vignettes/advanced-queries.R
In FlowWest/CDECRetrieve: Retrieve Data from the California Data Exchange Center
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
eval = FALSE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# cdec_query(station = c("ccr", "kwk"), dur_code = "h", sensor_num = "25")
## ---- message=FALSE-----------------------------------------------------------
# library(purrr)
# library(CDECRetrieve)
#
# stations_of_interest <- c("kwk", "ccr", "bsf")
#
# # 'map' through the stations of interest and apply them to the function
# map(stations_of_interest, function(s) {
# cdec_query(station = s, sensor_num = "25", dur_code = "h")
# })
# #> [[1]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
# #> 7 CDEC KWK 2020-12-08 05:00:00 25 52.8
# #> 8 CDEC KWK 2020-12-08 06:00:00 25 52.8
# #> 9 CDEC KWK 2020-12-08 07:00:00 25 52.8
# #> 10 CDEC KWK 2020-12-08 08:00:00 25 52.8
# #> # ... with 63 more rows
# #>
# #> [[2]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 63 more rows
# #>
# #> [[3]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC BSF 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC BSF 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC BSF 2020-12-08 01:00:00 25 52
# #> 4 CDEC BSF 2020-12-08 02:00:00 25 51.9
# #> 5 CDEC BSF 2020-12-08 03:00:00 25 51.7
# #> 6 CDEC BSF 2020-12-08 04:00:00 25 51.5
# #> 7 CDEC BSF 2020-12-08 05:00:00 25 51.2
# #> 8 CDEC BSF 2020-12-08 06:00:00 25 50.9
# #> 9 CDEC BSF 2020-12-08 07:00:00 25 50.7
# #> 10 CDEC BSF 2020-12-08 08:00:00 25 50.5
# #> # ... with 63 more rows
## ---- message=FALSE-----------------------------------------------------------
# temp_data <- map_df(stations_of_interest, function(s) {
# cdec_query(station = s, sensor_num = "25", dur_code = "h")
# })
#
# head(temp_data)
# #> # A tibble: 6 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
## ---- message=FALSE, warning=FALSE--------------------------------------------
# library(ggplot2)
#
# temp_data %>%
# ggplot(aes(datetime, parameter_value, color=location_id)) + geom_line()
## ---- message=FALSE-----------------------------------------------------------
# # here ~ tells map that this a function, and to interpret '.' as a value
# # being passed from the `stations_of_interest`
# map_df(stations_of_interest, ~cdec_query(., "25", "h"))
# #> # A tibble: 219 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
# #> 7 CDEC KWK 2020-12-08 05:00:00 25 52.8
# #> 8 CDEC KWK 2020-12-08 06:00:00 25 52.8
# #> 9 CDEC KWK 2020-12-08 07:00:00 25 52.8
# #> 10 CDEC KWK 2020-12-08 08:00:00 25 52.8
# #> # ... with 209 more rows
## -----------------------------------------------------------------------------
# stations_of_interest <- c("ccr", "kwk")
# sensors_of_interest <- c("25", "1")
# dur_code = "h"
## ---- message=FALSE-----------------------------------------------------------
# pmap_df(list(stations_of_interest,
# sensors_of_interest,
# dur_code), ~cdec_query(station = ..1, sensor_num = ..2, dur_code = ..3))
# #> # A tibble: 146 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 136 more rows
## ---- message=FALSE, warning=FALSE--------------------------------------------
# cdec_query_hourly <- purrr::partial(cdec_query, dur_code="h")
#
# # we only have to supply two arguments
# map2_df(stations_of_interest, sensors_of_interest,
# ~cdec_query_hourly(station=.x, sensor_num=.y))
# #> # A tibble: 146 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 136 more rows
## -----------------------------------------------------------------------------
# stations_of_interest <- c("bsf", "kwk")
# sensors_of_interest <- c("25", "27")
# dur_code <- "h"
#
# # data frame of all combinations
# ins <- expand.grid(x=stations_of_interest,
# y=sensors_of_interest,
# z=dur_code,
# stringsAsFactors = FALSE)
#
# temp_and_turb <-
# pmap_df(list(ins$x, ins$y, ins$z), ~cdec_query(..1, ..2, ..3))
#
# head(temp_and_turb)
# #> # A tibble: 6 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC BSF 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC BSF 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC BSF 2020-12-08 01:00:00 25 52
# #> 4 CDEC BSF 2020-12-08 02:00:00 25 51.9
# #> 5 CDEC BSF 2020-12-08 03:00:00 25 51.7
# #> 6 CDEC BSF 2020-12-08 04:00:00 25 51.5
## ---- warning=FALSE-----------------------------------------------------------
# param_names <- c("25" = "Temperature (F)", "27" = "Turbidity (NTU)")
# temp_and_turb %>%
# ggplot(aes(datetime, parameter_value, color=location_id)) +
# geom_line() +
# facet_wrap(. ~ param_names[parameter_cd], scales = "free")
FlowWest/CDECRetrieve documentation built on Oct. 10, 2022, 9:31 p.m.
