我有一个0.25 * 0.25度网格分辨率的数据集。我是另一个1 * 1度分辨率，并希望使它们具有可比性（均改为1 * 1分辨率）。该区域为28.5至36.5经度和-4.5至4.5纬度，具有2003年至2012年的月度数据。我已将前几行作为例如如果以下内容无效，我已附上完整数据的excel文件！ https://onedrive.live.com/redir?resid=9E74848E574367C6!2625&authkey=!AOcpftTyHVqU8CA&ithint=file%2ccsv我一直在调查＆＃39; sp＆＃39; （聚合）和＆＃39;光栅＆＃39;包但无法弄清楚这一点。任何帮助一如既往的赞赏！
> head(cur.data, 40)
Longitude Latitude Year Month DecimDate Rainfall RainAnom
1 28.625 -4.375 2012 12 2012.917 173.69343 73.74917
2 28.875 -4.375 2012 12 2012.917 186.91148 86.96723
3 29.125 -4.375 2012 12 2012.917 158.17921 58.23495
4 29.375 -4.375 2012 12 2012.917 194.71006 94.76581
5 29.625 -4.375 2012 12 2012.917 160.84774 60.90349
6 29.875 -4.375 2012 12 2012.917 192.01070 92.06645
7 30.125 -4.375 2012 12 2012.917 225.45180 125.50755
8 30.375 -4.375 2012 12 2012.917 234.98836 135.04410
9 30.625 -4.375 2012 12 2012.917 182.77813 82.83388
10 30.875 -4.375 2012 12 2012.917 226.08477 126.14052
11 31.125 -4.375 2012 12 2012.917 271.90741 171.96316
12 31.375 -4.375 2012 12 2012.917 272.48264 172.53839
13 31.625 -4.375 2012 12 2012.917 265.44591 165.50166
14 31.875 -4.375 2012 12 2012.917 266.44458 166.50033
15 32.125 -4.375 2012 12 2012.917 270.94755 171.00329
16 32.375 -4.375 2012 12 2012.917 257.85288 157.90863
17 32.625 -4.375 2012 12 2012.917 265.42979 165.48554
18 32.875 -4.375 2012 12 2012.917 255.23151 155.28726
19 33.125 -4.375 2012 12 2012.917 249.32812 149.38387
20 33.375 -4.375 2012 12 2012.917 165.93709 65.99284
21 33.625 -4.375 2012 12 2012.917 165.71675 65.77250
22 33.875 -4.375 2012 12 2012.917 201.09573 101.15148
23 34.125 -4.375 2012 12 2012.917 216.02753 116.08328
24 34.375 -4.375 2012 12 2012.917 255.53255 155.58830
25 34.625 -4.375 2012 12 2012.917 202.72544 102.78119
26 34.875 -4.375 2012 12 2012.917 71.00399 -28.94026
27 35.125 -4.375 2012 12 2012.917 69.55960 -30.38465
28 35.375 -4.375 2012 12 2012.917 84.89567 -15.04858
29 35.625 -4.375 2012 12 2012.917 144.54023 44.59598
30 35.875 -4.375 2012 12 2012.917 227.81630 127.87205
31 36.125 -4.375 2012 12 2012.917 193.73762 93.79337
32 36.375 -4.375 2012 12 2012.917 156.49207 56.54782
33 28.625 -4.125 2012 12 2012.917 182.18007 82.23582
34 28.875 -4.125 2012 12 2012.917 208.52020 108.57595
35 29.125 -4.125 2012 12 2012.917 175.20243 75.25817
36 29.375 -4.125 2012 12 2012.917 166.91644 66.97218
37 29.625 -4.125 2012 12 2012.917 156.38420 56.43994
38 29.875 -4.125 2012 12 2012.917 167.72313 67.77888
39 30.125 -4.125 2012 12 2012.917 175.48325 75.53899
40 30.375 -4.125 2012 12 2012.917 210.74390 110.79965
答案 0 :(得分：4)
我认为raster包是可行的方法：
require(raster)
cur.data <- read.csv("LVB_TRMM_Monthly_Rainfall.csv")
r <- cur.data
coordinates(r) <- ~ Longitude + Latitude
gridded(r) <- TRUE
# Stack each variable as a band in the raster:
tmp <- raster(r, layer = 1)
for (i in 2:5) {
tmp <- stack(tmp, raster(r, layer = i))
}
r <- tmp
# Aggregate to 1 degree resolution (4 times as coarse):
r <- aggregate(r, fact = 4, fun = mean)
# Get it back to a dataframe
coords <- as.matrix(coordinates(r))
df <- data.frame(Longitude = coords[, 1], Latitude = coords[, 2],
as.data.frame(r))
这给出了：
Longitude Latitude Year Month DecimDate Rainfall RainAnom
1 29 1 2003 1 2003 60.69725 -39.24700
2 30 1 2003 1 2003 39.60284 -60.34141
3 31 1 2003 1 2003 34.12878 -65.81548
4 32 1 2003 1 2003 46.32009 -53.62416
5 33 1 2003 1 2003 71.27627 -28.66799
6 34 1 2003 1 2003 82.92516 -17.01909
但是，我发现您有来自不同时间段的数据。也许您会想要在每个时间段重复上述步骤。