R中的for循环计算通货膨胀

Question

我想在包含电影信息的数据集中输出一个新列。 这些数字是电影的年份和总预算，它们包含在 df（下面的代码）“电影”中。 我有另一个包含通货膨胀率（百分比）的数据集。

预期的结果是每一行都有一个新列，其中包含一个针对通货膨胀指数的总数字。

# Adds percentage column
inflation$Percent = inflation$Annual/100

# Adds cumulative percentage column
inflation$Cum_Percent = cumsum(inflation$Percent)

# Adds column to movie df, ready for adjustment 
movies$gross_adjusted = movies$gross

for(val in movies$title_year){
  CumPercent = inflation[inflation$Year==val,4]
  YearPercent = inflation[inflation$Year==val,3]
  movies$gross_adjusted = movies$gross + movies$gross*(CumPercent-YearPercent)
}

运行时的错误代码：

There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In movie$gross * (CumPercent - YearPercent) :
  longer object length is not a multiple of shorter object length
2: In movie$gross * (CumPercent - YearPercent) :
  longer object length is not a multiple of shorter object length
3: In movie$gross * (CumPercent - YearPercent) :
  longer object length is not a multiple of shorter object length
......
50: In movie$gross * (CumPercent - YearPercent) :
  longer object length is not a multiple of shorter object length

dput():

