sumeer<-read.csv("D:/1 Teaching Material/R/importfile/sumeer.csv")
names(sumeer)[1]<-"SNO"
str(sumeer)
## 'data.frame': 50 obs. of 45 variables:
## $ SNO : int 375332 375326 372830 375704 368114 368069 365788 380816 380730 380536 ...
## $ AGE : int 23 49 27 54 49 75 20 20 58 75 ...
## $ SEX : int 0 0 0 1 0 1 0 1 1 0 ...
## $ DM : int 0 1 0 1 1 1 0 0 1 1 ...
## $ HTN : int 0 1 0 1 1 1 0 0 0 1 ...
## $ CKD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ SEPSIS : int 0 0 0 0 0 0 1 0 0 0 ...
## $ SEVERE_SEPSIS : int 0 0 1 1 1 0 0 0 0 1 ...
## $ SEPTIC_SHOCK : int 1 1 0 0 0 1 0 1 1 0 ...
## $ HB : num 12.8 11.2 11.6 11 11.5 7.6 11.7 4 6.2 12.5 ...
## $ TLC : int 8600 19100 12000 19800 17900 5400 30200 12000 31100 16900 ...
## $ PLATELET : int 30000 41000 150000 460000 320000 25000 270000 60000 31000 290000 ...
## $ pH : num 7.26 7.24 7.34 7.34 7.24 7.28 7.35 7.34 7.16 7.21 ...
## $ HCO3 : num 18 16 16 18 16 18 22 12.9 18 22.8 ...
## $ Na : int 132 140 140 132 130 132 132 130 140 130 ...
## $ K : num 3.6 3.8 35 3.8 3.8 3.6 3.6 5 5.4 4 ...
## $ CREATININE : num 2.04 2.16 0.74 1.42 5.3 1.53 0.84 2.25 2.9 1.21 ...
## $ TOTAL_BILIRUBIN : num 6.5 0.5 0.5 0.65 6.8 0.5 0.5 2.5 0.5 0.5 ...
## $ DIRECT_BILIRUBIN : num 4.5 0.25 0.25 0.35 4.6 0.25 0.25 1.25 0.25 0.25 ...
## $ SGOT : int 101 128 22 126 7 88 22 101 39 42 ...
## $ SGPT : int 68 134 18 171 14 39 15 120 32 41 ...
## $ MP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ DENGUE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PNEUMONIA_PROFILE: int 0 0 0 0 0 0 0 0 0 0 ...
