eda_height.Rmd

# 探索性数据分析-身高体重 {#eda-height}


```{r, eval=FALSE, include=FALSE}
# 数据模拟代码

library(tidyverse)

boy_mu_height       <- 168   
boy_mu_weight       <- 118    
sigma_a <-  5     # std dev in intercepts
sigma_b <-  3     # std dev in slopes
rho     <-  0.8   # correlation between intercepts and slopes

mu     <- c(boy_mu_height, boy_mu_weight)

sigmas <- c(sigma_a, sigma_b)          # standard deviations
rho    <- matrix(c(1, rho,             # correlation matrix
				   rho, 1), nrow = 2)

# now matrix multiply to get covariance matrix
sigma <- diag(sigmas) %*% rho %*% diag(sigmas)

# how many cafes would you like?
n_obs <- 1000

set.seed(13)  # used to replicate example
df_boys <- 
  MASS::mvrnorm(n_obs, mu, sigma) %>% 
  data.frame() %>% 
  set_names("height", "weight") %>% 
  as_tibble() %>% 
  mutate(gender = "male")



girl_mu_height       <- 165   
girl_mu_weight       <- 110    
sigma_a <-  5     # std dev in intercepts
sigma_b <-  4     # std dev in slopes
rho     <-  0.7   # correlation between intercepts and slopes

mu     <- c(girl_mu_height, girl_mu_weight)

sigmas <- c(sigma_a, sigma_b)          # standard deviations
rho    <- matrix(c(1, rho,             # correlation matrix
                   rho, 1), nrow = 2)

# now matrix multiply to get covariance matrix
sigma <- diag(sigmas) %*% rho %*% diag(sigmas)

# how many cafes would you like?
n_obs <- 1000


df_girls <- 
  MASS::mvrnorm(n_obs, mu, sigma) %>% 
  data.frame() %>% 
  set_names("height", "weight") %>% 
  as_tibble() %>% 
  mutate(gender = "female")



df <- bind_rows(df_boys, df_girls)
```


## 案例分析

这是一份身高和体重的数据集
```{r eda-height-2}
library(tidyverse)
d <- read_csv("./demo_data/weight-height.csv")
d
```

```{r eda-height-3}
d %>% summarise(
  across(everything(), ~ sum(is.na(.)))
)
```


## 可视化
### 画出不同性别的身高分布

常规答案
```{r eda-height-4}
d %>%
  ggplot(aes(x = Height, fill = Gender)) +
  geom_density(alpha = 0.5)
```


```{r eda-height-5}
d %>%
  ggplot(aes(x = Height, fill = Gender)) +
  geom_density(alpha = 0.5) +
  facet_wrap(vars(Gender))
```


## 来点高级的

刚才我们看到了分面的操作，全局数据按照某个变量分组后，形成的若干个子集在不同的面板中分别展示出来。

这种方法很适合子集之间对比。事实上，我们看到每个子集的情况后，还很想知道全局的情况，以及子集在全局中的分布、状态或者位置。也就说，想对比子集和全局的情况。

所以我们期望（**子集之间对比，子集与全局对比**）。

具体方法：**用分面的方法高亮展示子集，同时在每个分面上添加全局（灰色背景）**




- 第一步，先把子集用分面的方法，分别画出来

```{r eda-height-6, eval = FALSE}
d %>%
  ggplot(aes(x = Height)) +
  geom_density() +
  facet_wrap(vars(Gender))
```


- 第二步，添加整体的情况作为背景图层。因为第一步用到了分面，也就说会分组，但我们希望整体的背景图层不受分面信息影响，或者叫背景图层不需要分组，而是显示全部。也就说，要保证每个分面面板中的背景图都是一样的，因此，在这个geom_denstiy()图层中，构建不受facet_wrap()影响的数据，即删掉data的分组列。

```{r eda-height-7, eval = FALSE}
d %>%
  ggplot(aes(x = Height)) +
  geom_density(
    data = d %>% select(-Gender)
  ) +
  geom_density() +
  facet_wrap(vars(Gender))
```

