Summary of 2012 Surveys

library(dplyr)
library(magrittr)
library(purrr)

library(nlaR)

dt <- nla_load(2012)

tp <- select(dt$waterchem_wide, PTL_RESULT, UID) %>%
      left_join(select(dt$wide_siteinfo, AREA_HA, SITE_ID, UID)) %>%
      group_by(SITE_ID) %>%
      summarize(tp = median(PTL_RESULT), area = median(AREA_HA) / 100)

## Joining, by = "UID"

chl  <- select(dt$chla_wide, CHLX_RESULT, UID)  %>%
        left_join(select(dt$wide_siteinfo, SITE_ID, UID)) %>%
        group_by(SITE_ID) %>%
        summarize(chl = median(CHLX_RESULT))

## Joining, by = "UID"

secchi  <- select(dt$secchi, SECCHI, SITE_ID)  %>%
           group_by(SITE_ID) %>%
           summarize(secchi = median(SECCHI))

res <- reduce(list(tp, chl, secchi), left_join)

## Joining, by = "SITE_ID"

## Joining, by = "SITE_ID"

res <- left_join(res, select(dt$wide_phab, depth = DEPTH_AT_STATION, SITE_ID))

## Joining, by = "SITE_ID"

skimr::skim(res)

Data summary
Name	res
Number of rows	20838
Number of columns	6
_______________________
Column type frequency:
character	1
numeric	5
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
SITE_ID	0	1	12	13	0	1130	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
tp	0	1.00	120.94	286.04	4.00	21.00	41.00	100.00	3636.00	▇▁▁▁▁
area	0	1.00	4.62	39.54	0.01	0.11	0.30	1.05	1674.90	▇▁▁▁▁
chl	90	1.00	26.75	53.16	0.00	3.03	8.00	26.80	764.64	▇▁▁▁▁
secchi	1238	0.94	2.08	2.25	0.02	0.64	1.39	2.83	28.00	▇▁▁▁▁
depth	9051	0.57	1.60	1.71	0.00	0.70	1.10	1.90	34.10	▇▁▁▁▁

Jemma Stachelek

2021-08-03