Coding workshop: Week 6

Set up

Loading packages:

library(tidyverse) # general usage
library(here) # organization
library(naniar) # missing data visualization

library(skimr) # quick glimpse at data
library(plotly) # interactive plots
library(magick) # insert images into plots
library(NatParksPalettes) # one example of color palette package
library(wesanderson) # another example of color palette package
library(patchwork) # put plots together

load in the data

# create a new object called whales
# read in the whales data
whales <- read_csv(
  here("workshop", "data", "Graywhale_watching_2007_2019.csv")
)

look at missing data:

gg_miss_var(whales)

use skimr package to quickly glimpse the data:

skim(whales)

Data summary

Name	whales
Number of rows	5005
Number of columns	7
_______________________
Column type frequency:
character	3
Date	1
difftime	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
condition	75	0.99	4	13	0	37	0
direction	86	0.98	3	14	0	2589	0
species	0	1.00	21	21	0	1	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2007-02-01	2019-05-19	2014-04-07	1021

Variable type: difftime

skim_variable	n_missing	complete_rate	min	max	median	n_unique
local_time	284	0.94	2520 secs	63420 secs	12:26:00	547

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
total	0	1.00	2.25	2.46	0	1	2	2	65	▇▁▁▁▁
calves	141	0.97	0.33	0.66	0	0	0	1	6	▇▁▁▁▁

wrangling

whales_clean <- whales %>% 
  # creating new columns for years and months
  mutate(year_new = lubridate::year(date)) %>% 
  mutate(month_new = lubridate::month(date)) %>% 
  # create a new column for month names
  mutate(month_name = case_when(
    month_new == 2 ~ "February",
    month_new == 3 ~ "March",
    month_new == 4 ~ "April",
    month_new == 5 ~ "May"
  ),
  month_name = forcats::as_factor(month_name),
  month_name = forcats::fct_relevel(month_name, "February", "March", "April", "May")
  ) %>% 
  mutate(condition_new = case_when(
    stringr::str_detect(condition, "Excellent") ~ "excellent",
    stringr::str_detect(condition, "Good") ~ "good",
    stringr::str_detect(condition, "Fair") ~ "fair",
    stringr::str_detect(condition, "Poor") ~ "poor"
  ))

create a plot and insert an image

# create a new data frame to filter 2019 observations
whales_2019 <- whales_clean %>% 
  filter(year_new == 2019)

# read in the whale png as a raster
whale_image <- magick::image_read(
  here("workshop", "images", "noaa-gray-whale.png")
) %>% 
  as.raster()

# make a boxplot
whales_2019_boxplot <- ggplot(data = whales_2019, aes(x = month_name, y = total)) +
  geom_boxplot(aes(fill = month_name)) +
  geom_point(position = position_jitter(width = 0.3, height = 0, seed = 1000)) +
  scale_fill_manual(values = natparks.pals("RockyMtn", 4)) +
  annotation_raster(whale_image, xmin = 2.5, xmax = 4.5, ymin = 30, ymax = 60)

whales_2019_boxplot

ggplotly(whales_2019_boxplot)