summaryrefslogtreecommitdiffhomepage
path: root/r.html.markdown
blob: 93751df5fb824f9a16eb2db9ce94e28495be90bd (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
---
language: R
contributors:
    - ["e99n09", "http://github.com/e99n09"]
    - ["isomorphismes", "http://twitter.com/isomorphisms"]
filename: learnr.r
---

R is a statistical computing language. It has lots of libraries for uploading and cleaning data sets, running statistical procedures, and making graphs. You can also run `R` commands within a LaTeX document.

```r

# Comments start with number symbols.

# You can't make multi-line comments,
# but you can stack multiple comments like so.

# in Windows or Mac, hit COMMAND-ENTER to execute a line



#############################################################################
# Stuff you can do without understanding anything about programming
#############################################################################

# In this section, we show off some of the cool stuff you can do in
# R without understanding anything about programming. Do not worry
# about understanding everything the code does. Just enjoy!

data()	        # browse pre-loaded data sets
data(rivers)	# get this one: "Lengths of Major North American Rivers"
ls()	        # notice that "rivers" now appears in the workspace
head(rivers)	# peek at the data set
# 735 320 325 392 524 450

length(rivers)	# how many rivers were measured?
# 141
summary(rivers) # what are some summary statistics?
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#  135.0   310.0   425.0   591.2   680.0  3710.0

# make a stem-and-leaf plot (a histogram-like data visualization)
stem(rivers)

#  The decimal point is 2 digit(s) to the right of the |
#
#   0 | 4
#   2 | 011223334555566667778888899900001111223333344455555666688888999
#   4 | 111222333445566779001233344567
#   6 | 000112233578012234468
#   8 | 045790018
#  10 | 04507
#  12 | 1471
#  14 | 56
#  16 | 7
#  18 | 9
#  20 |
#  22 | 25
#  24 | 3
#  26 |
#  28 |
#  30 |
#  32 |
#  34 |
#  36 | 1

stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
# Take that, Bell curve fundamentalists.

#  The decimal point is 1 digit(s) to the left of the |
#
#  48 | 1
#  50 |
#  52 | 15578
#  54 | 44571222466689
#  56 | 023334677000124455789
#  58 | 00122366666999933445777
#  60 | 122445567800133459
#  62 | 112666799035
#  64 | 00011334581257889
#  66 | 003683579
#  68 | 0019156
#  70 | 079357
#  72 | 89
#  74 | 84
#  76 | 56
#  78 | 4
#  80 |
#  82 | 2

# make a histogram:
hist(rivers, col="#333333", border="white", breaks=25) # play around with these parameters
hist(log(rivers), col="#333333", border="white", breaks=25) # you'll do more plotting later

# Here's another neat data set that comes pre-loaded. R has tons of these.
data(discoveries)
plot(discoveries, col="#333333", lwd=3, xlab="Year",
     main="Number of important discoveries per year")
plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year",
     main="Number of important discoveries per year")

# Rather than leaving the default ordering (by year),
# we could also sort to see what's typical:
sort(discoveries)
#  [1]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2  2
# [26]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  3  3  3
# [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4
# [76]  4  4  4  4  5  5  5  5  5  5  5  6  6  6  6  6  6  7  7  7  7  8  9 10 12

stem(discoveries, scale=2)
#
#  The decimal point is at the |
#
#   0 | 000000000
#   1 | 000000000000
#   2 | 00000000000000000000000000
#   3 | 00000000000000000000
#   4 | 000000000000
#   5 | 0000000
#   6 | 000000
#   7 | 0000
#   8 | 0
#   9 | 0
#  10 | 0
#  11 |
#  12 | 0

max(discoveries)
# 12
summary(discoveries)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#    0.0     2.0     3.0     3.1     4.0    12.0

# Roll a die a few times
round(runif(7, min=.5, max=6.5))
# 1 4 6 1 4 6 4
# Your numbers will differ from mine unless we set the same random.seed(31337)

# Draw from a standard Gaussian 9 times
rnorm(9)
# [1]  0.07528471  1.03499859  1.34809556 -0.82356087  0.61638975 -1.88757271
# [7] -0.59975593  0.57629164  1.08455362



##################################################
# Data types and basic arithmetic
##################################################

# Now for the programming-oriented part of the tutorial.
# In this section you will meet the important data types of R:
# integers, numerics, characters, logicals, and factors.
# There are others, but these are the bare minimum you need to
# get started.

# INTEGERS
# Long-storage integers are written with L
5L # 5
class(5L) # "integer"
# (Try ?class for more information on the class() function.)
# In R, every single value, like 5L, is considered a vector of length 1
length(5L) # 1
# You can have an integer vector with length > 1 too:
c(4L, 5L, 8L, 3L) # 4 5 8 3
length(c(4L, 5L, 8L, 3L)) # 4
class(c(4L, 5L, 8L, 3L)) # "integer"

# NUMERICS
# A "numeric" is a double-precision floating-point number
5 # 5
class(5) # "numeric"
# Again, everything in R is a vector;
# you can make a numeric vector with more than one element
c(3,3,3,2,2,1) # 3 3 3 2 2 1
# You can use scientific notation too
5e4 # 50000
6.02e23 # Avogadro's number
1.6e-35 # Planck length
# You can also have infinitely large or small numbers
class(Inf)	# "numeric"
class(-Inf)	# "numeric"
# You might use "Inf", for example, in integrate(dnorm, 3, Inf);
# this obviates Z-score tables.

# BASIC ARITHMETIC
# You can do arithmetic with numbers
# Doing arithmetic on a mix of integers and numerics gives you another numeric
10L + 66L # 76      # integer plus integer gives integer
53.2 - 4  # 49.2    # numeric minus numeric gives numeric
2.0 * 2L  # 4       # numeric times integer gives numeric
3L / 4    # 0.75    # integer over numeric gives numeric
3 %% 2	  # 1       # the remainder of two numerics is another numeric
# Illegal arithmetic yeilds you a "not-a-number":
0 / 0 # NaN
class(NaN) # "numeric"
# You can do arithmetic on two vectors with length greater than 1,
# so long as the larger vector's length is an integer multiple of the smaller
c(1,2,3) + c(1,2,3) # 2 4 6

# CHARACTERS
# There's no difference between strings and characters in R
"Horatio" # "Horatio"
class("Horatio") # "character"
class('H') # "character"
# Those were both character vectors of length 1
# Here is a longer one:
c('alef', 'bet', 'gimmel', 'dalet', 'he')
# =>
# "alef"   "bet"    "gimmel" "dalet"  "he"
length(c("Call","me","Ishmael")) # 3
# You can do regex operations on character vectors:
substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
# R has several built-in character vectors:
letters
# =>
#  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
# [20] "t" "u" "v" "w" "x" "y" "z"
month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"

# LOGICALS
# In R, a "logical" is a boolean
class(TRUE)	# "logical"
class(FALSE)	# "logical"
# Their behavior is normal
TRUE == TRUE	# TRUE
TRUE == FALSE	# FALSE
FALSE != FALSE	# FALSE
FALSE != TRUE	# TRUE
# Missing data (NA) is logical, too
class(NA)	# "logical"
# Use | and & for logic operations.
# OR
TRUE | FALSE	# TRUE
# AND
TRUE & FALSE	# FALSE
# You can test if x is TRUE
isTRUE(TRUE)	# TRUE
# Here we get a logical vector with many elements:
c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE

# FACTORS
# The factor class is for categorical data
# Factors can be ordered (like childrens' grade levels) or unordered (like gender)
factor(c("female", "female", "male", NA, "female"))
#  female female male   <NA>   female
# Levels: female male
# The "levels" are the values the categorical data can take
# Note that missing data does not enter the levels
levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male"
# If a factor vector has length 1, its levels will have length 1, too
length(factor("male")) # 1
length(levels(factor("male"))) # 1
# Factors are commonly seen in data frames, a data structure we will cover later
data(infert) # "Infertility after Spontaneous and Induced Abortion"
levels(infert$education) # "0-5yrs"  "6-11yrs" "12+ yrs"

# NULL
# "NULL" is a weird one; use it to "blank out" a vector
class(NULL)	# NULL
parakeet = c("beak", "feathers", "wings", "eyes")
parakeet
# =>
# [1] "beak"     "feathers" "wings"    "eyes"
parakeet <- NULL
parakeet
# =>
# NULL

# TYPE COERCION
# Type-coercion is when you force a value to take on a different type
as.character(c(6, 8)) # "6" "8"
as.logical(c(1,0,1,1)) # TRUE FALSE  TRUE  TRUE
# If you put elements of different types into a vector, weird coercions happen:
c(TRUE, 4) # 1 4
c("dog", TRUE, 4) # "dog"  "TRUE" "4"
as.numeric("Bilbo")
# =>
# [1] NA
# Warning message:
# NAs introduced by coercion

