#Loading libraries: tidyverse , medicaldata (Install them first if not installed in your PC) You can check if the packages installed or not and if not installed, they will be istalled like thsi
if(!require(package name)){ install.packages(“package name”) library(somepackage) }
##installing packages##
packages = c(“dplyr”, “ggplot2”, “Publish”) #making list of package names
if(!require(packages)){ install.packages(packages)}
##Loading packages to R invironment
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(medicaldata)##loading data file (Cytomegalovirsu) >>Show head >>Show structure of the data >>Change class of “diagnosis” to factor >>Save the diagnosis of class factor into a new column name called diagnosisf >>Use the mutate method to make diagnosisf
data("cytomegalovirus")
head(cytomegalovirus)## ID age sex race diagnosis diagnosis.type
## 1 1 61 1 0 acute myeloid leukemia 1
## 2 2 62 1 1 non-Hodgkin lymphoma 0
## 3 3 63 0 1 non-Hodgkin lymphoma 0
## 4 4 33 0 1 Hodgkin lymphoma 0
## 5 5 54 0 1 acute lymphoblastic leukemia 0
## 6 6 55 1 1 myelofibrosis 1
## time.to.transplant prior.radiation prior.chemo prior.transplant recipient.cmv
## 1 5.16 0 2 0 1
## 2 79.05 1 3 0 0
## 3 35.58 0 4 0 1
## 4 33.02 1 4 0 1
## 5 11.40 0 5 0 1
## 6 2.43 0 0 0 1
## donor.cmv donor.sex TNC.dose CD34.dose CD3.dose CD8.dose TBI.dose C1/C2 aKIRs
## 1 0 0 18.31 2.29 3.21 0.95 200 0 1
## 2 0 1 4.26 2.04 NA NA 200 1 5
## 3 1 0 8.09 6.97 2.19 0.59 200 0 3
## 4 0 1 21.02 6.09 4.87 2.32 200 0 2
## 5 1 0 14.70 2.36 6.55 2.40 400 0 6
## 6 1 1 4.29 6.91 2.53 0.86 200 0 2
## cmv time.to.cmv agvhd time.to.agvhd cgvhd time.to.cgvhd
## 1 1 3.91 1 3.55 0 6.28
## 2 0 65.12 0 65.12 0 65.12
## 3 0 3.75 0 3.75 0 3.75
## 4 0 48.49 1 28.55 1 10.45
## 5 0 4.37 1 2.79 0 4.37
## 6 1 4.53 1 3.88 0 6.87str(cytomegalovirus)## 'data.frame': 64 obs. of 26 variables:
## $ ID : num 1 2 3 4 5 6 7 8 9 10 ...
## $ age : num 61 62 63 33 54 55 67 51 44 59 ...
## $ sex : num 1 1 0 0 0 1 1 1 0 1 ...
## $ race : num 0 1 1 1 1 1 1 1 0 1 ...
## $ diagnosis : chr "acute myeloid leukemia" "non-Hodgkin lymphoma" "non-Hodgkin lymphoma" "Hodgkin lymphoma" ...
## $ diagnosis.type : num 1 0 0 0 0 1 1 1 0 0 ...
## $ time.to.transplant: num 5.16 79.05 35.58 33.02 11.4 ...
## $ prior.radiation : num 0 1 0 1 0 0 0 0 1 0 ...
## $ prior.chemo : num 2 3 4 4 5 0 2 0 3 2 ...
## $ prior.transplant : num 0 0 0 0 0 0 0 1 1 0 ...
## $ recipient.cmv : num 1 0 1 1 1 1 1 1 1 0 ...
## $ donor.cmv : num 0 0 1 0 1 1 1 1 1 0 ...
## $ donor.sex : num 0 1 0 1 0 1 1 0 1 0 ...
## $ TNC.dose : num 18.31 4.26 8.09 21.02 14.7 ...
