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StatsToDo: R: Introduction and Setup fpr Using R

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Introduction R Studio Data I/O

Dataframe

R is able to handle data in numerous format, the most commonly used one is a data frame. Data frame is an object (a computer construct that contains large amount of information). In most cases, particularly in these pages, the dataframe contains a table with columns for variables, and rows for cases.

The first step in any analysis is therefore converting the table of data into a dataframe, which R can then analyse. The standard data table has columns for variables and rows for subjects. The first row containing the name of each column. The table can then be imported into R using one of the following methods

Direct data I/O

Nearly all examples of R codes on this site will use direct data I/O. This is the simplest form of data entry for small sets of data. The example codes are as follows

myTxt = ("
Col1  Col2  Col2	  
A     1     2
B     3     4
C     5     6
")                       # Example data in text
myDF <- read.table(textConnection(myTxt),header=TRUE)      
                         # Make data frame
#myDF             # Optional show dataframe in console

Please note that:

The table, in text, is in between (" and ")
The first row contains the column names
The columns are separated by spaces or tabs
The values of Col1 are text, which R called factors, and those of Col2 and Col3 are numerical. Please Note: the type of values in each column must be consistent.

Results of analysis are displayed in the console or the graphic panel. These can be copied to the clipboard by marking and the combined keys of ctrl+c and pasted into any other application.

Files

By default, R reads and writes all file using the standard Documents folder. It is useful to change and Set the default folder to the same as the current file loaded into the source condole. To do so the following procedures are used. At the menu bar at the top click Session->Set Working Directory->To Source File Location. To check whether the directory is correct or not, use the following codes

getwd()
list.files()

This will display the folder being used and the files it contains

Comma delimited (.csv) files

The codes for input from a comma delimited (.csv) file are

myDF <- read.csv("myCsvIn.csv") 
#myDF

Please Note that:

myCsvIn.csv is the name of the comma delimited file containing the data table.
The first line reads the table to the dataframe
The second line is optional display of the dataframe

The codes for writing an object, such as a dataframe, to a comma delimited (.csv) file are

write.csv(myDF, "myCsvOut.csv", row.names = FALSE)

Please Note that:

myDF is the name of the dataframe to be saved to the .csv. It should already exist
myCsvOut.csv is the name of the output comma delimited (.csv) file. User should change this name to one that is appropriate
If row.names= False is not used, then a first column containing row number will be included
If the named .csv file pre-exists and is closed, it will be overwritten. Attempt to write to an open file with the same name will fail and flag an error message

Excel worksheet (.xlsx) files

To access .xlsx files, the package xlsx must be already installed. If not installed, the command install.packages("xlsx") will do so. Once installed, each time a .xlsx file is accessed, the library xlsx must be called

For data input

#install.packages("xlsx")             #use only if the library is not already installed
library("xlsx")     
myDF <- read.xlsx("myXlsxIn.xlsx", sheetName="mySheet")
#myDF

Please note that:

The xlsx package needs to be already installed once in a computer
The library xlsx must be called
myXlsxIn.xlsx is the name of the excel .xlsx file to be read. User should change this to his/her own file name
mySheet is the name of the worksheet to be read. This can be the name of the sheet, or a number (without the quotes) counting from 1
When testing, uncomment the last command (remove #) to see if the correct data has been read

For output of an object such as a dataframe to an excel workbook (.xlsx)

lbrary("xlsx")
write.xlsx2(myDF, "myXlsxOut.xlsx", sheetName = "mySheet", 
            row.names = FALSE, append=TRUE)

Please Note that:

The package xlsx must be pre-installed and is called
myDF is a dataframe to be saved to file, a table with columns and rows.
myXlsxOut.xlsx is the name of the excel workbook to be saved to. Users should change this to his/her own file name
mySheet is the name of the sheet to save the data to
If row.names= False is not used, then a first column containing row number will be included
If the file pre-exists, it must be closed. Attempt to write to an open file will crash the program
If the append option is FALSE, any existing file containing a wroksheet with the same name will be overwritten
If the append option is TRUE and no worksheet with the same name exists, a new worksheet with the name will be created and the data written to it
If the append option is TRUE and a worksheet with the same name already exists, write to the worksheet will fail and the program crashes

Creating a report of analysis using Knit

RStudio provides an utility which will run the codes in the source panel, and provides all the results in a file, which can be a html, Word, or pdf file. To do so, the package markdown must be pre-installed using the code install.packages("rmarkdown").

