TidyRbasics.Rmd

---
title: "Tidying Data in R"
author: "T8"
date: "August 15, 2018"
output: html_document
---

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

Callings a masked function

packageName::functionName()
e.g.
stats::filter()

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

Read in data from [Mike Byerly. 2016. Alaska commercial salmon catches by management region (1886- 1997). Gulf of Alaska Data Portal.](https://knb.ecoinformatics.org/#view/df35b.304.2)

```{r}
catch_df <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.302.1", method = "libcurl"),
                    stringsAsFactors = FALSE)
head(catch_df)
```

```{r}
# Select not all & notesRegCode and overwite catch_df
catch_df <- catch_df %>% 
  select(-All,-notesRegCode)
```

Transpose form Wide to Long Format using gather and Rename Column Catch

```{r}
catch_df <- catch_df %>% 
  
  gather(key = "Species", value = "Catch", -Region, -Year) %>% 
  rename(catchThousands = Catch)


head(catch_df)
```

Let's Count Fish
1 fish 2 fish red fish lake
```{r}
# Convert Char to num
catchInt <- as.integer(catch_df$catchThousands)
naIx <- which(is.na(catchInt) == T)
catch_df$catchThousands[naIx]
```
turn this I into a 1 
coerce catchThousands into numerical type
convert catchThosands into units of fishes

```{r}
catch_df <- catch_df %>% 
  mutate(catchThousands = ifelse( catchThousands == "I", 1, catchThousands)) %>% 
  mutate(catchThousands = as.integer(catchThousands)) %>% 
  mutate(Catch = catchThousands*1000)
tail(catch_df)
```
# Split Apply Combine

```{r}
catchSummarized <- catch_df %>% 
  group_by(Region,Species) %>% 
  summarise(meanCatch = mean(Catch),
            sdCatch = sd(Catch),
            numObs = n()) %>%
  # Filter for Chinook
  filter(Species == "Chinook")

head(catchSummarized)
```
Filter by year then group
```{r}
catchYear <- catch_df %>% 
  filter(Year >1975 | Year < 1925) %>% 
  group_by(Year, Species) %>% 
  summarize(catchSum = sum(Catch))
 head(catchYear)
```
# Joins

Read Regions definitions file
```{r}
region_defs <- read.csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.303.1", method = "libcurl"),
                        stringsAsFactors = FALSE)
head(region_defs)


```

```{r}
region_defs <-region_defs %>% 
  select(code,mgmtArea)

head(region_defs)

```
```{r}
catch_joined <- left_join(catch_df,region_defs, by = c("Region" = "code"))
# Move magmtArea 
catch_joined <- select(catch_joined,Region,mgmtArea,Year,Species,catchThousands,Catch)

head(catch_joined)
```