## $ AMYLASE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PTINR : num 1.8 2 1 2 1 1.3 1 1.8 1.5 1.2 ...
## $ ALBUMIN : num 3.2 2.8 3 2.8 2.4 2.8 3.4 3 2.8 3 ...
## $ FIBRINOGEN : int 180 240 160 180 240 190 200 210 220 160 ...
## $ NIV_DAYS : int 3 0 1 0 2 2 0 1 0 2 ...
## $ MV_DAYS : int 0 1 0 0 4 0 0 4 1 0 ...
## $ INOTROPE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ SOFA : int 11 11 1 2 9 7 1 12 11 3 ...
## $ Number : int 3 3 1 1 2 1 1 3 2 1 ...
## $ lung : int 1 1 1 0 0 1 0 1 1 1 ...
## $ kidney : int 1 1 0 0 1 0 1 1 1 0 ...
## $ liver : int 1 1 0 1 1 0 0 1 0 0 ...
## $ STAY_IN_ICU_HDU : int 4 1 1 1 5 3 1 5 1 3 ...
## $ STAY_IN_WARD : int 5 3 5 3 7 5 4 3 3 3 ...
## $ bacterial : int 0 1 1 0 1 1 1 1 1 1 ...
## $ viral : int 0 0 0 0 0 0 0 0 0 0 ...
## $ parasitic : int 1 0 0 1 0 0 0 0 0 0 ...
## $ fungal : int 0 0 0 0 0 0 0 0 1 0 ...
## $ PCT : int 2 4 3 4 4 4 2 4 4 2 ...
## $ Mortality : int 2 3 2 2 2 2 2 3 3 2 ...
## $ Age_group : int 1 3 1 3 3 4 1 1 3 4 ...
names(sumeer)[3]<-"Gender"
str(sumeer)
## 'data.frame': 50 obs. of 45 variables:
## $ SNO : int 375332 375326 372830 375704 368114 368069 365788 380816 380730 380536 ...
## $ AGE : int 23 49 27 54 49 75 20 20 58 75 ...
## $ Gender : int 0 0 0 1 0 1 0 1 1 0 ...
## $ DM : int 0 1 0 1 1 1 0 0 1 1 ...
## $ HTN : int 0 1 0 1 1 1 0 0 0 1 ...
## $ CKD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ SEPSIS : int 0 0 0 0 0 0 1 0 0 0 ...
## $ SEVERE_SEPSIS : int 0 0 1 1 1 0 0 0 0 1 ...
## $ SEPTIC_SHOCK : int 1 1 0 0 0 1 0 1 1 0 ...
## $ HB : num 12.8 11.2 11.6 11 11.5 7.6 11.7 4 6.2 12.5 ...
## $ TLC : int 8600 19100 12000 19800 17900 5400 30200 12000 31100 16900 ...
## $ PLATELET : int 30000 41000 150000 460000 320000 25000 270000 60000 31000 290000 ...
## $ pH : num 7.26 7.24 7.34 7.34 7.24 7.28 7.35 7.34 7.16 7.21 ...
## $ HCO3 : num 18 16 16 18 16 18 22 12.9 18 22.8 ...
## $ Na : int 132 140 140 132 130 132 132 130 140 130 ...
## $ K : num 3.6 3.8 35 3.8 3.8 3.6 3.6 5 5.4 4 ...
## $ CREATININE : num 2.04 2.16 0.74 1.42 5.3 1.53 0.84 2.25 2.9 1.21 ...
## $ TOTAL_BILIRUBIN : num 6.5 0.5 0.5 0.65 6.8 0.5 0.5 2.5 0.5 0.5 ...
## $ DIRECT_BILIRUBIN : num 4.5 0.25 0.25 0.35 4.6 0.25 0.25 1.25 0.25 0.25 ...
## $ SGOT : int 101 128 22 126 7 88 22 101 39 42 ...
## $ SGPT : int 68 134 18 171 14 39 15 120 32 41 ...
## $ MP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ DENGUE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PNEUMONIA_PROFILE: int 0 0 0 0 0 0 0 0 0 0 ...