Once a set of R codes are tested and found to be satisfactory, at the menu bar on the top of RStudio,

The file will be created including all codes, results, and graphics. The user can then edit and improve on this as he/she thinks appropriate.

Please note: The knit program is quite temperamental, and have the following problems

It can be incompatible with some other packages, especially those that read or write to files or produce graphics. The program merely crashes without too much explanation. An example is that knit will crash when trying to read from or write to excel worksheets.
In some graphics programs, the labeling for the axis may be misplaced or absent in the report file

These are problems encountered so far, but there may be more.

Array & Matrices

This panel provides some useful templates for handling arrays and matrices.

1:Data Frames and Matrices

Template 1.1: Convert a text fields into a matrix. This is done by firstly import the text field (all columns must be numerical values) into a data frame, then convert the data frame into the matrix

txt = ("
Col1  Col2  Col3	  
7     1     2
8     3     4
9     5     6"
)                       
df <- read.table(textConnection(txt),header=TRUE)      
mx <- data.matrix(df)
colnames(mx) <- NULL    # optional removing column names
mx

The rresults are

> mx
     [,1] [,2] [,3]
[1,]    7    1    2
[2,]    8    3    4
[3,]    9    5    6

Template 1.2: Create matrix from the numerical fields of a mixed factor(text) and value (numerical) data frame

txt = ("
Col1  Col2  Col3	  
A     1     2
B     3     4
C     5     6"
)                       
df <- read.table(textConnection(txt),header=TRUE)      
#df              # data frame
mx <- matrix(c(df$Col2,df$Col3), ncol=2)
mx

Note: cols 2 and 3 are numerical, and extracted to form a 2 column matrix. The results are

> mx
     [,1] [,2]
[1,]    1    2
[2,]    3    4
[3,]    5    6

Template 1.3: Convert matrix into a data frame

# Input 3. matrix to data frame
#mx = matrix(c(1,2,3,4,5,6,7,8,9,10,11,12),nrow=4)  # alternative method to create matrix
col1 <- c(1,2,3,4)
col2 <- c(5,6,7,8)
col3 <- c(9,10,11,12)
mx <- cbind(col1, col2, col3)
mx         #matrix
# turn matrix into data frame
df = as.data.frame(mx)
df
# add another column to existing data frame
df$col4 <- c(20,21,22,23)             # method 1
df
ar <- c(30,31,32,33)                  # method 2
df$col5 <- ar
df

Results as follows

> df
  col1 col2 col3
1    1    5    9
2    2    6   10
3    3    7   11
4    4    8   12
> # add another column to existing data frame
> df$col4 <- c(20,21,22,23)             # method 1
> df
  col1 col2 col3 col4
1    1    5    9   20
2    2    6   10   21
3    3    7   11   22
4    4    8   12   23
> ar <- c(30,31,32,33)                   # method 2
> df$col5 <- ar
> df
  col1 col2 col3 col4 col5
1    1    5    9   20   30
2    2    6   10   21   31
3    3    7   11   22   32
4    4    8   12   23   33
>

2:Arrays

# create an array with contents
ar <- c(1,2,3,4)
ar

[1] 1 2 3 4

# Create an array with sequence
start = 1
finish = 10
increment = 2
ar <- seq(start, finish, by=increment)
ar

[1] 1 3 5 7 9

# Create an empty vector
ar <- vector()

# Create vector of defined length
default = 5
siz = 3
ar <- array(default,siz)
ar

[1] 5 5 5

# Operations
ar1 <- c(1,2,3)
ar2 <- c(4,5,6)

# add
ar1 + ar2

[1] 5 7 9

#subtract
ar2 - ar1

[1] 3 3 3

#multiply elements
ar1 * ar2

[1]  4 10 18

# multiply vectors col by row # default is column
ar1 %*% t(ar2)

     [,1] [,2] [,3]
[1,]    4    5    6
[2,]    8   10   12
[3,]   12   15   18

#multiply row by col
t(ar1) %*% ar2

     [,1]
[1,]   32

3:Matrices

#matrix with values
ar <- c(1,2,3,4,5,6)
mx <- matrix(data=ar, nrow=2,ncol=3) #byrow=FALSE is default
mx 
mx <- matrix(data=ar, nrow=2,ncol=3, byrow=TRUE)
mx

