Usually when you use {nasapower} to fetch data, you use a vector of longitude and latitude locations to make requests from the API.
But if you have coordinates in a data frame, e.g., sample locations with sampling dates that you want POWER weather data for, it may not be as intuitive.
Here’s a quick illustration how you can use a data frame object to feed into get_power() and fetch relative humidity data that way using {purrr} and {dplyr}.
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library("nasapower")
library("purrr")
library("dplyr")
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##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
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df <- data.frame(
stringsAsFactors = FALSE,
lon = c(151.81, 112.5, 115.5),
lat = c(-27.48, -55.5, -50.5),
date = c("3/3/2023", "5/12/2023", "1/3/2024")
)
xy_list <-
as.list(as.data.frame(t(df[, 1:2]),
stringsAsFactors = FALSE,
row.names = NA))
bind_rows(
map2(
.x = xy_list,
.y = df$date,
.f = get_power,
community = "ag",
pars = "RH2M",
temporal_api = "daily",
)
)
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## NASA/POWER CERES/MERRA2 Native Resolution Daily Data
## Dates (month/day/year): 03/03/2023 through 03/03/2023
## Location: Latitude -27.48 Longitude 151.81
## Elevation from MERRA-2: Average for 0.5 x 0.625 degree lat/lon region = 442.77 meters
## The value for missing source data that cannot be computed or is outside of the sources availability range: NA
## Parameter(s):
##
## Parameters:
## RH2M MERRA-2 Relative Humidity at 2 Meters (%)
##
## # A tibble: 3 × 8
## LON LAT YEAR MM DD DOY YYYYMMDD RH2M
## <dbl> <dbl> <dbl> <int> <int> <int> <date> <dbl>
## 1 152. -27.5 2023 3 3 62 2023-03-03 64.6
## 2 112. -55.5 2023 12 5 339 2023-12-05 90.5
## 3 116. -50.5 2024 3 1 61 2024-03-01 85.6