从数据框中分割行名

Question

对于文本挖掘项目，我必须调查单词列表随着时间的推移的发展。为此，我需要拆分行名称，以便有一列包含公司名称，一列包含年份。这是我的数据框中的摘录：

                    abs  access   allow     analysis application approach base big business challenge company 
Adidas_2010.txt     13    25       26          11       41        132   1      266        13     115       1
Adidas_2011.txt      1     3        1           0        0         8   0       11         2      10       0
Adidas_2012.txt     29    35       37          22      110        181   7      384        31     136       3
Adidas_2013.txt     28    47       38          32      180        184   4      451        30     129       3
Adidas_2014.txt     12    42       38          27      159        207   6      921        32     128       6
Adidas_2016.txt     30    47       50          47      162        251   9     1061        32     171      13
Nike_2009.txt       16    15       17          12       33        177   9      346        93     196       1
Nike_2011.txt       10    30        0           3        0         0    0       81         7      31       0
Nike_2012.txt       21    22       12          57      199        300   7      214        11     107       3
Nike_2013.txt       20    32       30          11      123        321   4      331        90     239       3
Nike_2014.txt       33    43       30          33      119        137   6      441        67     318       6
Nike_2015.txt       51    42       41          27      102        151   9     1061        32     221      13 

这是我的代码：

dtm <- DocumentTermMatrix(corpus, control=list(dictionary = word_list))
df1 <- data.frame(as.matrix(dtm), row.names = filenames_annualreports) 

我试过这个：

 names_plus_year <- rownames(df1)
 names_plus_year_split <- strsplit(names_plus_year, "_")
 rownames(df1) <- sapply(names_plus_year_split, "[", 1)

但我收到以下错误：

Error in `.rowNamesDF<-`(x, value = value) : 
  double 'row.names' not allowed 

还有其他方法来分割行名吗？多谢！:)

Answer 1

嗨，玛丽，使用 @Sotos 数据

library(tidyverse)
new_df <- df %>% 
  rownames_to_column(var = "row_name") %>% 
  separate(row_name,sep = "_",into = c("name","year")) %>% 
  mutate(year = year %>% str_remove(".txt"))


new_df %>% as_tibble()
# A tibble: 12 x 13
   name   year    abs access allow analysis application approach  base   big business challenge company
   <chr>  <chr> <int>  <int> <int>    <int>       <int>    <int> <int> <int>    <int>     <int>   <int>
 1 Adidas 2010     13     25    26       11          41      132     1   266       13       115       1
 2 Adidas 2011      1      3     1        0           0        8     0    11        2        10       0
 3 Adidas 2012     29     35    37       22         110      181     7   384       31       136       3
 4 Adidas 2013     28     47    38       32         180      184     4   451       30       129       3
 5 Adidas 2014     12     42    38       27         159      207     6   921       32       128       6
 6 Adidas 2016     30     47    50       47         162      251     9  1061       32       171      13
 7 Nike   2009     16     15    17       12          33      177     9   346       93       196       1
 8 Nike   2011     10     30     0        3           0        0     0    81        7        31       0
 9 Nike   2012     21     22    12       57         199      300     7   214       11       107       3
10 Nike   2013     20     32    30       11         123      321     4   331       90       239       3
11 Nike   2014     33     43    30       33         119      137     6   441       67       318       6
12 Nike   2015     51     42    41       27         102      151     9  1061       32       221      13