> mx <- matrix(data=ar, nrow=2,ncol=3) #byrow=FALSE is default
> mx 
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
> mx <- matrix(data=ar, nrow=2,ncol=3, byrow=TRUE)
> mx
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6

# matrix with default value
default = 5
mx <- matrix(data=default, nrow=2,ncol=3)
mx

> mx
     [,1] [,2] [,3]
[1,]    5    5    5
[2,]    5    5    5

#matrix joining two arrays
a1 <- c(1,2,3)
a2 <- c(4,5,6)
mx <- matrix(data=c(a1,a2), nrow=2,ncol=3, byrow=TRUE)
mx
mx <- matrix(data=c(a1,a2), nrow=3,ncol=2)
mx

> mx <- matrix(data=c(a1,a2), nrow=2,ncol=3, byrow=TRUE)
> mx
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6
> mx <- matrix(data=c(a1,a2), nrow=3,ncol=2)
> mx
     [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6

# rows
mx[1,]
mx[2,]
mx[3,]
#cols
mx[,1]
mx[,2]

> # rows
> mx[1,]
[1] 1 4
> mx[2,]
[1] 2 5
> mx[3,]
[1] 3 6
> #cols
> mx[,1]
[1] 1 2 3
> mx[,2]
[1] 4 5 6

# operations
mx1 <- matrix(c(c(3,5,1,2),c(4,1,3,1),c(2,3,1,1)),nrow=4,ncol=3)
mx1
mx2 <- matrix(c(c(1,2,1),c(2,3,1)),nrow=3,ncol=2)
mx2

> mx1
     [,1] [,2] [,3]
[1,]    3    4    2
[2,]    5    1    3
[3,]    1    3    1
[4,]    2    1    1
> mx2
     [,1] [,2]
[1,]    1    2
[2,]    2    3
[3,]    1    1

# transpose
mx3 <- t(mx1)
mx1
mx3

> mx1
     [,1] [,2] [,3]
[1,]    3    4    2
[2,]    5    1    3
[3,]    1    3    1
[4,]    2    1    1
> mx3
     [,1] [,2] [,3] [,4]
[1,]    3    5    1    2
[2,]    4    1    3    1
[3,]    2    3    1    1

# add
mx1 + mx1

> mx1 + mx1
     [,1] [,2] [,3]
[1,]    6    8    4
[2,]   10    2    6
[3,]    2    6    2
[4,]    4    2    2

# subtract
mx2 - mx2

> mx2 - mx2
     [,1] [,2]
[1,]    0    0
[2,]    0    0
[3,]    0    0

# multiply elements
mx2 * mx2

> mx2 * mx2
     [,1] [,2]
[1,]    1    4
[2,]    4    9
[3,]    1    1

#multiply matrices
mx1 %*% mx2

> mx1 %*% mx2
     [,1] [,2]
[1,]   13   20
[2,]   10   16
[3,]    8   12
[4,]    5    8

#division by elements
mx1 / mx1

> mx1 / mx1
     [,1] [,2] [,3]
[1,]    1    1    1
[2,]    1    1    1
[3,]    1    1    1
[4,]    1    1    1

# transpose
t(mx2)

> t(mx2)
     [,1] [,2] [,3]
[1,]    1    2    1
[2,]    2    3    1

# square matrix
mx <- matrix(c(c(4,2,3,2),c(2,5,3,1),c(3,3,6,2),c(2,1,2,3)),nrow=4,ncol=4)
mx

> mx
     [,1] [,2] [,3] [,4]
[1,]    4    2    3    2
[2,]    2    5    3    1
[3,]    3    3    6    2
[4,]    2    1    2    3

