通过聚合一行中的列来重塑数据
Reshaping data by aggregating columns in a row
我在重塑数据时遇到问题，但我不确定 reshape2 包是否是解决方案。我需要重塑的原始数据以一种特殊的方式存储。它们是 csv 文件中的每日温度数据，这是文件 header 的样子：
ID,YEAR,MONTH,NAME,ALTITUDE,REGION,LON,LAT,DATUM,TMAX1,TMAX2,......,TMAX31,TMIN1,TMIN2,..........,TMIN31
其中TMAX1代表MONTH第1天的最高气温。然后，以下值是所有月份的最高温度。 TMIN1 列给出了第 1 天的最低温度，依此类推，直到最后一列具有该月最后一天的最低温度。如果一个月少于 31 天，则该字段为空。
可以在 link
找到简短的示例数据文件
需要重新格式化以将数据保存在两个只有四列的新文件中（ID、DATE、TEMP、VALIDTEMP，其中 TEMP TMAX or TMIN) with station ID, date, temperature (TMAX or TMIN) value and validation flag 如图所示：
考虑到我的问题，我应该创建一个包含所有可能日期的向量（在原始数据中只说明年份和月份，日期来自数据列 number/name），然后进行某种转置将每天 TMAX/TMIN 数据与其对应的日期放入这个新数据框中。不确定这是否可以通过 reshape2 完成。
我用 reshape2 做了一个简单的第一次尝试，但是这给出了 TMAX1 和 TMAXn 作为不同的变量，而它们都是温度数据。我想在一个名为 TMAX/TMIN.
的变量中融化所有 TMAXn/TMIN
我会继续尝试解决问题，但非常感谢您的帮助
原始数据文件前20行的输出
> dput(kk)
structure(list(INDICATIVO = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("8008A",
"8036B", "8251E", "8325C", "8433I", "8472B", "8496E", "8520B"
), class = "factor"), ANYO = c(2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L), MES = c(3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L), NOMBRE = structure(c(8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("ALGEMESSI AUMAR",
"ARENOS (C.H. JUCAR)", "BENIDORM (AQUAGEST)", "CAUDIEL-MAS DE NOGUERA",
"EMBALSE DE ALCORA", "LA POBLA DE BENIFASS\xc0-FREDES I.", "VILLARGORDO DEL CABRIEL-CONTRERAS",
"VILLENA"), class = "factor"), ALTITUD = c(486L, 486L, 486L,
486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L,
486L, 486L, 486L, 486L, 486L, 486L), NOM_PROV = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("ALICANTE", "CASTELLON", "VALENCIA"), class = "factor"),
LONGITUD = c(51562L, 51562L, 51562L, 51562L, 51562L, 51562L,
51562L, 51562L, 51562L, 51562L, 51562L, 51562L, 51562L, 51562L,
51562L, 51562L, 51562L, 51562L, 51562L, 51562L), LATITUD = c(383437L,
383437L, 383437L, 383437L, 383437L, 383437L, 383437L, 383437L,
383437L, 383437L, 383437L, 383437L, 383437L, 383437L, 383437L,
383437L, 383437L, 383437L, 383437L, 383437L), DATUM = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = "ETRS89", class = "factor"), TMAX1 = c(230L,
220L, 310L, 280L, 370L, 310L, 330L, 270L, 200L, 180L, 180L,
190L, 170L, 160L, 210L, 290L, 340L, 320L, 300L, 310L), TMAX2 = c(270L,
200L, 310L, 295L, 330L, 330L, 310L, 270L, 160L, 195L, 150L,
200L, 220L, 180L, 225L, 290L, 360L, 330L, 330L, 300L), TMAX3 = c(240L,
220L, 370L, 300L, 370L, 250L, 330L, 230L, 190L, 200L, 170L,
170L, 180L, 190L, 240L, 290L, 340L, 360L, 350L, 290L), TMAX4 = c(230L,
200L, 330L, 300L, 380L, 360L, 290L, 290L, 230L, 200L, 210L,
130L, 220L, 190L, 230L, 300L, 320L, 400L, 390L, 300L), TMAX5 = c(180L,
240L, 290L, 310L, 400L, 360L, 240L, 300L, 220L, 180L, 170L,
140L, 140L, 120L, 170L, 290L, 360L, 330L, 425L, 270L), TMAX6 = c(170L,
150L, 290L, 320L, 390L, 360L, 190L, 280L, 235L, 160L, 110L,
180L, 140L, 180L, 210L, 310L, 340L, 310L, 360L, 330L), TMAX7 = c(250L,
150L, 260L, 310L, 425L, 400L, 220L, 250L, 230L, 160L, 190L,
130L, 90L, 195L, 190L, 310L, 310L, 310L, 340L, 310L), TMAX8 = c(180L,
170L, 290L, 320L, 400L, 325L, 250L, 220L, 200L, 200L, 220L,
160L, 140L, 150L, 180L, 340L, 360L, 340L, 340L, 290L), TMAX9 = c(200L,
180L, 290L, 330L, 340L, 330L, 240L, 230L, 220L, 160L, 200L,
180L, 140L, 200L, 190L, 360L, 350L, 330L, 320L, 270L), TMAX10 = c(195L,
200L, 270L, 310L, 360L, 390L, 320L, 250L, 240L, 135L, 170L,
210L, 120L, 190L, 190L, 350L, 360L, 250L, 320L, 260L), TMAX11 = c(260L,
180L, 310L, 240L, 340L, 350L, 300L, 270L, 230L, 140L, 100L,
210L, 150L, 200L, 210L, 320L, 370L, 290L, 300L, 300L), TMAX12 = c(260L,
190L, 340L, 300L, 355L, 380L, 290L, 270L, 190L, 140L, 140L,
220L, 130L, 230L, 210L, 310L, 370L, 290L, 320L, 230L), TMAX13 = c(185L,
200L, 380L, 290L, 360L, 350L, 320L, 240L, 210L, 170L, 150L,
