19
#> 5 0.51 0.21 0.00 0 0.11
#> 6 0.59 0.11 0.00 0 0.70
#> PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
#> 1 0.42 0.50 0.51 0.64 0.12 0.26
#> 2 0.50 0.34 0.60 0.52 0.02 0.12
#> 3 0.49 0.54 0.67 0.56 0.01 0.21
#> 4 0.30 0.73 0.64 0.65 0.02 0.39
#> 5 0.72 0.64 0.61 0.53 0.04 0.09
#> 6 0.42 0.49 0.73 0.64 0.01 0.58
#> PctUsePubTrans LemasPctOfficDrugUn ViolentCrimesPerPop
#> 1 0.20 0.32 0.20
#> 2 0.45 0.00 0.67
#> 3 0.02 0.00 0.43
#> 4 0.28 0.00 0.12
#> 5 0.02 0.00 0.03
#> 6 0.10 0.00 0.14
<- classic.to.sym(x = USCrime,
result concept = state,
variables= c(NumInShelters,
NumImmig,
ViolentCrimesPerPop),ViolentCrimesPerPop_hist = sym.histogram(ViolentCrimesPerPop,
breaks = pretty(USCrime$ViolentCrimesPerPop,5)))
result#> # A tibble: 46 × 4
#> ViolentCrimesPerPop_hist NumInShelters NumImmig ViolentCrimesPerPop
#> <symblc_h> <symblc_n> <symblc_n> <symblc_n>
#> 1 <hist> [0.00 : 0.32] [0.00 : 0.04] [0.01 : 1.00]
#> 2 <hist> [0.01 : 0.18] [0.01 : 0.09] [0.05 : 0.36]
#> 3 <hist> [0.00 : 1.00] [0.00 : 0.57] [0.05 : 0.57]
#> 4 <hist> [0.00 : 0.08] [0.00 : 0.02] [0.02 : 1.00]
#> 5 <hist> [0.00 : 1.00] [0.00 : 1.00] [0.01 : 1.00]
#> 6 <hist> [0.00 : 0.68] [0.00 : 0.23] [0.07 : 0.75]
#> 7 <hist> [0.00 : 0.79] [0.00 : 0.14] [0.00 : 0.94]
#> 8 <hist> [0.01 : 0.01] [0.01 : 0.01] [0.37 : 0.37]
#> 9 <hist> [1.00 : 1.00] [0.39 : 0.39] [1.00 : 1.00]
#> 10 <hist> [0.00 : 0.52] [0.00 : 1.00] [0.06 : 1.00]
#> # ℹ 36 more rows
data("ex_mcfa1")
head(ex_mcfa1)
#> suspect age hair eyes region
#> 1 1 42 h_red e_brown Bronx
#> 2 2 20 h_black e_green Bronx
#> 3 3 64 h_brown e_brown Brooklyn
#> 4 4 55 h_blonde e_brown Bronx
#> 5 5 4 h_brown e_green Manhattan
#> 6 6 61 h_blonde e_green Bronx
<- classic.to.sym(x = ex_mcfa1,
sym.table concept = suspect,
variables=c(hair,
eyes,
region),default.categorical = sym.set)
sym.table#> # A tibble: 100 × 3
#> hair eyes region
#> <symblc_s> <symblc_s> <symblc_s>
#> 1 {h_red} {e_brown,e_black} {Bronx}
#> 2 {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
We can modify the function that will be applied by default to the categorical variables
<- classic.to.sym(x = ex_mcfa1,
sym.table concept = suspect,
default.categorical = sym.set)
sym.table#> # A tibble: 100 × 4
#> age hair eyes region
#> <symblc_n> <symblc_s> <symblc_s> <symblc_s>
#> 1 [22.00 : 42.00] {h_red} {e_brown,e_black} {Bronx}
#> 2 [20.00 : 57.00] {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 [29.00 : 64.00] {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 [14.00 : 55.00] {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 [4.00 : 47.00] {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 [32.00 : 61.00] {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 [49.00 : 61.00] {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 [8.00 : 32.00] {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 [39.00 : 67.00] {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 [50.00 : 68.00] {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
<- SDS.to.RSDA(file.path = "hani3101.sds")
hani3101 #> Preprocessing file
#> Converting data to JSON format
#> Processing variable 1: R3101
#> Processing variable 2: RNINO12
#> Processing variable 3: RNINO3
#> Processing variable 4: RNINO4
#> Processing variable 5: RNINO34
#> Processing variable 6: RSOI
hani3101#> # A tibble: 32 × 6
#> R3101 RNINO12
#> <symblc_m> <symblc_m>
#> 1 X2:0.21 X4:0.18 X3:0.15 X5:... X1:0.17 X2:0.83 X3:0.00
#> 2 X2:0.30 X4:0.14 X3:0.19 X5:... X1:0.00 X2:0.25 X3:0.75
