单元格和列的类型
默认情况下，
read_excel()
会自动推断列的类型，当然你也可以通过
col_types
显式设置类型。
col_types
参数的使用是非常灵活的，你可以将实际类型与
skip
和
guess
混合使用，如果是单一的类型，会被重复使用。
read_excel("yo.xlsx")
read_excel("yo.xlsx", col_types = "numeric")
read_excel("yo.xlsx", col_types = c("date", "skip", "guess", "numeric"))
1. 类型推测
前面我们介绍过
readr
的类型推测，但是
readxl
与
readr
有点不太一样
-
readr
：根据数据猜测列类型
-
readxl
：根据
Excel
单元格类型猜测列类型
Excel
电子表格中的每个单元格都有其自己的类型
empty < boolean < numeric < text
read_excel()
会将
guess_max
行内或遍历完数据之后选择最大的类型作为该列的类型。
例如
> readxl_example("deaths.xlsx") %>%
+ read_excel(range = cell_rows(5:15))
# A tibble: 10 x 6
Name Profession Age `Has kids` `Date of birth` `Date of death`
<chr> <chr> <dbl> <lgl> <dttm> <dttm>
1 David Bowie musician 69 TRUE 1947-01-08 00:00:00 2016-01-10 00:00:00
2 Carrie Fisher actor 60 TRUE 1956-10-21 00:00:00 2016-12-27 00:00:00
3 Chuck Berry musician 90 TRUE 1926-10-18 00:00:00 2017-03-18 00:00:00
4 Bill Paxton actor 61 TRUE 1955-05-17 00:00:00 2017-02-25 00:00:00
5 Prince musician 57 TRUE 1958-06-07 00:00:00 2016-04-21 00:00:00
6 Alan Rickman actor 69 FALSE 1946-02-21 00:00:00 2016-01-14 00:00:00
7 Florence Henderson actor 82 TRUE 1934-02-14 00:00:00 2016-11-24 00:00:00
8 Harper Lee author 89 FALSE 1926-04-28 00:00:00 2016-02-19 00:00:00
9 Zsa Zsa Gábor actor 99 TRUE 1917-02-06 00:00:00 2016-12-18 00:00:00
10 George Michael musician 53 FALSE 1963-06-25 00:00:00 2016-12-25 00:00:00
2. 类型比较
|
Excel
|
base R
|
col_types
|
|
anything
|
non-existent
|
"skip"
|
|
empty
|
logical, but all NA
|
you cannot request this
|
|
boolean
|
logical
|
"logical"
|
|
numeric
|
numeric
|
"numeric"
|
|
datetime
|
POSIXct
|
"date"
|
|
text
|
character
|
"text"
|
|
anything
|
list
|
"list"
|
对于前两行进行说明：
-
如果你不希望解析并加载某一列，可以使用
skip
-
如果某一列全部为空，会自动跳过该列
例如，使用
skip
和
guess
> readxl_example("deaths.xlsx") %>%
+ read_excel(range = cell_rows(5:15),
+ col_types = c("guess", "skip", "guess", "skip", "skip", "skip"))
# A tibble: 10 x 2
Name Age
<chr> <dbl>
1 David Bowie 69
2 Carrie Fisher 60
3 Chuck Berry 90
4 Bill Paxton 61
5 Prince 57
6 Alan Rickman 69
7 Florence Henderson 82
8 Harper Lee 89
9 Zsa Zsa Gábor 99
10 George Michael 53
而对于
list
类型，会将该列解析为长度为
1
的
list
。如果某一列包含不同的数据类型，使用这种解析方式将会很方便
我们使用
clippy.xlsx
文件进行说明，它的第二列包含了关于
Clippy
的信息，如果只使用一种类型，就很难存储这些信息
> (clippy <- readxl_example("clippy.xlsx") %>%
+ read_excel(col_types = c("text", "list")))
# A tibble: 4 x 2
name value
<chr> <list>
1 Name <chr [1]>
2 Species <chr [1]>
3 Approx date of death <dttm [1]>
4 Weight in grams <dbl [1]>
# 将包含两列的数据框转换为命名的向量或列表
> tibble::deframe(clippy)
$Name
[1] "Clippy"
$Species
[1] "paperclip"
$`Approx date of death`
[1] "2007-01-01 UTC"
$`Weight in grams`
[1] 0.9
> sapply(clippy$value, class)
[[1]]
[1] "character"
[[2]]
[1] "character"
[[3]]
[1] "POSIXct" "POSIXt"
[[4]]
[1] "numeric"
工作流程
我们处理
Excel
文件是不是每次都要将其读取进来，然后通过一些列的操作将我们需要的数据从中提取出来呢？
其实不是的，我们完全可以将对我们有用的信息保存下来，而不是每次读取原始文件，然后再进行一般数据提取。
一般，我们可以将处理好的数据保存为
csv
文件，这样可以方便我们后续的读取与分析。
我们可以将
read_excel()
的输出直接传递到
readr::write_csv
，如下所示
iris_xl <- readxl_example("datasets.xlsx") %>%
read_excel(sheet = "iris") %>%
write_csv("iris-raw.csv")
readr::write_csv()
是一个便捷的写入函数，它在完成写入之后会以不可见的方式返回输入的内容。
上面的代码从
datasets.xlsx
中读取
iris
表，并将其写入文件中。
> iris_xl
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <chr>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
> dir(pattern = "iris")
[1] "iris-raw.csv"
写入
CSV
的数据是否与我们从
Excel
导入的数据完全相同?
