Module 1

Ong S.M.
April 2017

Module Flow

alt text

Activity 1 - Basic Operation

Working directory
Loading data
Install new packages
Load a package
Use an “add-in” function
Help? Google Search
Routine examination of a dataset - interface, summary, str, row & col
(Base vs. add-in) Functions in R

Working Directory

current working directory

getwd()

[1] "F:/Dropbox/Conference.Seminar.Talks/NIH Monash R Course/03 Module 1"

changing working directory

setwd("F:/Dropbox/")

Loading Data (1)

Loading Data (2)

Load a csv file

Data in WD

read.csv("cvdata.csv")

Loading Data (3)

Data in another folder

read.csv("F:/Dropbox/Conference.Seminar.Talks/NIH Monash R Course/03 Module 1/cvdata.csv")

?! Where is the dataset

Loading Data (4)

?! Where is the dataset

“Assignment” <-

dta <- read.csv("F:/Dropbox/Conference.Seminar.Talks/NIH Monash R Course/03 Module 1/cvdata.csv")

*check the “Environment”

Loading other file types

Flat files : read.table() and read.csv() - utils package.
Excel files - readxl package, the gdata package and XLConnect package.
SAS, STATA and SPSS data files - haven package , foreign package
Connecting with a database - RMySQL, RpostgreSQL and the ROracle package.
Accessing and manipulating the database happens via DBI.
Web scraping - rvest

Some common file types

Excel file

You need a function from a library

So, let's install the xlsx library

install.packages("xlsx")

after the installation is completed, load the library

library(xlsx)

Let's have a quick look at the library document.

Loading an excel file into R using xlsx libary

read.xlsx

dta_excel <- read.xlsx("F:/Dropbox/Conference.Seminar.Talks/NIH Monash R Course/03 Module 1/cvdata.xlsx")

Can you try to find out how can load a STATA file into R?

You have a “stata_file.dta” in your folder

Recap: Activity 1 - Basic Operation

Working directory
Loading data
Install new packages
Load a package
Use an “add-in” function
Help? Google Search
Routine examination of a dataset - interface, summary, str, row & col
(Base vs. add-in) Functions in R

Routine examination of a dataset

interface, summary, str, dimension, row & col alt text

Routine examination of a dataset - interface

alt text

Routine examination of a dataset - Summary

summary(dta)

     ptsex           ptrace       ptnationality    acsstratum   
 Min.   :1.000   Min.   : 1.000   Min.   :   0   Min.   :1.000  
 1st Qu.:1.000   1st Qu.: 1.000   1st Qu.:   1   1st Qu.:1.000  
 Median :1.000   Median : 1.000   Median :   1   Median :2.000  
 Mean   :1.212   Mean   : 2.096   Mean   :4169   Mean   :1.797  
 3rd Qu.:1.000   3rd Qu.: 3.000   3rd Qu.:9999   3rd Qu.:3.000  
 Max.   :2.000   Max.   :18.000   Max.   :9999   Max.   :3.000  
                 NA's   :15                                     
 smokingstatus      heartrate      death30d           bpsys      
 Min.   :   1.0   Min.   : 40.0   Mode :logical   Min.   : 60.0  
 1st Qu.:   1.0   1st Qu.: 70.0   FALSE:123       1st Qu.:118.0  
 Median :   2.0   Median : 82.0   TRUE :76        Median :137.0  
 Mean   : 450.3   Mean   : 84.3   NA's :801       Mean   :139.7  
 3rd Qu.:   3.0   3rd Qu.: 96.0                   3rd Qu.:158.0  
 Max.   :8888.0   Max.   :213.0                   Max.   :252.0  
 NA's   :9        NA's   :19                      NA's   :24     
     weight      
 Min.   : 35.00  
 1st Qu.: 60.00  
 Median : 67.00  
 Mean   : 67.18  
 3rd Qu.: 75.00  
 Max.   :112.00  
 NA's   :464

Routine examination of a dataset - Structure

str(dta)

'data.frame':   1000 obs. of  9 variables:
 $ ptsex        : int  2 1 1 2 2 1 1 1 1 1 ...
 $ ptrace       : int  1 2 2 1 1 2 2 2 1 1 ...
 $ ptnationality: int  0 1 1 9999 9999 0 1 9999 9999 0 ...
 $ acsstratum   : int  3 1 1 1 1 1 2 2 1 1 ...
 $ smokingstatus: int  1 3 1 1 1 1 8888 1 3 3 ...
 $ heartrate    : int  81 107 60 74 100 90 61 63 49 91 ...
 $ death30d     : logi  FALSE NA TRUE NA TRUE FALSE ...
 $ bpsys        : int  135 NA 98 122 157 111 177 135 98 127 ...
 $ weight       : num  52.3 58 NA 51.5 NA 69 NA NA 68 56 ...

