CODEWITHSUNDEEP
rreshape2
pacomet
pacomet

Reputation: 5141

Reshaping data by aggregating columns in a row

I'm facing a problem with reshaping my data but I'm not sure if reshape2 package is the solution. The original data I need to reshape are stored in a peculiar way. They are daily temperature data in csv files, this is how file header look like:

ID,YEAR,MONTH,NAME,ALTITUDE,REGION,LON,LAT,DATUM,TMAX1,TMAX2,......,TMAX31,TMIN1,TMIN2,..........,TMIN31

where TMAX1 stands for the maximum temperature of day 1 in MONTH. Then, following values are the maximum temperature for all month days. TMIN1 column gives minimum temperature for day 1 and so on until the last column with minimum temperature for last day in the month. If a month has less than 31 days the field is empty.

Short example data file can be found at link

Reformatting is needed to save data in two new files with just four columns (ID, DATE, TEMP, VALIDTEMP where TEMP is TMAX or TMIN) with station ID, date, temperature (TMAX or TMIN) value and validation flag as seen in the figure:

enter image description here

Thinking over my problem I should create a vector with all possible dates (in the original data only year and month are stated, day comes from the data column number/name) and then make some sort of transposing to fit every daily TMAX/TMIN data with its corresponding date in this new data frame.. Not sure if this can be accomplished by reshape2.

I made a simple first attempt with reshape2 but this gives TMAX1 and TMAXn as different variables while they are all temperature data. I want to melt all TMAXn/TMIN in a single variable called TMAX/TMIN.

I will continue trying to sort out the problem but any help is greatly appreciated

Output of 20 first rows of original data file 

> 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")

Upvotes: 1

Views: 176

Answers (3)

Paul Rougieux
Paul Rougieux

Reputation: 11379

This answer uses the tidyr and dplyr packages.

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)))

Filter minimum temperatures

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

You can do the same to filter tmax data

You might have noticed a warning from lubridate above Warning message: 40 failed to parse. That's because some dates such as February 30 don't exist and there is no data for them:

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>

Plot of the data

library(ggplot2)
ggplot(temp2, aes(x =  date, y = temp, color = key)) +
    geom_line()

enter image description here

Upvotes: 1

Henk
Henk

Reputation: 3656

I hope this is more or less what you are looking for:

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]

Upvotes: 1

lao_zhang
lao_zhang

Reputation: 178

You can use the melt function this way:

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

So all the temperatures will be under the value column. Is this what you want? Then you can factorize the different variables and do your calculation, mean or sum etc.

To add on, to create a new vector for the dates:

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"

Upvotes: 2

Related Questions