220L, 150L, 200L, 210L, 375L, 300L, 300L, 300L, 190L), TMAX14 = c(160L,
260L, 415L, 170L, 360L, 320L, 300L, 230L, 200L, 180L, 160L,
170L, 150L, 230L, 240L, 350L, 280L, 310L, 250L, 230L), TMAX15 = c(130L,
230L, 390L, 260L, 360L, 300L, 300L, 250L, 200L, 210L, 120L,
140L, 150L, 240L, 230L, 320L, 270L, 320L, 260L, 240L), TMAX16 = c(150L,
240L, 370L, 270L, 360L, 300L, 330L, 230L, 230L, 215L, 160L,
120L, 180L, 250L, 230L, 270L, 280L, 260L, 290L, 250L), TMAX17 = c(140L,
260L, 380L, 300L, 370L, 350L, 270L, 230L, 230L, 210L, 130L,
130L, 150L, 240L, 220L, 280L, 310L, 375L, 300L, 290L), TMAX18 = c(120L,
250L, 280L, 280L, 350L, 320L, 250L, 240L, 220L, 215L, 170L,
130L, 160L, 220L, 250L, 300L, 320L, 370L, 280L, 230L), TMAX19 = c(140L,
240L, 280L, 320L, 380L, 320L, 265L, 220L, 240L, 220L, 140L,
120L, 150L, 180L, 260L, 280L, 340L, 370L, 300L, 220L), TMAX20 = c(120L,
215L, 230L, 310L, 370L, 330L, 290L, 190L, 260L, 180L, 150L,
160L, 170L, 200L, 250L, 280L, 380L, 390L, 270L, 220L), TMAX21 = c(170L,
200L, 220L, 310L, 370L, 330L, 330L, 200L, 200L, 185L, 140L,
170L, 140L, 230L, 250L, 295L, 360L, 300L, 310L, 200L), TMAX22 = c(140L,
240L, 220L, 400L, 370L, 330L, 340L, 210L, 130L, 155L, 160L,
220L, 130L, 210L, 300L, 310L, 340L, 320L, 310L, 210L), TMAX23 = c(160L,
270L, 240L, 350L, 340L, 340L, 300L, 230L, 100L, 160L, 190L,
180L, 170L, 230L, 240L, 320L, 310L, 330L, 290L, 240L), TMAX24 = c(150L,
250L, 230L, 310L, 390L, 330L, 260L, 190L, 130L, 150L, 190L,
200L, 200L, 240L, 270L, 350L, 310L, 330L, 280L, NA), TMAX25 = c(110L,
260L, 250L, 310L, 350L, 320L, 280L, 190L, 180L, 160L, 190L,
160L, 230L, 200L, 300L, 330L, 310L, 330L, 290L, NA), TMAX26 = c(150L,
260L, 290L, 335L, 340L, 320L, 280L, 190L, 190L, 160L, 140L,
160L, 240L, 250L, 280L, 300L, 325L, 310L, 280L, NA), TMAX27 = c(230L,
220L, 260L, 360L, 420L, 330L, 260L, 230L, 160L, 160L, 130L,
85L, 210L, 250L, 300L, 320L, 340L, 340L, 270L, NA), TMAX28 = c(260L,
250L, 260L, 390L, 350L, 350L, 230L, 200L, 190L, 160L, 170L,
150L, 170L, 220L, 300L, 320L, 330L, 360L, 240L, NA), TMAX29 = c(260L,
250L, 300L, 370L, 390L, 330L, 210L, 230L, 180L, 180L, 130L,
150L, 250L, 130L, 260L, 330L, 350L, 350L, 260L, NA), TMAX30 = c(280L,
260L, 300L, 370L, 340L, 315L, 230L, 220L, 230L, 180L, 170L,
NA, 240L, 240L, 290L, 350L, 380L, 310L, 270L, NA), TMAX31 = c(310L,
NA, 290L, NA, 330L, 340L, NA, 200L, NA, 210L, 210L, NA, 175L,
NA, 270L, NA, 400L, 310L, NA, NA), TMIN1 = c(70L, 60L, 70L,
130L, 160L, 210L, 210L, 130L, 90L, -50L, 50L, 20L, 20L, 40L,
90L, 90L, 170L, 240L, 155L, 110L), TMIN2 = c(130L, 20L, 140L,
130L, 200L, 210L, 190L, 90L, 60L, -15L, 30L, -10L, 40L, -10L,
10L, 100L, 140L, 150L, 150L, 130L), TMIN3 = c(70L, 30L, 90L,
100L, 210L, 190L, 165L, 100L, 60L, -10L, 35L, 10L, 80L, 30L,
30L, 130L, 210L, 140L, 130L, 110L), TMIN4 = c(70L, 80L, 150L,
90L, 190L, 180L, 200L, 120L, 50L, 0L, 120L, -10L, -10L, 70L,
30L, 130L, 200L, 160L, 130L, 110L), TMIN5 = c(-20L, 100L,
150L, 100L, 150L, 220L, 170L, 150L, 90L, -10L, 70L, 20L,
60L, 80L, 55L, 120L, 180L, 210L, 160L, 150L), TMIN6 = c(-30L,
55L, 135L, 80L, 150L, 230L, 170L, 130L, 90L, 70L, 50L, 10L,
35L, 100L, 70L, 110L, 190L, 170L, 190L, 120L), TMIN7 = c(-30L,
80L, 70L, 70L, 150L, 240L, 170L, 120L, 100L, 80L, 40L, 65L,
-20L, 75L, 90L, 130L, 160L, 130L, 140L, 130L), TMIN8 = c(30L,
50L, 70L, 100L, 180L, 200L, 150L, 120L, 90L, 40L, 10L, 95L,
20L, 50L, 90L, 125L, 170L, 150L, 150L, 120L), TMIN9 = c(50L,
80L, 110L, 110L, 210L, 210L, 125L, 100L, 80L, 30L, 85L, 60L,
-20L, 0L, 110L, 140L, 210L, 130L, 160L, 140L), TMIN10 = c(-10L,
70L, 70L, 80L, 180L, 230L, 180L, 100L, 30L, 30L, 50L, 130L,
40L, 20L, 80L, 160L, 160L, 180L, 150L, 120L), TMIN11 = c(-10L,
80L, 50L, 110L, 160L, 220L, 170L, 130L, 25L, 30L, 50L, 120L,
-20L, 50L, 100L, 180L, 170L, 140L, 210L, 150L), TMIN12 = c(20L,
90L, 60L, 150L, 180L, 210L, 140L, 120L, 25L, 30L, 20L, 90L,
