1 Generate Matrixes

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1.1 Create a N by 2 Matrix from 3 arrays

Names of each array become row names automatically.

ar_row_one <- c(-1,+1)
ar_row_two <- c(-3,-2)
ar_row_three <- c(0.35,0.75)

mt_n_by_2 <- rbind(ar_row_one, ar_row_two, ar_row_three)
kable(mt_n_by_2) %>%
  kable_styling_fc()
ar_row_one -1.00 1.00
ar_row_two -3.00 -2.00
ar_row_three 0.35 0.75

1.2 Name Matrix Columns and Rows

# An empty matrix with Logical NA
mt_named <- matrix(data=NA, nrow=2, ncol=2)
colnames(mt_named) <- paste0('c', seq(1,2))
rownames(mt_named) <- paste0('r', seq(1,2))
mt_named
##    c1 c2
## r1 NA NA
## r2 NA NA

1.3 Generate NA Matrix

Allocate with NA or NA_real_ or NA_int_. Clarity in type definition is preferred.

# An empty matrix with Logical NA
mt_na <- matrix(data=NA, nrow=2, ncol=2)
str(mt_na)
##  logi [1:2, 1:2] NA NA NA NA
# An empty matrix with numerica NA
mt_fl_na <- matrix(data=NA_real_, nrow=2, ncol=2)
mt_it_na <- matrix(data=NA_integer_, nrow=2, ncol=2)

str(mt_fl_na)
##  num [1:2, 1:2] NA NA NA NA
str(mt_fl_na)
##  num [1:2, 1:2] NA NA NA NA

1.4 Generate Matrixes with values

Random draw from the normal distribution, random draw from the uniform distribution, and combine resulting matrixes.

# Generate 15 random normal, put in 5 rows, and 3 columns
mt_rnorm <- matrix(rnorm(15,mean=0,sd=1), nrow=5, ncol=3)

# Generate 15 random normal, put in 5 rows, and 3 columns
mt_runif <- matrix(runif(15,min=0,max=1), nrow=5, ncol=5)

# Combine
mt_rnorm_runif <- cbind(mt_rnorm, mt_runif)

# Display
kable(round(mt_rnorm_runif, 3)) %>% kable_styling_fc()
-0.237 0.696 0.857 0.077 0.406 0.715 0.077 0.406
-0.544 0.542 1.490 0.615 0.974 0.724 0.615 0.974
-1.295 -1.222 -0.214 0.234 0.916 0.694 0.234 0.916
-0.085 -0.730 -0.827 0.069 0.374 0.145 0.069 0.374
-0.177 -0.957 0.145 0.396 0.468 0.478 0.396 0.468

Now we generate a matrix with sequential integers, and either fill matrix by columns or fill matrix by rows.

# with byrow set to FALSE, will fill first col, then second col, etc..
mt_index_colbycol <- matrix(seq(0, 15), nrow=4, ncol=4, byrow=FALSE)
# Display
kable(mt_index_colbycol,
  caption= "with byrow=FALSE, the default, will fill col by col") %>%
  kable_styling_fc()
with byrow=FALSE, the default, will fill col by col
0 4 8 12
1 5 9 13
2 6 10 14
3 7 11 15 
# with byrow set to TRUE, will fill row by row
mt_index_rowbyrow <- matrix(seq(0, 15), nrow=4, ncol=4, byrow=TRUE)
# Display
kable(mt_index_rowbyrow,
  caption= " with byrow=TRUE, will fill row by row") %>%
  kable_styling_fc()
with byrow=TRUE, will fill row by row
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15

1.5 Replace a Subset of Matrix Values by NA_real_

For values in matrix that fall below or above some thresholds, we will replace these values by NA_real_.

fl_max_val <- 0.8
fl_min_val <- 0.2
mt_rnorm_runif_bd <- mt_rnorm_runif
mt_rnorm_runif_bd[which(mt_rnorm_runif < fl_min_val)] <- NA_real_
mt_rnorm_runif_bd[which(mt_rnorm_runif > fl_max_val)] <- NA_real_
# Print
print(mt_rnorm_runif_bd)
##      [,1]      [,2] [,3]      [,4]      [,5]      [,6]      [,7]      [,8]
## [1,]   NA 0.6963672   NA        NA 0.4062423 0.7153587        NA 0.4062423
## [2,]   NA 0.5418076   NA 0.6151660        NA 0.7240569 0.6151660        NA
## [3,]   NA        NA   NA 0.2341533        NA 0.6941645 0.2341533        NA
## [4,]   NA        NA   NA        NA 0.3736810        NA        NA 0.3736810
## [5,]   NA        NA   NA 0.3958477 0.4675057 0.4778807 0.3958477 0.4675057

1.6 Sort Each Matrix Row or Column

Now we sort within each row or within each column of the random matrix.

