R-根据其他列的计算值在数据框内选择年份
我被卡住了。我需要对所选年份进行一些计算，根据其他计算，不同地点的计算结果不同。 我有一个数据框，看起来像下面的示例df：R-根据其他列的计算值在数据框内选择年份,r,calculated-columns,R,Calculated Columns,我被卡住了。我需要对所选年份进行一些计算，根据其他计算，不同地点的计算结果不同。 我有一个数据框，看起来像下面的示例df： t0 <- 2017 tf <- 2046 pH_drift <- 0.5 fert_lim <- (pH_drift/0.1)*11.21 # amount of fertilizer needed to decrease pH level by the target drift threshold (i.e. pH_drift) state&l
t0 <- 2017
tf <- 2046
pH_drift <- 0.5
fert_lim <- (pH_drift/0.1)*11.21 # amount of fertilizer needed to decrease pH level by the target drift threshold (i.e. pH_drift)
state<-c(rep("IL",90),rep("IN",90))
county <- c(rep("a",30),rep("b",30),rep("c",30),rep("d",30),rep("e",30),rep("f",30))
for (i in length(county)) {year <- c(2017:2046)}
nfert <- c(sample(20,90,replace = TRUE),sample(13,90,replace = TRUE))
sand <- c(sample(100,90,replace = TRUE),sample(75,90,replace=TRUE))
fert_lim/n_loadlime_y <- NULL
st <- c(unique(state))
for (i in 1:length(st)) {
n_load <- mean(nfert)
lime_int <- round(fert_lim/n_load,digits = 0)
lime_yr <- c(seq(t0,tf,by=lime_int))
flag <- period %in% lime_yr
lime_yi <- ifelse(flag, 1,0)
lime_y <- c(lime_y,lime_yi)
}
df <- data.frame(state,county,year,nfert,sand,lime_y)
任何帮助或建议都将不胜感激。谢谢 两个循环中似乎存在一些语法问题
在您的第一个循环中，您得到了所有县的平均值，因为您有
nload感谢您的建议！我想我理解你的观点：如果不创建数据帧，它会将每个变量视为单独的向量。然后，仅创建一个子集，其中县与列表中的每个县匹配，并从每个县计算Nfert平均值和石灰间隔。但是，当试图直接从代码中识别与之匹配的年份时，我得到以下错误：seq.int（t0，tf，by=lime\u int）中的错误：无效'（to-from）/by'
。一定是我做错了什么，但我不知道是什么。再次感谢你的帮助。很抱歉，我打错了。我会编辑它。在ctyOh中，“County”应该大写！我懂了！非常感谢！！！注：@Sean给出的解决方案有效！不过，请注意，这是为了回答同一个问题的前一个版本，我在这里要求按县而不是按州平均。谢谢
lime_y <- NULL
st <- c(unique(state))
for (i in 1:length(st)) {
n_load <- mean(nfert)
lime_int <- round(fert_lim/n_load,digits = 0)
lime_yr <- c(seq(t0,tf,by=lime_int))
flag <- period %in% lime_yr
lime_yi <- ifelse(flag, 1,0)
lime_y <- c(lime_y,lime_yi)
}
df <- data.frame(state,county,year,nfert,sand,lime_y)
lime_y <- NULL
st <- c(unique(state))
for (i in 1:length(st)) {
n_load <- mean(df$nfert[st])
lime_int <- round(fert_lim/n_load,digits = 0)
lime_yr <- c(seq(t0,tf,by=lime_int))
flag <- period %in% lime_yr
lime_yi <- ifelse(flag, 1,0)
lime_y <- c(lime_y,lime_yi)
}
df <- data.frame(state,county,year,nfert,sand,lime_y)
state county year nfert sand lime_y
1 IL a 2017 11 99 1
2 IL a 2018 8 84 0
3 IL a 2019 7 43 0
4 IL a 2020 7 57 0
5 IL a 2021 20 15 0
6 IL a 2022 17 75 0
7 IL a 2023 13 21 0
8 IL a 2024 6 87 1
9 IL a 2025 5 56 0
10 IL a 2026 9 11 0
11 IL a 2027 19 50 0
12 IL a 2028 10 6 0
13 IL a 2029 3 99 0
14 IL a 2030 16 10 0
15 IL a 2031 7 4 1
16 IL a 2032 6 4 0
17 IL a 2033 10 52 0
18 IL a 2034 20 47 0
19 IL a 2035 17 27 0
20 IL a 2036 12 54 0
21 IL a 2037 16 81 0
22 IL a 2038 10 84 1
23 IL a 2039 15 68 0
24 IL a 2040 19 33 0
25 IL a 2041 14 80 0
26 IL a 2042 18 48 0
27 IL a 2043 10 58 0
28 IL a 2044 3 68 0
29 IL a 2045 10 62 1
30 IL a 2046 20 94 0
31 IL b 2017 13 92 1
32 IL b 2018 10 85 0
33 IL b 2019 17 33 0
34 IL b 2020 2 94 0
35 IL b 2021 4 9 0
36 IL b 2022 8 61 0
37 IL b 2023 3 16 0
38 IL b 2024 11 59 1
39 IL b 2025 12 71 0
40 IL b 2026 11 87 0
41 IL b 2027 2 83 0
42 IL b 2028 11 2 0
43 IL b 2029 3 11 0
44 IL b 2030 1 22 0
45 IL b 2031 5 51 1
46 IL b 2032 9 61 0
47 IL b 2033 20 63 0
48 IL b 2034 4 70 0
49 IL b 2035 1 92 0
50 IL b 2036 14 73 0
51 IL b 2037 10 51 0
52 IL b 2038 6 14 1
53 IL b 2039 1 58 0
54 IL b 2040 1 89 0
55 IL b 2041 18 30 0