-10L, 50L, 75L, 160L, 180L, 140L, 140L, 100L), TMIN13 = c(20L,
110L, 80L, 150L, 160L, 200L, 150L, 140L, 100L, 20L, -15L,
160L, -30L, 60L, 100L, 150L, 200L, 105L, 140L, 120L), TMIN14 = c(20L,
70L, 120L, 130L, 160L, 190L, 180L, 130L, 100L, 0L, -15L,
70L, -10L, 40L, 110L, 220L, 200L, 120L, 140L, 90L), TMIN15 = c(-5L,
115L, 110L, 140L, 170L, 180L, 125L, 40L, 60L, 0L, 40L, 60L,
40L, 50L, 110L, 170L, 180L, 120L, 80L, 60L), TMIN16 = c(50L,
100L, 60L, 100L, 155L, 160L, 120L, 115L, 50L, 20L, -30L,
55L, 10L, 50L, 80L, 170L, 150L, 160L, 100L, 50L), TMIN17 = c(-10L,
80L, 60L, 110L, 170L, 210L, 140L, 90L, 40L, 5L, -60L, -80L,
5L, 100L, 50L, 120L, 100L, 155L, 90L, 110L), TMIN18 = c(70L,
50L, 50L, 120L, 170L, 205L, 120L, 100L, 0L, 0L, 70L, 0L,
90L, 80L, 60L, 100L, 130L, 185L, 110L, 160L), TMIN19 = c(100L,
90L, 160L, 120L, 180L, 220L, 140L, 100L, 30L, -10L, 50L,
70L, 100L, 110L, 80L, 100L, 130L, 180L, 100L, 160L), TMIN20 = c(110L,
60L, 130L, 120L, 190L, 200L, 100L, 140L, 50L, -30L, -15L,
-15L, 50L, 110L, 80L, 110L, 180L, 200L, 160L, 130L), TMIN21 = c(90L,
40L, 80L, 130L, 175L, 175L, 120L, 90L, 50L, -10L, 0L, 40L,
50L, 80L, 80L, 100L, 170L, 220L, 140L, 130L), TMIN22 = c(100L,
100L, 60L, 130L, 195L, 175L, 120L, 70L, -10L, 0L, 25L, 60L,
80L, 80L, 80L, 100L, 150L, 170L, 130L, 110L), TMIN23 = c(70L,
70L, 50L, 150L, 190L, 170L, 130L, 50L, -10L, 20L, 15L, 20L,
85L, 50L, 120L, 110L, 180L, 150L, 130L, 150L), TMIN24 = c(80L,
60L, 60L, 140L, 200L, 210L, 100L, 90L, -45L, 0L, 20L, 20L,
25L, 80L, 100L, 110L, 140L, 135L, 125L, 130L), TMIN25 = c(30L,
110L, 65L, 150L, 230L, 150L, 90L, 130L, -20L, -20L, 70L,
90L, 20L, 40L, 110L, 125L, 140L, 130L, 135L, NA), TMIN26 = c(10L,
110L, 80L, 150L, 230L, 150L, 90L, 130L, 100L, 20L, 40L, 20L,
115L, 40L, 130L, 170L, 140L, 145L, 185L, NA), TMIN27 = c(70L,
100L, 100L, 150L, 200L, 140L, 130L, 120L, 10L, 35L, 40L,
40L, 70L, 70L, 120L, 170L, 150L, 140L, 160L, NA), TMIN28 = c(70L,
90L, 90L, 150L, 200L, 140L, 110L, 70L, 10L, -20L, 25L, 40L,
90L, 120L, 150L, 170L, 160L, 160L, 170L, NA), TMIN29 = c(60L,
60L, 90L, 150L, 200L, 160L, 100L, 80L, -30L, 50L, 60L, 60L,
75L, 110L, 150L, 200L, 140L, 200L, 120L, NA), TMIN30 = c(90L,
60L, 70L, 150L, 210L, 170L, 130L, 65L, -10L, 0L, 60L, NA,
10L, 110L, 140L, 180L, 170L, 220L, 130L, NA), TMIN31 = c(110L,
NA, 130L, NA, 210L, 220L, NA, 100L, NA, 70L, 80L, NA, 65L,
NA, 120L, NA, 210L, 170L, NA, NA)), .Names = c("INDICATIVO",
"ANYO", "MES", "NOMBRE", "ALTITUD", "NOM_PROV", "LONGITUD", "LATITUD",
"DATUM", "TMAX1", "TMAX2", "TMAX3", "TMAX4", "TMAX5", "TMAX6",
"TMAX7", "TMAX8", "TMAX9", "TMAX10", "TMAX11", "TMAX12", "TMAX13",
"TMAX14", "TMAX15", "TMAX16", "TMAX17", "TMAX18", "TMAX19", "TMAX20",
"TMAX21", "TMAX22", "TMAX23", "TMAX24", "TMAX25", "TMAX26", "TMAX27",
"TMAX28", "TMAX29", "TMAX30", "TMAX31", "TMIN1", "TMIN2", "TMIN3",
"TMIN4", "TMIN5", "TMIN6", "TMIN7", "TMIN8", "TMIN9", "TMIN10",
"TMIN11", "TMIN12", "TMIN13", "TMIN14", "TMIN15", "TMIN16", "TMIN17",
"TMIN18", "TMIN19", "TMIN20", "TMIN21", "TMIN22", "TMIN23", "TMIN24",
"TMIN25", "TMIN26", "TMIN27", "TMIN28", "TMIN29", "TMIN30", "TMIN31"
), row.names = 786:805, class = "data.frame")
您可以这样使用 melt 函数：
library(reshape2)
data <- read.csv("data_temp_orig.csv", header = TRUE)
allnames <- names(data)
idnames <- allnames[1:9]
tempnames <- allnames[10:71]
data_melt <- melt(data, id.vars = idnames, measure.vars = tempnames)
head(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM variable value
1 8008A 1942 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 155
2 8008A 1943 1 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 150
3 8008A 1943 2 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 190
4 8008A 1943 3 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 147
5 8008A 1943 4 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 215
