[R语言] readr包数据解析及导入《R for data science》 5

《R for Data Science》第十一章 Data import 啃书知识点积累
参考链接：R for Data Science

数据导入

- 常用函数

read_csv() reads comma delimited files, read_csv2() reads semicolon separated files, read_tsv() reads tab delimited files, and read_delim() reads in files with any delimiter.

read_fwf() reads fixed width files. You can specify fields either by their widths with fwf_widths() or their position with fwf_positions(). read_table() reads a common variation of fixed width files where columns are separated by white space.

read_log() reads Apache style log files.

Apache日志参考：Apache日志详解 (一般用不到)

- 读取和特殊创建

# It prints out a column specification that gives the name and type of each column. 
heights <- read_csv("data/heights.csv")
#> Parsed with column specification:
#> cols(
#>   earn = col_double(),
#>   height = col_double(),
#>   sex = col_character(),
#>   ed = col_double(),
#>   age = col_double(),
#>   race = col_character()
#> )

# 利用读取函数创建tibble
# 注意换行后不需要加,号
read_csv("a,b,c
1,2,3
4,5,6")
#> # A tibble: 2 x 3
#>       a     b     c
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2     4     5     6

# 等价于
read_csv("a,b,c\n1,2,3\n4,5,6")

# 按行创建tibble的经典方法
ts <- tribble(
    ~a, ~b, ~c,
    #--/--/----
     1,  2,  3,
     4,  5,  6
)

- read_csv()常用参数

# skip 跳过指定数量的行
read_csv("The first line of metadata
  The second line of metadata
  x,y,z
  1,2,3", skip = 2)
#> # A tibble: 1 x 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3

# comment 识别以comment开头的行为注释行，跳过
read_csv("# A comment I want to skip
  x,y,z
  1,2,3", comment = "#")
#> # A tibble: 1 x 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3

# col_names为FALSE时不识别第一行为列名，默认为TRUE
read_csv("1,2,3\n4,5,6", col_names = FALSE)
#> # A tibble: 2 x 3
#>      X1    X2    X3
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2     4     5     6

# col_names指定具体向量可以重新命名
read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z"))
#> # A tibble: 2 x 3
#>       x     y     z
#>   <dbl> <dbl> <dbl>
#> 1     1     2     3
#> 2     4     5     6

# na 将文件中满足条件的数据识别为NA
read_csv("a,b,c\n1,2,.", na = ".")
#> # A tibble: 1 x 3
#>       a     b c    
#>   <dbl> <dbl> <lgl>
#> 1     1     2 NA


# quote 指定符号用于限定字符串
# Single character used to quote strings.
read_csv("x,y\n1,'a,b'",quote="'")
# # A tibble: 1 x 2
#       x y    
#   <dbl> <chr>
# 1     1 a,b 
# 不指定quote会按照delim截断超过列数的列
read_csv("x,y\n1,'a,b'")   
# # A tibble: 1 x 2
#       x y    
#   <dbl> <chr>
# 1     1 'a        

# locale 中指定 encoding 编码类型
read_csv('数据1.csv',
         locale = locale(encoding = "GBK"))

- read_csv和原生read.csv的区别

They are typically much faster (~10x) than their base equivalents.
They produce tibbles.
They don’t convert character vectors to factors.
They are more reproducible. Base R functions inherit some behaviour from your operating system and environment variables, so import code that works on your computer might not work on someone else’s.

- Q: Apart from file, skip, and comment, what other arguments do read_csv() and read_tsv() have in common?

# read_csv() and read_tsv() are special cases of the general read_delim(). They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively.
# 以下参数二者公有

read_csv(file, col_names = TRUE, col_types = 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),
  progress = show_progress(), skip_empty_rows = TRUE)
  
read_tsv(file, col_names = TRUE, col_types = 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),
  progress = show_progress(), skip_empty_rows = TRUE)

解析数据

parse_*(): These functions take a character vector and return a more specialised vector like a logical, integer, or date

str(parse_logical(c("TRUE", "FALSE", "NA")))
#>  logi [1:3] TRUE FALSE NA
str(parse_integer(c("1", "2", "3")))
#>  int [1:3] 1 2 3
str(parse_date(c("2010-01-01", "1979-10-14")))
#>  Date[1:2], format: "2010-01-01" "1979-10-14"

