R Notebook

Kaitlin Kavlie, PSYC-541 Assignment 3: Google Trends Analysis

1. I created a graph of searches on Google of the term ‘psychologist’ over time. I used the first code shown below to create a data set of Google searches for ‘psychologist’ over time.

psych <- trendy("psychologist")

Then, I used the code below to create a labeled line graph of the data set created above.

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

As shown on the figure created above, Google searches for ‘psychologist’ were at the highest around the year 2020.

2. I created a graph of monthly search averages of the term ‘psychologist’. Using the first code below I created a data table of average searches per month for ‘psychologist’.

psych %>%
  get_interest() %>% 
  mutate(month = month(date)) %>%         # Create a new variable called month
  group_by(month) %>%                     # Combine the months across different weeks and years
  summarize(hits_per_month = mean(hits)) %>%         # Get average number of searches per month
  datatable(options = list(pageLength = 12)) %>% 
  formatRound(2, 2)

NA

Then, using the code below I created a line graph of the same data shown on the table above.

psych %>%
  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)) +
   theme_minimal() +
  labs(title = "Google searches for 'psychologist' per month on average")

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

As shown on the figure created above, searches for ‘psychologist’ are, on average, the highest in September and the lowest in December.

3. I created a datatable of searches for the term ‘psychologist’ organized by DMA, using the single code shown below.

psych %>%
  get_interest_dma() %>% 
  datatable()

The table created above shows the top 10 cities and areas that searched the most for the term ‘psychologist’.

4. I created a line graph comparing Google searches in the United States and Canada for ‘psychologist’, on a monthly basis. I used the first code shown below to create a data set with information on searches for the term in the US and Canada for a time period of 5 years.

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

Then I used the code below to create a line graph of the new data set, divided into monthly sections.

psych_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 'psychologist' over time, by country")

`summarise()` has grouped output by 'month'. You can override using the `.groups` argument.

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

As shown on the figure created above, the term ‘psychologist’ generally has similar search patterns on Google in the United States and Canada.

5. I compared searches for the term ‘psychologist’ to ‘psychiatrist’ over time, and then created a line graph. Using the first code below I created a data set with data for searches of both terms in the United States.

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

Then, using the code below I created a line graph of the data set uploaded above.

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

As shown on the figure created above, the term ‘psychologist’ was typically more searched for than ‘psychiatrist’ over time on Google.

6. I compared searches for ‘psychologist’ to ‘psychiatrist’ over time, specifically in Google Images. Using the first code below, I created a data set for searches of both of the terms in Google Images.

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

Then, using the code below I created a line graph of the data set created above.

psychologist_psychiatrist_images %>%
  get_interest() %>%
  ggplot(aes(x = date, y = hits, color = keyword)) +
  geom_line() +
  theme_minimal() +
labs(title = "Searches in Google Images for 'psychiatrist' & 'psychologist' over time")

As shown on the figure created above, the term ‘psychologist’ was generally more searched for than ‘psychiatrist’, over time on Google Images.