Prepare climate data
Antonio J.
Pérez-Luque
Last updated: 2023-07-26
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Knit directory: ms_mariposas_pheno/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7c33a30 | ajpelu | 2023-07-26 | add orcid |
| html | 32f6fa3 | ajpelu | 2023-07-26 | Build site. |
| Rmd | 00ea503 | ajpelu | 2023-07-26 | climate data |
| Rmd | 4e72a27 | ajpelu | 2023-07-11 | prepare climate data |
| html | baaf783 | ajpelu | 2023-07-10 | Build site. |
| Rmd | 1a4104b | ajpelu | 2023-07-10 | prepare data climate with new data |
| html | a834041 | ajpelu | 2023-06-26 | Build site. |
| Rmd | 3600071 | ajpelu | 2023-06-26 | get data from 1st oct to sep+1 |
| Rmd | 03899a4 | ajpelu | 2023-06-04 | add plots climate |
| html | 59c4576 | ajpelu | 2023-06-01 | Build site. |
| Rmd | 2f9f824 | ajpelu | 2023-06-01 | change name |
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| Rmd | 55a33ae | ajpelu | 2023-06-01 | add prepare climate data |
Introduction
library(here)
library(tidyverse)
library(purrr)
library(runner)
library(kableExtra)
library(DT)We used the climate data from REDIAM (500 x 500 grid for all Andalusian territory). Gridded data comes from meteorological stations. For each pixel values of maximum, minimum and median monthly temperatures are available from 1971 to 2021. Monthly rainfall are available from 1951 to 2022.
For each transect we obtained all the data of the pixels that contact with the transects (see
code/get_climate_transect.R). Then we generated the average value of each variable by transect and month.
files <- list.files("data/raw_data/climate_transects", pattern = "transect.csv", full.names = TRUE)
custom_f <- function(x) {
out <-
read_csv(x) |>
dplyr::select(-ID) |>
pivot_longer(-c("transectid","x", "y")) |>
separate(name, into = c("var", "year", "month", "cog")) |>
dplyr::select(-cog, -x, -y)
return(out)
}
raw <- files |>
map_df(~custom_f(.)) |>
mutate(var = recode(var,
"tm2" = "tmin",
"tm3" = "tmax"))
climate_transect <- raw |>
group_by(transectid, var, year, month) |>
summarise(avg_transect = mean(value)) |>
mutate(date = as.Date(paste(year, month, "01", sep="-"), format="%Y-%m-%d")) |> ungroup() |>
filter(date >= as.Date("2007-01-01", format="%Y-%m-%d")) |>
mutate(month_names = strftime(date, '%b'))
write_csv(climate_transect, "data/raw_data/climate_transects/climate_transect_all_avg.csv")climate_transect <- read_csv("data/raw_data/climate_transects/climate_transect_all_avg.csv")See aspect of the table:
Compute month, bi-month and three-month data
For each transect, we selected monthly data. Then we computed the bi-months and three-months average.
Prepare hydrological months
aux <- climate_transect |>
mutate_at(vars(month, year), as.numeric) |> # convert to numeric
mutate(
hydro_month = case_when(
month >= 10 ~ (month - 9),
TRUE ~ month + 3),
hydro_year = case_when(
month >= 10 ~ (year + 1),
TRUE ~ year),
name_m = case_when(
month >= 10 ~ paste0(month_names, "_pre"),
TRUE ~ month_names
)
)
# |>
# filter(hydro_year > 2007)Temperature data
Monthly data
We selected temperature data in the climate_transect
dataframe. For each variable (i.e. tmax, tmed, tmin) we pivot
the data to obtain the temperature data for each transect
(ID transect) and year (from 2007 to 2021). The resulted
dataframe is called d1mt (data-1-month-temperature).
d1mt <- aux |>
filter(var != "p") |>
dplyr::select(-month, -date, -month_names, -year, -hydro_month) |>
pivot_wider(values_from = avg_transect,
names_from = name_m) Explore the results
Bimonthly data
To obtain values of temperature aggregated each 2-months, we used the
runner() function of runner
package. This function computes a function over a temporal window. For
the temperature data we use the bimonth average temperature. As monthly
data, we pivot this dataframe to obtain for each variable, transect and
year the temperature value (in this case the 2-month average
temperature). The name of the variables will be a combination of the two
averaged month, i.e.: JanFeb. The resulting
dataset is called d2mt (data-2-month-temperature).
aux2 <- aux |>
filter(var != "p") |>
group_by(var) |>
mutate(
avg_two_months = runner::runner(
x = avg_transect,
k = 2,
f = mean,
na_pad = TRUE
)) |>
mutate(
month_names2 = runner::runner(
x = name_m,
k = 2,
f = paste,
na_pad = TRUE,
collapse="")
) |>
ungroup()
d2mt <- aux2 |>
filter(var != "p") |>
dplyr::select(-month, -date, -month_names, -year, -hydro_month, -avg_transect, -name_m) |>
filter(!(is.na(month_names2))) |>
pivot_wider(values_from = avg_two_months,
names_from = month_names2) See the results
Some test were performed to check the results:
d2008 <- aux |>
filter(var == "tmax") |>
filter(transectid == 14) |>
filter(hydro_year == 2008)
# test 1
janfeb <- mean(as.vector(d2008[4:5, "avg_transect"]$avg_transect))
test_aux2 <- aux2 |>
filter(var == "tmax") |>
filter(transectid == 14) |>
filter(hydro_year %in% c(2007, 2008))
identical(janfeb, (test_aux2 |>
filter(hydro_year == 2008) |>
filter(month_names2 == "JanFeb") |> pull(avg_two_months)))[1] TRUE# test 2
Dec_pre_jan <- mean(as.vector(d2008[3:4, "avg_transect"]$avg_transect))
identical(Dec_pre_jan, (test_aux2 |>
filter(hydro_year == 2008) |>
filter(month_names2 == "Dec_preJan") |> pull(avg_two_months)))[1] TRUETrimonthly data
To obtain values of temperature aggregated each 3-months, we again
used the runner() function of runner
package. As monthly data, we pivot this dataframe to obtain for each
variable, transect and year the temperature value (in this case the
3-month average temperature). The name of the variables will be a
combination of the three averaged month, i.e.:
JanFebMar. The resulting dataset is called
d3mt (data-3-month-temperature).
