The Basics

Keyboard Commands

Alt + - gives <- This is the assignment operator which is seen below.

Ctrl + Shift + M gives |> but if using either the magrittr, tidyverse or other packages it will give %>%. Whether these are different is unknown.

Further keyboard commands are at:

Keyboard Shortcuts

More shortcuts

R Markdown Stuff for further analysis

Yet more

To find information on a function, type ? in the console below followed by the function name to display help.

Arrays

Like a Rubic’s cube an array is a 3-dimensional “table”.

Syntax

The syntax is straight forward:

array(data, dim, dimnames)

Where:

data = the data
dim = the structure, or dimensions, of the array
dimnames = row and column names as a list, just like matrices

Example:

a1 <- array(c(1:75), dim = c(5, 5, 3), dimnames = list(letters[1:5], c(1:5), LETTERS[1:3]))
a1

## , , A
## 
##   1  2  3  4  5
## a 1  6 11 16 21
## b 2  7 12 17 22
## c 3  8 13 18 23
## d 4  9 14 19 24
## e 5 10 15 20 25
## 
## , , B
## 
##    1  2  3  4  5
## a 26 31 36 41 46
## b 27 32 37 42 47
## c 28 33 38 43 48
## d 29 34 39 44 49
## e 30 35 40 45 50
## 
## , , C
## 
##    1  2  3  4  5
## a 51 56 61 66 71
## b 52 57 62 67 72
## c 53 58 63 68 73
## d 54 59 64 69 74
## e 55 60 65 70 75

Accessing an element in an array is the same as a matrix but add the 3rd dimension i.e. row, column & dimension:

a1[4, 3, 2]

## [1] 39

Like matrices, rows or columns use the same syntax; 2nd row from 2nd matrix:

a1[2,, 2]

##  1  2  3  4  5 
## 27 32 37 42 47

3rd column from 1st matrix:

a1[, 3, 1]

##  a  b  c  d  e 
## 11 12 13 14 15

Or even the entire matrix:

a1[,, 3]

##    1  2  3  4  5
## a 51 56 61 66 71
## b 52 57 62 67 72
## c 53 58 63 68 73
## d 54 59 64 69 74
## e 55 60 65 70 75

Loops

All loops form a recurring function:

“When this condition is happening… do this.”

The format is generally the same:

when (something here is TRUE) {
do this
}

All loops have a block. The block is considered to be everything between the {} .

For Loops

A function that iterates over a set number of values to perform a repeating task. In its simplest form it says:

“For every value in this list - do this - but only after the condition is tested“

Example 1

# Print the square of each number from 1 to 10 inclusive. This also stores the value into a vector
for (i in 1:10) {
  x1 <- i^2
  print(x1)
}

## [1] 1
## [1] 4
## [1] 9
## [1] 16
## [1] 25
## [1] 36
## [1] 49
## [1] 64
## [1] 81
## [1] 100

Example 2

# Looping over a vector:
x2 <- c("Fred", "George", "Ginny", "Ron", "Percy")

for (i in x2) {
  print(i)
}

## [1] "Fred"
## [1] "George"
## [1] "Ginny"
## [1] "Ron"
## [1] "Percy"

Example 3

# A for loop can also iterate over a vector to create further data:
for (i in x2) {
  print(paste("The name", i, "contains", nchar(i), "characters."))
}

## [1] "The name Fred contains 4 characters."
## [1] "The name George contains 6 characters."
## [1] "The name Ginny contains 5 characters."
## [1] "The name Ron contains 3 characters."
## [1] "The name Percy contains 5 characters."

# Note that spaces do not need to be added to the printed output

Example 4

For loops can be nested to create integrated? values. This example uses the first 3 LETTERS of the alphabet and pastes then against the first 3 letters of the alphabet:

for (i in 1:3) {
  for (j in 1:3) {
    print(paste(LETTERS[i], letters[j], sep = " "))
  }
}

## [1] "A a"
## [1] "A b"
## [1] "A c"
## [1] "B a"
## [1] "B b"
## [1] "B c"
## [1] "C a"
## [1] "C b"
## [1] "C c"

While Loops

A while loop will also run the condition before the block is executed but will not iterate to the next value automatically; it needs to be executed in the block. It also requires an assigned vector beforehand:

In its simplest form it says:

“Whilst this condition is true - do this - but only after the condition is tested“

Example 1

x <- 0

while (x < 5) {
  print(x)
  x <- x + 1
}

## [1] 0
## [1] 1
## [1] 2
## [1] 3
## [1] 4

If Loops

Perhaps not a loop as the condition only executes once. Simply put:

