表格展示

Disply table

Tool
R
Python
Author

Tony D

Published

April 7, 2025

With the gt package, anyone can make wonderful-looking tables using the R/Python programming language.

GT package

Code 
library(gt)
library(dplyr) # Using dplyr for mutate, optional but convenient
library(gtExtras) # For image embedding
Code 
#exibble
Code 
exibble |> gt() |> opt_stylize(style=3,color = "green") |> fmt_auto()
num char fctr date time datetime currency row group
0.111 apricot one 2015-01-15 13:35 2018-01-01 02:22     49.95  row_1 grp_a
2.222 banana two 2015-02-15 14:40 2018-02-02 14:33     17.95  row_2 grp_a
33.33 coconut three 2015-03-15 15:45 2018-03-03 03:44      1.39  row_3 grp_a
444.4 durian four 2015-04-15 16:50 2018-04-04 15:55 65,100     row_4 grp_a
5,550 NA five 2015-05-15 17:55 2018-05-05 04:00  1,325.81  row_5 grp_b
NA fig six 2015-06-15 NA 2018-06-06 16:11     13.255 row_6 grp_b
777,000 grapefruit seven NA 19:10 2018-07-07 05:22 NA row_7 grp_b
8.880 × 106 honeydew eight 2015-08-15 20:20 NA      0.44  row_8 grp_b
Code 
import polars as pl
import polars.selectors as cs
from great_tables import GT, md,exibble
from great_tables.data import reactions
Code 
#exibble
Code 
GT(exibble).opt_stylize(style=3,color = "green")
num char fctr date time datetime currency row group
0.1111 apricot one 2015-01-15 13:35 2018-01-01 02:22 49.95 row_1 grp_a
2.222 banana two 2015-02-15 14:40 2018-02-02 14:33 17.95 row_2 grp_a
33.33 coconut three 2015-03-15 15:45 2018-03-03 03:44 1.39 row_3 grp_a
444.4 durian four 2015-04-15 16:50 2018-04-04 15:55 65100.0 row_4 grp_a
5550.0 five 2015-05-15 17:55 2018-05-05 04:00 1325.81 row_5 grp_b
fig six 2015-06-15 2018-06-06 16:11 13.255 row_6 grp_b
777000.0 grapefruit seven 19:10 2018-07-07 05:22 row_7 grp_b
8880000.0 honeydew eight 2015-08-15 20:20 0.44 row_8 grp_b

Table 1

origin table:

Code 
# Load the necessary library
library(gt)
library(dplyr) # Using dplyr for mutate, optional but convenient
library(gtExtras) # For image embedding
Code 
# 1. Create the data frame
# Note: Storing percentages as numbers (0-100) for easier formatting
hoosiers_data <- data.frame(
  TEAM = c("Wake Forest", "Indiana", "North Carolina", "Coppin St.", "Vermont", "New Mexico St."),
  logo_url = c(
    "https://a.espncdn.com/i/teamlogos/ncaa/500/154.png",      # Wake Forest
    "https://a.espncdn.com/i/teamlogos/ncaa/500/84.png",       # Indiana
    "https://a.espncdn.com/i/teamlogos/ncaa/500/153.png",      # North Carolina
    "https://a.espncdn.com/i/teamlogos/ncaa/500/2154.png",     # Coppin St.
    "https://a.espncdn.com/i/teamlogos/ncaa/500/261.png",     # Vermont
    "https://a.espncdn.com/i/teamlogos/ncaa/500/166.png"       # New Mexico St.
  ),
  `3FG_Text` = c("17-61", "19-79", "20-60", "21-60", "22-89", "22-72"), # Keep original text for display
  `3FG%` = c(27.87, 24.05, 33.33, 35.00, 24.72, 30.56),
  PER_GAME = c(2.83, 3.17, 3.33, 3.50, 3.67, 3.67),
  SEED = c(4, NA, 6, 16, 16, 13),
  ROUND = c("R64", NA, "R32", "R68", "R64", "R64"),
  YEAR = c(2009, 2024, 2014, 2008, 2010, 2014),
  stringsAsFactors = FALSE # Good practice
)
Code 
# 2. Create the gt table
gt_table <- hoosiers_data %>%
  gt() %>%
  # --- Add Logos ---
  gt_img_rows(columns = logo_url, height = 25) %>% # Use URL column, set image height
  # --- Move logo column ---
  cols_move_to_start(columns = logo_url) %>% # Place logo column first
  # Add title and subtitle
  tab_header(
    title = "History does not bode well for the Hoosiers",
    subtitle = "Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024."
  ) %>%
  # Create the spanner header over the shooting columns
  tab_spanner(
    label = "Shooting",
    columns = c(`X3FG_Text`, `X3FG.`, PER_GAME)
  ) %>%
  # Format column labels
  cols_label(
    logo_url = "", # No header text for the logo column
    TEAM = "TEAM",
    `X3FG_Text` = "3FG", # Use the text column for display
    `X3FG.` = "3FG%",
    PER_GAME = "PER GAME",
    SEED = "SEED",
    ROUND = "ROUND",
    YEAR = "YEAR"
  ) %>%
  # Format the percentage column
  fmt_percent(
    columns = `X3FG.`,
    decimals = 2,
    scale_values = FALSE # Values are already 0-100
  ) %>%
  # Format the 'PER GAME' column to two decimal places
  fmt_number(
    columns = PER_GAME,
    decimals = 2
  ) %>%
  # Replace NA values with "???"
  sub_missing(
    columns = c(SEED, ROUND),
    missing_text = "???"
  ) %>%
   # Align columns (optional, but often improves appearance)
  cols_align(
    align = "center",
    columns = c(`X3FG_Text`, `X3FG.`, PER_GAME, SEED, ROUND, YEAR)
  ) %>%
  cols_align(
    align = "left",
    columns = TEAM
  ) %>%
  # Highlight the Indiana row (using a light blue background as an example)
  tab_style(
    style = cell_fill(color = "#ADD8E6"), # AliceBlue, adjust as needed
    locations = cells_body(rows = TEAM == "Indiana")
  ) %>%
  tab_style(
    style = cell_borders(
      sides = c("top", 'bottom',"left", "right"), # Or "top", "left", "right", or "all"
      color = "black", # Or a hex code
      style = "dotted", # Or "dashed", "dotted", "double", "hidden"
      weight = px(2)  # Default is 1px
                         