#> 3 X2:0.16 X4:0.12 X3:0.20 X5:... X1:0.67 X2:0.33 X3:0.00
#> 4 X2:0.13 X4:0.15 X3:0.22 X5:... X1:0.17 X2:0.83 X3:0.00
#> 5 X2:0.14 X4:0.14 X3:0.18 X5:... X1:0.42 X2:0.58 X3:0.00
#> 6 X2:0.26 X4:0.06 X3:0.23 X5:... X1:0.00 X2:0.67 X3:0.33
#> 7 X2:0.28 X4:0.14 X3:0.10 X5:... X1:0.00 X2:1.00 X3:0.00
#> 8 X2:0.25 X4:0.15 X3:0.19 X5:... X1:0.00 X2:1.00 X3:0.00
#> 9 X2:0.20 X4:0.15 X3:0.19 X5:... X1:0.00 X2:1.00 X3:0.00
#> 10 X2:0.21 X4:0.16 X3:0.31 X5:... X1:0.08 X2:0.92 X3:0.00
#> # ℹ 22 more rows
#> # ℹ 4 more variables: RNINO3 <symblc_m>, RNINO4 <symblc_m>, RNINO34 <symblc_m>,
#> # RSOI <symblc_m>
# We can save the file in CSV to RSDA format as follows:
write.sym.table(hani3101,
file='hani3101.csv',
sep=';',
dec='.',
row.names=TRUE,
col.names=TRUE)
<- SODAS.to.RSDA("abalone.xml")
abalone #> Processing variable 1: LENGTH
#> Processing variable 2: DIAMETER
#> Processing variable 3: HEIGHT
#> Processing variable 4: WHOLE_WEIGHT
#> Processing variable 5: SHUCKED_WEIGHT
#> Processing variable 6: VISCERA_WEIGHT
#> Processing variable 7: SHELL_WEIGHT
abalone#> # A tibble: 24 × 7
#> LENGTH DIAMETER HEIGHT WHOLE_WEIGHT SHUCKED_WEIGHT
#> <symblc_n> <symblc_n> <symblc_n> <symblc_n> <symblc_n>
#> 1 [0.28 : 0.66] [0.20 : 0.48] [0.07 : 0.18] [0.08 : 1.37] [0.03 : 0.64]
#> 2 [0.30 : 0.74] [0.22 : 0.58] [0.02 : 1.13] [0.15 : 2.25] [0.06 : 1.16]
#> 3 [0.34 : 0.78] [0.26 : 0.63] [0.06 : 0.23] [0.20 : 2.66] [0.07 : 1.49]
#> 4 [0.39 : 0.82] [0.30 : 0.65] [0.10 : 0.25] [0.26 : 2.51] [0.11 : 1.23]
#> 5 [0.40 : 0.74] [0.32 : 0.60] [0.10 : 0.24] [0.35 : 2.20] [0.12 : 0.84]
#> 6 [0.45 : 0.80] [0.38 : 0.63] [0.14 : 0.22] [0.64 : 2.53] [0.16 : 0.93]
#> 7 [0.49 : 0.72] [0.36 : 0.58] [0.12 : 0.21] [0.68 : 2.12] [0.16 : 0.82]
#> 8 [0.55 : 0.70] [0.46 : 0.58] [0.18 : 0.22] [1.21 : 1.81] [0.32 : 0.71]
#> 9 [0.08 : 0.24] [0.06 : 0.18] [0.01 : 0.06] [0.00 : 0.07] [0.00 : 0.03]
#> 10 [0.13 : 0.58] [0.10 : 0.45] [0.00 : 0.15] [0.01 : 0.89] [0.00 : 0.50]
#> # ℹ 14 more rows
#> # ℹ 2 more variables: VISCERA_WEIGHT <symblc_n>, SHELL_WEIGHT <symblc_n>
write.sym.table(abalone,
file='abalone.csv',
sep=';',
dec='.',
row.names = TRUE,
col.names = TRUE)
data(example3)
mean(example3$F1)
#> [1] 1.628571
mean(example3[,1])
#> [1] 1.628571
mean(example3$F2)
#> [1] 5
mean(example3[,2])
#> [1] 5
mean(example3$F2,method = "interval")
#> <symbolic_interval[1]>
#> [1] [1.86 : 8.14]
mean(example3[,2],method = "interval")
#> <symbolic_interval[1]>
#> [1] [1.86 : 8.14]
median(example3$F1)
#> [1] 1.4
median(example3[,1])
#> [1] 1.4
median(example3$F2)
#> [1] 1.5
median(example3[,2])
#> [1] 1.5
median(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [5.00 : 89.00]
median(example3[,6], method = 'interval')
#> <symbolic_interval[1]>
#> [1] [5.00 : 89.00]
var(example3[,1])
#> [1] 15.98238
var(example3[,2])
#> [1] 90.66667
var(example3$F6)
#> [1] 1872.358
var(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [2,408.97 : 1,670.51]
var(example3$F6, method = 'billard')
#> [1] 1355.143
sd(example3$F1)
#> [1] 3.997797
sd(example3$F2)
#> [1] 6.733003
sd(example3$F6)
#> [1] 30.59704
sd(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [49.08 : 40.87]
sd(example3$F6, method = 'billard')
#> [1] 36.81226
cor(example3$F1, example3$F4)
#> [1] 0.2864553
cor(example3[,1], example3[,4])
#> [,1]
#> [1,] 0.2864553
cor(example3$F2, example3$F6, method = 'centers')
#> [1] -0.6693648
cor(example3$F2, example3$F6, method = 'billard')
#> [1] -0.6020041
library(ggpolypath)
#> Loading required package: ggplot2
