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R计算不同间隔长度的年增长率或标准化增长率
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R计算不同间隔长度的年增长率或标准化增长率
作者:
春风助手
发布时间:
2024-04-22 10:08:22 (2天前)
<p>我有一个结构如下所示的数据框。我想计算年增长率。问题在于，并非所有模型的时间步长都是相同的。在下面的示例中，REMIND每隔5年发送一次数据，而TIAM-ECN每隔10年发送一次数据。</p> <pre><code>model scenario region year value REMIND Base NORTH_AM 2010 314.1330 REMIND Base CHINA+ 2010 1325.9220 REMIND RefPol NORTH_AM 2010 314.1330 REMIND RefPol CHINA+ 2010 1325.9220 TIAM-ECN Base NORTH_AM 2010 344.4005 TIAM-ECN Base CHINA+ 2010 1341.3352 TIAM-ECN RefPol NORTH_AM 2010 344.4005 TIAM-ECN RefPol CHINA+ 2010 1341.3352 REMIND Base NORTH_AM 2015 327.6270 REMIND Base CHINA+ 2015 1354.3180 REMIND RefPol NORTH_AM 2015 327.6270 REMIND RefPol CHINA+ 2015 1354.3180 REMIND Base NORTH_AM 2020 340.8490 REMIND Base CHINA+ 2020 1372.4630 REMIND RefPol NORTH_AM 2020 340.8490 REMIND RefPol CHINA+ 2020 1372.4630 TIAM-ECN Base NORTH_AM 2020 374.2647 TIAM-ECN Base CHINA+ 2020 1387.7915 TIAM-ECN RefPol NORTH_AM 2020 374.2647 TIAM-ECN RefPol CHINA+ 2020 1387.7915 </code></pre><p>计算不同时间间隔的增长率很简单：</p> <pre><code>library(dplyr) tmp_gr <- group_by(df, model, scenario, region) %>% mutate(value = value / lag(value) - 1) %>% ungroup() </code></pre><p>收益率（我未列出2010年的资产净值）：</p> <pre><code>model scenario region year value REMIND Base NORTH_AM 2015 -0.7557456 REMIND Base CHINA+ 2015 3.1337191 REMIND RefPol NORTH_AM 2015 -0.7580871 REMIND RefPol CHINA+ 2015 3.1337191 REMIND Base NORTH_AM 2020 -0.7483242 REMIND Base CHINA+ 2020 3.0266012 REMIND RefPol NORTH_AM 2020 -0.7516516 REMIND RefPol CHINA+ 2020 3.0266012 TIAM-ECN Base NORTH_AM 2020 -0.7273044 TIAM-ECN Base CHINA+ 2020 2.7080483 TIAM-ECN RefPol NORTH_AM 2020 -0.7303164 TIAM-ECN RefPol CHINA+ 2020 2.7080483 </code></pre><p>但是现在，通过将间隔增长率除以间隔长度来计算年增长率</p> <pre><code>tmp_gr_yearly <- group_by(df, model, scenario, region) %>% mutate(value = (value / lag(value) - 1) / (year - lag(year))) %>% ungroup() </code></pre><p>产量：</p> <pre><code>model scenario region year value REMIND Base NORTH_AM 2015 -0.1511491 REMIND Base CHINA+ 2015 Inf REMIND RefPol NORTH_AM 2015 -Inf REMIND RefPol CHINA+ 2015 Inf REMIND Base NORTH_AM 2020 -0.1496648 REMIND Base CHINA+ 2020 Inf REMIND RefPol NORTH_AM 2020 -Inf REMIND RefPol CHINA+ 2020 Inf TIAM-ECN Base NORTH_AM 2020 -Inf TIAM-ECN Base CHINA+ 2020 Inf TIAM-ECN RefPol NORTH_AM 2020 -Inf TIAM-ECN RefPol CHINA+ 2020 Inf </code></pre><p>我不知道Inf从哪里来。</p><p>有任何想法吗？</p>
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只怕再见是故人
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2020-08-11 14-59
我计算简单的非标准化增长率的示例已经是错误的。 无论如何，我想我自己已经明白了： ``` tmp_gr <- group_by(df, model, scenario, region) %>% mutate(value = lag(value, n=0, order_by=year) / lag(value, order_by=year) - 1) %>% ungroup() tmp_gr_yearly <- group_by(df, model, scenario, region) %>% mutate(value = (lag(value, n=0, order_by=year) / lag(value, order_by=year) - 1) / (lag(year, n=0, order_by=year) - lag(year, order_by=year))) %>% ungroup() ``` 通过对所有值使用lag运算符并显式告知顺序，整个事情针对非顺序数据变得更加健壮。
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