The objective is to create dynamic reports using R Marksdown and generate the output in an HTML document.
To learn how to manipulate a given dataset using the package dplyr
knitr Global Options
# for development
knitr::opts_chunk$set(echo=TRUE, eval=TRUE, error=TRUE, warning=TRUE, message=TRUE, cache=FALSE, tidy=FALSE, fig.path='figures/')
# for production
#knitr::opts_chunk$set(echo=TRUE, eval=TRUE, error=FALSE, warning=FALSE, message=FALSE, cache=FALSE, tidy=FALSE, fig.path='figures/')Load Libraries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionRead Data
dfr <- read.csv("./data/patient-data.csv", header=T, stringsAsFactors=F)
head(dfr)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/001 Demetrius White Male False 182.87 76.57
## 2 AC/AH/017 Rosario White Male False 179.12 80.43
## 3 AC/AH/020 Julio Black Male False 169.15 75.48
## 4 AC/AH/022 Lupe White Male False 175.66 94.54
## 5 AC/AH/029 Lavern White Female False 164.47 71.78
## 6 AC/AH/033 Bernie Dog Female True 158.27 69.90
## BirthDate State Pet HealthGrade Died RecordDate
## 1 31-01-1972 Georgia,xxx Dog 2 False 25-11-2015
## 2 09-06-1972 Missouri Dog 2 False 25-11-2015
## 3 03-07-1972 Pennsylvania None 2 False 25-11-2015
## 4 11-08-1972 Florida Cat 1 False 25-11-2015
## 5 06-06-1973 Iowa NULL 2 True 25-11-2015
## 6 25-06-1973 Maryland Dog 2 False 25-11-2015Adding new column BMIValue
dfr <- mutate(dfr,BMIValue=(WeightInKgs*(10^4)/(HeightInCms)^2))
head(dfr)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/001 Demetrius White Male False 182.87 76.57
## 2 AC/AH/017 Rosario White Male False 179.12 80.43
## 3 AC/AH/020 Julio Black Male False 169.15 75.48
## 4 AC/AH/022 Lupe White Male False 175.66 94.54
## 5 AC/AH/029 Lavern White Female False 164.47 71.78
## 6 AC/AH/033 Bernie Dog Female True 158.27 69.90
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 31-01-1972 Georgia,xxx Dog 2 False 25-11-2015 22.89674
## 2 09-06-1972 Missouri Dog 2 False 25-11-2015 25.06859
## 3 03-07-1972 Pennsylvania None 2 False 25-11-2015 26.38080
## 4 11-08-1972 Florida Cat 1 False 25-11-2015 30.63867
## 5 06-06-1973 Iowa NULL 2 True 25-11-2015 26.53567
## 6 25-06-1973 Maryland Dog 2 False 25-11-2015 27.90487Adding new column BMILabel
dfr <- mutate(dfr, BMILabel= ifelse(BMIValue < 18.5,"Underweight",
ifelse(BMIValue > 18.5 & BMIValue < 25, "Normal",
ifelse(BMIValue > 25 & BMIValue < 30, "Overweight",
ifelse(BMIValue > 30, "Obese",NA)))))
head(dfr)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/001 Demetrius White Male False 182.87 76.57
## 2 AC/AH/017 Rosario White Male False 179.12 80.43
## 3 AC/AH/020 Julio Black Male False 169.15 75.48
## 4 AC/AH/022 Lupe White Male False 175.66 94.54
## 5 AC/AH/029 Lavern White Female False 164.47 71.78
## 6 AC/AH/033 Bernie Dog Female True 158.27 69.90
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 31-01-1972 Georgia,xxx Dog 2 False 25-11-2015 22.89674