“Do this if the condition is met”

Example

x <- 10

if(x == 10) {
  print("X is equal to 10")
}

## [1] "X is equal to 10"

Else If Loops

An extension to the if loop that simply says:

“Do this if the condition is met - if it is not met do this instead”

Example

x <- 10

if (x < 10) {
  print("x is lower than 10")
} else if (x > 10) {
  print("x is greater than 10")
} else {
  print("x is 10")
}

## [1] "x is 10"

Repeat Loops

As the name suggests these repeat until a further condition is met. They will read the block first. That further condition is an if loop:

“Repeat this condition until the secondary condition is met then execute that block”

Example

x <- 1

repeat {
  print(x)
  x <- x + 1
  if(x == 10) {
    break
  }
}

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

Switch

This last loop is perhaps more of a statement than a loop and is the only loop function that is not enclosed in { }It only iterates once as it will read the block before executing the condition. Note the commas in the example. Also note, only character switches have a default.

Example

x <- 8

switch (x,
  "Yes it's 1",
  "Yes it's 2",
  "Yes it's 3",
  "Yes it's 4",
)

# If the vector is a character each case needs to be defined:

y <- "x"

switch(y,
       fred = "It's Fred",
       george = "It's George",
       ron = "It's Ron",
       ginny = "It's Ginny",
       percy = "It's Percy",
       "It's no-one"
       )

## [1] "It's no-one"

For numeric switches, the default is to return NULL it will display nothing. To return a value add the below after the switch:

if (is.null(expression)) expression <- "No value"

Randoms

If a .csv file contains blank cells, these can be replaced with NA’s:

read.csv("file", na.strings = " ", NA)

class() gives the type of vector

head() gives the first 10 rows of a dataframe. This is the default setting

nchar() gives the number of characters in a vector

view() displays the dataframe

sheetindex = lets read.csv select the sheet

sort() a function to sort data by increasing or decreasing values

table() gives a tabulated frequency count

tail() gives the last 10 rows of a dataframe. This is the default setting

unique() similar to table() but only gives unique values

replace(vector, position, value) replace values in a vector

print() print characters etc

paste()

letters() lower case letters

LETTERS() upper case letters

Assign a variable outside a function <<-

To get the current directory:

getwd()

## [1] "C:/Users/steve/Desktop/R Files, Examples etc/Bibble"

# An alternative is:
path.expand("~/")

## [1] "C:/Users/steve/Documents/"

But this only returns the value to the penultimate folder and gives it in this format, however, when you set the working directory the / has to become \\.

setwd("C:\\Users\\steve\\Desktop\\R Files, Examples etc\\TEST")

# Display directory
getwd()

## [1] "C:/Users/steve/Desktop/R Files, Examples etc/TEST"

The alternate to having to change all the slashes is:

setwd(chartr("\\", "/", r"(C:\Users\steve\Desktop\V2C)"))

Dataframes

Dataframes are a type of table but differ significantly [define difference].

Syntax

The syntax for a dataframe is:

data.frame(data, row.names, check.rows, check.names, fix.empty.names, stringAsFactors)

Where:

data - the data to use

row.names - optional - specifies a column to be used as row names (like column 1)

check.rows - optional - if TRUE rows are checked for consistency

check.names - optional - if TRUE syntactically checks the names of the variables in the dataframe

fix.empty.names - optional - argument for replacing empty names

stringAsFactors - optional - should character vectors be converted to factors?

df <- data.frame(
  A = 1:5,
  B = 6:10,
  C = 11:15,
  D = 16:20,
  E = 21:25
)

# View the df
df

##   A  B  C  D  E
## 1 1  6 11 16 21
## 2 2  7 12 17 22
## 3 3  8 13 18 23
## 4 4  9 14 19 24
## 5 5 10 15 20 25

Using ‘data()’

You can view the dataframes using the data() function but this will not save anything to a variable; to do this you need the below format (note the head() function that displays the first x rows of code - tail() in-turn displays the last x rows of code):

df1 <- Seatbelts
df1 <- as.data.frame(df1)
head(df1)

##   DriversKilled drivers front rear   kms PetrolPrice VanKilled law
## 1           107    1687   867  269  9059   0.1029718        12   0
## 2            97    1508   825  265  7685   0.1023630         6   0
## 3           102    1507   806  319  9963   0.1020625        12   0
## 4            87    1385   814  407 10955   0.1008733         8   0
## 5           119    1632   991  454 11823   0.1010197        10   0
## 6           106    1511   945  427 12391   0.1005812        13   0

