[英]How to apply a function to only certain grid cells within raster files in R
我觉得这应该是一个简单的修复，但无法弄清楚。 我有两个栅格，一个带有温度数据，一个带有湿度数据。 我需要使用一组方程计算热量指数，并试图得到一个光栅 output。 我将热指数方程放入 function 中，如下所示（请忽略方程本身，我知道这是很多数字，这不是我的问题所在）：
fun_heat_index <- function(RH, T){
# https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml
#RH <- relative humidity in percent
#T <- maximum temperature in degrees F
# if HI is below 80 degrees, apply only this simple formula
iheat_index <- 0.5 * (T + 61 + ((T-68) * 1.2) + (RH * 0.094))
if (iheat_index > 80){
iheat_index <- -42.379 + 2.04901523 * T + 10.14333127 * RH - .22475541 * T *
RH - .00683783 * T * T - .05481717 * RH * RH + .00122874 * T * T * RH +
.00085282 * T * RH * RH - .00000199 * T * T * RH * RH
# ADJUSTMENT 1:
#if RH less than 13% and temp is between 80 and 112
if (RH < 13 && between(T, 80, 112)){
adj_1 <- ((13 - RH) / 4) * sqrt((17-abs(T-95))/17)
iheat_index <- iheat_index + adj_1
} # if statement Adjustment 1
# ADJUSTMENT 2:
#if RH is greater than 85% and temp is between 80 and 87
if (RH > 85 && between(T, 80, 87)){
adj_2 <- ((RH - 85) / 10) * ((87 - T) / 5)
iheat_index <- iheat_index + adj_2
} # if statement Adjustment 2
} # if statement simple equation over 80% HI
# Output the result after going through adjustments & equations
return(iheat_index)
} # fun_heat_index
这就是我试图使用上面的 function 运行的，它刚刚出现错误并且没有运行。 fun_heat_index(relative_humidity_data, tmax_data)
我的栅格具有相同的范围，并且每个栅格具有相同数量的网格单元。 我正在尝试应用 function 从两个栅格进行计算，以获得具有热指数值的第三个 output 栅格。 我通过每个网格单元分别找到了calc() function 使其成为 go ，我认为这可以解决问题。 我接下来尝试了这个：
calc(tmax_data, fun = fun_heat_index(relative_humidity_data, tmax_data))
在我添加它之后它开始工作了一点，但在最初的iheat_index变量创建之后它似乎仍在跳闸，这是 function 的第一步。 它将 output 来自该行的光栅，但是一旦它进入 if 语句的下一行if (iheat_index > 80){然后它说参数不可解释为逻辑，我假设这是因为它期待一个单一的值不是栅格。 有没有办法在 if 语句中再次应用calc() function ？ 或者是否有另一种方法让 R 只查看一些网格单元并将附加方程应用于仅满足特定标准的网格单元？
在四处寻找如何做到这一点时，我也发现了overlay()并尝试如下：
overlay(iloop_tmax, iloop_rh, fun = fun_heat_index(iloop_rh, iloop_tmax))
那也没有用。 我还尝试遍历每个单元格并使用getValues()删除值并将 function 应用于符合 if 语句标准的任何单元格，但我无法弄清楚如何将其恢复为栅格形式而不是表格形式在那之后。
任何帮助深表感谢： ：）
我尝试让简单的事情一步一步地工作，因为它使我的许多错误更容易检测和调试。 将上面删除的以 .gri 结尾的文件作为 T2 和 RH2，然后通过在每个中采样创建一系列值：
set.seed(42) # for reproducibility as we're sampling
values(RH2) <- sample(5:95, ncell(RH2), replace= TRUE)/100 # % relative humidity
values(T2) <- sample(70:115, ncell(T2), replace = TRUE)
# data T2 and RH2 at bottom
转到heatindex_equiation ，我们看到传统上，首先计算最简单的方程，然后将该结果与 T2 取平均值：
HI_tst1 = 0.5 * (T2 + 61.0 +((T2-68.0) * 1.2) + (RH2 * 0.094))
HI_tst1_avg = 0.5 * (T2 + HI_tst1)
因此，HI_tst1_avg 似乎是我们的基础数据集，并且根据规定的标准，对该数据集中的特定单元格进行了进一步的计算和调整。 更准确地说，HI_tst1_avg 是一个指向将应用进一步计算的单元格的索引（从概念上讲）。 所以现在，也许我们在我们的 avg 中做一些钓鱼来了解应该进行哪些计算和调整：