                         ), # AliceBlue, adjust as needed
    locations = cells_body(columns = `X3FG.`)
  ) %>%
  # Add the source note
  tab_source_note(
    source_note = "Viz. + Analysis by @andreweatherman"
  )%>%
  # Adjust width of the logo column if needed (optional)
  cols_width(
      logo_url ~ px(40) # Set logo column width to 40 pixels
  )
gt_table
History does not bode well for the Hoosiers
Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024.
TEAM
Shooting
SEED ROUND YEAR
3FG 3FG% PER GAME
Wake Forest 17-61 27.87% 2.83 4 R64 2009
Indiana 19-79 24.05% 3.17 ??? ??? 2024
North Carolina 20-60 33.33% 3.33 6 R32 2014
Coppin St. 21-60 35.00% 3.50 16 R68 2008
Vermont 22-89 24.72% 3.67 16 R64 2010
New Mexico St. 22-72 30.56% 3.67 13 R64 2014
Viz. + Analysis by @andreweatherman

the row Team Indiana background color is not correct,and also letter is not bold,So adjust it.

Code 
library(magick)

# Read the image
img <- image_read("images/my screenshots.png")

# Display the image to inspect it (optional, opens in a viewer)
#image_browse(img)
Code 
# Get image dimensions
img_info <- image_info(img)
print(img_info)
# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 PNG     1066    872 sRGB       TRUE    983185 57x57
Code 
# Crop a 50x50 pixel section from the Indiana row background
# Adjust coordinates based on your image dimensions
img_cropped <- image_crop(img, "50x50+200+400")

# Display the cropped section to confirm (optional)
#image_browse(img_cropped)
Code 
# Convert the cropped image to a raster
img_raster <- as.raster(img_cropped)

# Convert the raster to a matrix of colors
img_matrix <- as.matrix(img_raster)

# Extract RGB values from the matrix
# col2rgb expects a vector of colors, so we flatten the matrix
rgb_values <- col2rgb(img_matrix)

# Calculate the average RGB values (to approximate the dominant background color)
avg_rgb <- rowMeans(rgb_values, na.rm = TRUE)

# Convert the average RGB to Hex
# col2rgb returns values in the range 0-255, so we can use them directly
hex_code <- rgb(avg_rgb[1], avg_rgb[2], avg_rgb[3], maxColorValue = 255)

# Print the Hex code
print(hex_code)
[1] "#70AACA"
Code 
# 2. Create the gt table
gt_table <- hoosiers_data %>%
  gt() %>%
  # --- Add Logos ---
  gt_img_rows(columns = logo_url, height = 25) %>% # Use URL column, set image height
  # --- Move logo column ---
  cols_move_to_start(columns = logo_url) %>% # Place logo column first
  # Add title and subtitle
  tab_header(
    title = "History does not bode well for the Hoosiers",
    subtitle = "Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024."
  ) %>%
  # Create the spanner header over the shooting columns
  tab_spanner(
    label = "Shooting",
    columns = c(`X3FG_Text`, `X3FG.`, PER_GAME)
  ) %>%
  # Format column labels
  cols_label(
    logo_url = "", # No header text for the logo column
    TEAM = "TEAM",
    `X3FG_Text` = "3FG", # Use the text column for display
    `X3FG.` = "3FG%",
    PER_GAME = "PER GAME",
    SEED = "SEED",
    ROUND = "ROUND",
    YEAR = "YEAR"
  ) %>%
  # Format the percentage column
  fmt_percent(
    columns = `X3FG.`,
    decimals = 2,
    scale_values = FALSE # Values are already 0-100
  ) %>%
  # Format the 'PER GAME' column to two decimal places
  fmt_number(
    columns = PER_GAME,
    decimals = 2
  ) %>%
  # Replace NA values with "???"
  sub_missing(
    columns = c(SEED, ROUND),
    missing_text = "???"
  ) %>%
   # Align columns (optional, but often improves appearance)
  cols_align(
    align = "center",
    columns = c(`X3FG_Text`, `X3FG.`, PER_GAME, SEED, ROUND, YEAR)
  ) %>%
  cols_align(
    align = "left",
    columns = TEAM
  ) %>%
  # Highlight the Indiana row (using a light blue background as an example)
  tab_style(
    
    style = cell_fill(color = "#70AACA"), # AliceBlue, adjust as needed
    locations = cells_body(rows = TEAM == "Indiana")
  ) %>%
   tab_style(
     style = cell_text(weight = "bold"),
    locations = cells_body(rows = TEAM == "Indiana")
  ) %>%
  ### dotted line
  tab_style(
    style = cell_borders(
      sides = c("top"), # Or "top", "left", "right", or "all"
      color = "black", # Or a hex code
      style = "dotted", # Or "dashed", "dotted", "double", "hidden"
      weight = px(3)  # Default is 1px
                         
