if (!requireNamespace("librarian", quietly = TRUE)) {
# If not installed, install the package
install.packages("librarian")
}
librarian::shelf(
glue,
here,
skimr,
ggplot2
)
data <- read.csv(here("data/df_cleaned.csv"))
parameter_name <- params$parameter_namebounds <- read.csv(here("parameter_bounds.csv"), stringsAsFactors = FALSE, strip.white = T)
lower_bound <- bounds$min[bounds$param == parameter_name]
upper_bound <- bounds$max[bounds$param == parameter_name]
filter_condition <- (data$Parameter == parameter_name & (data$Value < lower_bound | data$Value > upper_bound))
tryCatch({ # this tryCatch is for when filter_condition is logical(0) i.e. no matches
data <- dplyr::filter(
data,
!filter_condition
)
print(glue("{sum(filter_condition)} rows dropped as < {lower_bound} or > {upper_bound}"))
}, error = function(e){
print(glue("no rows removed"))
})0 rows dropped as < 0 or > 6print(glue("{sum(filter_condition)} rows dropped as < {lower_bound} or > {upper_bound}"))0 rows dropped as < 0 or > 6write.csv(data, here("data/df_cleaned_02.csv"), row.names = FALSE)subset_data <- subset(data, Parameter == parameter_name)
print(skimr::skim(subset_data))── Data Summary ────────────────────────
Values
Name subset_data
Number of rows 23550
Number of columns 17
_______________________
Column type frequency:
character 4
numeric 13
________________________
Group variables None
── Variable type: character ────────────────────────────────────────────────────
skim_variable n_missing complete_rate min max empty n_unique whitespace
1 Source 0 1 3 21 0 9 0
2 Site 0 1 1 28 0 515 0
3 Parameter 0 1 12 12 0 1 0
4 Units 0 1 4 6 0 2 0
── Variable type: numeric ──────────────────────────────────────────────────────
skim_variable n_missing complete_rate mean sd p0
1 ...1 0 1 131266. 160487. 66448
2 Latitude 0 1 26.1 0.605 24.2
3 Longitude 0 1 -80.3 0.561 -83.6
4 Month 0 1 6.65 3.42 1
5 Day 30 0.999 11.5 7.28 1
6 Year 0 1 2020. 3.10 1998
7 Value 0 1 0.0845 0.243 0
8 Sample.Depth 552 0.977 3.49 23.9 0
9 Total.Depth 22248 0.0553 5.37 7.93 0.5
10 verbatimValue 0 1 0.0845 0.243 0
11 VerbatimLatitude 0 1 26.1 0.605 24.2
12 verbatimLongitude 0 1 -80.3 0.561 -83.6
13 Value_orig 0 1 0.0845 0.243 0
p25 p50 p75 p100 hist
1 78823. 84712. 90599. 696626 ▇▁▁▁▁
2 25.8 26.0 26.4 28.0 ▁▂▇▃▁
3 -80.3 -80.1 -80.1 -80.0 ▁▁▁▁▇
4 4 7 10 12 ▇▅▅▆▇
5 6 10 16 31 ▇▆▅▂▁
6 2019 2020 2021 2023 ▁▁▁▂▇
7 0.006 0.014 0.065 5.5 ▇▁▁▁▁
8 0.5 0.5 2.5 2494 ▇▁▁▁▁
9 0.708 3.4 6.7 62.2 ▇▁▁▁▁
10 0.006 0.014 0.065 5.5 ▇▁▁▁▁
11 25.8 26.0 26.4 28.0 ▁▂▇▃▁
12 -80.3 -80.1 -80.1 -80.0 ▁▁▁▁▇
13 0.006 0.014 0.065 5.5 ▇▁▁▁▁params$parameter_name histogramggplot2::ggplot(subset_data, aes(x=Value)) +
geom_histogram(bins=30, fill="blue", color="black") +
scale_y_log10() + # Transform the y-axis to a logarithmic scale
labs(title=paste("Histogram of Values for", params$parameter_name),
x="Value",
y="Log Frequency") +
theme_minimal()