- 第三步，y轴的调整，我们希望保持密度的形状，同时希望y轴不用比例值而是用具体的count个数，这样整体和局部能放在一个标度下，

```{r eda-height-8, eval = FALSE}
d %>%
  ggplot(aes(x = Height, y = after_stat(count))) +
  geom_density(
    data = d %>% select(-Gender)
  ) +
  geom_density() +
  facet_wrap(vars(Gender))
```


- 第四步， 配色。
[配色网站](https://coolors.co/50514f-f25f5c-ffe066-247ba0-70c1b3)选颜色

"Male", "Female" 是Gender已经存在的分组。另外，我们在背景图层，新增了一个组"all people"，这样，整个图就有三个分组（三个color组），那么，我们可以在scale_fill_manual中统一设置和指定。

```{r eda-height-9, eval = FALSE}
density_colors <- c(
  "Male" = "#247BA0",
  "Female" = "#F25F5C",
  "all people" = "grey85"
)
```


```{r eda-height-10, eval = FALSE}
d %>%
  ggplot(aes(x = Height, y = after_stat(count))) +
  geom_density(
    data = df %>% select(-Gender),
    aes(fill = "all people", color = "all people")
  ) +
  geom_density(aes(color = Gender, fill = Gender)) +
  facet_wrap(vars(Gender)) +
  scale_fill_manual(name = NULL, values = density_colors) +
  scale_color_manual(name = NULL, values = density_colors) +
  theme_minimal() +
  theme(legend.position = "bottom")
```




### 完整代码

```{r eda-height-11}
density_colors <- c(
  "Male" = "#247BA0",
  "Female" = "#F25F5C",
  "all people" = "grey80"
)

scales::show_col(density_colors)
```


```{r eda-height-12}
d %>%
  ggplot(aes(x = Height, y = after_stat(count))) +
  geom_density(
    data = d %>% dplyr::select(-Gender),
    aes(fill = "all people", color = "all people")
  ) +
  geom_density(aes(color = Gender, fill = Gender)) +
  facet_wrap(vars(Gender)) +
  scale_fill_manual(name = NULL, values = density_colors) +
  scale_color_manual(name = NULL, values = density_colors) +
  theme_minimal() +
  theme(legend.position = "bottom")
```





或者，用不同的主题风格
```{r eda-height-13}
density_colors <- c(
  "Male" = "#56B4E9",
  "Female" = "#EF8A17",
  "all participants" = "grey85"
)

d %>%
  ggplot(aes(x = Height, y = after_stat(count))) +
  geom_density(
    data = function(x) dplyr::select(x, -Gender),
    aes(fill = "all participants", color = "all participants")
  ) +
  geom_density(aes(fill = Gender, color = Gender)) +
  facet_wrap(vars(Gender)) +
  scale_color_manual(name = NULL, values = density_colors) +
  scale_fill_manual(name = NULL, values = density_colors) +
  cowplot::theme_minimal_hgrid(16) +
  theme(legend.position = "bottom", legend.justification = "center")
```





### 画出不同性别的体重分布

```{r eda-height-14}
d %>%
  ggplot(aes(x = Weight, fill = Gender)) +
  geom_density(alpha = 0.5)
```



## 建模
### 身高与体重的散点图
```{r eda-height-15}
d %>%
  ggplot(aes(x = Height, y = Weight, color = Gender)) +
  geom_point()
```


### 建立身高与体重的线性模型
```{r eda-height-16}
fit <- lm(Weight ~ 1 + Height, data = d)
summary(fit)
```


```{r eda-height-17}
broom::tidy(fit)
```


### 建立不同性别下的身高与体重的线性模型

```{r eda-height-18}
d %>%
  group_by(Gender) %>%
  group_modify(
    ~ broom::tidy(lm(Weight ~ 1 + Height, data = .))
  )
```


```{r eda-height-19}
d %>%
  ggplot(aes(x = Height, y = Weight, group = Gender)) +
  geom_point(aes(color = Gender)) +
  geom_smooth(method = lm)
```



```{r eda-height-20, echo = F}
# remove the objects
# rm(list=ls())
rm(d, fit, density_colors)
```

```{r eda-height-21, echo = F, message = F, warning = F, results = "hide"}
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
```