R Notebook

1. Create a graph of interest in it over time.

psychologist %>%
  get_interest() %>% 
  ggplot(aes(x = date, y = hits)) +
  geom_line() +
theme_minimal() +
  labs(title = "Google searches for 'psychologist' over time")

Here is the graph showing psychologist searches over time

2. Create a graph of monthly interest in it.

psychologist %>%
  get_interest() %>% 
  mutate(month = month(date)) %>%            # Create a new variable called month
  group_by(month) %>%                        # Combine months across weeks and years
  summarize(hits_per_month = mean(hits)) %>%      # Average number of searches for each month
  ggplot(aes(x = month, y = hits_per_month)) +    # graph it
  geom_line() +
  scale_x_discrete(limits = c(1:12)) +
labs(title = "Internet searches for psychologist over time showing monthly interest, by month")

Continuous limits supplied to discrete scale.
Did you mean `limits = factor(...)` or `scale_*_continuous()`?

Here is the graph showing monthly searches.

3. Create a datatable of interest by DMA.

psychologist_US <- trendy("psychologist", geo = "US", from = "2015-01-01", to = "2020-01-01")

psychologist_US %>%
  get_interest_dma() %>% 
  datatable()

NA

Here is a datatable of interest by DMA

4. Compare US vs. Canadian (CA) interest in psychologist by month, and create a line graph.

psychologist_countries <- trendy("psychologist", geo = c("US", "CA"), from = "2015-01-01", to = "2020-01-01")

psychologist_countries %>%
  get_interest() %>% 
  mutate(month = month(date)) %>%          
  group_by(month, geo) %>%                              
  summarize(hits_per_month = mean(hits)) %>%           
  ggplot(aes(x = month, y = hits_per_month, color = geo)) +       
  geom_line() +
  scale_x_discrete(limits = c(1:12)) +
  theme_minimal() +
  labs(title = "Internet searches for 'flu' over time, by country")

`summarise()` has grouped output by 'month'. You can override using the `.groups` argument.
Continuous limits supplied to discrete scale.
Did you mean `limits = factor(...)` or `scale_*_continuous()`?

This is a line graph comparing US and CA searches of psychologist per month.

5. Compare interest in psychologist to psychiatrist over time, and create a line graph.

psychologist_psychiatrist <- trendy(c("psychologist", "psychiatrist"), geo = "US")

psychologist_psychiatrist %>%
  get_interest() %>%
  ggplot(aes(x= date, y= hits, color= keyword)) +
  geom_line()+
  labs(title = "Internet searches for psychologist and psychiatrist over time")

A line graph comparing psychologist and psychiatrist over time.

6. Compare interest in psychologist to psychiatrist in google images over time, and create a line graph.

psychologist_psychiatrist_images <- trendy(c("psychologist", "psychiatrist"), gprop = "images")

psychologist_psychiatrist_images %>%
  get_interest() %>% 
  ggplot(aes(x = date, y = hits, color = keyword)) +       
  geom_line() +
  theme_minimal() +
  labs(title = "Google image searches for psychologist and psychiatrist over time")

This is showing the google image searches of psychologist and psychiatrist over time.