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edited README to include run instructions

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Ben Varick 2023-06-08 11:38:05 -05:00
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README.md
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# USGS NWIS data visualizations # USGS NWIS data visualizations
gets river discharge data from the USGS NWIS and makes visualizations Gets river discharge data from the USGS NWIS and makes visualizations
**To run: **
1. Clone the repository
2. Change the working directory in line 6 of `USGS_NWIS.R`. The script will make the `data` and `figures` directory in the working directory. 10 rivers will generate ~100MB of total data and figures.
3. Edit the list of rivers and their respective site IDs in `river_IDs.csv`
4. Run the script. The intitial download of the data will take some time. Subsequent runs will just download new data.
![example figure](https://git.dendroalsia.net/ben/USGS_NWIS/raw/commit/057ca3e2d4cdedbc80f9ffe05444163af1beae89/example_last_6_months.png) ![example figure](https://git.dendroalsia.net/ben/USGS_NWIS/raw/commit/057ca3e2d4cdedbc80f9ffe05444163af1beae89/example_last_6_months.png)

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USGS_NWIS.R
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@ -7,7 +7,7 @@ setwd("/home/ben/Documents/dataProjects/USGS_NWIS")
rivers <- read_csv(file = "river_IDs.csv", col_types = c("c", "c")) rivers <- read_csv(file = "river_IDs.csv", col_types = c("c", "c"))
rivers <- rivers %>% filter(names %in% c("Elwha")) #rivers <- rivers %>% filter(names %in% c("Elwha"))
#rivers <- rivers %>% filter(names %in% c("Duckabush")) #rivers <- rivers %>% filter(names %in% c("Duckabush"))
#rivers <- rivers %>% filter(names %in% c("Hoh")) #rivers <- rivers %>% filter(names %in% c("Hoh"))