56 IL b 2042 10 34 0
57 IL b 2043 9 69 0
58 IL b 2044 20 77 0
59 IL b 2045 7 16 1
60 IL b 2046 3 74 0
61 IL c 2017 4 12 1
62 IL c 2018 1 89 0
63 IL c 2019 14 44 0
64 IL c 2020 4 62 0
65 IL c 2021 5 62 0
66 IL c 2022 7 47 0
67 IL c 2023 18 25 0
68 IL c 2024 16 85 1
69 IL c 2025 10 26 0
70 IL c 2026 15 65 0
71 IL c 2027 3 33 0
72 IL c 2028 1 43 0
73 IL c 2029 3 43 0
74 IL c 2030 5 37 0
75 IL c 2031 4 5 1
76 IL c 2032 14 16 0
77 IL c 2033 7 100 0
78 IL c 2034 16 10 0
79 IL c 2035 15 84 0
80 IL c 2036 15 25 0
81 IL c 2037 15 48 0
82 IL c 2038 3 59 1
83 IL c 2039 20 73 0
84 IL c 2040 1 12 0
85 IL c 2041 2 19 0
86 IL c 2042 5 85 0
87 IL c 2043 9 27 0
88 IL c 2044 9 90 0
89 IL c 2045 1 49 1
90 IL c 2046 4 89 0
91 IN d 2017 8 19 1
92 IN d 2018 10 74 0
93 IN d 2019 10 19 0
94 IN d 2020 8 15 0
95 IN d 2021 7 4 0
96 IN d 2022 7 28 0
97 IN d 2023 12 34 0
98 IN d 2024 10 22 1
99 IN d 2025 6 37 0
100 IN d 2026 12 18 0
101 IN d 2027 13 27 0
102 IN d 2028 1 59 0
103 IN d 2029 8 27 0
104 IN d 2030 6 65 0
105 IN d 2031 13 43 1
106 IN d 2032 7 49 0
107 IN d 2033 12 45 0
108 IN d 2034 7 55 0
109 IN d 2035 7 6 0
110 IN d 2036 11 29 0
111 IN d 2037 12 66 0
112 IN d 2038 2 38 1
113 IN d 2039 2 27 0
114 IN d 2040 6 44 0
115 IN d 2041 5 50 0
116 IN d 2042 6 61 0
117 IN d 2043 8 33 0
118 IN d 2044 10 43 0
119 IN d 2045 5 31 1
120 IN d 2046 5 42 0
121 IN e 2017 13 30 1
122 IN e 2018 10 58 0
123 IN e 2019 12 75 0
124 IN e 2020 7 2 0
125 IN e 2021 4 29 0
126 IN e 2022 9 18 0
127 IN e 2023 13 24 0
128 IN e 2024 7 74 1
129 IN e 2025 7 70 0
130 IN e 2026 2 29 0
131 IN e 2027 5 64 0
132 IN e 2028 2 34 0
133 IN e 2029 3 48 0
134 IN e 2030 9 37 0
135 IN e 2031 8 67 1
136 IN e 2032 10 8 0
137 IN e 2033 4 56 0
138 IN e 2034 2 38 0
139 IN e 2035 2 3 0
140 IN e 2036 3 35 0
141 IN e 2037 8 67 0
142 IN e 2038 5 69 1
143 IN e 2039 2 44 0
144 IN e 2040 6 63 0
145 IN e 2041 7 51 0
146 IN e 2042 1 16 0
147 IN e 2043 7 60 0
148 IN e 2044 6 36 0
149 IN e 2045 12 17 1
150 IN e 2046 1 26 0
151 IN f 2017 4 1 1
152 IN f 2018 1 71 0
153 IN f 2019 4 55 0
154 IN f 2020 5 34 0
155 IN f 2021 4 69 0
156 IN f 2022 11 74 0
157 IN f 2023 1 13 0
158 IN f 2024 9 8 1
159 IN f 2025 8 18 0
160 IN f 2026 13 59 0
161 IN f 2027 2 14 0
162 IN f 2028 12 64 0
163 IN f 2029 1 71 0
164 IN f 2030 2 59 0
165 IN f 2031 12 6 1
166 IN f 2032 10 21 0
[ reached 'max' / getOption("max.print") -- omitted 14 rows ]
cty
[1] a, b, c, d, e, f
##Your example data
state<-c(rep("IL",90),rep("IN",90))
county <-c(rep("a",30),rep("b",30),rep("c",30),rep("d",30),rep("e",30),rep("f",30))
year <- rep(c(2017:2046), 6)
nfert <- c(sample(20,90,replace = TRUE),sample(13,90,replace = TRUE))
sand <- c(sample(100,90,replace = TRUE),sample(75,90,replace=TRUE))
DF<-data.frame(State=state, County=county, Nfert=nfert, Sand = sand, Period = year) #Using values from your first code block
fertlim<-180
cty<-unique(DF$County)
tmp<-list()
for(i in 1:length(cty)){
subs<-subset(DF, County==cty[i])
nload<-mean(subs$Nfert)
lime_int<-as.integer(fertlim/nload)
print(lime_int)
lime_yr <- seq.int(t0,tf,by=lime_int)
subs[, "Flag"]<-0
subs$Flag[subs$Period %in% lime_yr]<-1
tmp[[i]]<-subs
}
OUT<-do.call(rbind, tmp)
> head(OUT)
State County Nfert Sand Period Flag
1 IL a 18 18 2017 1
2 IL a 18 4 2018 0
3 IL a 18 49 2019 0
4 IL a 1 82 2020 0
5 IL a 15 82 2021 0
6 IL a 9 19 2022 0