1. Uploading and handling databases
Maria Novosolov

2. Understanding datasets
Example:
Continuous variable
Date variable
Ordinal variable
Case identifier
Nominal variable

3. variables Type of variables: Continuous Categorical Nominal Ordinal
Factors*
*determine how data will be analyzed and presented visually

4. factors
The function factor() stores the categorical values as a vector of integers in the range [1... k]
*k is the number of unique values in the nominal variable

5. example
*Nominal variables
diabetes <- c("Type1", "Type2", "Type1", "Type1")
diabetes <- factor(diabetes)
2 = Type 2
1 = Type 1
vector as (1, 2, 1, 1)

6. example
*Ordinal variables
status <- c("Poor", "Improved", "Excellent", "Poor")
status <- factor(status, ordered=TRUE)
2 = Improved
1 = Excellent
3 = Poor
vector as (3, 2, 1, 3)

7. Important!
By default, factor levels for character vectors are created in alphabetical order
To change it:
status <- factor(status,
ordered=TRUE,
levels=c("Poor", "Improved", "Excellent"))

8. Data structures

9. Data structures

10. Data structures

11. Data structures

12. Data structures

13. used to indicate a particular variable
The $
used to indicate a particular variable
from a given data frame
Example:
> table(patientdata$diabetes, patientdata$status)
Excellent Improved Poor
Type
Type

14. The attach() function adds the data frame to the R search path

15. The detach() function removes the data frame from the search path

16. Data input

17. Importing data files You can import data from:
delimited text files using read.table()
Comma-separated values using read.csv()
Excel spread sheet using read.xlsx()

18. Preparing your table
Things you need to remember in preparing your data for R:
Erase all irrelevant columns
Change the column names to short and easy names
Put NA in all the empty cells
Change all the spaces to “_” with ctrl + H

19. import. See help(read.table) for details
Importing from txt
Save your data in tab delimited txt
Set the working directory – setwd()
Use the function read.table()
The read.table() function has many additional options for fine-tuning the data
import. See help(read.table) for details
In R:
1. setwd("your path to the file")
2. mydataframe <- read.table("file.name",header=T/F,
sep="delimiter", row.names="name")
Or:
mydataframe <- read.table(“the.path.to.the.file+file.name",header=T/F,

20. Importing from csv Save your data in .csv file
Set working directory – setwd()
Import your data using read.csv()
In R:
1. setwd("your path to the file")
2. mydataframe <- read.csv("file.name.csv",header=T/F,
Sep=“comma", row.names="name")
Or:
mydataframe <- read.csv(“the.path.to.the.file+file.name.csv",header=T/F,
sep=”comma", row.names="name")

21. Importing data from Excel
use the RODBC package to access Excel files
The xlsx package can be used to access spreadsheets in xlsx format

22. RODBC package #installing the package in R install.packages("RODBC")
#Opens the package for use library(RODBC)
#Myfile.xls = your file; mysheet = the sheet in the file channel <- odbcConnectExcel("myfile.xls") mydataframe <- sqlFetch(channel, "mysheet")
odbcClose(channel)

23. Useful functions

24. Useful functions NA – not available NaN – not a number
The function is.na() allows you to test for the presence of missing values
Example:
y <- c(1, 2, 3, NA)
then the function
is.na(y)
returns
c(FALSE, FALSE, FALSE, TRUE)

25. Date values
Dates are typically entered into R as character strings and then translated into date variables
that are stored numerically
The function as.Date() is used to make this translation.
Syntex:
as.Date(x, "input_format")

26. Date values
Input format:

27. Date values Examples: strDates <- c("01/05/1965", "08/16/1975")
dates <- as.Date(strDates, "%m/%d/%Y")
myformat <- "%m/%d/%y"
leadership$date <- as.Date(leadership$date, myformat)

28. Type conversions