## 2 09-06-1972 Missouri Dog 2 False 25-11-2015 25.06859
## 3 03-07-1972 Pennsylvania None 2 False 25-11-2015 26.38080
## 4 11-08-1972 Florida Cat 1 False 25-11-2015 30.63867
## 5 06-06-1973 Iowa NULL 2 True 25-11-2015 26.53567
## 6 25-06-1973 Maryland Dog 2 False 25-11-2015 27.90487
## BMILabel
## 1 Normal
## 2 Overweight
## 3 Overweight
## 4 Obese
## 5 Overweight
## 6 OverweightData Cleaning
# converting yes or no to true or false
dfr$Smokes <- gsub("Yes","True", gsub("No","False", dfr$Smokes))
# converting null to none
dfr$Pet <- gsub("NULL","None", dfr$Pet)
# removing white spaces
dfr$Gender <- trimws(dfr$Gender)
# removing extra characters
dfr$State[dfr$State=="Georgia,xxx"] <- "Georgia"
# converting wrong values to invalid
dfr$Race <- ifelse(!dfr$Race=="White" & !dfr$Race=="Black" & !dfr$Race=="Hispanic" & !dfr$Race=="Asian" &
!dfr$Race=="Bi-Racial", NA, dfr$Race)
head(dfr)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/001 Demetrius White Male False 182.87 76.57
## 2 AC/AH/017 Rosario White Male False 179.12 80.43
## 3 AC/AH/020 Julio Black Male False 169.15 75.48
## 4 AC/AH/022 Lupe White Male False 175.66 94.54
## 5 AC/AH/029 Lavern White Female False 164.47 71.78
## 6 AC/AH/033 Bernie <NA> Female True 158.27 69.90
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 31-01-1972 Georgia Dog 2 False 25-11-2015 22.89674
## 2 09-06-1972 Missouri Dog 2 False 25-11-2015 25.06859
## 3 03-07-1972 Pennsylvania None 2 False 25-11-2015 26.38080
## 4 11-08-1972 Florida Cat 1 False 25-11-2015 30.63867
## 5 06-06-1973 Iowa None 2 True 25-11-2015 26.53567
## 6 25-06-1973 Maryland Dog 2 False 25-11-2015 27.90487
## BMILabel
## 1 Normal
## 2 Overweight
## 3 Overweight
## 4 Obese
## 5 Overweight
## 6 OverweightConvert Health Grade
dfr$HealthGrade <- ifelse(dfr$HealthGrade==1,"Good Heath",
ifelse(dfr$HealthGrade==2, "Normal",
ifelse(dfr$HealthGrade==3,"Bad Health",NA)))
head(dfr)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/001 Demetrius White Male False 182.87 76.57
## 2 AC/AH/017 Rosario White Male False 179.12 80.43
## 3 AC/AH/020 Julio Black Male False 169.15 75.48
## 4 AC/AH/022 Lupe White Male False 175.66 94.54
## 5 AC/AH/029 Lavern White Female False 164.47 71.78
## 6 AC/AH/033 Bernie <NA> Female True 158.27 69.90
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 31-01-1972 Georgia Dog Normal False 25-11-2015 22.89674
## 2 09-06-1972 Missouri Dog Normal False 25-11-2015 25.06859
## 3 03-07-1972 Pennsylvania None Normal False 25-11-2015 26.38080
## 4 11-08-1972 Florida Cat Good Heath False 25-11-2015 30.63867
## 5 06-06-1973 Iowa None Normal True 25-11-2015 26.53567
## 6 25-06-1973 Maryland Dog Normal False 25-11-2015 27.90487
## BMILabel
## 1 Normal
## 2 Overweight
## 3 Overweight
## 4 Obese
## 5 Overweight
## 6 OverweightDisplay top 10 records on BMIValue
dfr <- arrange(dfr, desc(BMIValue))
head(dfr, 10)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/SG/009 Sammy White Male False 166.84 88.25
