.Rhistory

'Hello World!'
22/7
install.packages("tidyverse")
library(tidyverse)
ggplot2::mpg
View(mpg)
summary(mpg)
?mpg
ggplot(data = mpg) + geom_point(mapping =aes(x = displ, y = hwy))
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color=class))
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, shape=class))
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~class,nrows=2)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~class,nrow=2)
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy,color=drv,show.legend=TRUE))
ggplot(data = mpg, mapping = aes(x = displ, y = hwy))
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +geom_point()+geom_smooth()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +geom_point(color=class)+geom_smooth()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +geom_point(color=class)+geom_smooth()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy,color=class)) +geom_point()+geom_smooth()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +geom_point(mapping=aes(color=class))+geom_smooth()
ggplot(data = mpg, mapping = aes(x = displ, y=hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth(
data = filter(mpg, class == "subcompact"),
se = FALSE
)
WineDataset <- read.csv("~/Desktop/DS/WineDataset.txt")
View(WineDataset)
wd <- WineDataset
ggplot(data=wd)+geom_point(mapping =aes(x = Alcohol, y = Flavanoids))
ggplot(data=wd)+geom_point(mapping =aes(x = Alcohol, y = Flavanoids,color=class))
ggplot(data=wd)+geom_point(mapping =aes(x = Alcohol, y = Flavanoids, color=class))
ggplot(data=wd)+geom_point(mapping =aes(x = Alcohol, y = Flavanoids, color=Class))
ggplot(data = wd, mapping = aes(x = TotalPhenols, y = Flavanoids)) +geom_point(mapping=aes(color=class))+geom_smooth()
ggplot(data = wd, mapping = aes(x = TotalPhenols, y = Flavanoids)) +geom_point(mapping=aes(color=Class))+geom_smooth()
ggplot(data = wd) + geom_point(mapping = aes(x = TotalPhenols, y = Flavanoids))+ facet_wrap(~Class,nrow=3)
ggplot(data = wd, mapping = aes(x = TotalPhenols, y = Flavanoids)) +geom_point(mapping=aes(color=Class))+geom_smooth()
summary(wd)
summary(wd,Class=3)
summary(wd(Class=3))
summary(wd,(Class=3))
## Answer the questions for the group activity and show your code here:
library(tidyverse)
ggplot(data = wd, mapping = aes(x = TotalPhenols, y = Flavanoids)) +geom_point(mapping=aes(color=Class))+geom_smooth()
ggplot(data=wd)+geom_point(mapping =aes(x = Alcohol, y = Flavanoids, color=Class))
ggplot(data = mpg) + geom_point(mapping =aes(x = displ, y = hwy), position = "jitter")
ggplot(data=mpg) +
stat_summary(
mapping = aes(x = class, y = displ),
fun.ymin = min,
fun.ymax = max,
fun.y = median
)
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, fill = class))
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, color = class))
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, fill = class))
ggplot(data = mpg, mapping = aes(x = class, y = hwy))
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, color = class))
ggplot(data=mpg) +
stat_summary(
mapping = aes(x = class, y = displ),
fun.ymin = min,
fun.ymax = max,
fun.y = median
)
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, fill = class))
ggplot(data = mpg) + geom_bar(mapping = aes(x = class, color = class))
ggplot(data = mpg, mapping = aes(x = class, y = hwy))
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut))
ggplot(data = mpg, mapping = aes(x = class, y = hwy))  + geom_boxplot()
### <CODE GOES HERE>
ggplot(data = mpg, mapping = aes(x = class, y = hwy))  + geom_boxplot()+ coord_flip()
## load the "diamonds" dataset included with the ggplot2 package:
ggplot::diamonds
## load the "diamonds" dataset included with the ggplot2 package:
ggplot2::diamonds
summary(diamonds)
?diamonds
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut))
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y= ..prop.., group=1))
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut, color = cut))
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut, fill = cut))
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity))
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut, fill = cut))
ggplot(data=diamonds) + geom_bar(mapping = aes(x = cut, fill = clarity))
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + geom_bar(position="dodge")
summary(mpg)
ggplot(data = mpg, mapping = aes(x = drv, fill = clarity)) + geom_bar(position="dodge")
ggplot(data = mpg, mapping = aes(x = drv, fill = cyl)) + geom_bar(position="dodge")
ggplot(data = mpg, mapping = aes(x = drv)) + geom_bar(position="dodge")
ggplot(data = mpg, mapping = aes(x = drv))
ggplot(data = mpg, mapping = aes(x = drv)) + geom_bar(position="dodge")
L= mgp$class== suv
L= mpg$class== suv
L= mpg$class== 'suv'
L
ggplot(data = mpg[L,], mapping = aes(x = drv)) + geom_bar(position="dodge")
