add eda_vaccine

_bookdown.yml

@@ -68,6 +68,8 @@ rmd_files: [
   "eda_caribou.Rmd",
   "eda_penguins.Rmd",
   "eda_career_decision.Rmd",
+  "eda_ames_houseprice.Rmd",
+  "eda_vaccine_effectiveness.Rmd",
   "rvest.Rmd",
   "tidygraph.Rmd",
   "tidytext.Rmd",

eda_ames_houseprice.Rmd

@@ -0,0 +1,319 @@
+# 探索性数据分析-ames房屋价格 {#eda-ames-houseprice}
+
+
+
+
+## 数据故事
+
+```{r out.width = '75%', echo = FALSE, fig.cap = "这是数据故事的地图"}
+knitr::include_graphics("images/ames.png")
+```
+
+
+这是一份**Ames**房屋[数据](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)，您可以把它想象为房屋中介推出的成都市武侯区、锦江区以及高新区等各区县的房屋信息
+
+```{r, message=FALSE, warning=FALSE}
+library(tidyverse)
+ames <- read_csv("./demo_data/ames_houseprice.csv") %>% 
+        janitor::clean_names()
+
+glimpse(ames)
+```
+
+
+
+
+
+感谢曾倬同学提供的解释说明文档
+```{r}
+explanation <- readxl::read_excel("./demo_data/ames_houseprice_explanation.xlsx")
+explanation %>% 
+  knitr::kable()
+```
+
+## 探索设想
+
+- 读懂数据描述，比如
+  - 房屋设施 (bedrooms, garage, fireplace, pool, porch, etc.),
+  - 地理位置 (neighborhood),
+  - 土地信息 (zoning, shape, size, etc.),
+  - 品相等级
+  - 出售价格
+
+
+- 探索影响房屋价格的因素
+  - 必要的预处理（缺失值处理、标准化、对数化等等）
+  - 必要的可视化（比如价格分布图等）
+  - 必要的统计（比如各地区房屋价格的均值）
+  - 合理选取若干预测变量，建立多元线性模型，并对模型结果给出解释
+  - 房屋价格与预测变量（房屋大小、在城市的位置、房屋类型、与街道的距离）
+
+
+
+## 变量选取
+
+```{r}
+d <- ames %>% 
+  select(sale_price, 
+         lot_frontage,   # 建筑离街道的距离
+         lot_area,       # 占地面积
+         neighborhood,   # 建筑在城市的位置
+         gr_liv_area,    # 地上居住面积
+         bldg_type,      # 住宅类别(联排别墅、独栋别墅...)
+         year_built      # 房屋修建日期
+         )
+d
+```
+
+
+
+
+
+
+## 缺失值处理
+
+```{r}
+d %>% 
+  summarise(
+    across(everything(), function(x) sum(is.na(x)) )
+  )
+```
+
+
+
+找出来看看
+```{r}
+d %>% 
+  filter_all(
+    any_vars(is.na(.))
+  )
+```
+
+
+```{r, eval=FALSE}
+library(visdat)
+
+d %>% vis_dat()
+```
+
+
+
+如果不选择`lot_frontage` 就不会有缺失值，如何选择，自己抉择
+
+```{r, eval=FALSE}
+d %>% 
+  select(-lot_frontage) %>% 
+  visdat::vis_dat()
+```
+
+
+我个人觉得这个变量很重要，所以还是保留，牺牲一点样本量吧
+```{r}
+d <- d %>% 
+  drop_na()
+```
+
+
+```{r, eval=FALSE}
+d %>% visdat::vis_dat()
+```
+
+
+
+
+
+
+## 预处理
+
+- 标准化
+
+```{r}
+standard <- function(x) {
+  (x - mean(x)) / sd(x)
+}
+
+d %>% 
+  mutate(
+    across(where(is.numeric), standard),
+    across(where(is.character), as.factor)
+  )
+```
+
+
+- 对数化
+
+```{r}
+d %>% 
+  mutate(
+    log_sale_price = log(sale_price)
+  )
+```
+
+
+```{r}
+d %>% 
+  mutate(
+    across(where(is.numeric), log),
+    across(where(is.character), as.factor)
+  )
+```
+
+
+- 标准化 vs 对数化
+
+选择哪一种，我们看图说话
+
+```{r}
+d %>% 
+  ggplot(aes(x = sale_price)) +
+  geom_density()
+```
+
+
+```{r}
+d %>% 