aux3 <- aux |>
filter(var != "p") |>
group_by(var) |>
mutate(
avg_three_months = runner::runner(
x = avg_transect,
k = 3,
f = mean,
na_pad = TRUE
)
) |>
mutate(
month_names3 = runner::runner(
x = name_m,
k = 3,
f = paste,
na_pad = TRUE,
collapse="")
) |> ungroup()
d3mt <- aux3 |>
filter(var != "p") |>
dplyr::select(-month, -date,
-month_names, -year,
-hydro_month, -avg_transect, -name_m) |>
filter(!(is.na(month_names3))) |>
pivot_wider(values_from = avg_three_months,
names_from = month_names3)See the results:
We generated a dataframe with monthly, 2-month and 3-month data
climate_temp <- d1mt |>
inner_join(d2mt) |>
inner_join(d3mt)Rainfall data
As we see in the previous section, we also computed the monthly, bimonthly and 3-monthly data for the rainfall, but in this case we used the cummulative precipitation (not the average), so the value for JanFeb corresponds to the cummulative rainfall of Jan and Feb.
auxp <- aux |>
filter(var == "p")
d1mp <- aux |>
filter(var == "p") |>
dplyr::select(-month, -date, -month_names, -year, -hydro_month) |>
pivot_wider(values_from = avg_transect,
names_from = name_m)
aux2p <- aux |>
filter(var == "p") |>
mutate(
sum_two_months = runner(
x = avg_transect,
k = 2,
f = sum,
na_pad = TRUE
)) |>
mutate(
month_names2 = runner(
x = name_m,
k = 2,
f = paste,
na_pad = TRUE,
collapse="")
) |> ungroup()
d2mp <- aux2p |>
dplyr::select(-month, -date, -month_names,
-year, -hydro_month, -avg_transect, -name_m) |>
filter(!(is.na(month_names2))) |>
pivot_wider(values_from = sum_two_months,
names_from = month_names2)
aux3p <- aux |>
filter(var == "p") |>
mutate(
sum_three_months = runner(
x = avg_transect,
k = 3,
f = mean,
na_pad = TRUE
)
) |>
mutate(
month_names3 = runner(
x = name_m,
k = 3,
f = paste,
na_pad = TRUE,
collapse="")
) |> ungroup()
d3mp <- aux3p |>
dplyr::select(-month, -date, -month_names,
-year, -hydro_month, -avg_transect, -name_m) |>
filter(!(is.na(month_names3))) |>
pivot_wider(values_from = sum_three_months,
names_from = month_names3)
climate_prec <- d1mp |>
inner_join(d2mp) |>
inner_join(d3mp)Generated data
The temperature and rainfall data were joinned and exported.
climate_data <- bind_rows(
climate_temp, climate_prec)# Filter erroneous date windows
climate_data <- climate_data |>
dplyr::select(-SepOct_pre, -AugSepOct_pre, -SepOct_preNov_pre)climate_data |>
mutate(across(-c(transectid, var, hydro_year), ~round(., digits = 2))) |>
DT::datatable()
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.26 kableExtra_1.3.4 runner_0.4.2 lubridate_1.9.2
[5] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[9] readr_2.1.4 tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1
[13] tidyverse_2.0.0 here_1.0.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 svglite_2.1.0 getPass_0.2-2 ps_1.7.1
[5] rprojroot_2.0.3 digest_0.6.31 utf8_1.2.2 R6_2.5.1
[9] evaluate_0.19 httr_1.4.4 pillar_1.8.1 rlang_1.1.0
[13] rstudioapi_0.14 whisker_0.4 callr_3.7.3 jquerylib_0.1.4
[17] rmarkdown_2.19 webshot_0.5.4 htmlwidgets_1.6.2 bit_4.0.4
[21] munsell_0.5.0 compiler_4.2.3 httpuv_1.6.8 xfun_0.39
[25] pkgconfig_2.0.3 systemfonts_1.0.4 htmltools_0.5.4 tidyselect_1.2.0
[29] fansi_1.0.3 viridisLite_0.4.1 crayon_1.5.2 tzdb_0.3.0
[33] withr_2.5.0 later_1.3.0 grid_4.2.3 jsonlite_1.8.4
[37] gtable_0.3.1 lifecycle_1.0.3 git2r_0.30.1 magrittr_2.0.3
[41] scales_1.2.1 vroom_1.6.3 cli_3.6.0 stringi_1.7.8
[45] cachem_1.0.6 fs_1.6.2 promises_1.2.0.1 xml2_1.3.3
[49] bslib_0.4.2 ellipsis_0.3.2 generics_0.1.3 vctrs_0.6.0
[53] tools_4.2.3 bit64_4.0.5 glue_1.6.2 crosstalk_1.2.0
[57] hms_1.1.2 processx_3.7.0 parallel_4.2.3 fastmap_1.1.0
[61] yaml_2.3.7 timechange_0.1.1 colorspace_2.0-3 rvest_1.0.3
[65] knitr_1.41 sass_0.4.5