> (iris_alt <- read_csv("iris-raw.csv"))
─ Column specification ────────────────────────────────────────
cols(
Sepal.Length = col_double(),
Sepal.Width = col_double(),
Petal.Length = col_double(),
Petal.Width = col_double(),
Species = col_character()
)
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <chr>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
# 删除上面的推测类型的信息
> attr(iris_alt, "spec") <- NULL
> identical(iris_xl, iris_alt)
[1] FALSE
合并数据表
要将
Excel
表中的所有数据表加载到列表中，主要分为两步
-
获取所有的数据表名称
-
使用
purrr::map
迭代读取数据表
> path <- readxl_example("datasets.xlsx")
> path %>%
+ excel_sheets() %>%
+ set_names() %>%
+ map(read_excel, path = path)
$iris
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <chr>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
$mtcars
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# … with 22 more rows
$chickwts
# A tibble: 71 x 2
weight feed
<dbl> <chr>
1 179 horsebean
2 160 horsebean
3 136 horsebean
4 227 horsebean
5 217 horsebean
6 168 horsebean
7 108 horsebean
8 124 horsebean
9 143 horsebean
10 140 horsebean
# … with 61 more rows
$quakes
# A tibble: 1,000 x 5
lat long depth mag stations
<dbl> <dbl> <dbl> <dbl> <dbl>
1 -20.4 182. 562 4.8 41
2 -20.6 181. 650 4.2 15
3 -26 184. 42 5.4 43
4 -18.0 182. 626 4.1 19
5 -20.4 182. 649 4 11
6 -19.7 184. 195 4 12
7 -11.7 166. 82 4.8 43
8 -28.1 182. 194 4.4 15
9 -28.7 182. 211 4.7 35
10 -17.5 180. 622 4.3 19
# … with 990 more rows
如果我们想一次读取所有的数据表并同时缓存到
CSV
呢？
我们定义如下函数，用于将单个数据表写入
CSV
文件中
read_then_csv <- function(sheet, path) {
pathbase <- path %>%
basename() %>%
tools::file_path_sans_ext()
path %>%
read_excel(sheet = sheet) %>%
write_csv(paste0(pathbase, "-", sheet, ".csv"))
}
应用
map
函数将
datasets.xlsx
的所有数据表写入
> path <- readxl_example("datasets.xlsx")
> path %>%
+ excel_sheets() %>%
+ set_names() %>%
+ map(read_then_csv, path = path)
既然已经能够一次性读取所有的数据表了，那怎么将它们合并成一个数据框呢？
我们使用
readxl
附带的数据
deaths.xlsx
，其中包含了
2016-2017
去世的著名人物。
还包含了两个名为
arts
和
other
的数据表，每个电子表格的布局都相同，并且数据表具有相同的变量，例如名称和死亡日期
purrr
中的
map_df
函数可以很容易的在数据表上迭代，并将数据合并
> path <- readxl_example("deaths.xlsx")
> deaths <- path %>%
+ excel_sheets() %>%
+ set_names() %>%
+ map_df(~ read_excel(path = path, sheet = .x, range = "A5:F15"), .id = "sheet")
> deaths
# A tibble: 20 x 7
sheet Name Profession Age `Has kids` `Date of birth` `Date of death`
<chr> <chr> <chr> <dbl> <lgl> <dttm> <dttm>
1 arts David Bowie musician 69 TRUE 1947-01-08 00:00:00 2016-01-10 00:00:00
2 arts Carrie Fisher actor 60 TRUE 1956-10-21 00:00:00 2016-12-27 00:00:00
3 arts Chuck Berry musician 90 TRUE 1926-10-18 00:00:00 2017-03-18 00:00:00
4 arts Bill Paxton actor 61 TRUE 1955-05-17 00:00:00 2017-02-25 00:00:00
5 arts Prince musician 57 TRUE 1958-06-07 00:00:00 2016-04-21 00:00:00
6 arts Alan Rickman actor 69 FALSE 1946-02-21 00:00:00 2016-01-14 00:00:00
7 arts Florence Henderson actor 82 TRUE 1934-02-14 00:00:00 2016-11-24 00:00:00
8 arts Harper Lee author 89 FALSE 1926-04-28 00:00:00 2016-02-19 00:00:00
