如何按年度与受援国汇总promised aid和provided aid的总金额？

Hey there! Let's walk through how to calculate those yearly aid totals per country—this is a standard aggregation task, and there are straightforward solutions depending on the tool you're using. I'll cover the most common options below:

If you're working with a spreadsheet, a Pivot Table is the easiest way to get this done:

• First, select your entire dataset (including the header row)
• Go to the Insert tab and click PivotTable, then choose where you want the result to appear
• In the PivotTable Fields pane:
  • Drag year and rcode into the Rows area (this will group your data by year first, then country)
  • Drag promised aid and provided aid into the Values area
  • Double-check the aggregation method: by default, it might use "Count"—click the value field, select Value Field Settings, and switch to Sum
• You’ll end up with a table that shows exactly what you need: e.g., 2002 → Afghanistan → total promised aid = 20M, total provided aid = 18M.

If you're using Python for data analysis, Pandas makes this a breeze (plus a filter if you need to limit to 2002-2012):

import pandas as pd

# Load your data into a DataFrame (adjust the path/loading method as needed)
df = pd.read_csv("your_aid_data.csv")

# Filter to only 2002-2012 if your dataset includes other years
filtered_df = df[(df["year"] >= 2002) & (df["year"] <= 2012)]

# Group by year and country code, then sum the two aid columns
summary_df = filtered_df.groupby(["year", "rcode"])[["promised aid", "provided aid"]].sum().reset_index()

# View the first few rows of the result
print(summary_df.head())

• The groupby method clusters your data by year and rcode, sum() calculates the total for each aid type, and reset_index() turns the grouped columns back into regular columns for easier viewing and further analysis.

For R users, the dplyr package provides a clean, readable workflow for this task:

library(dplyr)

# Load your data (adjust the file path or loading function as needed)
df <- read.csv("your_aid_data.csv")

# Filter to 2002-2012, group by year and country, then compute totals
summary_df <- df %>%
  filter(year >= 2002 & year <= 2012) %>%
  group_by(year, rcode) %>%
  summarise(
    total_promised = sum(`promised aid`, na.rm = TRUE),
    total_provided = sum(`provided aid`, na.rm = TRUE),
    .groups = "drop"
  )

# Preview the result
head(summary_df)

• filter() narrows down the dataset to your target year range, group_by() sets up the grouping structure, and summarise() calculates the total aid values. The na.rm = TRUE parameter ensures missing values don't break your sum calculations, and .groups = "drop" returns a regular data frame instead of a grouped one.

A quick tip: If your dataset has missing values in the aid columns, all the methods above include ways to ignore blank/NaN cells so they don't skew your totals.

内容的提问来源于stack exchange，提问作者nikip