任何人都可以告诉我如何只阅读下面数据的前6个月(7列),例如使用read.table()？
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2009 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2010 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2011 -21 -27 -2 -6 -10 -32 -13 -12 -27 -30 -38 -29
Rei*_*son 156
假设数据在文件中data.txt,您可以使用跳过列的colClasses参数read.table().这里前7列中的数据是"integer",我们将剩下的6列设置为"NULL"表示应该跳过它们
> read.table("data.txt", colClasses = c(rep("integer", 7), rep("NULL", 6)),
+ header = TRUE)
Year Jan Feb Mar Apr May Jun
1 2009 -41 -27 -25 -31 -31 -39
2 2010 -41 -27 -25 -31 -31 -39
3 2011 -21 -27 -2 -6 -10 -32
根据实际数据"integer"类型更改为详细信息中的一种可接受类型?read.table.
data.txt 看起来像这样:
$ cat data.txt
"Year" "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
2009 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2010 -41 -27 -25 -31 -31 -39 -25 -15 -30 -27 -21 -25
2011 -21 -27 -2 -6 -10 -32 -13 -12 -27 -30 -38 -29
并通过使用创建
write.table(dat, file = "data.txt", row.names = FALSE)
这里dat是
dat <- structure(list(Year = 2009:2011, Jan = c(-41L, -41L, -21L), Feb = c(-27L,
-27L, -27L), Mar = c(-25L, -25L, -2L), Apr = c(-31L, -31L, -6L
), May = c(-31L, -31L, -10L), Jun = c(-39L, -39L, -32L), Jul = c(-25L,
-25L, -13L), Aug = c(-15L, -15L, -12L), Sep = c(-30L, -30L, -27L
), Oct = c(-27L, -27L, -30L), Nov = c(-21L, -21L, -38L), Dec = c(-25L,
-25L, -29L)), .Names = c("Year", "Jan", "Feb", "Mar", "Apr",
"May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), class = "data.frame",
row.names = c(NA, -3L))
如果事先不知道列数,则效用函数count.fields将读取文件并计算每行中的字段数.
## returns a vector equal to the number of lines in the file
count.fields("data.txt", sep = "\t")
## returns the maximum to set colClasses
max(count.fields("data.txt", sep = "\t"))
Jaa*_*aap 67
要从数据集中读取特定的一组列,还有其他几个选项:
1)fread来自data.table-package:
您可以使用指定所需的列select从参数fread从data.table包.您可以使用列名称或列号向量指定列.
对于示例数据集:
library(data.table)
dat <- fread("data.txt", select = c("Year","Jan","Feb","Mar","Apr","May","Jun"))
dat <- fread("data.txt", select = c(1:7))
或者,您可以使用该drop参数指示不应读取哪些列:
dat <- fread("data.txt", drop = c("Jul","Aug","Sep","Oct","Nov","Dec"))
dat <- fread("data.txt", drop = c(8:13))
所有结果都是:
> data
Year Jan Feb Mar Apr May Jun
1 2009 -41 -27 -25 -31 -31 -39
2 2010 -41 -27 -25 -31 -31 -39
3 2011 -21 -27 -2 -6 -10 -32
更新:如果您不想fread返回data.table,请使用data.table = FALSE-parameter,例如:fread("data.txt", select = c(1:7), data.table = FALSE)
2)read.csv.sql来自sqldf-package:
另一个替代方案是包中的read.csv.sql功能sqldf:
library(sqldf)
dat <- read.csv.sql("data.txt",
sql = "select Year,Jan,Feb,Mar,Apr,May,Jun from file",
sep = "\t")
3)使用read_*-package中的readr-functions:
library(readr)
dat <- read_table("data.txt",
col_types = cols_only(Year = 'i', Jan = 'i', Feb = 'i', Mar = 'i',
Apr = 'i', May = 'i', Jun = 'i'))
dat <- read_table("data.txt",
col_types = list(Jul = col_skip(), Aug = col_skip(), Sep = col_skip(),
Oct = col_skip(), Nov = col_skip(), Dec = col_skip()))
dat <- read_table("data.txt", col_types = 'iiiiiii______')
从文档中使用的字符的解释col_types:
每个字符代表一列:c =字符,i =整数,n =数字,d = double,l =逻辑,D =日期,T =日期时间,t =时间,？= guess,或_/ - 跳过列
您也可以使用JDBC来实现此目的.让我们创建一个示例csv文件.