## $ AMYLASE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PTINR : num 1.8 2 1 2 1 1.3 1 1.8 1.5 1.2 ...
## $ ALBUMIN : num 3.2 2.8 3 2.8 2.4 2.8 3.4 3 2.8 3 ...
## $ FIBRINOGEN : int 180 240 160 180 240 190 200 210 220 160 ...
## $ NIV_DAYS : int 3 0 1 0 2 2 0 1 0 2 ...
## $ MV_DAYS : int 0 1 0 0 4 0 0 4 1 0 ...
## $ INOTROPE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ SOFA : int 11 11 1 2 9 7 1 12 11 3 ...
## $ Number : int 3 3 1 1 2 1 1 3 2 1 ...
## $ lung : int 1 1 1 0 0 1 0 1 1 1 ...
## $ kidney : int 1 1 0 0 1 0 1 1 1 0 ...
## $ liver : int 1 1 0 1 1 0 0 1 0 0 ...
## $ STAY_IN_ICU_HDU : int 4 1 1 1 5 3 1 5 1 3 ...
## $ STAY_IN_WARD : int 5 3 5 3 7 5 4 3 3 3 ...
## $ bacterial : int 0 1 1 0 1 1 1 1 1 1 ...
## $ viral : int 0 0 0 0 0 0 0 0 0 0 ...
## $ parasitic : int 1 0 0 1 0 0 0 0 0 0 ...
## $ fungal : int 0 0 0 0 0 0 0 0 1 0 ...
## $ PCT : int 2 4 3 4 4 4 2 4 4 2 ...
## $ Mortality : int 2 3 2 2 2 2 2 3 3 2 ...
## $ Age_group : int 1 3 1 3 3 4 1 1 3 4 ...
sumeer$Gender<-factor(sumeer$Gender, labels = c("Male", "Female"))
str(sumeer$Gender)
## Factor w/ 2 levels "Male","Female": 1 1 1 2 1 2 1 2 2 1 ...
sumeer$DM<-factor(sumeer$DM, labels = c("presence", "absence"))
str(sumeer$DM)
## Factor w/ 2 levels "presence","absence": 1 2 1 2 2 2 1 1 2 2 ...
sumeer$Mortality<-factor(sumeer$Mortality, labels = c("Death", "Survice") )
str(sumeer$Mortality)
## Factor w/ 2 levels "Death","Survice": 1 2 1 1 1 1 1 2 2 1 ...
exploration of the data set
summary(sumeer)
## SNO AGE Gender DM HTN
## Min. :365788 Min. :18.00 Male :22 presence:21 Min. :0.0
## 1st Qu.:376592 1st Qu.:34.25 Female:28 absence :29 1st Qu.:0.0
## Median :380860 Median :51.00 Median :0.5
## Mean :381339 Mean :50.34 Mean :0.5
## 3rd Qu.:387528 3rd Qu.:65.00 3rd Qu.:1.0
## Max. :391088 Max. :86.00 Max. :1.0
##
## CKD SEPSIS SEVERE_SEPSIS SEPTIC_SHOCK
## Min. :0.0 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:0.0 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
## Median :0.0 Median :0.00 Median :0.00 Median :0.00
## Mean :0.2 Mean :0.14 Mean :0.42 Mean :0.42
## 3rd Qu.:0.0 3rd Qu.:0.00 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :1.0 Max. :1.00 Max. :1.00 Max. :1.00
##
## HB TLC PLATELET pH
## Min. : 4.000 Min. : 4600 Min. : 10000 Min. :7.160
## 1st Qu.: 8.925 1st Qu.:13000 1st Qu.: 50000 1st Qu.:7.240
## Median :10.700 Median :15750 Median : 75000 Median :7.300
## Mean :10.388 Mean :18752 Mean :140680 Mean :7.290
## 3rd Qu.:12.175 3rd Qu.:23750 3rd Qu.:222500 3rd Qu.:7.348
## Max. :14.600 Max. :74500 Max. :820000 Max. :7.360
##
## HCO3 Na K CREATININE
## Min. :12.00 Min. :124.0 Min. : 2.40 Min. : 0.740
## 1st Qu.:18.00 1st Qu.:129.0 1st Qu.: 3.20 1st Qu.: 1.355
## Median :18.00 Median :132.0 Median : 3.40 Median : 2.000
## Mean :19.35 Mean :131.1 Mean : 4.09 Mean : 2.510
## 3rd Qu.:21.50 3rd Qu.:132.0 3rd Qu.: 3.60 3rd Qu.: 2.505
## Max. :28.00 Max. :140.0 Max. :35.00 Max. :10.000
##
## TOTAL_BILIRUBIN DIRECT_BILIRUBIN SGOT SGPT
## Min. :0.000 Min. :0.250 Min. : 7.0 Min. : 12.0
## 1st Qu.:0.500 1st Qu.:0.250 1st Qu.: 44.0 1st Qu.: 34.5
## Median :1.750 Median :1.000 Median : 100.0 Median : 75.0
## Mean :1.924 Mean :1.162 Mean : 128.1 Mean :112.2
## 3rd Qu.:2.500 3rd Qu.:1.400 3rd Qu.: 127.5 3rd Qu.:120.0
## Max. :7.600 Max. :4.600 Max. :1000.0 Max. :975.0
## NA's :1
## MP DENGUE PNEUMONIA_PROFILE AMYLASE PTINR
## Min. :0 Min. :0 Min. :0 Min. : 0.0 Min. :1.000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.: 0.0 1st Qu.:1.200
## Median :0 Median :0 Median :0 Median : 0.0 Median :1.300
## Mean :0 Mean :0 Mean :0 Mean : 1.2 Mean :1.424
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.: 0.0 3rd Qu.:1.650