# Within row sort
mt_rnorm_runif_row_sort <- t(apply(
  mt_rnorm_runif, 1, sort
))
# Within column sort, note no transpose
mt_rnorm_runif_col_sort <- apply(
  mt_rnorm_runif, 2, sort
)
# Display
kable(round(mt_rnorm_runif_row_sort, 3),
      caption="Each row sort low to high") %>%
  kable_styling_fc()
Each row sort low to high
-0.237 0.077 0.077 0.406 0.406 0.696 0.715 0.857
-0.544 0.542 0.615 0.615 0.724 0.974 0.974 1.490
-1.295 -1.222 -0.214 0.234 0.234 0.694 0.916 0.916
-0.827 -0.730 -0.085 0.069 0.069 0.145 0.374 0.374
-0.957 -0.177 0.145 0.396 0.396 0.468 0.468 0.478 
kable(round(mt_rnorm_runif_col_sort, 3),
      caption="Each column sort low to high") %>%
  kable_styling_fc()
Each column sort low to high
-1.295 -1.222 -0.827 0.069 0.374 0.145 0.069 0.374
-0.544 -0.957 -0.214 0.077 0.406 0.478 0.077 0.406
-0.237 -0.730 0.145 0.234 0.468 0.694 0.234 0.468
-0.177 0.542 0.857 0.396 0.916 0.715 0.396 0.916
-0.085 0.696 1.490 0.615 0.974 0.724 0.615 0.974

1.7 Compute Column and Row Statistics

Compute column and row means, and also column and row sums

print(paste0('colSums=',
             paste(round(
               colSums(mt_rnorm_runif),3), collapse=',')
             ))
## [1] "colSums=-2.337,-1.671,1.45,1.392,3.137,2.756,1.392,3.137"
print(paste0('colMeans=',
             paste(round(
               colMeans(mt_rnorm_runif),3), collapse=',')
             ))
## [1] "colMeans=-0.467,-0.334,0.29,0.278,0.627,0.551,0.278,0.627"
print(paste0('rowSums=',
             paste(round(
               rowSums(mt_rnorm_runif),3), collapse=',')
             ))
## [1] "rowSums=2.999,5.39,0.264,-0.611,1.215"
print(paste0('rowMeans=',
             paste(round(
               rowMeans(mt_rnorm_runif),3), collapse=',')
             ))
## [1] "rowMeans=0.375,0.674,0.033,-0.076,0.152"

1.8 Add Column to Matrix with Common Scalar Value

Given some matrix of information, add a column, where all rows of the column have the same numerical value. Use the matrix created prior. - R add column to matrix - r append column to matrix constant value

fl_new_first_col_val <- 111
fl_new_last_col_val <- 999
mt_with_more_columns <- cbind(rep(fl_new_first_col_val, dim(mt_rnorm_runif)[1]),
                              mt_rnorm_runif,
                              rep(fl_new_last_col_val, dim(mt_rnorm_runif)[1]))
# Display
kable(mt_with_more_columns) %>% kable_styling_fc_wide()
111 -0.2366139 0.6963672 0.8573265 0.0769430 0.4062423 0.7153587 0.0769430 0.4062423 999
111 -0.5437052 0.5418076 1.4897541 0.6151660 0.9736633 0.7240569 0.6151660 0.9736633 999
111 -1.2946558 -1.2221654 -0.2142978 0.2341533 0.9162547 0.6941645 0.2341533 0.9162547 999
111 -0.0847676 -0.7297558 -0.8274860 0.0694605 0.3736810 0.1447993 0.0694605 0.3736810 999
111 -0.1772940 -0.9573648 0.1448672 0.3958477 0.4675057 0.4778807 0.3958477 0.4675057 999