## $ CD34.dose : num 2.29 2.04 6.97 6.09 2.36 6.91 3.66 3.9 7 2.52 ...
## $ CD3.dose : num 3.21 NA 2.19 4.87 6.55 2.53 3.66 7.27 2.59 2.52 ...
## $ CD8.dose : num 0.95 NA 0.59 2.32 2.4 0.86 0.17 1.95 NA 1.22 ...
## $ TBI.dose : num 200 200 200 200 400 200 400 400 200 400 ...
## $ C1/C2 : num 0 1 0 0 0 0 0 0 1 1 ...
## $ aKIRs : num 1 5 3 2 6 2 1 2 2 4 ...
## $ cmv : num 1 0 0 0 0 1 0 0 1 0 ...
## $ time.to.cmv : num 3.91 65.12 3.75 48.49 4.37 ...
## $ agvhd : num 1 0 0 1 1 1 0 0 1 0 ...
## $ time.to.agvhd : num 3.55 65.12 3.75 28.55 2.79 ...
## $ cgvhd : num 0 0 0 1 0 0 0 0 1 1 ...
## $ time.to.cgvhd : num 6.28 65.12 3.75 10.45 4.37 ...as.factor(cytomegalovirus$diagnosis)## [1] acute myeloid leukemia non-Hodgkin lymphoma
## [3] non-Hodgkin lymphoma Hodgkin lymphoma
## [5] acute lymphoblastic leukemia myelofibrosis
## [7] acute myeloid leukemia acute myeloid leukemia
## [9] multiple myelomas chronic lymphocytic leukemia
## [11] multiple myelomas acute myeloid leukemia
## [13] myelodysplastic syndrome multiple myelomas
## [15] myelodysplastic syndrome non-Hodgkin lymphoma
## [17] acute myeloid leukemia myelodysplastic syndrome
## [19] non-Hodgkin lymphoma chronic lymphocytic leukemia
## [21] chronic myeloid leukemia chronic myeloid leukemia
## [23] myelodysplastic syndrome myelofibrosis
## [25] acute myeloid leukemia renal cell carcinoma
## [27] renal cell carcinoma chronic myeloid leukemia
## [29] myelodysplastic syndrome chronic lymphocytic leukemia
## [31] multiple myelomas non-Hodgkin lymphoma
## [33] Hodgkin lymphoma renal cell carcinoma
## [35] non-Hodgkin lymphoma chronic myeloid leukemia
## [37] non-Hodgkin lymphoma non-Hodgkin lymphoma
## [39] chronic lymphocytic leukemia renal cell carcinoma
## [41] multiple myelomas myelodysplastic syndrome
## [43] multiple myelomas aplastic anemia
## [45] Hodgkin lymphoma myelodysplastic syndrome
## [47] multiple myelomas chronic lymphocytic leukemia
## [49] myelofibrosis non-Hodgkin lymphoma
## [51] acute myeloid leukemia congenital anemia
## [53] non-Hodgkin lymphoma myelodysplastic syndrome
## [55] acute myeloid leukemia acute myeloid leukemia
## [57] acute myeloid leukemia myelofibrosis
## [59] non-Hodgkin lymphoma non-Hodgkin lymphoma
## [61] acute myeloid leukemia myelodysplastic syndrome
## [63] myeloproliferative disorder acute myeloid leukemia
## 13 Levels: acute lymphoblastic leukemia ... renal cell carcinomamutate(cytomegalovirus, diagnosisf = as.factor(cytomegalovirus$diagnosis))## ID age sex race diagnosis diagnosis.type
## 1 1 61 1 0 acute myeloid leukemia 