eda_penguins.Rmd

# 探索性数据分析-企鹅的故事 {#eda-penguins}


今天讲一个关于企鹅的数据故事。这个故事来源于科考人员记录的大量企鹅体征[数据](https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv)，图片来源[这里](https://github.com/allisonhorst/palmerpenguins). 

```{r eda-penguins-1, out.width = '100%', echo = FALSE}
knitr::include_graphics("images/penguins.png")
```



## 数据

### 导入数据
可通过宏包`palmerpenguins::penguins`获取数据，也可以读取本地`penguins.csv`文件，
我们采取后面一种方法：
```{r eda-penguins-2, eval=FALSE, include=FALSE}
library(tidyverse)
d <- palmerpenguins::penguins
d %>%
  tidyr::drop_na() %>%
  head()
```


```{r eda-penguins-3, message = FALSE, warning = FALSE}
library(tidyverse)
penguins <- read_csv("./demo_data/penguins.csv") %>%
  janitor::clean_names()

penguins %>%
  head()
```


### 变量含义

|variable          |class   |description |
|:-----------------|:-------|:-----------|
|species           |integer | 企鹅种类 (Adelie, Gentoo, Chinstrap) |
|island            |integer | 所在岛屿 (Biscoe, Dream, Torgersen) |
|bill_length_mm    |double  | 嘴峰长度 (单位毫米) |
|bill_depth_mm     |double  | 嘴峰深度 (单位毫米)|
|flipper_length_mm |integer | 鰭肢长度 (单位毫米) |
|body_mass_g       |integer | 体重 (单位克) |
|sex               |integer | 性别 |
|year              |integer | 记录年份 |



```{r eda-penguins-4, out.width = '86%', echo = FALSE}
knitr::include_graphics("images/culmen_depth.png")
```

### 数据清洗

检查缺失值(NA)这个很重要！

```{r eda-penguins-5}
penguins %>% summarise(
  across(everything(), ~ sum(is.na(.)))
)
```


有缺失值的地方找出来看看
```{r eda-penguins-6}
penguins %>% filter_all(
  any_vars(is.na(.))
)
```
发现共有11行至少有一处有缺失值，于是我们就删除这些行



```{r eda-penguins-7}
penguins <- penguins %>% drop_na()
penguins
```



## 探索性分析

大家可以提出自己想探索的内容：

- 每种类型企鹅有多少只？
- 每种类型企鹅各种属性的均值和分布？
- 嘴峰长度和深度的关联？
- 体重与翅膀长度的关联？
- 嘴峰长度与嘴峰深度的比例？
- 不同种类的宝宝，体重具有显著性差异？
- 这体征中哪个因素对性别影响最大？
- ...


### 每种类型企鹅有多少只
```{r eda-penguins-8}
penguins %>%
  count(species, sort = T)
```

### 每个岛屿有多少企鹅？
```{r eda-penguins-9}
penguins %>%
  count(island, sort = T)
```


### 每种类型企鹅各种体征属性的均值和分布
```{r eda-penguins-10}
penguins %>%
  group_by(species) %>%
  summarize(across(where(is.numeric), mean, na.rm = TRUE))
```




### 每种类型企鹅的嘴峰长度的分布
```{r eda-penguins-11}
penguins %>%
  ggplot(aes(x = bill_length_mm)) +
  geom_density() +
  facet_wrap(vars(species), scales = "free")
```

### 每种类型企鹅的嘴峰长度的分布（分性别）


```{r eda-penguins-12}
penguins %>%
  ggplot(aes(x = bill_length_mm)) +
  geom_density(aes(fill = sex)) +
  facet_wrap(vars(species), scales = "free")
```

男宝宝的嘴巴要长些，哈哈。



来张更好看点的
```{r eda-penguins-13}
penguins %>%
  ggplot(aes(x = bill_length_mm, fill = sex)) +
  geom_histogram(
    position = "identity",
    alpha = 0.7,
    bins = 25
  ) +
  scale_fill_manual(values = c("#66b3ff", "#8c8c8c")) +
  ylab("number of penguins") +
  xlab("length (mm)") +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.text = element_text(size = 11),
    legend.title = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title = element_text(color = "white", size = 10),
    plot.title = element_text(size = 20),
    plot.subtitle = element_text(size = 12, hjust = 1)
  ) +
  facet_wrap(vars(species), scales = "free")
```