# ? How many HI_tst1_avg >= 80
length(which(HI_tst1_avg@data@values >= 80))
[1] 402
# ? which cells in T2 and RH2 are these, here just indexed
gt80_idx <- which(HI_tst1_avg@data@values >= 80)
# ? How many HI_tst1_avg < 80 and subject to simple calc
length(which(HI_tst1_avg@data@values < 80))
[1] 138
# which cells are those indexed
HI_simple_idx <- which(HI_tst1_avg@data@values < 80)
# ? How many subject to first adjustment (RH <= 0.13 cells also T btw 80:87)
length(which(HI_tst1_avg@data@values >= 80 & HI_tst1_avg@data@values <=87 & RH2@data@values <= 0.13))
[1] 7
# ? Which cells in T2 & RH2 do these mean
intersect((which(HI_tst1_avg@data@values >= 80 & HI_tst1_avg@data@values <=87)),(which(RH2@data@values <= 0.13)))
[1] 20 45 72 177 184 422 441
# so, `adj1_idx = intersect((which...` save as index
# ? how many will be subject to adjustment 2
length(which(HI_tst1_avg@data@values >= 80 & HI_tst1_avg@data@values <=87 & RH2@data@values >= 0.85))
[1] 11
# ? which cells to index for adj2
intersect((which(HI_tst1_avg@data@values >= 80 & HI_tst1_avg@data@values <=87)),(which(RH2@data@values >= 0.85)))
[1] 56 221 230 300 307 346 353 393 412 470 485
现在我认为我有正确的索引来开始插入创建热指数栅格。 因此，使用 NA 值创建它。
heatindex <- T2
values(heatindex) <- NA
names(heatindex) <- 'summer_day'
heatindex
class : RasterLayer
dimensions : 18, 30, 540 (nrow, ncol, ncell)
resolution : 0.0625, 0.0625 (x, y)
extent : -78.125, -76.25, 38.375, 39.5 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : memory
names : summer_day
values : NA, NA (min, max)
按照建议的顺序工作，1）简单 2）回归 3）adj1 4）adj2
#simple
values(heatindex)[HI_simple_idx] = 0.5 * (values(T2)[HI_simple_idx] + 61.0 + ((values(T2)[HI_simple_idx] - 68.0) * 1.2) + (values(RH2)[HI_simple_idx] * 0.094))
# regression coefficients - Yikes, trusting to precedence
values(heatindex)[gt80_idx] = -42.379 + 2.04901523 * values(T2)[gt80_idx] + 10.14333127 * values(RH2)[gt80_idx] - .22475541 * values(T2)[gt80_idx] *
values(RH2)[gt80_idx] - .00683783 * values(T2)[gt80_idx] * values(T2)[gt80_idx] - .05481717 * values(RH2)[gt80_idx] * values(RH2)[gt80_idx] + .00122874 * values(T2)[gt80_idx] * values(T2)[gt80_idx] * values(RH2)[gt80_idx] +
.00085282 * values(T2)[gt80_idx] * values(RH2)[gt80_idx] * values(RH2)[gt80_idx] - .00000199 * values(T2)[gt80_idx] * values(T2)[gt80_idx] * values(RH2)[gt80_idx] * values(RH2)[gt80_idx]
# god help me, appears to work
# adjustment 1
values(heatindex)[adj1_idx] = values(heatindex)[adj1_idx] - ((13 - values(RH2)[adj1_idx]) /4) * sqrt((17 - abs(values(T2)[adj1_idx]- 95)/17))
#adjustment 2
values(heatindex)[adj2_idx] = values(heatindex)[adj2_idx] + ((values(RH2)[adj2_idx] - 85) / 10) * ((87 - values(T2)[adj2_idx]) / 5)