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## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
eval = FALSE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# cdec_query(station = c("ccr", "kwk"), dur_code = "h", sensor_num = "25")
## ---- message=FALSE-----------------------------------------------------------
# library(purrr)
# library(CDECRetrieve)
#
# stations_of_interest <- c("kwk", "ccr", "bsf")
#
# # 'map' through the stations of interest and apply them to the function
# map(stations_of_interest, function(s) {
# cdec_query(station = s, sensor_num = "25", dur_code = "h")
# })
# #> [[1]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
# #> 7 CDEC KWK 2020-12-08 05:00:00 25 52.8
# #> 8 CDEC KWK 2020-12-08 06:00:00 25 52.8
# #> 9 CDEC KWK 2020-12-08 07:00:00 25 52.8
# #> 10 CDEC KWK 2020-12-08 08:00:00 25 52.8
# #> # ... with 63 more rows
# #>
# #> [[2]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 63 more rows
# #>
# #> [[3]]
# #> # A tibble: 73 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC BSF 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC BSF 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC BSF 2020-12-08 01:00:00 25 52
# #> 4 CDEC BSF 2020-12-08 02:00:00 25 51.9
# #> 5 CDEC BSF 2020-12-08 03:00:00 25 51.7
# #> 6 CDEC BSF 2020-12-08 04:00:00 25 51.5
# #> 7 CDEC BSF 2020-12-08 05:00:00 25 51.2
# #> 8 CDEC BSF 2020-12-08 06:00:00 25 50.9
# #> 9 CDEC BSF 2020-12-08 07:00:00 25 50.7
# #> 10 CDEC BSF 2020-12-08 08:00:00 25 50.5
# #> # ... with 63 more rows
## ---- message=FALSE-----------------------------------------------------------
# temp_data <- map_df(stations_of_interest, function(s) {
# cdec_query(station = s, sensor_num = "25", dur_code = "h")
# })
#
# head(temp_data)
# #> # A tibble: 6 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
## ---- message=FALSE, warning=FALSE--------------------------------------------
# library(ggplot2)
#
# temp_data %>%
# ggplot(aes(datetime, parameter_value, color=location_id)) + geom_line()
## ---- message=FALSE-----------------------------------------------------------
# # here ~ tells map that this a function, and to interpret '.' as a value
# # being passed from the `stations_of_interest`
# map_df(stations_of_interest, ~cdec_query(., "25", "h"))
# #> # A tibble: 219 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC KWK 2020-12-07 23:00:00 25 52.9
# #> 2 CDEC KWK 2020-12-08 00:00:00 25 52.9
# #> 3 CDEC KWK 2020-12-08 01:00:00 25 52.9
# #> 4 CDEC KWK 2020-12-08 02:00:00 25 52.8
# #> 5 CDEC KWK 2020-12-08 03:00:00 25 52.8
# #> 6 CDEC KWK 2020-12-08 04:00:00 25 52.8
# #> 7 CDEC KWK 2020-12-08 05:00:00 25 52.8
# #> 8 CDEC KWK 2020-12-08 06:00:00 25 52.8
# #> 9 CDEC KWK 2020-12-08 07:00:00 25 52.8
# #> 10 CDEC KWK 2020-12-08 08:00:00 25 52.8
# #> # ... with 209 more rows
## -----------------------------------------------------------------------------
# stations_of_interest <- c("ccr", "kwk")
# sensors_of_interest <- c("25", "1")
# dur_code = "h"
## ---- message=FALSE-----------------------------------------------------------
# pmap_df(list(stations_of_interest,
# sensors_of_interest,
# dur_code), ~cdec_query(station = ..1, sensor_num = ..2, dur_code = ..3))
# #> # A tibble: 146 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 136 more rows
## ---- message=FALSE, warning=FALSE--------------------------------------------
# cdec_query_hourly <- purrr::partial(cdec_query, dur_code="h")
#
# # we only have to supply two arguments
# map2_df(stations_of_interest, sensors_of_interest,
# ~cdec_query_hourly(station=.x, sensor_num=.y))
# #> # A tibble: 146 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC CCR 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC CCR 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC CCR 2020-12-08 01:00:00 25 52.1
# #> 4 CDEC CCR 2020-12-08 02:00:00 25 52
# #> 5 CDEC CCR 2020-12-08 03:00:00 25 51.9
# #> 6 CDEC CCR 2020-12-08 04:00:00 25 51.9
# #> 7 CDEC CCR 2020-12-08 05:00:00 25 51.8
# #> 8 CDEC CCR 2020-12-08 06:00:00 25 51.7
# #> 9 CDEC CCR 2020-12-08 07:00:00 25 51.6
# #> 10 CDEC CCR 2020-12-08 08:00:00 25 51.6
# #> # ... with 136 more rows
## -----------------------------------------------------------------------------
# stations_of_interest <- c("bsf", "kwk")
# sensors_of_interest <- c("25", "27")
# dur_code <- "h"
#
# # data frame of all combinations
# ins <- expand.grid(x=stations_of_interest,
# y=sensors_of_interest,
# z=dur_code,
# stringsAsFactors = FALSE)
#
# temp_and_turb <-
# pmap_df(list(ins$x, ins$y, ins$z), ~cdec_query(..1, ..2, ..3))
#
# head(temp_and_turb)
# #> # A tibble: 6 x 5
# #> agency_cd location_id datetime parameter_cd parameter_value
# #> <chr> <chr> <dttm> <chr> <dbl>
# #> 1 CDEC BSF 2020-12-07 23:00:00 25 52.4
# #> 2 CDEC BSF 2020-12-08 00:00:00 25 52.2
# #> 3 CDEC BSF 2020-12-08 01:00:00 25 52
# #> 4 CDEC BSF 2020-12-08 02:00:00 25 51.9
# #> 5 CDEC BSF 2020-12-08 03:00:00 25 51.7
# #> 6 CDEC BSF 2020-12-08 04:00:00 25 51.5
## ---- warning=FALSE-----------------------------------------------------------
# param_names <- c("25" = "Temperature (F)", "27" = "Turbidity (NTU)")
# temp_and_turb %>%
# ggplot(aes(datetime, parameter_value, color=location_id)) +
# geom_line() +
# facet_wrap(. ~ param_names[parameter_cd], scales = "free")
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