## Max. :0 Max. :0 Max. :0 Max. :30.0 Max. :2.400
##
## ALBUMIN FIBRINOGEN NIV_DAYS MV_DAYS
## Min. :2.400 Min. :160.0 Min. :0.00 Min. : 0.00
## 1st Qu.:2.800 1st Qu.:188.5 1st Qu.:0.00 1st Qu.: 0.00
## Median :2.900 Median :200.0 Median :0.50 Median : 0.00
## Mean :2.906 Mean :203.0 Mean :1.12 Mean : 0.84
## 3rd Qu.:3.000 3rd Qu.:220.0 3rd Qu.:2.00 3rd Qu.: 0.00
## Max. :3.400 Max. :240.0 Max. :5.00 Max. :10.00
##
## INOTROPE SOFA Number lung kidney
## Min. :0 Min. : 1.00 Min. :1.00 Min. :0.00 Min. :0.00
## 1st Qu.:0 1st Qu.: 5.00 1st Qu.:1.00 1st Qu.:0.00 1st Qu.:0.00
## Median :0 Median : 7.00 Median :2.00 Median :1.00 Median :1.00
## Mean :0 Mean : 7.06 Mean :1.86 Mean :0.66 Mean :0.66
## 3rd Qu.:0 3rd Qu.:10.00 3rd Qu.:2.00 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :0 Max. :13.00 Max. :3.00 Max. :1.00 Max. :1.00
## NA's :2
## liver STAY_IN_ICU_HDU STAY_IN_WARD bacterial
## Min. :0.00 Min. : 1.0 Min. :0.00 Min. :0.0
## 1st Qu.:0.00 1st Qu.: 1.0 1st Qu.:3.00 1st Qu.:0.0
## Median :1.00 Median : 2.0 Median :5.00 Median :1.0
## Mean :0.52 Mean : 2.8 Mean :4.26 Mean :0.7
## 3rd Qu.:1.00 3rd Qu.: 4.0 3rd Qu.:5.00 3rd Qu.:1.0
## Max. :1.00 Max. :10.0 Max. :7.00 Max. :1.0
##
## viral parasitic fungal PCT Mortality
## Min. :0.00000 Min. :0.0 Min. :0.00 Min. :1.0 Death :42
## 1st Qu.:0.00000 1st Qu.:0.0 1st Qu.:0.00 1st Qu.:2.0 Survice: 8
## Median :0.00000 Median :0.0 Median :0.00 Median :3.0
## Mean :0.06122 Mean :0.3 Mean :0.06 Mean :2.9
## 3rd Qu.:0.00000 3rd Qu.:1.0 3rd Qu.:0.00 3rd Qu.:3.0
## Max. :1.00000 Max. :1.0 Max. :1.00 Max. :4.0
## NA's :1
## Age_group
## Min. :1.00
## 1st Qu.:2.00
## Median :3.00
## Mean :2.62
## 3rd Qu.:4.00
## Max. :4.00
##
fivenum(sumeer$AGE)
## [1] 18 34 51 65 86
fivenum(sumeer$DM)
## Warning in Ops.factor(x[floor(d)], x[ceiling(d)]): '+' not meaningful for
## factors
## [1] NA NA NA NA NA
fivenum(sumeer$HB)
## [1] 4.0 8.9 10.7 12.2 14.6
library(descr)
descr(sumeer)
##
## SNO
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 365788 376592 380860 381339 387528 391088
##
## AGE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.00 34.25 51.00 50.34 65.00 86.00
##
## Gender
## Male Female
## 22 28
##
## DM
## presence absence
## 21 29
##
## HTN
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.5 0.5 1.0 1.0
##
## CKD
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.0 0.2 0.0 1.0
##
## SEPSIS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.14 0.00 1.00
##
## SEVERE_SEPSIS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.42 1.00 1.00
##
## SEPTIC_SHOCK
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.42 1.00 1.00
##
## HB
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.000 8.925 10.700 10.388 12.175 14.600
##
## TLC
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4600 13000 15750 18752 23750 74500