.xlsx Files

These files require a library to be opened. There are two ways to do this:

open the entire library - library(readxl)
use a one-time call to a specific function from a library as seen above: readxl::read_xlsx

Other Functions

Creating a dataframe

The simplest way to create a dataframe is to create a matrix then convert it to a dataframe:

m1 <- matrix(c(1:30), 5, 6)
m1 <- as.data.frame(m1)
m1

##   V1 V2 V3 V4 V5 V6
## 1  1  6 11 16 21 26
## 2  2  7 12 17 22 27
## 3  3  8 13 18 23 28
## 4  4  9 14 19 24 29
## 5  5 10 15 20 25 30

The easiest way to get a dataframe is to download one if you have one to hand.

df2 <- read.csv(chartr("\\", "/", r"(C:\Users\steve\Desktop\R Files, Examples etc\Bibble\10by10.csv)"))

A .csv file is normally set to the first column. If these need splitting use sep = ", " to split the data up.

To easily review a dataset (although this does not print here it is displayed within the RStudio environment):

View(df2)

Or to get the structure of the data:

str(df2)

## 'data.frame':    10 obs. of  10 variables:
##  $ A: int  1 11 21 31 41 51 61 71 81 91
##  $ B: int  2 12 22 32 42 52 62 72 82 92
##  $ C: int  3 13 23 33 43 53 63 73 83 93
##  $ D: int  4 14 24 34 44 54 64 74 84 94
##  $ E: int  5 15 25 35 45 55 65 75 85 95
##  $ F: int  6 16 26 36 46 56 66 76 86 96
##  $ G: int  7 17 27 37 47 57 67 77 87 97
##  $ H: int  8 18 28 38 48 58 68 78 88 98
##  $ I: int  9 19 29 39 49 59 69 79 89 99
##  $ J: int  10 20 30 40 50 60 70 80 90 100

Or the summary:

summary(df2)

##        A              B              C              D              E       
##  Min.   : 1.0   Min.   : 2.0   Min.   : 3.0   Min.   : 4.0   Min.   : 5.0  
##  1st Qu.:23.5   1st Qu.:24.5   1st Qu.:25.5   1st Qu.:26.5   1st Qu.:27.5  
##  Median :46.0   Median :47.0   Median :48.0   Median :49.0   Median :50.0  
##  Mean   :46.0   Mean   :47.0   Mean   :48.0   Mean   :49.0   Mean   :50.0  
##  3rd Qu.:68.5   3rd Qu.:69.5   3rd Qu.:70.5   3rd Qu.:71.5   3rd Qu.:72.5  
##  Max.   :91.0   Max.   :92.0   Max.   :93.0   Max.   :94.0   Max.   :95.0  
##        F              G              H              I              J        
##  Min.   : 6.0   Min.   : 7.0   Min.   : 8.0   Min.   : 9.0   Min.   : 10.0  
##  1st Qu.:28.5   1st Qu.:29.5   1st Qu.:30.5   1st Qu.:31.5   1st Qu.: 32.5  
##  Median :51.0   Median :52.0   Median :53.0   Median :54.0   Median : 55.0  
##  Mean   :51.0   Mean   :52.0   Mean   :53.0   Mean   :54.0   Mean   : 55.0  
##  3rd Qu.:73.5   3rd Qu.:74.5   3rd Qu.:75.5   3rd Qu.:76.5   3rd Qu.: 77.5  
##  Max.   :96.0   Max.   :97.0   Max.   :98.0   Max.   :99.0   Max.   :100.0

Which provides:

min - the minimum value

1st quantile - the first 25% of the value

median - the middle value

mean - the mean value (total / amount)

3rd quantile - the first 75% of the value

max- the maximum value

NA - the number of NAs

Deleting Rows & Columns

Very useful in a table, matrix or dataframe. This deletes columns 1, 3 & 4 from df2.

df2 <- df2[-c(1, 3:4)]
df2

##     B  E  F  G  H  I   J
## 1   2  5  6  7  8  9  10
## 2  12 15 16 17 18 19  20
## 3  22 25 26 27 28 29  30
## 4  32 35 36 37 38 39  40
## 5  42 45 46 47 48 49  50
## 6  52 55 56 57 58 59  60
## 7  62 65 66 67 68 69  70
## 8  72 75 76 77 78 79  80
## 9  82 85 86 87 88 89  90
## 10 92 95 96 97 98 99 100