# invert
solve(mx)

> solve(mx)
            [,1]        [,2]        [,3]        [,4]
[1,]  0.50000000 -0.07142857 -0.14285714 -0.21428571
[2,] -0.07142857  0.29591837 -0.12244898  0.03061224
[3,] -0.14285714 -0.12244898  0.32653061 -0.08163265
[4,] -0.21428571  0.03061224 -0.08163265  0.52040816

# Rank
qr(mx)$rank  # uses qr decomposition

[1] 4

# Determinant
det(mx)

> det(mx)
[1] 98

# Eigen values and matrices
eigenResults <- eigen(mx)
eigenResults

> eigenResults
$values
[1] 11.501474  3.143052  2.000000  1.355473
$vectors
           [,1]        [,2]       [,3]        [,4]
[1,] -0.4779381 -0.36246433  0.3779645  0.70522169
[2,] -0.4961149  0.76831305  0.3779645 -0.14390264
[3,] -0.6487310 -0.05918796 -0.7559289 -0.06493346
[4,] -0.3234090 -0.52422462  0.3779645 -0.69118597

#Square root of matrix after obtaining eigen
sr <- eigenResults$vectors %*% diag(sqrt(eigenResults$values)) %*% solve(eigenResults$vectors)
sr

> sr
          [,1]      [,2]      [,3]      [,4]
[1,] 1.7886506 0.3942986 0.6321685 0.4956015
[2,] 0.3942986 2.1073918 0.6176967 0.1479165
[3,] 0.6321685 0.6176967 2.2465112 0.4147301
[4,] 0.4956015 0.1479165 0.4147301 1.6001559

# Validate square root matrix
sr %*% sr

> sr %*% sr
     [,1] [,2] [,3] [,4]
[1,]    4    2    3    2
[2,]    2    5    3    1
[3,]    3    3    6    2
[4,]    2    1    2    3

Missing Data

Explanations & References

This panel explains and provides example codes for handling missing data using R.

In research and handling data, missing data is a common occurrence. Subjects are lost, errors are made in collecting and transcribing information, and whole host of reasons creating holes in the data table.

R provides an option for how to handle missing data in nearly all its formulae, but this requires the analyst to be familiar with how missing data may affect a particular procedure and which option each procedure provides for handling missing data.

For the sake of simplicity, all the codes provided in StatsToDo assume that the data is already clean and contain no missing data. This separates the procedures handling missing data from the statistical algorithms.

This panel therefore provides the algorithms for handling missing data at the final stages of data preparation, to produce a complete set of data for analysis

How missing data are represented in R

Within the object dataframe, missing values are represented by NA in the numerical columns and <NA> in text columns. However, in data I/O, the following is used

When data is presented directly in the R Code as a text table, or when read in from a comma delimited file (.csv), missing data is represented by NA. Any other representation is interpreted by R as values in the data
When data is read in from a Excel worksheet using the package xlsx, missing data are blank cells in the Excel worksheet. Anything else will be interpreted as actual values and processed accordingly

When deciding on how data are to be inputed into R, how missing values are prepresented in the different input media should be carefully considered, and testing with a small dataset would avoid error later.

Different options in dealing with missing data

R provides an extensive collection of methods of handling missing data. Only a few of the more commonly used ones are presented in this page. This panel discusses the options conceptually, the complete set of codes and how the codes work are presented in the R Code panel

Option 1. Casewise deletion

This is the easiest, and widely used method. All records containing missing values are deleted.

This method is appropriate if the analyst is sure that data is lost at random, so that removing records containing missing data would not create a bias leading to misinterpretation. The amount of missing data should also be small, say in less than 1% of the cases

Option 2. K Nearest Neighbour

For each missing value, the program searches for k completed records that are nearest (similarity not location) to it, replacing it with the average for a numerical (value) column, and the most frequent value for a text (factor) column. k can be specified in the formula. If not specified, the default k=10 is used.

This is a robust method, and can be used even if some bias process is implicated in data loss, as the missing value is replaced by values from similar records.

There are, however, some issues involved. Firstly, for every missing value, k (10) completed records are required. Secondly, the whole database is searched for the nearest records, and this is time consuming if the database is large and missing data numerous.