                         ), # AliceBlue, adjust as needed
    locations = cells_body(columns = `X3FG.`,rows = TEAM == "Wake Forest") # Apply to Indiana row)
  ) %>%
    ### dotted line
    tab_style(
    style = cell_borders(
      sides = c("bottom"), # Or "top", "left", "right", or "all"
      color = "black", # Or a hex code
      style = "dotted", # Or "dashed", "dotted", "double", "hidden"
      weight = px(3)  # Default is 1px
                         
                         ), # AliceBlue, adjust as needed
    locations = cells_body(columns = `X3FG.`,rows = TEAM == "New Mexico St.")
  ) %>%
  ### dotted line
    tab_style(
    style = cell_borders(
      sides = c("left","right"), # Or "top", "left", "right", or "all"
      color = "black", # Or a hex code
      style = "dotted", # Or "dashed", "dotted", "double", "hidden"
      weight = px(3)  # Default is 1px
                         
                         ), # AliceBlue, adjust as needed
    locations = cells_body(columns = `X3FG.`)
  ) %>%
  # Add the source note
  tab_source_note(
    source_note = "Viz. + Analysis by @andreweatherman"
  )%>%
  # Adjust width of the logo column if needed (optional)
  cols_width(
      logo_url ~ px(40) # Set logo column width to 40 pixels
  )
gt_table
History does not bode well for the Hoosiers
Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024.
TEAM
Shooting
SEED ROUND YEAR
3FG 3FG% PER GAME
Wake Forest 17-61 27.87% 2.83 4 R64 2009
Indiana 19-79 24.05% 3.17 ??? ??? 2024
North Carolina 20-60 33.33% 3.33 6 R32 2014
Coppin St. 21-60 35.00% 3.50 16 R68 2008
Vermont 22-89 24.72% 3.67 16 R64 2010
New Mexico St. 22-72 30.56% 3.67 13 R64 2014
Viz. + Analysis by @andreweatherman
Code 
import polars as pl
import pandas as pd
import numpy as np
from great_tables import GT, md, html, loc, style, px# Import necessary components
Code 
# 1. Create the Pandas DataFrame
# Using a dictionary preserves column names with special characters/spaces
hoosiers_data = pd.DataFrame({
    "TEAM": ["Wake Forest", "Indiana", "North Carolina", "Coppin St.", "Vermont", "New Mexico St."],
    "logo_url": [
        "https://a.espncdn.com/i/teamlogos/ncaa/500/154.png",      # Wake Forest
        "https://a.espncdn.com/i/teamlogos/ncaa/500/84.png",       # Indiana
        "https://a.espncdn.com/i/teamlogos/ncaa/500/153.png",      # North Carolina
        "https://a.espncdn.com/i/teamlogos/ncaa/500/2154.png",     # Coppin St.
        "https://a.espncdn.com/i/teamlogos/ncaa/500/261.png",      # Vermont
        "https://a.espncdn.com/i/teamlogos/ncaa/500/166.png"       # New Mexico St.
    ],
    "3FG_Text": ["17-61", "19-79", "20-60", "21-60", "22-89", "22-72"], # Keep original text
    "3FG%": [27.87, 24.05, 33.33, 35.00, 24.72, 30.56],
    "PER_GAME": [2.83, 3.17, 3.33, 3.50, 3.67, 3.67],
    "SEED": [4, np.nan, 6, 16, 16, 13], # Use numpy.nan for missing values
    "ROUND": ["R64", np.nan, "R32", "R68", "R64", "R64"],
    "YEAR": [2009, 2024, 2014, 2008, 2010, 2014],
})

hoosiers_data_pl=pl.from_pandas(hoosiers_data)
Code 
# 2. Create the great_tables table (Using lambda for older versions)
# NOTE: This assumes fmt_image and loc.body exist in your version.
# If you get errors on those, your version might be very old.
gt_table = (
    GT(data=hoosiers_data_pl)
    .fmt_image(columns="logo_url", height=25)
    .cols_move_to_start(columns=["logo_url"])
    .tab_header(
        title="History does not bode well for the Hoosiers",
        subtitle="Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024."
    )
    .tab_spanner(
        label="Shooting",
        columns=["3FG_Text", "3FG%", "PER_GAME"]
    )
    .cols_label(
        logo_url = "",
        TEAM = "TEAM",
        **{"3FG_Text": "3FG"},
        **{"3FG%": "3FG%"},
        PER_GAME = "PER GAME",
        SEED = "SEED",
        ROUND = "ROUND",
        YEAR = "YEAR"
    )
    .fmt_percent(
        columns="3FG%",
        decimals=2,
        scale_values=False
    )
    .fmt_number(
        columns="PER_GAME",
        decimals=2
    )
    .sub_missing(
        columns=["SEED", "ROUND"],
        missing_text="???"
    )
    .cols_align(
        align="center",
        columns=["3FG_Text", "3FG%", "PER_GAME", "SEED", "ROUND", "YEAR"]
    )
    .cols_align(
        align="left",
        columns="TEAM"
    )
    # Highlight the Indiana row (Using lambda function)
    .tab_style(
        style=style.fill(color="#70AACA"),
        # pandas way   Use a lambda function to define the row condition
      # locations=[loc.body(rows=lambda x: x["TEAM"] == "Indiana")]
        locations=[loc.body(rows=pl.col("TEAM")  == "Indiana")]
    )
    # Make letter bold the Indiana row (Using lambda function)
    .tab_style(
        style=style.text(weight = "bold"),
        # pandas way   Use a lambda function to define the row condition
      # locations=[loc.body(rows=lambda x: x["TEAM"] == "Indiana")]
        locations=[loc.body(rows=pl.col("TEAM")  == "Indiana")]
    )

