Coded data access in R
Dave Bosworth
CA Department of Water
Resources
April 17, 2024
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsSimple Methods
read_csv() with URL
library(readr)
# Import data from a URL using read_csv()
url_dayflow_2023 <- "https://data.cnra.ca.gov/dataset/06ee2016-b138-47d7-9e85-f46fae674536/resource/f7c1ba7f-bd64-4762-88e3-6db9b2501b38/download/dayflowcalculations2023.csv"
df_dayflow_2023 <- read_csv(url_dayflow_2023)
df_dayflow_2023## # A tibble: 365 × 29
## Year Mo Date SAC YOLO CSMR MOKE MISC SJR EAST TOT CCC SWP
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022 10 10/1… 7210 35 25 88 150 212 476 7721 132 494
## 2 2022 10 10/2… 7470 36 26 0 145 218 389 7895 132 499
## 3 2022 10 10/3… 7550 36 26 0 147 245 418 8004 136 499
## 4 2022 10 10/4… 7640 36 26 0 129 234 390 8065 128 498
## 5 2022 10 10/5… 7590 36 26 0 93 222 341 7967 170 490
## 6 2022 10 10/6… 7440 35 26 0 63 213 301 7776 159 486
## 7 2022 10 10/7… 7460 33 25 0 50 216 291 7784 136 494
## 8 2022 10 10/8… 7200 33 24 0 59 248 331 7564 111 493
## 9 2022 10 10/9… 7010 32 24 0 66 308 398 7440 113 491
## 10 2022 10 10/1… 6870 32 24 0 71 305 400 7302 116 490
## # ℹ 355 more rows
## # ℹ 16 more variables: CVP <dbl>, NBAQ <dbl>, EXPORTS <dbl>, GCD <dbl>,
## # PREC <dbl>, MISDV <dbl>, CD <dbl>, XGEO <dbl>, WEST <dbl>, RIO <dbl>,
## # OUT <dbl>, EXPIN <dbl>, DIVER <dbl>, EFFEC <dbl>, EFFDIV <dbl>, X2 <dbl>download.file() then import
# Import data from EDI to temporary directory
url_edi_emp_2022 <- "https://portal.edirepository.org/nis/dataviewer?packageid=edi.458.10&entityid=cf231071093ac2861893793517db26f3"
download.file(url_edi_emp_2022, file.path(tempdir(), "EMP_DWQ_1975_2022.csv"), mode = "wb")
# Then import data using read_csv()
df_emp_2022 <- read_csv(file.path(tempdir(), "EMP_DWQ_1975_2022.csv"))
df_emp_2022## # A tibble: 17,366 × 73
## Station Date Time SampleDescription Flag FlagDescription FieldNotes
## <chr> <date> <time> <chr> <chr> <chr> <chr>
## 1 D11 1975-01-07 13:00 <NA> <NA> <NA> <NA>
## 2 D15 1975-01-07 14:00 <NA> <NA> <NA> <NA>
## 3 D16 1975-01-07 16:00 <NA> <NA> <NA> <NA>
## 4 D19 1975-01-07 15:00 <NA> <NA> <NA> <NA>
## 5 D22 1975-01-07 13:00 <NA> <NA> <NA> <NA>
## 6 D24 1975-01-07 14:00 <NA> <NA> <NA> <NA>
## 7 D26 1975-01-07 15:00 <NA> <NA> <NA> <NA>
## 8 D4 1975-01-07 12:00 <NA> <NA> <NA> <NA>
## 9 D10 1975-01-08 13:00 <NA> <NA> <NA> <NA>
## 10 D12 1975-01-08 14:00 <NA> <NA> <NA> <NA>
## # ℹ 17,356 more rows
## # ℹ 66 more variables: Weather <chr>, AirTemp <dbl>, WindVelocity <dbl>,
## # WindDirection <dbl>, NorthLat <dbl>, WestLong <dbl>, Chla_Sign <chr>,
## # Chla <dbl>, Pheophytin_Sign <chr>, Pheophytin <dbl>,
## # TotAlkalinity_Sign <chr>, TotAlkalinity <dbl>, TotAmmonia_Sign <chr>,
## # TotAmmonia <dbl>, DissAmmonia_Sign <chr>, DissAmmonia <dbl>,
## # DissBromide_Sign <chr>, DissBromide <dbl>, DissCalcium_Sign <chr>, …Dedicated R Packages
EDIutils
Package documentation: https://docs.ropensci.org/EDIutils/
Available on
CRAN: https://cloud.r-project.org/web/packages/EDIutils/index.html
List data package revisions
library(EDIutils)
edi_scope <- "edi"
edi_emp_id <- 458
list_data_package_revisions(scope = edi_scope, identifier = edi_emp_id)## [1] 1 2 3 4 5 6 7 8 9 10edi_emp_rev <- list_data_package_revisions(
scope = edi_scope,
identifier = edi_emp_id,
filter = "newest"
)
edi_emp_pid <- paste(edi_scope, edi_emp_id, edi_emp_rev, sep = ".")
edi_emp_pid## [1] "edi.458.10"List data entities
df_edi_emp_ent <- read_data_entity_names(packageId = edi_emp_pid)
df_edi_emp_ent## entityId entityName
## 1 cf231071093ac2861893793517db26f3 EMP_DWQ_1975_2022
## 2 86dd696bc3f8407ff52954094e1e9dcf EMP_DWQ_Stations_1975-2022
## 3 afc5b55a61e9a16d29fcaef4d802f5be EMP_DWQ_FlagCodes
## 4 b399c042c893809547dc196a762b929f EMP_DWQ_metadata_methodsImport data entity
edi_emp_ent_id <- df_edi_emp_ent %>%
filter(entityName == "EMP_DWQ_1975_2022") %>%
pull(entityId)
raw_emp_2022_edi <- read_data_entity(packageId = edi_emp_pid, entityId = edi_emp_ent_id)
df_emp_2022_edi <- read_csv(raw_emp_2022_edi)
df_emp_2022_edi## # A tibble: 17,366 × 73
## Station Date Time SampleDescription Flag FlagDescription FieldNotes
## <chr> <date> <time> <chr> <chr> <chr> <chr>
## 1 D11 1975-01-07 13:00 <NA> <NA> <NA> <NA>
## 2 D15 1975-01-07 14:00 <NA> <NA> <NA> <NA>
## 3 D16 1975-01-07 16:00 <NA> <NA> <NA> <NA>
## 4 D19 1975-01-07 15:00 <NA> <NA> <NA> <NA>
## 5 D22 1975-01-07 13:00 <NA> <NA> <NA> <NA>
## 6 D24 1975-01-07 14:00 <NA> <NA> <NA> <NA>
## 7 D26 1975-01-07 15:00 <NA> <NA> <NA> <NA>
## 8 D4 1975-01-07 12:00 <NA> <NA> <NA> <NA>
## 9 D10 1975-01-08 13:00 <NA> <NA> <NA> <NA>
## 10 D12 1975-01-08 14:00 <NA> <NA> <NA> <NA>
## # ℹ 17,356 more rows
## # ℹ 66 more variables: Weather <chr>, AirTemp <dbl>, WindVelocity <dbl>,
## # WindDirection <dbl>, NorthLat <dbl>, WestLong <dbl>, Chla_Sign <chr>,
## # Chla <dbl>, Pheophytin_Sign <chr>, Pheophytin <dbl>,
## # TotAlkalinity_Sign <chr>, TotAlkalinity <dbl>, TotAmmonia_Sign <chr>,
## # TotAmmonia <dbl>, DissAmmonia_Sign <chr>, DissAmmonia <dbl>,
## # DissBromide_Sign <chr>, DissBromide <dbl>, DissCalcium_Sign <chr>, …dataRetrieval
Package documentation: https://doi-usgs.github.io/dataRetrieval/
Available
on CRAN: https://cloud.r-project.org/web/packages/dataRetrieval/index.html