##
## PLATELET
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10000 50000 75000 140680 222500 820000
##
## pH
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.160 7.240 7.300 7.290 7.348 7.360
##
## HCO3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.00 18.00 18.00 19.35 21.50 28.00
##
## Na
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 124.0 129.0 132.0 131.1 132.0 140.0
##
## K
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.40 3.20 3.40 4.09 3.60 35.00
##
## CREATININE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.740 1.355 2.000 2.510 2.505 10.000
##
## TOTAL_BILIRUBIN
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.500 1.750 1.924 2.500 7.600
##
## DIRECT_BILIRUBIN
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.250 0.250 1.000 1.162 1.400 4.600 1
##
## SGOT
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.0 44.0 100.0 128.1 127.5 1000.0
##
## SGPT
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.0 34.5 75.0 112.2 120.0 975.0
##
## MP
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## DENGUE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## PNEUMONIA_PROFILE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0
##
## AMYLASE
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.0 1.2 0.0 30.0
##
## PTINR
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.200 1.300 1.424 1.650 2.400
##
## ALBUMIN
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.400 2.800 2.900 2.906 3.000 3.400
##
## FIBRINOGEN
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 160.0 188.5 200.0 203.0 220.0 240.0
##
## NIV_DAYS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.50 1.12 2.00 5.00
##
## MV_DAYS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.84 0.00 10.00
##
## INOTROPE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 0 0 0 0 0 2
##
## SOFA
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 7.00 7.06 10.00 13.00
##
## Number
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 1.00 2.00 1.86 2.00 3.00
##
## lung
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 1.00 0.66 1.00 1.00
##
## kidney
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 1.00 0.66 1.00 1.00
##
## liver
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 1.00 0.52 1.00 1.00
##
## STAY_IN_ICU_HDU
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 1.0 2.0 2.8 4.0 10.0
##
## STAY_IN_WARD
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.00 5.00 4.26 5.00 7.00
##
## bacterial
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 1.0 0.7 1.0 1.0
##
## viral
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00000 0.00000 0.06122 0.00000 1.00000 1
##
## parasitic
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.0 0.3 1.0 1.0
##
## fungal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 0.06 0.00 1.00
##
## PCT
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 2.0 3.0 2.9 3.0 4.0
##
## Mortality
## Death Survice
## 42 8
##
## Age_group
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 2.62 4.00 4.00
library(pastecs)
## Warning: package 'pastecs' was built under R version 3.5.3
stat.desc(sumeer)
