R codebase（二）数据转换

段首语

此系列文章用来做R语言的学习，以及对于使用R语言进行数据处理和作图的代码汇总，方便大家随时进行查找、使用。

一、数据的导入与输出

1.导入

表格

read_*(file, col_names = TRUE, col_types = NULL, locale = default_locale(), na = c("", "NA"),
      quoted_na = TRUE, comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, 
      guess_max = min(1000, n_max), progress = interactive())

逗号分隔：read_csv(“file.csv”)

分号分隔：read_csv2(“file2.csv”)

任意分隔符：read_delim(“file.txt”, delim = “|”)

任意宽度的空格分隔：read_fwf(“file.fwf”, col_positions = c(1, 3, 5))

tab键分隔：read_tsv(“file.tsv”) or read_table()

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参数

去除表头：read_csv(f, col_names = FALSE)

自定义表头：read_csv(f, col_names = c(“x”, “y”, “z”))

跳行：read_csv(f, skip = 1)

选定行：read_csv(f, n_max = 1)

替换值为NA：read_csv(f, na = c(“1”, “.”))

文件

文件读入为一个字符串：read_file(file, locale = default_locale())

文件读入为多行字符串：read_lines(file, skip = 0, n_max = -1L, na = character(), locale = default_locale(), progress = interactive())

文件读入为一个向量：read_file_raw(file)

文件读入为多行向量：read_lines_raw(file, skip = 0, n_max = -1L, progress = interactive())

2.输出

与导入类似：

逗号分隔：write_csv(x, path, na = “NA”, append = FALSE, col_names = !append)

指定分隔：write_delim(x, path, delim = “ “, na = “NA”, append = FALSE, col_names = !append)

Excel：write_excel_csv(x, path, na = “NA”, append = FALSE, col_names = !append)

字符串到文件：write_file(x, path, append = FALSE)

字符串向量到文件，每行一个分隔符：write_lines(x,path, na = “NA”, append = FALSE)

RDS文件：write_rds(x, path, compress = c(“none”, “gz”, “bz2”, “xz”), …)

tab键分隔write_tsv(x, path, na = “NA”, append = FALSE, col_names = !append)

二、数据的扩展、合并、分解（tidyr）

1.数据的重构

扩展

gather(data, key, value, …, na.rm = FALSE, convert = FALSE, factor_key = FALSE)

library(tidyr)
data1 <- data.frame(country = c("A","B","D"),"Y1999" = c("2.7K","3.8K","1.4K"),"Y2000" = c("3.0K","4.5K","2.0K"))
data1
##   country Y1999 Y2000
## 1       A  2.7K  3.0K
## 2       B  3.8K  4.5K
## 3       D  1.4K  2.0K
gather(data1, "Y1999", "Y2000", key = "year", value = "cases")
##   country  year cases
## 1       A Y1999  2.7K
## 2       B Y1999  3.8K
## 3       D Y1999  1.4K
## 4       A Y2000  3.0K
## 5       B Y2000  4.5K
## 6       D Y2000  2.0K

合并

spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)

library(tidyr)
data2 <- data.frame(country = rep(c("A","B","C"),each=4),
                    year = rep(rep(c("Y1999","Y2000"),each=2),times=3),
                    type = rep(c("cases","pop"),times=6),
                    count = c("2.7K","2.6K","5.6K","3.5K","3.7K","1.7K","3.9K","2.4K","2.9K","2.0K","2.1K","4.6K")
                    )
data2
##    country  year  type count
## 1        A Y1999 cases  2.7K
## 2        A Y1999   pop  2.6K
## 3        A Y2000 cases  5.6K
## 4        A Y2000   pop  3.5K
## 5        B Y1999 cases  3.7K
## 6        B Y1999   pop  1.7K
## 7        B Y2000 cases  3.9K
## 8        B Y2000   pop  2.4K
## 9        C Y1999 cases  2.9K
## 10       C Y1999   pop  2.0K
## 11       C Y2000 cases  2.1K
## 12       C Y2000   pop  4.6K
spread(data2, type, count)
##   country  year cases  pop
## 1       A Y1999  2.7K 2.6K
## 2       A Y2000  5.6K 3.5K
## 3       B Y1999  3.7K 1.7K
## 4       B Y2000  3.9K 2.4K
## 5       C Y1999  2.9K 2.0K
## 6       C Y2000  2.1K 4.6K

