`gather`无法处理行名 [英] `gather` can't handle rownames

问题描述

allcsvs = list.files(pattern = "*.csv$", recursive = TRUE)

library(tidyverse)

##LOOP to redact the snow data csvs##
for(x in 1:length(allcsvs)) {
  df = read.csv(allcsvs[x], check.names = FALSE)

  newdf = df %>%
    gather(COL_DATE, SNOW_DEPTH, -PT_ID, -DATE) %>%
    mutate( 
      DATE = as.Date(DATE,format = "%m/%d/%Y"),
      COL_DATE = as.Date(COL_DATE, format = "%Y.%m.%d")
    ) %>%
    filter(DATE == COL_DATE) %>%
    select(-COL_DATE)

  ####TURN DATES UNAMBIGUOUS HERE####
  df$DATE = lubridate::mdy(df$DATE)
  finaldf = merge(newdf, df, all.y = TRUE)
  write.csv(finaldf, allcsvs[x])

  df = read.csv(allcsvs[x])
  newdf = df[, -grep("X20", colnames(df))]
  write.csv(newdf, allcsvs[x])
}

我正在使用上面的代码逐行填充新列使用来自不同现有列的值g日期作为选择标准。如果我在excel中手动打开每个.csv并删除第一列，则此代码效果很好。但是，如果我按原样在.csvs上运行

I am using the code above to populate a new column row-by-row using values from different existing columns, using date as selection criteria. If I manually open each .csv in excel and delete the first column, this code works great. However, if I run it on the .csvs "as is"

我会收到以下消息：

错误：必须命名第1列

到目前为止，我已经尝试过放置 -rownames 放在 gather 括号内，我尝试将 remove_rownames％>％放在下面 newdf = df％&％;％，但似乎无济于事。我尝试读取没有第一列 [，-1] 的csv或删除R df [，1]< -NULL ，但是由于某些原因，我的代码返回了一个空表，而不是我想要的。 换句话说，我可以删除Excel中的行名，并且效果很好，如果我在R中删除行名，则会发生一些时髦的事情。

So far I've tried putting -rownames within the parenthesis of gather, I've tried putting remove_rownames %>% below newdf = df %>%, but nothing seems to work. I tried reading the csv without the first column [,-1] or deleting the first column in R df[,1]<-NULL but for some reason when I do that my code returns an empty table instead of what I want it to. In other words, I can delete the rownames in Excel and it works great, if I delete them in R something funky happens.

这里是一些示例数据： https://drive.google.com/file/ d / 1RiMrx4wOpUdJkN4il6IopciSF6pKeNLr / view？usp = sharing

Here is some sample data: https://drive.google.com/file/d/1RiMrx4wOpUdJkN4il6IopciSF6pKeNLr/view?usp=sharing

推荐答案

您可以考虑使用> reader :: read_csv 。

使用 tidyverse 的简单解决方案：

allcsvs %>%
            map(read_csv) %>% 
            reduce(bind_rows) %>%
            gather(COL_DATE, SNOW_DEPTH, -PT_ID, -DATE) %>% 
            mutate( 
                            DATE = as.Date(DATE,format = "%m/%d/%Y"),
                            COL_DATE = as.Date(COL_DATE, format = "%Y.%m.%d")
            ) %>%
            filter(DATE == COL_DATE) %>%
            select(-COL_DATE)

其中 utils :: read.csv ，您导入字符串是因素。 as.Date（DATE，format =％m /％d /％Y）得出 NA 。

With utils::read.csv, you are importing strings are factors. as.Date(DATE,format = "%m/%d/%Y") evaluates NA.

更新

以上解决方案返回一个单个数据帧。要使用for循环分别写入每个数据文件：

Above solution returns one single dataframe. To write the each data file separately with the for loop:

for(x in 1:length(allcsvs)) {
  read_csv(allcsvs[x]) %>% 
    gather(COL_DATE, SNOW_DEPTH, -PT_ID, -DATE) %>% 
    mutate( 
      COL_DATE = as.Date(COL_DATE, format = "%Y.%m.%d")
    ) %>%
    filter(DATE == COL_DATE) %>%
    select(-COL_DATE) %>%
    write_csv(paste('tidy', allcsvs[x], sep = '_'))
}

比较

• purrr：map 和 purrr：reduce 代替for循环。这些函数将另一个函数用作参数。


• readr :: read_csv 通常比基本R等效项快10倍。 （更多信息： http://r4ds.had.co.nz/data-import。 html ）。它还可以更好地处理CSV文件。

• purrr:map and purrr:reduce can be used instead of for loop in some cases. Those functions take another functions as arguments.
• readr::read_csv is typically 10x faster than base R equivalents. (More info: http://r4ds.had.co.nz/data-import.html). Also it can handle CSV files better.

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