Routine examination of a dataset - Names

names(dta)

[1] "ptsex"         "ptrace"        "ptnationality" "acsstratum"   
[5] "smokingstatus" "heartrate"     "death30d"      "bpsys"        
[9] "weight"

Routine examination of a dataset - Dimension, row & column

dim(dta)

[1] 1000    9

nrow(dta)

[1] 1000

ncol(dta)

[1] 9

Routine examination of a dataset - Class

class(dta)

[1] "data.frame"

Routine examination of a dataset - Class

Extract a variable(col)

The “$” in R

class(dta$ptsex)

[1] "integer"

str(dta$ptsex)

 int [1:1000] 2 1 1 2 2 1 1 1 1 1 ...

Module Flow

alt text

Simple description

Continuous data (max, min, missing, mean, median, SD, IQR)
Categorical data (tabulation, proportion)

Continuous data (1)

Mean

mean(dta$heartrate)

[1] NA

?? Why

Continuous data (1)

Because …

mean(dta$heartrate, na.rm = TRUE)

[1] 84.29766

?? Why

Continuous data (1)

Let's read the help file

?mean

Median

median(dta$heartrate,na.rm = TRUE)

Continuous data (2)

Maximum

max(dta$heartrate,na.rm = TRUE)

[1] 213

Minimum

min(dta$heartrate,na.rm = TRUE)

[1] 40

Continuous data (3)

Standard Deviation & IQR

sd(dta$heartrate,na.rm = T)

[1] 21.42964

IQR(dta$heartrate,na.rm = T)

[1] 26

Categorical data - univariate

Tabulation & Proportion

table(dta$ptsex)


  1   2 
788 212

? missing data

788+212

[1] 1000

Categorical data

Tabulation & Proportion

table(dta$ptrace)


  1   2   3   5   7   8   9  10  12  13  18 
501 230 210   7   1   6   6   6   4   6   8

? missing data

501+230+210+7+1+6+6+6+4+6+8

[1] 985

A better way to add them up

add margin

addmargins(table(dta$ptrace))


  1   2   3   5   7   8   9  10  12  13  18 Sum 
501 230 210   7   1   6   6   6   4   6   8 985

?addmargins

Categorical data

Proportion

prop.table(table(dta$ptrace))


          1           2           3           5           7           8 
0.508629442 0.233502538 0.213197970 0.007106599 0.001015228 0.006091371 
          9          10          12          13          18 
0.006091371 0.006091371 0.004060914 0.006091371 0.008121827

?prop.table

Categorical data

Proportion

addmargins(prop.table(table(dta$ptrace)))


          1           2           3           5           7           8 
0.508629442 0.233502538 0.213197970 0.007106599 0.001015228 0.006091371 
          9          10          12          13          18         Sum 
0.006091371 0.006091371 0.004060914 0.006091371 0.008121827 1.000000000

IQR(dta$heartrate,na.rm = T)

[1] 26

Categorical data - bivariate

Tabulation & Proportion

table(dta$ptsex,dta$death30d)


    FALSE TRUE
  1    87   56
  2    36   20

Categorical data - bivariate

Tabulation & Proportion

prop.table(table(dta$ptsex,dta$death30d))


        FALSE      TRUE
  1 0.4371859 0.2814070
  2 0.1809045 0.1005025

Categorical data - bivariate

By row

prop.table(table(dta$ptsex,dta$death30d),1)


        FALSE      TRUE
  1 0.6083916 0.3916084
  2 0.6428571 0.3571429

By col

prop.table(table(dta$ptsex,dta$death30d),2)


        FALSE      TRUE
  1 0.7073171 0.7368421
  2 0.2926829 0.2631579

Can you tabulate "trivariate" ?

prop.table(table(dta$ptsex,dta$smokingstatus,dta$death30d),1)

, ,  = FALSE


              1           2           3        8888
  1 0.148936170 0.255319149 0.177304965 0.028368794
  2 0.563636364 0.054545455 0.000000000 0.036363636

, ,  = TRUE


              1           2           3        8888
  1 0.120567376 0.134751773 0.127659574 0.007092199
  2 0.309090909 0.000000000 0.018181818 0.018181818

Your turn - How to find out number of missing in this variable

find out which function identify a missing data point
Tabulate missing vs. non-missing
Compute the % of missing

Answer

is.na(dta$death30d)