data(oils)
<- RSDA:::to.v3(RSDA:::to.v2(oils))
oils sym.radar.plot(oils[2:3,])
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
sym.radar.plot(oils[2:5,])
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
<- interval.histogram.plot(oils[,2],
res n.bins = 4,
col = c(2,3,4,5))
res#> $frequency
#> [1] 25 49 1 25
#>
#> $histogram
#> [,1]
#> [1,] 0.7
#> [2,] 1.9
#> [3,] 3.1
#> [4,] 4.3
<- interval.histogram.plot(oils[,3],
res n.bins = 3,
main = "Histogram",
col = c(2, 3, 4))
res#> $frequency
#> [1] 50 25 25
#>
#> $histogram
#> [,1]
#> [1,] 0.7
#> [2,] 1.9
#> [3,] 3.1
data("oils")
<- sym.dist.interval(sym.data = oils[,1:4],
DM method = "Gowda.Diday")
<- hclust(DM)
model plot(model, hang = -1)
<- sym.dist.interval(sym.data= oils[,1:4],
DM method = "Ichino")
<- hclust(DM)
model plot(model, hang = -1)
<- sym.dist.interval(sym.data = oils[,c(1,2,4)],
DM gamma = 0.5,
method = "Hausdorff",
normalize = FALSE,
SpanNormalize = TRUE,
euclidea = TRUE,
q = 2)
<- hclust(DM)
model plot(model, hang = -1)
data(int_prost_train)
data(int_prost_test)
<- sym.lm(formula = lpsa~., sym.data = int_prost_train, method = 'cm')
res.cm
res.cm#>
#> Call:
#> stats::lm(formula = formula, data = centers)
#>
#> Coefficients:
#> (Intercept) lcavol lweight age lbph svi
#> 0.411537 0.579327 0.614128 -0.018659 0.143918 0.730937
#> lcp gleason pgg45
#> -0.205536 -0.030924 0.009507
<- sym.predict(model = res.cm, new.sym.data = int_prost_test) pred.cm
RMSE.L(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.7229999
RMSE.U(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.7192467
R2.L(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.501419
R2.U(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.5058389
deter.coefficient(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.4962964
data(int_prost_train)
data(int_prost_test)
<- sym.glm(sym.data = int_prost_train,
res.cm.lasso response = 9,
method = 'cm',
alpha = 1,
nfolds = 10,
grouped = TRUE)
<- sym.predict(res.cm.lasso,
pred.cm.lasso response = 9,
int_prost_test,method = 'cm')
plot(res.cm.lasso)
plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)
RMSE.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.7014621
RMSE.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.6982014
R2.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.531049
R2.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.5348845
deter.coefficient(int_prost_test$lpsa, pred.cm.lasso)
#> [1] 0.4896842
data(int_prost_train)
data(int_prost_test)
<- sym.glm(sym.data = int_prost_train,
res.cm.ridge response = 9,
method = 'cm',
alpha = 0,
nfolds = 10,
grouped = TRUE)
<- sym.predict(res.cm.ridge,
pred.cm.ridge response = 9,
int_prost_test,method = 'cm')
plot(res.cm.ridge)
plot(res.cm.ridge$glmnet.fit, "lambda", label=TRUE)
RMSE.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.703543
RMSE.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.7004145
R2.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5286114
R2.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5322683
deter.coefficient(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.4808652
data("oils")
<- sym.pca(oils,'centers')
res plot(res, choix = "ind")
plot(res, choix = "var")
<- sym.pca(oils,'tops')
res #> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
plot(res, choix = "ind")
<- sym.pca(oils, 'principal.curves')
res #> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
plot(res, choix = "ind")
<- sym.pca(oils,'optimized.distance')
