我有以下数据框，其中包含长期运行的数据。我的目标是计算下面我所说的“aroc”函数的年化变化率。然而，在应用函数时，我得到的 aroc 值是 NA。我一再尝试并决定接受你的帮助。如您所知，对于第一个时间点，aroc 必须为 0。在本例中，第一个时间点是 1990 年。代码确实按要求粘贴了零，但不计算 aroc。我需要你的帮助。请！
df <- data.frame (year_id = c(1990, 1990, 1991, 1991, 1992, 1992, 1993, 1993, 1994, 1994, 1995, 1995, 1996, 1996, 1997, 1997, 1998, 1998, 1999, 1999, 2000, 2000, 2001, 2001, 2002, 2002, 2003, 2003, 2004, 2004, 2005, 2005, 2006, 2006, 2007, 2007, 2008, 2008, 2009, 2009, 2010, 2010, 2011, 2011, 2012, 2012, 2013, 2013, 2014, 2014, 2015, 2015, 2016, 2016, 2017, 2017, 2018, 2018, 2019, 2019, 2020, 2020, 2021, 2021, 1990, 1990, 1991, 1991, 1992, 1992, 1993, 1993, 1994, 1994, 1995, 1995, 1996, 1996, 1997, 1997, 1998, 1998, 1999, 1999, 2000, 2000, 2001, 2001, 2002, 2002),
sex = c("male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male" , "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male", "female", "male" , "female", "male" , "female", "male" , "female", "male", "female", "male", "female", "male", "female"),
loc = c("C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C","C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C","C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D", "D" ,"D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D", "D"),
reg1 = c( "SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO","SOAEAO", "SOAEAO","SOAEAO","SOAEAO","SOAEAO"), reg2 = c("EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA","EA"), val = c(0.000043700, 0.000019600, 0.000049300, 0.000022900, 0.000058200 ,0.000027000, 0.000068200, 0.000031700, 0.000080100, 0.000037800, 0.000093800, 0.000044800,0.000107661, 0.000051300, 0.000123680, 0.000058200, 0.000143318, 0.000066600, 0.000167694, 0.000076700, 0.000198397, 0.000089100, 0.000235314, 0.000103256,0.000277176, 0.000118437, 0.000321671, 0.000133436, 0.000368679, 0.000148275, 0.000411686, 0.000160694, 0.000437294, 0.000165787, 0.000456603, 0.000169191, 0.000473239, 0.000172542, 0.000475592, 0.000169768 ,0.000480068, 0.000168260, 0.000480103, 0.000166006, 0.000470809, 0.000159872, 0.000473221, 0.000159643, 0.000483188, 0.000161755, 0.000486762, 0.000162135, 0.000485582, 0.000160320, 0.000479532, 0.000157872, 0.000473521, 0.000154745, 0.000465836, 0.000150780, 0.000440927, 0.000141668, 0.000414025, 0.000131982, 0.000008850, 0.000002930, 0.000011700, 0.000004080, 0.000016800, 0.000006140, 0.000024500, 0.000009350, 0.000036500, 0.000014500, 0.000052200, 0.000021300, 0.000069400, 0.000029000, 0.000089700, 0.000038200, 0.000114664, 0.000049800, 0.000144987, 0.000063900, 0.000181219, 0.000080900, 0.000222981, 0.000100655, 0.000269623, 0.000122767) )
这是我创建的用于计算年化变化率的函数
#functionaroc <- function(x, t, mean = FALSE){t <- order(t)res <- numeric(length(x)-1). for(i in 1:(length(x)-1)){res[i] <- log((x[i+1]/x[i]))/(t[i+1]-t[I]) }if(mean == TRUE){res <- mean(res)}return(aroc = res)}
这里我正在应用该函数。 #计算
df %>%select(year_id,loc,sex,val, reg1, reg2) %>%group_by(year_id,loc,sex) %>%mutate(aroc_pop = ifelse(as.numeric(year_id != 1990),aroc(val,as.numeric(year_id)),0)) %>% data.frame()
Here is the output with NAs on the new column 'aroc_pop'. How do I get the right values in the new column?#outputyear_id loc sex val reg1 reg2 aroc_pop1 1990 C male 0.000043700 SOAEAO EA 02 1990 C female 0.000019600 SOAEAO EA 03 1991 C male 0.000049300 SOAEAO EA NA4 1991 C female 0.000022900 SOAEAO EA NA5 1992 C male 0.000058200 SOAEAO EA NA6 1992 C female 0.000027000 SOAEAO EA NA7 1993 C male 0.000068200 SOAEAO EA NA8 1993 C female 0.000031700 SOAEAO EA NA9 1994 C male 0.000080100 SOAEAO EA NA10 1994 C female 0.000037800 SOAEAO EA NA11 1995 C male 0.000093800 SOAEAO EA NA12 1995 C female 0.000044800 SOAEAO EA NA13 1996 C male 0.000107661 SOAEAO EA NA14 1996 C female 0.000051300 SOAEAO EA NA15 1997 C male 0.000123680 SOAEAO EA NA16 1997 C female 0.000058200 SOAEAO EA NA17 1998 C male 0.000143318 SOAEAO EA NA18 1998 C female 0.000066600 SOAEAO