R:仅导入和合并特定列
R: import and merge only specific columns
我有一个来自网络的 CSV URL 列表,并将它们合并到一个向量中。
现在,我想用 read_csv
.
阅读这个列表
示例:
files <- c("csv_link1.csv",
"csv_link2.csv",
"csv_link3.csv",
and so on....)
data <- map_dfr(files, read_csv)
这没问题。问题是在 CSV 文件中有些列填充了不同的值。因此,例如,在 CSV1 中有列“V1”,它用双精度填充,而在 CSV 中,同一列是“V1”,用字符填充。合并 CSV 无效,因为它们是不同的数据类型。
就我而言,我认为有两种可能性可以解决这个问题。
- 我只导入某些列,所以我说
read_csv
只读取列(V2 和 V3)而不是 V1
或
- 我将列合并为与
col_types
相同的数据类型
我都试过了,但由于语法不正确而失败了。
我试过
data <- map_dfr(files, read_csv(cols_only(the col names)))
但是,这行不通。
如何只导入和合并特定的列?
我的具体例子:
library(data.table)
library(readr)
library(purrr)
files <- c("https://www.football-data.co.uk/mmz4281/1920/EC.csv",
"https://www.football-data.co.uk/mmz4281/1819/EC.csv",
"https://www.football-data.co.uk/mmz4281/1718/EC.csv",
"https://www.football-data.co.uk/mmz4281/1617/EC.csv",
"https://www.football-data.co.uk/mmz4281/1516/EC.csv",
"https://www.football-data.co.uk/mmz4281/1415/EC.csv",
"https://www.football-data.co.uk/mmz4281/1314/EC.csv",
"https://www.football-data.co.uk/mmz4281/1213/EC.csv",
"https://www.football-data.co.uk/mmz4281/1112/EC.csv",
"https://www.football-data.co.uk/mmz4281/1011/EC.csv")
data <- map_dfr(files, read_csv)
Error: Can't combine `BbAH` <character> and `BbAH` <double>.
所以我的列 BbAH
有不同的数据类型。但我不需要这个专栏。
如果我可以选择将在合并 运行 之前合并到错误中的列,那将会很酷,因为这个不同的数据类型问题。
这个怎么样:
library(data.table)
library(readr)
rbindlist(lapply(files, read_csv, col_types = "character"))
这会将 所有 列导入为 character
,因此您需要在合并后将它们转换为您最初想要的任何内容。
我们可以 select
阅读 csvs 后您需要的列,并使用 map_df
组合它们。
library(tidyverse)
result <- map_df(files, ~read_csv(.x) %>% select(Date,HomeTeam,AwayTeam,FTHG,FTAG,FTR))
由于您只需要这七个变量,您可以使用 fread
读取这些特定变量以避免 BbAH
变量的问题。
library(data.table)
library(dplyr)
library(purrr)
files <- c("https://www.football-data.co.uk/mmz4281/1920/EC.csv",
"https://www.football-data.co.uk/mmz4281/1819/EC.csv",
"https://www.football-data.co.uk/mmz4281/1718/EC.csv",
"https://www.football-data.co.uk/mmz4281/1617/EC.csv",
"https://www.football-data.co.uk/mmz4281/1516/EC.csv",
"https://www.football-data.co.uk/mmz4281/1415/EC.csv",
"https://www.football-data.co.uk/mmz4281/1314/EC.csv",
"https://www.football-data.co.uk/mmz4281/1213/EC.csv",
"https://www.football-data.co.uk/mmz4281/1112/EC.csv",
"https://www.football-data.co.uk/mmz4281/1011/EC.csv")
# Identify columns you need
myColumns = c("Date","Time","HomeTeam","AwayTeam","FTHG","FTAG","FTR")
# Modified function found in
# takes a filename and a vector of columns as input
fread_allfiles <- function(file, columns){
x <- fread(file, select = columns) %>%
select(everything()) #
return(x)
}
df_all <- files %>%
map_df(~ fread_allfiles(.,myColumns))
head(df_all)
生成以下格式:
Date Time HomeTeam AwayTeam FTHG FTAG FTR
1: 03/08/2019 12:30 Stockport Maidenhead 0 1 A
2: 03/08/2019 15:00 Aldershot Fylde 1 2 A
3: 03/08/2019 15:00 Barnet Yeovil 1 0 H
4: 03/08/2019 15:00 Chesterfield Dover Athletic 1 2 A
5: 03/08/2019 15:00 Chorley Bromley 0 0 D
6: 03/08/2019 15:00 Dag and Red Woking 0 2 A
然后您可以根据需要重新格式化 Date
和 Time
列。看起来第一个文件有 Time
的任何值?所以剩下的都填成NA
> str(df_all)
Classes ‘data.table’ and 'data.frame': 5429 obs. of 7 variables:
$ Date : chr "03/08/2019" "03/08/2019" "03/08/2019" "03/08/2019" ...
$ Time : chr "12:30" "15:00" "15:00" "15:00" ...
$ HomeTeam: chr "Stockport" "Aldershot" "Barnet" "Chesterfield" ...
$ AwayTeam: chr "Maidenhead" "Fylde" "Yeovil" "Dover Athletic" ...
$ FTHG : int 0 1 1 1 0 0 1 1 2 1 ...
$ FTAG : int 1 2 0 2 0 2 0 4 2 3 ...
$ FTR : chr "A" "A" "H" "A" ...