同理，可以画出其他属性的分布。当然，我更喜欢用山峦图来呈现不同分组的分布，因为竖直方向可以更方便比较
```{r eda-penguins-14}
library(ggridges)
penguins %>%
  ggplot(aes(x = bill_length_mm, y = species, fill = species)) +
  ggridges::geom_density_ridges()
```


同样，我们也用颜色区分下性别，这样不同种类、不同性别企鹅的嘴峰长度分布一目了然

```{r eda-penguins-15}
penguins %>%
  ggplot(aes(x = bill_length_mm, y = species, fill = sex)) +
  geom_density_ridges(alpha = 0.5)
```


同样的代码，类似地画个其他体征的分布，

```{r eda-penguins-16}
penguins %>%
  ggplot(aes(x = bill_depth_mm, fill = species)) +
  ggridges::geom_density_ridges(aes(y = species))
```


```{r eda-penguins-17}
penguins %>%
  ggplot(aes(x = bill_depth_mm, fill = sex)) +
  ggridges::geom_density_ridges(aes(y = species))
```



```{r eda-penguins-18}
penguins %>%
  ggplot(aes(x = body_mass_g, y = species, fill = sex)) +
  ggridges::geom_density_ridges(alpha = 0.5)
```


但这样一个特征一个特征的画，好麻烦。你知道程序员都是偷懒的，于是我们还有更骚的操作
```{r eda-penguins-19}
penguins %>%
  dplyr::select(species, bill_length_mm:body_mass_g) %>%
  pivot_longer(-species, names_to = "measurement", values_to = "value") %>%
  ggplot(aes(x = value)) +
  geom_density(aes(color = species, fill = species), size = 1.2, alpha = 0.2) +
  facet_wrap(vars(measurement), ncol = 2, scales = "free")
```

```{r eda-penguins-20}
penguins %>%
  dplyr::select(species, bill_length_mm:body_mass_g) %>%
  pivot_longer(-species, names_to = "measurement", values_to = "value") %>%
  ggplot(aes(x = species, y = value)) +
  geom_boxplot(aes(color = species, fill = species), size = 1.2, alpha = 0.2) +
  facet_wrap(vars(measurement), ncol = 2, scales = "free")
```


```{r eda-penguins-21}
penguins %>%
  dplyr::select(species, bill_length_mm:body_mass_g) %>%
  pivot_longer(-species, names_to = "measurement", values_to = "value") %>%
  ggplot(aes(x = value, y = species, fill = species)) +
  ggridges::geom_density_ridges() +
  facet_wrap(vars(measurement), scales = "free")
```


```{r eda-penguins-22}
penguins %>%
  dplyr::select(species,sex, bill_length_mm:body_mass_g) %>%
  pivot_longer(
    -c(species, sex), 
    names_to = "measurement", 
    values_to = "value"
    ) %>%
  ggplot(aes(x = value, y = species, fill = sex)) +
  ggridges::geom_density_ridges() +
  facet_wrap(vars(measurement), scales = "free")
```

我若有所思的看着这张图，似乎看到了一些特征（pattern）了。



### 嘴峰长度和深度的关联

嘴巴越长，嘴巴也会越厚？

```{r eda-penguins-23}
penguins %>%
  ggplot(aes(
    x = bill_length_mm, y = bill_depth_mm,
    shape = species, color = species
  )) +
  geom_point()
```


我们把不同的种类，用不同的颜色区分看看
```{r eda-penguins-24}
penguins %>%
  ggplot(aes(
    x = bill_length_mm, y = bill_depth_mm,
    shape = species, color = species
  )) +
  geom_point(aes(size = body_mass_g))
```


感觉这是一个辛普森佯谬， 我们画图看看
```{r eda-penguins-25}
penguins %>%
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point(aes(color = species, shape = species)) +
  geom_smooth(method = lm) +
  geom_smooth(method = lm, aes(color = species))
```









