如果没有可重现的示例，这是一项艰巨的任务。我使用gapminder数据来尝试解决您的问题：
数据
gapminder %>% select(country, year, lifeExp) %>% filter(year %in% c(1952, 1977)) %>% pivot_wider(names_from = year, values_from = lifeExp) %>% mutate(difference = abs(`1952`- `1977`))
输出
# A tibble: 142 x 4 country `1952` `1977` difference <fct> <dbl> <dbl> <dbl> 1 Afghanistan 28.8 38.4 9.64 2 Albania 55.2 68.9 13.7 3 Algeria 43.1 58.0 14.9 4 Angola 30.0 39.5 9.47 5 Argentina 62.5 68.5 6.00 6 Australia 69.1 73.5 4.37 7 Austria 66.8 72.2 5.37 8 Bahrain 50.9 65.6 14.7 9 Bangladesh 37.5 46.9 9.44 10 Belgium 68 72.8 4.8 # ... with 132 more rows
转换为您的（假定的）dataframe，可能是：
dataframe
dataframe %>% select(Year, Country, percentagecolumn) %>% filter(year %in% c(1990, 2019)) %>% pivot_wider(names_from = Year, values_from = percentagecolumn) %>% mutate(percentage_difference = abs(`1990`- `2019`))
Without a reproducible example that's a difficult task to perform. I used the gapminder data to try to solve your problem:
Data
Output
Translated to your (presumed) dataframe that might be:
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如果没有可重现的示例，这是一项艰巨的任务。我使用gapminder数据来尝试解决您的问题：
数据
输出
转换为您的（假定的）
dataframe，可能是：Without a reproducible example that's a difficult task to perform. I used the gapminder data to try to solve your problem:
Data
Output
Translated to your (presumed)
dataframethat might be: