forked from rafalab/dsbook
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy patherrors.txt
314 lines (200 loc) · 9.01 KB
/
errors.txt
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
Preface, Page xxvii
Zofia Gajdos instead of Zzofia Gajdos
Introduction, Page xxx
In the intro "scrapping" should be "scraping"
Page 3, 31 in book
the New File
Should be
hen New File
Page 48, 77 in pdf
name space
Should be
namespace
Page 49, 77 in pdf
If we want to be absolutely sure we use the __dplyr__ `filter` we can use
Should be
If we want to be absolutely sure that we use the __dplyr__ `filter`, we can use
Page 63, 91 in pdf
this is now a special data frame called a grouped data frame and dplyr functions
Add comma:
this is now a special data frame called a grouped data frame, and dplyr functions
Page 64, 92 in pdf
To see the states by population, from smallest to largest, we arrange by `rate` instead
Should be
To see the states by murder rate, from lowest to highest, we arrange by `rate` instead:
Page 65, 94 in pdf
"Remember that the main summarization function"
Change to
"The `mean` and `sd` functions"
Page 65, 94 in pdf
if left blank, top_n, filters
Remove comma
if left blank, top_n filters
Page 70, 98 in pdf
For example to access
Add comma
For example, to access
Page 71, 99 in pdf
__no argument
No __ and Should be bold
So in markdown right:
__no argument__
Page 73, 101 in pdf
three groups
Should be
four groups
Page 73, 101 in pdf
for example to check
Should be
for example, to check
Page 74, 102 in pdf
In exercise 3 murders is in the wrong font. In code,
murders
Should be
`murders`
Page 82, 110 in pdf
Remove:
Two other functions that are helpful are `scan`.
Page 84, 112 in pdf
Although there are R packages designed to read this format, if you are choosing a file format
to save your own data, you generally want to avoid Microsoft Excel. We recommend Google
Sheets as a free software tool for organizing data. We provide more recommendations in
the section Data Organization with Spreadsheets. This book focuses on data analysis. Yet
often a data scientist needs to collect data or work with others collecting data. Filling out
a spreadsheet by hand is a practice we highly discourage and instead recommend that the
process be automatized as much as possible. But sometimes you just have to do it. In this
section, we provide recommendations on how to store data in a spreadsheet.
Should be
Although this book focuses almost exclusively on data analysis, data management is also an important part of data science. As explained in the introduction, we do not cover this topic. However, quite often data analysts needs to collect data, or work with others collecting data, in a way that is most conveniently stored in a spreadsheet. Although filling out a spreadsheet by hand is a practice we highly discourage, we instead recommend the process be automatized as much as possible, sometimes you just have to do it. Therefore, in this section, we provide recommendations on how to organize data in a spreadsheet. Although there are R packages designed to read Microsoft Excel spreadsheets, we generally want to avoid this format. Instead, we recommend Google Sheets as a free software tool. Below
we summarize the recommendations made in paper by Karl Broman and Kara Woo^[https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1375989]. Please read the paper for important details.
Page 132, 160 in pdf
10\. Notice that the approximation calculated in question *two* is very close to the exact calculation in the first question. Now perform the same task for more extreme values. Compare the exact calculation and the normal approximation for the interval (79,81]. How many times bigger is the actual proportion than the approximation?
Two should be nine
page 108, 136 in pdf
9. Rewrite the code above to abbreviation as the label through aes
should be
9. Rewrite the code above to use abbreviations as the label through aes
page 108, 136 in pdf
10. Change the color of the labels through blue. How will we do this?
should be
10. Change the color of the labels to blue. How will we do this?
Page 132, 160 in pdf
15. In answering the previous questions, we found that it is not at all rare for a seven footer to become an NBA player. What would be a fair critique of our calculations:
a. Practice and talent are what make a great basketball player, not height.
b. The normal approximation is not appropriate for heights.
c. As seen in question 3, the normal approximation tends to underestimate the extreme values. It’s possible that there are more seven footers than we predicted.
d. As seen in question 3, the normal approximation tends to overestimate the extreme
values. It’s possible that there are fewer seven footers than we predicted.
Question 3 should be question 10
Page171, 199 in pdf
For each year, we are simply comparing four quantities – the four percentages. A widely used graphical representation of percentages, popularized by Microsoft Excel, is the pie chart:
Should be (four should be five)
For each year, we are simply comparing five quantities – the five percentages. A widely used graphical representation of percentages, popularized by Microsoft Excel, is the pie chart:
Page 199, 147 in pdf
closet should be closest
Page 344, 372 in pdf
get_slope <- function(x, y) cor(x, y) / (sd(x) * sd(y))
Should be
get_slope <- function(x, y) cor(x, y) * sd(y) / sd(x)
Page 344, 372 in pdf
summarize(slope = get_slope(R_per_game, BB_per_game))
Should be:
summarize(slope = get_slope(BB_per_game, R_per_game))
Page 344, 372 in pdf
So does this mean that if we go and hire low salary players with many BB, and who therefore increase the number of walks per game by 2, our team will score 4.2 more runs per game?