    # Add the source note
    .tab_source_note(
        source_note=md("Viz. + Analysis by @andreweatherman")
    )
     # Adjust width of the logo column
     .cols_width(
        logo_url = px(40)
     )
)
Code 
# save:
# gt_table.save("hoosiers_table.html")

# To display(In Jupyter):
gt_table # In Jupyter
History does not bode well for the Hoosiers
Only one future tournament team made fewer 3PTs through their first six games than Indiana in 2024.
TEAM Shooting SEED ROUND YEAR
3FG 3FG% PER GAME
Wake Forest 17-61 27.87% 2.83 4.0 R64 2009
Indiana 19-79 24.05% 3.17 ??? ??? 2024
North Carolina 20-60 33.33% 3.33 6.0 R32 2014
Coppin St. 21-60 35.00% 3.50 16.0 R68 2008
Vermont 22-89 24.72% 3.67 16.0 R64 2010
New Mexico St. 22-72 30.56% 3.67 13.0 R64 2014
Viz. + Analysis by @andreweatherman

Table 2

origin table:

Code 
library(gt)
library(gtExtras)
library(dplyr)
library(jsonlite)
library(scales)
library(lubridate)
Code 
# URL for the data
data_url <- "https://github.com/machow/coffee-sales-data/raw/main/data/coffee-sales.ndjson"
coffee_data_raw =stream_in(url(data_url), simplifyDataFrame = TRUE)

 Found 14 records...
 Imported 14 records. Simplifying...

Prompt gemini 2.5

using R GT package to recreate this table.data source from https://github.com/machow/coffee-sales-data/blob/main/data/coffee-sales.ndjson

Step 1 load package and read in data from github library(gt) library(gtExtras) library(dplyr) library(jsonlite) library(scales) library(lubridate) data_url <- “https://github.com/machow/coffee-sales-data/raw/main/data/coffee-sales.ndjson” coffee_data_raw =stream_in(url(data_url), simplifyDataFrame = TRUE)

no need to no need to Aggregate data for the table.all number are given

Step 2 create GT table,using cols_nanoplot() to add the bar plot for column Monthly Sales

no need to create the icon at left side

Since gt v0.3.0, columns = vars(...) has been deprecated. • Please use columns = c(...) instead.

Code 
# Step 1: Load necessary packages
# ----------------------------------------------------
# Ensure gt and gtExtras are installed and loaded
# install.packages("gt")
# install.packages("gtExtras")
library(gt)
library(gtExtras)
library(dplyr) # Still useful for data frame creation/manipulation
library(tibble) # Good for defining data frames

# Step 2: Define the data frame manually based on the image
# ----------------------------------------------------
# Note:
# - Amounts are entered as numbers (e.g., 904000 for $904K, 2.05e6 for $2.05M)
#   so fmt_currency(scale_suffixing = TRUE) works correctly.
# - Percentages are entered as proportions (e.g., 0.03 for 3%) for fmt_percent.
# - Monthly Sales data needs to be a list of numeric vectors. Since we don't
#   have the exact monthly data, we create approximate data representative
#   of the bar patterns shown in the image. The length (14) is arbitrary.

# coffee_table_data <- tibble(
#   Product = c("Grinder", "Moka pot", "Cold brew", "Filter", "Drip machine",
#               "AeroPress", "Pour over", "French press", "Cezve", "Chemex",
#               "Scale", "Kettle", "Espresso Machine"),
#   Revenue_Amount = c(904000, 2050000, 289000, 404000, 2630000,
#                      2600000, 846000, 1110000, 2510000, 3140000,
#                      3800000, 756000, 8410000),
#   Revenue_Percent = c(0.03, 0.07, 0.01, 0.01, 0.09,
#                       0.09, 0.03, 0.04, 0.09, 0.11,
#                       0.13, 0.03, 0.29),
#   Profit_Amount = c(568000, 181000, 242000, 70000, 1370000,
#                     1290000, 365000, 748000, 1970000, 818000,
#                     2910000, 618000, 3640000),
#   Profit_Percent = c(0.04, 0.01, 0.02, 0.00, 0.09,
#                      0.09, 0.02, 0.05, 0.13, 0.06,
#                      0.20, 0.04, 0.25),
#   # --- Estimated Monthly Sales Data (List Column) ---
#   # Create lists of numbers that approximate the bar patterns
#   Monthly_Sales = list(
#     c(8,9,8,9,8,9,8,9,8,9,8,9,8,9),         # Grinder (Consistent high)
#     c(7,8,7,8,7,8,7,8,7,8,7,8,7,8),         # Moka pot (Consistent medium-high)
#     c(2,3,4,5,6,7,8,7,6,5,4,3,2,1),         # Cold brew (Peak in middle)
#     c(6,7,6,7,6,7,6,7,6,7,6,7,6,7),         # Filter (Consistent medium)
#     c(8,9,8,9,8,9,8,9,8,9,8,9,8,9),         # Drip machine (Consistent high)
#     c(8,9,8,9,8,9,8,9,8,9,8,9,8,9),         # AeroPress (Consistent high)
#     c(5,6,7,6,5,6,7,6,5,6,7,6,5,6),         # Pour over (Slight variation medium)
#     c(7,8,7,8,7,8,7,8,7,8,7,8,7,8),         # French press (Consistent medium-high)
#     c(6,7,8,7,6,7,8,7,6,7,8,7,6,7),         # Cezve (Slight variation medium-high)
#     c(7,8,9,8,7,8,9,8,7,8,9,8,7,8),         # Chemex (Slight variation high)
#     c(8,9,8,9,8,9,8,9,8,9,8,9,8,9),         # Scale (Consistent high)
#     c(7,8,7,8,7,8,7,8,7,8,7,8,7,8),         # Kettle (Consistent medium-high)
#     c(1,2,3,4,5,6,7,7,6,6,5,5,4,4)          # Espresso Machine (Ramp up, plateau/dip)
#   )
# )