Sacramento River at Freeport CA (USGS station 11447650): https://waterdata.usgs.gov/monitoring-location/11447650/
NWIS Web Services
Station Information
library(dataRetrieval)
df_srf_sta_info <- whatNWISsites(sites = "11447650")
glimpse(df_srf_sta_info)## Rows: 1
## Columns: 8
## $ agency_cd <chr> "USGS"
## $ site_no <chr> "11447650"
## $ station_nm <chr> "SACRAMENTO R A FREEPORT CA"
## $ site_tp_cd <chr> "ST"
## $ dec_lat_va <dbl> 38.45566
## $ dec_long_va <dbl> -121.5016
## $ colocated <lgl> FALSE
## $ queryTime <dttm> 2024-04-17 16:12:33Data availability
df_srf_uv_data_avail <- whatNWISdata(siteNumber = "11447650", service = "uv")
glimpse(df_srf_uv_data_avail)## Rows: 15
## Columns: 24
## $ agency_cd <chr> "USGS", "USGS", "USGS", "USGS", "USGS", "USGS", "US…
## $ site_no <chr> "11447650", "11447650", "11447650", "11447650", "11…
## $ station_nm <chr> "SACRAMENTO R A FREEPORT CA", "SACRAMENTO R A FREEP…
## $ site_tp_cd <chr> "ST", "ST", "ST", "ST", "ST", "ST", "ST", "ST", "ST…
## $ dec_lat_va <dbl> 38.45566, 38.45566, 38.45566, 38.45566, 38.45566, 3…
## $ dec_long_va <dbl> -121.5016, -121.5016, -121.5016, -121.5016, -121.50…
## $ coord_acy_cd <chr> "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "…
## $ dec_coord_datum_cd <chr> "NAD83", "NAD83", "NAD83", "NAD83", "NAD83", "NAD83…
## $ alt_va <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ alt_acy_va <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ alt_datum_cd <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ huc_cd <chr> "18020163", "18020163", "18020163", "18020163", "18…
## $ data_type_cd <chr> "uv", "uv", "uv", "uv", "uv", "uv", "uv", "uv", "uv…
## $ parm_cd <chr> "00010", "00010", "00060", "00065", "00095", "00300…
## $ stat_cd <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ts_id <chr> "15731", "15760", "236032", "15738", "236034", "157…
## $ loc_web_ds <chr> "[Right Bank Pump Stand]", "BGC PROJECT, [East Fend…
## $ medium_grp_cd <chr> "wat", "wat", "wat", "wat", "wat", "wat", "wat", "w…
## $ parm_grp_cd <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ srs_id <chr> "1645597", "1645597", "1645423", "17164583", "16466…
## $ access_cd <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "…
## $ begin_date <date> 2007-10-01, 2013-08-30, 2007-10-01, 2007-10-01, 20…
## $ end_date <date> 2024-04-17, 2024-04-17, 2024-04-17, 2024-04-17, 20…
## $ count_nu <dbl> 6043, 3883, 6043, 6043, 3883, 3883, 3883, 3883, 388…USGS parameter codes
as_tibble(parameterCdFile)## # A tibble: 24,200 × 6
## parameter_cd parameter_group_nm parameter_nm casrn srsname parameter_units
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 00001 Information Location in cr… " " " " ft
## 2 00002 Information Location in cr… " " " " %
## 3 00003 Information Sampling depth… " " " " ft
## 4 00004 Physical Stream width, … " " "Instr… ft
## 5 00005 Information Location in cr… " " " " %
## 6 00008 Information Sample account… " " " " nu
## 7 00009 Information Location in cr… " " " " ft
## 8 00010 Physical Temperature, w… " " "Tempe… deg C
## 9 00011 Physical Temperature, w… " " "Tempe… deg F
## 10 00012 Physical Evaporation te… " " " " deg C
## # ℹ 24,190 more rowssrf_uv_parm_cd <- unique(df_srf_uv_data_avail$parm_cd)
srf_uv_parm_cd## [1] "00010" "00060" "00065" "00095" "00300" "00301" "00400" "00480" "32295"
## [10] "32316" "63680" "72137" "72255"df_srf_uv_parm_cd <- as_tibble(parameterCdFile) %>%
filter(parameter_cd %in% srf_uv_parm_cd) %>%
select(parameter_cd, parameter_nm, parameter_units)
df_srf_uv_parm_cd## # A tibble: 13 × 3
## parameter_cd parameter_nm parameter_units
## <chr> <chr> <chr>
## 1 00010 Temperature, water, degrees Celsius deg C
## 2 00060 Discharge, cubic feet per second ft3/s
## 3 00065 Gage height, feet ft
## 4 00095 Specific conductance, water, unfiltered, micros… uS/cm @25C
## 5 00300 Dissolved oxygen, water, unfiltered, milligrams… mg/l
## 6 00301 Dissolved oxygen, water, unfiltered, percent of… % saturatn
## 7 00400 pH, water, unfiltered, field, standard units std units
## 8 00480 Salinity, water, unfiltered, parts per thousand ppth
## 9 32295 Dissolved organic matter fluorescence (fDOM), w… ug/l QSE
## 10 32316 Chlorophyll fluorescence (fChl), water, in situ… ug/l
## 11 63680 Turbidity, water, unfiltered, monochrome near i… FNU
## 12 72137 Discharge, tidally filtered, cubic feet per sec… ft3/s
## 13 72255 Mean water velocity for discharge computation, … ft/secas_tibble(df_srf_uv_data_avail) %>%
select(parm_cd, loc_web_ds, begin_date, end_date, count_nu) %>%
left_join(df_srf_uv_parm_cd, by = join_by(parm_cd == parameter_cd))## # A tibble: 15 × 7
## parm_cd loc_web_ds begin_date end_date count_nu parameter_nm
## <chr> <chr> <date> <date> <dbl> <chr>
## 1 00010 [Right Bank Pump Stand] 2007-10-01 2024-04-17 6043 Temperature…
## 2 00010 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 Temperature…
## 3 00060 <NA> 2007-10-01 2024-04-17 6043 Discharge, …
## 4 00065 [PRIMARY] 2007-10-01 2024-04-17 6043 Gage height…
## 5 00095 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 Specific co…
## 6 00300 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 Dissolved o…
## 7 00301 BGC PROJECT, [Computed] 2013-08-30 2024-04-17 3883 Dissolved o…
## 8 00400 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 pH, water, …
## 9 00480 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 Salinity, w…
## 10 32295 BGC PROJECT, [East Fende… 2021-04-18 2024-04-17 1095 Dissolved o…
## 11 32316 BGC PROJECT 2013-08-30 2024-04-17 3883 Chlorophyll…
## 12 63680 MEDIAN TS087: YSI model … 2009-12-03 2013-10-01 1398 Turbidity, …
## 13 63680 BGC PROJECT, [East Fende… 2013-08-30 2024-04-17 3883 Turbidity, …
## 14 72137 <NA> 2015-10-01 2024-04-16 3120 Discharge, …
## 15 72255 <NA> 2015-10-01 2024-04-17 3121 Mean water …
## # ℹ 1 more variable: parameter_units <chr>Continuous data - instantaneous
df_srf_spc_inst <- readNWISuv(
