list_inmet()First, we add a pathway for the file to be downloaded and converted into useful information. The file is retained, and its content can be extracted later. For this example, we will use a temporary file that doesn’t have much influence at this stage.
file.down <- tempfile()
info.inmet <- DataMetProcess::list_inmet(
year="2000",
filename = file.down
)
head(info.inmet)
#> $Adresses
#> Name
#> 1 2000/
#> 2 2000/INMET_CO_DF_A001_BRASILIA_07-05-2000_A_31-12-2000.CSV
#> 3 2000/INMET_NE_BA_A401_SALVADOR_13-05-2000_A_31-12-2000.CSV
#> 4 2000/INMET_N_AM_A101_MANAUS_09-05-2000_A_31-12-2000.CSV
#> 5 2000/INMET_SE_RJ_A601_ECOLOGIA AGRICOLA_07-05-2000_A_31-12-2000.CSV
#> 6 2000/INMET_S_RS_A801_PORTO ALEGRE_22-09-2000_A_31-12-2000.CSV
#>
#> $Details
#> Year Origin Region State Id City Start.Date End.Date
#> 1 2000 INMET CO DF A001 BRASILIA 2000-05-07 2000-12-31
#> 2 2000 INMET SE RJ A601 ECOLOGIA AGRICOLA 2000-05-07 2000-12-31
#> 3 2000 INMET N AM A101 MANAUS 2000-05-09 2000-12-31
#> 4 2000 INMET S RS A801 PORTO ALEGRE 2000-09-22 2000-12-31
#> 5 2000 INMET NE BA A401 SALVADOR 2000-05-13 2000-12-31
#>
#> $Saved
#> [1] "C:\\Users\\wagne\\AppData\\Local\\Temp\\RtmpKCaDlR\\file6b1415115f83"Now we have an object containing a list of files in
$Addresses within the path shown in $Saved,
and a structured table with information extracted from the file
names.
Having done that, now we can make use of a very useful function from
the utils package, called unzip(). At this
point, we can also use the files parameter of the unzip()
function to extract only the files of interest. Please refer to
?utils::unzip for more details.
adjustDate()To perform the time zone correction, we can use the
adjustDate() function. To do this, we will use an example
file provided with the package, At this point, we could indeed use a
file obtained in the previous topic. You can check the available time
zones by using OlsonNames().
address <-
base::system.file("extdata",
"ex1_inmet.CSV",
package = "DataMetProcess")
df <-
read.table(
address,
h=TRUE,
sep = ";",
dec = ",",
skip = 8,
na.strings = -9999,
check.names = FALSE
) #see ?read.table for more details...
#Converting to R standard (when necessary)
df$Data = as.Date(df$Data,format = "%d/%m/%Y")
head(df[1:3]) #We are only viewing a part of it.
#> Data Hora precipita_o_total_hor_rio_mm
#> 1 2008-01-01 00:00 0
#> 2 2008-01-01 01:00 0
#> 3 2008-01-01 02:00 0
#> 4 2008-01-01 03:00 0
#> 5 2008-01-01 04:00 0
#> 6 2008-01-01 05:00 0
df <-
adjustDate(df,
colnames(df)[1],
colnames(df)[2],
fuso = "America/Bahia")
#date and time are now in a single column
head(df[1:2]) #We are only viewing a part of it.
#> Date_Hour precipita_o_total_hor_rio_mm
#> 1 2007-12-31 21:00:00 0
#> 2 2007-12-31 22:00:00 0
#> 3 2007-12-31 23:00:00 0
#> 4 2008-01-01 00:00:00 0
#> 5 2008-01-01 01:00:00 0
#> 6 2008-01-01 02:00:00 0calculateDMY()We can then calculate daily, monthly, and yearly data using the
calculateDMY() function. First, we adjust the data_hora
column defined by the previous function to ensure there are no
differences between the same dates. Then, we populate the parameters
with the column names in string format (“string”) and define the type as
"Daily," "Monthly," or "Yearly".
df.daily <-
calculateDMY(
data = df.new,
col_date = colnames(df)[c(1)],
col_sum = colnames(df)[c(2,6)], #simplest way to pass column names as string
col_mean = colnames(df)[-c(1,2,6)], #remove the previous steps in the parameter above
type = "Daily"
)
head(df.daily[1:2]) #We are only viewing a part of it.
#> # A tibble: 6 × 2
#> Date precipita_o_total_hor_rio_mm
#> <date> <dbl>
#> 1 2007-12-31 0
#> 2 2008-01-01 2.6
#> 3 2008-01-02 1.2
#> 4 2008-01-03 0
#> 5 2008-01-04 0
#> 6 2008-01-05 14.2We use the processed df.daily file from the previous
topic.
df.monthly <-
calculateDMY(
data = df.daily,
col_date = colnames(df.daily)[c(1)],
col_sum = colnames(df.daily)[c(2)],
col_mean = colnames(df.daily)[-c(1,2)],
type = "Monthly"
)
head(df.monthly[1:2]) #We are only viewing a part of it.
#> # A tibble: 6 × 2
#> Date precipita_o_total_hor_rio_mm
#> <date> <dbl>
#> 1 2007-12-01 0
#> 2 2008-01-01 183.
#> 3 2008-02-01 191.
#> 4 2008-03-01 174.
#> 5 2008-04-01 98.8
#> 6 2008-05-01 31.6df.yearly <-
calculateDMY(
data = df.monthly,
col_date = colnames(df.monthly)[c(1)],
col_sum = colnames(df.monthly)[c(2)],
col_mean = colnames(df.monthly)[-c(1,2)],
type = "Yearly"
)
head(df.yearly[1:2]) #We are only viewing a part of it.
#> # A tibble: 2 × 2
#> Date precipita_o_total_hor_rio_mm
#> <dbl> <dbl>
#> 1 2007 0
#> 2 2008 1105.calculateETrefPM()We can calculate reference evapotranspiration for daily data using
the calculateETrefPM() function. This function is based on
the FAO Penman-Monteith method, according to:”
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration – guidelines for computing crop water requirements – FAO Irrigation and Drainage Paper 56. FAO, 1998. ISBN 92-5-104219-5
address <-
base::system.file("extdata",
"ex2_daily.CSV",
package = "DataMetProcess")
df <- read.table(
address,
h = TRUE,
sep = ";"
)
#converting to Mj/m
df$radiacao_global_kj_m <- df$radiacao_global_kj_m/1000
colnames(df)[3] <- "radiacao_global_mj_m"
df.Eto <-
calculateETrefPM(
data = df,
lat = -21.980353,
alt = 859.29,
za = 10,
DAP = 1,
date = colnames(df)[1],
Ta = colnames(df)[7],
G = NULL,
RH = colnames(df)[15],
Rg = colnames(df)[3],
AP = colnames(df)[4],
WS = colnames(df)[18],
Kc = NULL
)
head(df.Eto)
#> Data etref JD DAP es ea delta y rn
#> 1 2008-01-01 8.269305 1 1 3.138558 2.198246 0.1871697 0.06096587 10.534448
#> 2 2008-01-02 5.987792 2 2 2.877179 2.201042 0.1734963 0.06101973 9.618784
#> 3 2008-01-03 8.090657 3 3 2.913816 1.998295 0.1754227 0.06113810 10.746086
#> 4 2008-01-04 9.051521 4 4 2.943771 1.919633 0.1769954 0.06115673 13.339185
#> 5 2008-01-05 6.495420 5 5 2.842662 2.109539 0.1716782 0.06105032 10.076827
#> 6 2008-01-06 2.308457 6 6 2.387142 2.131480 0.1473864 0.06096122 8.308235