trendy asssignment

library(tidyverse)
library(DT)
library(trendyy)                # Package to access google search data
library(lubridate)               # Handles dates and times

psychologist <- trendy("psychologist")

psychologist%>% 
  get_interest() %>% 
  glimpse()

Observations: 260
Variables: 6
$ date     [3m[90m<dttm>[39m[23m 2015-02-22, 2015-03-01, 2015-03-08, 2015-03-15,…
$ hits     [3m[90m<int>[39m[23m 69, 65, 67, 65, 66, 61, 61, 65, 65, 66, 65, 65, …
$ keyword  [3m[90m<chr>[39m[23m "psychologist", "psychologist", "psychologist", …
$ geo      [3m[90m<chr>[39m[23m "world", "world", "world", "world", "world", "wo…
$ gprop    [3m[90m<chr>[39m[23m "web", "web", "web", "web", "web", "web", "web",…
$ category [3m[90m<chr>[39m[23m "All categories", "All categories", "All categor…

psychologist %>%
  get_interest_dma() %>% 
  datatable()

psychologist %>%
  get_interest() %>% 
  ggplot(aes(x = date, y = hits)) +
  geom_line() +
  theme_minimal() +
  labs(x = "date", 
       y = "hits", 
       color = "Event", 
       title = "Psychologist Searches")

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))

psychologist %>%
  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)

psychologist <- trendy("psychologist", geo = c("US", "CA"), from = "2015-01-01", to = "2018-01-01")

psychologist %>%
  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 month")

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

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

psychologist_psychiatrist <- trendy("interest in psychologist to psychiatrist", gprop = "google images")

psychologist_psychiatrist %>%
  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 = c(1:12)) +
  theme_minimal() +
  labs(title = "Google image searches for 'psychologist vs psychiatrist' ")