siteNumbers = "11447650",
parameterCd = "00095",
startDate = "2023-01-01", endDate = "2023-12-31",
tz = "America/Los_Angeles"
)
as_tibble(df_srf_spc_inst)## # A tibble: 34,216 × 6
## agency_cd site_no dateTime X_00095_00000 X_00095_00000_cd tz_cd
## <chr> <chr> <dttm> <dbl> <chr> <chr>
## 1 USGS 11447650 2023-01-01 00:00:00 122 P Americ…
## 2 USGS 11447650 2023-01-01 00:15:00 121 P Americ…
## 3 USGS 11447650 2023-01-01 00:30:00 121 P Americ…
## 4 USGS 11447650 2023-01-01 00:45:00 120 P Americ…
## 5 USGS 11447650 2023-01-01 01:00:00 120 P Americ…
## 6 USGS 11447650 2023-01-01 01:15:00 120 P Americ…
## 7 USGS 11447650 2023-01-01 01:30:00 119 P Americ…
## 8 USGS 11447650 2023-01-01 01:45:00 119 P Americ…
## 9 USGS 11447650 2023-01-01 02:00:00 118 P Americ…
## 10 USGS 11447650 2023-01-01 02:15:00 117 P Americ…
## # ℹ 34,206 more rowsContinuous data - daily averages
df_srf_tfq_davg <- readNWISdv(
siteNumbers = "11447650",
parameterCd = "72137",
startDate = "2023-01-01", endDate = "2023-12-31"
)
as_tibble(df_srf_tfq_davg)## # A tibble: 365 × 5
## agency_cd site_no Date X_72137_00003 X_72137_00003_cd
## <chr> <chr> <date> <dbl> <chr>
## 1 USGS 11447650 2023-01-01 56300 A
## 2 USGS 11447650 2023-01-02 67700 A
## 3 USGS 11447650 2023-01-03 70500 A
## 4 USGS 11447650 2023-01-04 67400 A
## 5 USGS 11447650 2023-01-05 65000 A
## 6 USGS 11447650 2023-01-06 67900 A
## 7 USGS 11447650 2023-01-07 73800 A
## 8 USGS 11447650 2023-01-08 78700 A
## 9 USGS 11447650 2023-01-09 79800 A
## 10 USGS 11447650 2023-01-10 79900 A
## # ℹ 355 more rowsWQP Web Services
Station Information
whatWQPsites(siteid = "USGS-11447650")## # A tibble: 1 × 37
## OrganizationIdentifier OrganizationFormalName MonitoringLocationId…¹
## <chr> <chr> <chr>
## 1 USGS-CA USGS California Water Science C… USGS-11447650
## # ℹ abbreviated name: ¹MonitoringLocationIdentifier
## # ℹ 34 more variables: MonitoringLocationName <chr>,
## # MonitoringLocationTypeName <chr>, MonitoringLocationDescriptionText <chr>,
## # HUCEightDigitCode <chr>, DrainageAreaMeasure.MeasureValue <dbl>,
## # DrainageAreaMeasure.MeasureUnitCode <chr>,
## # ContributingDrainageAreaMeasure.MeasureValue <dbl>,
## # ContributingDrainageAreaMeasure.MeasureUnitCode <chr>, …Data availability
df_srf_nutr_data_avail <- readWQPsummary(
siteid = "USGS-11447650",
characteristicType = "Nutrient"
)## NEWS: USGS data availability and format are changing.
## Beginning in mid-March 2024 the data obtained from legacy profiles
## will not include new USGS data or recent updates to existing data.
## To view the status of changes in data availability and code functionality, visit:
## https://doi-usgs.github.io/dataRetrieval/articles/Status.html
## If you have additional questions about these changes,
## email CompTools@usgs.gov.df_srf_nutr_data_avail## # A tibble: 480 × 19
## Provider MonitoringLocationIdentifier YearSummarized CharacteristicType
## <chr> <chr> <dbl> <chr>
## 1 NWIS USGS-11447650 1958 Nutrient
## 2 NWIS USGS-11447650 1959 Nutrient
## 3 NWIS USGS-11447650 1960 Nutrient
## 4 NWIS USGS-11447650 1961 Nutrient
## 5 NWIS USGS-11447650 1962 Nutrient
## 6 NWIS USGS-11447650 1963 Nutrient
## 7 NWIS USGS-11447650 1964 Nutrient
## 8 NWIS USGS-11447650 1965 Nutrient
## 9 NWIS USGS-11447650 1966 Nutrient
## 10 NWIS USGS-11447650 1966 Nutrient
## # ℹ 470 more rows
## # ℹ 15 more variables: CharacteristicName <chr>, ActivityCount <dbl>,
## # ResultCount <dbl>, LastResultSubmittedDate <chr>,
## # OrganizationIdentifier <chr>, OrganizationFormalName <chr>,
## # MonitoringLocationName <chr>, MonitoringLocationTypeName <chr>,
## # ResolvedMonitoringLocationTypeName <chr>, HUCEightDigitCode <chr>,
## # MonitoringLocationUrl <chr>, CountyName <chr>, StateName <chr>, …df_srf_nutr_data_avail %>% summarize(
min_yr = min(YearSummarized),
max_yr = max(YearSummarized),
num_samp = sum(ResultCount),
.by = CharacteristicName
)## # A tibble: 12 × 4
## CharacteristicName min_yr max_yr num_samp
## <chr> <dbl> <dbl> <dbl>
## 1 Nitrate 1958 2023 1470
## 2 Phosphate-phosphorus 1966 1992 54
## 3 Phosphorus 1970 2023 1053
## 4 Inorganic nitrogen (nitrate and nitrite) 1971 2023 714
## 5 Kjeldahl nitrogen 1973 2023 851
## 6 Nitrite 1973 2023 1110
## 7 Nitrogen, mixed forms (NH3), (NH4), organic, (NO2) an… 1973 2023 1298
## 8 Ammonia and ammonium 1977 2023 1349
## 9 Organic Nitrogen 1977 2023 919
## 10 Orthophosphate 1981 2023 1171
## 11 Nitrogen 2001 2023 222
## 12 Particulate nitrogen/particulate organic carbon 2001 2018 115Discrete WQ data
Import discrete nitrate data from the Water Quality Portal (WQP)
df_srf_nitrate <- readWQPqw(
siteNumbers = "USGS-11447650",
parameterCd = "Nitrate",
startDate = "2023-01-01", endDate = "2023-12-31",
tz = "America/Los_Angeles"
)## NEWS: USGS data availability and format are changing.