> dput(movies)
structure(list(gross = c(760505847L, 309404152L, 200074175L, 
448130642L, NA, 73058679L, 336530303L, 200807262L, 458991599L, 
301956980L, 330249062L, 200069408L, 168368427L, 423032628L, 89289910L, 
291021565L, 141614023L, 623279547L, 241063875L, 179020854L, 255108370L, 
262030663L, 105219735L, 258355354L, 70083519L, 218051260L, 658672302L, 
407197282L, 65173160L, 652177271L, 304360277L, 373377893L, 408992272L, 
334185206L, 234360014L, 268488329L, 402076689L, 245428137L, 234903076L, 
202853933L, 172051787L, 191450875L, 116593191L, 414984497L, 125320003L, 
350034110L, 202351611L, 233914986L, 228756232L, 65171860L, 144812796L, 
90755643L, 101785482L, 352358779L, 317011114L, 123070338L, 237282182L, 
130468626L, 223806889L, 140080850L, 166112167L, 137850096L, 47375327L, 
124051759L, 291709845L, 154985087L, 533316061L, 292979556L, 198332128L, 
318298180L, 73820094L, 113745408L, 102176165L, 161087183L, 100289690L, 
100189501L, 88246220L, 150167630L, 356454367L, 362645141L, 312057433L, 
155111815L, 241407328L, 208543795L, NA, 38297305L, 259746958L, 
238371987L, 93417865L, 222487711L, 189412677L, 665426L, 102315545L, 
217387997L, 150350192L, 333130696L, 187991439L, 292568851L, NA, 
303001229L, 144512310L, 127490802L, 146405371L, 281666058L, 63143812L, 
60655503L, 76846624L, 320706665L, 46978995L, 89732035L, 104383624L, 
198539855L, 318759914L, 34293771L, 292000866L, 289994397L, 227946274L, 
256386216L, 206456431L, 206435493L, 205343774L, 179982968L, 177243721L, 
179883016L, 139259759L, 400736600L, 281492479L, 206360018L, 153629485L, 
133375846L, 181015141L, 114053579L, 119420252L, 83640426L, 79711678L, 
195000874L, 61937495L, 124051759L, 126597121L, 165230261L, 131564731L, 
133382309L, 73103784L, 21379315L, 64459316L, 34964818L, 111505642L, 
133228348L, 216366733L, 160201106L, 118099659L, 201573391L, 190418803L, 
82161969L, 143523463L, 209364921L, 103400692L, 110332737L, 111110575L, 
65007045L, 257704099L, 403706375L, 176997107L, 31141074L, 31704416L, 
107503316L, 129734803L, 132122995L, 122512052L, 68642452L, 32131830L, 
176636816L, 126930660L, 93926386L, 292298923L, 63992328L, 134518390L, 
NA, 52792307L, 183635922L, 83024900L, 123207194L, 83348920L, 
227137090L, 215395021L, 180191634L, 424645577L, 292298923L, 177343675L, 
234277056L, 138396624L, 149234747L, 118311368L, 101160529L, 77564037L, 
249358727L, 49551662L, 60522097L, 137748063L, NA), title_year = c(2009L, 
2007L, 2015L, 2012L, NA, 2012L, 2007L, 2010L, 2015L, 2009L, 2016L, 
2006L, 2008L, 2006L, 2013L, 2013L, 2008L, 2012L, 2011L, 2012L, 
2014L, 2012L, 2010L, 2013L, 2007L, 2005L, 1997L, 2016L, 2012L, 
2015L, 2012L, 2004L, 2013L, 2010L, 2006L, 2013L, 2009L, 2014L, 
2013L, 2014L, 2010L, 2011L, 2011L, 2010L, 2009L, 2015L, 2013L, 
2014L, 2013L, 2013L, 2013L, 2010L, 2013L, 2011L, 2008L, 2015L, 
2012L, 2016L, 2008L, 2007L, 2009L, 2009L, 2015L, 2016L, 2005L, 
2016L, 2008L, 2009L, 2009L, 2008L, 2011L, 1999L, 2008L, 2016L, 
2007L, 2014L, 1995L, 2009L, 2015L, 2016L, 2010L, 2012L, 2014L, 
2014L, 2015L, 2013L, 2014L, 2010L, 2015L, 2014L, 2012L, 2004L, 
2016L, 2010L, 2003L, 2014L, 2014L, 2010L, 2016L, 2012L, 2001L, 
2008L, 2011L, 2015L, 2010L, 2006L, 2016L, 2007L, 2016L, 2015L, 
2010L, 2001L, 2007L, 2004L, 2007L, 2005L, 2008L, 2007L, 2005L, 
2007L, 2005L, 2008L, 2009L, 2009L, 2003L, 2013L, 2003L, 2013L, 
2015L, 2009L, 2011L, 2008L, 2009L, 2012L, 2012L, 2015L, 2010L, 
2016L, 2007L, 2011L, 2010L, 2006L, 2013L, 2011L, 2006L, 2015L, 
2014L, 2004L, 2012L, 2002L, 2016L, 1998L, 2002L, 2007L, 2016L, 
2011L, 2012L, 2005L, 2003L, 2014L, 2009L, 2002L, 2014L, 2016L, 
2005L, 2009L, 1998L, 2003L, 2013L, 2005L, 2001L, 2011L, 1999L, 
2003L, 2012L, 2011L, 2008L, NA, 2016L, 2015L, 2013L, 2011L, 2014L, 
2007L, 2008L, 2015L, 2013L, 2012L, 2015L, 2005L, 2003L, 2011L, 
2010L, 2014L, 2011L, 2004L, 2008L, 2013L, 2000L, 2011L)), row.names = c(NA, 
200L), class = "data.frame")
> dput(inflation)
structure(list(Year = 2016:1916, Annual = c(1.3, 0.1, 1.6, 1.5, 
2.1, 3.2, 1.6, -0.4, 3.8, 2.8, 3.2, 3.4, 2.7, 2.3, 1.6, 2.8, 
3.4, 2.2, 1.6, 2.3, 3, 2.8, 2.6, 3, 3, 4.2, 5.4, 4.8, 4.1, 3.6, 
1.9, 3.6, 4.3, 3.2, 6.2, 10.3, 13.5, 11.3, 7.6, 6.5, 5.8, 9.1, 
11, 6.2, 3.2, 4.4, 5.7, 5.5, 4.2, 3.1, 2.9, 1.6, 1.3, 1.3, 1, 
1, 1.7, 0.7, 2.8, 3.3, 1.5, -0.4, 0.7, 0.8, 1.9, 7.9, 1.3, -1.2, 
8.1, 14.4, 8.3, 2.3, 1.7, 6.1, 10.9, 5, 0.7, -1.4, -2.1, 3.6, 
1.5, 2.2, 3.1, -5.1, -9.9, -9, -2.3, 0, -1.7, -1.7, 1.1, 2.3, 
0, 1.8, -6.1, -10.5, 15.6, 14.6, 18, 17.4, 7.9), Percent = c(0.013, 
0.001, 0.016, 0.015, 0.021, 0.032, 0.016, -0.004, 0.038, 0.028, 
0.032, 0.034, 0.027, 0.023, 0.016, 0.028, 0.034, 0.022, 0.016, 
0.023, 0.03, 0.028, 0.026, 0.03, 0.03, 0.042, 0.054, 0.048, 0.041, 
0.036, 0.019, 0.036, 0.043, 0.032, 0.062, 0.103, 0.135, 0.113, 
0.076, 0.065, 0.058, 0.091, 0.11, 0.062, 0.032, 0.044, 0.057, 
0.055, 0.042, 0.031, 0.029, 0.016, 0.013, 0.013, 0.01, 0.01, 
0.017, 0.007, 0.028, 0.033, 0.015, -0.004, 0.007, 0.008, 0.019, 
0.079, 0.013, -0.012, 0.081, 0.144, 0.083, 0.023, 0.017, 0.061, 
0.109, 0.05, 0.007, -0.014, -0.021, 0.036, 0.015, 0.022, 0.031, 
-0.051, -0.099, -0.09, -0.023, 0, -0.017, -0.017, 0.011, 0.023, 
0, 0.018, -0.061, -0.105, 0.156, 0.146, 0.18, 0.174, 0.079), 
    Cum_Percent = c(0.013, 0.014, 0.03, 0.045, 0.066, 0.098, 
    0.114, 0.11, 0.148, 0.176, 0.208, 0.242, 0.269, 0.292, 0.308, 
    0.336, 0.37, 0.392, 0.408, 0.431, 0.461, 0.489, 0.515, 0.545, 
    0.575, 0.617, 0.671, 0.719, 0.76, 0.796, 0.815, 0.851, 0.894, 
    0.926, 0.988, 1.091, 1.226, 1.339, 1.415, 1.48, 1.538, 1.629, 
    1.739, 1.801, 1.833, 1.877, 1.934, 1.989, 2.031, 2.062, 2.091, 
    2.107, 2.12, 2.133, 2.143, 2.153, 2.17, 2.177, 2.205, 2.238, 
    2.253, 2.249, 2.256, 2.264, 2.283, 2.362, 2.375, 2.363, 2.444, 
    2.588, 2.671, 2.694, 2.711, 2.772, 2.881, 2.931, 2.938, 2.924, 
    2.903, 2.939, 2.954, 2.976, 3.007, 2.956, 2.857, 2.767, 2.744, 
    2.744, 2.727, 2.71, 2.721, 2.744, 2.744, 2.762, 2.701, 2.596, 
    2.752, 2.898, 3.078, 3.252, 3.331)), row.names = c(NA, -101L
), class = "data.frame")