LS0tCnRpdGxlOiAidHJlbmR5IGFzc3NpZ25tZW50IgpvdXRwdXQ6IGh0bWxfbm90ZWJvb2sKLS0tCgpgYGB7cn0KbGlicmFyeSh0aWR5dmVyc2UpCmxpYnJhcnkoRFQpCmxpYnJhcnkodHJlbmR5eSkgICAgICAgICAgICAgICAgIyBQYWNrYWdlIHRvIGFjY2VzcyBnb29nbGUgc2VhcmNoIGRhdGEKbGlicmFyeShsdWJyaWRhdGUpICAgICAgICAgICAgICAgIyBIYW5kbGVzIGRhdGVzIGFuZCB0aW1lcwpgYGAKCmBgYHtyfQpwc3ljaG9sb2dpc3QgPC0gdHJlbmR5KCJwc3ljaG9sb2dpc3QiKQoKcHN5Y2hvbG9naXN0JT4lIAogIGdldF9pbnRlcmVzdCgpICU+JSAKICBnbGltcHNlKCkKYGBgCgpgYGB7cn0KcHN5Y2hvbG9naXN0ICU+JQogIGdldF9pbnRlcmVzdF9kbWEoKSAlPiUgCiAgZGF0YXRhYmxlKCkKYGBgCgoKCmBgYHtyfQpwc3ljaG9sb2dpc3QgJT4lCiAgZ2V0X2ludGVyZXN0KCkgJT4lIAogIGdncGxvdChhZXMoeCA9IGRhdGUsIHkgPSBoaXRzKSkgKwogIGdlb21fbGluZSgpICsKICB0aGVtZV9taW5pbWFsKCkgKwogIGxhYnMoeCA9ICJkYXRlIiwgCiAgICAgICB5ID0gImhpdHMiLCAKICAgICAgIGNvbG9yID0gIkV2ZW50IiwgCiAgICAgICB0aXRsZSA9ICJQc3ljaG9sb2dpc3QgU2VhcmNoZXMiKQpgYGAKCgoKCgoKCmBgYHtyfQpwc3ljaG9sb2dpc3QgJT4lCiAgZ2V0X2ludGVyZXN0KCkgJT4lIAogIG11dGF0ZShtb250aCA9IG1vbnRoKGRhdGUpKSAlPiUgICAgICAgICAgICAjIENyZWF0ZSBhIG5ldyB2YXJpYWJsZSBjYWxsZWQgbW9udGgKICBncm91cF9ieShtb250aCkgJT4lICAgICAgICAgICAgICAgICAgICAgICAgIyBDb21iaW5lIG1vbnRocyBhY3Jvc3Mgd2Vla3MgYW5kIHllYXJzCiAgc3VtbWFyaXplKGhpdHNfcGVyX21vbnRoID0gbWVhbihoaXRzKSkgJT4lICAgICAgIyBBdmVyYWdlIG51bWJlciBvZiBzZWFyY2hlcyBmb3IgZWFjaCBtb250aAogIGdncGxvdChhZXMoeCA9IG1vbnRoLCB5ID0gaGl0c19wZXJfbW9udGgpKSArICAgICMgZ3JhcGggaXQKICBnZW9tX2xpbmUoKSArCiAgc2NhbGVfeF9kaXNjcmV0ZShsaW1pdHMgPSBjKDE6MTIpKQpgYGAKCgoKYGBge3J9CnBzeWNob2xvZ2lzdCAlPiUKICBnZXRfaW50ZXJlc3QoKSAlPiUgCiAgbXV0YXRlKG1vbnRoID0gbW9udGgoZGF0ZSkpICU+JSAgICAgICAgICMgQ3JlYXRlIGEgbmV3IHZhcmlhYmxlIGNhbGxlZCBtb250aAogIGdyb3VwX2J5KG1vbnRoKSAlPiUgICAgICAgICAgICAgICAgICAgICAjIENvbWJpbmUgdGhlIG1vbnRocyBhY3Jvc3MgZGlmZmVyZW50IHdlZWtzIGFuZCB5ZWFycwogIHN1bW1hcml6ZShoaXRzX3Blcl9tb250aCA9IG1lYW4oaGl0cykpICU+JSAgICAgICAgICMgR2V0IGF2ZXJhZ2UgbnVtYmVyIG9mIHNlYXJjaGVzIHBlciBtb250aAogIGRhdGF0YWJsZShvcHRpb25zID0gbGlzdChwYWdlTGVuZ3RoID0gMTIpKSAlPiUgCiAgZm9ybWF0Um91bmQoMiwgMikKYGBgCgoKCgpgYGB7cn0KcHN5Y2hvbG9naXN0IDwtIHRyZW5keSgicHN5Y2hvbG9naXN0IiwgZ2VvID0gYygiVVMiLCAiQ0EiKSwgZnJvbSA9ICIyMDE1LTAxLTAxIiwgdG8gPSAiMjAxOC0wMS0wMSIpCmBgYAoKYGBge3J9CnBzeWNob2xvZ2lzdCAlPiUKICBnZXRfaW50ZXJlc3QoKSAlPiUgCiAgbXV0YXRlKG1vbnRoID0gbW9udGgoZGF0ZSkpICU+JSAgICAgICAgICAKICBncm91cF9ieShtb250aCwgZ2VvKSAlPiUgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICBzdW1tYXJpemUoaGl0c19wZXJfbW9udGggPSBtZWFuKGhpdHMpKSAlPiUgICAgICAgICAgIAogIGdncGxvdChhZXMoeCA9IG1vbnRoLCB5ID0gaGl0c19wZXJfbW9udGgsIGNvbG9yID0gZ2VvKSkgKyAgICAgICAKICBnZW9tX2xpbmUoKSArCiAgc2NhbGVfeF9kaXNjcmV0ZShsaW1pdHMgPSBjKDE6MTIpKSArCiAgdGhlbWVfbWluaW1hbCgpICsKICBsYWJzKHRpdGxlID0gIkludGVybmV0IHNlYXJjaGVzIGZvciAncHN5Y2hvbG9naXN0JyBvdmVyIHRpbWUsIGJ5IG1vbnRoIikKYGBgCgpgYGB7cn0KcHN5Y2hvbG9naXN0X3BzeWNoaWF0cmlzdCA8LSB0cmVuZHkoYygicHN5Y2hvbG9naXN0IiwgInBzeWNoaWF0cmlzdCIpLCBnZW8gPSAiVVMiKQpgYGAKCgoKCgpgYGB7cn0KcHN5Y2hvbG9naXN0X3BzeWNoaWF0cmlzdCAlPiUKICBnZXRfaW50ZXJlc3QoKSAlPiUgCiAgZ2dwbG90KGFlcyh4ID0gZGF0ZSwgeSA9IGhpdHMsIGNvbG9yID0ga2V5d29yZCkpICsKICBnZW9tX2xpbmUoKQpgYGAKCgoKCmBgYHtyfQpwc3ljaG9sb2dpc3RfcHN5Y2hpYXRyaXN0IDwtIHRyZW5keSgiaW50ZXJlc3QgaW4gcHN5Y2hvbG9naXN0IHRvIHBzeWNoaWF0cmlzdCIsIGdwcm9wID0gImdvb2dsZSBpbWFnZXMiKQpgYGAKCgpgYGB7cn0KcHN5Y2hvbG9naXN0X3BzeWNoaWF0cmlzdCAlPiUKICBnZXRfaW50ZXJlc3QoKSAlPiUgCiAgbXV0YXRlKG1vbnRoID0gbW9udGgoZGF0ZSkpICU+JSAgICAgICAgICAKICBncm91cF9ieShtb250aCkgJT4lICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgc3VtbWFyaXplKGhpdHNfcGVyX21vbnRoID0gbWVhbihoaXRzKSkgJT4lICAgICAgICAgICAKICBnZ3Bsb3QoYWVzKHggPSBtb250aCwgeSA9IGhpdHNfcGVyX21vbnRoKSkgKyAgICAgICAKICBnZW9tX2xpbmUoKSArCiAgc2NhbGVfeF9kaXNjcmV0ZShsaW1pdHMgPSBjKDE6MTIpKSArCiAgdGhlbWVfbWluaW1hbCgpICsKICBsYWJzKHRpdGxlID0gIkdvb2dsZSBpbWFnZSBzZWFyY2hlcyBmb3IgJ3BzeWNob2xvZ2lzdCB2cyBwc3ljaGlhdHJpc3QnICIpCmBgYAoKCgoK