## SNO AGE Gender DM HTN CKD
## nbr.val 5.000000e+01 50.0000000 NA NA 50.00000000 50.00000000
## nbr.null 0.000000e+00 0.0000000 NA NA 25.00000000 40.00000000
## nbr.na 0.000000e+00 0.0000000 NA NA 0.00000000 0.00000000
## min 3.657880e+05 18.0000000 NA NA 0.00000000 0.00000000
## max 3.910880e+05 86.0000000 NA NA 1.00000000 1.00000000
## range 2.530000e+04 68.0000000 NA NA 1.00000000 1.00000000
## sum 1.906695e+07 2517.0000000 NA NA 25.00000000 10.00000000
## median 3.808595e+05 51.0000000 NA NA 0.50000000 0.00000000
## mean 3.813389e+05 50.3400000 NA NA 0.50000000 0.20000000
## SE.mean 8.953497e+02 2.8070771 NA NA 0.07142857 0.05714286
## CI.mean.0.95 1.799273e+03 5.6410326 NA NA 0.14354109 0.11483287
## var 4.008256e+07 393.9840816 NA NA 0.25510204 0.16326531
## std.dev 6.331079e+03 19.8490323 NA NA 0.50507627 0.40406102
## coef.var 1.660223e-02 0.3942994 NA NA 1.01015254 2.02030509
## SEPSIS SEVERE_SEPSIS SEPTIC_SHOCK HB
## nbr.val 50.00000000 50.00000000 50.00000000 50.0000000
## nbr.null 43.00000000 29.00000000 29.00000000 0.0000000
## nbr.na 0.00000000 0.00000000 0.00000000 0.0000000
## min 0.00000000 0.00000000 0.00000000 4.0000000
## max 1.00000000 1.00000000 1.00000000 14.6000000
## range 1.00000000 1.00000000 1.00000000 10.6000000
## sum 7.00000000 21.00000000 21.00000000 519.4000000
## median 0.00000000 0.00000000 0.00000000 10.7000000
## mean 0.14000000 0.42000000 0.42000000 10.3880000
## SE.mean 0.04956958 0.07050836 0.07050836 0.3295097
## CI.mean.0.95 0.09961379 0.14169185 0.14169185 0.6621746
## var 0.12285714 0.24857143 0.24857143 5.4288327
## std.dev 0.35050983 0.49856938 0.49856938 2.3299855
## coef.var 2.50364166 1.18706996 1.18706996 0.2242959
## TLC PLATELET pH HCO3
## nbr.val 5.000000e+01 5.000000e+01 5.000000e+01 50.0000000
## nbr.null 0.000000e+00 0.000000e+00 0.000000e+00 0.0000000
## nbr.na 0.000000e+00 0.000000e+00 0.000000e+00 0.0000000
## min 4.600000e+03 1.000000e+04 7.160000e+00 12.0000000
## max 7.450000e+04 8.200000e+05 7.360000e+00 28.0000000
## range 6.990000e+04 8.100000e+05 2.000000e-01 16.0000000
## sum 9.376000e+05 7.034000e+06 3.644800e+02 967.7000000
## median 1.575000e+04 7.500000e+04 7.300000e+00 18.0000000
## mean 1.875200e+04 1.406800e+05 7.289600e+00 19.3540000
## SE.mean 1.592360e+03 2.148310e+04 8.300283e-03 0.4532802
## CI.mean.0.95 3.199967e+03 4.317191e+04 1.668004e-02 0.9109007
## var 1.267805e+08 2.307618e+10 3.444735e-03 10.2731469
## std.dev 1.125968e+04 1.519085e+05 5.869186e-02 3.2051750
## coef.var 6.004525e-01 1.079816e+00 8.051452e-03 0.1656079
## Na K CREATININE TOTAL_BILIRUBIN
## nbr.val 5.000000e+01 50.0000000 50.0000000 50.0000000
## nbr.null 0.000000e+00 0.0000000 0.0000000 1.0000000
## nbr.na 0.000000e+00 0.0000000 0.0000000 0.0000000
## min 1.240000e+02 2.4000000 0.7400000 0.0000000
## max 1.400000e+02 35.0000000 10.0000000 7.6000000
## range 1.600000e+01 32.6000000 9.2600000 7.6000000
## sum 6.553000e+03 204.5000000 125.4800000 96.2000000
## median 1.320000e+02 3.4000000 2.0000000 1.7500000
## mean 1.310600e+02 4.0900000 2.5096000 1.9240000
## SE.mean 4.894061e-01 0.6339719 0.2767390 0.2406803
## CI.mean.0.95 9.834985e-01 1.2740143 0.5561279 0.4836652
## var 1.197592e+01 20.0960204 3.8292243 2.8963510
## std.dev 3.460624e+00 4.4828585 1.9568404 1.7018669
## coef.var 2.640488e-02 1.0960534 0.7797419 0.8845462
