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:

answer <- 7/9 + sin(2)
answer
## [1] 1.687075
num <- 2
text_me <- "chester"
happy <- "very happy"
Name <- "billy"
name <- "bernie"
nAme <- "Barack"
answer_squared <- answer^2
answer_squared
## [1] 2.846223

Including Plots

Top Level header

including plots

Third level header

fourth level header

fifth level header
sixth level header

we can add italics by surrounding the word by a single underscore: italics

we could also surround the word by a singl asterisk to give the same result italic

to bold a word or give it strong emhasis we surround it with two unerscores or two asterisk: bolded or bolded

we can out both italics and emphasis together: both italics and bold.

Lastly, we can strikethrough a ohrase by using two tildes: strikethrough this sentence

next, we will add a link to Chester Ismay’s webpage. Here is the link

list

unordered

let’s create a grocery list:

  • apples
  • celery
  • kale

ordered

we can also create an ordered lost using numbers.

  1. wake up
    • get out of bed
  2. warm up food
    • open kitchen door
    • get plate out of cupboard
  3. make coffee
    1. warm up water
    2. grind beans
  4. make breakfast

If we forgot that we need to start heating up food before we make breakfast, we can add it into the list

line breaks

Here is an example of text with only a line break.

You may expect this line to appear in a new paragraph but it doesn’t.

horizontal line above

blockquotes

Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. - Roger Peng

comments

equations

Equations: \(y=mx+b\)

count20 <- 1:20
Count100_by_5 <- seq(from = 5, to = 100, by = 5)
Count100_by_5
##  [1]   5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  85
## [18]  90  95 100
prod <- count20 * Count100_by_5
prod
##  [1]    5   20   45   80  125  180  245  320  405  500  605  720  845  980
## [15] 1125 1280 1445 1620 1805 2000

multiplying 7 by 14 gives us the result of 98.

The value of one_value is less than 10.

You can also embed plots, for example:

Chemistry Example

we download the file here

download.file(url = "http://ismayc.github.io/periodic-table-data.csv",
 destfile = "periodic-table-data.csv")

we save the CSV into an R markdown

periodic_table <- read.csv("periodic-table-data.csv",
              stringsAsFactors = FALSE)
str(periodic_table)
## 'data.frame':    118 obs. of  22 variables:
##  $ atomic_number      : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ symbol             : chr  "H" "He" "Li" "Be" ...
##  $ name               : chr  "Hydrogen" "Helium" "Lithium" "Beryllium" ...
##  $ name_origin        : chr  "composed of the Greek elements hydro- and -gen meaning 'water-forming'" "the Greek helios, 'sun'" "the Greek lithos, 'stone'" "beryl, a mineral" ...
##  $ group              : int  1 18 1 2 13 14 15 16 17 18 ...
##  $ period             : int  1 1 2 2 2 2 2 2 2 2 ...
##  $ block              : chr  "s" "s" "s" "s" ...
##  $ state_at_stp       : chr  "Gas" "Gas" "Solid" "Solid" ...
##  $ occurrence         : chr  "Primordial" "Primordial" "Primordial" "Primordial" ...
##  $ description        : chr  "Non-metal" "Noble gas" "Alkali metal" "Alkaline earth metal" ...
##  $ atomic_weight      : num  1.01 4 6.94 9.01 10.81 ...
##  $ aw_uncertainty     : int  NA 2 NA 5 NA NA NA NA 6 6 ...
##  $ any_stable_nuclides: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ density            : num  8.99e-05 1.78e-04 5.34e-01 1.85 2.34 ...
##  $ density_predicted  : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ melting_point      : num  14.01 0.956 453.69 1560 2349 ...
##  $ mp_predicted       : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ boiling_point      : num  20.28 4.22 1560 2742 4200 ...
##  $ bp_predicted       : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ heat_capacity      : num  14.3 5.19 3.58 1.82 1.03 ...
##  $ electronegativity  : num  2.2 NA 0.98 1.57 2.04 2.55 3.04 3.44 3.98 NA ...
##  $ abundance          : num  1.40e+03 8.00e-03 2.00e+01 2.80 1.00e+01 2.00e+02 1.90e+01 4.61e+05 5.85e+02 5.00e-03 ...
periodic_table$name[10:20]
##  [1] "Neon"       "Sodium"     "Magnesium"  "Aluminium"  "Silicon"   
##  [6] "Phosphorus" "Sulfur"     "Chlorine"   "Argon"      "Potassium" 
## [11] "Calcium"
periodic_table[41:50, c(1, 2, 4)]
##    atomic_number symbol
## 41            41     Nb
## 42            42     Mo
## 43            43     Tc
## 44            44     Ru
## 45            45     Rh
## 46            46     Pd
## 47            47     Ag
## 48            48     Cd
## 49            49     In
## 50            50     Sn
##                                                                      name_origin
## 41                         Niobe, daughter of king Tantalus from Greek mythology
## 42                                             the Greek molybdos meaning 'lead'
## 43                                     the Greek tekhn??tos meaning 'artificial'
## 44                                       Ruthenia, the New Latin name for Russia
## 45                                     the Greek rhodos, meaning 'rose coloured'
## 46 the then recently discovered asteroid Pallas, considered a planet at the time
## 47                                              English word (argentum in Latin)
## 48                                        the New Latin cadmia, from King Kadmos
## 49                                                                        indigo
## 50                                               English word (stannum in Latin)
periodic_table[(periodic_table$name %in% c("Hydrogen", "Oxygen")),
               c("atomic_weight", "state_at_stp")]
##   atomic_weight state_at_stp
## 1      1.008235          Gas
## 8     15.999000          Gas