6 8008A 1943 5 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 170
tail(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM variable value
2289 8008A 1945 7 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 154
2290 8008A 1945 8 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 90
2291 8008A 1945 9 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 NA
2292 8008A 1945 10 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 75
2293 8008A 1945 11 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 NA
2294 8008A 1945 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 -20
因此所有温度都将在 value 列下。这是你想要的吗？然后你可以分解不同的变量并进行计算，mean 或 sum 等
要添加，为日期创建一个新向量：
data_melt <- separate(data = data_melt,
col = variable,
into = c("max/min", "day"),
sep = 4)
data_melt$date <- as.Date(paste0(data_melt$YEAR, "-", data_melt$MONTH, "-", data_melt$day))
head(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM max/min day value date
1 8008A 1942 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 155 1942-12-01
2 8008A 1943 1 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 150 1943-01-01
3 8008A 1943 2 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 190 1943-02-01
4 8008A 1943 3 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 147 1943-03-01
5 8008A 1943 4 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 215 1943-04-01
6 8008A 1943 5 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 170 1943-05-01
class(data_melt$date)
[1] "Date"
我希望这或多或少是您要找的：
library(data.table)
library(lubridate)
x = fread("/your/directory/data_temp_orig.csv")
m = melt(x,
id.vars = c("ID", "YEAR", "MONTH"),
measure.vars = patterns("^TM"))
m[, fecha := ymd(paste(YEAR, MONTH, "01", sep = "-"))]
m[, c("YEAR", "MONTH") := NULL]
library(tidyr)
library(dplyr)
library(lubridate)
temp_orig <- read.csv("data-raw/data_temp_orig.csv",
stringsAsFactors = FALSE)
# I prefer lowercase column names
names(temp_orig) <- tolower(names(temp_orig))
temp2 <- temp_orig %>%
# select only interesting columns
select(id, year, month, tmax1:tmin31) %>%
# reshape in long format
gather(key, temp, -id, -year, -month) %>%
# separate at the fourth character
separate(key, c("key", "day"), sep = 4) %>%
# Combine year, month, day in a single date
mutate(date = ymd(paste(year,month,day)))
过滤最低温度
tmindata <- temp2 %>%
# filter for existing dates
filter(key == "tmin" & !is.na(date)) %>%
# Remove year month day
select(-year, -month, -day)
head(tmindata)
# id key temp date
# 1 8008A tmin 20 1942-12-01
# 2 8008A tmin 5 1943-01-01
# 3 8008A tmin 55 1943-02-01
# 4 8008A tmin 20 1943-03-01
# 5 8008A tmin 40 1943-04-01
# 6 8008A tmin 109 1943-05-01
您可以执行相同的操作来过滤 tmax 数据
您可能已经注意到上面 lubridate 的警告 Warning message:
40 failed to parse. 那是因为某些日期（例如 2 月 30 日）不存在并且没有它们的数据：
tmissingdate <- temp2 %>%
filter(is.na(date))
head(tmissingdate)
# id year month key day temp date
# 1 8008A 1943 2 tmax 29 NA <NA>
# 2 8008A 1945 2 tmax 29 NA <NA>
# 3 8008A 1943 2 tmax 30 NA <NA>
# 4 8008A 1944 2 tmax 30 NA <NA>
# 5 8008A 1945 2 tmax 30 NA <NA>
# 6 8008A 1943 2 tmax 31 NA <NA>
数据图
library(ggplot2)
ggplot(temp2, aes(x = date, y = temp, color = key)) +
geom_line()
我在重塑数据时遇到问题，但我不确定 reshape2 包是否是解决方案。我需要重塑的原始数据以一种特殊的方式存储。它们是 csv 文件中的每日温度数据，这是文件 header 的样子：
ID,YEAR,MONTH,NAME,ALTITUDE,REGION,LON,LAT,DATUM,TMAX1,TMAX2,......,TMAX31,TMIN1,TMIN2,..........,TMIN31
其中TMAX1代表MONTH第1天的最高气温。然后，以下值是所有月份的最高温度。 TMIN1 列给出了第 1 天的最低温度，依此类推，直到最后一列具有该月最后一天的最低温度。如果一个月少于 31 天，则该字段为空。
可以在 link
找到简短的示例数据文件需要重新格式化以将数据保存在两个只有四列的新文件中（ID、DATE、TEMP、VALIDTEMP，其中 TEMP TMAX or TMIN) with station ID, date, temperature (TMAX or TMIN) value and validation flag 如图所示：
考虑到我的问题，我应该创建一个包含所有可能日期的向量（在原始数据中只说明年份和月份，日期来自数据列 number/name），然后进行某种转置将每天 TMAX/TMIN 数据与其对应的日期放入这个新数据框中。不确定这是否可以通过 reshape2 完成。
我用 reshape2 做了一个简单的第一次尝试，但是这给出了 TMAX1 和 TMAXn 作为不同的变量，而它们都是温度数据。我想在一个名为 TMAX/TMIN.