res #> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
plot(res, choix = "ind")
plot(res, choix = "var")
<- sym.pca(oils,'optimized.variance')
res #> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
#> tcltk DLL is linked to '/opt/X11/lib/libX11.6.dylib'
plot(res, choix = "ind")
plot(res, choix = "var")
data("ex_mcfa1")
ex_mcfa1#> suspect age hair eyes region
#> 1 1 42 h_red e_brown Bronx
#> 2 2 20 h_black e_green Bronx
#> 3 3 64 h_brown e_brown Brooklyn
#> 4 4 55 h_blonde e_brown Bronx
#> 5 5 4 h_brown e_green Manhattan
#> 6 6 61 h_blonde e_green Bronx
#> 7 7 61 h_white e_black Queens
#> 8 8 32 h_blonde e_brown Manhattan
#> 9 9 39 h_blonde e_black Brooklyn
#> 10 10 50 h_brown e_brown Manhattan
#> 11 11 41 h_red e_blue Manhattan
#> 12 12 35 h_blonde e_green Brooklyn
#> 13 13 56 h_blonde e_brown Bronx
#> 14 14 52 h_red e_brown Queens
#> 15 15 55 h_red e_green Brooklyn
#> 16 16 25 h_brown e_brown Queens
#> 17 17 52 h_blonde e_brown Brooklyn
#> 18 18 28 h_red e_brown Manhattan
#> 19 19 21 h_white e_blue Manhattan
#> 20 20 66 h_black e_black Brooklyn
#> 21 21 67 h_blonde e_brown Queens
#> 22 22 13 h_white e_blue Brooklyn
#> 23 23 39 h_brown e_green Manhattan
#> 24 24 47 h_black e_green Brooklyn
#> 25 25 54 h_blonde e_brown Bronx
#> 26 26 75 h_brown e_blue Brooklyn
#> 27 27 3 h_white e_green Manhattan
#> 28 28 40 h_white e_green Manhattan
#> 29 29 58 h_red e_blue Queens
#> 30 30 41 h_brown e_green Bronx
#> 31 31 25 h_white e_black Brooklyn
#> 32 32 75 h_blonde e_blue Manhattan
#> 33 33 58 h_white e_brown Bronx
#> 34 34 61 h_white e_brown Manhattan
#> 35 35 52 h_white e_blue Bronx
#> 36 36 19 h_red e_black Queens
#> 37 37 58 h_red e_black Bronx
#> 38 38 46 h_black e_green Manhattan
#> 39 39 74 h_brown e_black Manhattan
#> 40 40 26 h_blonde e_brown Brooklyn
#> 41 41 63 h_blonde e_blue Queens
#> 42 42 40 h_brown e_black Queens
#> 43 43 65 h_black e_brown Brooklyn
#> 44 44 51 h_blonde e_brown Brooklyn
#> 45 45 15 h_white e_black Brooklyn
#> 46 46 32 h_blonde e_brown Bronx
#> 47 47 68 h_white e_black Manhattan
#> 48 48 51 h_white e_black Queens
#> 49 49 14 h_red e_green Queens
#> 50 50 72 h_white e_brown Brooklyn
#> 51 51 7 h_red e_blue Brooklyn
#> 52 52 22 h_red e_brown Bronx
#> 53 53 52 h_red e_brown Brooklyn
#> 54 54 62 h_brown e_green Bronx
#> 55 55 41 h_black e_brown Queens
#> 56 56 32 h_black e_black Manhattan
#> 57 57 58 h_brown e_brown Queens
#> 58 58 25 h_black e_brown Queens
#> 59 59 70 h_blonde e_green Brooklyn
#> 60 60 64 h_brown e_blue Queens
#> 61 61 25 h_white e_blue Bronx
#> 62 62 42 h_black e_black Brooklyn
#> 63 63 56 h_red e_black Brooklyn
#> 64 64 41 h_blonde e_black Brooklyn
#> 65 65 8 h_white e_black Manhattan
#> 66 66 7 h_black e_green Brooklyn
#> 67 67 42 h_white e_brown Queens
#> 68 68 10 h_white e_blue Manhattan
#> 69 69 60 h_brown e_black Bronx
#> 70 70 52 h_blonde e_brown Brooklyn
#> 71 71 39 h_brown e_blue Manhattan
#> 72 72 69 h_brown e_green Queens
#> 73 73 67 h_blonde e_green Manhattan
#> 74 74 46 h_red e_black Brooklyn
#> 75 75 72 h_black e_black Queens
#> 76 76 66 h_red e_blue Queens
#> 77 77 4 h_black e_blue Manhattan
#> 78 78 62 h_black e_green Brooklyn
#> 79 79 10 h_blonde e_blue Bronx
#> 80 80 16 h_blonde e_black Manhattan
#> 81 81 59 h_blonde e_brown Bronx
#> 82 82 63 h_blonde e_blue Manhattan
#> 83 83 54 h_red e_blue Queens
#> 84 84 14 h_brown e_blue Brooklyn
#> 85 85 48 h_black e_green Manhattan
#> 86 86 59 h_blonde e_black Bronx
#> 87 87 73 h_blonde e_black Bronx
#> 88 88 51 h_brown e_brown Bronx