2.对于na值的处理

x <- data.frame(x1 = LETTERS[seq(1,5)],
                x2 = c(1,NA,NA,3,NA))
x
##   x1 x2
## 1  A  1
## 2  B NA
## 3  C NA
## 4  D  3
## 5  E NA
## 去除 drop_na(data, ...)
drop_na(x, x2)
##   x1 x2
## 1  A  1
## 2  D  3
## 填充 fill(data, ..., .direction = c("down", "up"))
fill(x, x2)
##   x1 x2
## 1  A  1
## 2  B  1
## 3  C  1
## 4  D  3
## 5  E  3
## 替换 replace_na(data, replace = list(), ...)
replace_na(x, list(x2 = 2))
##   x1 x2
## 1  A  1
## 2  B  2
## 3  C  2
## 4  D  3
## 5  E  2

3.填充和完善

head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02
## Valiant           18.1   6  225 105 2.76 3.460 20.22
##                   vs am gear carb
## Mazda RX4          0  1    4    4
## Mazda RX4 Wag      0  1    4    4
## Datsun 710         1  1    4    1
## Hornet 4 Drive     1  0    3    1
## Hornet Sportabout  0  0    3    2
## Valiant            1  0    3    1
## complete(data, ..., fill = list())
complete(mtcars, cyl, gear, carb)
## # A tibble: 74 × 11
##      cyl  gear  carb   mpg  disp    hp  drat    wt
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1     4     3     1  21.5 120.     97  3.7   2.46
##  2     4     3     2  NA    NA      NA NA    NA   
##  3     4     3     3  NA    NA      NA NA    NA   
##  4     4     3     4  NA    NA      NA NA    NA   
##  5     4     3     6  NA    NA      NA NA    NA   
##  6     4     3     8  NA    NA      NA NA    NA   
##  7     4     4     1  22.8 108      93  3.85  2.32
##  8     4     4     1  32.4  78.7    66  4.08  2.2 
##  9     4     4     1  33.9  71.1    65  4.22  1.84
## 10     4     4     1  27.3  79      66  4.08  1.94
## # … with 64 more rows, and 3 more variables:
## #   qsec <dbl>, vs <dbl>, am <dbl>
## expand(data, ...)
expand(mtcars, cyl, gear, carb)
## # A tibble: 54 × 3
##      cyl  gear  carb
##    <dbl> <dbl> <dbl>
##  1     4     3     1
##  2     4     3     2
##  3     4     3     3
##  4     4     3     4
##  5     4     3     6
##  6     4     3     8
##  7     4     4     1
##  8     4     4     2
##  9     4     4     3
## 10     4     4     4
## # … with 44 more rows

4.单元格的分解与合并

datax <- data.frame(country = rep(c("A","B","C"),each = 2),
                    year = rep(c("Y1999","Y2000"),times = 3),
                    rate = c("0.7K/19M","2K/20M","37K/172M","80K/174M","212K/1T","213K/1T"))
datax
##   country  year     rate
## 1       A Y1999 0.7K/19M
## 2       A Y2000   2K/20M
## 3       B Y1999 37K/172M
## 4       B Y2000 80K/174M
## 5       C Y1999  212K/1T
## 6       C Y2000  213K/1T

#### 分解为多列
## separate(data, col, into, sep = "[^[:alnum:]] +", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ...)

separate(datax, rate, sep = "/",into = c("cases", "pop"))
##   country  year cases  pop
## 1       A Y1999  0.7K  19M
## 2       A Y2000    2K  20M
## 3       B Y1999   37K 172M
## 4       B Y2000   80K 174M
## 5       C Y1999  212K   1T
## 6       C Y2000  213K   1T

#### 分解为多行
## separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE)

separate_rows(datax, rate)
## # A tibble: 12 × 3
##    country year  rate 
##    <chr>   <chr> <chr>
##  1 A       Y1999 0.7K 
##  2 A       Y1999 19M  
##  3 A       Y2000 2K   
##  4 A       Y2000 20M  
##  5 B       Y1999 37K  
##  6 B       Y1999 172M 
##  7 B       Y2000 80K  
##  8 B       Y2000 174M 
##  9 C       Y1999 212K 
## 10 C       Y1999 1T   
## 11 C       Y2000 213K 
## 12 C       Y2000 1T

#### 合并
## unite(data, col, ..., sep = "_", remove = TRUE)

unite(datax, country, year, col = "c_y", sep = "")
##      c_y     rate
## 1 AY1999 0.7K/19M
## 2 AY2000   2K/20M
## 3 BY1999 37K/172M
## 4 BY2000 80K/174M
## 5 CY1999  212K/1T
## 6 CY2000  213K/1T