# Also note: those were just the basic data types
# There are many more data types, such as for dates, time series, etc.



##################################################
# Variables, loops, if/else
##################################################

# A variable is like a box you store a value in for later use.
# We call this "assigning" the value to the variable.
# Having variables lets us write loops, functions, and if/else statements

# VARIABLES
# Lots of way to assign stuff:
x = 5 # this is possible
y <- "1" # this is preferred
TRUE -> z # this works but is weird

# LOOPS
# We've got for loops
for (i in 1:4) {
  print(i)
}
# We've got while loops
a <- 10
while (a > 4) {
	cat(a, "...", sep = "")
	a <- a - 1
}
# Keep in mind that for and while loops run slowly in R
# Operations on entire vectors (i.e. a whole row, a whole column)
# or apply()-type functions (we'll discuss later) are preferred

# IF/ELSE
# Again, pretty standard
if (4 > 3) {
	print("4 is greater than 3")
} else {
	print("4 is not greater than 3")
}
# =>
# [1] "4 is greater than 3"

# FUNCTIONS
# Defined like so:
jiggle <- function(x) {
	x = x + rnorm(1, sd=.1)	#add in a bit of (controlled) noise
	return(x)
}
# Called like any other R function:
jiggle(5)	# 5±ε. After set.seed(2716057), jiggle(5)==5.005043



###########################################################################
# Data structures: Vectors, matrices, data frames, and arrays
###########################################################################

# ONE-DIMENSIONAL

# Let's start from the very beginning, and with something you already know: vectors.
vec <- c(8, 9, 10, 11)
vec	#  8  9 10 11
# We ask for specific elements by subsetting with square brackets
# (Note that R starts counting from 1)
vec[1]		# 8
letters[18]	# "r"
LETTERS[13]	# "M"
month.name[9]	# "September"
c(6, 8, 7, 5, 3, 0, 9)[3]	# 7
# We can also search for the indices of specific components,
which(vec %% 2 == 0)	# 1 3
# grab just the first or last few entries in the vector,
head(vec, 1)	# 8
tail(vec, 2)	# 10 11
# or figure out if a certain value is in the vector
any(vec == 10) # TRUE
# If an index "goes over" you'll get NA:
vec[6]	# NA
# You can find the length of your vector with length()
length(vec)	# 4
# You can perform operations on entire vectors or subsets of vectors
vec * 4	# 16 20 24 28
vec[2:3] * 5	# 25 30
any(vec[2:3] == 8) # FALSE
# and R has many built-in functions to summarize vectors
mean(vec)	# 9.5
var(vec)	# 1.666667
sd(vec)		# 1.290994
max(vec)	# 11
min(vec)	# 8
sum(vec)	# 38
# Some more nice built-ins:
5:15	# 5  6  7  8  9 10 11 12 13 14 15
seq(from=0, to=31337, by=1337)
# =>
#  [1]     0  1337  2674  4011  5348  6685  8022  9359 10696 12033 13370 14707
# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751

# TWO-DIMENSIONAL (ALL ONE CLASS)

# You can make a matrix out of entries all of the same type like so:
mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
mat
# =>
#      [,1] [,2]
# [1,]    1    4
# [2,]    2    5
# [3,]    3    6
# Unlike a vector, the class of a matrix is "matrix", no matter what's in it
class(mat) # => "matrix"
# Ask for the first row
mat[1,]	# 1 4
# Perform operation on the first column
3 * mat[,1]	# 3 6 9
# Ask for a specific cell
mat[3,2]	# 6

# Transpose the whole matrix
t(mat)
# =>
#      [,1] [,2] [,3]
# [1,]    1    2    3
# [2,]    4    5    6

# Matrix multiplication
mat %*% t(mat)
# =>
#      [,1] [,2] [,3]
# [1,]   17   22   27
# [2,]   22   29   36
# [3,]   27   36   45