TMAXn/TMIN
我会继续尝试解决问题，但非常感谢您的帮助
原始数据文件前20行的输出
> dput(kk)
structure(list(INDICATIVO = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("8008A",
"8036B", "8251E", "8325C", "8433I", "8472B", "8496E", "8520B"
), class = "factor"), ANYO = c(2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L), MES = c(3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L), NOMBRE = structure(c(8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("ALGEMESSI AUMAR",
"ARENOS (C.H. JUCAR)", "BENIDORM (AQUAGEST)", "CAUDIEL-MAS DE NOGUERA",
"EMBALSE DE ALCORA", "LA POBLA DE BENIFASS\xc0-FREDES I.", "VILLARGORDO DEL CABRIEL-CONTRERAS",
"VILLENA"), class = "factor"), ALTITUD = c(486L, 486L, 486L,
486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L, 486L,
486L, 486L, 486L, 486L, 486L, 486L), NOM_PROV = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("ALICANTE", "CASTELLON", "VALENCIA"), class = "factor"),
LONGITUD = c(51562L, 51562L, 51562L, 51562L, 51562L, 51562L,
51562L, 51562L, 51562L, 51562L, 51562L, 51562L, 51562L, 51562L,
51562L, 51562L, 51562L, 51562L, 51562L, 51562L), LATITUD = c(383437L,
383437L, 383437L, 383437L, 383437L, 383437L, 383437L, 383437L,
383437L, 383437L, 383437L, 383437L, 383437L, 383437L, 383437L,
383437L, 383437L, 383437L, 383437L, 383437L), DATUM = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = "ETRS89", class = "factor"), TMAX1 = c(230L,
220L, 310L, 280L, 370L, 310L, 330L, 270L, 200L, 180L, 180L,
190L, 170L, 160L, 210L, 290L, 340L, 320L, 300L, 310L), TMAX2 = c(270L,
200L, 310L, 295L, 330L, 330L, 310L, 270L, 160L, 195L, 150L,
200L, 220L, 180L, 225L, 290L, 360L, 330L, 330L, 300L), TMAX3 = c(240L,
220L, 370L, 300L, 370L, 250L, 330L, 230L, 190L, 200L, 170L,
170L, 180L, 190L, 240L, 290L, 340L, 360L, 350L, 290L), TMAX4 = c(230L,
200L, 330L, 300L, 380L, 360L, 290L, 290L, 230L, 200L, 210L,
130L, 220L, 190L, 230L, 300L, 320L, 400L, 390L, 300L), TMAX5 = c(180L,
240L, 290L, 310L, 400L, 360L, 240L, 300L, 220L, 180L, 170L,
140L, 140L, 120L, 170L, 290L, 360L, 330L, 425L, 270L), TMAX6 = c(170L,
150L, 290L, 320L, 390L, 360L, 190L, 280L, 235L, 160L, 110L,
180L, 140L, 180L, 210L, 310L, 340L, 310L, 360L, 330L), TMAX7 = c(250L,
150L, 260L, 310L, 425L, 400L, 220L, 250L, 230L, 160L, 190L,
130L, 90L, 195L, 190L, 310L, 310L, 310L, 340L, 310L), TMAX8 = c(180L,
170L, 290L, 320L, 400L, 325L, 250L, 220L, 200L, 200L, 220L,
160L, 140L, 150L, 180L, 340L, 360L, 340L, 340L, 290L), TMAX9 = c(200L,
180L, 290L, 330L, 340L, 330L, 240L, 230L, 220L, 160L, 200L,
180L, 140L, 200L, 190L, 360L, 350L, 330L, 320L, 270L), TMAX10 = c(195L,
200L, 270L, 310L, 360L, 390L, 320L, 250L, 240L, 135L, 170L,
210L, 120L, 190L, 190L, 350L, 360L, 250L, 320L, 260L), TMAX11 = c(260L,
180L, 310L, 240L, 340L, 350L, 300L, 270L, 230L, 140L, 100L,
210L, 150L, 200L, 210L, 320L, 370L, 290L, 300L, 300L), TMAX12 = c(260L,
190L, 340L, 300L, 355L, 380L, 290L, 270L, 190L, 140L, 140L,
220L, 130L, 230L, 210L, 310L, 370L, 290L, 320L, 230L), TMAX13 = c(185L,
200L, 380L, 290L, 360L, 350L, 320L, 240L, 210L, 170L, 150L,
220L, 150L, 200L, 210L, 375L, 300L, 300L, 300L, 190L), TMAX14 = c(160L,
260L, 415L, 170L, 360L, 320L, 300L, 230L, 200L, 180L, 160L,
170L, 150L, 230L, 240L, 350L, 280L, 310L, 250L, 230L), TMAX15 = c(130L,
230L, 390L, 260L, 360L, 300L, 300L, 250L, 200L, 210L, 120L,
140L, 150L, 240L, 230L, 320L, 270L, 320L, 260L, 240L), TMAX16 = c(150L,
240L, 370L, 270L, 360L, 300L, 330L, 230L, 230L, 215L, 160L,