1
## 2 2 62 1 1 non-Hodgkin lymphoma 0
## 3 3 63 0 1 non-Hodgkin lymphoma 0
## 4 4 33 0 1 Hodgkin lymphoma 0
## 5 5 54 0 1 acute lymphoblastic leukemia 0
## 6 6 55 1 1 myelofibrosis 1
## 7 7 67 1 1 acute myeloid leukemia 1
## 8 8 51 1 1 acute myeloid leukemia 1
## 9 9 44 0 0 multiple myelomas 0
## 10 10 59 1 1 chronic lymphocytic leukemia 0
## 11 11 45 1 1 multiple myelomas 0
## 12 12 57 1 1 acute myeloid leukemia 1
## 13 13 52 0 1 myelodysplastic syndrome 1
## 14 14 38 0 1 multiple myelomas 0
## 15 15 35 1 1 myelodysplastic syndrome 1
## 16 16 61 0 1 non-Hodgkin lymphoma 0
## 17 17 62 0 1 acute myeloid leukemia 1
## 18 18 45 0 1 myelodysplastic syndrome 1
## 19 19 62 1 0 non-Hodgkin lymphoma 0
## 20 20 51 0 1 chronic lymphocytic leukemia 0
## 21 21 52 0 1 chronic myeloid leukemia 1
## 22 22 62 0 0 chronic myeloid leukemia 1
## 23 23 62 1 1 myelodysplastic syndrome 1
## 24 24 52 0 1 myelofibrosis 1
## 25 25 45 0 1 acute myeloid leukemia 1
## 26 26 48 1 1 renal cell carcinoma NA
## 27 27 48 1 1 renal cell carcinoma NA
## 28 28 57 1 1 chronic myeloid leukemia 1
## 29 29 60 0 1 myelodysplastic syndrome 1
## 30 30 48 0 0 chronic lymphocytic leukemia 0
## 31 31 49 1 1 multiple myelomas 0
## 32 32 58 1 1 non-Hodgkin lymphoma 0
## 33 33 36 1 1 Hodgkin lymphoma 0
## 34 34 29 0 1 renal cell carcinoma NA
## 35 35 57 1 1 non-Hodgkin lymphoma 0
## 36 36 39 0 1 chronic myeloid leukemia 1
## 37 37 46 1 1 non-Hodgkin lymphoma 0
## 38 38 51 1 1 non-Hodgkin lymphoma 0
## 39 39 56 1 1 chronic lymphocytic leukemia 0
## 40 40 46 0 1 renal cell carcinoma NA
## 41 41 36 0 0 multiple myelomas 0
## 42 42 62 0 1 myelodysplastic syndrome 1
## 43 43 60 0 1 multiple myelomas 0
## 44 44 36 1 1 aplastic anemia NA
## 45 45 62 1 1 Hodgkin lymphoma 0
## 46 46 62 1 1 myelodysplastic syndrome 1
## 47 47 34 1 1 multiple myelomas 0
## 48 48 54 1 1 chronic lymphocytic leukemia 0
## 49 49 57 1 1 myelofibrosis 1
## 50 50 57 0 1 non-Hodgkin lymphoma 0
## 51 51 50 1 1 acute myeloid leukemia 1
## 52 52 42 1 1 congenital anemia NA
## 53 53 55 0 1 non-Hodgkin lymphoma 0
## 54 54 64 0 1 myelodysplastic syndrome 1
## 55 55 61 1 1 acute myeloid leukemia 1
## 56 56 57 1 1 acute myeloid leukemia 1
## 57 57 61 0 1 acute myeloid leukemia 1
## 58 58 57 0 1 myelofibrosis 1
## 59 59 58 1 1 non-Hodgkin lymphoma 0
## 60 60 41 0 1 non-Hodgkin lymphoma 0
## 61 61 57 0 1 acute myeloid leukemia 1
## 62 62 61 1 1 myelodysplastic syndrome 1
## 63 63 50 1 1 myeloproliferative disorder 1
## 64 64 64 0 1 acute myeloid leukemia 1
## time.to.transplant prior.radiation prior.chemo prior.transplant
