readme.md

Please add alt text to your posts

Please add alt text (alternative text) to all of your posted graphics for #TidyTuesday.

Twitter provides guidelines for how to add alt text to your images.

The DataViz Society/Nightingale by way of Amy Cesal has an article on writing good alt text for plots/graphs.

Here’s a simple formula for writing alt text for data visualization:

Chart type

It’s helpful for people with partial sight to know what chart type it is and gives context for understanding the rest of the visual. Example: Line graph

Type of data

What data is included in the chart? The x and y axis labels may help you figure this out. Example: number of bananas sold per day in the last year

Reason for including the chart

Think about why you’re including this visual. What does it show that’s meaningful. There should be a point to every visual and you should tell people what to look for. Example: the winter months have more banana sales

Link to data or source

Don’t include this in your alt text, but it should be included somewhere in the surrounding text. People should be able to click on a link to view the source data or dig further into the visual. This provides transparency about your source and lets people explore the data. Example: Data from the USDA

Penn State has an article on writing alt text descriptions for charts and tables.

Charts, graphs and maps use visuals to convey complex images to users. But since they are images, these media provide serious accessibility issues to colorblind users and users of screen readers. See the examples on this page for details on how to make charts more accessible.

The {rtweet} package includes the ability to post tweets with alt text programatically.

Need a reminder? There are extensions that force you to remember to add Alt Text to Tweets with media.

Commercial Fishing

The data this week comes from Great Lakes Fishery Commission. Full details on the data can be found on their statistic notes and background notes.

Commercial fish catch data (called production) were published by the Great Lakes Fishery Commission in 1962 (Technical Report No.3) and covered the period 1867-1960. A supplement covering the years 1961-1968 was released in 1970, and a revised edition covering the years 1867-1977 was published in 1979. This third update of a web-based version covers the period 1867-2015. The intent is to update at approximately five-year intervals. The files are intended for open use by the public. We ask only that the commission be acknowledged when these records are used in presentations and publications.

Another article from the Detroit Free Press on King salmon reign becomes more precarious on changing Great Lakes.

It's the undisputed king sport fish of the Great Lakes — it says so right in its name.

For a half-century, the chinook, or king salmon — an ocean fish transplanted into the Great Lakes from the Pacific Northwest — has sent fisherman piling into boats every spring and summer, or queuing up on the banks of inland rivers every fall, its fierce fight on the line like a siren's song.

First stocked regularly in the Great Lakes beginning in 1966, the kings led a complete turnaround in the Great Lakes fishery, helping create a $7 billion economic impact. As recently as 2012, Michigan's Great Lakes fishing charters averaged 7.4 king salmon caught per trip.

It's unlikely it will ever be like that again.

Invasive zebra and quagga mussels have spread so pervasively throughout the Great Lakes, their filtration of nutrients from lake water has caused ripple effects throughout the food web. It's led to steep declines in the populations of another invasive species that is the chinook salmon's almost exclusive diet — the alewife, a silvery herring.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-06-08')
tuesdata <- tidytuesdayR::tt_load(2021, week = 24)

fishing <- tuesdata$fishing

# Or read in the data manually

fishing <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-08/fishing.csv')
stocked <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-08/stocked.csv')

Data Dictionary

fishing.csv

variable	class	description
year	double	Year of measurement
lake	character	Lake Name
species	character	Species of fish
grand_total	double	Grand total of observed
comments	character	Comments from the dataset providers
region	character	Region of the US/Canada, note there is some inconsistency
values	double	Production amounts have been rounded to the nearest thousand pounds

stocked.csv

variable	class	description
SID	double	Unique ID
YEAR	double	Year
MONTH	double	Month
DAY	double	Day
LAKE	character	Lake
STATE_PROV	character	State Province
SITE	character	Site name
ST_SITE	character	Site code
LATITUDE	logical	Latitude
LONGITUDE	logical	Longitude
GRID	double	Army Corps of Engineers grid
STAT_DIST	character	Statistical district
LS_MGMT	character	Lake trout management units
SPECIES	character	Species
STRAIN	character	Strain of fish
NO_STOCKED	double	Number of fish stocked
YEAR_CLASS	double	Closest year in which hatching and onset of exogenous feeding occurs.
STAGE	character	Life history of stage
AGEMONTH	double	Age in months
MARK	character	Marking method
MARK_EFF	double	Marking efficiency
TAG_NO	logical	Tag Number
TAG_RET	double	Tag Retention in percent
LENGTH	double	Average length of stock in mm
WEIGHT	double	Average weight of fish stocked in kg
CONDITION	double	Qualitative condition of fish at stocking
LOT_CODE	character	Lot Code
STOCK_METH	character	Stock method code
AGENCY	character	Stocking agency code
VALIDATION	double	Validation accuracy
NOTES	character	Notes/comments

Cleaning Script

library(tidyverse)
library(readxl)

raw_df <- read_excel("commercial.xls")

raw_df %>%
  pivot_longer(
    names_to = "region",
    values_to = "values",
    cols = 4:9
  ) %>% 
  janitor::clean_names()


clean_and_pivot <- function(sheet, cols_long){
  
  raw_df <- read_excel("commercial.xls", sheet = sheet)
  
  raw_df %>%
    select(-contains("...")) %>% 
    pivot_longer(
      names_to = "region",
      values_to = "values",
      cols = all_of(cols_long)
    ) %>% 
    janitor::clean_names() %>% 
    rename_with(~str_replace(.x, "totals", "total"))
  
}

clean_and_pivot(6, 4:5)

all_data <- tribble(
  ~sheet, ~cols_long,
  1, 4:9,
  2, 4:5,
  3, 4:10,
  4, 4:8,
  5, 4:12,
  6, 4:5
) %>% 
  pmap_dfr(clean_and_pivot) 

all_data %>% 
  filter(str_detect(region, "U.S. Total")) %>% 
  filter(!is.na(region)) %>% 
  mutate(species = fct_lump(species, 12)) %>% 
  filter(species != "Other") %>% 
  ggplot(aes(x = year, y = values, color = lake)) +
  geom_line() +
  facet_wrap(~species) +
  theme(legend.position = "top")

all_data %>% 
  write_csv("2021/2021-06-08/fishing.csv")