- 解析异常及处理

parse_integer(c("1", "231", ".", "456"), na = ".")
#> [1]   1 231  NA 456


parse_integer(c("123", "345", "abc", "123.45"))
#> [1] 123 345  NA  NA
#> attr(,"problems")
#> # A tibble: 2 x 4
#>     row   col expected               actual
#>   <int> <int> <chr>                  <chr> 
#> 1     3    NA an integer             abc   
#> 2     4    NA no trailing characters .45

用problems(x)捕获异常形成tibble

x <- parse_integer(c("123", "345", "abc", "123.45"))
problems(x)
#> # A tibble: 2 x 4
#>     row   col expected               actual
#>   <int> <int> <chr>                  <chr> 
#> 1     3    NA an integer             abc   
#> 2     4    NA no trailing characters .45

- 解析数字

parse_number可以只提取数字而忽略数字旁的符号

parse_number("$100")
#> [1] 100

parse_number("20%")
#> [1] 20

parse_number("It cost $123.45")
#> [1] 123.45

分组符号和小数符号

parse_double("1.23")
#> [1] 1.23
parse_double("1,23", locale = locale(decimal_mark = ","))
#> [1] 1.23


# Used in America
parse_number("$123,456,789")
#> [1] 1.23e+08
# Used in many parts of Europe
parse_number("123.456.789", locale = locale(grouping_mark = "."))
#> [1] 1.23e+08
# Used in Switzerland
parse_number("123'456'789", locale = locale(grouping_mark = "'"))
#> [1] 1.23e+08

Tips
(1) decimal_mark 和 grouping_mark相同会报错

parse_number("1,23", locale = locale(decimal_mark = ","))
# [1] 1.23
parse_number("1,23", locale = locale(grouping_mark = ","))
# [1] 123
parse_number("1,23", locale = locale(
                                     grouping_mark = ",",
                                     decimal_mark = ","))
# 错误: `decimal_mark` and `grouping_mark` must be different

decimal_mark默认为.，grouping_mark默认为,，如果占位了对方的默认符号可视作交换

# decimal_mark为','时grouping_mark默认为'.'
parse_number("2.221,23", locale = locale(decimal_mark = ","))
# [1] 2221.23

# grouping_mark为'.'时decimal_mark默认为','
parse_number("2.221,23", locale = locale(grouping_mark = "."))
# [1] 2221.23

integer double number对比

parse_logical(c("TRUE", "FALSE", "1", "0", "true", "t", "NA"))
# [1]  TRUE FALSE  TRUE FALSE  TRUE  TRUE    NA

parse_integer(c("1235", "0134", "NA"))
# [1] 1235  134   NA
parse_number(c("1235", "0134", "NA"))
# [1] 1235  134   NA
parse_double(c("1235", "0134", "NA"))
# [1] 1235  134   NA

parse_integer(c("1000", "$1,000", "10.00"))
# Warning: 2 parsing failures.
# row col               expected actual
# 2  -- an integer             $1,000
# 3  -- no trailing characters .00   
# 
# [1] 1000   NA   NA
# attr(,"problems")
# # A tibble: 2 x 4
# row   col expected               actual
# <int> <int> <chr>                  <chr> 
#   1     2    NA an integer             $1,000
#   2     3    NA no trailing characters .00   
parse_number(c("1000", "$1,000", "10.00"))
# [1] 1000 1000   10
parse_double(c("1000", "$1,000", "10.00"))
# Warning: 1 parsing failure.
# row col expected actual
# 2  -- a double $1,000
# 
# [1] 1000   NA   10
# attr(,"problems")
# # A tibble: 1 x 4
#  row   col expected actual
# <int> <int> <chr>    <chr> 
#   1     2    NA a double $1,000

- 解析字符串

charToRaw("Hadley")
#> [1] 48 61 64 6c 65 79

The mapping from hexadecimal number to character is called the encoding

给乱码重新编码

x1 <- "El Ni\xf1o was particularly bad this year"
x2 <- "\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd"

parse_character(x1, locale = locale(encoding = "Latin1"))
#> [1] "El Niño was particularly bad this year"
parse_character(x2, locale = locale(encoding = "Shift-JIS"))
#> [1] "こんにちは"

