Conditioning Problem Set
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School
University of California, Berkeley *
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Course
20
Subject
Aerospace Engineering
Date
Apr 3, 2024
Type
Pages
3
Uploaded by HighnessNeutronSeal65
9/17/23, 2:16 PM
Conditioning
https://stat20.datahub.berkeley.edu/user/lijahliu/rstudio/p/5075c682/
1/3
library
(tidyverse)
library
(stat20data)
ggplot
(penguins, aes
(
x = bill_length_mm,
y = bill_depth_mm,
color = species)) +
geom_point
() +
lims
(
x = c
(
30
, 60
),
y = c
(
12
, 23
))
9/17/23, 2:16 PM
Conditioning
https://stat20.datahub.berkeley.edu/user/lijahliu/rstudio/p/5075c682/
2/3
# A tibble: 187 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 46.1 13.2 211 4500
2 Gentoo Biscoe 50 16.3 230 5700
3 Gentoo Biscoe 48.7 14.1 210 4450
4 Gentoo Biscoe 50 15.2 218 5700
5 Gentoo Biscoe 47.6 14.5 215 5400
6 Gentoo Biscoe 46.5 13.5 210 4550
7 Gentoo Biscoe 45.4 14.6 211 4800
8 Gentoo Biscoe 46.7 15.3 219 5200
9 Gentoo Biscoe 43.3 13.4 209 4400
10 Gentoo Biscoe 46.8 15.4 215 5150
# ℹ
177 more rows
# ℹ
2 more variables: sex <fct>, year <int>
# A tibble: 135 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 50 16.3 230 5700
2 Gentoo Biscoe 50 15.2 218 5700
3 Gentoo Biscoe 50 15.3 220 5550
4 Gentoo Biscoe 50 15.9 224 5350
5 Chinstrap Dream 50 19.5 196 3900
6 Gentoo Biscoe 49.9 16.1 213 5400
7 Gentoo Biscoe 49.8 16.8 230 5700
8 Gentoo Biscoe 49.8 15.9 229 5950
9 Chinstrap Dream 49.8 17.3 198 3675
10 Chinstrap Dream 49.7 18.6 195 3600
# ℹ
125 more rows
# ℹ
2 more variables: sex <fct>, year <int>
filter
(penguins, species != "Adelie"
)
penguins %>%
filter
(species != "Adelie"
,
bill_length_mm >=
40
, bill_length_mm <=
50
) %>%
arrange
(
desc
(bill_length_mm))
9/17/23, 2:16 PM
Conditioning
https://stat20.datahub.berkeley.edu/user/lijahliu/rstudio/p/5075c682/
3/3
# A tibble: 3 × 3
species bill_length_mean bill_depth_mean
<fct> <dbl> <dbl>
1 Adelie 38.8 18.3
2 Chinstrap 48.8 18.4
3 Gentoo 47.6 15.0
bill_size
`summarise()` has grouped output by 'species'. You can override using the
`.groups` argument.
# A tibble: 5 × 4
# Groups: species [3]
species island bill_size_mean bill_size_sd
<fct> <fct> <dbl> <dbl>
1 Chinstrap Dream 67.3 4.17
2 Gentoo Biscoe 62.6 3.82
3 Adelie Torgersen 57.5 3.57
4 Adelie Biscoe 57.3 3.21
5 Adelie Dream 56.8 3.22
penguins %>%
group_by
(species) %>%
summarize
(
bill_length_mean = mean
(bill_length_mm),
bill_depth_mean = mean
(bill_depth_mm))
penguins %>%
group_by
(species, island) %>%
mutate
(
bill_size = bill_length_mm + bill_depth_mm)
%>%
summarize
(
bill_size_mean = mean
(bill_size),
bill_size_sd = sd
(bill_size)
) %>%
arrange
(
desc
(bill_size_mean))
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