RIntrinsics.Rmd

---
title: "R Intrinsics"
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---

```{r setup, include=FALSE}
knitr::opts_chunk$set(message=FALSE,warning=FALSE, cache=TRUE)
```
This is a page that lists functions and has a brief description of how they work.

##Assignment Number 2

###General functions
1.
Help(topic)

This function shows you all the help systems for a problem
```{r}
help(sum)
```


2.
?topic

This function is used when you are looking for the help page documentation of a certain topic in R.
For example you want to understand the summing function
```{r}
?sum
```
3.
Is()

This function is used to see if a certain object in R is part of a class you selected. This is a logical statement.If it is in the class or if it is not in the class then True or False will be returned
```{r}
A<-"H"
is(A, "character")
```
4.
dir()

This function is used to create a vector of characters for a name of files or folders in a directory
```{r}
A<-"hello"
dir(A)
```

5.
list.files()

Similar to the dir function, this function creates a vector of the names of files or folders in a directory

###Input and Output
1.
save()

This function is used to create a temporary file to show that you saved your current work.
```{r}


```


2.
load()

This is used to reload what you previously saved with the save func

3.
data()

This function loads the data sets you have
```{r}
A<-c(1,2,3)
B<-c(5,6,7)
data(A,B)
```


4.
library()

This function loads, attaches, and lists packages


5.
read.csv()

This function transposes a data table to a data frame.

6.
read.table()

Similar to  the previous function this reads table data and creates a data frame 

7. 
scan()

Reads data in a vector or list

8.
print()

This function prints out the argument
```{r}
A<-1
B<-2
print(sum(A,B))

```


9.
cat()

This concatenates outputs of objects

10. 
write.table()

This prints an arguement after its converted from a data frame

###Data Creation
1.
c()

This function combines arguments into a vector.
```{r}
V<-c(1,2,3)
V
```
2.
from:to (where from and to are replaced with numbers, e.g. 1:10)

This creates a vector/variable of a data range starting from and ending to
```{r}
T<-(2:8)
T
```
3.
seq()

This will put numbers from a vector in what ever order you arrage them/set them up
```{r}
Y<-c(2:10)
seq(Y)
```
4.
rep()

This function replicates the values you choose from a vector or a list
```{r}
U<-c(2,3,4,5)
rep(U[2])
```
5.
data.frame()

This creates  data in a frame that closely resembles matrices or lists
```{r}
fr<-c(1,2,3)
dr<-c(6,7,8)
data.frame(fr,dr)
```


6.
list()

This constructs and checks for differnt types of lists in R

7.
matrix()

This creates a matrix from the data given
```{r}
M <- matrix(c(3:14), nrow = 4, byrow = TRUE)
print(M)

```


8.
factor()

This labels a vector as a factor

9.
rbind()

This combines data rows
```{r}
o <- c(7, 4, 4, 9)                  
p <- c(5, 2, 8, 9)                 
k <- c(1, 2, 3, 4)                  
data_1 <- data.frame(o, p, k)
vec_1 <- c(9, 8, 7) 
rbind(data_1, vec_1)

```


10.
cbind()

This combines data columns

###Slicing and extracting data
indexing vectors
x[n] nth element
x[-n] all but nth element
x[1:n] first n elements
x[-(1:n)] elements from n+1 to the end
x[c(1,4,2)] specific elements
x[“name”] elements named “name”
x[x>3] all elements greater than 3
x[x > 3 & x < 5] all elements between 3 and 5
x[x %in% c(“a”,“and”,“the”)] all elements in given set

###Indexing lists
x[n] list with elements n
x[[n]] nt h element of the list
x[[“name”]] element of the list named “name”
x$name id.