## Beginning in mid-March 2024 the data obtained from legacy profiles
## will not include new USGS data or recent updates to existing data.
## To view the status of changes in data availability and code functionality, visit:
## https://doi-usgs.github.io/dataRetrieval/articles/Status.html
## If you have additional questions about these changes,
## email CompTools@usgs.gov.as_tibble(df_srf_nitrate) %>%
select(MonitoringLocationIdentifier, ActivityStartDateTime,
CharacteristicName, USGSPCode, ResultMeasureValue)## # A tibble: 42 × 5
## MonitoringLocationIdenti…¹ ActivityStartDateTime CharacteristicName USGSPCode
## <chr> <dttm> <chr> <chr>
## 1 USGS-11447650 2023-01-04 10:15:00 Nitrate 00618
## 2 USGS-11447650 2023-01-04 10:15:00 Nitrate 71851
## 3 USGS-11447650 2023-01-11 08:45:00 Nitrate 71851
## 4 USGS-11447650 2023-01-11 08:45:00 Nitrate 00618
## 5 USGS-11447650 2023-01-12 09:30:00 Nitrate 00618
## 6 USGS-11447650 2023-01-12 09:30:00 Nitrate 71851
## 7 USGS-11447650 2023-01-31 09:30:00 Nitrate 00618
## 8 USGS-11447650 2023-01-31 09:30:00 Nitrate 71851
## 9 USGS-11447650 2023-02-07 13:15:00 Nitrate 00618
## 10 USGS-11447650 2023-02-07 13:15:00 Nitrate 71851
## # ℹ 32 more rows
## # ℹ abbreviated name: ¹MonitoringLocationIdentifier
## # ℹ 1 more variable: ResultMeasureValue <dbl>Other Information
More useful links for accessing USGS data through
dataRetrieval:
- Parameter codes: https://help.waterdata.usgs.gov/codes-and-parameters/parameters
- Stat (Statistic) codes: https://help.waterdata.usgs.gov/code/stat_cd_nm_query?stat_nm_cd=%25&fmt=html
USGS County and State codes
as_tibble(countyCd)## # A tibble: 3,253 × 5
## STATE COUNTY COUNTY_NAME STUSAB COUNTY_ID
## <chr> <chr> <chr> <chr> <chr>
## 1 00 000 Unspecified <NA> <NA>
## 2 01 001 Autauga County AL H1
## 3 01 003 Baldwin County AL H1
## 4 01 005 Barbour County AL H1
## 5 01 007 Bibb County AL H1
## 6 01 009 Blount County AL H1
## 7 01 011 Bullock County AL H1
## 8 01 013 Butler County AL H1
## 9 01 015 Calhoun County AL H1
## 10 01 017 Chambers County AL H1
## # ℹ 3,243 more rowsas_tibble(stateCd)## # A tibble: 58 × 4
## STATE STATE_NAME STUSAB STATENS
## <chr> <chr> <chr> <chr>
## 1 00 Unspecified <NA> <NA>
## 2 01 Alabama AL 01779775
## 3 02 Alaska AK 01785533
## 4 04 Arizona AZ 01779777
## 5 05 Arkansas AR 00068085
## 6 06 California CA 01779778
## 7 08 Colorado CO 01779779
## 8 09 Connecticut CT 01779780
## 9 10 Delaware DE 01779781
## 10 11 District of Columbia DC 01702382
## # ℹ 48 more rowscder
Package documentation: https://hydroecology.net/cder/index.html
Available
on CRAN: https://cran.r-project.org/web/packages/cder/index.html
Station Information
library(cder)cdec_meta(station = "FPT")Import data
df_fpt_turb <- cdec_query(
stations = "FPT", sensors = 221, durations = "E",
start.date = "2024-03-01", end.date = "2024-03-31"
)
df_fpt_turb## # A tibble: 2,877 × 9
## StationID Duration SensorNumber SensorType DateTime
## <chr> <chr> <int> <chr> <dttm>
## 1 FPT E 221 TURB WF 2024-03-01 00:00:00
## 2 FPT E 221 TURB WF 2024-03-01 00:15:00
## 3 FPT E 221 TURB WF 2024-03-01 00:30:00
## 4 FPT E 221 TURB WF 2024-03-01 00:45:00
## 5 FPT E 221 TURB WF 2024-03-01 01:00:00
## 6 FPT E 221 TURB WF 2024-03-01 01:15:00
## 7 FPT E 221 TURB WF 2024-03-01 01:30:00
## 8 FPT E 221 TURB WF 2024-03-01 01:45:00
## 9 FPT E 221 TURB WF 2024-03-01 02:00:00
## 10 FPT E 221 TURB WF 2024-03-01 02:15:00
## # ℹ 2,867 more rows
## # ℹ 4 more variables: ObsDate <dttm>, Value <dbl>, DataFlag <chr>,
## # SensorUnits <chr>IEP Integrated Datasets
deltafish
Available on GitHub: https://github.com/Delta-Stewardship-Council/deltafish
EDI data repository: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=1075
# install.packages("devtools")
# devtools::install_github("Delta-Stewardship-Council/deltafish")
library(deltafish)## Loading required package: arrow##
## Attaching package: 'arrow'## The following object is masked from 'package:lubridate':
##
## duration## The following object is masked from 'package:utils':
##
## timestamp# Build the database - this takes a while, use update = TRUE to
# re-build cached database
create_fish_db()## Reading data from cache directory, revision edi.1075.1## [1] "C:\\Users\\dboswort\\AppData\\Local/deltafish/deltafish/Cache"# Open two data files
surv <- open_survey()
fish <- open_fish()## Note: fish length units are not all compatible across surveys. The Suisun study uses standard length while the other surveys use fork/total length. Use deltafish::convert_lengths to convert the length units of the 20 taxa for which we have conversion equations from standard length to fork/total length.# Filter for sources and taxa of interest and join them together
surv_FMWT <- surv %>% filter(Source == "FMWT") %>% select(SampleID, Date)
fish_smelt <- fish %>%
filter(Taxa %in% c("Dorosoma petenense", "Morone saxatilis", "Spirinchus thaleichthys"))
df_fish <- left_join(surv_FMWT, fish_smelt)
# Collect the resulting data frame - collect executes
# the SQL query and gives you a table
df_fish_c <- collect(df_fish)
df_fish_c## # A tibble: 175,009 × 6