# Deleting rows is exactly the same except a comma is added before the closing bracket ]

df2 <- df2[-c(3),]
df2

##     B  E  F  G  H  I   J
## 1   2  5  6  7  8  9  10
## 2  12 15 16 17 18 19  20
## 4  32 35 36 37 38 39  40
## 5  42 45 46 47 48 49  50
## 6  52 55 56 57 58 59  60
## 7  62 65 66 67 68 69  70
## 8  72 75 76 77 78 79  80
## 9  82 85 86 87 88 89  90
## 10 92 95 96 97 98 99 100

# To delete a row that contains a value
df2 <- df2[!grepl("46", "F"),]
df2

##     B  E  F  G  H  I   J
## 1   2  5  6  7  8  9  10
## 2  12 15 16 17 18 19  20
## 4  32 35 36 37 38 39  40
## 5  42 45 46 47 48 49  50
## 6  52 55 56 57 58 59  60
## 7  62 65 66 67 68 69  70
## 8  72 75 76 77 78 79  80
## 9  82 85 86 87 88 89  90
## 10 92 95 96 97 98 99 100

Changing Row & Column Names

Changing these is easy and there are several styles to change them to.

df <- read.csv(chartr("\\", "/", r"(C:\Users\steve\Desktop\R Files, Examples etc\Bibble\10by10.csv)"))

# All examples work for both rows and columns
rownames(df) <- c("Row 1", "Row 2", "Row 3", "Row 4", "Row 5", "Row 6", "Row 7", "Row 8", "Row 9", "Row 10")
df

##         A  B  C  D  E  F  G  H  I   J
## Row 1   1  2  3  4  5  6  7  8  9  10
## Row 2  11 12 13 14 15 16 17 18 19  20
## Row 3  21 22 23 24 25 26 27 28 29  30
## Row 4  31 32 33 34 35 36 37 38 39  40
## Row 5  41 42 43 44 45 46 47 48 49  50
## Row 6  51 52 53 54 55 56 57 58 59  60
## Row 7  61 62 63 64 65 66 67 68 69  70
## Row 8  71 72 73 74 75 76 77 78 79  80
## Row 9  81 82 83 84 85 86 87 88 89  90
## Row 10 91 92 93 94 95 96 97 98 99 100

# Or
colnames(df) <- c(letters[1:10])
df

##         a  b  c  d  e  f  g  h  i   j
## Row 1   1  2  3  4  5  6  7  8  9  10
## Row 2  11 12 13 14 15 16 17 18 19  20
## Row 3  21 22 23 24 25 26 27 28 29  30
## Row 4  31 32 33 34 35 36 37 38 39  40
## Row 5  41 42 43 44 45 46 47 48 49  50
## Row 6  51 52 53 54 55 56 57 58 59  60
## Row 7  61 62 63 64 65 66 67 68 69  70
## Row 8  71 72 73 74 75 76 77 78 79  80
## Row 9  81 82 83 84 85 86 87 88 89  90
## Row 10 91 92 93 94 95 96 97 98 99 100

# Or
colnames(df) <- c(LETTERS[1:10])
df

##         A  B  C  D  E  F  G  H  I   J
## Row 1   1  2  3  4  5  6  7  8  9  10
## Row 2  11 12 13 14 15 16 17 18 19  20
## Row 3  21 22 23 24 25 26 27 28 29  30
## Row 4  31 32 33 34 35 36 37 38 39  40
## Row 5  41 42 43 44 45 46 47 48 49  50
## Row 6  51 52 53 54 55 56 57 58 59  60
## Row 7  61 62 63 64 65 66 67 68 69  70
## Row 8  71 72 73 74 75 76 77 78 79  80
## Row 9  81 82 83 84 85 86 87 88 89  90
## Row 10 91 92 93 94 95 96 97 98 99 100

Replacing Missing Data

To replace NAs in a dataframe:

# First, introduce an NA
df2[7,8] <- NA

# Then use is.na to replace
df2[is.na(df2)] <- 'A'

Other Dataframe Snippets

Length of row:

length(df2[,1])

## [1] 9

Or column:

length(df2[1,])

## [1] 8

# Dimensions
dim(df)

## [1] 10 10

# Get row names (without changing them)
rownames(df)

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

# Ditto column names
colnames(df)

##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"

A single cell, row, rows and/or columns in a dataframe can be saved to a vector:

