TP: Environmental Risk

Work Sheet 1

Task 1.2

Take the palmerpenguins data set (saved as dat_penguins) and make a similar plot:


                                 
1   ggplot(data = ______,
2          aes(x = ______,
              y = ______,
3              col = ______)) +
4  geom_point(aes(shape = ______),
5             size = 3,
6             alpha = 0.8) +
7  geom_smooth(method = "lm", se = FALSE) +
8  scale_color_manual(values = c("darkorange","purple","cyan4")) +
9  labs(title = ______,
       x = ______,
       y = ______) +
10  theme_minimal()
1
Take the dat_penguins data set, and then,
2
Use bill_length_mm as x and bill_depth_mm as y axis
3
Use different colors for the different species
4
Add a point layer, use different point shapes for the different species
5
Define point size
6
and transparency
7
Add lines through the point clouds for each species (takes col = species from the 2
8
Define a manual color scale
9
Define the title, x, y axes, and a name for the colour legend
10
Add a theme

Use the filter() function from dplyr:

1  ggplot(data = dat_penguins,
2          aes(x = bill_length_mm,
              y = bill_depth_mm,
3              col = species)) +
4  geom_point(aes(shape = species),
5             size = 3,
6             alpha = 0.8) +
7  geom_smooth(method = "lm", se = FALSE) +
8  scale_color_manual(values = c("darkorange","purple","cyan4")) +
9  labs(title = "Penguin bill dimensions",
       x = "Bill length (mm)",
       y = "Bill depth (mm)",
       color = "Penguin species",
       shape = "Penguin species") +
10  theme_minimal()
1
Take the dat_penguins data set, and then,
2
Use bill_length_mm as x and bill_depth_mm as y axis
3
Use different colors for the different species
4
Add a point layer, use different point shapes for the different species
5
Define point size
6
and transparency
7
Add lines through the point clouds for each species (takes col = species from the 2
8
Define a manual color scale
9
Define the title, x, y axes, and a name for the color legend
10
Add a theme

Task 1.3

Calculate the mean, standard deviation (sd()), and replicates (n()) of body_mass_gfor the different species and sexes in dat_penguins.

1dat_penguins %>%
2  group_by(______, ______) %>%
3  summarize(______ = ______,
            ______ = ______,
            ______ = ______)
1
Take the dat_penguins data
2
Group it by species and sex
3
Summarise for mean(), sd(), and n() 
1dat_penguins %>%
2  group_by(species, sex) %>%
3  summarize(mean_body_mass_g = mean(body_mass_g),
            sd_body_mass_g = sd(body_mass_g),
            n = n())
1
Take the dat_penguins data
2
Group it by species and sex
3
Summarise for mean(), sd(), and n()

Task 1.4

Take the dat_penguins data and

  • calculate the mean body_mass_g per year and species
  • change the format from long to wide, year should be distributed across columns
1dat_penguins %>%
2  group_by(______, ______) %>%
3  summarise(______ = mean(______)) %>%
4  pivot_wider(names_from = ______,
5  values_from = ______)
1
Take the dat_penguins data
2
Group it by species and year
3
Calculate the mean() of body_mass_g
4
Transform into a wide data frame, take the column names from year
5
and the values from the mean body_mass_g 
1dat_penguins %>%
2  group_by(species, year) %>%
3  summarise(mean_body_mass_g = mean(body_mass_g)) %>%
4  pivot_wider(names_from = year,
5  values_from = mean_body_mass_g)
1
Take the dat_penguins data
2
Group it by species and year
3
Calculate the mean() of body_mass_g
4
Transform into a wide data frame, take the column names from year
5
and the values from mean_body_mass_g

Task 1.4 Bonus

Why are there NA values and how can you avoid it?

Have a look at the actual data:

dat_penguins

The data contains NA values. Since these values are not known, R cannot calculate a mean for groups containing NA values. To ignore NA values, set na.rm = TRUE in the mean() function:

dat_penguins %>%
  group_by(species, year) %>%
  summarise(mean_body_mass_g = mean(body_mass_g, na.rm = TRUE)) %>%
  pivot_wider(names_from = year,
  values_from = mean_body_mass_g)