> For the complete documentation index, see [llms.txt](https://r-pedia.gitbook.io/cevi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://r-pedia.gitbook.io/cevi/r-data/r-management.md).
# -R management
## #R functions for objects
Useful functions for dealing with objects
```r
rm(list=ls()) #delete all objectslength(object)
# number of elements str(object)
# structure of an object class(object)
# class or type of an objectnames(object)
# names of a objectobject
# prints the objectls()
# list current objectsrm(object)
# delete an objectc(object,object,...)
# combine objects into a vectorcbind(object, object, ...)
# combine objects as columnsrbind(object, object, ...)
# combine objects as rows
```
## #R data import/export
Importing data into R is fairly easy methode. The important thing that you must know the function for every sources. The most sources from getting data in R come from CSV plain text and Excel.
#### #1- Import from CSV plain text: [read.table()](https://www.rdocumentation.org/packages/utils/versions/3.4.3/topics/read.table)
* If you’ve already [set a working directory in R](https://itsmecevi.gitbook.io/r/untitled-1/r-folder):
```r
csvdata <- read.table("csv.txt", header=TRUE,sep=",")
```
* If you haven't set a working directory: **copy the file and paste in file directory (PASTE HERE)**
```
csvdata<-read.table("PASTE HERE",header=TRUE,sep=",")
```
* Export as CSV
```
write.table(csvdata, "c:/mydata.txt", sep=",")
```
Save the dataset as .csv with plain text editor or another program such as excel
```
Sally Whittaker,2018,McCarren House,312,3.75
Belinda Jameson,2017,Cushing House,148,3.52
Jeff Smith,2018,Prescott House,17-D,3.20
Sandy Allen,2019,Oliver House,108,3.48
```
#### #2-Import from Excel:
* The best ways to read an Excel file is to export it to a csv , but we can do it direct in R
```r
library(readxl) #install the readxl package
dataexcel <- read_excel("excel.xlsx")
#saving the data into a variableView(dataexcel)
```
Note: If you haven't set a working directory: copy the file and paste in function read\_excel("PASTE HERE")
* Export as excel.xlsx?????
```
```
**Going further** : [R Data Import Tutorial](https://www.datacamp.com/community/tutorials/r-data-import-tutorial) and [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.html)
## #R dataset information
How to get information from the dataset
```r
# See the dataset
dataset
#or
print(dataset)
#or
View(dataset
# print first 10 rows of dataset
head(dataset, n=10)
# print last 7 rows of dataset
tail(dataset, n=7)
# list all objects
ls()
# list the variables
names(dataset)
# list the structure of dataset
str(dataset)
# class of an object or variable (numeric, matrix, data frame, etc)
class(object)
# dimensions of an object
dim(object)
```
## #R missing data
The symbol NA (not available) are represented missing value. And NaN are not a number (e.g., dividing by zero).
Going further for more [NaN values](https://en.wikipedia.org/wiki/NaN)
#### Missing Values test:
```
x<- c(1,2,3,NA)
is.na(x) # returns a vector (F F F T)
############
[1] FALSE FALSE FALSE TRUE
```
#### Excluding Missing Values from Analyses:
```
x <- c(1,2,NA)
mean(x) # returns NA
mean(x, na.rm=TRUE) # returns 1.5
#######
[1] 1.5
```
#### Advanced Handling of Missing Data:
* [Amelia II](http://gking.harvard.edu/amelia/), [Mice](https://www.rdocumentation.org/packages/mice/versions/2.25/topics/mice), and [mitools](http://cran.us.r-project.org/web/packages/mitools/index.html).
## #R date Values
Dates are represented as the number of days since 1970-01-01, with negative values for earlier dates.