write.table(x=mtcars, file="mtcars.csv", sep=",", row.names=F, col.names=T) # create example csv file
从以下链接下载并保存CSV JDBC驱动程序:http://sourceforge.net/projects/csvjdbc/files/latest/download
> library(RJDBC)
> path.to.jdbc.driver <- "jdbc//csvjdbc-1.0-18.jar"
> drv <- JDBC("org.relique.jdbc.csv.CsvDriver", path.to.jdbc.driver)
> conn <- dbConnect(drv, sprintf("jdbc:relique:csv:%s", getwd()))
> head(dbGetQuery(conn, "select * from mtcars"), 3)
mpg cyl disp hp drat wt qsec vs am gear carb
1 21 6 160 110 3.9 2.62 16.46 0 1 4 4
2 21 6 160 110 3.9 2.875 17.02 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
> head(dbGetQuery(conn, "select mpg, gear from mtcars"), 3)
MPG GEAR
1 21 4
2 21 4
3 22.8 4
vroom包提供了一种在导入期间按名称选择/删除列的“整洁”方法。文档：https://www.tidyverse.org/blog/2019/05/vroom-1-0-0/#column-selection
\nvroom 参数“col_select”使得选择要保留（或省略）的列更加简单。col_select 的接口与 dplyr::select() 相同。
\n按名称选择列\ndata <- vroom("flights.tsv", col_select = c(year, flight, tailnum))\n#> Observations: 336,776\n#> Variables: 3\n#> chr [1]: tailnum\n#> dbl [2]: year, flight\n#> \n#> Call `spec()` for a copy-pastable column specification\n#> Specify the column types with `col_types` to quiet this message\ndata <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))\n#> Observations: 336,776\n#> Variables: 13\n#> chr [4]: carrier, tailnum, origin, dest\n#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...\n#> \n#> Call `spec()` for a copy-pastable column specification\n#> Specify the column types with `col_types` to quiet this message\nUse the selection helpers\ndata <- vroom("flights.tsv", col_select = ends_with("time"))\n#> Observations: 336,776\n#> Variables: 5\n#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time\n#> \n#> Call `spec()` for a copy-pastable column specification\n#> Specify the column types with `col_types` to quiet this message\ndata <- vroom("flights.tsv", col_select = list(plane = tailnum, everything()))\n#> Observations: 336,776\n#> Variables: 19\n#> chr [ 4]: carrier, tailnum, origin, dest\n#> dbl [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...\n#> dttm [ 1]: time_hour\n#> \n#> Call `spec()` for a copy-pastable column specification\n#> Specify the column types with `col_types` to quiet this message\ndata\n#> # A tibble: 336,776 x 19\n#> plane year month day dep_time sched_dep_time dep_delay arr_time\n#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>\n#> 1 N142\xe2\x80\xa6 2013 1 1 517 515 2 830\n#> 2 N242\xe2\x80\xa6 2013 1 1 533 529 4 850\n#> 3 N619\xe2\x80\xa6 2013 1 1 542 540 2 923\n#> 4 N804\xe2\x80\xa6 2013 1 1 544 545 -1 1004\n#> 5 N668\xe2\x80\xa6 2013 1 1 554 600 -6 812\n#> 6 N394\xe2\x80\xa6 2013 1 1 554 558 -4 740\n#> 7 N516\xe2\x80\xa6 2013 1 1 555 600 -5 913\n#> 8 N829\xe2\x80\xa6 2013 1 1 557 600 -3 709\n#> 9 N593\xe2\x80\xa6 2013 1 1 557 600 -3 838\n#> 10 N3AL\xe2\x80\xa6 2013 1 1 558 600 -2 753\n#> # \xe2\x80\xa6 with 336,766 more rows, and 11 more variables: sched_arr_time <dbl>,\n#> # arr_delay <dbl>, carrier <chr>, flight <dbl>, origin <chr>,\n#> # dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,\n#> # time_hour <dttm>\n