### 体重与翅膀长度的关联

翅膀越长，体重越大？

```{r eda-penguins-26}
penguins %>%
  group_by(species, island, sex) %>%
  ggplot(aes(
    x = body_mass_g, y = reorder(species, -body_mass_g),
    color = species
  )) +
  geom_jitter(position = position_jitter(seed = 2020, width = 0.2), alpha = 0.4, size = 2) +
  stat_summary(fun = mean, geom = "point", size = 5, alpha = 1)
```



```{r eda-penguins-27}
library(ggtext)
penguins %>%
  ggplot(aes(flipper_length_mm, body_mass_g, group = species)) +
  geom_point(aes(colour = species, shape = species), alpha = 0.7) +
  scale_color_manual(values = c("darkorange", "purple", "cyan4")) +
  labs(
    title = "Penguin Size, Palmer Station LTER",
    subtitle = "Flipper length and body mass for <span style = 'color:darkorange;'>Adelie</span>, <span style = 'color:purple;'>Chinstrap</span> and <span style = 'color:cyan4;'>Gentoo</span> Penguins",
    x = "flipper length (mm)",
    y = "body mass (g)"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    # text = element_text(family = "Futura"),
    # (I only have 'Light' )
    plot.title = element_text(size = 16),
    plot.subtitle = element_markdown(), # element_markdown from `ggtext` to parse the css in the subtitle
    plot.title.position = "plot",
    plot.caption = element_text(size = 8, colour = "grey50"),
    plot.caption.position = "plot"
  )
```



### 不同种类的宝宝，体重具有显著性差异？

先分组计算体重的均值和标准差

```{r eda-penguins-28}
penguins %>%
  group_by(species) %>%
  summarise(
    count = n(),
    mean_body_mass = mean(body_mass_g),
    sd_body_mass = sd(body_mass_g)
  )
```


```{r eda-penguins-29}
penguins %>%
  ggplot(aes(x = species, y = body_mass_g)) +
  geom_boxplot() +
  geom_jitter()
```


用统计方法验证下我们的猜测吧。记住，我们是有科学精神的的人！


#### 参数检验

- one-way ANOVA(要求等方差)

```{r eda-penguins-30}
stats::aov(formula = body_mass_g ~ species, data = penguins) %>%
  summary()
```

p-value 很小，说明不同种类企鹅之间体重是有显著差异的，但aov只给出了species在整体上引起了体重差异（只要有任意两组之间有显著差异，aov给出的p-value都很小），如果想知道不同种类两两之间是否有显著差异，这就需要用到TukeyHSD().


- one-way ANOVA(不要求等方差)，相关介绍看[here](http://www.sthda.com/english/wiki/one-way-anova-test-in-r)
```{r eda-penguins-31}
oneway.test(body_mass_g ~ species, data = penguins)
```

```{r eda-penguins-32}
stats::aov(formula = body_mass_g ~ species, data = penguins) %>%
  TukeyHSD(which = "species") %>%
  broom::tidy()
```

表格第一行instrap-Adelie 的 p-value = 0.916，没通过显著性检验；而Gentoo-Adelie 和 Gentoo-Chinstrap 他们的p-value都接近0，通过显著性检验，这和图中的结果是一致的。


作为统计出生的R语言，有很多宏包可以帮助我们验证我们的结论，我这里推荐**可视化学统计**的宏包[ggstatsplot](https://indrajeetpatil.github.io/ggstatsplot/)宏包将统计分析的结果写在图片里，统计结果和图形融合在一起，让统计结果更容易懂了。（使用这个宏包辅助我们学习统计） 


```{r eda-penguins-33, eval=FALSE}
library(ggstatsplot)

penguins %>%
  ggstatsplot::ggbetweenstats(
    x = species, # > 2 groups
    y = body_mass_g,
    type = "parametric",
    pairwise.comparisons = TRUE, 
    pairwise.display = "all",
    messages = FALSE,
    var.equal = FALSE
  )
```

#### 非参数检验

相关介绍看[here](http://www.sthda.com/english/wiki/kruskal-wallis-test-in-r)
```{r eda-penguins-34}
kruskal.test(body_mass_g ~ species, data = penguins)
```



```{r eda-penguins-35, eval=FALSE}
penguins %>%
  ggstatsplot::ggbetweenstats(
    x = species,
    y = body_mass_g,
    type = "nonparametric",
    mean.ci = TRUE,
    pairwise.comparisons = TRUE, # <<
    pairwise.display = "all",    # ns = only non-significant
    p.adjust.method = "fdr",     # <<
    messages = FALSE
  )
```