coffee_table_data=coffee_data_raw |> rename(
  Product=product,
  Revenue_Amount=revenue_dollars,
  Revenue_Percent=revenue_pct,
  Profit_Amount=profit_dollars,
  Profit_Percent=profit_pct,
  Monthly_Sales=monthly_sales
)

# Step 3: Create the GT table
# --------------------------------------------------

coffee_gt_table_manual <- coffee_table_data %>%
  # Initialize gt table, using 'Product' column as row labels (stub)
  gt(rowname_col = "icon") %>%

  # --- Add Column Spanners ---
  tab_spanner(
    label = "Revenue",
    columns = c(Revenue_Amount, Revenue_Percent)
  ) %>%
  tab_spanner(
    label = "Profit",
    columns = c(Profit_Amount, Profit_Percent)
  ) %>%

  # --- Format Columns ---
  # Format Amounts using short scale (K, M)
  fmt_currency(
      columns = c(Revenue_Amount, Profit_Amount),
      currency = "USD", # Assuming USD
      decimals = 1,     # One decimal place shown in image for totals, apply consistently
      suffixing = TRUE # Key for K, M suffixes
  ) %>%
  # Format percentage columns
  fmt_percent(
      columns = c(Revenue_Percent, Profit_Percent),
      decimals = 0 # Zero decimal places
  ) %>%

  # --- Add Nanoplot (Bar Chart) ---
  cols_nanoplot(
     columns = Monthly_Sales,
     plot_type = "bar",
     options = nanoplot_options(
         data_bar_fill_color = "steelblue",
         data_bar_stroke_color = "steelblue"
     )
  ) %>%

  # --- Add Grand Summary Row ---
  # Calculate sums directly from the numeric columns we created
  # Verify these match the totals in the image ($29.4M, 100%, $14.8M, 100%)
  # grand_summary_rows(
  #   columns = c(Revenue_Amount, Profit_Amount),
  #   fns = list(
  #     Total = ~sum(., na.rm = TRUE)
  #   ),
  #   formatter = fmt_currency,
  #    #currency = "USD",
  #    decimals = 1,
  #    suffixing = TRUE
  # ) %>%
  #  grand_summary_rows(
  #   columns = c(Revenue_Percent, Profit_Percent),
  #   fns = list(
  #       Total = ~sum(., na.rm = TRUE)
  #   ),
  #   formatter = fmt_percent,
  #   decimals = 0
  #  ) %>%

  # --- Add Title and Labels ---
  tab_header(
    title = "Sales of Coffee Equipment"
  ) %>%
  cols_label(
    Revenue_Amount = "Amount",
    Revenue_Percent = "Percent",
    Profit_Amount = "Amount",
    Profit_Percent = "Percent",
    Monthly_Sales = "Monthly Sales"
  ) %>%

