1

我正在使用 gt 显示两列表示特定时间点的值，第三列显示百分比变化，这是在预处理中计算的。我想要一个汇总行，其中包含每列中值的总和，以及各个观察值的百分比变化列下方的这些总和之间的百分比变化。

为了说明，我使用 sp500 表修改了一个示例。

library(gt)
library(tidyverse)

start_date <- "2010-06-07"
end_date <- "2010-06-14"

data <- sp500 %>%
  filter(date >= start_date & date <= end_date) %>%
  select(date, open, close) %>%
  mutate(intraday_pct_ch = (close/open - 1)*100)

data
#> # A tibble: 6 x 4
#>   date        open close intraday_pct_ch
#>   <date>     <dbl> <dbl>           <dbl>
#> 1 2010-06-14 1095  1090.          -0.490
#> 2 2010-06-11 1083. 1092.           0.827
#> 3 2010-06-10 1059. 1087.           2.65 
#> 4 2010-06-09 1063. 1056.          -0.664
#> 5 2010-06-08 1051. 1062            1.06 
#> 6 2010-06-07 1066. 1050.          -1.44

data %>% gt() %>%
  summary_rows(
    fns = list(Week_Sum = ~sum(.)),
    columns = vars(open, close, intraday_pct_ch))

由reprex 包于 2021-04-20 创建 (v2.0.0 )

如所写，我得到了百分比变化的总和，这显然是我不想要的。有没有办法在这里编写一个函数，将前两列作为参数并将结果保持在同一行中？

谢谢！

更新： 这是给我想要的结果的代表，但随着“_stock#”组数量的增加，它会变得笨拙。我从第一个代表中删除了 html。

library(gt)
library(tidyverse)

set.seed(42)  
data1 <- sp500 %>%
  filter(date >= "2010-06-07" & date <= "2010-06-14") %>%
  select(date, open, close) %>%
  mutate(intraday_pct_ch = (close/open - 1)*100)

data2 <- data1 %>%
  mutate(open = (open + rnorm(1, 100, 10)),
         close = (close + rnorm(1, 100, 10)),
         intraday_pct_ch = (close/open - 1)*100)

data_bind <- data1 %>% bind_cols(data2, .name_repair = "minimal") %>%
  rename_with(~ str_replace(., "$", "_stock1"), .cols = 1:4) %>%
  rename_with(~ str_replace(., "$", "_stock2"), .cols = 5:8) %>%
  select(!starts_with("date"))

data_bind
#> # A tibble: 6 x 6
#>   open_stock1 close_stock1 intraday_pct_ch_stock1 open_stock2 close_stock2
#>         <dbl>        <dbl>                  <dbl>       <dbl>        <dbl>
#> 1       1095         1090.                 -0.490       1209.        1184.
#> 2       1083.        1092.                  0.827       1196.        1186.
#> 3       1059.        1087.                  2.65        1172.        1181.
#> 4       1063.        1056.                 -0.664       1176.        1150.
#> 5       1051.        1062                   1.06        1165.        1156.
#> 6       1066.        1050.                 -1.44        1180.        1145.
#> # … with 1 more variable: intraday_pct_ch_stock2 <dbl>

#vectorized William Gram's suggested solution
intraday_pct_ch_vec <- c((sum(data_bind$close_stock1) / sum(data_bind$open_stock1)-1)*100 , (sum(data_bind$close_stock2) / sum(data_bind$open_stock2)-1)*100 )

data_bind %>% gt() %>%
  summary_rows(
    fns = list(Week_Sum = ~sum(.)),
    columns = starts_with(c("o", "c"))) %>%
  summary_rows(
    fns = list(Week_Sum = ~ return(intraday_pct_ch_vec[1])),
    columns = 4) %>%
  summary_rows(
    fns = list(Week_Sum = ~ return(intraday_pct_ch_vec[2])),
    columns = 7)


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  border-top-style: solid;
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}

