>> 读入文本文档：readr 包

☑ 一致的函数命名方式

read_csv()   | read_csv2()      | read_tsv()   | read_delim()    |
read_table() | read_fwf()       |
read_lines() | read_lines_raw() | read_file()  | read_file_raw() | read_log()  |

☑ 基本一致的参数设置

read_*(file,  # delim = NULL, escape_backslash = FALSE, escape_double = TRUE,
       col_names = TRUE, col_types = NULL, col_select = NULL, 
       id = NULL, locale = default_locale(), 
       na = c("", "NA"), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, 
       skip = 0, n_max = Inf, guess_max = min(1000, n_max), 
       name_repair = "unique", num_threads = readr_threads(), 
       progress = show_progress(), show_col_types = should_show_types(), 
       skip_empty_rows = TRUE, lazy = should_read_lazy())
# file: Either a path to a file, a connection, or literal data. 
#       Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed.
#       Files starting with http://, https://, ftp://, or ftps:// will be downloaded.
#       Using a value of clipboard() will read from the system clipboard.

>> 读入文本文档：readr 包

☑ 和内置 utils 包中 read.*() 比较

?`tbl_df-class`
tibble: a subclass of data.frame and the central data structure for the tidyverse
lazy and surly - do less and complain more than base data.frames.
======================================================================================
1. 列数据不进行自动类型转换（如字符->因子），且原生直接支持列表列
2. 只对长度为1的“标量”向量进行循环操作
3. tibble支持不合法的数据变量名，如 tibble(`:)` = "smile")
4. 不增加行名，也不鼓励使用行名来存储数据信息
5. 用 [ 对tibble取子集操作总是返回新的tibble，[[ 和 $ 总是返回向量，且 $ 不支持变量名部分匹配
6. tibble在屏幕上打印输出时会适应屏幕大小，并提供更多有用信息（类似str()）

>> 读入文本文档：readr 包

常用参数示例：read_csv()

library(tidyverse)
read_csv("The 1st line of metadata
         The 2nd line of metadata
         # A comment to skip
         x,y,z
         1,2,3", 
         skip = 2,
         comment = "#")

#> Rows: 1 Columns: 3
#> ── Column specification ────────────────
#> Delimiter: ","
#> dbl (3): x, y, z
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#> # A tibble: 1 × 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3

read_csv("1,2,3\n4,5,6")

#> # A tibble: 1 × 3
#>     `1`   `2`   `3`
#>   <dbl> <dbl> <dbl>
#> 1     4     5     6

read_csv("1,2,3\n4,5,6", 
         col_names = FALSE)

#> # A tibble: 2 × 3
#>      X1    X2    X3
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2     4     5     6

read_csv("1,2,3\n4,5,6", 
         col_names = c("x", "y", "z"))

#> # A tibble: 2 × 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2     4     5     6

>> 读入文本文档：readr 包

readr 包采用启发式策略来解析文本文档中数据

#  生成示例数据文档
set.seed(123456)   # 设定随机数种子
nycflights13::flights %>% 
  mutate(dep_delay = if_else(dep_delay <= 0, FALSE, TRUE)) %>% 
  select(month:dep_time, dep_delay, tailnum, time_hour) %>% 
  slice_sample(n = 20) %>%  
  mutate(across(everything(), ~ifelse(runif(20) <= 0.1, NA, .))) %>% 
  mutate(time_hour = lubridate::as_datetime(time_hour)) %>% 
  print(n = 5) %>%
  write_csv(file = "data/ex_flights.csv", na = "--")  # 默认na = "NA"

#> # A tibble: 20 × 6
#>   month   day dep_time dep_delay tailnum time_hour          
#>   <int> <int>    <int> <lgl>     <chr>   <dttm>             
#> 1     6    26      932 TRUE      <NA>    2013-06-26 12:00:00
#> 2    NA     5       NA NA        <NA>    2013-12-05 17:00:00
#> 3     7    20      656 FALSE     N17245  2013-07-20 10:00:00
#> 4     5    16       NA FALSE     N27152  2013-05-16 12:00:00
#> 5    12    23       NA NA        <NA>    2013-12-23 23:00:00
#> # ℹ 15 more rows

>> 读入文本文档：readr 包

readr 包采用启发式策略来解析文本文档中数据

(ex_flights <- read_csv("data/ex_flights.csv"))  # 默认na = c("", "NA")

#> Rows: 20 Columns: 6
#> ── Column specification ──────────────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): month, day, dep_time, dep_delay, tailnum, time_hour
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#> # A tibble: 20 × 6
#>   month day   dep_time dep_delay tailnum time_hour           
#>   <chr> <chr> <chr>    <chr>     <chr>   <chr>               
#> 1 6     26    932      TRUE      --      2013-06-26T12:00:00Z
#> 2 --    5     --       --        --      2013-12-05T17:00:00Z
#> 3 7     20    656      FALSE     N17245  2013-07-20T10:00:00Z
#> 4 5     16    --       FALSE     N27152  2013-05-16T12:00:00Z
#> 5 12    23    --       --        --      2013-12-23T23:00:00Z
#> # ℹ 15 more rows