## SampleID Date Length Count Notes_catch Taxa
## <chr> <date> <dbl> <dbl> <chr> <chr>
## 1 FMWT 1 1992-01-10 NA 0 <NA> Morone saxatilis
## 2 FMWT 1 1992-01-10 NA 0 <NA> Dorosoma petenense
## 3 FMWT 1 1992-01-10 NA 0 <NA> Spirinchus thaleichthys
## 4 FMWT 2 1992-02-07 NA 0 <NA> Morone saxatilis
## 5 FMWT 2 1992-02-07 NA 0 <NA> Dorosoma petenense
## 6 FMWT 2 1992-02-07 NA 0 <NA> Spirinchus thaleichthys
## 7 FMWT 3 1992-03-18 NA 0 <NA> Morone saxatilis
## 8 FMWT 3 1992-03-18 NA 0 <NA> Dorosoma petenense
## 9 FMWT 3 1992-03-18 NA 0 <NA> Spirinchus thaleichthys
## 10 FMWT 4 1992-09-16 NA 0 <NA> Morone saxatilis
## # ℹ 174,999 more rowszooper
Available on GitHub: https://github.com/InteragencyEcologicalProgram/zooper
EDI data repository: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=539
# install.packages("devtools")
# devtools::install_github("InteragencyEcologicalProgram/zooper")
library(zooper)
df_zoop <- Zoopsynther(
Data_type = "Community", Response = c("CPUE", "BPUE"),
Sources = c("EMP", "FRP", "FMWT"), Size_class = "Meso",
Date_range = c("1990-10-01", "2000-09-30")
)## [1] "No disclaimers here! Enjoy the clean data!"df_zoop## # A tibble: 151,478 × 35
## Source SizeClass Volume Lifestage Taxname Phylum Class Order Family Genus
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 EMP Meso 10.6 Adult Acanthocyc… Arthr… Cope… Cycl… Cyclo… Acan…
## 2 EMP Meso 10.6 Adult Acartia_Un… Arthr… Cope… Cala… Acart… Acar…
## 3 EMP Meso 10.6 Adult Acartiella… Arthr… Cope… Cala… Acart… Acar…
## 4 EMP Meso 10.6 Adult Asplanchna… Rotif… Euro… Ploi… Aspla… Aspl…
## 5 EMP Meso 10.6 Adult Bosmina lo… Arthr… Bran… Clad… Bosmi… Bosm…
## 6 EMP Meso 10.6 Adult Calanoida_… Arthr… Cope… Cala… <NA> <NA>
## 7 EMP Meso 10.6 Adult Cladocera_… Arthr… Bran… Clad… <NA> <NA>
## 8 EMP Meso 10.6 Adult Cyclopoida… Arthr… Cope… Cycl… <NA> <NA>
## 9 EMP Meso 10.6 Adult Daphnia_Un… Arthr… Bran… Clad… Daphn… Daph…
## 10 EMP Meso 10.6 Adult Diaphanoso… Arthr… Bran… Clad… Sidid… Diap…
## # ℹ 151,468 more rows
## # ℹ 25 more variables: Species <chr>, Taxlifestage <chr>, SampleID <chr>,
## # CPUE <dbl>, BPUE <dbl>, Undersampled <lgl>, Date <dttm>, Station <chr>,
## # Chl <dbl>, Secchi <dbl>, Temperature <dbl>, BottomDepth <dbl>, Tide <chr>,
## # TowType <chr>, Datetime <dttm>, Turbidity <dbl>, pH <dbl>, DO <dbl>,
## # Microcystis <chr>, Year <dbl>, AmphipodCode <chr>, SalSurf <dbl>,
## # SalBott <dbl>, Latitude <dbl>, Longitude <dbl>discretewq
Available on GitHub: https://github.com/InteragencyEcologicalProgram/discretewq
EDI data repository: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=731
# install.packages("devtools")
# devtools::install_github("InteragencyEcologicalProgram/discretewq")
library(discretewq)
df_dwq <- wq(
Sources = c("EMP", "NCRO", "USGS_CAWSC", "USGS_SFBS"),
Start_year = 2020, End_year = 2022
)
df_dwq## # A tibble: 5,176 × 78
## Source Station Latitude Longitude Field_coords Date
## <chr> <chr> <dbl> <dbl> <lgl> <dttm>
## 1 EMP C10A 37.7 -121. FALSE 2020-01-14 00:00:00
## 2 EMP C3A 38.4 -122. FALSE 2020-01-14 00:00:00
## 3 EMP C9 37.8 -122. FALSE 2020-01-14 00:00:00
## 4 EMP D12 38.0 -122. FALSE 2020-01-15 00:00:00
## 5 EMP D19 38.0 -122. FALSE 2020-01-15 00:00:00
## 6 EMP D28A 38.0 -122. FALSE 2020-01-15 00:00:00
## 7 EMP D16 38.1 -122. FALSE 2020-01-16 00:00:00
## 8 EMP D26 38.1 -122. FALSE 2020-01-16 00:00:00
## 9 EMP MD10A 38.0 -121. FALSE 2020-01-16 00:00:00
## 10 EMP P8 38.0 -121. FALSE 2020-01-16 00:00:00
## # ℹ 5,166 more rows
## # ℹ 72 more variables: Datetime <dttm>, Notes <chr>, Depth <dbl>, Tide <chr>,
## # Microcystis <dbl>, Chlorophyll_Sign <chr>, Chlorophyll <dbl>, Secchi <dbl>,
## # Temperature <dbl>, Temperature_bottom <dbl>, Conductivity <dbl>,
## # Conductivity_bottom <dbl>, DissolvedOxygen <dbl>,
## # DissolvedOxygen_bottom <dbl>, DissolvedOxygenPercent <dbl>,
## # DissolvedOxygenPercent_bottom <dbl>, pH <dbl>, pH_bottom <dbl>, …deltamapr
Available on GitHub: https://github.com/InteragencyEcologicalProgram/deltamapr
# install.packages("devtools")
# devtools::install_github("InteragencyEcologicalProgram/deltamapr")
library(deltamapr)
library(sf)## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUEWW_Delta## Simple feature collection with 282 features and 9 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -122.6408 ymin: 37.41522 xmax: -120.9357 ymax: 38.67781
## Geodetic CRS: NAD83
## # A tibble: 282 × 10
## AREA PERIMETER HYDRO_POLY HYDRO_PO_1 HYDRO_24K_ TYPE HNAME Shape_Leng
## <dbl> <dbl> <int> <int> <int> <chr> <chr> <dbl>
## 1 73544304 1033340 791 797 798 MR SACRAM… 2.45
## 2 87637. 3319. 1965 1963 1964 S W 0.0357
## 3 7915130 87428. 1967 1965 1966 C SACTO.… 0.829
## 4 103906 2719. 1970 1969 1970 L GREENS… 0.0264
## 5 106371 2798. 1977 1974 1975 L LAKE W… 0.0283
## 6 159485 3392. 1982 1978 1979 S W 0.0314
## 7 42597. 1003. 1992 1989 1990 S W 0.00952
## 8 5648. 498. 2001 2008 2009 MR SOUTH … 0.00548
## 9 4139. 502. 2006 2012 2013 MR SOUTH … 0.00536
## 10 97840. 6630. 2008 2011 2012 MR SOUTH … 0.0746
## # ℹ 272 more rows
## # ℹ 2 more variables: Shape_Area <dbl>, geometry <POLYGON [°]>ggplot(WW_Delta) + geom_sf() + theme_bw()
The following is additional info that’s useful but not included in tutorial.
