res <- gtrends("pizza",
geo = "US",
time=("2020-08-01 2020-08-31"))
names(res)
## [1] "interest_over_time" "interest_by_country" "interest_by_region"
## [4] "interest_by_dma" "interest_by_city" "related_topics"
## [7] "related_queries"
res$interest_by_region %>%
arrange(desc(hits)) %>%
head(10)
## location hits keyword geo gprop
## 1 Rhode Island 100 pizza US web
## 2 Connecticut 95 pizza US web
## 3 Delaware 91 pizza US web
## 4 Pennsylvania 90 pizza US web
## 5 New Hampshire 89 pizza US web
## 6 Massachusetts 84 pizza US web
## 7 Ohio 81 pizza US web
## 8 Michigan 79 pizza US web
## 9 Iowa 74 pizza US web
## 10 Indiana 72 pizza US web
res$interest_by_dma %>%
arrange(desc(hits)) %>%
head(10)
## location hits keyword geo gprop
## 1 Salisbury MD 100 pizza US web
## 2 Hartford & New Haven CT 93 pizza US web
## 3 Zanesville OH 92 pizza US web
## 4 Providence RI-New Bedford MA 91 pizza US web
## 5 Youngstown OH 89 pizza US web
## 6 Pittsburgh PA 85 pizza US web
## 7 Wilkes Barre-Scranton PA 83 pizza US web
## 8 Buffalo NY 82 pizza US web
## 9 Traverse City-Cadillac MI 81 pizza US web
## 10 Philadelphia PA 81 pizza US web
res$interest_by_city %>%
arrange(desc(hits)) %>%
head(10)
## location hits keyword geo gprop
## 1 Philadelphia 100 pizza US web
## 2 Detroit 92 pizza US web
## 3 Columbus 78 pizza US web
## 4 Milwaukee 72 pizza US web
## 5 Indianapolis 63 pizza US web
## 6 Chicago 62 pizza US web
## 7 Raleigh 60 pizza US web
## 8 Ferris 55 pizza US web
## 9 Denver 54 pizza US web
## 10 Phoenix 53 pizza US web
plot.gtrends.silent <- function(x, ...) {
df <- x$interest_over_time
df$date <- as.Date(df$date)
df$hits <- if(typeof(df$hits) == 'character'){
as.numeric(gsub('<','',df$hits))
} else {
df$hits
}
df$legend <- paste(df$keyword, " (", df$geo, ")", sep = "")
p <- ggplot(df, aes_string(x = "date", y = "hits", color = "legend")) +
geom_line() +
xlab("Date") +
ylab("Search hits") +
ggtitle("Interest over time") +
theme_bw() +
theme(legend.title = element_blank())
invisible(p)
}
my_plot <- plot.gtrends.silent(res)
my_plot +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
theme(legend.position = "none")
#----------------------------
res <- gtrends("Amazon",
geo = "US",
time=("2019-01-01 2020-08-31"))
plot(res)
state <- map_data("state")
res$interest_by_region %>%
mutate(region = tolower(location)) %>%
filter(region %in% state$region) %>%
select(region, hits) -> my_df
ggplot() +
labs(title = "Amazon : Keyword Google search",subtitle = "From 2019-01-01 to 2020-09-01",
caption = "Datasource: Google Trends\nIllustration by @JoeLongSanDiego")+
geom_map(data = state,
map = state,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = state,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'brown') +
theme(panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
## Warning: Ignoring unknown aesthetics: x, y
#----------------------------
res <- gtrends("Home Depot",
geo = "US",
time=("2019-01-01 2020-09-01"))
plot(res)
state <- map_data("state")
res$interest_by_region %>%
mutate(region = tolower(location)) %>%
filter(region %in% state$region) %>%
select(region, hits) -> my_df
ggplot() +
labs(title = "Home_Depot : Keyword Google search",
subtitle = "From 2019-01-01 to 2020-09-01",
caption = "Datasource: Google Trends\nIllustration by @JoeLongSanDiego")+
geom_map(data = state,
map = state,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = state,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'red') +
theme(panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
## Warning: Ignoring unknown aesthetics: x, y
#----------------------------
res <- gtrends("walmart",
geo = "US",
time=("2019-01-01 2020-08-31"))
plot(res,main="Google search for WALMART",
xlab="August 2020",
ylab="Count",
sub="Data source: Google Trends")
state <- map_data("state")
res$interest_by_region %>%
mutate(region = tolower(location)) %>%
filter(region %in% state$region) %>%
select(region, hits) -> my_df
ggplot() +
labs(title = "Walmart : Keyword Google search",subtitle = "From 2019-01-01 to 2020-09-01",
caption = "Datasource: Google Trends\nIllustration by @JoeLongSanDiego" )+
geom_map(data = state,
map = state,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = state,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'blue') +
theme(panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
## Warning: Ignoring unknown aesthetics: x, y
#----------------------------
res <- gtrends("Lowe's",
geo = "US",
time=("2019-01-01 2020-08-31"))
plot(res,main="Google search for LOWE'S",
xlab="",
ylab="Count",
sub="Data source: Google Trends")
state <- map_data("state")
res$interest_by_region %>%
mutate(region = tolower(location)) %>%
filter(region %in% state$region) %>%
select(region, hits) -> my_df
ggplot() +
labs(title = "LOWE'S : Keyword Google search",subtitle = "From 2019-01-01 to 2020-09-01",
caption = "Datasource: Google Trends\nIllustration by @JoeLongSanDiego" )+
geom_map(data = state,
map = state,
aes(x = long, y = lat, map_id = region),
fill="#ffffff", color="#ffffff", size=0.15) +
geom_map(data = my_df,
map = state,
aes(fill = hits, map_id = region),
color="#ffffff", size=0.15) +
scale_fill_continuous(low = 'grey', high = 'green') +
theme(panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
## Warning: Ignoring unknown aesthetics: x, y
