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library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(modelr)
options(na.action = na.warn)
library(nycflights13)
library(lubridate)
## 
## 载入程辑包：'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
ggplot(diamonds, aes(cut, price))+geom_boxplot()

ggplot(diamonds, aes(color, price))+geom_boxplot()

ggplot(diamonds, aes(clarity, price))+geom_boxplot()

ggplot(diamonds, aes(carat, price))+geom_hex(bins = 50)

diamonds2 <- diamonds %>% 
  filter(carat <= 2.5) %>% 
  mutate(lprice = log2(price), lcarat = log2(carat))
ggplot(diamonds2, aes(lcarat, lprice)) + 
  geom_hex(bins = 50)

mod_diamond <- lm(lprice ~ lcarat, data = diamonds2)

grid <- diamonds2 %>% 
  data_grid(carat = seq_range(carat, 20)) %>% 
  mutate(lcarat = log2(carat)) %>% 
  add_predictions(mod_diamond, "lprice") %>% 
  mutate(price = 2 ^ lprice)

ggplot(diamonds2, aes(carat, price)) + 
  geom_hex(bins = 50) +
  geom_line(data = grid, colour = "red", size = 1)

diamonds2 <- diamonds2 %>% 
  add_residuals(mod_diamond, "lresid")

ggplot(diamonds2, aes(lcarat, lresid)) + 
  geom_hex(bins = 50)

ggplot(diamonds2, aes(cut, lresid)) + geom_boxplot()

ggplot(diamonds2, aes(color, lresid)) + geom_boxplot()

ggplot(diamonds2, aes(clarity, lresid)) + geom_boxplot()

mod_diamond2 <- lm(lprice ~ lcarat + color + cut + clarity, data = diamonds2)

grid <- diamonds2 %>% 
  data_grid(cut, .model = mod_diamond2) %>% 
  add_predictions(mod_diamond2)
grid
## # A tibble: 5 × 5
##   cut       lcarat color clarity  pred
##   <ord>      <dbl> <chr> <chr>   <dbl>
## 1 Fair      -0.515 G     VS2      11.2
## 2 Good      -0.515 G     VS2      11.3
## 3 Very Good -0.515 G     VS2      11.4
## 4 Premium   -0.515 G     VS2      11.4
## 5 Ideal     -0.515 G     VS2      11.4
ggplot(grid, aes(cut, pred)) + 
  geom_point()

diamonds2 <- diamonds2 %>% 
  add_residuals(mod_diamond2, "lresid2")

ggplot(diamonds2, aes(lcarat, lresid2)) + 
  geom_hex(bins = 50)

diamonds2 %>% 
  filter(abs(lresid2) > 1) %>% 
  add_predictions(mod_diamond2) %>% 
  mutate(pred = round(2 ^ pred)) %>% 
  select(price, pred, carat:table, x:z) %>% 
  arrange(price)
## # A tibble: 16 × 11
##    price  pred carat cut       color clarity depth table     x     y     z
##    <int> <dbl> <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  1013   264  0.25 Fair      F     SI2      54.4    64  4.3   4.23  2.32
##  2  1186   284  0.25 Premium   G     SI2      59      60  5.33  5.28  3.12
##  3  1186   284  0.25 Premium   G     SI2      58.8    60  5.33  5.28  3.12
##  4  1262  2644  1.03 Fair      E     I1       78.2    54  5.72  5.59  4.42
##  5  1415   639  0.35 Fair      G     VS2      65.9    54  5.57  5.53  3.66
##  6  1415   639  0.35 Fair      G     VS2      65.9    54  5.57  5.53  3.66
##  7  1715   576  0.32 Fair      F     VS2      59.6    60  4.42  4.34  2.61
##  8  1776   412  0.29 Fair      F     SI1      55.8    60  4.48  4.41  2.48
##  9  2160   314  0.34 Fair      F     I1       55.8    62  4.72  4.6   2.6 
## 10  2366   774  0.3  Very Good D     VVS2     60.6    58  4.33  4.35  2.63
## 11  3360  1373  0.51 Premium   F     SI1      62.7    62  5.09  4.96  3.15
## 12  3807  1540  0.61 Good      F     SI2      62.5    65  5.36  5.29  3.33
## 13  3920  1705  0.51 Fair      F     VVS2     65.4    60  4.98  4.9   3.23
## 14  4368  1705  0.51 Fair      F     VVS2     60.7    66  5.21  5.11  3.13
## 15 10011  4048  1.01 Fair      D     SI2      64.6    58  6.25  6.2   4.02
## 16 10470 23622  2.46 Premium   E     SI2      59.7    59  8.82  8.76  5.25

```