120L, 180L, 250L, 230L, 270L, 280L, 260L, 290L, 250L), TMAX17 = c(140L,
260L, 380L, 300L, 370L, 350L, 270L, 230L, 230L, 210L, 130L,
130L, 150L, 240L, 220L, 280L, 310L, 375L, 300L, 290L), TMAX18 = c(120L,
250L, 280L, 280L, 350L, 320L, 250L, 240L, 220L, 215L, 170L,
130L, 160L, 220L, 250L, 300L, 320L, 370L, 280L, 230L), TMAX19 = c(140L,
240L, 280L, 320L, 380L, 320L, 265L, 220L, 240L, 220L, 140L,
120L, 150L, 180L, 260L, 280L, 340L, 370L, 300L, 220L), TMAX20 = c(120L,
215L, 230L, 310L, 370L, 330L, 290L, 190L, 260L, 180L, 150L,
160L, 170L, 200L, 250L, 280L, 380L, 390L, 270L, 220L), TMAX21 = c(170L,
200L, 220L, 310L, 370L, 330L, 330L, 200L, 200L, 185L, 140L,
170L, 140L, 230L, 250L, 295L, 360L, 300L, 310L, 200L), TMAX22 = c(140L,
240L, 220L, 400L, 370L, 330L, 340L, 210L, 130L, 155L, 160L,
220L, 130L, 210L, 300L, 310L, 340L, 320L, 310L, 210L), TMAX23 = c(160L,
270L, 240L, 350L, 340L, 340L, 300L, 230L, 100L, 160L, 190L,
180L, 170L, 230L, 240L, 320L, 310L, 330L, 290L, 240L), TMAX24 = c(150L,
250L, 230L, 310L, 390L, 330L, 260L, 190L, 130L, 150L, 190L,
200L, 200L, 240L, 270L, 350L, 310L, 330L, 280L, NA), TMAX25 = c(110L,
260L, 250L, 310L, 350L, 320L, 280L, 190L, 180L, 160L, 190L,
160L, 230L, 200L, 300L, 330L, 310L, 330L, 290L, NA), TMAX26 = c(150L,
260L, 290L, 335L, 340L, 320L, 280L, 190L, 190L, 160L, 140L,
160L, 240L, 250L, 280L, 300L, 325L, 310L, 280L, NA), TMAX27 = c(230L,
220L, 260L, 360L, 420L, 330L, 260L, 230L, 160L, 160L, 130L,
85L, 210L, 250L, 300L, 320L, 340L, 340L, 270L, NA), TMAX28 = c(260L,
250L, 260L, 390L, 350L, 350L, 230L, 200L, 190L, 160L, 170L,
150L, 170L, 220L, 300L, 320L, 330L, 360L, 240L, NA), TMAX29 = c(260L,
250L, 300L, 370L, 390L, 330L, 210L, 230L, 180L, 180L, 130L,
150L, 250L, 130L, 260L, 330L, 350L, 350L, 260L, NA), TMAX30 = c(280L,
260L, 300L, 370L, 340L, 315L, 230L, 220L, 230L, 180L, 170L,
NA, 240L, 240L, 290L, 350L, 380L, 310L, 270L, NA), TMAX31 = c(310L,
NA, 290L, NA, 330L, 340L, NA, 200L, NA, 210L, 210L, NA, 175L,
NA, 270L, NA, 400L, 310L, NA, NA), TMIN1 = c(70L, 60L, 70L,
130L, 160L, 210L, 210L, 130L, 90L, -50L, 50L, 20L, 20L, 40L,
90L, 90L, 170L, 240L, 155L, 110L), TMIN2 = c(130L, 20L, 140L,
130L, 200L, 210L, 190L, 90L, 60L, -15L, 30L, -10L, 40L, -10L,
10L, 100L, 140L, 150L, 150L, 130L), TMIN3 = c(70L, 30L, 90L,
100L, 210L, 190L, 165L, 100L, 60L, -10L, 35L, 10L, 80L, 30L,
30L, 130L, 210L, 140L, 130L, 110L), TMIN4 = c(70L, 80L, 150L,
90L, 190L, 180L, 200L, 120L, 50L, 0L, 120L, -10L, -10L, 70L,
30L, 130L, 200L, 160L, 130L, 110L), TMIN5 = c(-20L, 100L,
150L, 100L, 150L, 220L, 170L, 150L, 90L, -10L, 70L, 20L,
60L, 80L, 55L, 120L, 180L, 210L, 160L, 150L), TMIN6 = c(-30L,
55L, 135L, 80L, 150L, 230L, 170L, 130L, 90L, 70L, 50L, 10L,
35L, 100L, 70L, 110L, 190L, 170L, 190L, 120L), TMIN7 = c(-30L,
80L, 70L, 70L, 150L, 240L, 170L, 120L, 100L, 80L, 40L, 65L,
-20L, 75L, 90L, 130L, 160L, 130L, 140L, 130L), TMIN8 = c(30L,
50L, 70L, 100L, 180L, 200L, 150L, 120L, 90L, 40L, 10L, 95L,
20L, 50L, 90L, 125L, 170L, 150L, 150L, 120L), TMIN9 = c(50L,
80L, 110L, 110L, 210L, 210L, 125L, 100L, 80L, 30L, 85L, 60L,
-20L, 0L, 110L, 140L, 210L, 130L, 160L, 140L), TMIN10 = c(-10L,
70L, 70L, 80L, 180L, 230L, 180L, 100L, 30L, 30L, 50L, 130L,
40L, 20L, 80L, 160L, 160L, 180L, 150L, 120L), TMIN11 = c(-10L,
80L, 50L, 110L, 160L, 220L, 170L, 130L, 25L, 30L, 50L, 120L,
-20L, 50L, 100L, 180L, 170L, 140L, 210L, 150L), TMIN12 = c(20L,
90L, 60L, 150L, 180L, 210L, 140L, 120L, 25L, 30L, 20L, 90L,
-10L, 50L, 75L, 160L, 180L, 140L, 140L, 100L), TMIN13 = c(20L,
