1 + 1[1] 2week 3 exercises
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1/200*30[1] 0.15sqrt(24)[1] 4.8989797*7[1] 49a <- 1+2
b <- 3+4
c <- "objects can be of different types"
c[1] "objects can be of different types"(d <- "another new string")[1] "another new string"a+b[1] 10this_is_a_really_long_name <- 2.5this_is_too <- 3.5r_rocks <- 2^3
colour <- "blue"seq(1,20) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20x <- "hello world"(y <- seq(1,10, length.out = 5))[1] 1.00 3.25 5.50 7.75 10.00library(palmerpenguins)
Attaching package: 'palmerpenguins'The following objects are masked from 'package:datasets':
penguins, penguins_rawlibrary(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.3.0
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.1.0 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorspenguins |>
filter(species == "adelie") |>
arrange(body_mass_g)# A tibble: 0 × 8
# ℹ 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
# bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
# year <int># which penguins weigh more than 5000g?
penguins %>%
filter(body_mass_g > 5000)# A tibble: 61 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 50 16.3 230 5700
2 Gentoo Biscoe 50 15.2 218 5700
3 Gentoo Biscoe 47.6 14.5 215 5400
4 Gentoo Biscoe 46.7 15.3 219 5200
5 Gentoo Biscoe 46.8 15.4 215 5150
6 Gentoo Biscoe 49 16.1 216 5550
7 Gentoo Biscoe 48.4 14.6 213 5850
8 Gentoo Biscoe 49.3 15.7 217 5850
9 Gentoo Biscoe 49.2 15.2 221 6300
10 Gentoo Biscoe 48.7 15.1 222 5350
# ℹ 51 more rows
# ℹ 2 more variables: sex <fct>, year <int># shows the relationship between a penguins flipper length and its body mass
## do heavier penguins tend to have longer flippers?
penguins |>
ggplot(aes(x = flipper_length_mm,
y= body_mass_g,
colour = species)) +
geom_point()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
#
penguins |>
ggplot(aes(x= flipper_length_mm,
y= body_mass_g)) +
geom_point() +
labs(
tittle = "body mass by flipper length",
x= "flipper length (mm)",
y= "body mass (g)"
)Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
# compares average body mass across different species
## which penguiin species is the heaviest on average?
penguins |>
ggplot(aes(x= species,
y= body_mass_g)) +
geom_point() +
labs(
tittle = "body mass by species",
x = "species",
y = "body mass (g)"
)Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
# displays the distribution, spread, and outliers of body mass for each species
## which species has the most variation in body mass?
penguins |>
group_by(species) |>
summarise(mean_body_mass_g =
mean(body_mass_g, na.rm = TRUE)) |>
ungroup() |>
ggplot(aes(x= species,
y= mean_body_mass_g)) +
geom_col() +
labs(
title = "body mass by species",
x = "species",
y= "mean body mass (g)"
)
# tracks population trends over time for each species
## how has the number of adelie penguins changed over the years?
penguins |>
count(year, species) |>
ggplot(aes(x = year,
y = n,
colour = species)) +
geom_line() +
labs(
title = "penguin count per year",
suntitle = "and species",
x= "year",
y= "number of penguin sightings"
)
# shows the frequency distribution of bill depth each year, seperated by species.
## did the average bill depth of gentoo penguins change over time?
penguins |>
ggplot(aes(x = bill_depth_mm,
colour = species)) +
geom_histogram(binwidth = 1) +
labs(
title = "bill depth histogram by species",
x = "bill depth",
y = "count"
) +
facet_wrap(~species)Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).
# compares the distribution of bill depth between species
## which species has the deepest bills on average?
penguins |>
ggplot(aes(x = species,
y = bill_length_mm,
fill = species)) +
geom_boxplot() +
labs(
title = "bill depth by species",
x = "species",
y= "bill depth"
)Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).
# shows how bill deoth varies across species and islands, highlighting both species differences and location effects.
## do penguins of the same species have different bill depths depending on the island they live on?
penguins |>
ggplot(aes(x = species,
y= bill_depth_mm,
fill = island)) +
geom_boxplot() +
labs(
title = "bill depth by species and island",
x = "species",
y = "bill depth",
fill = "island"
)Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_boxplot()`).