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library(yarrr)
## Loading required package: jpeg
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(beanplot)
require(beanplot)

+++ here now +++

Question 1

# A
x <- rnorm(100, mean = 100, sd = 10)
y <- x + rnorm(100, mean = 20, sd = 20)
x
##   [1]  92.66460 102.46348  96.78262  92.23534 112.19286  99.40359  85.60302
##   [8]  95.94630  98.89008 101.09260  94.01286  91.76836 116.25819 100.36473
##  [15] 105.88279 103.27604  99.89662 106.37429  88.86085  98.38024  99.05075
##  [22]  96.07376  80.01162  82.46762  86.96490 109.38879  95.10622  82.20975
##  [29] 112.23021  88.42871  96.73067  97.32888 108.96151 102.03100  97.94437
##  [36] 109.85728 101.70906 119.28109  89.25859  98.43414 100.83494  82.07022
##  [43] 123.36878 121.14532  92.47592 105.08055  85.91407 103.72720 113.13637
##  [50]  91.99819  95.25260  81.86964  92.63915  86.81363  99.63643  89.95275
##  [57] 109.10437  93.70529 102.02750 116.78436 104.94522 100.73432  91.36302
##  [64]  83.48644  97.15278 106.52897  95.16353  94.14947 100.96071  83.74045
##  [71] 101.02263 101.74737  92.48485 101.04388  94.57970  99.15485  90.65331
##  [78] 102.02790 109.58575  87.17877 116.73828  99.83106  90.03414  86.59814
##  [85] 107.45404 112.19252  98.47692  96.34682 109.57363 103.10484  95.95827
##  [92] 112.58005 104.15573 110.84794  90.85878  90.87442  97.51983  98.20716
##  [99] 118.80560 115.60481
y
##   [1] 100.58178 126.04133 117.69816 111.18223  84.04412  72.10042  95.68427
##   [8] 107.94221  97.10619 116.99643  53.07600 128.66005 143.09836 110.18982
##  [15] 112.96276 125.17067 111.72032  97.03256 129.99642 138.72420 104.71478
##  [22] 116.82676 122.93851  70.20308  80.71397 128.71071 129.19643  95.95905
##  [29] 140.67243  93.86026 112.84890 153.03089 129.41920 111.26041 119.39587
##  [36] 134.77353  98.78702 118.21928 112.41301 106.68142 116.56667 135.23226
##  [43] 130.14859 123.03212 110.41308 106.90843  71.78240 128.92005 106.98639
##  [50] 120.28114 108.57172  78.98433 115.49132 112.93026 107.25638 109.40659
##  [57] 146.67708  98.62860 119.36177 145.62460 124.30546 107.37762  85.69435
##  [64]  97.18738 138.13844 104.24550 136.38799 126.56850  83.13455  96.85728
##  [71] 134.39209 136.70106  95.45075 116.55870  88.45429 102.02003 121.13224
##  [78] 104.40590 151.95087 138.79659 130.19067 121.42373 117.40860  92.55474
##  [85] 126.70940 140.98916 129.79174 118.25322 119.09056 106.79902  97.69280
##  [92] 154.03681 122.28050 117.96297 108.36268 140.06218 117.08055 122.00741
##  [99] 145.25231 113.51888
# B
hist(x = x,
     main = "Histogram of x",
     xlab = "This is th x-label",
     ylab = "Frequency")

# c
hist(x = y,
     main = "Histogram of y",
     xlab = "This is the x-label",
     ylab = "Frequency")

# D
plot(x, y,
     main = "This is the title",
     xlab = "This is the x-label",
     ylab = "This is the y-label")
 # E
mean(x)
## [1] 99.16823
mean(y)
## [1] 114.8507
abline(v = (mean(x)), lty = 2)
abline(h = (mean(y)), lty = 2)

Question 2

colors()[1:10]
##  [1] "white"         "aliceblue"     "antiquewhite"  "antiquewhite1"
##  [5] "antiquewhite2" "antiquewhite3" "antiquewhite4" "aquamarine"   
##  [9] "aquamarine1"   "aquamarine2"
sample(x = 1:100, 
       size = 10)
##  [1] 27 40 65 80 12 75 71 81 84 82
samp.numbers <- sample(1:657, size = 10)
colors.to.use <- colors()[samp.numbers]

plot(1:10, 
     col = colors.to.use,
     pch = 16,
     cex = 2,
     xlim = c(0, 11),
     ylim = c(0, 11)
     )

text(1:10, 
     1:10, 
     colors()[samp.numbers],
     pos = 3
     )

Question 3

pirates <- read.table(file = “http://nathanieldphillips.com/wp-content/uploads/2015/11/pirates1.txt”, header = T, sep = “”, # tab-delimited stringsAsFactors = F )

boxplot(pirates$sword.time ~ pirates$sword.type,
data = pirates,
xlab = "Swordtype",
ylab = "Sword Swing Time",
main = "Sword swinging time by sword type")

Question 4

beanplot(pirates$sword.time ~ pirates$sword.type,
data = pirates,
main = "Sword Swinging Time by Sword Type",
xlab = "sword type",
ylab = "sword swing time",
col  =  "white",
lwd  =   1,
what = c(1, 1, 1, 1), log = ""
)

Question 5

# A
plot(x = 1,
xlab = "Age",
ylab = "Beard Length",
xaxt = "n", yaxt = "n",
type = "n",
xlim = c(0, 50), ylim = c(0, 40),
main = "Here is a plot with gridlines waiting for data!")

abline(v = 10:50,
col = gray(.8))

abline(h = 0:40,
lwd = 1,
col = gray(.8))

# B
points(x = pirates$age[pirates$sex == "male"],
       y = pirates$beard.length[pirates$sex == "male"],
       pch = 16,
       col = "red")
#C

points(x = pirates$age[pirates$sex == "female"],
       y = pirates$beard.length[pirates$sex == "female"],
       pch = 16,
       col = "red")