+  ggplot(aes(x = log(sale_price))) +
+  geom_density()
+```
+
+
+
+我们选择对数化，并保存结果
+```{r}
+d <- d %>% 
+  mutate(
+    across(where(is.numeric), 
+           .fns = list(log = log), 
+           .names = "{.fn}_{.col}"
+           ),
+    across(where(is.character), as.factor)
+  )
+```
+
+
+
+
+
+## 开始探索
+
+
+### 各区域的房屋价格均值
+
+```{r}
+d %>% count(neighborhood)
+```
+
+
+
+```{r}
+d %>% 
+  group_by(neighborhood) %>% 
+  summarise(
+    mean_sale = mean(sale_price)
+  ) %>% 
+  
+  ggplot(
+    aes(x = mean_sale, y = fct_reorder(neighborhood, mean_sale))
+  ) +
+  geom_col(aes(fill = mean_sale < 150000), show.legend = FALSE) +
+  geom_text(aes(label = round(mean_sale, 0)), hjust = 1) +
+  # scale_x_continuous(
+  #   expand = c(0, 0),
+  #   breaks = c(0, 100000, 200000, 300000),
+  #   labels = c(0, "1w", "2w", "3w")    
+  #   ) +
+  scale_x_continuous(
+    expand = c(0, 0),
+    labels = scales::dollar
+  ) +
+  scale_fill_viridis_d(option = "D") + 
+  theme_classic() +
+  labs(x = NULL, y = NULL)
+```
+
+
+
+### 房屋价格与占地面积
+
+```{r}
+d %>%
+  ggplot(aes(x = log_lot_area, y = log_sale_price)) +
+  geom_point(colour = "blue") +
+  geom_smooth(method = lm, se = FALSE, formula = "y ~ x")
+```
+
+
+
+
+
+```{r}
+d %>%
+  ggplot(aes(x = log_lot_area, y = log_sale_price)) +
+  geom_point(aes(colour = neighborhood)) +
+  geom_smooth(method = lm, se = FALSE, formula = "y ~ x")
+```
+
+
+
+
+
+```{r}
+d %>%
+  ggplot(aes(x = log_lot_area, y = log_sale_price)) +
+  geom_point(colour = "blue") +
+  geom_smooth(method = lm, se = FALSE, formula = "y ~ x", fullrange = TRUE) +
+  facet_wrap(~neighborhood) +
+  theme(strip.background = element_blank())
+```
+
+
+
+### 房屋价格与房屋居住面积
+
+```{r}
+d %>%
+  ggplot(aes(x = log_gr_liv_area, y = log_sale_price)) +
+  geom_point(aes(colour = neighborhood)) +
+  geom_smooth(method = lm, se = FALSE, formula = "y ~ x")
+```
+
+
+```{r}
+d %>%
+  ggplot(aes(x = log_gr_liv_area, y = log_sale_price)) +
+  geom_point() +
+  geom_smooth(method = lm, se = FALSE, formula = "y ~ x", fullrange = TRUE) +
+  facet_wrap(~neighborhood) +
+  theme(strip.background = element_blank())
+```
+
+
+
+## 建模
+
+```{r}
+lm(log_sale_price ~ 1 + log_gr_liv_area + neighborhood, data = d) %>% 
+  broom::tidy()
+```
+
+
+```{r}
+library(lme4)
+lmer(log_sale_price ~ 1 + log_gr_liv_area + (log_gr_liv_area | neighborhood), 
+    data = d) %>% 
+   broom.mixed::tidy()
+```
+
+
+
+```{r, echo = F}
+# remove the objects
+# ls() %>% stringr::str_flatten(collapse = ", ")
+rm(ames, d, explanation)
+```
+
+
+
+```{r, echo = F, message = F, warning = F, results = "hide"}
+pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
+```