## 1 5.16 0 2 0
## 2 79.05 1 3 0
## 3 35.58 0 4 0
## 4 33.02 1 4 0
## 5 11.40 0 5 0
## 6 2.43 0 0 0
## 7 9.59 0 2 0
## 8 NA 0 0 1
## 9 43.43 1 3 1
## 10 92.71 0 2 0
## 11 39.00 1 3 1
## 12 17.84 0 2 0
## 13 4.53 0 3 1
## 14 21.32 1 3 1
## 15 16.33 1 0 1
## 16 162.40 0 5 0
## 17 13.70 0 2 0
## 18 8.71 0 4 1
## 19 76.09 0 7 0
## 20 30.98 0 2 0
## 21 14.23 0 1 0
## 22 10.35 0 1 0
## 23 4.53 0 0 0
## 24 5.45 0 0 0
## 25 9.66 0 3 0
## 26 3.65 0 0 0
## 27 6.90 0 1 0
## 28 3.19 0 0 0
## 29 8.67 0 1 0
## 30 136.87 0 4 0
## 31 4.07 0 1 0
## 32 18.79 0 3 1
## 33 27.76 1 5 1
## 34 5.19 0 1 0
## 35 19.42 0 2 0
## 36 9.92 0 2 0
## 37 12.88 0 2 0
## 38 99.42 1 4 0
## 39 88.57 0 4 0
## 40 8.48 1 1 0
## 41 11.70 0 2 1
## 42 13.04 0 0 0
## 43 14.00 0 2 1
## 44 1.84 0 1 0
## 45 43.89 0 5 0
## 46 7.82 0 1 0
## 47 14.62 0 2 1
## 48 18.33 0 2 0
## 49 13.70 0 1 0
## 50 46.82 0 3 0
## 51 30.39 0 3 0
## 52 24.34 0 0 0
## 53 10.74 0 2 0
## 54 10.97 0 1 0
## 55 2.53 0 2 0
## 56 4.50 0 1 0
## 57 4.01 0 2 0
## 58 5.68 0 0 0
## 59 70.74 1 8 0
## 60 151.59 1 5 0
## 61 33.54 0 1 0
## 62 8.94 0 1 0
## 63 173.83 0 1 0
## 64 24.44 0 2 0
## recipient.cmv donor.cmv donor.sex TNC.dose CD34.dose CD3.dose CD8.dose
## 1 1 0 0 18.31 2.29 3.21 0.95
## 2 0 0 1 4.26 2.04 NA NA
## 3 1 1 0 8.09 6.97 2.19 0.59
## 4 1 0 1 21.02 6.09 4.87 2.32
## 5 1 1 0 14.70 2.36 6.55 2.40
## 6 1 1 1 4.29 6.91 2.53 0.86
## 7 1 1 1 7.96 3.66 3.66 0.17
## 8 1 1 0 15.63 3.90 7.27 1.95
## 9 1 1 1 6.86 7.00 2.59 NA
## 10 0 0 0 7.54 2.52 2.52 1.22
## 11 0 1 1 13.55 7.00 5.23 2.48
## 12 0 1 0 11.67 7.00 NA NA
## 13 1 1 0 14.25 4.06 6.56 NA
## 14 1 1 0 8.08 7.03 3.23 NA
## 15 0 1 1 12.45 7.00 3.62 1.74
## 16 1 1 0 12.58 3.46 3.08 NA
## 17 1 1 1 2.06 7.00 2.06 NA
## 18 0 0 1 9.76 5.76 5.76 1.19
## 19 1 1 0 16.50 7.00 8.18 3.05
## 20 1 0 0 11.30 6.87 NA NA
## 21 0 0 1 8.38 7.04 2.78 0.93
## 22 1 1 0 8.10 6.92 8.10 NA
## 23 0 0 1 3.37 7.00 1.08 NA
## 24 0 1 0 11.99 6.11 4.34 NA
## 25 1 0 1 15.16 6.99 5.24 1.53
## 26 0 1 0 8.68 4.85 4.25 NA
## 27 1 1 1 15.25 3.58 3.97 1.13
## 28 1 1 1 11.71 3.13 3.98 2.11
## 29 1 1 1 10.62 6.58 3.92 1.40
## 30 0 1 1 14.58 7.03 3.30 1.07
## 31 0 1 0 10.97 5.80 4.43 1.57
## 32 0 0 1 10.35 4.51 4.00 1.46
## 33 0 0 0 17.87 3.04 4.44 2.01
## 34 0 0 0 12.94 6.74 3.91 1.55
## 35 0 0 1 5.09 7.33 2.48 0.47
## 36 1 1 0 14.87 12.51 5.99 1.59
## 37 1 1 1 14.34 7.21 5.48 2.96
## 38 0 0 1 6.90 6.42 1.74 0.68
## 39 1 0 1 2.85 2.90 1.50 0.27
## 40 1 1 0 7.29 6.92 3.58 1.29
## 41 1 1 1 9.62 6.64 3.93 1.21
## 42 1 1 0 9.58 6.99 3.12 0.53
## 43 1 0 1 18.04 5.98 4.33 1.20
## 44 1 1 