  # --- Styling ---
   cols_width(
      c(Product) ~ px(150),
      contains("Amount") ~ px(100),
      contains("Percent") ~ px(80),
      Monthly_Sales ~ px(150)
   ) %>%
   tab_style(
      style = cell_text(align = "center", weight = "bold"),
      locations = list(
          cells_column_spanners(),
          cells_column_labels()
          )
   ) %>%
   cols_align(
      align = "right",
      columns = c(Revenue_Amount, Profit_Amount, Revenue_Percent, Profit_Percent)
   ) %>%
    cols_align( # Center align product names (stub) and monthly sales plot
      align = "center",
      columns = c(Product, Monthly_Sales)
   ) %>%
   cols_align( # Left align product names (stub)
      align = "left",
      columns = Product
   ) %>%
   tab_options(
       column_labels.padding = px(5),
       data_row.padding = px(5),
       summary_row.padding = px(5), # Controls grand summary padding too
       grand_summary_row.padding = px(5)
   )
Code 
# --- Display the table ---
coffee_gt_table_manual |> tab_style(
        style= list(cell_fill(color ="aliceblue")),
        locations=cells_body(columns = c(Revenue_Amount, Revenue_Percent))
        )|> tab_style(
        style= list(cell_fill(color ="papayawhip")),
        locations=cells_body(columns = c(Profit_Amount, Profit_Percent))
    )|> tab_style(
        style= list(cell_text(weight="bold")),
        locations=cells_body(rows = Revenue_Amount == max(Revenue_Amount))
    ) |> fmt_image("icon", path="assets") |> sub_missing(missing_text="")
Sales of Coffee Equipment
Product
Revenue
Profit
nanoplots
Amount Percent Amount Percent
Grinder $904.5K 3% $568.0K 4%
765 0 521 494 596 613 667 748 765 686 607 594 568 751
Moka pot $2.0M 7% $181.1K 1%
6.87K 0 4.73K 4.74K 4.79K 5.51K 6.16K 6.62K 6.87K 6.03K 5.30K 4.88K 4.65K 6.28K
Cold brew $288.8K 1% $241.8K 2%
2.70K 0 244 249 438 981 1.77K 2.70K 2.61K 2.35K 1.74K 896 499 244
Filter $404.2K 1% $70.0K 0%
2.74K 0 2.07K 1.81K 1.84K 2.12K 2.25K 2.63K 2.56K 2.37K 2.16K 2.19K 2.07K 2.74K
Drip machine $2.6M 9% $1.4M 9%
2.58K 0 2.14K 1.62K 1.97K 2.10K 2.58K 2.46K 2.34K 2.32K 2.05K 1.97K 1.84K 2.33K
AeroPress $2.6M 9% $1.3M 9%
9.27K 0 6.33K 5.20K 6.37K 7.02K 7.91K 8.70K 8.69K 7.80K 6.83K 6.96K 6.88K 9.27K
Pour over $846.0K 3% $364.5K 2%
2.18K 0 1.56K 1.29K 1.51K 1.69K 1.94K 2.18K 2.14K 1.86K 1.72K 1.81K 1.60K 2.16K
French press $1.1M 4% $748.1K 5%
4.82K 0 3.51K 2.88K 3.35K 3.79K 3.90K 4.10K 4.18K 4.43K 3.28K 3.42K 3.30K 4.82K
Cezve $2.5M 9% $2.0M 13%
17.1K 0 12.2K 11.5K 11.8K 13.6K 15.4K 16.5K 17.1K 14.4K 13.0K 12.9K 11.6K 15.9K
Chemex $3.1M 11% $817.7K 6%
7.22K 0 4.94K 4.17K 5.24K 6.00K 6.36K 6.77K 7.11K 6.25K 5.60K 6.08K 4.98K 7.22K
Scale $3.8M 13% $2.9M 20%
3.18K 0 1.54K 1.57K 1.68K 2.03K 2.43K 2.55K 2.57K 2.23K 2.04K 2.09K 1.69K 3.18K
Kettle $756.2K 3% $617.5K 4%
1.53K 0 1.14K 1.02K 1.09K 1.13K 1.41K 1.48K 1.46K 1.30K 1.14K 1.23K 1.19K 1.53K
Espresso Machine $8.4M 29% $3.6M 25%
2.58K 0 686 840 618 598 2.15K 533 797 996 1.00K 668 858 2.58K

Total $29.4M 100% $14.8M 100%
Code 
import polars as pl
import polars.selectors as cs
from great_tables import GT, loc, style

coffee_sales = pl.read_ndjson("coffee-sales.ndjson")
sel_rev = cs.starts_with("revenue")
sel_prof = cs.starts_with("profit")


coffee_table = (
    GT(coffee_sales)
    .tab_header("Sales of Coffee Equipment")
    .tab_spanner(label="Revenue", columns=sel_rev)
    .tab_spanner(label="Profit", columns=sel_prof)
    .cols_label(
        revenue_dollars="Amount",
        profit_dollars="Amount",
        revenue_pct="Percent",
        profit_pct="Percent",
        monthly_sales="Monthly Sales",
        icon="",
        product="Product",
    )
    # formatting ----
    .fmt_number(
        columns=cs.ends_with("dollars"),
        compact=True,
        pattern="${x}",
        n_sigfig=3,
    )
    .fmt_percent(columns=cs.ends_with("pct"), decimals=0)
    # style ----
    .tab_style(
        style=style.fill(color="aliceblue"),
        locations=loc.body(columns=sel_rev),
    )
    .tab_style(
        style=style.fill(color="papayawhip"),
        locations=loc.body(columns=sel_prof),
    )
    .tab_style(
        style=style.text(weight="bold"),
        locations=loc.body(rows=pl.col("product") == "Total"),
    )
    .fmt_nanoplot("monthly_sales", plot_type="bar")
    .fmt_image("icon", path="assets")
    .sub_missing(missing_text="")
)

coffee_table
Sales of Coffee Equipment
Product Revenue Profit Monthly Sales
Amount Percent Amount Percent
Grinder $904K 3% $568K 4%
7650521494596613667748765686607594568751
Moka pot $2.05M 7% $181K 1%
6.87K04.73K4.74K4.79K5.51K6.16K6.62K6.87K6.03K5.30K4.88K4.65K6.28K
Cold brew $289K 1% $242K 2%
2.70K02442494389811.77K2.70K2.61K2.35K1.74K896499244
Filter $404K 1% $70.0K 0%
2.74K02.07K1.81K1.84K2.12K2.25K2.63K2.56K2.37K2.16K2.19K2.07K2.74K
Drip machine $2.63M 9% $1.37M 9%
2.58K02.14K1.62K1.97K2.10K2.58K2.46K2.34K2.32K2.05K1.97K1.84K2.33K
AeroPress $2.60M 9% $1.29M 9%
9.27K06.33K5.20K6.37K7.02K7.91K8.70K8.69K7.80K6.83K6.96K6.88K9.27K
Pour over $846K 3% $365K 2%
2.18K01.56K1.29K1.51K1.69K1.94K2.18K2.14K1.86K1.72K1.81K1.60K2.16K
French press $1.11M 4% $748K 5%
4.82K03.51K2.88K3.35K3.79K3.90K4.10K4.18K4.43K3.28K3.42K3.30K4.82K
Cezve $2.51M 9% $1.97M 13%
17.1K012.2K11.5K11.8K13.6K15.4K16.5K17.1K14.4K13.0K12.9K11.6K15.9K
Chemex $3.14M 11% $818K 6%
7.22K04.94K4.17K5.24K6.00K6.36K6.77K7.11K6.25K5.60K6.08K4.98K7.22K
Scale $3.80M 13% $2.91M 20%
3.18K01.54K1.57K1.68K2.03K2.42K2.55K2.57K2.23K2.04K2.09K1.69K3.18K
Kettle $756K 3% $618K 4%
1.53K01.14K1.02K1.09K1.13K1.41K1.48K1.46K1.30K1.14K1.23K1.19K1.53K
Espresso Machine $8.41M 29% $3.64M 25%
2.58K06868406185982.15K5337979961.00K6688582.58K
Total $29.4M 100% $14.8M 100%
Code 
#coffee_table.save("data/coffee-table.png",  scale=2)