Should be (4.2 is now 1.5)
So does this mean that if we go and hire low salary players with many BB, and who therefore increase the number of walks per game by 2, our team will score 1.5 more runs per game?
Page 345, 373 in pdf
#> 1 1.3
Should be
#> 1 0.449
Page 346, 374 in pdf
#> 1 0.4 4.19
#> 2 0.5 3.30
#> 3 0.6 2.55
#> 4 0.7 2.03
#> 5 0.8 2.47
Should be
#> 1 0.4 0.734
#> 2 0.5 0.566
#> 3 0.6 0.412
#> 4 0.7 0.285
#> 5 0.8 0.365
Page 346, 374 in pdf
In fact, the values above are closer to the slope we obtained from singles, 1.3, which is more consistent with our intuition.
Should be (1.3 should b3 0.45)
In fact, the values above are closer to the slope we obtained from singles, 0.45, which is more consistent with our intuition.
Page 347, 375 in pdf
#> 1 2.8 6.47
#> 2 2.9 8.87
#> 3 3 7.40
#> 4 3.1 7.34
#> 5 3.2 5.89
Should be:
#> 1 2.8 1.52
#> 2 2.9 1.57
#> 3 3 1.52
#> 4 3.1 1.49
#> 5 3.2 1.58
Page 347, 375 in pdf
#> 1 5.33
Should be:
#> 1 1.84
Page 355, 383 in pdf
get_slope <- function(x, y) cor(x, y) / (sd(x) * sd(y))
Should be
get_slope <- function(x, y) cor(x, y) * sd(y) / sd(x)
Page 373, 401 in pdf
over interpret should be over-interpret
three common ways should be four common ways
Page 391, 419 in pdf
Add a return after + here
new_tidy_data %>% ggplot(aes(year, fertility, color = country)) +
geom_point()
page 453, 481 in pdf
The make_date function can be used to quickly create a date object. It takes three ar- guments: year, month, day, hour, minute, seconds, and time zone defaulting to the epoch values on UTC time. So create an date object representing, for example, July 6, 2019 we write:
make_date(1970, 7, 6)
#> [1] "1970-07-06"
Should be
make_date(2019, 7, 6)
#> [1] "2019-07-06"
Page 449, 477 in pdf
"It turns them into days since the epoch.The as.Date function can convert a character into a date. So to see that the epoch is day 0 we can type"
There should be a space between "epoch" and "The".
Page 485, 513 in pdf
If the outcomes are binary, both RMSE and MSE are equivalent to accuracy, since
Should be
If the outcomes are binary, both RMSE and MSE are equivalent to one minus accuracy, since
Page 538, 566 in pdf
fit_glm <- glm(y ~ x_1 + x_2, data=mnist_27$train, family = "binomial")
p_hat_glm <- predict(fit_glm, mnist_27$test)
y_hat_glm <- factor(ifelse(p_hat_glm > 0.5, 7, 2))
confusionMatrix(y_hat_glm, mnist_27$test$y)$overall["Accuracy"]
#> Accuracy #> 0.76
Should be (type response, and accuracy changes a bit)
fit_glm <- glm(y ~ x_1 + x_2, data=mnist_27$train, family = "binomial")
p_hat_glm <- predict(fit_glm, mnist_27$test, type = "response")
y_hat_glm <- factor(ifelse(p_hat_glm > 0.5, 7, 2))
confusionMatrix(y_hat_glm, mnist_27$test$y)$overall["Accuracy"]
#> Accuracy #> 0.75
Page 667, 695 in pdf
Remove sentence "We have already shown how we can do this with RStudio."
Improvements (not errors)
Page 67
describe in the next
Should be
Describe next
Page 73, 101 in pdf
case_when(x < 0 ~ "Negative", x > 0 ~ "Positive", TRUE ~ "Zero")
Should be
case_when(x < 0 ~ "Negative",
x > 0 ~ "Positive",
TRUE ~ "Zero")
Page 81, 109 in pdf
it will overwrite existing files without warning.
Should be bolded:
__it will overwrite existing files without warning__.
page 442, 470 in pdf
paste0("http://www.pnas.org/content/suppl/2015/09/16/",
should be
paste0("https://www.pnas.org/content/suppl/2015/09/16/",