# are we nearly done?
which(is.na(values(heatindex)) == TRUE)
[1] 20 45 72 177 184 422 441
length(which(is.na(values(heatindex)) == TRUE))/ncell(heatindex)
[1] 0.01296296
all.equal(which(is.na(values(heatindex)) == TRUE), adj1_idx)
[1] TRUE
正如他们所说，“热杀”。 仍然需要弄清楚为什么调整 1 不起作用，但这些是我在制作包含这些步骤的 function 之前要完成的步骤。 我还发现这些base方法在几个月后重新访问时更容易掌握和推理。
数据：
# RH2
new("RasterLayer", file = new(".RasterFile", name = "", datanotation = "INT4U",
byteorder = c(value = "little"), nodatavalue = 2147483647,
NAchanged = FALSE, nbands = 1L, bandorder = c(value = "BIL"),
offset = 0L, toptobottom = TRUE, blockrows = 0L, blockcols = 0L,
driver = "", open = FALSE), data = new(".SingleLayerData",
values = c(0.38, 0.21, 0.65, 0.87, 0.47, 0.83, 0.19, 0.09,
0.5, 0.93, 0.78, 0.51, 0.57, 0.15, 0.76, 0.45, 0.32, 0.12,
0.88, 0.08, 0.86, 0.84, 0.84, 0.66, 0.67, 0.43, 0.67, 0.28,
0.17, 0.6, 0.06, 0.6, 0.66, 0.63, 0.32, 0.46, 0.75, 0.71,
0.86, 0.49, 0.32, 0.61, 0.89, 0.11, 0.12, 0.44, 0.49, 0.61,
0.48, 0.82, 0.84, 0.39, 0.3, 0.15, 0.49, 0.91, 0.55, 0.41,
0.18, 0.16, 0.18, 0.61, 0.48, 0.8, 0.52, 0.49, 0.72, 0.9,
0.24, 0.23, 0.05, 0.07, 0.37, 0.29, 0.21, 0.21, 0.89, 0.69,
0.42, 0.08, 0.87, 0.72, 0.12, 0.6, 0.67, 0.44, 0.54, 0.9,
0.93, 0.43, 0.65, 0.6, 0.49, 0.63, 0.18, 0.75, 0.78, 0.35,
0.53, 0.4, 0.62, 0.82, 0.7, 0.09, 0.81, 0.82, 0.08, 0.47,
0.67, 0.89, 0.67, 0.91, 0.1, 0.77, 0.91, 0.25, 0.58, 0.24,
0.34, 0.4, 0.52, 0.73, 0.31, 0.3, 0.39, 0.68, 0.21, 0.07,
0.82, 0.05, 0.62, 0.39, 0.29, 0.44, 0.15, 0.11, 0.27, 0.31,
0.61, 0.59, 0.28, 0.38, 0.93, 0.91, 0.74, 0.61, 0.44, 0.29,
0.35, 0.94, 0.11, 0.54, 0.15, 0.39, 0.14, 0.18, 0.68, 0.95,
0.65, 0.73, 0.48, 0.5, 0.52, 0.23, 0.24, 0.54, 0.66, 0.72,
0.63, 0.91, 0.24, 0.14, 0.33, 0.53, 0.61, 0.9, 0.13, 0.85,
0.73, 0.49, 0.78, 0.83, 0.08, 0.11, 0.65, 0.06, 0.52, 0.55,
0.77, 0.31, 0.44, 0.11, 0.42, 0.91, 0.71, 0.88, 0.58, 0.83,
0.12, 0.37, 0.12, 0.78, 0.88, 0.76, 0.66, 0.12, 0.28, 0.5,
0.48, 0.68, 0.53, 0.29, 0.19, 0.12, 0.42, 0.86, 0.7, 0.36,
0.34, 0.44, 0.87, 0.45, 0.2, 0.71, 0.77, 0.39, 0.23, 0.28,
0.41, 0.95, 0.57, 0.13, 0.44, 0.95, 0.24, 0.17, 0.21, 0.6,
0.79, 0.1, 0.05, 0.17, 0.42, 0.83, 0.81, 0.5, 0.26, 0.74,
0.47, 0.95, 0.81, 0.35, 0.49, 0.76, 0.95, 0.94, 0.24, 0.85,
0.42, 0.31, 0.46, 0.58, 0.85, 0.52, 0.41, 0.09, 0.39, 0.65,