## DIRECT_BILIRUBIN SGOT SGPT MP DENGUE
## nbr.val 49.0000000 50.000000 50.00000 50 50
## nbr.null 0.0000000 0.000000 0.00000 50 50
## nbr.na 1.0000000 0.000000 0.00000 0 0
## min 0.2500000 7.000000 12.00000 0 0
## max 4.6000000 1000.000000 975.00000 0 0
## range 4.3500000 993.000000 963.00000 0 0
## sum 56.9400000 6406.000000 5610.00000 0 0
## median 1.0000000 100.000000 75.00000 0 0
## mean 1.1620408 128.120000 112.20000 0 0
## SE.mean 0.1527177 22.328432 22.47453 0 0
## CI.mean.0.95 0.3070596 44.870665 45.16426 0 0
## var 1.1428124 24927.944490 25255.22449 0 0
## std.dev 1.0690240 157.885859 158.91892 0 0
## coef.var 0.9199540 1.232328 1.41639 NaN NaN
## PNEUMONIA_PROFILE AMYLASE PTINR ALBUMIN
## nbr.val 50 50.0000000 50.00000000 50.00000000
## nbr.null 50 48.0000000 0.00000000 0.00000000
## nbr.na 0 0.0000000 0.00000000 0.00000000
## min 0 0.0000000 1.00000000 2.40000000
## max 0 30.0000000 2.40000000 3.40000000
## range 0 30.0000000 1.40000000 1.00000000
## sum 0 60.0000000 71.20000000 145.30000000
## median 0 0.0000000 1.30000000 2.90000000
## mean 0 1.2000000 1.42400000 2.90600000
## SE.mean 0 0.8398251 0.04969786 0.03042689
## CI.mean.0.95 0 1.6876916 0.09987159 0.06114513
## var 0 35.2653061 0.12349388 0.04628980
## std.dev 0 5.9384599 0.35141696 0.21515064
## coef.var NaN 4.9487166 0.24678157 0.07403669
## FIBRINOGEN NIV_DAYS MV_DAYS INOTROPE SOFA
## nbr.val 50.000000 50.0000000 50.0000000 48 50.0000000
## nbr.null 0.000000 25.0000000 38.0000000 48 0.0000000
## nbr.na 0.000000 0.0000000 0.0000000 2 0.0000000
## min 160.000000 0.0000000 0.0000000 0 1.0000000
## max 240.000000 5.0000000 10.0000000 0 13.0000000
## range 80.000000 5.0000000 10.0000000 0 12.0000000
## sum 10151.000000 56.0000000 42.0000000 0 353.0000000
## median 200.000000 0.5000000 0.0000000 0 7.0000000
## mean 203.020000 1.1200000 0.8400000 0 7.0600000
## SE.mean 3.192273 0.2111968 0.2730926 0 0.4741566
## CI.mean.0.95 6.415112 0.4244158 0.5488002 0 0.9528534
## var 509.530204 2.2302041 3.7289796 0 11.2412245
## std.dev 22.572776 1.4933868 1.9310566 0 3.3527935
## coef.var 0.111185 1.3333811 2.2988769 NaN 0.4748999
## Number lung kidney liver
## nbr.val 50.0000000 50.00000000 50.00000000 50.00000000
## nbr.null 0.0000000 17.00000000 17.00000000 24.00000000
## nbr.na 0.0000000 0.00000000 0.00000000 0.00000000
## min 1.0000000 0.00000000 0.00000000 0.00000000
## max 3.0000000 1.00000000 1.00000000 1.00000000
## range 2.0000000 1.00000000 1.00000000 1.00000000
## sum 93.0000000 33.00000000 33.00000000 26.00000000
## median 2.0000000 1.00000000 1.00000000 1.00000000
## mean 1.8600000 0.66000000 0.66000000 0.52000000
## SE.mean 0.1106935 0.06767268 0.06767268 0.07137141
## CI.mean.0.95 0.2224470 0.13599335 0.13599335 0.14342621
## var 0.6126531 0.22897959 0.22897959 0.25469388
## std.dev 0.7827216 0.47851812 0.47851812 0.50467205
## coef.var 0.4208181 0.72502746 0.72502746 0.97052317
## STAY_IN_ICU_HDU STAY_IN_WARD bacterial viral
## nbr.val 50.0000000 50.0000000 50.00000000 49.00000000
## nbr.null 0.0000000 2.0000000 15.00000000 46.00000000
## nbr.na 0.0000000 0.0000000 0.00000000 1.00000000
## min 1.0000000 0.0000000 0.00000000 0.00000000
## max 10.0000000 7.0000000 1.00000000 1.00000000
## range 9.0000000 7.0000000 