>> 读入文本文档：readr 包

readr 包采用启发式策略来解析文本文档中数据

1. 根据给定或猜测的分隔符将文本文档分割为字符串矩阵
2. 确定每列字符串向量的类型
      2.1 由 col_types 参数直接给定
      2.2 猜测：读入文档 guess_max 行（v2 vs. v1），并按如下顺序猜测每列变量的类型：logical -> integer -> double -> number -> time -> date -> date-time -> character
3. 将每列字符串解析为相应类型的向量

当发现 readr 自动解析碰到问题时

• 可用 problems() 查看读入时遇到的问题

• 根据需要设定 read_*() 的相关参数，如 na = "--"

• 直接设定 read_*() 的参数 col_types = cols(...)

• 将全部数据读入为字符型变量，然后再用 type_convert() 转换变量类型

• 在后续的数据整理步骤中进行相应的处理（如 dplyr::muate() + parse_*()）

>> 读入文本文档：readr 包

# 设定缺失值参数
read_csv("data/ex_flights.csv", 
         na = "--") %>% spec()

#> cols(
#>   month = col_double(),
#>   day = col_double(),
#>   dep_time = col_double(),
#>   dep_delay = col_logical(),
#>   tailnum = col_character(),
#>   time_hour = col_datetime(format = "")
#> )

# 直接设定参数col_types，?cols
ex_flights <- read_csv(
  "data/ex_flights.csv", 
  col_types = cols(  # cols_only()
    dep_delay = col_logical(),    # "l"
    tailnum   = col_character(),  # "c"
    time_hour = col_datetime(),   # "T"
    .default  = col_integer()
  ) # col_types = "iiilcT"
)

#> Warning: One or more parsing issues, call `problems()` on
#> your data frame for details, e.g.:
#>   dat <- vroom(...)
#>   problems(dat)

problems(ex_flights)

#> # A tibble: 17 × 5
#>     row   col expected           actual file                                              
#>   <int> <int> <chr>              <chr>  <chr>                                             
#> 1     3     1 an integer         --     C:/Users/admin/Desktop/QFwR_2023.09/slides/L05_Im…
#> 2     3     3 an integer         --     C:/Users/admin/Desktop/QFwR_2023.09/slides/L05_Im…
#> 3     3     4 1/0/T/F/TRUE/FALSE --     C:/Users/admin/Desktop/QFwR_2023.09/slides/L05_Im…
#> # ℹ 14 more rows

# 全部读入，再行转换类型
read_csv(
  "data/ex_flights.csv",
  col_types = cols(
    .default = col_character()
  )
) %>% 
  type_convert(col_types = "iiilcT")

#> # A tibble: 20 × 6
#>   month   day dep_time dep_delay tailnum time_hour          
#>   <int> <int>    <int> <lgl>     <chr>   <dttm>             
#> 1     6    26      932 TRUE      --      2013-06-26 12:00:00
#> 2    NA     5       NA NA        --      2013-12-05 17:00:00
#> 3     7    20      656 FALSE     N17245  2013-07-20 10:00:00
#> # ℹ 17 more rows

>> 读入文本文档：readr 包

一次性读入多个相同性质的文档

# 生成相同性质的示例文档
nycflights13::flights %>% split(.$carrier) %>% 
  purrr::iwalk(~ write_tsv(.x, glue::glue("data/flights_{.y}.tsv")))

# 文件路径向量
(files <- dir(path = "data/", pattern = "\\.tsv$", full.names = TRUE))

#>  [1] "data/flights_9E.tsv" "data/flights_AA.tsv" "data/flights_AS.tsv"
#>  [4] "data/flights_B6.tsv" "data/flights_DL.tsv" "data/flights_EV.tsv"
#>  [7] "data/flights_F9.tsv" "data/flights_FL.tsv" "data/flights_HA.tsv"
#> [10] "data/flights_MQ.tsv" "data/flights_OO.tsv" "data/flights_UA.tsv"
#> [13] "data/flights_US.tsv" "data/flights_VX.tsv" "data/flights_WN.tsv"
#> [16] "data/flights_YV.tsv"

# 一次性读入（并纵向合并为 tibble）
read_tsv(files, id = "fpath")  # 这里通过设置 id 参数将文件路径存入指定变量fpath中

#> # A tibble: 336,776 × 20
#>   fpath         year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <chr>        <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>          <dbl>
#> 1 data/flight…  2013     1     1      810            810         0     1048           1037
#> 2 data/flight…  2013     1     1     1451           1500        -9     1634           1636
#> 3 data/flight…  2013     1     1     1452           1455        -3     1637           1639
#> # ℹ 336,773 more rows
#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <dbl>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#> #   time_hour <dttm>

>> 读入文本文档：readr 包

readr 的图形化用户界面 ^*

* RStudio 右上 Environment 标签页 > Import Dataset > From Text (readr) ...