   [1] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
  [12]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
  [23]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
  [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
  [45] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
  [56]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
  [67]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
  [78]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
  [89]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [100]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [111]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [122]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [133]  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [144]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [155] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [166] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [177]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [188]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
 [199]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [210]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [221]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE
 [232]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [243]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
 [254]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [265] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [276] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [287]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [298]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [309]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [320]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [331]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [342]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [353]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [364]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [375]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
 [386] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
 [397]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [408]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [419]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [430]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [441]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [452]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [463]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [474]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [485]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [496]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
 [507]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [518]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
 [529]  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
 [540]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [551]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [562]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
 [573]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [584]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [595]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [606]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [617] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [628]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [639] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [650]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
 [661] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [672]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [683]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [694] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 [705] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [716]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE
 [727]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
 [738] FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
 [749]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [760]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [771]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [782]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [793]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [804]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [815]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE
 [826]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE
 [837]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
 [848]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
 [859]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [870]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [881]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [892]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE
 [903] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
 [914]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [925]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
 [936]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [947] FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
 [958]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [969]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
 [980]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [991]  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

table(is.na(dta$death30d))


FALSE  TRUE 
  199   801

prop.table(table(is.na(dta$death30d)))


FALSE  TRUE 
0.199 0.801

Missing data analysis - briefly

complete cases

head(complete.cases(dta))

[1]  TRUE FALSE FALSE FALSE FALSE  TRUE

- complete cases

the which function

which(complete.cases(dta))

use the which function to extract the complete cases

Missing data analysis - briefly

packages for missing data analysis

prop.table(table(is.na(dta$death30d)))


FALSE  TRUE 
0.199 0.801

Recoding or making a new variable

the recode() function in car package

??recode

install.packages("car")

library(car)

The `recode()` function in `car` package

summary(dta$heartrate)
recode(dta$heartrate, "0:82='below.avg' ; 83:213='above.avg'")

[1] "below.avg" "above.avg" "below.avg" "below.avg" "above.avg" "above.avg"

Adding the recoded variable back to the dataset

dta$heartrate2 <- recode(dta$heartrate, "0:82='below.avg' ; 83:213='above.avg'")

table(dta$heartrate2)


above.avg below.avg 
      488       493

Recoding or making a new variable

Or, you can use the ifelse() function

dta$heartrate2 <- ifelse(dta$heartrate < 83, "below.avg", "above.avg")

table(dta$heartrate2)


above.avg below.avg 
      488       493

Subseting

Often use to derive a subgroup / target cohort

Subseting

table(dta$acsstratum)


  1   2   3 
468 267 265

Subseting

derive the ACS = 1 cohort (UA)

head(subset(dta,acsstratum==1))

  ptsex ptrace ptnationality acsstratum smokingstatus heartrate death30d
2     1      2             1          1             3       107       NA
3     1      2             1          1             1        60     TRUE
4     2      1          9999          1             1        74       NA
5     2      1          9999          1             1       100     TRUE
6     1      2             0          1             1        90    FALSE
9     1      1          9999          1             3        49       NA
  bpsys weight heartrate2
2    NA   58.0  above.avg
3    98     NA  below.avg
4   122   51.5  below.avg
5   157     NA  above.avg
6   111   69.0  above.avg
9    98   68.0  below.avg

Subseting

derive the ACS = 1 cohort (UA)

UA.cohort <- subset(dta,acsstratum==1)

2 variables

UA.cohort.male <- subset(dta, acsstratum==1 & ptsex==1)

Practice

Subset STEMI cohort (acstratum=3)
Subset Female (ptsex=2) STEMI cohort (acstratum=3)

The "Table 1" in research paper

How to build one quickly
the tableone package

??tableone

install the package
load the package

The "Table 1" in research paper

install.packages("tableone")

library(tableone)

The "Table 1" in research paper

library(tableone)
?tableone

CreateTableOne(data=dta)


                             Overall          
  n                             1000          
  ptsex (mean (sd))             1.21 (0.41)   
  ptrace (mean (sd))            2.10 (2.22)   
  ptnationality (mean (sd))  4168.82 (4931.12)
  acsstratum (mean (sd))        1.80 (0.83)   
  smokingstatus (mean (sd))   450.29 (1945.96)
  heartrate (mean (sd))        84.30 (21.43)  
  death30d = TRUE (%)             76 (38.2)   
  bpsys (mean (sd))           139.67 (30.40)  
  weight (mean (sd))           67.18 (12.17)  
  heartrate2 = below.avg (%)     493 (50.3)

Practice

Load a new dataset
Call the new dataset “data.new”
Find out % of missing for all variables
Extract only the complete cases and assign it to “data.new.comp”
Recode age variable to old (>/=60) and young (<60)
Extract the male sub cohort
Quickly build a Table 1 using the “tableone” package

One group to present