0.54, 0.28, 0.18, 0.35, 0.11, 0.87, 0.18, 0.37, 0.36, 0.43,
0.66, 0.71, 0.83, 0.23, 0.45, 0.36, 0.93, 0.85, 0.73, 0.32,
0.65, 0.4, 0.34, 0.63, 0.15, 0.85, 0.21, 0.78, 0.68, 0.54,
0.28, 0.91, 0.73, 0.95, 0.23, 0.63, 0.74, 0.86, 0.93, 0.84,
0.55, 0.8, 0.63, 0.6, 0.88, 0.51, 0.47, 0.88, 0.31, 0.27,
0.73, 0.14, 0.88, 0.4, 0.95, 0.77, 0.32, 0.44, 0.5, 0.86,
0.74, 0.07, 0.88, 0.61, 0.79, 0.41, 0.07, 0.83, 0.55, 0.31,
0.37, 0.34, 0.66, 0.51, 0.78, 0.45, 0.35, 0.87, 0.51, 0.34,
0.26, 0.89, 0.76, 0.33, 0.9, 0.8, 0.76, 0.87, 0.53, 0.29,
0.9, 0.69, 0.54, 0.46, 0.14, 0.39, 0.85, 0.2, 0.47, 0.65,
0.41, 0.49, 0.59, 0.28, 0.52, 0.55, 0.76, 0.3, 0.56, 0.64,
0.05, 0.25, 0.86, 0.69, 0.64, 0.46, 0.18, 0.6, 0.88, 0.43,
0.48, 0.08, 0.7, 0.38, 0.95, 0.48, 0.55, 0.6, 0.44, 0.61,
0.05, 0.12, 0.44, 0.95, 0.23, 0.78, 0.92, 0.59, 0.82, 0.94,
0.58, 0.38, 0.5, 0.87, 0.27, 0.24, 0.53, 0.16, 0.76, 0.41,
0.91, 0.61, 0.32, 0.05, 0.18, 0.8, 0.15, 0.13, 0.1, 0.09,
0.56, 0.53, 0.33, 0.24, 0.21, 0.59, 0.95, 0.11, 0.79, 0.57,
0.91, 0.8, 0.13, 0.58, 0.8, 0.24, 0.08, 0.92, 0.24, 0.14,
0.22, 0.46, 0.82, 0.48, 0.64, 0.66, 0.91, 0.69, 0.75, 0.16,
0.63, 0.5, 0.85, 0.75, 0.58, 0.51, 0.74, 0.79, 0.61, 0.31,
0.42, 0.86, 0.58, 0.55, 0.43, 0.83, 0.49, 0.53, 0.69, 0.32,
0.66, 0.63, 0.09, 0.17, 0.56, 0.79, 0.95, 0.72, 0.79, 0.23,
0.8, 0.13, 0.16, 0.89, 0.91, 0.2, 0.35, 0.79, 0.45, 0.45,
0.9, 0.28, 0.2, 0.16, 0.28, 0.87, 0.55, 0.46, 0.08, 0.11,
0.22, 0.37, 0.81, 0.44, 0.24, 0.2, 0.23, 0.38, 0.63, 0.64,
0.75, 0.45, 0.34, 0.82, 0.94, 0.64, 0.19, 0.08, 0.77, 0.25,
0.18, 0.76, 0.08, 0.81, 0.62, 0.31, 0.28, 0.36, 0.39, 0.22,
0.13, 0.39), offset = 0, gain = 1, inmemory = TRUE, fromdisk = FALSE,
isfactor = FALSE, attributes = list(), haveminmax = TRUE,
min = 0.05, max = 0.95, band = 1L, unit = "", names = "X73147"),
legend = new(".RasterLegend", type = character(0), values = logical(0),
color = logical(0), names = logical(0), colortable = logical(0)),
title = character(0), extent = new("Extent", xmin = -78.125,
xmax = -76.25, ymin = 38.375, ymax = 39.5), rotated = FALSE,
rotation = new(".Rotation", geotrans = numeric(0), transfun = function ()
NULL), ncols = 30L, nrows = 18L, crs = new("CRS", projargs = "+proj=longlat +datum=WGS84 +no_defs"),
history = list(), z = list())
和 T2：