#> 89 89 14 h_white e_black Bronx
#> 90 90 58 h_blonde e_black Queens
#> 91 91 56 h_red e_green Manhattan
#> 92 92 26 h_red e_blue Brooklyn
#> 93 93 59 h_brown e_black Manhattan
#> 94 94 27 h_white e_green Manhattan
#> 95 95 38 h_black e_green Manhattan
#> 96 96 5 h_blonde e_green Bronx
#> 97 97 14 h_black e_blue Queens
#> 98 98 13 h_black e_brown Manhattan
#> 99 99 54 h_white e_blue Brooklyn
#> 100 100 66 h_white e_green Manhattan
#> 101 1 22 h_red e_black Bronx
#> 102 2 57 h_blonde e_black Manhattan
#> 103 3 29 h_white e_green Queens
#> 104 4 14 h_blonde e_black Manhattan
#> 105 5 47 h_red e_green Bronx
#> 106 6 32 h_white e_blue Queens
#> 107 7 49 h_red e_blue Bronx
#> 108 8 8 h_white e_black Brooklyn
#> 109 9 67 h_white e_brown Bronx
#> 110 10 68 h_black e_green Bronx
#> 111 11 15 h_black e_brown Manhattan
#> 112 12 46 h_white e_brown Bronx
#> 113 13 68 h_white e_black Manhattan
#> 114 14 55 h_blonde e_blue Manhattan
#> 115 15 7 h_white e_green Bronx
#> 116 16 10 h_black e_brown Brooklyn
#> 117 17 49 h_red e_blue Manhattan
#> 118 18 12 h_brown e_blue Brooklyn
#> 119 19 41 h_white e_blue Bronx
#> 120 20 10 h_brown e_blue Bronx
#> 121 21 12 h_white e_green Manhattan
#> 122 22 53 h_white e_blue Manhattan
#> 123 23 5 h_black e_black Manhattan
#> 124 24 46 h_brown e_black Queens
#> 125 25 14 h_brown e_black Queens
#> 126 26 55 h_white e_green Brooklyn
#> 127 27 53 h_red e_brown Manhattan
#> 128 28 31 h_black e_brown Manhattan
#> 129 29 31 h_blonde e_brown Queens
#> 130 30 55 h_brown e_black Brooklyn
<- classic.to.sym(x = ex_mcfa1,
sym.table concept = suspect,
default.categorical = sym.set)
sym.table#> # A tibble: 100 × 4
#> age hair eyes region
#> <symblc_n> <symblc_s> <symblc_s> <symblc_s>
#> 1 [22.00 : 42.00] {h_red} {e_brown,e_black} {Bronx}
#> 2 [20.00 : 57.00] {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 [29.00 : 64.00] {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 [14.00 : 55.00] {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 [4.00 : 47.00] {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 [32.00 : 61.00] {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 [49.00 : 61.00] {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 [8.00 : 32.00] {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 [39.00 : 67.00] {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 [50.00 : 68.00] {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
<- sym.mcfa(sym.table, c(2,3))
res mcfa.scatterplot(res[,2], res[,3], sym.data = sym.table, pos.var = c(2,3))
<- sym.mcfa(sym.table, c(2,3,4))
res mcfa.scatterplot(res[,2], res[,3], sym.data = sym.table, pos.var = c(2,3,4))
<- oils
datos
datos#> # A tibble: 8 × 4
#> GRA FRE IOD SAP
#> * <symblc_n> <symblc_n> <symblc_n> <symblc_n>
#> 1 [0.93 : 0.94] [-27.00 : -18.00] [170.00 : 204.00] [118.00 : 196.00]
#> 2 [0.93 : 0.94] [-5.00 : -4.00] [192.00 : 208.00] [188.00 : 197.00]
#> 3 [0.92 : 0.92] [-6.00 : -1.00] [99.00 : 113.00] [189.00 : 198.00]
#> 4 [0.92 : 0.93] [-6.00 : -4.00] [104.00 : 116.00] [187.00 : 193.00]
#> 5 [0.92 : 0.92] [-25.00 : -15.00] [80.00 : 82.00] [189.00 : 193.00]
#> 6 [0.91 : 0.92] [0.00 : 6.00] [79.00 : 90.00] [187.00 : 196.00]
#> 7 [0.86 : 0.87] [30.00 : 38.00] [40.00 : 48.00] [190.00 : 199.00]
#> 8 [0.86 : 0.86] [22.00 : 32.00] [53.00 : 77.00] [190.00 : 202.00]
<- sym.umap(datos)
x
x#> V1 V2 V3 V4
#> 1 -9.486419 -2.437560 11.09450514 3.8597450
#> 2 -9.291773 -2.242912 11.28922078 3.6651031
#> 3 -9.322376 -2.273513 11.25873422 3.6957040
