tidyverse_tidyr2.Rmd

# 数据规整2 {#tidyverse-tidyr2}

接着上一章，罗列一些`tidyr`的函数

```{r, message = FALSE, warning = FALSE}
library(tidyverse)
```


## `fill()` 缺失值填充

利用**所在列**的上下值进行缺失值填充
```{r}
sales <- tibble::tribble(
  ~quarter, ~year, ~sales,
      "Q1",  2000,  66013,
      "Q2",    NA,  69182,
      "Q3",    NA,  53175,
      "Q4",    NA,  21001,
      "Q1",  2001,  46036,
      "Q2",    NA,  58842,
      "Q3",    NA,  44568,
      "Q4",    NA,  50197,
      "Q1",  2002,  39113,
      "Q2",    NA,  41668,
      "Q3",    NA,  30144,
      "Q4",    NA,  52897
  )
sales
```


```{r}
sales %>% fill(year)
```
也可以控制填充的方向
```{r}
sales %>% fill(year, .direction = "up")
```


## `expand()` 与 `complete()` 

指定数据框的若干列，根据其向量元素值，产生所有可能的交叉组合
```{r}
df <- tibble::tribble(
  ~x, ~y, ~z,
  1L, 1L, 4L,
  1L, 2L, 5L,
  2L, 1L, NA,
  3L, 2L, 6L
)


df %>% expand(x, y)
```

`nesting()`用于限定只产生数据框已出现的组合。
```{r}
df %>% expand(nesting(x, y))
```


```{r}
df %>% expand(nesting(x, y), z)
```

 


`complete()` 补全，可以看做是 `expand(nesting()) + fill()`

```{r}
df %>% complete(x, y)
```


```{r}
df %>% complete(x, y, fill = list(z = 0))
```


数据在complete补全的时候，会面临有两种缺失值：

1. 补位的时候造成的空缺
2. 数据原先就存在缺失值

```{r}
df %>% complete(x, y)
```

- 补位的时候造成的空缺，可通过`fill = list(x = 0)` 控制填充

```{r}
df %>% complete(x, y, fill = list(z = 0))
```


- 数据原先就存在缺失值，最好通过 `explicit = FALSE`显式地控制是否填充

```{r}
df %>% complete(x, y, fill = list(z = 0), explicit = FALSE)
```





## `expand_grid()` 与 `crossing()`
产生一个新的数据框，每行对应着向量元素的所有交叉组合
```{r}
expand_grid(x = 1:3, y = 1:2)
```


```{r}
crossing(x = 1:3, y = 1:2)
```

向量换成数据框也可以，其结果就是数据框行与元素的交叉组合
```{r}
expand_grid(df = data.frame(x = 1:2, y = c(2, 1)), z = 1:3)
```


```{r}
crossing(df = data.frame(x = 1:2, y = c(2, 1)), z = 1:3)
```

`crossing()`可以看作是`expand_grid() + distinct()`， 即`crossing()`在完成交叉组合之后会自动去重，比如

```{r}
expand_grid(x = c(1, 1), y = c(1:2))  # 不考虑去重
```


```{r}
crossing(x = c(1, 1), y = c(1:2))    # 考虑去重 
```



## `separate()` 与 `unite()`

```{r}
tb <- tibble::tribble(
  ~day, ~price,
  1,   "30-45",
  2,   "40-95",
  3,   "89-65",
  4,   "45-63",
  5,   "52-42"
)
```

```{r}
tb1 <- tb %>%
  separate(price, into = c("low", "high"), sep = "-")
tb1
```


```{r}
tb1 %>%
  unite(col = "price", c(low, high), sep = ":", remove = FALSE)
```



有时候分隔符搞不定的，可以用正则表达式，将捕获的每组弄成一列
```{r}
dfc <- tibble(x = c("1-12week", "1-10wk", "5-12w", "01-05weeks"))
dfc
```


```{r}
dfc %>% tidyr::extract(
  x,
  c("start", "end", "letter"), "(\\d+)-(\\d+)([a-z]+)",
  remove = FALSE
)
```






## 删除缺失值所在行drop_na()与replace_na() 

```{r}
df <- tibble::tribble(
    ~name,     ~type, ~score, ~extra,
  "Alice", "english",     80,     10,
  "Alice",    "math",     NA,      5,
    "Bob", "english",     NA,      9,
    "Bob",    "math",     69,     NA,
  "Carol", "english",     80,     10,
  "Carol",    "math",     90,      5
  )

df
```

如果score列中有缺失值`NA`，就删除所在的row
```{r}
df %>%
  filter(!is.na(score))
```

或者用`across()`
```{r}
df %>%
  filter(
    across(score, ~ !is.na(.x))
  )
```


所有列，如果有缺失值`NA`，就删除所在的row
```{r}
df %>%
  filter(
    across(everything(), ~ !is.na(.x))
  )
```



现在有更简便的方法
```{r}
df %>%
  drop_na()
```

也可指定某一列
```{r}
df %>%
  drop_na(score)
```


没来参加考试，视为0分，可以用`replace_na()`
```{r}
df %>% mutate(score = replace_na(score, 0))
```

或者使用`coalesce()`

```{r}
df %>% mutate(score = coalesce(score, 0))
```


```{r}
df %>%
  mutate(
    across(c(score, extra), ~ coalesce(.x, 0))
  )
```



没来参加考试，用平均分代替
```{r}
df %>%
  mutate(
    score = replace_na(score, mean(score, na.rm = TRUE))
  )
```

当然也可以用`if_else()`来做

```{r}
df %>%
  mutate(
    score = if_else(is.na(score), mean(score, na.rm = TRUE), score)
  )
```



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