Web Scraping
Package documentation: https://rvest.tidyverse.org/index.html
Available on
CRAN: https://cloud.r-project.org/web/packages/rvest/index.html
library(rvest)##
## Attaching package: 'rvest'## The following object is masked from 'package:readr':
##
## guess_encodingurl_pforb <- "https://invasions.si.edu/nemesis/species_summary/-218"
html_pforb <- read_html(url_pforb)
html_elements(html_pforb, "table")## {xml_nodeset (6)}
## [1] <table>\n<tr>\n<td><strong>Kingdom:</strong></td>\n <t ...
## [2] <table class="table table-striped">\n<tr>\n<td>General Habitat</td>\n<td> ...
## [3] <table class="table table-striped">\n<tr>\n<td>Minimum Temperature (ºC)</ ...
## [4] <table class="table table-striped">\n<tr>\n<td>NEP-V</td>\n<td colspan="2 ...
## [5] <table class="table table-striped" id="dist_table">\n<thead><tr>\n<th>Bio ...
## [6] <table class="table table-striped" id="occ_table">\n<thead><tr>\n<th>OCC_ ...df_pforb_traits <- html_elements(html_pforb, "table")[[3]] %>% html_table()
df_pforb_traits## # A tibble: 8 × 3
## X1 X2 X3
## <chr> <chr> <chr>
## 1 Minimum Temperature (ºC) 3 Field, Columbia River estuary (Bollens et al. …
## 2 Maximum Temperature (ºC) 27 Maximum in San Joaquin river, near Stockton (O…
## 3 Minimum Salinity (‰) 0 Field, San Francisco Bay (Orsi and Walter 1991)
## 4 Maximum Salinity (‰) 16 Field, San Francisco Bay (Orsi and Walter 1991…
## 5 Minimum Length (mm) 1 Minimum adult length, male (Orsi and Walter 19…
## 6 Maximum Length (mm) 1.2 Maximum adult length, female (Orsi and Walter …
## 7 Broad Temperature Range None Warm temperate
## 8 Broad Salinity Range None Nontidal Limnetic-MesohalineUse the SelectorGadget tool to find CSS selectors on a webpage: https://selectorgadget.com/
Extract data from .pdf
Package documentation: https://docs.ropensci.org/pdftools/
Available on
CRAN: https://cloud.r-project.org/web/packages/pdftools/index.html
library(pdftools)## Using poppler version 22.04.0# Download Jan 2024 Delta Outflow Computation report from the USBR website to
# the temporary R directory
url_usbr_dout <- "https://www.usbr.gov/mp/cvo/vungvari/dout0124.pdf"
download.file(url_usbr_dout, file.path(tempdir(), "Delta_Outflow_0124.pdf"), mode = "wb")
usbr_pdf_txt <- pdf_text(file.path(tempdir(), "Delta_Outflow_0124.pdf")) %>% read_lines()
usbr_pdf_txt## [1] "02/01/24"
## [2] " U.S. Bureau of Reclamation - Central Valley Operations Office"
## [3] " Delta Outflow Computation (values in c.f.s.)"
## [4] ""
## [5] "Preliminary Data - Subject to Change January 2024"
## [6] "Estimated numbers are in bold Italic print"
## [7] " DELTA INFLOW DELTA EXPORTS OUTFLOW INDEX EXPORT/INFLOW"
## [8] " Sacto R Yolo + East Side S. Joaquin River @ Vernalis Total Clifton Contra Byron Total 3-day"
## [9] " Date NDCU"
## [10] " @Freeport SRTP Misc Streams 7-day Monthly Delta Court Tracy Costa Bethany NBA Delta Avg NDOI 7-day Monthly Daily 3 Day 14 Day"
## [11] " prev dy prev wk prev dy prev dy prev dy Avg Avg Inflow (CLT) (TRA) (CCC) (BBID) Exports TRA & CLT daily Avg Avg (%) (%) (%)"
## [12] " 01/01/24 19,294 210 159 618 1,429 1,356 1,377 21,710 -4,818 2,290 3,547 104 5 32 5,968 7,978 20,561 11,399 20,561 27% 41% 43%"
## [13] " 01/02/24 23,070 227 89 610 1,377 1,361 1,371 25,373 -3,777 2,298 3,540 108 3 30 5,974 6,907 23,177 13,381 21,869 23% 31% 35%"
## [14] " 01/03/24 23,756 244 63 608 1,365 1,375 1,384 26,036 -5,881 2,097 3,546 108 3 33 5,782 5,773 26,136 15,980 23,291 22% 24% 28%"
## [15] " 01/04/24 22,446 261 90 691 1,409 1,389 1,393 24,897 -2,280 2,093 3,532 104 5 29 5,753 5,702 21,424 17,927 22,824 23% 22% 27%"
## [16] " 01/05/24 20,925 279 109 696 1,421 1,397 1,391 23,430 -960 2,191 3,507 109 4 35 5,838 5,655 18,552 19,423 21,970 24% 23% 27%"
## [17] " 01/06/24 21,079 296 70 604 1,385 1,395 1,389 23,434 -1,010 2,395 3,476 110 3 32 6,012 5,731 18,432 20,479 21,380 25% 24% 27%"
## [18] " 01/07/24 21,235 313 64 584 1,379 1,422 1,422 23,575 -1,745 2,393 3,484 111 5 35 6,018 5,815 19,302 21,083 21,083 25% 25% 27%"
## [19] " 01/08/24 20,019 330 73 583 1,615 1,484 1,470 22,620 31 2,389 3,480 110 4 28 6,004 5,872 16,586 20,515 20,521 26% 25% 27%"
## [20] " 01/09/24 18,320 330 64 562 1,812 1,553 1,513 21,088 487 2,697 3,466 92 4 36 6,288 5,970 14,312 19,249 19,831 29% 27% 28%"
## [21] " 01/10/24 17,364 330 57 541 1,852 1,621 1,550 20,144 437 2,696 3,471 80 4 34 6,277 6,067 13,430 17,434 19,191 31% 28% 28%"
## [22] " 01/11/24 16,624 330 58 555 1,885 1,687 1,580 19,452 159 2,599 3,468 82 4 29 6,175 6,133 13,118 16,247 18,639 31% 30% 28%"
## [23] " 01/12/24 16,920 330 59 703 1,881 1,756 1,604 19,893 844 2,593 3,488 98 5 31 6,206 6,105 12,844 15,432 18,156 31% 31% 28%"
## [24] " 01/13/24 16,540 330 59 670 1,868 1,824 1,624 19,467 1,022 2,592 3,553 90 5 38 6,268 6,098 12,177 14,538 17,696 32% 31% 27%"
## [25] " 01/14/24 16,299 330 59 631 1,857 1,861 1,641 19,176 -1,489 1,791 3,554 62 5 43 5,445 5,857 