###Indexing matrices
x[i,j] element at row i, column j
x[i,] row i
x[,j] column j
x[,c(1,3)] columns 1 and 3
x[“name”,] row named “name”

###Indexing data frames (matrix indexing plus the following)
x[[“name”]] column named “name”
x$nameid.
###Variable conversion
1.
as.data.frame(x)

Checks is objects are a data frame
```{r}
f <- as.data.frame(matrix(1:10,2,4,9),1:5)
print(f)

```
2.
as.numeric(x)

checks is objects interpretable as numbers
```{r}
xt <- as.numeric(matrix(1:10,2,4,9),1:5)
print(xt)

```
3.
as.logical(x)

creates test of the logic in objects

4.
as.character(x)

creates test if object is a character

###Variable information
is.na(x)
is.null(x)
is.data.frame(x)
is.numeric(x)
is.character(x)
length(x)
dim(x)
dimnames(x)
nrow(x)
ncol(x)
class()
attributes()
###Data selection and manipulation

1.
which.max()

This finds the location of the maximun number in a vector
```{r}
d<-c(2,7,10,1,3)
which.max(d)
```
2.
which.min()

This finds the location of the minumum number in a vector
```{r}
w<-c(2,10,11,15,1,20)
which.min(w)
```
3.
which()

This asks which of these logical statements is true

4.
sort()

This ordered data in a vector into ascending or descending order

5.
unique()

This returns a vector like the vector created but its a duplicate

6.
table()

This builds a tables from cross classifying factors
```{r}
q<-c(2,2,3,1,4,5,5,5,6)
table(q)
```

7.
sample()

This creates a sample of data with or without replacement
```{r}
s<-c(3,5,7,9,10)
sample(s,3)
```

###Math
1.
max()

This finds the highest number in a list
```{r}
ABC<-c(1,4,17,3)

max(ABC)

```

2.
min()

This finds the smallest number in a list
```{r}
Y<-c(4,17,36,1)

min(Y)
```

3.
range()

This diplays the lowest and highest number in a list
```{r}
X<-c(3,6,9,10,13,15)

range(X)
```

4.
sum()

This adds all the the numbers you have selected

```{r}
J<-13
K<-15

sum(J,K)
```

5.
mean()

This adds all the numbers in a list and divdes it by the total number

```{r}
I<-c(1,2,3,4,5)

mean(I)
```

6.
median()

This finds the middle most number in a list

```{r}
U<-c(7,3,8,1,10,4)

median(U)
```

7.
var()

This computes the variance of a number set, i.e. how much numbers vary from mean

```{r}
l<-c(4,2,6,11,13)

var(l)
```

8.
sd()

This computes the standard deviations the numbers have from there mean

```{r}
sd(l)
```

9.
cor()

This calculates the covariance or correlation between two matrices
```{r}
D<-c(2,5,7,9,12)

cor(l,D)
```

10.
round()

This rounds a number up to o decimal places

```{r}
T<-12.6

round(T)
```

11.
abs()

This computes the absoulte value of a numer
```{r}
Q<--13

abs(Q)
```
###Matrices

1.
t()

This returns the transposed of x from a matrix or data frame

2.
diag()

This finds the diagonal of a matrix 

3.
rowSums()

This finds the sum of a row in a martix or data frame

4.
colSums()

This finds the sum of a column in a matrix or data frame

5.
rowMeans()

This finds the mean of a row in a matrix 

6.
colMeans()

This finds the mean of a column in a matrix

###Advanced Data processing
1.
apply()

This returns a vector/list of values that have a margin of array

2.
aggregate()

This splits data into subsets
```{r}

```


###Strings

1.
paste()

This concatenates vector

2.
strsplit()

This splits elements of a character vector
```{r}
Vec<-"AngelinaVasquez"
strsplit(Vec,split="")
```
3.
tolower()

This translates characters to lower case
```{r}
e<-"T"
tolower(e)
```
4.
toupper

This translates characters to upper case
```{r}
u<-"w"
toupper(u)
```


###Plotting
1.
hist()

This creates a histogram of given values
```{r}
w<-c(1,2,4,3,3,3,5,6,6,1)
hist(w)
```
2.
plot()

This plots r objects

###Distributions

1.
rnorm()

This runs a normal distribution
```{r}
y<-c(2,2,5,7,8,9)
rnorm(y)
```

2.
runif()

This generates random number
```{r}
runif(2:17)
```
###Programming
show that you can define a function
show that you can write a for loop
show that you can write a while loop
show that you can write an if else statement
Explain what return() does inside a function, show you can use it
Explain what break() does, show you can use it