110L, 80L, 150L, 160L, 200L, 150L, 140L, 100L, 20L, -15L,
160L, -30L, 60L, 100L, 150L, 200L, 105L, 140L, 120L), TMIN14 = c(20L,
70L, 120L, 130L, 160L, 190L, 180L, 130L, 100L, 0L, -15L,
70L, -10L, 40L, 110L, 220L, 200L, 120L, 140L, 90L), TMIN15 = c(-5L,
115L, 110L, 140L, 170L, 180L, 125L, 40L, 60L, 0L, 40L, 60L,
40L, 50L, 110L, 170L, 180L, 120L, 80L, 60L), TMIN16 = c(50L,
100L, 60L, 100L, 155L, 160L, 120L, 115L, 50L, 20L, -30L,
55L, 10L, 50L, 80L, 170L, 150L, 160L, 100L, 50L), TMIN17 = c(-10L,
80L, 60L, 110L, 170L, 210L, 140L, 90L, 40L, 5L, -60L, -80L,
5L, 100L, 50L, 120L, 100L, 155L, 90L, 110L), TMIN18 = c(70L,
50L, 50L, 120L, 170L, 205L, 120L, 100L, 0L, 0L, 70L, 0L,
90L, 80L, 60L, 100L, 130L, 185L, 110L, 160L), TMIN19 = c(100L,
90L, 160L, 120L, 180L, 220L, 140L, 100L, 30L, -10L, 50L,
70L, 100L, 110L, 80L, 100L, 130L, 180L, 100L, 160L), TMIN20 = c(110L,
60L, 130L, 120L, 190L, 200L, 100L, 140L, 50L, -30L, -15L,
-15L, 50L, 110L, 80L, 110L, 180L, 200L, 160L, 130L), TMIN21 = c(90L,
40L, 80L, 130L, 175L, 175L, 120L, 90L, 50L, -10L, 0L, 40L,
50L, 80L, 80L, 100L, 170L, 220L, 140L, 130L), TMIN22 = c(100L,
100L, 60L, 130L, 195L, 175L, 120L, 70L, -10L, 0L, 25L, 60L,
80L, 80L, 80L, 100L, 150L, 170L, 130L, 110L), TMIN23 = c(70L,
70L, 50L, 150L, 190L, 170L, 130L, 50L, -10L, 20L, 15L, 20L,
85L, 50L, 120L, 110L, 180L, 150L, 130L, 150L), TMIN24 = c(80L,
60L, 60L, 140L, 200L, 210L, 100L, 90L, -45L, 0L, 20L, 20L,
25L, 80L, 100L, 110L, 140L, 135L, 125L, 130L), TMIN25 = c(30L,
110L, 65L, 150L, 230L, 150L, 90L, 130L, -20L, -20L, 70L,
90L, 20L, 40L, 110L, 125L, 140L, 130L, 135L, NA), TMIN26 = c(10L,
110L, 80L, 150L, 230L, 150L, 90L, 130L, 100L, 20L, 40L, 20L,
115L, 40L, 130L, 170L, 140L, 145L, 185L, NA), TMIN27 = c(70L,
100L, 100L, 150L, 200L, 140L, 130L, 120L, 10L, 35L, 40L,
40L, 70L, 70L, 120L, 170L, 150L, 140L, 160L, NA), TMIN28 = c(70L,
90L, 90L, 150L, 200L, 140L, 110L, 70L, 10L, -20L, 25L, 40L,
90L, 120L, 150L, 170L, 160L, 160L, 170L, NA), TMIN29 = c(60L,
60L, 90L, 150L, 200L, 160L, 100L, 80L, -30L, 50L, 60L, 60L,
75L, 110L, 150L, 200L, 140L, 200L, 120L, NA), TMIN30 = c(90L,
60L, 70L, 150L, 210L, 170L, 130L, 65L, -10L, 0L, 60L, NA,
10L, 110L, 140L, 180L, 170L, 220L, 130L, NA), TMIN31 = c(110L,
NA, 130L, NA, 210L, 220L, NA, 100L, NA, 70L, 80L, NA, 65L,
NA, 120L, NA, 210L, 170L, NA, NA)), .Names = c("INDICATIVO",
"ANYO", "MES", "NOMBRE", "ALTITUD", "NOM_PROV", "LONGITUD", "LATITUD",
"DATUM", "TMAX1", "TMAX2", "TMAX3", "TMAX4", "TMAX5", "TMAX6",
"TMAX7", "TMAX8", "TMAX9", "TMAX10", "TMAX11", "TMAX12", "TMAX13",
"TMAX14", "TMAX15", "TMAX16", "TMAX17", "TMAX18", "TMAX19", "TMAX20",
"TMAX21", "TMAX22", "TMAX23", "TMAX24", "TMAX25", "TMAX26", "TMAX27",
"TMAX28", "TMAX29", "TMAX30", "TMAX31", "TMIN1", "TMIN2", "TMIN3",
"TMIN4", "TMIN5", "TMIN6", "TMIN7", "TMIN8", "TMIN9", "TMIN10",
"TMIN11", "TMIN12", "TMIN13", "TMIN14", "TMIN15", "TMIN16", "TMIN17",
"TMIN18", "TMIN19", "TMIN20", "TMIN21", "TMIN22", "TMIN23", "TMIN24",
"TMIN25", "TMIN26", "TMIN27", "TMIN28", "TMIN29", "TMIN30", "TMIN31"
), row.names = 786:805, class = "data.frame")
您可以这样使用 melt 函数：
library(reshape2)
data <- read.csv("data_temp_orig.csv", header = TRUE)
allnames <- names(data)
idnames <- allnames[1:9]
tempnames <- allnames[10:71]
data_melt <- melt(data, id.vars = idnames, measure.vars = tempnames)
head(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM variable value
1 8008A 1942 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 155