15,219 13,955 17,519 28% 30% 26%"
## [26] " 01/15/24 16,793 330 350 882 1,870 1,873 1,658 20,225 -1,539 1,793 3,548 55 4 30 5,422 5,610 16,342 13,920 17,441 26% 29% 25%"
## [27] " 01/16/24 18,048 330 202 921 1,897 1,878 1,672 21,398 -1,310 1,795 3,541 54 5 35 5,420 5,341 17,288 14,345 17,431 25% 26% 25%"
## [28] " 01/17/24 24,096 330 140 773 1,885 1,883 1,687 27,224 -3,643 1,796 3,534 67 6 31 5,423 5,336 25,443 16,062 17,902 20% 23% 24%"
## [29] " 01/18/24 25,440 330 164 852 1,921 1,893 1,702 28,707 -3,871 1,799 3,547 84 4 33 5,458 5,337 27,120 18,062 18,415 19% 21% 24%"
## [30] " 01/19/24 23,501 330 157 969 1,950 1,894 1,711 26,907 -1,410 1,894 3,556 81 4 20 5,547 5,375 22,771 19,480 18,644 20% 19% 24%"
## [31] " 01/20/24 24,646 330 133 872 1,879 1,895 1,719 27,860 -1,639 1,796 3,582 85 4 35 5,494 5,391 24,004 21,170 18,912 19% 19% 24%"
## [32] " 01/21/24 25,386 330 307 910 1,866 1,910 1,731 28,799 -3,236 1,794 3,589 84 3 31 5,496 5,404 26,539 22,787 19,275 19% 19% 23%"
## [33] " 01/22/24 25,500 330 444 1,337 1,973 1,981 1,761 29,584 -3,971 1,893 3,597 88 4 31 5,606 5,417 27,949 24,445 19,669 19% 19% 23%"
## [34] " 01/23/24 29,219 330 3,004 1,757 2,391 2,198 1,832 36,701 -9,904 293 3,586 103 5 31 4,009 4,917 42,597 28,060 20,666 11% 16% 20%"
## [35] " 01/24/24 38,607 330 794 1,760 3,408 2,517 1,929 44,899 -10,183 470 2,665 104 3 23 3,260 4,168 51,822 31,829 21,964 7% 11% 16%"
## [36] " 01/25/24 42,466 330 480 1,424 4,154 2,784 2,005 48,854 -11,780 1,089 2,666 102 4 33 3,886 3,590 56,748 36,061 23,356 8% 8% 13%"
## [37] " 01/26/24 44,134 330 417 1,356 3,817 3,014 2,062 50,054 -10,183 1,287 2,658 100 5 33 4,073 3,612 56,163 40,832 24,617 8% 8% 12%"
## [38] " 01/27/24 45,171 330 272 1,241 3,488 3,234 2,112 50,502 -7,216 1,087 2,651 103 3 31 3,870 3,813 53,847 45,095 25,700 7% 8% 12%"
## [39] " 01/28/24 44,661 330 214 1,106 3,409 3,453 2,161 49,720 -1,104 1,095 2,651 105 4 31 3,878 3,810 46,946 48,010 26,459 8% 8% 11%"
## [40] " 01/29/24 42,797 330 184 1,069 3,502 3,595 2,203 47,882 -876 1,992 1,826 118 3 32 3,965 3,767 44,793 50,417 27,091 8% 8% 10%"
## [41] " 01/30/24 38,357 330 166 1,050 3,385 3,577 2,240 43,288 950 1,995 1,827 93 0 35 3,951 3,795 38,387 49,815 27,468 9% 8% 10%"
## [42] " 01/31/24 34,283 330 152 990 3,286 3,448 2,272 39,041 950 1,096 2,763 103 4 32 3,990 3,833 34,101 47,284 27,682 10% 9% 10%"
## [43] ""
## [44] " Total 816,996 9,750 8,653 27,925 68,616 64,508 53,166 931,940 (88,945) 58,081 100,898 2,899 116 994 162,756 164,179 858,128 764,716 657,222 - - -"
## [45] " Average 26,355 315 279 901 2,213 2,081 1,715 30,063 (2,869) 1,874 3,255 94 4 32 5,250 5,296 27,682 24,668 21,201 20% 21% 23%"
## [46] ""
## [47] ""
## [48] ""
## [49] ""
## [50] " PRELIMINARY"
## [51] " 2/1/2024 7:24 AM"# Keep "rows" 12-42 in the data and convert to a matrix with 23 columns
usbr_pdf_mat <- usbr_pdf_txt[12:42] %>%
str_squish() %>%
str_split_fixed(pattern = " ", n = 23)
usbr_pdf_mat## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "01/01/24" "19,294" "210" "159" "618" "1,429" "1,356" "1,377"
## [2,] "01/02/24" "23,070" "227" "89" "610" "1,377" "1,361" "1,371"
## [3,] "01/03/24" "23,756" "244" "63" "608" "1,365" "1,375" "1,384"
## [4,] "01/04/24" "22,446" "261" "90" "691" "1,409" "1,389" "1,393"
## [5,] "01/05/24" "20,925" "279" "109" "696" "1,421" "1,397" "1,391"
## [6,] "01/06/24" "21,079" "296" "70" "604" "1,385" "1,395" "1,389"
## [7,] "01/07/24" "21,235" "313" "64" "584" "1,379" "1,422" "1,422"
## [8,] "01/08/24" "20,019" "330" "73" "583" "1,615" "1,484" "1,470"
## [9,] "01/09/24" "18,320" "330" "64" "562" "1,812" "1,553" "1,513"
## [10,] "01/10/24" "17,364" "330" "57" "541" "1,852" "1,621" "1,550"
## [11,] "01/11/24" "16,624" "330" "58" "555" "1,885" "1,687" "1,580"
## [12,] "01/12/24" "16,920" "330" "59" "703" "1,881" "1,756" "1,604"
## [13,] "01/13/24" "16,540" "330" "59" "670" "1,868" "1,824" "1,624"
## [14,] "01/14/24" "16,299" "330" "59" "631" "1,857" "1,861" "1,641"
## [15,] "01/15/24" "16,793" "330" "350" "882" "1,870" "1,873" "1,658"
## [16,] "01/16/24" "18,048" "330" "202" "921" "1,897" "1,878" "1,672"
## [17,] "01/17/24" "24,096" "330" "140" "773" "1,885" "1,883" "1,687"
## [18,] "01/18/24" "25,440" "330" "164" "852" "1,921" "1,893" "1,702"
## [19,] "01/19/24" "23,501" "330" "157" "969" "1,950" "1,894" "1,711"
## [20,] "01/20/24" "24,646" "330" "133" "872" "1,879" "1,895" "1,719"
## [21,] "01/21/24" "25,386" "330" "307" "910" "1,866" "1,910" "1,731"
## [22,] "01/22/24" "25,500" "330" "444" "1,337" "1,973" "1,981" "1,761"
## [23,] "01/23/24" "29,219" "330" "3,004" "1,757" "2,391" "2,198" "1,832"
## [24,] "01/24/24" "38,607" "330" "794" "1,760" "3,408" "2,517" "1,929"
## [25,] "01/25/24" "42,466" "330" "480" "1,424" "4,154" "2,784" "2,005"
## [26,] "01/26/24" "44,134" "330" "417" "1,356" "3,817" "3,014" "2,062"
## [27,] "01/27/24" "45,171" "330" "272" "1,241" "3,488" "3,234" "2,112"