## 2 AC/SG/064 Jon White Male False 169.16 90.08
## 3 AC/AH/076 Albert White Male False 176.22 97.67
## 4 AC/AH/104 Jeremy White Male True 169.85 90.63
## 5 AC/AH/022 Lupe White Male False 175.66 94.54
## 6 AC/AH/248 Andrea White Male False 178.64 97.05
## 7 AC/SG/067 Thomas White Male False 167.51 84.15
## 8 AC/AH/052 Courtney White Male True 175.39 92.22
## 9 AC/AH/159 Edward White Male False 181.64 96.91
## 10 AC/AH/127 Jame White Male False 167.75 82.06
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 04-03-1972 Vermont Dog Good Heath False 25-06-2016 31.70402
## 2 04-10-1972 Illinois Cat Normal True 25-07-2016 31.47988
## 3 08-04-1973 Louisiana Cat Normal False 25-12-2015 31.45218
## 4 12-04-1972 Kentucky None Good Heath True 25-12-2015 31.41528
## 5 11-08-1972 Florida Cat Good Heath False 25-11-2015 30.63867
## 6 12-01-1973 Indiana Cat Good Heath True 25-05-2016 30.41152
## 7 19-07-1972 Pennsylvania Bird Normal True 25-07-2016 29.98974
## 8 16-03-1972 Indiana Bird Bad Health False 25-12-2015 29.97888
## 9 04-12-1972 Connecticut Cat Normal False 25-02-2016 29.37282
## 10 29-10-1972 Texas Dog Good Heath True 25-01-2016 29.16127
## BMILabel
## 1 Obese
## 2 Obese
## 3 Obese
## 4 Obese
## 5 Obese
## 6 Obese
## 7 Overweight
## 8 Overweight
## 9 Overweight
## 10 OverweightDisplay bottom 10 records on BMIValue
dfr <- arrange(dfr, BMIValue)
head(dfr, 10)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/SG/193 Ronnie White Male True 185.43 73.63
## 2 AC/AH/061 Lester Black Male False 181.13 72.33
## 3 AC/SG/099 Leslie Asian Male False 172.72 67.62
## 4 AC/AH/001 Demetrius White Male False 182.87 76.57
## 5 AC/AH/210 Keith Hispanic Female True 170.03 66.68
## 6 AC/AH/086 Kyle Black Male True 180.11 75.72
## 7 AC/AH/045 Shirley White Male False 181.32 76.90
## 8 AC/AH/089 Dong White Male False 179.24 75.54
## 9 AC/AH/164 Shane Hispanic Male True 177.03 74.04
## 10 AC/AH/114 Kris Hispanic Male False 177.75 74.84
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 05-06-1973 Iowa Dog Bad Health False 25-09-2016 21.41385
## 2 16-11-1972 Wisconsin Dog <NA> True 25-12-2015 22.04640
## 3 04-02-1972 Ohio Cat Good Heath False 25-07-2016 22.66678
## 4 31-01-1972 Georgia Dog Normal False 25-11-2015 22.89674
## 5 28-08-1972 New York Dog <NA> False 25-03-2016 23.06452
## 6 12-05-1973 Georgia Cat Bad Health False 25-12-2015 23.34183
## 7 25-12-1971 Louisiana Dog Good Heath False 25-11-2015 23.39025
## 8 11-03-1972 California None Normal True 25-12-2015 23.51295
## 9 18-02-1972 Florida None Normal False 25-02-2016 23.62505
## 10 19-11-1972 Pennsylvania Bird Bad Health False 25-01-2016 23.68725
## BMILabel
## 1 Normal
## 2 Normal
## 3 Normal
## 4 Normal
## 5 Normal
## 6 Normal
## 7 Normal
## 8 Normal
## 9 Normal
## 10 NormalDisplay Frequency of Gender > Race
summarise(group_by(dfr, Gender, Race), n())## Source: local data frame [10 x 3]
## Groups: Gender [?]
##
## Gender Race `n()`
## <chr> <chr> <int>
## 1 Female Asian 3
## 2 Female Black 3
## 3 Female Hispanic 7
## 4 Female White 41
## 5 Female <NA> 1
## 6 Male Asian 2
## 7 Male Bi-Racial 1
## 8 Male Black 5
## 9 Male Hispanic 10
## 10 Male White 27Display Max, Min & Avg of BMIValues for Race > Gender
summarise(group_by(dfr, Race , Gender), max(BMIValue), min(BMIValue), mean(BMIValue))## Source: local data frame [10 x 5]
