R Notebook

library(tidyverse)
library(DT)
library(trendyy)              
library(lubridate)               

Psychologist <- trendy("Psychologist")

Psychologist %>% 
  get_interest() %>% 
  glimpse()

Rows: 262
Columns: 7
$ date     <dttm> 2019-08-11, 2019-08-18, 2019-08-25, 2019…
$ hits     <int> 68, 73, 75, 70, 82, 77, 72, 72, 72, 70, 7…
$ keyword  <chr> "Psychologist", "Psychologist", "Psycholo…
$ geo      <chr> "world", "world", "world", "world", "worl…
$ time     <chr> "today+5-y", "today+5-y", "today+5-y", "t…
$ gprop    <chr> "web", "web", "web", "web", "web", "web",…
$ category <chr> "All categories", "All categories", "All …

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

The shows the searches for psychologist over a span of years.

Psychologist %>%
  get_interest() %>% 
  mutate(month = month(date)) %>%            
  group_by(month) %>%                        
  summarize(hits_per_month = mean(hits)) %>%      
  ggplot(aes(x = month, y = hits_per_month)) +    
  geom_line() +
  scale_x_discrete(limits = factor(1:12))

This graph shows the searches for psychologist by month over a year.

Psychologist %>%
  get_interest_dma() %>% 
  datatable() %>%

suppressWarnings()

This table shows the searches for psychologist by geographical location.

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

Psychologist %>% 
  get_interest() %>% 
  glimpse()

Rows: 262
Columns: 7
$ date     <dttm> 2019-08-11, 2019-08-18, 2019-08-25, 2019…
$ hits     <int> 68, 73, 75, 70, 82, 77, 72, 72, 72, 70, 7…
$ keyword  <chr> "Psychologist", "Psychologist", "Psycholo…
$ geo      <chr> "world", "world", "world", "world", "worl…
$ time     <chr> "today+5-y", "today+5-y", "today+5-y", "t…
$ gprop    <chr> "web", "web", "web", "web", "web", "web",…
$ category <chr> "All categories", "All categories", "All …

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 = factor(1:12)) +
  theme_minimal() +
  labs(title = "Internet searches for 'Psychologist' over time, by Canada and US")

This shows a comparison between searches for psychologist in Canada and the US by month, over a year.

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

This shows a comparison of the number of searches for psychologist and the number of searches for psychiatrist over the span of years.

Psychologist_Psychiatrist_images <- trendy(c("Psychologist", "Psychiatrist"), geo = "US", gprop = "images")

Psychologist_Psychiatrist_images %>%
  get_interest() %>%
  ggplot(aes(x = date, y = hits, color = keyword, gprop = "images")) +
  geom_line()

This shows a comparison between the number of image searches for psychologist and psychiatrist.