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  background-color: #FFFFFF;
  text-transform: inherit;
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  padding-bottom: 8px;
  padding-left: 5px;
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</style>
<div id="jdrzqoxgdy" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"><table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">open_stock1</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">close_stock1</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">intraday_pct_ch_stock1</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">open_stock2</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">close_stock2</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">intraday_pct_ch_stock2</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1095.00</td>
      <td class="gt_row gt_right">1089.63</td>
      <td class="gt_row gt_right">-0.4904110</td>
      <td class="gt_row gt_right">1208.71</td>
      <td class="gt_row gt_right">1183.983</td>
      <td class="gt_row gt_right">-2.0456995</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1082.65</td>
      <td class="gt_row gt_right">1091.60</td>
      <td class="gt_row gt_right">0.8266753</td>
      <td class="gt_row gt_right">1196.36</td>
      <td class="gt_row gt_right">1185.953</td>
      <td class="gt_row gt_right">-0.8698527</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1058.77</td>
      <td class="gt_row gt_right">1086.84</td>
      <td class="gt_row gt_right">2.6511896</td>
      <td class="gt_row gt_right">1172.48</td>
      <td class="gt_row gt_right">1181.193</td>
      <td class="gt_row gt_right">0.7431629</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1062.75</td>
      <td class="gt_row gt_right">1055.69</td>
      <td class="gt_row gt_right">-0.6643237</td>
      <td class="gt_row gt_right">1176.46</td>
      <td class="gt_row gt_right">1150.043</td>
      <td class="gt_row gt_right">-2.2454376</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1050.81</td>
      <td class="gt_row gt_right">1062.00</td>
      <td class="gt_row gt_right">1.0648832</td>
      <td class="gt_row gt_right">1164.52</td>
      <td class="gt_row gt_right">1156.353</td>
      <td class="gt_row gt_right">-0.7012905</td>
    </tr>
    <tr>
      <td class="gt_row gt_left gt_stub"></td>
      <td class="gt_row gt_right">1065.84</td>
      <td class="gt_row gt_right">1050.47</td>
      <td class="gt_row gt_right">-1.4420551</td>
      <td class="gt_row gt_right">1179.55</td>
      <td class="gt_row gt_right">1144.823</td>
      <td class="gt_row gt_right">-2.9440531</td>
    </tr>
    <tr>
      <td class="gt_row gt_stub gt_right gt_grand_summary_row gt_first_grand_summary_row">Week_Sum</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">6,415.82</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">6,436.23</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">0.32</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">7,098.08</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">7,002.35</td>
      <td class="gt_row gt_right gt_grand_summary_row gt_first_grand_summary_row">&minus;1.35</td>
    </tr>
  </tbody>
  
  
</table></div>

<sup>Created on 2021-04-21 by the [reprex package](https://reprex.tidyverse.org) (v2.0.0)</sup>
4

1 回答 1

1

我完全误解了你的意图gt()，为此道歉。

您可以添加一个附加值summary_rows并单独计算总和：

intraday_pct_ch_total <- (sum(data$close) / sum(data$open)-1)*100 

data %>% 
  gt() %>%
  summary_rows(
    fns = list(Week_Sum = ~ sum(.)),
    columns = vars(open, close)) %>% 
  summary_rows(
    fns = list(Week_Sum = ~ return(intraday_pct_ch_total)),
    columns = vars(intraday_pct_ch)
  )

让我知道这是否会产生您期望的表格。

更新 我一直在尽我所能使答案可扩展，但无济于事。

首先，我使intraday_pct_chwhich 可以包含任意数量的列：

pct_changes <- data %>% 
  summarise(
    across(contains('open'), sum),
    across(contains('close'), sum)
  ) %>% 
  pivot_longer(
    cols = everything(),
    names_to = c('open_close', 'no'),
    names_pattern = '(open|close)(.)',
    values_to = 'sum'
  ) %>% 
  pivot_wider(
    names_from = 'open_close',
    values_from = 'sum'
  ) %>% 
  mutate(diff = (close/open-1)*100, .keep='unused') %>% 
  pivot_wider(
    names_from = 'no',
    values_from = 'diff',
    names_prefix = 'intraday_pct_ch'
  )

然后我必须将它们添加到表中：

table_info <- data %>% gt() %>%
  summary_rows(
    fns = list(Week_Sum = ~sum(.)),
    columns = starts_with(c("o", "c"))
  )

table_info %>%
  summary_rows(
    fns = list(Week_Sum = ~ return(as.numeric(pct_changes[1, 1]))),
    columns = names(tmp)[1]) %>%
  summary_rows(
    fns = list(Week_Sum = ~ return(as.numeric(pct_changes[1, 2]))),
    columns = names(tmp)[2]
  )

我尝试制作一个愚蠢的循环，所以这不必是手动的，但出于某种原因，至少在我这边，它不起作用。

for (i in seq_along(pct_changes)) {
  table_info
  print(paste0('change: ', i, ': ', names(pct_changes)[i], ': ', pct_changes %>% pull(names(pct_changes)[i])))
  table_info <- table_info %>% 
    summary_rows(
      fns = list(Week_Sum = ~ pct_changes %>% pull(names(pct_changes)[i])),
      columns = names(pct_changes)[i]
    )
}
于 2021-04-20T16:39:44.713 回答