猜编码：正确率有限，文本量大时正确率高

guess_encoding(charToRaw(x1))
#> # A tibble: 2 x 2
#>   encoding   confidence
#>   <chr>           <dbl>
#> 1 ISO-8859-1       0.46
#> 2 ISO-8859-9       0.23


guess_encoding(charToRaw(x2))
#> # A tibble: 1 x 2
#>   encoding confidence
#>   <chr>         <dbl>
#> 1 KOI8-R         0.42
# 猜错了，实际应该是Shift-JIS

- 解析因子

fruit <- c("apple", "banana")
parse_factor(c("apple", "banana", "bananana"), levels = fruit)
#> Warning: 1 parsing failure.
#> row col           expected   actual
#>   3  -- value in level set bananana
#> [1] apple  banana <NA>  
#> attr(,"problems")
#> # A tibble: 1 x 4
#>     row   col expected           actual  
#>   <int> <int> <chr>              <chr>   
#> 1     3    NA value in level set bananana
#> Levels: apple banana

- 解析日期时间

基础用法

parse_datetime("2010-10-01T2010")
#> [1] "2010-10-01 20:10:00 UTC"
# If time is omitted, it will be set to midnight
parse_datetime("20101010")
#> [1] "2010-10-10 UTC"

parse_date("2010-10-01")
#> [1] "2010-10-01"

library(hms)
parse_time("01:10 am")
#> 01:10:00
parse_time("20:10:01")
#> 20:10:01

date-time format

常用格式

可以用通配符跳过

parse_time("现在的时间是：20点10分!以及01秒",
           '%*%H%*%M%*%S%*')
# 20:10:01

指定不同的格式会出现不同的结果

parse_date("01/02/15", "%m/%d/%y")
#> [1] "2015-01-02"

parse_date("01/02/15", "%d/%m/%y")
#> [1] "2015-02-01"

parse_date("01/02/15", "%y/%m/%d")
#> [1] "2001-02-15"

locale封装时间格式

# 封装
new_locale <- locale(date_format = "%d/%m/%Y")
parse_date("04/11/2020", locale = new_locale)
# [1] "2020-11-04"

Exercises

d1 <- "January 1, 2010"
parse_date(d1, '%B %d, %Y')
# [1] "2010-01-01"

d2 <- "2015-Mar-07"
parse_date(d2, '%Y-%b-%d')
# [1] "2015-03-07"

d3 <- "06-Jun-2017"
parse_date(d3, '%d-%b-%Y')
# [1] "2017-06-06"

d4 <- c("August 19 (2015)", "July 1 (2015)")
parse_date(d4, '%B %d (%Y)')
# [1] "2015-08-19" "2015-07-01"

d5 <- "12/30/14" # Dec 30, 2014
parse_date(d5, '%M/%d/%y')
# [1] "2014-01-30"

t1 <- "1705"
parse_time(t1, '%H%M')
# 17:05:00

t2 <- "11:15:10.12 PM"
parse_time(t2, '%I:%M:%OS %p')
# 23:15:10.12

解析导入的文件各列

- 经典解析策略

readr uses a heuristic to figure out the type of each column: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column.

guess_parser("2010-10-01")
#> [1] "date"
guess_parser("15:01")
#> [1] "time"
guess_parser(c("TRUE", "FALSE"))
#> [1] "logical"
guess_parser(c("1", "5", "9"))
#> [1] "double"
guess_parser(c("12,352,561"))
#> [1] "number"

str(parse_guess("2010-10-10"))
#>  Date[1:1], format: "2010-10-10

具体解析策略
The heuristic tries each of the following types, stopping when it finds a match:

logical: contains only “F”, “T”, “FALSE”, or “TRUE”.
integer: contains only numeric characters (and -).
double: contains only valid doubles (including numbers like 4.5e-5).
number: contains valid doubles with the grouping mark inside.
time: matches the default time_format.
date: matches the default date_format.
date-time: any ISO8601 date.
If none of these rules apply, then the column will stay as a vector of strings.

只通过前1000列启发式解析会带来的问题：

The first thousand rows might be a special case, and readr guesses a type that is not sufficiently general. For example, you might have a column of doubles that only contains integers in the first 1000 rows.

The column might contain a lot of missing values. If the first 1000 rows contain only NAs, readr will guess that it’s a logical vector, whereas you probably want to parse it as something more specific.