EA NA19 1999 C male 0.000167694 SOAEAO EA NA20 1999 C female 0.000076700 SOAEAO EA NA21 2000 C male 0.000198397 SOAEAO EA NA22 2000 C female 0.000089100 SOAEAO EA NA23 2001 C male 0.000235314 SOAEAO EA NA24 2001 C female 0.000103256 SOAEAO EA NA25 2002 C male 0.000277176 SOAEAO EA NA26 2002 C female 0.000118437 SOAEAO EA NA27 2003 C male 0.000321671 SOAEAO EA NA28 2003 C female 0.000133436 SOAEAO EA NA29 2004 C male 0.000368679 SOAEAO EA NA30 2004 C female 0.000148275 SOAEAO EA NA31 2005 C male 0.000411686 SOAEAO EA NA32 2005 C female 0.000160694 SOAEAO EA NA33 2006 C male 0.000437294 SOAEAO EA NA34 2006 C female 0.000165787 SOAEAO EA NA35 2007 C male 0.000456603 SOAEAO EA NA36 2007 C female 0.000169191 SOAEAO EA NA37 2008 C male 0.000473239 SOAEAO EA NA38 2008 C female 0.000172542 SOAEAO EA NA39 2009 C male 0.000475592 SOAEAO EA NA40 2009 C female 0.000169768 SOAEAO EA NA41 2010 C male 0.000480068 SOAEAO EA NA42 2010 C female 0.000168260 SOAEAO EA NA43 2011 C male 0.000480103 SOAEAO EA NA44 2011 C female 0.000166006 SOAEAO EA NA45 2012 C male 0.000470809 SOAEAO EA NA46 2012 C female 0.000159872 SOAEAO EA NA47 2013 C male 0.000473221 SOAEAO EA NA48 2013 C female 0.000159643 SOAEAO EA NA49 2014 C male 0.000483188 SOAEAO EA NA50 2014 C female 0.000161755 SOAEAO EA NA51 2015 C male 0.000486762 SOAEAO EA NA52 2015 C female 0.000162135 SOAEAO EA NA53 2016 C male 0.000485582 SOAEAO EA NA54 2016 C female 0.000160320 SOAEAO EA NA55 2017 C male 0.000479532 SOAEAO EA NA56 2017 C female 0.000157872 SOAEAO EA NA57 2018 C male 0.000473521 SOAEAO EA NA58 2018 C female 0.000154745 SOAEAO EA NA59 2019 C male 0.000465836 SOAEAO EA NA60 2019 C female 0.000150780 SOAEAO EA NA61 2020 C male 0.000440927 SOAEAO EA NA62 2020 C female 0.000141668 SOAEAO EA NA63 2021 C male 0.000414025 SOAEAO EA NA64 2021 C female 0.000131982 SOAEAO EA NA65 1990 D male 0.000008850 SOAEAO EA 066 1990 D female 0.000002930 SOAEAO EA 067 1991 D male 0.000011700 SOAEAO EA NA68 1991 D female 0.000004080 SOAEAO EA NA69 1992 D male 0.000016800 SOAEAO EA NA70 1992 D female 0.000006140 SOAEAO EA NA71 1993 D male 0.000024500 SOAEAO EA NA72 1993 D female 0.000009350 SOAEAO EA NA73 1994 D male 0.000036500 SOAEAO EA NA74 1994 D female 0.000014500 SOAEAO EA NA75 1995 D male 0.000052200 SOAEAO EA NA76 1995 D female 0.000021300 SOAEAO EA NA77 1996 D male 0.000069400 SOAEAO EA NA78 1996 D female 0.000029000 SOAEAO EA NA79 1997 D male 0.000089700 SOAEAO EA NA80 1997 D female 0.000038200 SOAEAO EA NA81 1998 D male 0.000114664 SOAEAO EA NA82 1998 D female 0.000049800 SOAEAO EA NA83 1999 D male 0.000144987 SOAEAO EA NA84 1999 D female 0.000063900 SOAEAO EA NA85 2000 D male 0.000181219 SOAEAO EA NA86 2000 D female 0.000080900 SOAEAO EA NA87 2001 D male 0.000222981 SOAEAO EA NA88 2001 D female 0.000100655 SOAEAO EA NA89 2002 D male 0.000269623 SOAEAO EA NA90 2002 D female 0.000122767 SOAEAO EA NA
如上所述，我编写了 aroc 函数，并期望它能够计算 aroc_pop 值。 此外，在对女性群体应用 aroc 函数后，如下：
aroc(df[df$sex == 'female', ]$val,as.numeric(df[df$sex == 'female', ]$year_id))%>% data.frame()
在这种情况下，我得到女性 aroc_pop 的正确值，如下所示：
4.862730e-032 -5.312902e-033 5.014994e-034 -5.677175e-035 5.309345e-036 -4.370407e-037 3.943581e-038 -4.349007e-039 4.412410e-0310 -4.834117e-0311 4.607875e-0312 -4.424833e-0313 3.726275e-0314 -3.401506e-0315 2.513540e-0316 -1.006513e-0317 6.351382e-0418 -6.326591e-0419 -5.064964e-0420 2.878193e-0421 -4.214522e-0422 1.214530e-0323 -4.479446e-0524 -4.239604e-0425 7.332740e-0526 3.631453e-0427 -1.538724e-0228 -2.000598e-0229 -2.595678e-0230 -6.233553e-0231 -7.082074e-0232 -3.807663e+0033 3.310946e-0134 4.087278e-0135 4.205516e-0136 4.387723e-0137 3.845584e-0138 3.085888e-0139 2.755397e-0140 2.651795e-0141 2.493044e-0142 2.358945e-0143 2.184850e-0144 1.985894e-01我可以将“女性”更改为“男性”并仍然计算这些值。 但由于 NA，这在整个数据集中是不可能的
格式使得这很难遵循，但我很确定这就是答案：您的
group_by(year_id, loc, sex)arocx <- 4.37e-05
x[2]
> NA
您必须删除分组变量或在您拥有的组中添加更多记录。