1 5.25 4.45 2.31 0.65
## 45 0 1 0 13.38 3.54 5.43 2.29
## 46 1 1 0 11.32 4.25 4.63 1.20
## 47 0 0 1 4.84 3.73 1.08 0.16
## 48 1 0 1 9.37 5.53 3.78 0.54
## 49 0 0 1 13.00 5.77 4.70 1.46
## 50 1 1 1 9.44 4.91 5.19 2.56
## 51 0 0 0 6.42 6.42 5.71 0.61
## 52 1 1 0 6.28 5.15 5.15 0.93
## 53 1 1 0 17.79 6.99 8.12 1.73
## 54 1 0 1 11.81 5.79 7.28 1.64
## 55 0 1 0 11.69 4.93 6.09 3.19
## 56 0 0 0 5.56 2.95 2.95 0.17
## 57 1 1 0 7.86 5.11 2.47 0.81
## 58 1 0 0 5.77 5.77 6.98 0.79
## 59 1 0 1 13.04 5.77 3.29 0.84
## 60 1 0 0 12.90 4.20 3.46 1.07
## 61 1 1 1 14.99 2.21 5.52 1.28
## 62 1 1 1 5.30 5.88 5.88 1.16
## 63 0 0 0 6.42 6.41 2.27 0.68
## 64 1 1 0 6.51 7.03 7.03 1.15
## TBI.dose C1/C2 aKIRs cmv time.to.cmv agvhd time.to.agvhd cgvhd time.to.cgvhd
## 1 200 0 1 1 3.91 1 3.55 0 6.28
## 2 200 1 5 0 65.12 0 65.12 0 65.12
## 3 200 0 3 0 3.75 0 3.75 0 3.75
## 4 200 0 2 0 48.49 1 28.55 1 10.45
## 5 400 0 6 0 4.37 1 2.79 0 4.37
## 6 200 0 2 1 4.53 1 3.88 0 6.87
## 7 400 0 1 0 2.99 0 2.99 0 2.99
## 8 400 0 2 0 4.80 0 4.80 0 4.80
## 9 200 1 2 1 2.73 1 0.69 1 6.21
## 10 400 1 4 0 22.05 0 22.05 1 6.77
## 11 200 1 2 0 14.72 1 2.14 0 14.72
## 12 200 1 4 0 2.40 1 1.41 0 2.40
## 13 200 0 5 1 9.72 0 10.58 1 8.28
## 14 200 1 2 1 11.96 0 12.48 1 11.33
## 15 200 0 2 0 9.59 0 9.59 0 9.59
## 16 200 1 2 1 0.89 0 77.54 1 3.88
## 17 200 1 1 1 3.48 0 21.32 1 3.55
## 18 400 0 2 0 23.95 0 23.95 0 23.95
## 19 400 0 1 0 2.66 1 0.72 0 2.66
## 20 200 1 1 1 4.34 0 85.19 1 3.91
## 21 200 1 5 0 17.71 0 17.71 0 17.71
## 22 200 0 5 0 84.47 0 84.47 1 2.30
## 23 200 0 1 0 15.93 1 3.25 1 10.41
## 24 200 0 5 0 78.29 0 78.29 1 6.44
## 25 200 0 1 0 6.80 0 6.80 0 6.80
## 26 200 0 6 0 0.82 0 0.82 0 0.82
## 27 200 0 4 1 1.12 1 2.63 1 4.34
## 28 200 0 5 1 0.43 0 44.71 0 44.71
## 29 200 1 5 0 16.26 0 16.26 0 16.26
## 30 200 1 1 1 1.12 0 18.53 0 18.53
## 31 200 0 2 1 27.10 1 2.04 1 17.45
## 32 200 1 2 1 3.84 1 0.66 1 6.77
## 33 400 1 1 0 36.67 1 0.76 1 12.91
## 34 200 0 2 0 9.00 1 1.35 0 9.00
## 35 200 1 2 0 6.64 1 2.73 1 3.32
## 36 200 1 1 1 1.12 0 21.68 1 17.22
## 37 200 1 2 1 8.51 0 53.78 1 7.56
## 38 200 0 5 0 50.27 1 1.15 1 3.42
## 39 400 1 4 1 1.02 1 1.15 0 4.57
## 40 200 1 3 1 0.43 0 3.65 0 3.65
## 41 200 1 1 1 3.98 1 2.99 1 3.98
## 42 200 1 4 1 5.26 1 3.29 1 29.17
## 43 200 1 1 1 1.12 1 3.22 0 45.40
## 44 400 0 2 0 3.88 1 2.60 0 3.88
## 45 200 0 1 1 1.35 0 9.66 0 9.66
## 46 400 1 4 1 0.95 1 0.95 0 1.45
## 47 400 0 5 0 29.31 0 29.31 1 6.47
## 48 400 0 5 0 23.66 0 23.66 1 5.26
## 49 400 1 2 0 34.00 0 34.00 1 4.44
## 50 400 1 3 0 32.16 1 2.20 1 3.48
## 51 400 1 1 0 3.61 0 3.61 