- attr(*, ".internal.selfref")=<externalptr>
我有一个来自网络的 CSV URL 列表,并将它们合并到一个向量中。
现在,我想用 read_csv
.
示例:
files <- c("csv_link1.csv",
"csv_link2.csv",
"csv_link3.csv",
and so on....)
data <- map_dfr(files, read_csv)
这没问题。问题是在 CSV 文件中有些列填充了不同的值。因此,例如,在 CSV1 中有列“V1”,它用双精度填充,而在 CSV 中,同一列是“V1”,用字符填充。合并 CSV 无效,因为它们是不同的数据类型。
就我而言,我认为有两种可能性可以解决这个问题。
- 我只导入某些列,所以我说
read_csv
只读取列(V2 和 V3)而不是 V1
或
- 我将列合并为与
col_types
相同的数据类型
我都试过了,但由于语法不正确而失败了。
我试过
data <- map_dfr(files, read_csv(cols_only(the col names)))
但是,这行不通。
如何只导入和合并特定的列?
我的具体例子:
library(data.table)
library(readr)
library(purrr)
files <- c("https://www.football-data.co.uk/mmz4281/1920/EC.csv",
"https://www.football-data.co.uk/mmz4281/1819/EC.csv",
"https://www.football-data.co.uk/mmz4281/1718/EC.csv",
"https://www.football-data.co.uk/mmz4281/1617/EC.csv",
"https://www.football-data.co.uk/mmz4281/1516/EC.csv",
"https://www.football-data.co.uk/mmz4281/1415/EC.csv",
"https://www.football-data.co.uk/mmz4281/1314/EC.csv",
"https://www.football-data.co.uk/mmz4281/1213/EC.csv",
"https://www.football-data.co.uk/mmz4281/1112/EC.csv",
"https://www.football-data.co.uk/mmz4281/1011/EC.csv")
data <- map_dfr(files, read_csv)
Error: Can't combine `BbAH` <character> and `BbAH` <double>.
所以我的列 BbAH
有不同的数据类型。但我不需要这个专栏。
如果我可以选择将在合并 运行 之前合并到错误中的列,那将会很酷,因为这个不同的数据类型问题。
这个怎么样:
library(data.table)
library(readr)
rbindlist(lapply(files, read_csv, col_types = "character"))
这会将 所有 列导入为 character
,因此您需要在合并后将它们转换为您最初想要的任何内容。
我们可以 select
阅读 csvs 后您需要的列,并使用 map_df
组合它们。
library(tidyverse)
result <- map_df(files, ~read_csv(.x) %>% select(Date,HomeTeam,AwayTeam,FTHG,FTAG,FTR))
由于您只需要这七个变量,您可以使用 fread
读取这些特定变量以避免 BbAH
变量的问题。
library(data.table)
library(dplyr)
library(purrr)
files <- c("https://www.football-data.co.uk/mmz4281/1920/EC.csv",
"https://www.football-data.co.uk/mmz4281/1819/EC.csv",
"https://www.football-data.co.uk/mmz4281/1718/EC.csv",
"https://www.football-data.co.uk/mmz4281/1617/EC.csv",
"https://www.football-data.co.uk/mmz4281/1516/EC.csv",
"https://www.football-data.co.uk/mmz4281/1415/EC.csv",
"https://www.football-data.co.uk/mmz4281/1314/EC.csv",
"https://www.football-data.co.uk/mmz4281/1213/EC.csv",
"https://www.football-data.co.uk/mmz4281/1112/EC.csv",
"https://www.football-data.co.uk/mmz4281/1011/EC.csv")
# Identify columns you need
myColumns = c("Date","Time","HomeTeam","AwayTeam","FTHG","FTAG","FTR")
# Modified function found in
# takes a filename and a vector of columns as input
fread_allfiles <- function(file, columns){
x <- fread(file, select = columns) %>%
select(everything()) #
return(x)
}
df_all <- files %>%
map_df(~ fread_allfiles(.,myColumns))
head(df_all)
生成以下格式:
Date Time HomeTeam AwayTeam FTHG FTAG FTR 1: 03/08/2019 12:30 Stockport Maidenhead 0 1 A 2: 03/08/2019 15:00 Aldershot Fylde 1 2 A 3: 03/08/2019 15:00 Barnet Yeovil 1 0 H 4: 03/08/2019 15:00 Chesterfield Dover Athletic 1 2 A 5: 03/08/2019 15:00 Chorley Bromley 0 0 D 6: 03/08/2019 15:00 Dag and Red Woking 0 2 A
然后您可以根据需要重新格式化 Date
和 Time
列。看起来第一个文件有 Time
的任何值?所以剩下的都填成NA
> str(df_all)
Classes ‘data.table’ and 'data.frame': 5429 obs. of 7 variables:
$ Date : chr "03/08/2019" "03/08/2019" "03/08/2019" "03/08/2019" ...
$ Time : chr "12:30" "15:00" "15:00" "15:00" ...
$ HomeTeam: chr "Stockport" "Aldershot" "Barnet" "Chesterfield" ...
$ AwayTeam: chr "Maidenhead" "Fylde" "Yeovil" "Dover Athletic" ...
$ FTHG : int 0 1 1 1 0 0 1 1 2 1 ...
$ FTAG : int 1 2 0 2 0 2 0 4 2 3 ...
$ FTR : chr "A" "A" "H" "A" ...
- attr(*, ".internal.selfref")=<externalptr>