# Indexing vectors

survey_years <- seq(2008, 2024, by = 2)
coral_cover_pct <- c(
  34.53,
  22.26,
  11.66,
  9.25,
  14.05,
  22.30,
  30.35,
  16.35,
  5.74
)
n_observations <- c(Acropora = 13882, Pocillopora = 13988, Porites = 3192)

# Indexing with positive integers
coral_cover_pct[8:9]
coral_cover_pct[c(1, length(coral_cover_pct))]
# out of order works too
coral_cover_pct[9:8]
coral_cover_pct[c(8, 8, 8)]

# Negative integers
coral_cover_pct[-c(1, 3, 5, 7, 9)]
coral_cover_pct[-(2:length(coral_cover_pct))]
# Oh no, a bug
coral_cover_pct[-1:2]
# Fix it
coral_cover_pct[-(1:2)]

# Names
n_observations["Porites"]
n_observations[c("Porites", "Pocillopora")]

# Logic
cyclone_year <- 2010
survey_years == cyclone_year
survey_years[survey_years == cyclone_year]
coral_cover_pct[survey_years == cyclone_year]

# 2d data structures

# Up first: matrices
simple_matrix <- matrix(1:12, nrow = 3, ncol = 4)
simple_matrix

# Index a single value
simple_matrix[3, 4]

# Entire rows or columns with _blanks_
simple_matrix[2, ]
simple_matrix[, 2]

# Index a submatrix
simple_matrix[1:2, 2:3]

# Indexing can assign to elements too
simple_matrix[2, 3] <- 100
simple_matrix

# Data frames represent things like data sheets
bird_species <- c("California Towhee", "House Finch", "House Sparrow")
bird_counts <- c(9L, 6L, 8L)
song_freq_khz <- c(4.99, 2.66, 4.08)
is_native <- c(TRUE, TRUE, FALSE)


songbird_survey <- data.frame(
  species = bird_species,
  count = bird_counts,
  song_freq_khz = song_freq_khz,
  is_native = is_native
)

# To pull out columns, index by name
songbird_survey[, "species"]
# The "buck" operator $
songbird_survey$species

# To get rows:
songbird_survey[1, ]
songbird_survey[1:2, ]