The method was devised by those working on big data and artificial intelligence, when thousands or even millions of records are available, and the data can be analysed using powerful computers over prolonged periods.

Clinical data are caught between having database not large enough so that k has to be reduced, and the long time required for processing using desk top computers.

Although the method is excellent in theory, it cannot always be successfully used in the clinical setting. However, it is worth a try. If the program crashes or takes too long to run, k can be progressively reduced until the program works. Be aware however that, as k is reduced, the risk of producing bias replacements increases

Option 3. General Imputation

The program randomly selects a missing data value, and replaces it with an estimate using the available data and multiple regression. This is then included in the available data to estimate the next randomly selected missing value. This process is repeated until all missing values are replaced by estimated (imputated) values.

As latter estimations are influenced by earlier estimated values, the results are slightly different depending on the random sequence. The program copes with this by iterating the process a number of times (m) and averaged the results. The number of iterations (m) can be specified by the user. If not specified, the default is m=5. Controversy exists as to what m should be, and some statisticians argue that m should be the same as the number of missing values in the data.

This method is most suited to the small data sets that are common in clinical studies, especially in survey and clinical trials where the sample size is around 100.

The only proviso is that at least one (1) numerical column must exist in the data set for the algorithm to work.

Users should also be aware that the same program and data will produce slightly different results when repeated, as the random sequence is generated at run time so are different each time

Option 4. Numerical Imputation

The program is a mathematical algorithm using existing values in the same column of the data set to estimate a replacement value for the missing values. The methods available are mean. median, mode, and interpolation (average of the available values on the two sides of the missing value).

The method only works in columns of numerical data, and ignores missing values in columns that are text. It is quick to implement and the results easy to interpret. It can be used if such mathematical replacement is appropriate to the analyst's needs

The interpolation method is especially useful in time series data such as continuous monitoring, as the interpolation result is close to what the missing data should be.

Additional information

Checking the results

It is important to check the results of fixing missing data before the data set is used for analysis. There are numerous methods for doing so, but they are not covered in this page. The example codes provide the basic comparisons using the summary command, which will count the different values in text columns, and minimum, maximum, quartile values, means and standard deviation in numerical columns

References

https://en.wikipedia.org/wiki/Missing_data

https://www.kdnuggets.com/2017/09/missing-data-imputation-using-r.html

https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4

R Codes

This panel explains the codes presented in the previous panel in segments

Data handling

Direct data entry

myTxt = ("
Sex   Ethn    Gest BWt
Girl  Greek   37   3048
Boy   German  36   2813
Girl  French	41   3622
NA    Greek   36   2706
Boy   German  35   2581
Boy   NA      NA   3442
Girl  Greek   40   3453
Boy   German  37   3172
Girl  French  35   NA 
Boy   Greek   39   3555
Girl  German  37   3029
Boy   French  37   3185
Girl  NA      36   2670
Boy   German  NA   3314
Girl  French  41   3596
Boy   Greek   38   3312
NA    NA      39   3200
Boy   French  41   3667
Boy   Greek   40   3643
Girl  German  38   3212
Girl  French  38   3135
Girl  Greek   39   3366               
")
myDF <- read.table(textConnection(myTxt),header=TRUE)      
summary(myDF)
#myDF

The example data is computer generated, and purports to come from a study of birth weight. Sex being sex of the baby, Ethn the ethnicity of the mother, Gest the number of completed weeks in gestation, and BWt the weight of the baby at birth.

The table has columns for variables and rows for subjects (each baby). The first row contains the names for each column. NA represents missing data

myTxt is the name given to this table. User can change this to any other name

This is followed by 3 lines of codes

Imports the data into the dataframe object myDF
Displays the summary, which can be used for comparison with the results of changes to the data set
Optional display of the data as represented within the dataframe. This can be activated by uncommenting the line (remove #)

Summary is as follows

   Sex         Ethn        Gest            BWt      
 Boy :10   French:6   Min.   :35.00   Min.   :2581  
 Girl:10   German:6   1st Qu.:36.75   1st Qu.:3048  
 NA's: 2   Greek :7   Median :38.00   Median :3212  
           NA's  :3   Mean   :38.00   Mean   :3225  
                      3rd Qu.:39.25   3rd Qu.:3453  
                      Max.   :41.00   Max.   :3667  
                      NA's   :2       NA's   :1