## Groups: Race [?]
##
## Race Gender `max(BMIValue)` `min(BMIValue)` `mean(BMIValue)`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Asian Female 28.19431 24.42511 26.06524
## 2 Asian Male 27.24885 22.66678 24.95782
## 3 Bi-Racial Male 24.83473 24.83473 24.83473
## 4 Black Female 26.71407 24.64441 25.52777
## 5 Black Male 26.60586 22.04640 24.43950
## 6 Hispanic Female 27.84206 23.06452 26.02787
## 7 Hispanic Male 28.78164 23.62505 26.29876
## 8 White Female 28.24834 24.21459 26.39055
## 9 White Male 31.70402 21.41385 27.67323
## 10 <NA> Female 27.90487 27.90487 27.90487Display records for Dead People
filter(dfr, Died == "True")## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/061 Lester Black Male False 181.13 72.33
## 2 AC/AH/089 Dong White Male False 179.24 75.54
## 3 AC/AH/150 Brett White Male True 181.56 79.54
## 4 AC/SG/056 Merrill Asian Female True 166.19 67.46
## 5 AC/SG/179 Logan White Male False 183.10 82.47
## 6 AC/AH/112 Pat Black Female False 160.57 63.54
## 7 AC/SG/182 Jamie Hispanic Male True 171.08 72.51
## 8 AC/AH/233 Marion White Female False 163.97 66.71
## 9 AC/SG/234 Luis Hispanic Female False 164.88 68.07
## 10 AC/AH/180 Drew White Female False 160.80 64.77
## 11 AC/AH/154 Tony White Female False 160.03 64.30
## 12 AC/SG/003 Walter White Female False 161.83 66.03
## 13 AC/SG/010 Theo Asian Female False 159.32 64.92
## 14 AC/AH/133 Clyde Hispanic Male False 181.15 83.93
## 15 AC/AH/192 Dominique White Male False 180.61 83.59
## 16 AC/AH/244 Sean White Female False 160.09 65.93
## 17 AC/SG/165 Elmer White Female False 162.18 67.81
## 18 AC/AH/221 Carlos White Female False 165.34 70.84
## 19 AC/SG/197 Stacy White Female False 159.44 66.21
## 20 AC/SG/134 Daryl White Female True 162.59 69.76
## 21 AC/AH/171 Devin White Female False 163.35 70.46
## 22 AC/SG/216 Alva White Female False 159.13 66.96
## 23 AC/SG/068 Valentine Hispanic Female False 160.47 68.20
## 24 AC/AH/029 Lavern White Female False 164.47 71.78
## 25 AC/SG/116 Connie Black Male False 184.34 90.41
## 26 AC/SG/084 Brian Hispanic Male False 174.25 80.93
## 27 AC/SG/016 Jimmie Black Female False 161.84 69.97
## 28 AC/AH/249 Jesus Hispanic Female True 159.78 68.31
## 29 AC/SG/008 Dana White Male True 169.66 77.30
## 30 AC/SG/065 Shayne White Female False 157.01 66.56
## 31 AC/AH/186 Christopher White Female False 157.95 67.41
## 32 AC/AH/176 Jerry Asian Male False 175.21 83.65
## 33 AC/AH/219 Jay White Female False 163.47 72.89
## 34 AC/SG/101 Jason White Female False 159.23 69.96
## 35 AC/SG/123 Darnell White Female True 162.32 72.72
## 36 AC/SG/167 Jimmy White Female False 159.38 70.37
## 37 AC/SG/217 Dean White Female False 160.58 71.49
## 38 AC/AH/185 Ronald White Male False 166.46 76.83
## 39 AC/SG/155 Raymond White Female False 158.35 69.72
## 40 AC/SG/046 Carl Hispanic Male False 171.41 81.70
## 41 AC/SG/191 Lacy Hispanic Female False 159.33 70.68
## 42 AC/AH/156 George White Male False 165.62 76.72
## 43 AC/AH/160 Rory Asian Female False 159.67 71.88
## 44 AC/AH/211 Son White Female False 157.16 69.64
## 45 AC/AH/049 Martin White Female False 160.06 72.37
## 46 AC/SG/181 Terry Hispanic Male False 177.14 88.70
## 47 AC/SG/055 Evan White Male False 166.75 79.06
## 48 AC/SG/172 Whitney White Male False 171.45 84.29
## 49 AC/SG/015 Shaun White Male True 170.51 84.35
## 50 AC/AH/127 Jame White Male False 167.75 82.06
## 51 AC/SG/067 Thomas White Male False 167.51 84.15
## 52 AC/AH/248 Andrea White Male False 178.64 97.05
## 53 AC/AH/104 Jeremy White Male True 169.85 90.63
## 54 AC/SG/064 Jon White Male False 169.16 90.08
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 16-11-1972 Wisconsin Dog <NA> True 25-12-2015 22.04640
