---
title: "Data Wrangling 2: Table Summaries, Joins, & Reshaping"
output: 
  html_notebook:
    toc: yes
    toc_depth: 2
    toc_float: yes
---

```{css echo = FALSE}
h1,h2,h3 {font-weight:bold;}
h1 {font-size:24px;}
h2 {font-size:20px;}
h3 {font-size:16px;}
```
\

# Load Packages 

```{r chunk01}
library(dplyr)

# Specify package preferences for filter, count, select, and arrange
library(conflicted)
conflict_prefer("filter", "dplyr", quiet = TRUE)
conflict_prefer("count", "dplyr", quiet = TRUE)
conflict_prefer("select", "dplyr", quiet = TRUE)
conflict_prefer("arrange", "dplyr", quiet = TRUE)
```

# Table Summaries

The main dplyr function for creating summaries of data is `summarise()`. The syntax is similar to `mutate()`. Summary columns can use any aggregate function from base R or dplyr (see the [dplyr cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-transformation.pdf) for a list of functions that are designed to use with `summarise()`).

\

## Summarise an entire tibble

Compute the mean of several columns (using the `na.rm = TRUE` to ignore NA values):

```{r chunk02}

library(palmerpenguins)
penguins |> 
  summarise(bill_len_mean = mean(bill_length_mm, na.rm = TRUE), 
            bill_depth_mean = mean(bill_depth_mm, na.rm = TRUE), 
            body_mass_mean = mean(body_mass_g, na.rm = TRUE))
```

\

## Summarise groups of rows

Often you want to create summaries for groups of rows (i.e., a different unit of analysis). 

If we wanted to compute summary stats for each species, you would first group the rows by species with `group_by()`, and then use `summarise()`.

```{r chunk03}
penguins |> 
  group_by(species) |> 
  summarise(bill_len_mean = mean(bill_length_mm, na.rm = TRUE), 
            bill_depth_mean = mean(bill_depth_mm, na.rm = TRUE), 
            body_mass_mean = mean(body_mass_g, na.rm = TRUE))
```

\

**How many rows per island?**

For this question, we can use the `n()` function in `summarise()`:

```{r chunk04}
penguins |> 
  group_by(island) |> 
  summarise(num_records = n())
```

\

# CHALLENGE #1

Compute the number of observations per year.

```{r chunk05}
## Your answer here

```

\

# CHALLENGE #2

Compute the number of observations per year and species.

Hint: `group_by()` can take more than one column

```{r chunk06}
# Your answer here

```

\

# Join tables

Import a dataset with some (fake) genetic information about Penguins:

```{r chunk07}
library(readr)
genetics_tbl <- read_csv("./data/penguin_genetic_diversity.csv")
genetics_tbl
```

\

We can join these columns to the Palmer Penguins dataset:

```{r chunk08}
penguins |>
  left_join(genetics_tbl, by = "species") |> 
  head()
```

# Reshape Data

## Wide to Long

Start by creating a 'wide' data frame:

```{r chunk09}
cases_df <- data.frame(country = c("FR", "DE", "US"),
                       `2011` = c(7000,5800,15000),
                       `2012` = c(6900,6000,14000),
                       `2013` = c(7000,6200,13000),
                       check.names = FALSE)
cases_df
```

\

Next, we combine the values of three year columns into one, with another column for the year:

```{r chunk10}
library(tidyr)

cases_tidy_df <- cases_df |> 
  pivot_longer(cols = c("2011", "2012", "2013"), 
               names_to = "year", 
               values_to = "cases")

cases_tidy_df
```

\

## Long to wide

We begin by importing the January 2050 projected daily minimum and maximum temperature for Sacramento:

```{r chunk11}
sac_temps_tbl <- read_csv("./data/sacramento_daily_temp_jan2050.csv")
sac_temps_tbl |> arrange(dt)
```

\

Convert from a long to wide format:

```{r chunk12}
sac_temps_tbl |> 
  pivot_wider(names_from = clim_var, values_from = temp_f)
```

\

## Compute Daily Temperature Range

Use the 'new' columns to compute the daily temperature range:

```{r chunk13}
sac_temps_tbl |> 
  pivot_wider(names_from = clim_var, values_from = temp_f) |> 
  mutate(diurnal_range_f = tasmax - tasmin) |> 
  head()
```

\

# End

Remember to save your work to render a HTML copy of the notebook.