new("RasterLayer", file = new(".RasterFile", name = "", datanotation = "INT4U",
byteorder = c(value = "little"), nodatavalue = 2147483647,
NAchanged = FALSE, nbands = 1L, bandorder = c(value = "BIL"),
offset = 0L, toptobottom = TRUE, blockrows = 0L, blockcols = 0L,
driver = "", open = FALSE), data = new(".SingleLayerData",
values = c(104L, 87L, 90L, 89L, 78L, 97L, 105L, 98L, 79L,
94L, 86L, 111L, 72L, 85L, 101L, 114L, 77L, 75L, 72L, 86L,
105L, 70L, 86L, 84L, 114L, 93L, 94L, 70L, 110L, 75L, 108L,
101L, 108L, 102L, 93L, 76L, 83L, 111L, 109L, 91L, 111L, 108L,
106L, 72L, 85L, 115L, 93L, 85L, 107L, 83L, 115L, 93L, 76L,
99L, 90L, 87L, 90L, 113L, 73L, 113L, 106L, 78L, 86L, 112L,
94L, 102L, 79L, 100L, 114L, 90L, 88L, 85L, 84L, 97L, 71L,
98L, 72L, 109L, 98L, 97L, 100L, 102L, 70L, 105L, 96L, 94L,
84L, 98L, 99L, 110L, 72L, 102L, 74L, 83L, 94L, 72L, 93L,
73L, 102L, 95L, 95L, 76L, 83L, 91L, 72L, 92L, 115L, 72L,
110L, 101L, 107L, 94L, 88L, 113L, 102L, 83L, 90L, 111L, 113L,
102L, 100L, 88L, 112L, 81L, 83L, 77L, 104L, 100L, 100L, 93L,
111L, 93L, 93L, 80L, 81L, 72L, 74L, 88L, 71L, 105L, 108L,
110L, 108L, 100L, 102L, 73L, 89L, 79L, 95L, 73L, 107L, 79L,
85L, 103L, 100L, 107L, 105L, 102L, 78L, 102L, 73L, 72L, 77L,
70L, 114L, 86L, 100L, 93L, 109L, 113L, 91L, 102L, 92L, 85L,
105L, 76L, 83L, 76L, 100L, 103L, 70L, 80L, 110L, 84L, 73L,
70L, 74L, 102L, 107L, 97L, 85L, 101L, 89L, 78L, 108L, 74L,
106L, 73L, 98L, 99L, 104L, 95L, 94L, 74L, 91L, 91L, 96L,
71L, 84L, 84L, 101L, 83L, 74L, 108L, 94L, 101L, 103L, 107L,
106L, 85L, 84L, 100L, 98L, 88L, 104L, 105L, 111L, 85L, 82L,
84L, 115L, 97L, 86L, 78L, 89L, 99L, 73L, 112L, 99L, 75L,
81L, 104L, 80L, 73L, 82L, 96L, 84L, 96L, 108L, 106L, 82L,
86L, 78L, 92L, 101L, 88L, 87L, 75L, 100L, 92L, 100L, 83L,
78L, 76L, 80L, 106L, 101L, 88L, 93L, 82L, 113L, 91L, 90L,
102L, 93L, 94L, 102L, 99L, 100L, 101L, 107L, 87L, 93L, 114L,
73L, 78L, 97L, 107L, 79L, 84L, 94L, 77L, 112L, 75L, 74L,
83L, 94L, 92L, 101L, 87L, 77L, 103L, 82L, 107L, 89L, 114L,
84L, 112L, 115L, 76L, 89L, 94L, 92L, 98L, 111L, 101L, 79L,
81L, 92L, 90L, 100L, 74L, 113L, 115L, 70L, 105L, 102L, 110L,
107L, 97L, 113L, 80L, 81L, 93L, 76L, 96L, 94L, 105L, 98L,
96L, 90L, 88L, 75L, 78L, 104L, 82L, 79L, 77L, 105L, 73L,
80L, 78L, 85L, 99L, 107L, 90L, 91L, 110L, 98L, 100L, 78L,
