Matrix Dataframe and DPLYR

Matrix

Ctrl+Alt+I create a chunk

A<-matrix(c(2,4,6,3,12,7,9,13,20,23),nrow=2, byrow=T)
A

     [,1] [,2] [,3] [,4] [,5]
[1,]    2    4    6    3   12
[2,]    7    9   13   20   23

rownames(A)<-c("Anna","Ben")
colnames(A)<- c("Var1","Var2","Var3","Var4","Var5")
A

     Var1 Var2 Var3 Var4 Var5
Anna    2    4    6    3   12
Ben     7    9   13   20   23

nrow(A)

[1] 2

ncol(A)

[1] 5

dim(A)

[1] 2 5

colnames(A)<-month.name[1:5]
A

     January February March April May
Anna       2        4     6     3  12
Ben        7        9    13    20  23

colnames(A)<-month.abb[1:5]
A

     Jan Feb Mar Apr May
Anna   2   4   6   3  12
Ben    7   9  13  20  23

colnames(A)<-LETTERS[1:5]
A

     A B  C  D  E
Anna 2 4  6  3 12
Ben  7 9 13 20 23

colnames(A)<-letters[1:5]
A

     a b  c  d  e
Anna 2 4  6  3 12
Ben  7 9 13 20 23

A[1,4]

[1] 3

A[1,]

 a  b  c  d  e 
 2  4  6  3 12 

A[2,c(1,2,5)]

 a  b  e 
 7  9 23 

A*2

      a  b  c  d  e
Anna  4  8 12  6 24
Ben  14 18 26 40 46

A %*% t(A)

     Anna  Ben
Anna  209  464
Ben   464 1228

A*x=b 2x+1y=3 5x-6y=0

A=matrix(c(2,1,5,-6),nrow = 2, byrow = T)
A

     [,1] [,2]
[1,]    2    1
[2,]    5   -6

b<-c(3,0)
solve(A,b)

[1] 1.0588235 0.8823529

diag(A)

[1]  2 -6

det(A)

[1] -17

a<-c(1,3,4,5,6,7)
b<-c("no","no","yes","no","no","yes")
Data_ab<-cbind(a,b)
Data_ab

     a   b    
[1,] "1" "no" 
[2,] "3" "no" 
[3,] "4" "yes"
[4,] "5" "no" 
[5,] "6" "no" 
[6,] "7" "yes"

Data_ab[4,]

   a    b 
 "5" "no" 

Data_ab[4,2]

   b 
"no" 

Dataframe

library(tibble)
df_ab<-data.frame(a,b)
df_ab

df_ab_tibble<-tibble(a,b)
df_ab_tibble

df_ab_tibble$a

[1] 1 3 4 5 6 7

df_ab_tibble[5,2]="YES"
df_ab_tibble

df_ab_tibble[1,1]=NA
df_ab_tibble

NA

data_1<-data.frame(state.abb,state.area,state.center,state.division)
head(data_1)

data_2<-data.frame(state.area,state.name,state.region)
head(data_2)

dlpyr

library(dplyr)
left_join(data_1,data_2,by="state.area")

right_join(data_1,data_2, by="state.area")

data_3<- data_1 %>% filter(state.area>=10000 & state.area<=100000)
data_3

dim(data_2)

[1] 50  3

dim(data_3)

[1] 34  5

left_join(data_2,data_3)

Joining, by = "state.area"

right_join(data_2,data_3)

Joining, by = "state.area"

data_4<-right_join(data_1,data_2, by="state.area")
data_4

NA

d5<-data_4 %>% filter(state.region=="West")
d5

table(data_4$state.region)

    Northeast         South North Central          West 
            9            16            12            13 

d6<-data_4 %>% filter(state.region==c("West","South"))
d6

d7<-data_4 %>% filter(state.region %in% c("West","South"),state.area>=8000 & state.area<=100000)
d7

d8<- data_4 %>% select(state.area,state.name,state.region)
d8

d9<- data_4 %>% select(-c(1,3,5))
d9

head(data_4)

NA

d10<-data_4 %>% mutate(new_area=state.area/1000)
d10

YN<-sample(c("No","Yes"),nrow(data_4),replace = T)
d11<-d10 %>% mutate(new_YN=YN)
d11

IF<-ifelse(data_4$state.region=="West",1,0)
IF

 [1] 0 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
[27] 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1

d12<-d11 %>% mutate(new_IF=IF)
d12

NA