2 8008A 1943 1 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 150
3 8008A 1943 2 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 190
4 8008A 1943 3 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 147
5 8008A 1943 4 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 215
6 8008A 1943 5 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX1 170
tail(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM variable value
2289 8008A 1945 7 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 154
2290 8008A 1945 8 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 90
2291 8008A 1945 9 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 NA
2292 8008A 1945 10 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 75
2293 8008A 1945 11 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 NA
2294 8008A 1945 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMIN31 -20
因此所有温度都将在 value 列下。这是你想要的吗？然后你可以分解不同的变量并进行计算，mean 或 sum 等
要添加，为日期创建一个新向量：
data_melt <- separate(data = data_melt,
col = variable,
into = c("max/min", "day"),
sep = 4)
data_melt$date <- as.Date(paste0(data_melt$YEAR, "-", data_melt$MONTH, "-", data_melt$day))
head(data_melt)
ID YEAR MONTH NAME ALTITUDE REGION LON LAT DATUM max/min day value date
1 8008A 1942 12 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 155 1942-12-01
2 8008A 1943 1 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 150 1943-01-01
3 8008A 1943 2 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 190 1943-02-01
4 8008A 1943 3 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 147 1943-03-01
5 8008A 1943 4 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 215 1943-04-01
6 8008A 1943 5 VILLENA 486 ALICANTE 51562 383437 ETRS89 TMAX 1 170 1943-05-01
class(data_melt$date)
[1] "Date"
我希望这或多或少是您要找的：
library(data.table)
library(lubridate)
x = fread("/your/directory/data_temp_orig.csv")
m = melt(x,
id.vars = c("ID", "YEAR", "MONTH"),
measure.vars = patterns("^TM"))
m[, fecha := ymd(paste(YEAR, MONTH, "01", sep = "-"))]
m[, c("YEAR", "MONTH") := NULL]
library(tidyr)
library(dplyr)
library(lubridate)
temp_orig <- read.csv("data-raw/data_temp_orig.csv",
stringsAsFactors = FALSE)
# I prefer lowercase column names
names(temp_orig) <- tolower(names(temp_orig))
temp2 <- temp_orig %>%
# select only interesting columns
select(id, year, month, tmax1:tmin31) %>%
# reshape in long format
gather(key, temp, -id, -year, -month) %>%
# separate at the fourth character
separate(key, c("key", "day"), sep = 4) %>%
# Combine year, month, day in a single date
mutate(date = ymd(paste(year,month,day)))
过滤最低温度
tmindata <- temp2 %>%
# filter for existing dates
filter(key == "tmin" & !is.na(date)) %>%
# Remove year month day
select(-year, -month, -day)
head(tmindata)
# id key temp date
# 1 8008A tmin 20 1942-12-01
# 2 8008A tmin 5 1943-01-01
# 3 8008A tmin 55 1943-02-01
# 4 8008A tmin 20 1943-03-01
# 5 8008A tmin 40 1943-04-01
# 6 8008A tmin 109 1943-05-01
您可以执行相同的操作来过滤 tmax 数据
您可能已经注意到上面 lubridate 的警告 Warning message:
40 failed to parse. 那是因为某些日期（例如 2 月 30 日）不存在并且没有它们的数据：
tmissingdate <- temp2 %>%
filter(is.na(date))
head(tmissingdate)
# id year month key day temp date
# 1 8008A 1943 2 tmax 29 NA <NA>
# 2 8008A 1945 2 tmax 29 NA <NA>
# 3 8008A 1943 2 tmax 30 NA <NA>
# 4 8008A 1944 2 tmax 30 NA <NA>
# 5 8008A 1945 2 tmax 30 NA <NA>
# 6 8008A 1943 2 tmax 31 NA <NA>
数据图
library(ggplot2)
ggplot(temp2, aes(x = date, y = temp, color = key)) +
geom_line()