Answer 1

这是一个dplyr解决方案，不需要循环。

首先是你的累积通胀计算低估了真实的影响。 比较下面的最后两列。

inflation %>% 
  mutate(Compound = cumprod(1+Percent)) %>% 
  arrange(Year) %>% 
  head()

  Year Annual Percent Cum_Percent Compound
1 1916    7.9   0.079       3.331   23.75975
2 1917   17.4   0.174       3.252   22.02015
3 1918   18.0   0.180       3.078   18.75652
4 1919   14.6   0.146       2.898   15.89536
5 1920   15.6   0.156       2.752   13.87029
6 1921  -10.5  -0.105       2.596   11.99852

因为每年的收益（或损失）会影响下一年使用的基值，所以您需要一个复合公式。 例如，1916 年和 1917 年的累积率应该是 (1 + 0.079) * (1 + 0.174) - 1。

以下是您的数据集的解决方案。 注意： lead函数将百分比移动一行。 根据您的代码，我认为您不希望在年份匹配的情况下提高价格，只是为了之后的每一年。 我使用领先而不是滞后，因为日期正在下降。 此外，您的movies数据框缺少值，所以我刚刚排除了 NA

library(dplyr)
library(tidyr)

# Lag rates
inflation <- inflation %>% 
  mutate(lagged = replace_na(lead(Percent), 0))

# Compound 
new_inflation <- inflation %>% 
  mutate(Compound = cumprod(1+lagged)) %>% 
  select(Year, Compound)

# Left Join with movies 
movies <- movies %>% 
  na.omit() %>% 
  rename(Year = title_year) %>% 
  left_join(new_inflation, by = "Year") %>% 
  mutate(adjusted = gross * Compound)