0 3.61
## 52 400 1 4 0 11.10 0 11.10 1 1.02
## 53 400 0 6 1 3.29 0 31.54 0 31.54
## 54 400 1 1 1 1.12 0 20.34 0 20.34
## 55 400 0 6 0 7.92 0 7.92 0 7.92
## 56 400 1 1 0 8.90 0 8.90 0 8.90
## 57 400 0 1 0 2.07 0 2.07 0 2.07
## 58 400 1 3 1 3.15 1 3.06 0 8.15
## 59 400 0 1 0 3.19 1 0.99 0 3.19
## 60 400 1 2 0 21.68 0 21.68 1 6.70
## 61 400 1 2 1 1.15 0 11.30 0 11.30
## 62 400 1 5 0 7.43 0 7.43 0 7.43
## 63 400 0 5 0 12.88 1 3.32 0 12.88
## 64 400 0 4 0 8.05 0 8.05 1 5.16
## diagnosisf
## 1 acute myeloid leukemia
## 2 non-Hodgkin lymphoma
## 3 non-Hodgkin lymphoma
## 4 Hodgkin lymphoma
## 5 acute lymphoblastic leukemia
## 6 myelofibrosis
## 7 acute myeloid leukemia
## 8 acute myeloid leukemia
## 9 multiple myelomas
## 10 chronic lymphocytic leukemia
## 11 multiple myelomas
## 12 acute myeloid leukemia
## 13 myelodysplastic syndrome
## 14 multiple myelomas
## 15 myelodysplastic syndrome
## 16 non-Hodgkin lymphoma
## 17 acute myeloid leukemia
## 18 myelodysplastic syndrome
## 19 non-Hodgkin lymphoma
## 20 chronic lymphocytic leukemia
## 21 chronic myeloid leukemia
## 22 chronic myeloid leukemia
## 23 myelodysplastic syndrome
## 24 myelofibrosis
## 25 acute myeloid leukemia
## 26 renal cell carcinoma
## 27 renal cell carcinoma
## 28 chronic myeloid leukemia
## 29 myelodysplastic syndrome
## 30 chronic lymphocytic leukemia
## 31 multiple myelomas
## 32 non-Hodgkin lymphoma
## 33 Hodgkin lymphoma
## 34 renal cell carcinoma
## 35 non-Hodgkin lymphoma
## 36 chronic myeloid leukemia
## 37 non-Hodgkin lymphoma
## 38 non-Hodgkin lymphoma
## 39 chronic lymphocytic leukemia
## 40 renal cell carcinoma
## 41 multiple myelomas
## 42 myelodysplastic syndrome
## 43 multiple myelomas
## 44 aplastic anemia
## 45 Hodgkin lymphoma
## 46 myelodysplastic syndrome
## 47 multiple myelomas
## 48 chronic lymphocytic leukemia
## 49 myelofibrosis
## 50 non-Hodgkin lymphoma
## 51 acute myeloid leukemia
## 52 congenital anemia
## 53 non-Hodgkin lymphoma
## 54 myelodysplastic syndrome
## 55 acute myeloid leukemia
## 56 acute myeloid leukemia
## 57 acute myeloid leukemia
## 58 myelofibrosis
## 59 non-Hodgkin lymphoma
## 60 non-Hodgkin lymphoma
## 61 acute myeloid leukemia
## 62 myelodysplastic syndrome
## 63 myeloproliferative disorder
## 64 acute myeloid leukemia###data.table
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposecytomegalovirus= setDT(cytomegalovirus)
cytomegalovirus[, diagnosisf:= as.factor(diagnosis)]myelo_data = cytomegalovirus %>% filter(diagnosis == "myelofibrosis")
mean(myelo_data$age)## [1] 55.25mean(cytomegalovirus$age)## [1] 52.4375