Convert strings to dates and opposite:
```r
# convert strings dates with as.Date( )
dates <- as.Date(c("2018-04-22", "2014-02-13"))
# number of days between date
days <-dates[1] - dates[2] #Time difference of 1529 day#
#convert dates to character datastr
Dates <- as.character(days)
```
#### Todays date and time:
```r
Sys.Date( ) #returns today's date.
date() #returns the current date and time.
```
* **format( )** function to print dates.
| **Symbol** | **Meaning** | **Example** |
| ---------- | ---------------------- | ----------- |
| **%Y** | 4-digit year | 2007 |
| **%y** | 2-digit year | 07 |
| **%B** | unabbreviated month | January |
| **%b** | abbreviated month | Jan |
| **%m** | month (00-12) | 00-12 |
| **%A** | unabbreviated weekday | Monday |
| **%a** | abbreviated weekday | Mon |
| **%d** | day as a number (0-31) | 01-31 |
* **An example:**
```r
#today's date
now <- date()
format(now, format="%B %d %Y
```
## #R create variable
Use operator "<-" or "=" to create variable
#### Assign a variable:
```r
cevi<-c(1:10)
data<-c("Berlin", "Frankfurt")
```
#### Categorial variable:
```r
data<-c(1:10)
datacat<-ifelse(data%%2,"odd num","even num")
#odd num:= modulo 2 (%%2)
datacat
############
[1] "odd num" "even num" "odd num" "even num" "odd num" "even num"
[7] "odd num" "even num" "odd num" "even num"
```
## #R operators
#### Arithmetic Operators:
| **Operator** | **Description** |
| ------------- | --------------------------- |
| **+** | addition |
| **-** | subtraction |
| **\*** | multiplication |
| **/** | division |
| **^ or \*\*** | exponentiation |
| **x %% y** | modulus (x mod y) 5%%2 is 1 |
| **x %/% y** | integer division 5%/%2 is 2 |
#### Logical Operators:
| **Operator** | **Description** |
| ------------- | ------------------------ |
| **==** | exactly equal to |
| **!=** | not equal to |
| **>** | greater than |
| **>=** | greater than or equal to |
| **<** | less than |
| **<=** | less than or equal to |
| **!x** | Not x |
| **x \| y** | x OR y |
| **x & y** | x AND y |
| **isTRUE(x)** | test if X is TRUE |
```
# example
x <- c(1:5)
x[(x>1) & (x<5)]
####################
# result: 2 3 4
# how it works?
#x <- c(1:5)
# x
# 1 2 3 4 5
# x > 1
# F T T T T
# x < 5
# T T T T F
# x > 1 & x < 5
# F T T T F
# x[c(F,T,T,T,F)]
# 2 3 4
```
## #R built in functions-X
Numeric Functions:
| **Function** | **Description** |
| -------------------------------------------- | ------------------------------ |
| **abs(***x***)** | absolute value |
| **sqrt(***x***)** | square root |
| **log(***x***)** | natural logarithm |
| l**og10(***x***)** | common logarithm |
| **exp(***x***)** | e^*x* |
| **cos(***x***), sin(***x***), tan(***x***)** | - |
| **signif(***x***, digits=***n***)** | signif(3.475, digits=2) is 3.5 |
| **round(***x***, digits=***n***)** | round(3.475, digits=2) is 3.48 |
| **trunc(***x***)** | trunc(5.99) is 5 |
| **floor(***x***)** | floor(3.475) is 3 |
| **ceiling(***x***)** | ceiling(3.475) is 4 |
Character Functions:
| **Function** | **Description** |
| -------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| **tolower(***x***)** | Lowercase |
| **toupper(***x***)** | Uppercase |
| **substr(***x***, start=***n1***, stop=***n2***)** | Extract or replace substrings x <- "abcdef" substr(x, 2, 3) is "bc" substr(x, 2, 4) <- "22222" is "a22def" |
| **strsplit(***x***,** *split***)** | Split the elements strsplit("abc", "") returns 3 element vector "a","b","c" |
| **paste(..., sep="")** | |
## #R control structures-X