R preliminaries

class: center, middle, inverse, title-slide

.title[
# R preliminaries
]
.author[
### Mikhail Dozmorov
]
.institute[
### Virginia Commonwealth University
]
.date[
### 10-26-2022
]

---

## Summary statistics

- Simple statistical functions: `min()`, `max()`, `mean()`, `median()`, `sd()`, `cor()`, `summary()`, `quantile()`)

- These, and many other functions, have settings to properly handle NAs, e.g., `mean(x, na.rm = FALSE, ...)`

- `complete.cases()` on a matrix/data frame returns row-wise logical with TRUE for rows without NAs

- `unique()` - unique elements in a vector. Combine with `length()` to get the number of unique elements

- `table()` - contingency table for a vector (the number of elements per unique level)

---
## Summary statistics

```r
data(mtcars)    # simple summary 
# ?mtcars
head(mtcars)
```

```
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
```

```r
mean(mtcars$mpg) # Try median, sd, var, min, max
```

```
## [1] 20.09062
```

```r
summary(mtcars$mpg)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.40   15.43   19.20   20.09   22.80   33.90
```

---
## Summary statistics

```r
quantile(mtcars$mpg, probs = c(.20, .80))
```

```
##   20%   80% 
## 15.20 24.08
```

```r
cor(mtcars$mpg, mtcars$hp) # sample correlation coeficient
```

```
## [1] -0.7761684
```

```r
table(mtcars$cyl)
```

```
## 
##  4  6  8 
## 11  7 14
```

```r
table(mtcars$cyl)/length(mtcars$cyl) # normalized by the total number of observations = 32
```

```
## 
##       4       6       8 
## 0.34375 0.21875 0.43750
```

---
## Control structures inside R/functions

- `if, else`
- `for`
- `while`
- `repeat`
- `break`
- `next`

---
## If-else statement

Conditional code execution

``` r
if (condition) {
  # do something
} else {
  # do something else
}
```

- `==`: Equal to
- `!=`: Not equal to
- `>`, `>=`: Greater than, greater than or equal to
- `<`, `<=`: Less than, less than or equal to

```r
x <- 1:15
if (sample(x, 1) <= 10) {
  print("x is less than 10")
} else {
  print("x is greater than 10")
}
```

```
## [1] "x is greater than 10"
```

---
## For loop

Repetitive code execution

```r
for (i in 1:5) {
  cat(i)
}
```

```
## 12345
```

Compare with

```r
for (i in 1:5) {
  print(i)
}
```

```
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
```

---
## More uses of For loops

```r
x <- c("apples", "oranges", "bananas", "strawberries")

for (i in x) {
  cat(i); cat(" ")
}
```

```
## apples oranges bananas strawberries
```

```r
for (i in 1:4) {
  cat(x[i]); cat(" ")
}
```

```
## apples oranges bananas strawberries
```

```r
for (i in seq(x)) {
  cat(x[i]); cat(" ")
}
```

```
## apples oranges bananas strawberries
```

---
## Nested For loops

```r
m <- matrix(1:10, 2)
m
```

```
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10
```

```r
for (i in seq(nrow(m))) {
  for (j in seq(ncol(m))) {
    print(m[i, j])
  }
}
```

```
## [1] 1
## [1] 3
## [1] 5
## [1] 7
## [1] 9
## [1] 2
## [1] 4
## [1] 6
## [1] 8
## [1] 10
```

---
## while, repeat loops

```r
i <- 1
while (i < 10) {
  print(i)
  i <- i + 1
} # Be sure there is a way to exit out of a while loop
```

```
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
```

``` r
repeat {
  # simulations; generate some value have an expectation if within some range,
  # then exit the loop
  if ((value - expectation) <= threshold) {
    break
  }
}
```

---
## Combine any statements/functions

```r
for (i in 1:20) {           
  if (i%%2 == 1) {
    next                # skip printing over odd numbers
  } else {
    print(i)
  }
}
```

```
## [1] 2
## [1] 4
## [1] 6
## [1] 8
## [1] 10
## [1] 12
## [1] 14
## [1] 16
## [1] 18
## [1] 20
```

---
## Vectorized operation

Many operations in R are already vectorized, making code more efficient, concise, and easier to read

```r
x <- 1:4; y <- 6:9
x
```

```
## [1] 1 2 3 4
```

```r
y
```

```
## [1] 6 7 8 9
```

```r
x * y
```

```
## [1]  6 14 24 36
```

```r
x / y
```

```
## [1] 0.1666667 0.2857143 0.3750000 0.4444444
```

---
## Manipulating vectors

```r
ages <- c(40, 50, 60, 70, 80)
# add a value to end of vector
ages <- c(ages, 90) 
# add value at the beginning
ages <- c(30, ages)
# extracting second value
ages[2]
```

```
## [1] 40
```

```r
# excluding second value
ages[-2]
```

```
## [1] 30 50 60 70 80 90
```

```r
# extracting first and third values
ages[c(1, 3)] 
```

```
## [1] 30 50
```

---
## `apply` family of functions

Writing for, while loops in R are inefficient, and we want to vectorize computation in R.