# get row 3, column E
v1 <- df[3, 5]

# Get column B
v2 <- df2$B

# Get rows 2 & 3
v3 <- df2$c[2:3]

# Want multiple cells? Columns 1, 3, 5 - 7 of row 1
v4 <- df2$A[1, c(1, 3, 5:7)]

Dataframes (and other types of storage vectors) will, at some point, contain blank data which will need to be dealt with. Dependant on the way the blank data is presented dictates the process required but an easy rule is:

var[data == "?"] <- NA

Meaning that any cell containing a ? will be replaced with NA. var & data is the name of the dataframe. This is shown below:

df4 <- data.frame(matrix(c(1:4, "?", 6:11, "?", 13:15, "?", 17:23, "?", "?"), 5, 5))
df4

##   X1 X2 X3 X4 X5
## 1  1  6 11  ? 21
## 2  2  7  ? 17 22
## 3  3  8 13 18 23
## 4  4  9 14 19  ?
## 5  ? 10 15 20  ?

df4[df4 == "?"] <- NA
df4

##     X1 X2   X3   X4   X5
## 1    1  6   11 <NA>   21
## 2    2  7 <NA>   17   22
## 3    3  8   13   18   23
## 4    4  9   14   19 <NA>
## 5 <NA> 10   15   20 <NA>

Once all blank data is formatted to the same variable a further function can be used to bring the dataframe back together:

is.na() Function

Checks a variable for missing data.

v1 <- c(99L, 43L)

# Returns FALSE for valid data
is.na(v1)

## [1] FALSE FALSE

# Returns TRUE for valid data. Note the ! at the beginning
!is.na(v1)

## [1] TRUE TRUE

Vectors

There are 5 types of vectors:

Logical - boolean (TRUE or FALSE)

{r} v1 <- c(TRUE, FALSE, FALSE) # c() Allows for a range of values to be stored}

Integer - whole numbers

{r} v2 <- c(99L, 43L) # The 'L' specifies integer}
Numeric - mixed numbers

{r} v3 <- c(15, 19.35, 0.86, 32.456, 18.2)}
Complex - algebraic numbers

{r} v4 <- c(4+3i, 8+7i) # The 'i' specifies a complex number}
Character - strings

{r} v5 <- c("This", 'is', "today") # n.b. This will print "Thisistoday" as spaces need to be added. Note each word is braced by either ' or " - these must be paired for each brace and not eg. "This'}

Variables can be concatenated together:

{r} v6 <- v2 + v2 v6}

Can be checked against:

{r} v2 > 44}

And a single value, or a range of values can be taken:

{r} v3[2] v3[c(3:5)]}

{r} x1 <- c((1:3), (1:3)) x1 class(x1) x2 <- as.character(x1) x2 class(x2) x3 <- as.factor(x1) x3 class(x3) x4 <- as.integer(x3) x4 class(x4)}

Markdown Info

Back ticks form the basis of small bits of code.

Three back ticks create a chunk of code: ```

A chunk of code

The chunk must be closed of by three back ticks too.

A single line of code can be written with just one back tick:

A line of code

And must be closed with another single back tick. Or click the </> button on the menu (next to the bold and italics buttons).

Bold text is encapsulated by ** whereas Italics are encapsulated by *

Code can be inserted into these chunks to. This:

“I counted ‘r sum(c(1, 2, 3))’ blue cars on the motorway.”

Becomes: “I counted 6 blue cars on the motorway.”

Special Characters

Code-based

A’s

\u00C0 → À

\u00C1 → Á

\u00C2 → Â

\u00C3 → Ã

\u00C4 → Ä

\u00C5 → Å

\u00E0 → à

B’s

\u00E1 → á

\u00E2 → â

\u00E3 → ã

\u00E4 → ä

\u00E5 → å

C’s

\u00C7 → Ç

\u00E7 → ç

E’s

\u00C8 → È

\u00C9 → É

\u00CA → Ê

\u00CB → Ë

\u00E8 → è

\u00E9 → é

\u00EA → ê

\u00EB → ë

I’s

\u00CC → Ì

\u00CD → Í

\u00CE → Î

\u00CF → Ï

\u00EC → ì

\u00ED → í

\u00EE → î

\u00EF → ï

N’s

\u00D1 → Ñ

\u00F1 → ñ

O’s

\u00D2 → Ò

\u00D3 → Ó

\u00D4 → Ô

\u00D5 → Õ

\u00D6 → Ö

\u00F2 → ò

\u00F3 → ó

\u00F4 → ô

\u00F5 → õ

\u00F6 → ö

U’s

\u00D9 → Ù

\u00DA → Ú

\u00DB → Û

\u00DC → Ü

\u00F9 → ù

\u00FA → ú

\u00FB → û

\u00FC → ü

Y’s

\u00DD → Ý

\u00FD → ý

\u00FF → ÿ

Ligatures

\u00C6 → Æ # Ash

\u00E6 → æ

\u0152 → Œ # Ethel

\u0153 → œ

German

\u00DF → ß

Guillemets

\u00AB → «

\u00BB → »