## 2 11-03-1972 California None Normal True 25-12-2015 23.51295
## 3 03-05-1972 Kentucky Dog Good Heath True 25-02-2016 24.12933
## 4 27-11-1972 Indiana None Bad Health True 25-07-2016 24.42511
## 5 24-10-1972 Ohio Dog Bad Health True 25-09-2016 24.59910
## 6 26-06-1973 California <NA> <NA> True 25-01-2016 24.64441
## 7 25-03-1973 Louisiana None Bad Health True 25-09-2016 24.77419
## 8 23-12-1971 Ohio Cat Bad Health True 25-04-2016 24.81202
## 9 10-11-1971 Pennsylvania Cat Bad Health True 25-10-2016 25.03916
## 10 18-02-1973 Oregon CAT Good Heath True 25-03-2016 25.04966
## 11 30-08-1973 California DOG Good Heath True 25-02-2016 25.10777
## 12 11-07-1972 Oregon None Normal True 25-05-2016 25.21292
## 13 29-01-1973 New York Cat Normal True 25-06-2016 25.57631
## 14 13-10-1973 Washington Cat Bad Health True 25-02-2016 25.57647
## 15 24-03-1972 Michigan None Bad Health True 25-03-2016 25.62541
## 16 25-01-1973 Maryland None <NA> True 25-05-2016 25.72496
## 17 25-03-1972 Washington Bird Good Heath True 25-08-2016 25.78096
## 18 01-02-1972 Michigan Dog <NA> True 25-04-2016 25.91330
## 19 08-11-1972 New York Cat Good Heath True 25-10-2016 26.04528
## 20 28-05-1972 Texas CAT Normal True 25-08-2016 26.38875
## 21 16-04-1973 California Bird Bad Health True 25-03-2016 26.40611
## 22 19-06-1972 Alabama None Good Heath True 25-10-2016 26.44304
## 23 15-04-1972 Tennessee Cat Bad Health True 25-07-2016 26.48480
## 24 06-06-1973 Iowa None Normal True 25-11-2015 26.53567
## 25 05-06-1972 Florida None Bad Health True 25-08-2016 26.60586
## 26 06-03-1972 Virginia DOG Normal True 25-07-2016 26.65410
## 27 03-04-1972 Arizona Cat Bad Health True 25-06-2016 26.71407
## 28 23-04-1972 Alabama Cat Normal True 25-05-2016 26.75713
## 29 26-05-1973 Nevada Dog Good Heath True 25-05-2016 26.85472
## 30 05-04-1972 California Dog Bad Health True 25-07-2016 26.99968
## 31 06-05-1972 New Jersey Dog Bad Health True 25-03-2016 27.01998
## 32 01-05-1973 Virginia Dog Bad Health True 25-03-2016 27.24885
## 33 07-04-1972 North Carolina Bird Good Heath True 25-04-2016 27.27670
## 34 28-09-1973 Michigan Dog Normal True 25-07-2016 27.59307
## 35 03-09-1972 North Carolina Bird Good Heath True 25-08-2016 27.60005
## 36 30-09-1973 Washington None Normal True 25-09-2016 27.70256
## 37 11-11-1972 Ohio None Good Heath True 25-10-2016 27.72441
## 38 17-08-1972 Colorado None <NA> True 25-03-2016 27.72752
## 39 02-06-1972 California Cat Bad Health True 25-08-2016 27.80489
## 40 05-08-1973 Mississippi Bird Normal True 25-06-2016 27.80672
## 41 21-06-1973 Texas None Bad Health True 25-09-2016 27.84206
## 42 09-07-1972 California Dog Good Heath True 25-02-2016 27.96939
## 43 22-09-1973 Florida Cat Normal True 25-02-2016 28.19431
## 44 14-07-1973 California Cat Normal True 25-04-2016 28.19517
## 45 28-04-1972 California Horse Normal True 25-12-2015 28.24834
## 46 24-11-1971 Indiana CAT Bad Health True 25-09-2016 28.26769
## 47 24-02-1972 Illinois Bird Bad Health True 25-07-2016 28.43316
## 48 25-02-1972 Florida Dog Normal True 25-09-2016 28.67484
## 49 09-11-1972 New Jersey DOG Bad Health True 25-06-2016 29.01252
## 50 29-10-1972 Texas Dog Good Heath True 25-01-2016 29.16127
## 51 19-07-1972 Pennsylvania Bird Normal True 25-07-2016 29.98974
## 52 12-01-1973 Indiana Cat Good Heath True 25-05-2016 30.41152
## 53 12-04-1972 Kentucky None Good Heath True 25-12-2015 31.41528
## 54 04-10-1972 Illinois Cat Normal True 25-07-2016 31.47988