93L, 71L, 73L, 101L, 76L, 110L, 96L, 96L, 115L, 70L, 115L,
71L, 82L, 74L, 90L, 97L, 84L, 111L, 75L, 101L, 103L, 113L,
103L, 113L, 87L, 76L, 77L, 95L, 71L, 89L, 72L, 83L, 100L,
84L, 90L, 83L, 110L, 77L, 103L, 93L, 80L, 70L, 89L, 100L,
85L, 84L, 96L, 82L, 106L, 74L, 85L, 102L, 109L, 77L, 79L,
113L, 108L, 89L, 101L, 85L, 87L, 78L, 80L, 104L, 90L, 104L,
79L, 89L, 103L, 110L, 102L, 91L, 112L, 105L, 77L, 115L, 113L,
114L, 96L, 83L, 113L, 71L, 103L, 110L, 111L, 101L, 102L,
81L, 98L, 74L, 100L, 82L, 110L, 73L, 109L, 71L, 77L, 91L,
75L, 72L, 115L, 71L, 75L, 103L, 107L, 96L, 84L, 113L, 86L,
73L, 107L, 88L, 79L, 113L, 110L, 110L, 102L, 95L, 93L, 79L,
95L, 79L, 108L, 87L, 84L, 115L, 111L, 94L, 105L, 99L, 98L,
113L, 90L, 92L, 82L, 106L, 73L, 96L, 70L, 81L, 91L, 102L,
114L, 78L, 90L, 70L, 78L, 106L, 94L, 102L, 108L, 97L, 83L,
83L, 78L, 73L, 90L, 78L, 101L, 73L, 112L, 77L, 115L, 93L,
71L, 74L, 97L, 103L, 109L, 99L, 114L, 102L, 105L, 94L, 73L,
111L, 109L, 81L, 115L), offset = 0, gain = 1, inmemory = TRUE,
fromdisk = FALSE, isfactor = FALSE, attributes = list(),
haveminmax = TRUE, min = 70L, max = 115L, band = 1L, unit = "",
names = "X2000.04.09"), legend = new(".RasterLegend", type = character(0),
values = logical(0), color = logical(0), names = logical(0),
colortable = logical(0)), title = character(0), extent = new("Extent",
xmin = -78.125, xmax = -76.25, ymin = 38.375, ymax = 39.5),
rotated = FALSE, rotation = new(".Rotation", geotrans = numeric(0),
transfun = function ()
NULL), ncols = 30L, nrows = 18L, crs = new("CRS", projargs = "+proj=longlat +datum=WGS84 +no_defs"),
history = list(), z = list())
当栅格像元的值大于栅格中的某个阈值时，如何获取每个栅格像元的日期？
[英]How to get dates of each grid cells when grid cells values are greater than certain threshold in r raster?
如何高效统计R中栅格像元周围一定距离内的空间点数？
[英]How to efficiently count the number of spatial points within a certain distance around raster cells in R?
如何在 R 的光栅上应用一个 function 和多个 arguments？
[英]How to apply a function with multiple arguments on raster in R?
如何过滤 Raster 或仅在 R 中绘制某些栅格类别值？
[英]How to filter Raster or only plot certain raster category values in R?
如何基于另一个栅格网格像元值对栅格进行子集化（分类）？
[英]How to subset (classify ) raster based on another raster grid cells values?
声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.