Currency

\u00A3 → £

\u20AC → €

Greek Letters

\u0391 → Α # Alpha

\u03B1 → α

\u0392 → Β # Beta

\u03B2 → β

\u0393 → Γ # Gamma

\u03B3 → γ

\u0394 → Δ # Delta

\u03B4 → δ

\u0395 → Ε # Epsilon

\u03B5 → ε

\u0396 → Ζ # Zeta

\u03B6 → ζ

\u0397 → Η # Eta

\u03B7 → η

\u0398 → Θ # Theta

\u03B8 → θ

\u0399 → Ι # Iota

\u03B9 → ι

\u039A → Κ # Kappa

\u03BA → κ

\u039B → Λ # Lambda

\u03BB → λ

\u039C → Μ # Mu

\u03BC → μ

\u039D → Ν # Nu

\u03BD → ν

\u039E → Ξ # Xi

\u03BE → ξ

\u039F → Ο # Omicron

\u03BF → ο

\u03A0 → Π # Pi

\u03C0 → π

\u03A1 → Ρ # Rho

\u03C1 → ρ

\u03A3 → Σ # Sigma

\u03C3 → σ

\u03C2 → ς (used if the last letter in a word is s)

\u03A4 → Τ # Tau

\u03C4 → τ

\u03A5 → Υ # Upsilon

\u03C5 → υ

\u03A6 → Φ # Phi

\u03C6 → φ

\u03A7 → Χ # Chi

\u03C7 → χ

\u03A8 → Ψ # Psi

\u03C8 → ψ

\u03A9 → Ω # Omega

\u03C9 → ω

Maths Symbols

\u00D7 → ×

\u00F7 → ÷

\u2260 → ≠

\u2264 → ≤

\u2265 → ≥

\u2248 → ≈

\u223C → ∼

\u00B1 → ±

\u221E → ∞

\u221A → √

\u222B → ∫

\u222C → ∬

\u222D → ∭

\u2220 → ∠

\u00B0 → °

\u2208 → ∈

\u2282 → ⊂

\u2283 → ⊃

\u2227 → ∧

\u2228 → ∨

\u00AC → ¬

\u2190 → ←

\u2192 → →

\u2191 → ↑

\u2193 → ↓

\u21D2 → ⇒

\u21D4 → ⇔

Alt x

These will only work on the numerical side of a keyboard.

0227

0128

AltGr

a = á

A = Á

e = é

E = É

Details on Markdown can be found here http://rmarkdown.rstudio.com. To paste a weblink encapsulate the url in < and >.

To generate a report from the file, run the render command:

Working Directories

# Get working directory
getwd()

## [1] "C:/Users/steve/Desktop/R Files, Examples etc/Bibble"

# Set working directory

# When the path is pasted into setwd() each \ needs to be replaced with / so:
# setwd("C:\Users\steve\Desktop\R Files, Examples etc\Bibble") 
# fails but:

setwd("C:/Users/steve/Desktop/R Files, Examples etc/Bibble")


# or do this:

df <- read.csv(chartr("\\", "/", r"(C:\Users\steve\Desktop\V2C\EPCs.csv)"))

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

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Bibble

Steve Silcox

2026-09-30

The Basics

Keyboard Commands

Arrays

Syntax

Loops

For Loops

Example 1

Example 2

Example 3

Example 4

While Loops

Example 1

If Loops

Example

Else If Loops

Example

Repeat Loops

Example

Switch

Example

Randoms

Dataframes

Syntax

Using ‘data()’

.xlsx Files

Other Functions

Creating a dataframe

Deleting Rows & Columns

Changing Row & Column Names

Replacing Missing Data

Other Dataframe Snippets

is.na() Function

Vectors

Markdown Info

Special Characters

Code-based

A’s

B’s

C’s

E’s

I’s

N’s

O’s

U’s

Y’s

Ligatures

German

Guillemets

Currency

Greek Letters

Maths Symbols

Alt x

AltGr

R Markdown

Including Plots