# cbind() sticks vectors together column-wise to make a matrix
mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
mat2
# =>
#      [,1] [,2]
# [1,] "1"  "dog"
# [2,] "2"  "cat"
# [3,] "3"  "bird"
# [4,] "4"  "dog"
class(mat2)	# matrix
# Again, note what happened!
# Because matrices must contain entries all of the same class,
# everything got converted to the character class
c(class(mat2[,1]), class(mat2[,2]))

# rbind() sticks vectors together row-wise to make a matrix
mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
mat3
# =>
#      [,1] [,2] [,3] [,4]
# [1,]    1    2    4    5
# [2,]    6    7    0    4
# Ah, everything of the same class. No coercions. Much better.

# TWO-DIMENSIONAL (DIFFERENT CLASSES)

# For columns of different types, use a data frame
# This data structure is so useful for statistical programming,
# a version of it was added to Python in the package "pandas".

students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
                       c(3,2,2,1,0,-1),
                       c("H", "G", "G", "R", "S", "G"))
names(students) <- c("name", "year", "house") # name the columns
class(students)	# "data.frame"
students
# =>
#     name year house
# 1 Cedric    3     H
# 2   Fred    2     G
# 3 George    2     G
# 4    Cho    1     R
# 5  Draco    0     S
# 6  Ginny   -1     G
class(students$year)	# "numeric"
class(students[,3])	# "factor"
# find the dimensions
nrow(students)	# 6
ncol(students)	# 3
dim(students)	# 6 3
# The data.frame() function converts character vectors to factor vectors
# by default; turn this off by setting stringsAsFactors = FALSE when
# you create the data.frame
?data.frame

# There are many twisty ways to subset data frames, all subtly unalike
students$year	# 3  2  2  1  0 -1
students[,2]	# 3  2  2  1  0 -1
students[,"year"]	# 3  2  2  1  0 -1

# An augmented version of the data.frame structure is the data.table
# If you're working with huge or panel data, or need to merge a few data
# sets, data.table can be a good choice. Here's a whirlwind tour:
install.packages("data.table") # download the package from CRAN
require(data.table) # load it
students <- as.data.table(students)
students # note the slightly different print-out
# =>
#      name year house
# 1: Cedric    3     H
# 2:   Fred    2     G
# 3: George    2     G
# 4:    Cho    1     R
# 5:  Draco    0     S
# 6:  Ginny   -1     G
students[name=="Ginny"] # get rows with name == "Ginny"
# =>
#     name year house
# 1: Ginny   -1     G
students[year==2] # get rows with year == 2
# =>
#      name year house
# 1:   Fred    2     G
# 2: George    2     G
# data.table makes merging two data sets easy
# let's make another data.table to merge with students
founders <- data.table(house=c("G","H","R","S"),
                       founder=c("Godric","Helga","Rowena","Salazar"))
founders
# =>
#    house founder
# 1:     G  Godric
# 2:     H   Helga
# 3:     R  Rowena
# 4:     S Salazar
setkey(students, house)
setkey(founders, house)
students <- founders[students] # merge the two data sets by matching "house"
setnames(students, c("house","houseFounderName","studentName","year"))
students[,order(c("name","year","house","houseFounderName")), with=F]
# =>
#    studentName year house houseFounderName
# 1:        Fred    2     G           Godric
# 2:      George    2     G           Godric
# 3:       Ginny   -1     G           Godric
# 4:      Cedric    3     H            Helga
# 5:         Cho    1     R           Rowena
# 6:       Draco    0     S          Salazar

# data.table makes summary tables easy
students[,sum(year),by=house]
# =>
#    house V1
# 1:     G  3
# 2:     H  3
# 3:     R  1
# 4:     S  0

# To drop a column from a data.frame or data.table,
# assign it the NULL value
students$houseFounderName <- NULL
students
# =>
#    studentName year house
# 1:        Fred    2     G
# 2:      George    2     G
# 3:       Ginny   -1     G
# 4:      Cedric    3     H
# 5:         Cho    1     R
# 6:       Draco    0     S

# Drop a row by subsetting
# Using data.table:
students[studentName != "Draco"]