## BMILabel
## 1 Normal
## 2 Normal
## 3 Normal
## 4 Normal
## 5 Normal
## 6 Normal
## 7 Normal
## 8 Normal
## 9 Overweight
## 10 Overweight
## 11 Overweight
## 12 Overweight
## 13 Overweight
## 14 Overweight
## 15 Overweight
## 16 Overweight
## 17 Overweight
## 18 Overweight
## 19 Overweight
## 20 Overweight
## 21 Overweight
## 22 Overweight
## 23 Overweight
## 24 Overweight
## 25 Overweight
## 26 Overweight
## 27 Overweight
## 28 Overweight
## 29 Overweight
## 30 Overweight
## 31 Overweight
## 32 Overweight
## 33 Overweight
## 34 Overweight
## 35 Overweight
## 36 Overweight
## 37 Overweight
## 38 Overweight
## 39 Overweight
## 40 Overweight
## 41 Overweight
## 42 Overweight
## 43 Overweight
## 44 Overweight
## 45 Overweight
## 46 Overweight
## 47 Overweight
## 48 Overweight
## 49 Overweight
## 50 Overweight
## 51 Overweight
## 52 Obese
## 53 Obese
## 54 ObeseDisplay records for Hispanic Females
filter(dfr, Race == "Hispanic" & Gender == "Female")## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 1 AC/AH/210 Keith Hispanic Female True 170.03 66.68
## 2 AC/SG/234 Luis Hispanic Female False 164.88 68.07
## 3 AC/AH/208 Lawrence Hispanic Female False 165.80 71.77
## 4 AC/SG/068 Valentine Hispanic Female False 160.47 68.20
## 5 AC/AH/249 Jesus Hispanic Female True 159.78 68.31
## 6 AC/SG/122 Michal Hispanic Female False 160.09 68.94
## 7 AC/SG/191 Lacy Hispanic Female False 159.33 70.68
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 1 28-08-1972 New York Dog <NA> False 25-03-2016 23.06452
## 2 10-11-1971 Pennsylvania Cat Bad Health True 25-10-2016 25.03916
## 3 07-08-1973 Louisiana None Good Heath False 25-03-2016 26.10802
## 4 15-04-1972 Tennessee Cat Bad Health True 25-07-2016 26.48480
## 5 23-04-1972 Alabama Cat Normal True 25-05-2016 26.75713
## 6 16-12-1971 South Carolina DOG Good Heath False 25-08-2016 26.89942
## 7 21-06-1973 Texas None Bad Health True 25-09-2016 27.84206
## BMILabel
## 1 Normal
## 2 Overweight
## 3 Overweight
## 4 Overweight
## 5 Overweight
## 6 Overweight
## 7 OverweightDisplay 7 sample records
set.seed(707)
sample_n(dfr, 7)## ID Name Race Gender Smokes HeightInCms WeightInKgs
## 10 AC/AH/114 Kris Hispanic Male False 177.75 74.84
## 44 AC/SG/204 Anthony White Female False 164.11 70.66
## 27 AC/AH/154 Tony White Female False 160.03 64.30
## 52 AC/SG/121 Rudy White Female False 163.94 71.47
## 73 AC/SG/046 Carl Hispanic Male False 171.41 81.70
## 67 AC/SG/123 Darnell White Female True 162.32 72.72
## 83 AC/AH/241 Lindsay White Female False 161.38 73.55
## BirthDate State Pet HealthGrade Died RecordDate BMIValue
## 10 19-11-1972 Pennsylvania Bird Bad Health False 25-01-2016 23.68725
## 44 17-06-1972 California Dog Bad Health False 25-10-2016 26.23636
## 27 30-08-1973 California DOG Good Heath True 25-02-2016 25.10777
## 52 12-03-1973 Michigan Cat Bad Health False 25-08-2016 26.59218
## 73 05-08-1973 Mississippi Bird Normal True 25-06-2016 27.80672
## 67 03-09-1972 North Carolina Bird Good Heath True 25-08-2016 27.60005
## 83 08-02-1972 Florida Cat Bad Health False 25-05-2016 28.24121
## BMILabel
## 10 Normal
## 44 Overweight
## 27 Overweight
## 52 Overweight
## 73 Overweight
## 67 Overweight
## 83 OverweightNote
The patient dataset consist of a lot of errors and missing data. These inconsistencies have been removed using various functions provided by the dplyr package so that the required data manipulation can be done. Once the data has been manipulated, the required outputs can be generated using different filters.
Objectives
The objectives set at the start have been met.