Data Wrangling 2: Table Summaries, Joins, & Reshaping

Load Packages

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 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):

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().

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():

penguins |> 
  group_by(island) |> 
  summarise(num_records = n())

CHALLENGE #1

Compute the number of observations per year.

## Your answer here

CHALLENGE #2

Compute the number of observations per year and species.

Hint: group_by() can take more than one column

# Your answer here

Join tables

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

library(readr)
genetics_tbl <- read_csv("./data/penguin_genetic_diversity.csv")

Rows: 3 Columns: 5── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): species, photo
dbl (3): haplotype_div, nucleotide_div, tajima_d
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

genetics_tbl

We can join these columns to the Palmer Penguins dataset:

penguins |>
  left_join(genetics_tbl, by = "species") |> 
  head()

Reshape Data

Wide to Long

Start by creating a ‘wide’ data frame:

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:

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:

sac_temps_tbl <- read_csv("./data/sacramento_daily_temp_jan2050.csv")

Rows: 248 Columns: 6── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (4): clim_var, period, gcm, scenario
dbl  (1): temp_f
date (1): dt
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sac_temps_tbl |> arrange(dt)

Convert from a long to wide format:

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:

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.