Fetch NASA POWER Data Using a Data Frame of Locations

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")

## 
## 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",
  )
)

## ────────────────────────────────────────────────────────────────────────────────

## 

## ── NASA/POWER Source Native Resolution Daily Data  ─────────────────────────────

## Dates (month/day/year): 03/03/2023 through 03/03/2023 in LST

## 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