python to R with gemini 2.5

translate following python code to R code,using cols_nanoplot in R to replace fmt_nanoplot()

Error in fmt_currency(., columns = ends_with(“dollars”), currency = “USD”, : unused arguments (use_sigfig = TRUE, sigfig = 3)

Code 
# --- Load necessary libraries ---
library(gt)
library(dplyr)
library(jsonlite)
library(gtExtras)

# --- Data Loading ---
# (Assuming coffee_sales is loaded as before)
con <- file("coffee-sales.ndjson", "r")
coffee_sales <- stream_in(con, simplifyDataFrame = TRUE) %>%
  as_tibble()

 Found 14 records...
 Imported 14 records. Simplifying...
Code 
close(con)

# --- Table Creation and Styling with gt ---
coffee_table <-
  gt(coffee_sales) %>%

  # --- Headers and Spanners ---
  tab_header(title = "Sales of Coffee Equipment") %>%
  tab_spanner(label = "Revenue", columns = starts_with("revenue")) %>%
  tab_spanner(label = "Profit", columns = starts_with("profit")) %>%

  # --- Column Labels ---
  cols_label(
    revenue_dollars = "Amount",
    profit_dollars = "Amount",
    revenue_pct = "Percent",
    profit_pct = "Percent",
    monthly_sales = "Monthly Sales",
    icon = "",
    product = "Product"
  ) %>%

  # --- Formatting ---
  # *** CORRECTED SECTION ***
  # Format numeric columns using significant figures and add '$' prefix with pattern
  fmt_number(
      columns = ends_with("dollars"),
      pattern = "${x}",  # Use pattern to add the dollar sign
      n_sigfig = 3 ,      # Specify 3 significant figures
      # If you also wanted the compact K/M notation like in Python's compact=True:
      suffixing = TRUE
  ) %>%
  # Format percentage columns
  fmt_percent(columns = ends_with("pct"), decimals = 0) %>%

  # --- Styling ---
  tab_style(
    style = cell_fill(color = "aliceblue"),
    locations = cells_body(columns = starts_with("revenue"))
  ) %>%
  tab_style(
    style = cell_fill(color = "papayawhip"),
    locations = cells_body(columns = starts_with("profit"))
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(columns = everything(), rows = product == "Total")
  ) %>%

  # --- Special Formatting ---
  #gt_plt_bar(column = monthly_sales, color="grey", background = "lightgrey") %>%
  # text_transform(
  #   locations = cells_body(columns = icon),
  #   fn = function(x) {
  #       image_path <- file.path("assets", x)
  #        if (!file.exists(image_path)) {
  #           warning("Image file not found: ", image_path)
  #           return("")
  #         }
  #       local_image(filename = image_path, height = px(25))
  #   }
  # ) %>%
  sub_missing(missing_text = "")



# --- Save the table (optional) ---
# gtsave(coffee_table, filename = "data/coffee-table.png", zoom = 2)
Code 
# --- Display the table ---
coffee_table|> fmt_image("icon", path="assets") |>  
  # --- Add Nanoplot (Bar Chart) ---
  cols_nanoplot(
     columns = monthly_sales,
     plot_type = "bar",
     options = nanoplot_options(
         data_bar_fill_color = "steelblue",
         data_bar_stroke_color = "steelblue"
     )
  )
Sales of Coffee Equipment
Product
Revenue
Profit
nanoplots
Amount Percent Amount Percent
Grinder $904K 3% $568K 4%
765 0 521 494 596 613 667 748 765 686 607 594 568 751
Moka pot $2.05M 7% $181K 1%
6.87K 0 4.73K 4.74K 4.79K 5.51K 6.16K 6.62K 6.87K 6.03K 5.30K 4.88K 4.65K 6.28K
Cold brew $289K 1% $242K 2%
2.70K 0 244 249 438 981 1.77K 2.70K 2.61K 2.35K 1.74K 896 499 244
Filter $404K 1% $70.0K 0%
2.74K 0 2.07K 1.81K 1.84K 2.12K 2.25K 2.63K 2.56K 2.37K 2.16K 2.19K 2.07K 2.74K
Drip machine $2.63M 9% $1.37M 9%
2.58K 0 2.14K 1.62K 1.97K 2.10K 2.58K 2.46K 2.34K 2.32K 2.05K 1.97K 1.84K 2.33K
AeroPress $2.60M 9% $1.29M 9%
9.27K 0 6.33K 5.20K 6.37K 7.02K 7.91K 8.70K 8.69K 7.80K 6.83K 6.96K 6.88K 9.27K
Pour over $846K 3% $365K 2%
2.18K 0 1.56K 1.29K 1.51K 1.69K 1.94K 2.18K 2.14K 1.86K 1.72K 1.81K 1.60K 2.16K
French press $1.11M 4% $748K 5%
4.82K 0 3.51K 2.88K 3.35K 3.79K 3.90K 4.10K 4.18K 4.43K 3.28K 3.42K 3.30K 4.82K
Cezve $2.51M 9% $1.97M 13%
17.1K 0 12.2K 11.5K 11.8K 13.6K 15.4K 16.5K 17.1K 14.4K 13.0K 12.9K 11.6K 15.9K
Chemex $3.14M 11% $818K 6%
7.22K 0 4.94K 4.17K 5.24K 6.00K 6.36K 6.77K 7.11K 6.25K 5.60K 6.08K 4.98K 7.22K
Scale $3.80M 13% $2.91M 20%
3.18K 0 1.54K 1.57K 1.68K 2.03K 2.43K 2.55K 2.57K 2.23K 2.04K 2.09K 1.69K 3.18K
Kettle $756K 3% $618K 4%
1.53K 0 1.14K 1.02K 1.09K 1.13K 1.41K 1.48K 1.46K 1.30K 1.14K 1.23K 1.19K 1.53K
Espresso Machine $8.41M 29% $3.64M 25%
2.58K 0 686 840 618 598 2.15K 533 797 996 1.00K 668 858 2.58K

