module_8.Rmd

---
title: "module_8"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T)
```

```{r, message=F, warning=F}
library(dplyr)
library(tidyr)
library(readr)


```

```{r}
catch_original <- read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.302.1")

```

```{r}
catch_data <- catch_original %>% 
  select(Region, Year, Chinook, Sockeye, Coho, Pink, Chum)
catch_data

```

```{r}

summary(catch_data)
head(catch_data)

```

```{r}
catch_clean <- catch_data %>% 
  mutate(Chinook = as.numeric(Chinook))

```

```{r}
i <- which(is.na(catch_clean$Chinook))
i

```

```{r}
catch_original[i,]

```


```{r}

catch_clean <- catch_data %>% 
  mutate(Chinook = ifelse(Chinook == "I", 1, Chinook)) %>%
  mutate(Chinook = as.integer(Chinook))

head(catch_clean)
catch_clean


```


```{r}
catch_long <- catch_clean %>% 
  pivot_longer(cols = -c(Region, Year), names_to = "species", values_to = "catch")

head(catch_long)


```

```{r}
catch_wide <- catch_long %>% 
  pivot_wider(names_from = species, values_from = catch)

head(catch_wide)


```
```{r}

catch_long <- catch_long %>% 
  rename(catch_thousands = catch)

head(catch_long)

```
```{r}

catch_long <- catch_long %>% 
  mutate(catch = catch_thousands * 1000) %>% 
  select(-catch_thousands)

head(catch_long)


```
```{r}

mean_region <- catch_long %>% 
  group_by(Region) %>%
  summarise(catch_mean = mean(catch))

```

```{r}
n_region <- catch_long %>% 
  group_by(Region) %>%
  summarize(n = n())

```

```{r}
SSE_catch <- catch_long %>% 
  filter(Region == "SSE")

head(SSE_catch)

```

```{r}

mean_region <- catch_long %>% 
  group_by(Region) %>% 
  summarise(mean_catch = mean(catch)) %>% 
  arrange(desc(mean_catch))
mean_region

```


```{r}
region_defs <- read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.303.1") %>% 
    select(code, mgmtArea)

```
```{r}
catch_joined <- left_join(catch_long, region_defs, by = c("Region" = "code"))

head(catch_joined)

```
```{r}
region_defs <- region_defs %>% 
  rename(Region = code, Region_Name = mgmtArea)

catch_joined <- left_join(catch_long, region_defs, by = c("Region"))

head(catch_joined)

```


```{r}
sites_df <- data.frame(site = c("HAW-101",
                                "HAW-103",
                                "OAH-320",
                                "OAH-219",
                                "MAI-039"),
                       stringsAsFactors = FALSE)

sites_df %>% 
  separate(site, c("island", "site_number"), "-")


```

```{r}
cities_df <- data.frame(city = c("Juneau AK", 
                                 "Sitka AK", 
                                 "Anchorage AK"),
                        stringsAsFactors = FALSE)
```
```{r}
dates_df <- data.frame(year = c("1930",
                                "1930",
                                "1930"),
                       month = c("12",
                                "12",
                                "12"),
                       day = c("14",
                               "15",
                               "16"),
                       stringsAsFactors = FALSE)

dates_df %>% 
  unite(date, year, month, day, sep = "-")

```
```{r}
catch_original <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.302.1", method = "libcurl"),
                  stringsAsFactors = FALSE)
region_defs <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.303.1", method = "libcurl"),
                        stringsAsFactors = FALSE) %>% 
    select(code, mgmtArea)

mean_region <- catch_original %>%
  select(-All, -notesRegCode) %>% 
  mutate(Chinook = ifelse(Chinook == "I", 1, Chinook)) %>% 
  mutate(Chinook = as.numeric(Chinook)) %>% 
  pivot_longer(-c(Region, Year), names_to = "species", values_to = "catch") %>%
  mutate(catch = catch*1000) %>% 
  group_by(Region) %>% 
  summarize(mean_catch = mean(catch)) %>% 
  left_join(region_defs, by = c("Region" = "code"))
```