1.00000000 1.00000000
## sum 140.0000000 213.0000000 35.00000000 3.00000000
## median 2.0000000 5.0000000 1.00000000 0.00000000
## mean 2.8000000 4.2600000 0.70000000 0.06122449
## SE.mean 0.2913725 0.1954795 0.06546537 0.03460372
## CI.mean.0.95 0.5855350 0.3928308 0.13155758 0.06957545
## var 4.2448980 1.9106122 0.21428571 0.05867347
## std.dev 2.0603150 1.3822490 0.46291005 0.24222607
## coef.var 0.7358268 0.3244716 0.66130007 3.95635916
## parasitic fungal PCT Mortality Age_group
## nbr.val 50.00000000 50.00000000 50.0000000 NA 50.0000000
## nbr.null 35.00000000 47.00000000 0.0000000 NA 0.0000000
## nbr.na 0.00000000 0.00000000 0.0000000 NA 0.0000000
## min 0.00000000 0.00000000 1.0000000 NA 1.0000000
## max 1.00000000 1.00000000 4.0000000 NA 4.0000000
## range 1.00000000 1.00000000 3.0000000 NA 3.0000000
## sum 15.00000000 3.00000000 145.0000000 NA 131.0000000
## median 0.00000000 0.00000000 3.0000000 NA 3.0000000
## mean 0.30000000 0.06000000 2.9000000 NA 2.6200000
## SE.mean 0.06546537 0.03392669 0.1115750 NA 0.1638566
## CI.mean.0.95 0.13155758 0.06817824 0.2242183 NA 0.3292821
## var 0.21428571 0.05755102 0.6224490 NA 1.3424490
## std.dev 0.46291005 0.23989794 0.7889544 NA 1.1586410
## coef.var 1.54303350 3.99829896 0.2720532 NA 0.4422294
Library (skmir)
skim(data1)
Library(dataExplorer)
create_report(data1)
Frequency distribution tables
one way
onewaytable1<-table(sumeer$Gender)
onewaytable1
##
## Male Female
## 22 28
two way
twowaytable1<-table(sumeer$Gender, sumeer$DM)
twowaytable1
##
## presence absence
## Male 10 12
## Female 11 17
three way
threewaytable1<-table(sumeer$Gender, sumeer$DM, sumeer$Mortality)
threewaytable1
## , , = Death
##
##
## presence absence
## Male 8 11
## Female 10 13
##
## , , = Survice
##
##
## presence absence
## Male 2 1
## Female 1 4
use ftable for the same
cell proportions
first create a table as r object then use it in prop.table
prop.table(onewaytable1)
##
## Male Female
## 0.44 0.56
prop.table(twowaytable1)
##
## presence absence
## Male 0.20 0.24
## Female 0.22 0.34
prop.table(threewaytable1)
## , , = Death
##
##
## presence absence
## Male 0.16 0.22
## Female 0.20 0.26
##
## , , = Survice
##
##
## presence absence
## Male 0.04 0.02
## Female 0.02 0.08
row proportion
first create a table as r object then use it in prop.table
prop.table(onewaytable1,1)
##
## Male Female
## 1 1
prop.table(twowaytable1,1)
##
## presence absence
## Male 0.4545455 0.5454545
## Female 0.3928571 0.6071429
prop.table(threewaytable1,1)
## , , = Death
##
##
## presence absence
## Male 0.36363636 0.50000000
## Female 0.35714286 0.46428571
##
## , , = Survice
##
##
## presence absence
## Male 0.09090909 0.04545455
## Female 0.03571429 0.14285714
column proportion
first create a table as r object then use it in prop.table
prop.table(onewaytable1)
##
## Male Female
## 0.44 0.56
prop.table(twowaytable1,2)
##
## presence absence
## Male 0.4761905 0.4137931
## Female 0.5238095 0.5862069
prop.table(threewaytable1,2)
## , , = Death
##
##
## presence absence
## Male 0.38095238 0.37931034
## Female 0.47619048 0.44827586
##
## , , = Survice
##
##
## presence absence
## Male 0.09523810 0.03448276
## Female 0.04761905 0.13793103
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