#> 4 -9.240148 -2.191262 11.34096205 3.6134746
#> 5 -9.497748 -2.448876 11.08339404 3.8710728
#> 6 -9.645452 -2.596570 10.93580344 4.0187760
#> 7 -9.306796 -2.257918 11.27452456 3.6801182
#> 8 -9.328169 -2.279298 11.25315937 3.7014873
#> 9 -1.235054 1.836879 -2.94972019 -5.7066594
#> 10 -1.151552 1.838129 -2.93052399 -5.7656798
#> 11 -1.233733 1.799005 -3.05162192 -5.5995454
#> 12 -1.109717 1.527585 -3.05026821 -5.5966663
#> 13 -1.115887 1.974561 -2.87027101 -5.7007239
#> 14 -1.084133 2.008087 -2.94475355 -5.6159948
#> 15 -1.332376 1.902570 -2.90051234 -5.6109137
#> 16 -1.234441 1.818597 -3.13425707 -5.7771997
#> 17 -1.943464 2.224879 -3.87359369 -5.8978057
#> 18 -2.260242 1.959183 -3.50008662 -5.8856918
#> 19 -1.991149 2.264097 -3.81272396 -5.8480879
#> 20 -2.084464 1.864579 -3.65592624 -6.1601024
#> 21 -2.058150 2.248290 -3.76852481 -5.5536431
#> 22 -2.201742 2.108917 -3.69485394 -5.8168247
#> 23 -2.146909 2.212785 -3.88390350 -5.7021996
#> 24 -2.359044 2.007334 -3.72549972 -5.9458950
#> 25 -2.016807 2.218834 -3.18711095 -5.7165063
#> 26 -1.842308 1.829503 -3.41755379 -5.7894437
#> 27 -2.027974 1.980831 -3.41816637 -5.5431891
#> 28 -1.967136 2.020815 -3.32940908 -5.7332410
#> 29 -2.133592 1.903024 -3.52063772 -5.3200989
#> 30 -2.335501 1.954604 -3.58358895 -5.6684793
#> 31 -2.045006 1.683916 -3.47629672 -5.5372667
#> 32 -2.319471 1.800426 -3.48923740 -5.5594852
#> 33 -9.836918 -2.530528 -1.09181973 3.6016449
#> 34 -9.976300 -2.481613 -1.19294984 3.4584848
#> 35 -9.731401 -2.726910 -1.42070558 3.5256548
#> 36 -9.557896 -2.815222 -1.60457834 3.6888385
#> 37 -10.203365 -2.534262 -1.06815875 3.2857082
#> 38 -10.186821 -2.528668 -0.89248767 3.1737354
#> 39 -10.023885 -2.754892 -1.22478957 3.0948667
#> 40 -9.893945 -2.722448 -1.09201864 3.1092877
#> 41 -8.886738 -2.925786 -0.10866759 3.2194084
#> 42 -8.894895 -2.918994 -0.04656192 3.1817061
#> 43 -8.558576 -3.108756 -0.15292170 3.5209042
#> 44 -8.553762 -3.119990 -0.12832467 3.4020375
#> 45 -9.045744 -2.795375 0.04350897 3.0364241
#> 46 -9.132956 -2.710859 0.06946699 2.9646078
#> 47 -8.798646 -2.957939 0.23781326 2.9099614
#> 48 -8.907719 -2.834238 0.16215991 2.9779378
#> 49 -10.089641 -2.271160 -1.31993117 3.4227533
#> 50 -10.326117 -2.056373 -1.07295955 3.2034754
#> 51 -10.108617 -2.445900 -1.24756434 3.4866513
#> 52 -10.391770 -2.104110 -1.04970560 3.1561217
#> 53 -10.331175 -2.375337 -1.04743462 3.0492796
#> 54 -10.403801 -2.049200 -0.87999277 2.8163371
#> 55 -10.367941 -2.559443 -1.05958357 2.9863523
#> 56 -10.344861 -2.181893 -0.97013310 2.9003257
#> 57 -9.652443 -2.475703 -0.40052757 3.1934088
#> 58 -9.889868 -2.140432 -0.38851591 3.1232234
#> 59 -9.556867 -2.601785 -0.56241976 3.2800251
#> 60 -9.762875 -2.136516 -0.39710383 3.4568988
#> 61 -9.799112 -2.458597 -0.37626909 2.9104039
#> 62 -9.874689 -2.247742 -0.31200241 2.8245910
#> 63 -9.748234 -2.507264 -0.37915756 2.9420952
#> 64 -10.072827 -2.049071 -0.36990687 2.8731328
#> 65 -2.291260 15.292681 -3.36417253 -1.2406825
#> 66 -2.368837 15.368845 -3.42577180 -1.3182622
#> 67 -3.308272 11.785336 4.29575519 0.9460292
#> 68 -3.258976 11.736871 4.25181787 0.9942804
#> 69 -2.460637 15.469962 -3.54812902 -1.4077661
#> 70 -2.196794 15.196405 -3.26246098 -1.1464073
#> 71 -3.113254 11.625637 4.16980406 1.1042603
#> 72 -3.265602 11.562247 4.07516338 1.1693015
#> 73 -2.397863 15.396676 -3.45708888 -1.3474379
#> 74 -2.289662 15.287897 -3.35199103 -1.2394340
#> 75 -3.545106 11.746703 4.16099539 0.9866755
#> 76 -3.382411 11.756020 4.27460654 0.9753418
