R Notebook

By running the following chunk, I am retrieving the data of how many searches for psychologist there have been on google over time.

psychologist <- trendy("psychologist")

psychologist %>% 
  glimpse()

List of 1
 $ :List of 7
  ..$ interest_over_time :'data.frame': 261 obs. of  7 variables:
  .. ..$ date    : POSIXct[1:261], format:  ...
  .. ..$ hits    : int [1:261] 66 66 66 64 65 66 64 62 65 68 ...
  .. ..$ keyword : chr [1:261] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ geo     : chr [1:261] "world" "world" "world" "world" ...
  .. ..$ time    : chr [1:261] "today+5-y" "today+5-y" "today+5-y" "today+5-y" ...
  .. ..$ gprop   : chr [1:261] "web" "web" "web" "web" ...
  .. ..$ category: int [1:261] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ interest_by_country:'data.frame': 250 obs. of  5 variables:
  .. ..$ location: chr [1:250] "Australia" "South Africa" "Ireland" "New Zealand" ...
  .. ..$ hits    : chr [1:250] "100" "75" "47" "43" ...
  .. ..$ keyword : chr [1:250] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ geo     : chr [1:250] "world" "world" "world" "world" ...
  .. ..$ gprop   : chr [1:250] "web" "web" "web" "web" ...
  ..$ interest_by_region : NULL
  ..$ interest_by_dma    :'data.frame': 306 obs. of  5 variables:
  .. ..$ location: chr [1:306] "Miami-Ft. Lauderdale FL" "New York NY" "Philadelphia PA" "West Palm Beach-Ft. Pierce FL" ...
  .. ..$ hits    : int [1:306] 100 97 96 93 93 90 88 88 86 85 ...
  .. ..$ keyword : chr [1:306] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ geo     : chr [1:306] "world" "world" "world" "world" ...
  .. ..$ gprop   : chr [1:306] "web" "web" "web" "web" ...
  ..$ interest_by_city   :'data.frame': 200 obs. of  5 variables:
  .. ..$ location: chr [1:200] "Ghan" "Lismore" "Mornington" "Canberra" ...
  .. ..$ hits    : int [1:200] NA NA NA 100 100 NA 100 94 94 94 ...
  .. ..$ keyword : chr [1:200] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ geo     : chr [1:200] "world" "world" "world" "world" ...
  .. ..$ gprop   : chr [1:200] "web" "web" "web" "web" ...
  ..$ related_topics     :'data.frame': 34 obs. of  5 variables:
  .. ..$ subject       : chr [1:34] "100" "16" "11" "9" ...
  .. ..$ related_topics: chr [1:34] "top" "top" "top" "top" ...
  .. ..$ value         : chr [1:34] "Psychologist" "Psychology" "Psychiatrist" "Clinical psychologist" ...
  .. ..$ keyword       : chr [1:34] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ category      : int [1:34] 0 0 0 0 0 0 0 0 0 0 ...
  .. ..- attr(*, "reshapeLong")=List of 4
  ..$ related_queries    :'data.frame': 50 obs. of  5 variables:
  .. ..$ subject        : chr [1:50] "100" "99" "73" "73" ...
  .. ..$ related_queries: chr [1:50] "top" "top" "top" "top" ...
  .. ..$ value          : chr [1:50] "psychology" "the psychologist" "psychiatrist" "psychiatrist psychologist" ...
  .. ..$ keyword        : chr [1:50] "psychologist" "psychologist" "psychologist" "psychologist" ...
  .. ..$ category       : int [1:50] 0 0 0 0 0 0 0 0 0 0 ...
  .. ..- attr(*, "reshapeLong")=List of 4
  ..- attr(*, "class")= chr [1:2] "gtrends" "list"
 - attr(*, "class")= chr "trendy"

psychologist %>%
  get_interest() %>% 
  ggplot(aes(x = date, y = hits)) +
  geom_line()

This chunk then turned the data I ran from before into a graph of the hits for ‘psychologist’ on google over time.

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))

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

This chunk then narrowed the last graph even more by changing the timeline from years to months. This makes the data clearer and easier to understand.

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

This is retrieving the data from google of how many searches for ‘psychologist’ there have been in the US from January 2015 to January 2020

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

The above chunk then converted the previous data into a datatable to organize it better.

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

This chunk retrieves data about searches for ‘psychologist’ in both the US and Canada from 2015-2020. The chunk below then turns this data into a line graph comparing the two searches over 12 months.

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 'psychologist' 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()`?

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

This chunk is retrieving the searches for psychologist and psychiatrist in the US. The below chunk is then running this data to become a line graph comparing the amount of searches for each over the years.

  psychologist_psychiatrist %>%
  get_interest() %>%
  ggplot(aes(x = date, y = hits, color = keyword)) +
  geom_line()

psychiatrist_images <- trendy("psychiatrist", gprop = "images")

This is retrieving the search for images of psychiatrist from google images

psychologist_images <- trendy("psychologist", gprop = "images")

This is retrieving the search for images of psychologist from google images

psychiatrist_images %>%
psychologist_images

Error in psychologist_images(.) : 
  could not find function "psychologist_images"

This was one of many attempts to achieve a line graph of the searches for both psychologist and psychiatrist on google images over time. However, I was not able to figure out the right code.