R Language Practical reading notes (iv) Basic data management

4.2 Creating a new variable

Several operators:

^ or * *: exponentiation

X%%y: Seeking Redundancy

X%/%y: integer except

4.3 Re-Encoding of variables

With ():

Within (): Can modify data frame

4.4 Variable renaming

Package reshape has a function rename, can be renamed rename (df,c (manage= ' ManagerID ', date= ' testdate '))

Or

Names (DF) [2]<-' NewName '

4.5 Missing values

Is.na (): Check for missing value, return True, no return false

The Na.rm=true option can be used, such as

Y<-sum (X,na.rm=true)

Removing missing values

Newdf<-na.omit (DF)

4.6 Date value

As. Date (): It is hard to remember the value of the parameter Input_format, the default date is YYYY-MM-DD

%d: numeric date

%a: Abbreviated Week name

%A: Non-abbreviated weekday name

%m:00~12

%B: Abbreviated Month

%B: Non-abbreviated month

%y: Two-bit year

%Y: four-bit year

Sys.date (): Current date

Date (): Returns the current date and time, why not make a good name, not to call date and date

You can use format to extract something.

Today <-Sys.date ()
Format (today, format = "%B%d%Y")
Format (today, format = "%A")

Date can be subtracted

StartDate <-as. Date ("2004-02-13")
EndDate <-as. Date ("2009-06-22")
Days <-Enddate-startdate

can also

Today <-Sys.date ()
Format (today, format = "%B%d%Y")
Dob <-as. Date ("1956-10-10")
Format (DOB, format = "%A")

4.6.1 Converting a date to a character variable

As.character

4.7 Type Conversions

Is.numeric-As.numeric

Is.character

Is.vector

Is.data.frame

Is.factor

Is.logical

4.8 Sorting Data

Order ()

NewData <-Leadership[order (leadership$age),] This is ascending, preceded by a minus sign is descending

NewData <-Leadership[order (Gender,-age),] this is sorted by sex ascending, age descending

4.9 Merging of datasets

4.9.1 Adding columns

Merge two data frames horizontally, with merge ()

Newdf<-merge (dfa,dfb,by= "ID")

Newdf<-merge (Dfa,dfb,by=c ("ID", "country"))

If you do not need to connect, you can use Cbind

4.9.2 Adding rows

Rbind

4.10 Subset of data sets

4.10.1 Selecting variables

Select columns

Data<-df[,c (6:10)]

or select by name

Myvars <-C ("Q1", "Q2", "Q3", "Q4", "Q5")
NewData <-Leadership[myvars]

4.10.2 Culling variables

Myvars <-Names (leadership)%in% C ("Q3", "Q4") get intersection
NewData <-Leadership[!myvars] Take the reverse, the result is to remove q3,q4

Or

NewData <-Leadership[c (-7,-8)]

4.10.3 Selective observation

which function

4.10.4 subset

NewData <-Subset (leadership, age >= | Age <, select = C (Q1, Q2, Q3, Q4))
NewData <-Subset (leadership, gender = "M" & Age >, select = Gender:q4)

4.10.5 Random Sampling

Sample

Sample<-df[sample (1:nrow* (DF), 3,replace=false] do not put back sampling

4.11 Manipulating data frames with SQL

Library (SQLDF)
NEWDF <-sqldf ("select * from Mtcars where carb=1 order by mpg", row.names = TRUE)
NEWDF <-sqldf ("Select AVG (MPG) as Avg_mpg, AVG (disp) as avg_disp,gear from Mtcars where Cyl in (4, 6) GROUP by Gear")

R Language Practical reading notes (iv) Basic data management