Friends Examples

friend_names <- c("Bertha", "Herbert", "Alice", "Nathaniel")
friend_names
## [1] "Bertha"    "Herbert"   "Alice"     "Nathaniel"
friend_ages <- c(25L, 37L, 22L, 30L)
friend_ages
## [1] 25 37 22 30
class(friend_names)
## [1] "character"
class(friend_ages)
## [1] "integer"
periodic_table$block <- factor(periodic_table$block,
                levels = c("s", "p", "d", "f"))
table(periodic_table$block)
## 
##  s  p  d  f 
## 14 36 40 28
friends <- data.frame(names = friend_names,
                      ages = friend_ages,
                      stringsAsFactors = FALSE)

friends
##       names ages
## 1    Bertha   25
## 2   Herbert   37
## 3     Alice   22
## 4 Nathaniel   30
friend_names[1:3]
## [1] "Bertha"  "Herbert" "Alice"
friend_names[c(1,3)]
## [1] "Bertha" "Alice"
friend_names[-c(2, 4)]
## [1] "Bertha" "Alice"
friend_names[c(TRUE, FALSE, TRUE, FALSE)]
## [1] "Bertha" "Alice"
friend_names == "Bertha"
## [1]  TRUE FALSE FALSE FALSE
friend_names[friend_names %in% c("Bertha", "Alice")]
## [1] "Bertha" "Alice"

sequence

sequence_by_2 <- seq(from = 0L, to = 100L, by = 2L)
sequence_by_2
##  [1]   0   2   4   6   8  10  12  14  16  18  20  22  24  26  28  30  32
## [18]  34  36  38  40  42  44  46  48  50  52  54  56  58  60  62  64  66
## [35]  68  70  72  74  76  78  80  82  84  86  88  90  92  94  96  98 100
class(sequence_by_2)
## [1] "integer"
dec_frac_seq <- seq(from = 10, to = 3, by = -0.2)
dec_frac_seq
##  [1] 10.0  9.8  9.6  9.4  9.2  9.0  8.8  8.6  8.4  8.2  8.0  7.8  7.6  7.4
## [15]  7.2  7.0  6.8  6.6  6.4  6.2  6.0  5.8  5.6  5.4  5.2  5.0  4.8  4.6
## [29]  4.4  4.2  4.0  3.8  3.6  3.4  3.2  3.0
class(dec_frac_seq)
## [1] "numeric"

vectorized operations

ages_numeric <- c(25L, 37L, 22L, 30L)
five_years_older <- ages_numeric + 5L
five_years_older
## [1] 30 42 27 35

##Functions

seq()
## [1] 1
ages_numeric
## [1] 25 37 22 30
mean( x = ages_numeric)
## [1] 28.5
mean(ages_numeric, na.rm = TRUE)
## [1] 28.5
mean(ages_numeric, na.rm = TRUE)
## [1] 28.5