这里的解析再一次说明了NA是逻辑型：

readr contains a challenging CSV that illustrates both of these problems

challenge <- read_csv(readr_example("challenge.csv"))
#> Parsed with column specification:
#> cols(
#>   x = col_double(),
#>   y = col_logical()
#> )
#> Warning: 1000 parsing failures.
#>  row col           expected     actual                                                 file
#> 1001   y 1/0/T/F/TRUE/FALSE 2015-01-16 '/home/travis/R/Library/readr/extdata/challenge.csv'
#> 1002   y 1/0/T/F/TRUE/FALSE 2018-05-18 '/home/travis/R/Library/readr/extdata/challenge.csv'
#> 1003   y 1/0/T/F/TRUE/FALSE 2015-09-05 '/home/travis/R/Library/readr/extdata/challenge.csv'
#> 1004   y 1/0/T/F/TRUE/FALSE 2012-11-28 '/home/travis/R/Library/readr/extdata/challenge.csv'
#> 1005   y 1/0/T/F/TRUE/FALSE 2020-01-13 '/home/travis/R/Library/readr/extdata/challenge.csv'
#> .... ... .................. .......... ....................................................
#> See problems(...) for more details.

# 使用problem详细捕获异常
problems(challenge)
#> # A tibble: 1,000 x 5
#>     row col   expected         actual    file                                   
#>   <int> <chr> <chr>            <chr>     <chr>                                  
#> 1  1001 y     1/0/T/F/TRUE/FA… 2015-01-… '/home/travis/R/Library/readr/extdata/…
#> 2  1002 y     1/0/T/F/TRUE/FA… 2018-05-… '/home/travis/R/Library/readr/extdata/…
#> 3  1003 y     1/0/T/F/TRUE/FA… 2015-09-… '/home/travis/R/Library/readr/extdata/…
#> 4  1004 y     1/0/T/F/TRUE/FA… 2012-11-… '/home/travis/R/Library/readr/extdata/…
#> 5  1005 y     1/0/T/F/TRUE/FA… 2020-01-… '/home/travis/R/Library/readr/extdata/…
#> 6  1006 y     1/0/T/F/TRUE/FA… 2016-04-… '/home/travis/R/Library/readr/extdata/…
#> # … with 994 more rows

非字符型的解析：指定解析类型

tail(challenge)
#> # A tibble: 6 x 2
#>       x y    
#>   <dbl> <lgl>
#> 1 0.805 NA   
#> 2 0.164 NA   
#> 3 0.472 NA   
#> 4 0.718 NA   
#> 5 0.270 NA   
#> 6 0.608 NA

challenge <- read_csv(
  readr_example("challenge.csv"), 
  col_types = cols(
    x = col_double(),
    y = col_date()
  )
)

tail(challenge)
#> # A tibble: 6 x 2
#>       x y         
#>   <dbl> <date>    
#> 1 0.805 2019-11-21
#> 2 0.164 2018-03-29
#> 3 0.472 2014-08-04
#> 4 0.718 2015-08-16
#> 5 0.270 2020-02-04
#> 6 0.608 2019-01-06

Every parse_xyz() function has a corresponding col_xyz() function. You use parse_xyz() when the data is in a character vector in R already; you use col_xyz() when you want to tell readr how to load the data.

stop_for_problems：严格的捕获错误
遇到error就会终止，warning不会

stop_for_problems(
  x <- parse_double("It cost $123.45")
)
# 错误: 1 parsing failure

- 其他解析策略

修改默认启发解析的行数：提高解析成功的准确度

challenge2 <- read_csv(readr_example("challenge.csv"), guess_max = 1001)
#> Parsed with column specification:
#> cols(
#>   x = col_double(),
#>   y = col_date(format = "")
#> )