#> 77 -2.458594 15.457283 -3.50851262 -1.4080999
#> 78 -2.214867 15.212769 -3.27525274 -1.1647830
#> 79 -3.384951 11.674387 4.17874816 1.0572961
#> 80 -3.346659 11.730304 4.24668638 1.0010304
#> 81 -8.867230 -3.134426 -1.83467389 3.8794114
#> 82 -9.040418 -3.170801 -1.97790725 3.9464580
#> 83 -8.705343 -3.178814 -1.90515115 3.9184059
#> 84 -8.604041 -3.073533 -1.81332373 3.7838838
#> 85 -9.200930 -3.079470 -1.87522761 3.8814084
#> 86 -9.211750 -3.092860 -1.89270530 3.8901012
#> 87 -8.755734 -3.038026 -1.83392976 3.7886873
#> 88 -8.716574 -3.098808 -1.74781074 3.8731309
#> 89 -8.363832 -3.215765 -0.50292266 3.6252172
#> 90 -8.348101 -3.231469 -0.57134010 3.6465990
#> 91 -8.151781 -3.232610 -0.79619503 3.7603195
#> 92 -8.191471 -3.329333 -0.68333982 3.8512038
#> 93 -8.296942 -3.308535 -0.42334865 3.5034851
#> 94 -8.428702 -3.209744 -0.26311152 3.5452289
#> 95 -7.999973 -3.233331 -0.61463189 3.9026834
#> 96 -8.069542 -3.352646 -0.79053443 3.8819472
#> 97 19.352550 -3.924938 0.65459181 -1.6611328
#> 98 19.043797 -3.847698 0.54736249 -1.4208922
#> 99 18.958306 -3.975861 0.75753001 -1.3841676
#> 100 18.849054 -3.936887 0.83629385 -1.1584206
#> 101 19.439143 -4.057120 0.50828662 -1.5295567
#> 102 19.187417 -3.973146 0.53374518 -1.4905529
#> 103 18.959896 -4.158357 0.77881712 -1.2221065
#> 104 18.831169 -3.791918 0.94135624 -1.2977460
#> 105 18.945999 -3.014966 0.85037065 -1.6113620
#> 106 18.730601 -3.099109 0.79001701 -1.3469204
#> 107 18.649123 -3.474216 1.11494565 -1.2505490
#> 108 18.732295 -3.242920 1.04941745 -1.1964077
#> 109 18.861805 -2.973715 0.94464752 -1.5991106
#> 110 18.836312 -3.202012 0.72966173 -1.4139295
#> 111 18.719034 -3.467854 1.10640031 -1.3316311
#> 112 18.693757 -3.316346 1.10866560 -1.1472152
#> 113 19.383382 -3.662464 0.72718527 -2.1308934
#> 114 19.448150 -3.796827 0.69857061 -2.0870552
#> 115 19.265631 -4.169768 0.46361947 -1.6824031
#> 116 19.356645 -4.167678 0.40988015 -1.4935245
#> 117 19.631782 -3.827665 0.82881992 -2.2635247
#> 118 19.606308 -3.789762 0.76449216 -2.2436630
#> 119 19.564081 -3.981105 0.55390372 -2.1188136
#> 120 19.498718 -3.815132 0.45100089 -2.0715520
#> 121 19.194869 -3.119667 0.83294182 -1.9365282
#> 122 19.236718 -2.979468 0.72660549 -2.0186949
#> 123 18.749964 -2.854605 1.03286682 -1.5407410
#> 124 18.770605 -2.922677 0.93169195 -1.5192153
#> 125 19.244634 -3.110865 0.93151183 -2.2420016
#> 126 19.431952 -3.036827 1.15436822 -2.2028455
#> 127 19.157741 -2.869348 1.17223802 -1.8961034
#> 128 19.163809 -2.891441 1.11713129 -1.9606745
plot(x)
<- Cardiological
datos
datos#> # A tibble: 11 × 3
#> Pulse Syst Diast
#> <symblc_n> <symblc_n> <symblc_n>
#> 1 [44.00 : 68.00] [90.00 : 100.00] [50.00 : 70.00]
#> 2 [60.00 : 72.00] [90.00 : 130.00] [70.00 : 90.00]
#> 3 [56.00 : 90.00] [140.00 : 180.00] [90.00 : 100.00]
#> 4 [70.00 : 112.00] [110.00 : 142.00] [80.00 : 108.00]
#> 5 [54.00 : 72.00] [90.00 : 100.00] [50.00 : 70.00]
#> 6 [70.00 : 100.00] [130.00 : 160.00] [80.00 : 110.00]
#> 7 [63.00 : 75.00] [60.00 : 100.00] [140.00 : 150.00]
#> 8 [72.00 : 100.00] [130.00 : 160.00] [76.00 : 90.00]
#> 9 [76.00 : 98.00] [110.00 : 190.00] [70.00 : 110.00]
#> 10 [86.00 : 96.00] [138.00 : 180.00] [90.00 : 110.00]
#> 11 [86.00 : 100.00] [110.00 : 150.00] [78.00 : 100.00]
<- sym.umap(datos)
x
x#> V1 V2 V3
#> 1 2.19817997 -0.30633757 3.086790110
#> 2 1.17523197 -0.37798511 3.036328634
#> 3 2.31531052 -0.17350881 2.935875215
#> 4 1.34554176 -0.36745627 2.927284222