哇，原来统计可以这样学！


### 嘴峰长度与嘴峰深度的比例
```{r eda-penguins-36}
penguins %>%
  mutate(ratio = bill_length_mm / bill_depth_mm) %>%
  group_by(species) %>%
  summarise(mean = mean(ratio))
```

```{r eda-penguins-37}
penguins %>%
  mutate(ratio = bill_length_mm / bill_depth_mm) %>%
  ggplot(aes(x = ratio, fill = species)) +
  ggridges::geom_density_ridges(aes(y = species))
```

男宝宝和女宝宝颜色区分下，代码只需要修改一个地方，留给大家自己实践下吧。



### 建立模型

建模需要标准化数据，并对分类变量（比如sex）编码为 1 和 0; （这是第二个好习惯）

```{r eda-penguins-38}
scale_fun <- function(x) {  
  (x - mean(x)) / sd(x)
}

d <- penguins %>%
  select(sex, species, bill_length_mm:body_mass_g) %>%
  mutate(
    across(where(is.numeric), scale_fun)
  ) %>%
  mutate(male = if_else(sex == "male", 1, 0))
d
```

按照species分组后，对flipper_length_mm标准化？这样数据会聚拢到一起了喔, 还是不要了
```{r eda-penguins-39, eval=FALSE}
penguins %>%
  select(sex, species, bill_length_mm:body_mass_g) %>%
  group_by(species) %>%
  mutate(
    across(where(is.numeric), scale_fun)
  ) %>%
  ungroup()
```



#### model_01

我们将性别sex视为响应变量，其他变量为预测变量。这里性别变量是二元的（0 或者 1），所以我们用logistic回归

```{r eda-penguins-40}
logit_mod1 <- glm(
  male ~ 1 + species + bill_length_mm + bill_depth_mm +
    flipper_length_mm + body_mass_g,
  data = d,
  family = binomial(link = "logit")
)

summary(logit_mod1)
```




计算每个变量的平均边际效应
```{r eda-penguins-41}
library(margins)

logit_mod1_m <- logit_mod1 %>% 
 margins() %>% 
 summary() %>% 
 as_tibble()

logit_mod1_m
```



```{r eda-penguins-42}
logit_mod1_m %>%
  ggplot(aes(
    x = reorder(factor, AME),
    y = AME, ymin = lower, ymax = upper
  )) +
  geom_hline(yintercept = 0, color = "gray80") +
  geom_pointrange() +
  coord_flip() +
  labs(x = NULL, y = "Average Marginal Effect")
```




```{r eda-penguins-43, eval=FALSE}
library(ggeffects)
ggpredict(logit_mod1, terms = "bill_length_mm") 
```




#### model_02

```{r eda-penguins-44, eval=FALSE}
library(brms)

brms_mod2 <- brm(
  male ~ 1 + bill_length_mm + bill_depth_mm + flipper_length_mm + body_mass_g + (1 | species),
  data = d,
  family = binomial(link = "logit")
)
```


```{r eda-penguins-45, eval=FALSE}
summary(brms_mod2)
```


```{r eda-penguins-46, eval=FALSE}
library(ggeffects)
ggpredict(brms_mod2, "bill_depth_mm [all]") %>%
  plot()
```





#### model_03

```{r eda-penguins-47, eval=FALSE}
penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_length_mm, color = species)) +
  geom_point()
```



```{r eda-penguins-48, eval=FALSE}
brms_mod3 <- brm(bill_length_mm ~ flipper_length_mm + (1|species),
  data = penguins
)
```


```{r eda-penguins-49, eval=FALSE}
penguins %>%
  group_by(species) %>%
  modelr::data_grid(flipper_length_mm) %>%
  tidybayes::add_fitted_draws(brms_mod3, n = 100) %>%
  ggplot() +
  geom_point(
    data = penguins,
    aes(flipper_length_mm, bill_length_mm, color = species, shape = species)
  ) +
  geom_line(aes(flipper_length_mm, .value, group = interaction(.draw, species), color = species), alpha = 0.1)
```




```{r eda-penguins-50, echo = F}
# remove the objects
# rm(list=ls())
rm(d, logit_mod1, logit_mod1_m, penguins, scale_fun)
```



```{r eda-penguins-51, echo = F, message = F, warning = F, results = "hide"}
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
```