# =>
#    house studentName year
# 1:     G        Fred    2
# 2:     G      George    2
# 3:     G       Ginny   -1
# 4:     H      Cedric    3
# 5:     R         Cho    1
# Using data.frame:
students <- as.data.frame(students)
students[students$house != "G",]
# =>
#   house houseFounderName studentName year
# 4     H            Helga      Cedric    3
# 5     R           Rowena         Cho    1
# 6     S          Salazar       Draco    0

# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)

# Arrays creates n-dimensional tables
# All elements must be of the same type
# You can make a two-dimensional table (sort of like a matrix)
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
# =>
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    8    3
# [2,]    2    5    9    6
# You can use array to make three-dimensional matrices too
array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
# =>
# , , 1
#
#      [,1] [,2]
# [1,]    2    8
# [2,]  300    9
# [3,]    4    0
#
# , , 2
#
#      [,1] [,2]
# [1,]    5   66
# [2,]   60    7
# [3,]    0  847

# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)

# Finally, R has lists (of vectors)
list1 <- list(time = 1:40)
list1$price = c(rnorm(40,.5*list1$time,4)) # random
list1
# You can get items in the list like so
list1$time # one way
list1[["time"]] # another way
list1[[1]] # yet another way
# =>
#  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
# [34] 34 35 36 37 38 39 40
# You can subset list items like any other vector
list1$price[4]

# Lists are not the most efficient data structure to work with in R;
# unless you have a very good reason, you should stick to data.frames
# Lists are often returned by functions that perform linear regressions

##################################################
# The apply() family of functions
##################################################

# Remember mat?
mat
# =>
#      [,1] [,2]
# [1,]    1    4
# [2,]    2    5
# [3,]    3    6
# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
# over rows (MAR = 1) or columns (MAR = 2)
# That is, R does FUN to each row (or column) of X, much faster than a
# for or while loop would do
apply(mat, MAR = 2, jiggle)
# =>
#      [,1] [,2]
# [1,]    3   15
# [2,]    7   19
# [3,]   11   23
# Other functions: ?lapply, ?sapply

# Don't feel too intimidated; everyone agrees they are rather confusing

# The plyr package aims to replace (and improve upon!) the *apply() family.
install.packages("plyr")
require(plyr)
?plyr



#########################
# Loading data
#########################

# "pets.csv" is a file on the internet
# (but it could just as easily be be a file on your own computer)
pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
pets
head(pets, 2) # first two rows
tail(pets, 1) # last row

# To save a data frame or matrix as a .csv file
write.csv(pets, "pets2.csv") # to make a new .csv file
# set working directory with setwd(), look it up with getwd()

# Try ?read.csv and ?write.csv for more information



#########################
# Plots
#########################

# BUILT-IN PLOTTING FUNCTIONS
# Scatterplots!
plot(list1$time, list1$price, main = "fake data")
# Regressions!
linearModel <- lm(price  ~ time, data = list1)
linearModel # outputs result of regression
# Plot regression line on existing plot
abline(linearModel, col = "red")
# Get a variety of nice diagnostics
plot(linearModel)
# Histograms!
hist(rpois(n = 10000, lambda = 5), col = "thistle")
# Barplots!
barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))

# GGPLOT2
# But these are not even the prettiest of R's plots
# Try the ggplot2 package for more and better graphics
install.packages("ggplot2")
require(ggplot2)
?ggplot2
pp <- ggplot(students, aes(x=house))
pp + geom_histogram()
ll <- as.data.table(list1)
pp <- ggplot(ll, aes(x=time,price))
pp + geom_point()
# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)



```

## How do I get R?

* Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/)
* [RStudio](http://www.rstudio.com/ide/) is another GUI