#> 5 2.22181861 -0.25586565 2.705633081
#> 6 1.17995049 -0.17437956 2.436965713
#> 7 2.37636155 -0.36473550 2.610893274
#> 8 1.19934747 -0.40935891 2.181472833
#> 9 1.84876290 -0.22779015 2.637356633
#> 10 0.89379481 -0.27763813 2.435300110
#> 11 0.27531686 -1.04739854 0.738395997
#> 12 0.32173415 -0.92292759 0.711066277
#> 13 1.55624202 -0.22379563 2.093209373
#> 14 1.25893769 -0.15067865 1.816577298
#> 15 0.56042289 -1.39972579 0.240666390
#> 16 0.56719776 -0.95465501 0.075102367
#> 17 0.36992129 -1.46986465 0.134610177
#> 18 -1.55880355 0.80264402 -1.206952707
#> 19 -0.02170622 -1.32708203 -0.469212845
#> 20 -2.46073121 1.16588072 -2.145462264
#> 21 0.56280273 -1.45751666 -0.023964495
#> 22 -1.22062853 0.88593901 -1.365273529
#> 23 0.15025897 -1.42400231 -0.679198360
#> 24 -2.15722074 1.14813040 -2.539568731
#> 25 0.86935951 -0.51088025 1.728281099
#> 26 -1.58615295 1.58563455 -0.248456462
#> 27 0.05540525 -1.08992640 0.298414768
#> 28 -2.22944649 1.47554005 -0.706767428
#> 29 1.41642597 -0.95561032 -0.268715098
#> 30 -0.99581267 1.53988508 -0.763739710
#> 31 0.72926620 -1.09901216 -0.495037766
#> 32 -1.20359577 1.57417945 -1.350797365
#> 33 1.91019154 -0.07261958 3.032927503
#> 34 1.25395159 -0.27064467 3.007390877
#> 35 2.24152775 -0.19549945 2.998060819
#> 36 1.23665897 -0.55220460 2.783210438
#> 37 1.98454010 -0.18287350 2.648082775
#> 38 0.97218701 -0.13821983 2.212728636
#> 39 2.09919504 -0.39594377 2.827348906
#> 40 0.74717898 -0.40511906 2.214213714
#> 41 0.63187345 -0.96799195 0.343831029
#> 42 -1.83107239 1.42464276 -0.606324825
#> 43 -0.25699810 -0.95315253 -0.102095499
#> 44 -2.50275748 1.28716902 -1.289566219
#> 45 1.02705237 -1.12828742 -0.439201655
#> 46 -0.91482175 1.38914896 -1.299377816
#> 47 0.33086119 -1.00662656 -0.643494945
#> 48 -1.63930622 1.41660527 -2.059942615
#> 49 2.91699373 -1.61705487 -0.786488524
#> 50 2.94311901 -1.67406604 -0.800185222
#> 51 2.49149970 -1.72634189 -0.773446542
#> 52 2.58026916 -1.45910406 -0.812480993
#> 53 3.05458666 -1.58873378 -0.678271191
#> 54 2.87641171 -1.79922705 -0.938459488
#> 55 2.62808785 -1.49218059 -1.013667120
#> 56 2.57357999 -1.63566030 -0.996269909
#> 57 0.52081716 -0.95882325 0.567126452
#> 58 -2.05324133 1.24747578 -0.400429550
#> 59 -0.37526263 -1.23863753 -0.004406741
#> 60 -2.64788426 1.18402016 -1.271033929
#> 61 0.85041907 -1.05872109 0.146530347
#> 62 -1.70552642 1.24596980 -0.787629867
#> 63 -0.15030967 -0.94930666 -0.286377449
#> 64 -2.08625231 1.43151717 -1.558107092
#> 65 0.42182815 -0.36270350 1.875523761
#> 66 -1.75467237 1.23804387 -0.085598018
#> 67 -0.70221556 -0.91025848 -0.426497570
#> 68 -2.72481515 1.13147383 -1.647862996
#> 69 1.46196052 -0.88093787 -0.375031336
#> 70 -0.68915700 1.22687722 -0.859093172
#> 71 -1.95435600 0.82160221 -2.686902896
#> 72 -2.15775070 1.15791305 -2.494454667
#> 73 -1.49366682 0.63297594 -1.133141574
#> 74 -1.81384850 1.03172243 -1.129250407
#> 75 -2.37096593 0.86320653 -2.405701836
#> 76 -2.58703813 1.19894789 -2.074975923
#> 77 -1.00653839 0.72984502 -1.514560726
#> 78 -1.05213583 1.18314811 -1.601207985
#> 79 -1.79382977 1.07753394 -2.675773266
#> 80 -2.03386351 1.40488390 -2.390379237
#> 81 -1.18398669 0.73071404 0.360980074
#> 82 -1.75743623 1.17966870 0.007556736
#> 83 -1.75284596 0.48127687 -1.136052369
#> 84 -2.42032325 1.35591524 -0.960040136
#> 85 -0.45800068 0.90382081 -0.521984572
#> 86 -0.90379292 1.42858454 -0.675901051
#> 87 -1.38637284 0.60416942 -1.573392457
#> 88 -1.60723918 1.40236578 -1.677529495
plot(x)