There are 22 subjects
Sex and Ethn are text columns, and the number of rows with each value is counted
Gest and BWt are numerical columns, and the quartile and mean values are presented
Missing values, represented as NA are also counted for each column

Missing Values

Option 1: casewise deletion

casewiseDeletedDataFrame <- na.omit(myDF)
summary(casewiseDeletedDataFrame)                
#casewiseDeletedDataFrame

The first line creates a new dataframe casewiseDeletedDataFrame containing only those rows with no missing data
The second line provides the summary which can be used to compare with the input data
The third line is an optional display of the result data, so it can be copied and pasted to other applications. This can be activated by removing the #

The summary is as follows

   Sex        Ethn        Gest            BWt      
 Boy :8   French:5   Min.   :35.00   Min.   :2581  
 Girl:8   German:5   1st Qu.:37.00   1st Qu.:3113  
          Greek :6   Median :38.00   Median :3262  
                     Mean   :38.38   Mean   :3274  
                     3rd Qu.:40.00   3rd Qu.:3565  
                     Max.   :41.00   Max.   :3667

Six (6) rows with one or more missing values are deleted, so the data set now has 16 rows.

Option 2: K Nearest Neighbours

#install.packages("DMwR")
library(DMwR)
knnDataFrame <- knnImputation(myDF,k=10)
summary(knnDataFrame)
#knnDataFrame

Line 1 installs the package "DMwR" which is a package for deep learning, from which this algorithm is obtained. This is commented out, as this is not needed repeatedly once the package is installed on the computer
Line 2 calls the installed library. This must be done prior to running the program
Line 3 creates a new dataframe, which has the missing values replaced by the estimated values. The value for k can be specified by the user. If not specified, the default is k=10. If the amount of completed record is insufficient, or if the run time of the program is too long, k can be reduced.
Line 4 displays the summary of the new dataframe, which can be compared with that from the input data
Line 5 is an optional display of the result data, so it can be copied and pasted to other applications. This can be activated by removing the #

The summary is as follows

   Sex         Ethn        Gest            BWt      
 Boy :11   French:6   Min.   :35.00   Min.   :2581  
 Girl:11   German:7   1st Qu.:37.00   1st Qu.:3046  
           Greek :9   Median :38.00   Median :3206  
                      Mean   :38.07   Mean   :3217  
                      3rd Qu.:39.12   3rd Qu.:3450  
                      Max.   :41.00   Max.   :3667

Twentytwo (22) rows remain in the data set, but the counts, interquartile values, and means have changed as the missing values are replaced by the estimated values

Option 3: General Imputation

#install.packages("mice")  
library(mice)                                  
impute <- mice(myDF, m = 5, print = FALSE)

fit<-with(data = impute, lm(BWt ~ Sex+Ethn+Gest)) 
pool<-pool(fit) 
miceDataFrame<-complete(impute)

summary(miceDataFrame)
#miceDataFrame

Line 1 installs the package "mice" which contains the imputation program. This is commented out, as this is not needed repeatedly once the package is installed on the computer
Line 2 calls the installed library. This must be done prior to running the program
Line 3 creates a data matrix containing the estimated values from the iterations. The number of iterations (m) can be specified by the user. If not specified, the default is m=5.
Line 4 estimates the imputed values, using a regression formula. The formula should contain the names of all the columns that the analyst intend to use to estimate missing values.
Lines 5 and 6 pool the results, and created a new database with the missing values replaced by the imputation estimates.

Please note the following

The variables used in the regression formula are the column names in the inputed data. Analysts should replace these with those from his/her own data
At least one (1) column in the data must be a numerical one. Without this, the program fails and an error is flagged.