- `apply()` - apply a function over the margins of an array

- `lapply()` - loop over a list and evaluate a function on each element

- `sapply()` - same as lapply but try to simplify results, if the result is a list where every element is length 1, then a vector is returned

- `mapply()` - multivariate version of lapply

- `tapply()` -  apply a function over subsets of a vector

---
## apply examples

```r
x <- 1:4
lapply(x, runif)
```

```
## [[1]]
## [1] 0.8818407
## 
## [[2]]
## [1] 0.04883314 0.40796025
## 
## [[3]]
## [1] 0.1738366 0.9037754 0.7629888
## 
## [[4]]
## [1] 0.62638605 0.39945354 0.02538207 0.11469272
```

```r
x <- list(a = 1:4, b = rnorm(10), c = rnorm(20, 1))
sapply(x, mean)
```

```
##          a          b          c 
##  2.5000000 -0.1146121  1.0226676
```

---
## apply examples

```r
#If the result is a list where every element is a vector of the same length (> 1), a matrix is returned.
x <- list(rnorm(100), runif(100), rpois(100, 1))
sapply(x, quantile, probs = c(0.25, 0.75))
```

```
##           [,1]      [,2] [,3]
## 25% -0.6915946 0.2156673    0
## 75%  0.7931717 0.7460333    2
```

```r
x <- matrix(rnorm(200), 20, 10)
apply(x, 1, sum)
```

```
##  [1]  4.5910361  0.1467918 -0.8917816  4.1782556 -1.8283390  3.5942265
##  [7]  1.8619504 -2.0195184 -1.9222401  1.4993943 -4.2959375  0.2893697
## [13] -1.6865616  1.8719928 -2.5612439 -1.0264835  4.4188670  5.1879845
## [19] -0.8778389  3.3824086
```

```r
apply(x, 2, mean)
```

```
##  [1]  0.21287216  0.12690330  0.38845135 -0.04634389  0.03047213  0.05851572
##  [7]  0.03920000 -0.18908547  0.11039650 -0.03576514
```

---
## apply examples

For sums and means of matrix dimensions, we have some shortcuts

```r
rowSums  = apply(x, 1, sum)
rowMeans = apply(x, 1, mean)
colSums  = apply(x, 2, sum)
colMeans = apply(x, 2, mean)
```

Check `?rowSums` help on these base R functions

---
## tapply

Apply a function to groups of values of an array defined by the levels of certain factors.

``` r
function (X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE)
X is a vector
INDEX is a factor or a list of factors (or else they are coerced to factors)
FUN is a function to be applied
... contains other arguments to be passed FUN
simplify, should we simplify the result?
```

```r
x <- c(rnorm(10), runif(10), rnorm(10, 1))
f <- gl(3, 10)
tapply(x, f, mean)
```

```
##          1          2          3 
## -0.4701166  0.4143934  0.6149223
```

---
## mapply

.panelset[
.panel[.panel-name[mapply]
mapply is a multivariate version of sapply. mapply applies FUN to the first elements of each ... argument, the second elements, the third elements, and so on. Arguments are recycled if necessary.

``` r
function (FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE)
FUN is a function to apply
... contains arguments to apply over
MoreArgs is a list of other arguments to FUN.
SIMPLIFY indicates whether the result should be simplified
```
]
.panel[.panel-name[Example 1]

```r
mapply(rep, 1:4, 4:1)
```

```
## [[1]]
## [1] 1 1 1 1
## 
## [[2]]
## [1] 2 2 2
## 
## [[3]]
## [1] 3 3
## 
## [[4]]
## [1] 4
```
rep(x, length.out)
]
.panel[.panel-name[Example 1]

```r
mapply(rnorm, mean = 1:3, sd = 1:3, n = seq(5, 15, by = 5))
```

```
## [[1]]
## [1] -1.0085953  2.2791268 -0.1355583  2.1717461  1.4338130
## 
## [[2]]
##  [1] 3.180368 3.029437 2.479470 1.673017 4.851908 2.577321 5.262167 2.244685
##  [9] 2.116074 1.207478
## 
## [[3]]
##  [1]  6.4892297  2.5103664  3.6762450  1.6827791  2.1785540  0.5672901
##  [7] -0.8010352  1.5578697  0.5731406  1.9819280 -0.5586595  2.1592128
## [13]  3.6467275  6.9318631  6.7501765
```
rnorm(n, mean = 0, sd = 1)
]
]

---
## Formatting

```r
round(c(3.14159, 2.71828), digits = 2)
```

```
## [1] 3.14 2.72
```

```r
formatC(c(3.14159, 2.71828), digits = 2, format = "e")
```

```
## [1] "3.14e+00" "2.72e+00"
```

---
## Handling NAs

- NAs create problems in calculations. Many functions have built-in mechanism to handle NAs. Check `?mean` and the 'na.rm' argument

- Check for presence of NAs in your data using `is.na()`

```r
vec_with_NAs <- c(1, 2, NA, 3)
sum(is.na(vec_with_NAs))
```

```
## [1] 1
```

- Remove all **rows** containing NAs from a data frame

```r
dat <- dat[complete.cases(dat), ]
```

---
## Modifying vector elements

.can-edit[

```r
a <- c("Heineken", "Tuborg", "Carlsberg")
paste("Beer", a)
```

```
## [1] "Beer Heineken"  "Beer Tuborg"    "Beer Carlsberg"
```

```r
paste("Beer", a, sep = ":")
```

```
## [1] "Beer:Heineken"  "Beer:Tuborg"    "Beer:Carlsberg"
```

```r
paste("Beer", a, collapse = ":")
```

```
## [1] "Beer Heineken:Beer Tuborg:Beer Carlsberg"
```

```r
paste0("e", "yes")
```

```
## [1] "eyes"
```
]

---
## Modifying vector elements

```r
a <- c("but", "cut")
sub("u", "a", a)
```

```
## [1] "bat" "cat"
```

```r
a <- c("Column.1", "Column.2")
strsplit(a, ".", fixed = TRUE)
```

```
## [[1]]
## [1] "Column" "1"     
## 
## [[2]]
## [1] "Column" "2"
```

```r
sapply(a, function(x) strsplit(x, ".", fixed = TRUE)[[1]][2])
```

```
## Column.1 Column.2 
##      "1"      "2"
```

`stringr` - functions for string manipulations https://stringr.tidyverse.org/