## [28,] "01/28/24" "44,661" "330" "214" "1,106" "3,409" "3,453" "2,161"
## [29,] "01/29/24" "42,797" "330" "184" "1,069" "3,502" "3,595" "2,203"
## [30,] "01/30/24" "38,357" "330" "166" "1,050" "3,385" "3,577" "2,240"
## [31,] "01/31/24" "34,283" "330" "152" "990" "3,286" "3,448" "2,272"
## [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17]
## [1,] "21,710" "-4,818" "2,290" "3,547" "104" "5" "32" "5,968" "7,978"
## [2,] "25,373" "-3,777" "2,298" "3,540" "108" "3" "30" "5,974" "6,907"
## [3,] "26,036" "-5,881" "2,097" "3,546" "108" "3" "33" "5,782" "5,773"
## [4,] "24,897" "-2,280" "2,093" "3,532" "104" "5" "29" "5,753" "5,702"
## [5,] "23,430" "-960" "2,191" "3,507" "109" "4" "35" "5,838" "5,655"
## [6,] "23,434" "-1,010" "2,395" "3,476" "110" "3" "32" "6,012" "5,731"
## [7,] "23,575" "-1,745" "2,393" "3,484" "111" "5" "35" "6,018" "5,815"
## [8,] "22,620" "31" "2,389" "3,480" "110" "4" "28" "6,004" "5,872"
## [9,] "21,088" "487" "2,697" "3,466" "92" "4" "36" "6,288" "5,970"
## [10,] "20,144" "437" "2,696" "3,471" "80" "4" "34" "6,277" "6,067"
## [11,] "19,452" "159" "2,599" "3,468" "82" "4" "29" "6,175" "6,133"
## [12,] "19,893" "844" "2,593" "3,488" "98" "5" "31" "6,206" "6,105"
## [13,] "19,467" "1,022" "2,592" "3,553" "90" "5" "38" "6,268" "6,098"
## [14,] "19,176" "-1,489" "1,791" "3,554" "62" "5" "43" "5,445" "5,857"
## [15,] "20,225" "-1,539" "1,793" "3,548" "55" "4" "30" "5,422" "5,610"
## [16,] "21,398" "-1,310" "1,795" "3,541" "54" "5" "35" "5,420" "5,341"
## [17,] "27,224" "-3,643" "1,796" "3,534" "67" "6" "31" "5,423" "5,336"
## [18,] "28,707" "-3,871" "1,799" "3,547" "84" "4" "33" "5,458" "5,337"
## [19,] "26,907" "-1,410" "1,894" "3,556" "81" "4" "20" "5,547" "5,375"
## [20,] "27,860" "-1,639" "1,796" "3,582" "85" "4" "35" "5,494" "5,391"
## [21,] "28,799" "-3,236" "1,794" "3,589" "84" "3" "31" "5,496" "5,404"
## [22,] "29,584" "-3,971" "1,893" "3,597" "88" "4" "31" "5,606" "5,417"
## [23,] "36,701" "-9,904" "293" "3,586" "103" "5" "31" "4,009" "4,917"
## [24,] "44,899" "-10,183" "470" "2,665" "104" "3" "23" "3,260" "4,168"
## [25,] "48,854" "-11,780" "1,089" "2,666" "102" "4" "33" "3,886" "3,590"
## [26,] "50,054" "-10,183" "1,287" "2,658" "100" "5" "33" "4,073" "3,612"
## [27,] "50,502" "-7,216" "1,087" "2,651" "103" "3" "31" "3,870" "3,813"
## [28,] "49,720" "-1,104" "1,095" "2,651" "105" "4" "31" "3,878" "3,810"
## [29,] "47,882" "-876" "1,992" "1,826" "118" "3" "32" "3,965" "3,767"
## [30,] "43,288" "950" "1,995" "1,827" "93" "0" "35" "3,951" "3,795"
## [31,] "39,041" "950" "1,096" "2,763" "103" "4" "32" "3,990" "3,833"
## [,18] [,19] [,20] [,21] [,22] [,23]
## [1,] "20,561" "11,399" "20,561" "27%" "41%" "43%"
## [2,] "23,177" "13,381" "21,869" "23%" "31%" "35%"
## [3,] "26,136" "15,980" "23,291" "22%" "24%" "28%"
## [4,] "21,424" "17,927" "22,824" "23%" "22%" "27%"
## [5,] "18,552" "19,423" "21,970" "24%" "23%" "27%"
## [6,] "18,432" "20,479" "21,380" "25%" "24%" "27%"
## [7,] "19,302" "21,083" "21,083" "25%" "25%" "27%"
## [8,] "16,586" "20,515" "20,521" "26%" "25%" "27%"
## [9,] "14,312" "19,249" "19,831" "29%" "27%" "28%"
## [10,] "13,430" "17,434" "19,191" "31%" "28%" "28%"
## [11,] "13,118" "16,247" "18,639" "31%" "30%" "28%"
## [12,] "12,844" "15,432" "18,156" "31%" "31%" "28%"
## [13,] "12,177" "14,538" "17,696" "32%" "31%" "27%"
## [14,] "15,219" "13,955" "17,519" "28%" "30%" "26%"
## [15,] "16,342" "13,920" "17,441" "26%" "29%" "25%"
## [16,] "17,288" "14,345" "17,431" "25%" "26%" "25%"
## [17,] "25,443" "16,062" "17,902" "20%" "23%" "24%"
## [18,] "27,120" "18,062" "18,415" "19%" "21%" "24%"
## [19,] "22,771" "19,480" "18,644" "20%" "19%" "24%"
## [20,] "24,004" "21,170" "18,912" "19%" "19%" "24%"
## [21,] "26,539" "22,787" "19,275" "19%" "19%" "23%"
## [22,] "27,949" "24,445" "19,669" "19%" "19%" "23%"
## [23,] "42,597" "28,060" "20,666" "11%" "16%" "20%"
## [24,] "51,822" "31,829" "21,964" "7%" "11%" "16%"
## [25,] "56,748" "36,061" "23,356" "8%" "8%" "13%"
## [26,] "56,163" "40,832" "24,617" "8%" "8%" "12%"
## [27,] "53,847" "45,095" "25,700" "7%" "8%" "12%"
## [28,] "46,946" "48,010" "26,459" "8%" "8%" "11%"
## [29,] "44,793" "50,417" "27,091" "8%" "8%" "10%"
## [30,] "38,387" "49,815" "27,468" "9%" "8%" "10%"
## [31,] "34,101" "47,284" "27,682" "10%" "9%" "10%"# Keep the columns for Date, Export, and Outflow; rename them; convert to tibble
usbr_pdf_mat2 <- usbr_pdf_mat[,c(1, 16, 18)]
colnames(usbr_pdf_mat2) <- c("Date", "Export", "Outflow")
df_usbr_dout <- as_tibble(usbr_pdf_mat2)
df_usbr_dout## # A tibble: 31 × 3
## Date Export Outflow
## <chr> <chr> <chr>
## 1 01/01/24 5,968 20,561
## 2 01/02/24 5,974 23,177
## 3 01/03/24 5,782 26,136
## 4 01/04/24 5,753 21,424
## 5 01/05/24 5,838 18,552
## 6 01/06/24 6,012 18,432
## 7 01/07/24 6,018 19,302
## 8 01/08/24 6,004 16,586
## 9 01/09/24 6,288 14,312
## 10 01/10/24 6,277 13,430
## # ℹ 21 more rows