The extract_summary() function

Use a modified version of sp500 the dataset to create a gt table with row groups and row labels. Create summary rows labeled as min, max, and avg for every row group with summary_rows(). Then, extract the summary rows as a list object.

summary_extracted <-
  sp500 |>
  dplyr::filter(date >= "2015-01-05" & date <="2015-01-30") |>
  dplyr::arrange(date) |>
  dplyr::mutate(week = paste0("W", strftime(date, format = "%V"))) |>
  dplyr::select(-adj_close, -volume) |>
  gt(
    rowname_col = "date",
    groupname_col = "week"
  ) |>
  summary_rows(
    groups = everything(),
    columns = c(open, high, low, close),
    fns = list(
      min = ~min(.),
      max = ~max(.),
      avg = ~mean(.)
    ),
  ) |>
  extract_summary()

summary_extracted

$summary_df_data_list
$summary_df_data_list$W02
# A tibble: 3 × 9
  group_id row_id rowname  date  open  high   low close  week
  <chr>    <chr>  <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 W02      min    min        NA 2006. 2030. 1992. 2003.    NA
2 W02      max    max        NA 2063. 2064. 2038. 2062.    NA
3 W02      avg    avg        NA 2035. 2049. 2017. 2031.    NA

$summary_df_data_list$W03
# A tibble: 3 × 9
  group_id row_id rowname  date  open  high   low close  week
  <chr>    <chr>  <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 W03      min    min        NA 1992. 2018. 1988. 1993.    NA
2 W03      max    max        NA 2046. 2057. 2023. 2028.    NA
3 W03      avg    avg        NA 2020. 2033. 2000. 2015.    NA

$summary_df_data_list$W04
# A tibble: 3 × 9
  group_id row_id rowname  date  open  high   low close  week
  <chr>    <chr>  <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 W04      min    min        NA 2020. 2029. 2004. 2023.    NA
2 W04      max    max        NA 2063. 2065. 2051. 2063.    NA
3 W04      avg    avg        NA 2035. 2049. 2023. 2042.    NA

$summary_df_data_list$W05
# A tibble: 3 × 9
  group_id row_id rowname  date  open  high   low close  week
  <chr>    <chr>  <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 W05      min    min        NA 2002. 2023. 1989. 1995.    NA
2 W05      max    max        NA 2050. 2058. 2041. 2057.    NA
3 W05      avg    avg        NA 2030. 2039. 2009. 2021.    NA

Use the summary list to make a new gt table. The key thing is to use dplyr::bind_rows() and then pass the tibble to gt().

summary_extracted |>
  unlist(recursive = FALSE) |>
  dplyr::bind_rows() |>
  gt(groupname_col = "group_id") |>
  cols_hide(columns = row_id)

	date	open	high	low	close	week
W02
min	NA	2005.550	2029.610	1992.440	2002.610	NA
max	NA	2063.450	2064.430	2038.330	2062.140	NA
avg	NA	2035.240	2048.562	2016.854	2031.208	NA
W03
min	NA	1992.250	2018.400	1988.120	1992.670	NA
max	NA	2046.130	2056.930	2022.580	2028.260	NA
avg	NA	2020.422	2033.288	1999.772	2014.930	NA
W04
min	NA	2020.190	2028.940	2004.490	2022.550	NA
max	NA	2062.980	2064.620	2050.540	2063.150	NA
avg	NA	2034.557	2048.707	2023.362	2042.410	NA
W05
min	NA	2002.450	2023.320	1989.180	1994.990	NA
max	NA	2050.420	2057.620	2040.970	2057.090	NA
avg	NA	2030.484	2039.186	2008.986	2021.008	NA