指定默认解析结局的类型

# 默认全部列均解析为character
challenge2 <- read_csv(readr_example("challenge.csv"), 
  col_types = cols(.default = col_character())
)

type_convert启发式转换类型

library(tibble)
df <- tribble(
  ~x,  ~y,
  #--/----
  "1", "1.21",
  "2", "2.32",
  "3", "4.56"
)

str(df)
# tibble [3 x 2] (S3: tbl_df/tbl/data.frame)
# $ x: chr [1:3] "1" "2" "3"
# $ y: chr [1:3] "1.21" "2.32" "4.56"
df
## A tibble: 3 x 2
#  x     y    
#  <chr> <chr>
#  1 1     1.21 
#  2 2     2.32 
#  3 3     4.56 


df <- type_convert(df)
str(df)
# tibble [3 x 2] (S3: tbl_df/tbl/data.frame)
# $ x: num [1:3] 1 2 3
# $ y: num [1:3] 1.21 2.32 4.56
df
## A tibble: 3 x 2
#  x     y
# <dbl> <dbl>
#  1     1  1.21
#  2     2  2.32
#  3     3  4.56

写入文件

readr also comes with two useful functions for writing data back to disk: write_csv() and write_tsv().
Both functions increase the chances of the output file being read back in correctly by:

Always encoding strings in UTF-8.

Saving dates and date-times in ISO8601 format so they are easily parsed elsewhere.

If you want to export a csv file to Excel, use write_excel_csv() — this writes a special character (a “byte order mark”) at the start of the file which tells Excel that you’re using the UTF-8 encoding.

write_csv(x, path, na = "NA", append = FALSE, col_names = !append,
  quote_escape = "double")

# x 文件，path 写入路径，na 将指定识别为缺失值
# col_names 是否将第一行视为列名，append 是否追加写入

- 需要注意的细节

写入csv后列属性会丢失

challenge
#> # A tibble: 2,000 x 2
#>       x y         
#>   <dbl> <date>    
#> 1   404 NA        
#> 2  4172 NA        
#> 3  3004 NA        
#> 4   787 NA        
#> 5    37 NA        
#> 6  2332 NA        
#> # … with 1,994 more rows
write_csv(challenge, "challenge-2.csv")

read_csv("challenge-2.csv")
#> Parsed with column specification:
#> cols(
#>   x = col_double(),
#>   y = col_logical()
#> )
#> # A tibble: 2,000 x 2
#>       x y    
#>   <dbl> <lgl>
#> 1   404 NA   
#> 2  4172 NA   
#> 3  3004 NA   
#> 4   787 NA   
#> 5    37 NA   
#> 6  2332 NA   
#> # … with 1,994 more rows

解决办法：

以rds形式写入

write_rds(challenge, "challenge.rds")

read_rds("challenge.rds")
#> # A tibble: 2,000 x 2
#>       x y         
#>   <dbl> <date>    
#> 1   404 NA        
#> 2  4172 NA        
#> 3  3004 NA        
#> 4   787 NA        
#> 5    37 NA        
#> 6  2332 NA        
#> # … with 1,994 more rows

feather包

library(feather)
write_feather(challenge, "challenge.feather")

read_feather("challenge.feather")
#> # A tibble: 2,000 x 2
#>       x      y
#>   <dbl> <date>
#> 1   404   <NA>
#> 2  4172   <NA>
#> 3  3004   <NA>
#> 4   787   <NA>
#> 5    37   <NA>
#> 6  2332   <NA>
#> # ... with 1,994 more rows

Feather tends to be faster than RDS and is usable outside of R.

RDS supports list-columns; feather currently does not.

[R语言] readr包数据解析及导入《R for data science》 5

[R语言] readr包数据解析及导入《R for data science》 5

数据导入

- 常用函数

- 读取和特殊创建

- read_csv()常用参数

- read_csv和原生read.csv的区别

- Q: Apart from file, skip, and comment, what other arguments do read_csv() and read_tsv() have in common?

解析数据

- 解析异常及处理

- 解析数字

- 解析字符串

- 解析因子

- 解析日期时间

解析导入的文件各列

- 经典解析策略

- 其他解析策略

写入文件

- 需要注意的细节

Others

推荐阅读更多精彩内容

[R语言] readr包 数据解析及导入《R for data science》 5

数据导入

- 常用函数

- 读取和特殊创建

- read_csv()常用参数

- read_csv和原生read.csv的区别

- Q: Apart from file, skip, and comment, what other arguments do read_csv() and read_tsv() have in common?

解析数据

- 解析异常及处理

- 解析数字

- 解析字符串

- 解析因子

- 解析日期时间

解析导入的文件各列

- 经典解析策略

- 其他解析策略

写入文件

- 需要注意的细节

Others

推荐阅读更多精彩内容

[R语言] readr包数据解析及导入《R for data science》 5