Total $29.4M 100% $14.8M 100%

Table 3

origin table:

Prompt

write R code using GT pacakge to recreate this table

1.opt_align change to cols_align 2.cols_labels change to cols_label

input data

Code 
library(gt)
library(dplyr)

# Create the data frame (replace with your actual data loading if needed)
data <-read.csv("power-generation.csv")

make GT table

Code 
# Create the gt table
gt_table <- data %>%
  gt() %>%
  # Title and subtitle
  tab_header(
    title = md("2023 Mean **Carbon Intensity** (gCO2eq/kWh) and **Power Consumption** Breakdown (%)")
  ) %>%
  # Column labels
  cols_label(
    CO2.Intensity = "CO2 Intensity",
    Hydro.Discharge = "Hydro Discharge",
    Battery.Discharge = "Battery Discharge"
  ) %>%
  # Format the numeric columns to have one decimal place
  fmt_number(
    columns = -Zone, # Apply to all columns except Zone
    decimals = 1
  ) %>%
  # Add a spanning header for the power consumption breakdown
  # tab_spanner(
  #   label = "Power Consumption Breakdown (%)",
  #   columns = c(Hydro, Nuclear, Wind, Solar, Geothermal, Biomass, Gas, Coal, Oil, Unknown, Hydro.Discharge, Battery.Discharge)
  # ) %>%
  # Add source note
  tab_source_note(md("Source: [Your Data Source Information Here]")) %>%
  # Add a footnote about the methodology
  tab_footnote(
    md("Some emissions factors are based on IPCC 2014 defaults, while some are based on more accurate regional factors.")
  ) %>%
  # Style the table (optional, customize as needed)
   cols_align(align = "center") %>%
  opt_row_striping()

# Display the table
gt_table
2023 Mean Carbon Intensity (gCO2eq/kWh) and Power Consumption Breakdown (%)
Zone CO2 Intensity Hydro Nuclear Wind Solar Geothermal Biomass Gas Coal Oil Unknown Hydro Discharge Battery Discharge
Sweden 23.5 0.4 0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Iceland 27.6 0.7 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Quebec 30.6 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
France 52.7 0.1 0.6 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
Ontario 72.6 0.3 0.5 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
Finland 87.2 0.2 0.4 0.2 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0
Tasmania 92.2 0.7 0.0 0.2 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0
New Zealand 94.5 0.6 0.0 0.1 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.0
Belgium 139.6 0.0 0.4 0.2 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
West Denmark 143.1 0.2 0.0 0.5 0.1 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.0
East Denmark 147.6 0.1 0.1 0.4 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0
Spain 154.0 0.1 0.2 0.2 0.2 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
South Australia 185.8 0.0 0.0 0.4 0.2 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.0
Great Britain 199.8 0.0 0.2 0.3 0.1 0.0 0.1 0.3 0.0 0.0 0.0 0.0 0.0
California 257.7 0.1 0.1 0.1 0.2 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0
Netherlands 272.8 0.0 0.0 0.3 0.2 0.0 0.1 0.3 0.1 0.0 0.0 0.0 0.0
New York ISO 280.0 0.2 0.2 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0
Italy (North) 307.3 0.2 0.1 0.0 0.1 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0
Texas 383.2 0.0 0.1 0.3 0.1 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0
Germany 396.8 0.1 0.0 0.3 0.1 0.0 0.1 0.1 0.2 0.0 0.0 0.0 0.0
Western Australia 433.3 0.0 0.0 0.2 0.2 0.0 0.0 0.4 0.3 0.0 0.0 0.0 0.0
Alberta 438.9 0.0 0.0 0.1 0.0 0.0 0.0 0.7 0.1 0.0 0.0 0.0 0.0
Victoria 506.4 0.1 0.0 0.2 0.1 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0
New South Wales 556.3 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0
India (North) 558.2 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0
Queensland 607.0 0.0 0.0 0.0 0.2 0.0 0.0 0.1 0.7 0.0 0.0 0.0 0.0
South Africa 701.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0
Source: [Your Data Source Information Here]
Some emissions factors are based on IPCC 2014 defaults, while some are based on more accurate regional factors.

Reference:

https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file