>> 读入其它类型的数据

>> 齐整数据

齐整数据的三个条件 📐

>> 齐整数据

齐整数据的好处 💪

>> 齐整数据

但……并非所有的数据集都是齐整的，😭

table1

#> # A tibble: 6 × 4
#>   country      year cases population
#>   <chr>       <dbl> <dbl>      <dbl>
#> 1 Afghanistan  1999   745   19987071
#> 2 Afghanistan  2000  2666   20595360
#> 3 Brazil       1999 37737  172006362
#> # ℹ 3 more rows

table2

#> # A tibble: 12 × 4
#>   country      year type          count
#>   <chr>       <dbl> <chr>         <dbl>
#> 1 Afghanistan  1999 cases           745
#> 2 Afghanistan  1999 population 19987071
#> 3 Afghanistan  2000 cases          2666
#> # ℹ 9 more rows

table3

#> # A tibble: 6 × 3
#>   country      year rate           
#>   <chr>       <dbl> <chr>          
#> 1 Afghanistan  1999 745/19987071   
#> 2 Afghanistan  2000 2666/20595360  
#> 3 Brazil       1999 37737/172006362
#> # ℹ 3 more rows

table4a   # cases

#> # A tibble: 3 × 3
#>   country     `1999` `2000`
#>   <chr>        <dbl>  <dbl>
#> 1 Afghanistan    745   2666
#> 2 Brazil       37737  80488
#> 3 China       212258 213766

table4b   # population

#> # A tibble: 3 × 3
#>   country         `1999`     `2000`
#>   <chr>            <dbl>      <dbl>
#> 1 Afghanistan   19987071   20595360
#> 2 Brazil       172006362  174504898
#> 3 China       1272915272 1280428583

>> 该 tidyr 包出场啦 🎉

tidyr包中的函数大致可分为5大类：

>> pivot_longer() 和 pivot_wider()

pivot_longer(
  data, cols,
  ..., 
  cols_vary = "fastest", 
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL, 
  names_pattern = NULL, 
  names_ptypes = NULL, 
  names_transform = NULL, 
  names_repair = "check_unique", 
  values_to = "value",
  values_drop_na = FALSE, 
  values_ptypes = NULL,
  values_transform = NULL
)

table4a %>% 
  pivot_longer(
    cols = c(`1999`, `2000`),
    names_to = "year", 
    values_to = "cases"
  )
# table4b %>% ...

#> # A tibble: 6 × 3
#>   country     year  cases
#>   <chr>       <chr> <dbl>
#> 1 Afghanistan 1999    745
#> 2 Afghanistan 2000   2666
#> 3 Brazil      1999  37737
#> # ℹ 3 more rows

>> pivot_longer() 和 pivot_wider()

pivot_wider(
  data,
  ..., 
  id_cols = NULL, id_expand = FALSE,
  names_from = name,
  names_prefix = "", names_sep = "_", 
  names_glue = NULL, 
  names_sort = FALSE, 
  names_vary = "fastest",
  names_expand = FALSE, 
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL, values_fn = NULL, 
  unused_fn = NULL
)

table2 %>% 
  pivot_wider(
    names_from = type, 
    values_from = count
  )

#> # A tibble: 6 × 4
#>   country      year cases population
#>   <chr>       <dbl> <dbl>      <dbl>
#> 1 Afghanistan  1999   745   19987071
#> 2 Afghanistan  2000  2666   20595360
#> 3 Brazil       1999 37737  172006362
#> # ℹ 3 more rows

>> separate_wider_*()、separate_longer_*() 和 unite()

separate_wider_delim|position|regex(
  data, cols,
  delim | widths | patterns,
  ..., 
  names, names_sep = NULL, names_repair,
  too_few, too_many, cols_remove = TRUE
)
separate_longer_delim|position(
  data, cols,
  delim | width,
  ..., keep_empty = FALSE
)

table3 %>% 
  separate_wider_delim(
    rate, delim = "/", 
    names = c("cases", "population")
  )

#> # A tibble: 6 × 4
#>   country      year cases population
#>   <chr>       <dbl> <chr> <chr>     
#> 1 Afghanistan  1999 745   19987071  
#> 2 Afghanistan  2000 2666  20595360  
#> 3 Brazil       1999 37737 172006362 
#> # ℹ 3 more rows

>> separate_wider_*()、separate_longer_*() 和 unite()

unite(
  data, 
  col,
  ...,
  sep = "_", 
  remove = TRUE, 
  na.rm = FALSE
)

table5 %>% 
  unite(
    col = "year",
    century, year,
    sep = ""
  )

#> # A tibble: 6 × 3
#>   country     year  rate           
#>   <chr>       <chr> <chr>          
#> 1 Afghanistan 1999  745/19987071   
#> 2 Afghanistan 2000  2666/20595360  
#> 3 Brazil      1999  37737/172006362
#> # ℹ 3 more rows