9 arts Zsa Zsa Gábor actor 99 TRUE 1917-02-06 00:00:00 2016-12-18 00:00:00
10 arts George Michael musician 53 FALSE 1963-06-25 00:00:00 2016-12-25 00:00:00
11 other Vera Rubin scientist 88 TRUE 1928-07-23 00:00:00 2016-12-25 00:00:00
12 other Mohamed Ali athlete 74 TRUE 1942-01-17 00:00:00 2016-06-03 00:00:00
13 other Morley Safer journalist 84 TRUE 1931-11-08 00:00:00 2016-05-19 00:00:00
14 other Fidel Castro politician 90 TRUE 1926-08-13 00:00:00 2016-11-25 00:00:00
15 other Antonin Scalia lawyer 79 TRUE 1936-03-11 00:00:00 2016-02-13 00:00:00
16 other Jo Cox politician 41 TRUE 1974-06-22 00:00:00 2016-06-16 00:00:00
17 other Janet Reno lawyer 78 FALSE 1938-07-21 00:00:00 2016-11-07 00:00:00
18 other Gwen Ifill journalist 61 FALSE 1955-09-29 00:00:00 2016-11-14 00:00:00
19 other John Glenn astronaut 95 TRUE 1921-07-28 00:00:00 2016-12-08 00:00:00
20 other Pat Summit coach 64 TRUE 1952-06-14 00:00:00 2016-06-28 00:00:00
在这里我们使用了
range = "A5:E15"
来提取矩形区域，因为表格顶部和底部包含了一些非数据行。
最后，我们可以将这些代码串联起来，完成一个完整的从读取到合并再写出的过程
path <- readxl_example("deaths.xlsx")
sheets <- path %>%
excel_sheets() %>%
set_names()
ranges <- list("A5:F15", cell_rows(5:15))
deaths <- map2_df(
sheets,
ranges,
~ read_excel(path, sheet = .x, range = .y),
.id = "sheet"
) %>%
write_csv("deaths.csv")
下面我们使用基础的
R
代码来实现同样的功能的，以便大家进行比较
-
缓存为
CSV
iris_xl <- read_excel(readxl_example("datasets.xlsx"), sheet = "iris")
write.csv(iris_xl, "iris-raw.csv", row.names = FALSE, quote = FALSE)
iris_alt <- read.csv("iris-raw.csv", stringsAsFactors = FALSE)
## coerce iris_xl back to a data.frame
identical(as.data.frame(iris_xl), iris_alt)
-
迭代
Excel
文件的所有数据表
path <- readxl_example("datasets.xls")
sheets <- excel_sheets(path)
xl_list <- lapply(excel_sheets(path), read_excel, path = path)
names(xl_list) <- sheets
-
迭代并缓存
read_then_csv <- function(sheet, path) {
pathbase <- tools::file_path_sans_ext(basename(path))
df <- read_excel(path = path, sheet = sheet)
write.csv(df, paste0(pathbase, "-", sheet, ".csv"),
quote = FALSE, row.names = FALSE)
df
}
path <- readxl_example("datasets.xlsx")
sheets <- excel_sheets(path)
xl_list <- lapply(excel_sheets(path), read_then_csv, path = path)
names(xl_list) <- sheets
-
合并为数据框
path <- readxl_example("deaths.xlsx")
sheets <- excel_sheets(path)
xl_list <-
lapply(excel_sheets(path), read_excel, path = path, range = "A5:F15")
xl_list <- lapply(seq_along(sheets), function(i) {
data.frame(sheet = I(sheets[i]), xl_list[[i]])
})
xl_list <- do.call(rbind, xl_list)
-
串联整个流程
path <- readxl_example("deaths.xlsx")
sheets <- excel_sheets(path)
ranges <- list("A5:F15", cell_rows(5:15))
xl_list <- mapply(function(x, y) {
read_excel(path = path, sheet = x, range = y)
}, sheets, ranges, SIMPLIFY = FALSE)
xl_list <- lapply(seq_along(sheets), function(i) {
data.frame(sheet = I(sheets[i]), xl_list[[i]])
})
xl_list <- do.call(rbind, xl_list)
write.csv(xl_list, "deaths.csv", row.names = FALSE, quote = FALSE)
