Visualize the total production of honey across years by state Use color to highlight the west coast (Washington, Oregon, and California) with a different color used for each west coast state.

p1 <- ggplot(df, aes(year, totalprod)) + 
  geom_line(aes(group=state),
            color="lightgrey") + 
  geom_line(data = df %>% filter(state %in% c("CA", "OR", "WA")), 
            aes(color=state), 
            size=1.5) + 
  theme_minimal()

p1

Reproduce the plot according three different kinds of color blindness, as well as a desaturated version.

cvd_grid(p1)

Reproduce the plot using a color blind safe palette.

p2 = p1 + scale_color_viridis_d()

p2

cvd_grid(p2)

Download the file here denoting the region and division of each state.

Join the file with your honey file.

df2 <- read_csv(here::here("data", "lab3", "regions_divisions.txt"))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   State = col_character(),
##   `State Code` = col_character(),
##   Region = col_character(),
##   Division = col_character()
## )
df3 <- df %>% left_join(df2, by=c("state"="State Code"))

Produce a bar plot displaying the average honey for each state (collapsing across years).

df_avg = df3 %>% 
  group_by(Region, state) %>% 
  summarise(year_m = mean(totalprod))
## `summarise()` has grouped output by 'Region'. You can override using the `.groups` argument.
ggplot(df_avg, aes(x=state, y=year_m)) + 
  geom_bar(stat="identity") + 
  coord_flip() + 
  theme_minimal()

Use color to highlight the region of the country the state is from.

Note patterns you notice: The production of honey is an exponential distribution, and most of tail are Northeast and South.

ggplot(df_avg, aes(x=reorder(state, year_m), 
                   y=year_m, 
                   fill=Region)) + 
  geom_bar(stat="identity") + 
  coord_flip() + 
  scale_fill_OkabeIto() +
  labs(x="State", 
       y="Total production (averaged over years)") + 
  theme_minimal()

Create a heatmap displaying the average honey production across years by region (averaging across states within region).

df_avg2 = df3 %>% 
  group_by(Region, year) %>% 
  summarise(year_m = mean(totalprod))
## `summarise()` has grouped output by 'Region'. You can override using the `.groups` argument.
ggplot(df_avg2, aes(x=year, 
                    y=Region, 
                    fill=year_m)) + 
  geom_tile() + 
  scale_fill_viridis_c() + 
  labs(fill = "Production") + 
  coord_fixed() + 
  theme_minimal()

Create at least one more plot of your choosing using color to distinguish, represent data values, or highlight.

I tried for a while but had difficulties in installing the “albersusa” package, which seems to be caused by a failure to install the “units” package. When I tried to install the “unit” manually, the error message says I need the “libudunits2.so” to install the “units”, so I tried to install udunits through command line. Then the command line says it was installed already, so I am a bit run out of ideas for the installation.

df_avg3 = df3 %>% 
  group_by(state) %>% 
  summarise(m=mean(priceperlb))

ggplot(df3, aes(year, 
                priceperlb, 
                color=state)) + 
  geom_line(aes(group=state)) + 
  facet_wrap(~state)+
  gghighlight(max(priceperlb) > 3.5, 
              use_direct_label=FALSE) + 
  scale_color_OkabeIto(darken = 0.2) +
  theme_minimal() +
  labs(title = "States with honey unit price over $3.5 over time", 
       y = "price per lb") + 
  theme(legend.position = "none")