Line 6 displays the summary of the new dataframe, which can be compared with that from the input data
Line 7 is an optional display of the result data, so it can be copied and pasted to other applications. This can be activated by removing the #

The summary is as follows

   Sex         Ethn        Gest            BWt      
 Boy :10   French:8   Min.   :35.00   Min.   :2581  
 Girl:12   German:6   1st Qu.:37.00   1st Qu.:3034  
           Greek :8   Median :38.00   Median :3206  
                      Mean   :38.05   Mean   :3206  
                      3rd Qu.:39.00   3rd Qu.:3450  
                      Max.   :41.00   Max.   :3667

Twentytwo (22) rows remain in the data set, but the counts, interquartile values, and means have changed as the missing values are replaced by the estimated values

Option 3: General Imputation

Activate library

#install.packages("ggplot2")   #only if not already installed
#install.packages("imputeTS")  #only if not already installed
library(imputeTS)

The package imputeTS is required for numerical imputation. The algorithms in this package call functions in the package ggplot2, so that package also needs to be installed. The 2 installation commands are commented out because they only need to be installed to the computer once.

For each of the numerical imputation method, the library imputeTS needs to be activated.

Once the library is called, the analyst can choose one of the mathematical models. Please note: that only numerical data are imputed. Columns containing text data are ignored.

Replace missing values by column mean

meanDataFrame<-na.mean(myDF, option = "mean")
summary(meanDataFrame)
#meanDataFrame

The summary is

   Sex         Ethn        Gest         BWt      
 Boy :10   French:6   Min.   :35   Min.   :2581  
 Girl:10   German:6   1st Qu.:37   1st Qu.:3070  
 NA's: 2   Greek :7   Median :38   Median :3218  
           NA's  :3   Mean   :38   Mean   :3225  
                      3rd Qu.:39   3rd Qu.:3450  
                      Max.   :41   Max.   :3667

Replace missing values by column median

medianDataFrame<-na.mean(myDF, option = "median")
summary(medianDataFrame)
#medianDataFrame

The summary is

   Sex         Ethn        Gest         BWt      
 Boy :10   French:6   Min.   :35   Min.   :2581  
 Girl:10   German:6   1st Qu.:37   1st Qu.:3070  
 NA's: 2   Greek :7   Median :38   Median :3212  
           NA's  :3   Mean   :38   Mean   :3224  
                      3rd Qu.:39   3rd Qu.:3450  
                      Max.   :41   Max.   :3667

Replace missing values by column mode

modeDataFrame<-na.mean(myDF, option = "mode")
summary(modeDataFrame)
#modeDataFrame

The summary is

   Sex         Ethn        Gest            BWt      
 Boy :10   French:6   Min.   :35.00   Min.   :2581  
 Girl:10   German:6   1st Qu.:37.00   1st Qu.:3034  
 NA's: 2   Greek :7   Median :37.50   Median :3206  
           NA's  :3   Mean   :37.91   Mean   :3196  
                      3rd Qu.:39.00   3rd Qu.:3450  
                      Max.   :41.00   Max.   :3667

Replace missing values by interpolation (average of the values on each side of the missing value)

interpolationDataFrame<-na.interpolation(modeDataFrame)
summary(interpolationDataFrame) 
#interpolationDataFrame

The summary is

   Sex         Ethn        Gest            BWt      
 Boy :10   French:6   Min.   :35.00   Min.   :2581  
 Girl:10   German:6   1st Qu.:37.00   1st Qu.:3034  
 NA's: 2   Greek :7   Median :37.50   Median :3206  
           NA's  :3   Mean   :37.91   Mean   :3196  
                      3rd Qu.:39.00   3rd Qu.:3450  
                      Max.   :41.00   Max.   :3667

StatsToDo: R: Introduction and Setup fpr Using R

Setting up R and RStudio

RStudio

Anatomy of RStudio :

Packages

How to use the example codes

Help and further information

Dataframe

Direct data I/O

Files

Comma delimited (.csv) files

Excel worksheet (.xlsx) files

Creating a report of analysis using Knit

1:Data Frames and Matrices

2:Arrays

3:Matrices

How missing data are represented in R

Different options in dealing with missing data

Option 1. Casewise deletion

Option 2. K Nearest Neighbour

Option 3. General Imputation

Option 4. Numerical Imputation

Additional information

Checking the results

References

Data handling

Direct data entry

Missing Values

Option 1: casewise deletion

Option 2: K Nearest Neighbours

Option 3: General Imputation

Option 3: General Imputation