ggplot(data = mpg[L,], mapping = aes(x = drv, color = drv)) + geom_bar(position="dodge")
ggplot(data = mpg[L,], mapping = aes(x = drv, fill = drv)) + geom_bar(position="dodge")
library(tidyverse)
library(dplyr)
x <- 6*3
x
x
x
x x
x +x
install.packages("nycflights13")
library(nycflights13)
library(tidyverse)
## Let's dive deeper into the filter() function:
filter(flights, month == 1, day ==1)
?nycflights13
??nycflights13
## The nycflights13 data contains all (what # of) flights that departed from New York City in 2013
## Source:US Bureau of Transportation
summarise(nycflights13)
## The nycflights13 data contains all (what # of) flights that departed from New York City in 2013
## Source:US Bureau of Transportation
summarise(flights)
## The nycflights13 data contains all (what # of) flights that departed from New York City in 2013
## Source:US Bureau of Transportation
flights
?flights
summary(wd)
View(mpg)
library(tidyverse)
ggplot2::mpg
View(mpg)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~class,nrow=2)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~year,nrow=2)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~city,nrow=2)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))+ facet_wrap(~cty,nrow=2)
ggplot(data = mpg) + geom_point(mapping = aes(x = cty, y = hwy))+ facet_wrap(~displ,nrow=2)
ggplot2::msleep
sleep = msleep
sleep <- msleep
flights
## The nycflights13 data contains all (what # of) flights that departed from New York City in 2013
## Source:US Bureau of Transportation
flights
library(nycflights13)
library(tidyverse)
?nycflights13
## The nycflights13 data contains all (what # of) flights that departed from New York City in 2013
## Source:US Bureau of Transportation
flights
arrange(flights, desc(arr_delay))
arrange(flights, desc(arr_delay, day))
arrange(flights, desc(month, day))
?flights
arrange(flights, desc(day,month))
arrange(flights, desc(day))
arrange(flights, desc(day,dep_delay))
arrange(flights,day,dep_delay)
install.packages("nycflights13")
library(nycflights13)
library(tidyverse)
flights
?flights
flights
newFlights <- mutate(flights,
new_delay=sched_dep_time-dep_time
)
newFlights
arrange(newFlighst,desc(new_delay))
arrange(newFlights,desc(new_delay))
transmute(flights,
dep_time,
hour = dep_time %/% 100,
minute = dep_time %% 100
)
newFlights <- transmute(flights,
dep_time,
sched_dep_time,
dep_delay,
new_delay=dep_time-sched_dep_time
)
arrange(newFlights,desc(new_delay))
newFlights <- transmute(flights,
dep_time,
sched_dep_time,
dep_delay,
new_delay=dep_time-sched_dep_time)
## 2.) (15 pts. total)  Remember  the struggle we had last week when we tried to plot
##     a bar plot representing the distribution of drive (front-wheel, rear-wheel, four-wheel)
##     for SUVs only?  Now take advantage of the fliter() and/or select() functions and
##     use in conjunction with your ggplot command in order to create this plot properly.
##     Submit your plot in addition to your code, either by saving it or by taking a
##     screenshot.  Make sure your last name and Week3Assignment are in the title.
ggplot2::mpg
ggplot(data = mpg, mapping = aes(x = displ, y=hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth(
data = filter(mpg, class == "subcompact"),
se = FALSE
)
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
geom_boxplot()
ggplot(data = mpg[L,], mapping = aes(x = drv, fill = drv)) + geom_bar(position="dodge")
ggplot(data = filter(mpg,class==suv), mapping = aes(x = drv, fill = drv)) + geom_bar(position="dodge")
ggplot(data = filter(mpg,class=='suv'), mapping = aes(x = drv, fill = drv)) + geom_bar(position="dodge")
#### AND FINALLY A NEW DATASET! #################################################
## LOAD THE MSLEEP DATASET:
ggplot2::msleep
arrage(msleep,desc(sleep_total))
arrange(msleep,desc(sleep_total))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))
ggplot(data = mpg) + geom_smooth(mapping = aes(x = displ, y = hwy,color=drv,show.legend=TRUE))
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +geom_point(mapping=aes(color=class))+geom_smooth()
ggplot(data = mpg) + geom_point(mapping =aes(x = displ, y = hwy), position = "jitter")
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_total,y= sleep_rem))
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_total,y= sleep_rem,color=genus))
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_total,y= sleep_rem,color=vore))
#The animal that sleeps the most is the Little brown bat
msleep?
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_total,y= sleep_rem))
#The animal that sleeps the most is the Little brown bat
msleep?
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_total,y= sleep_rem))
#The animal that sleeps the most is the Little brown bat
?msleep
ggplot(data=msleep)+ geom_point(mapping=aes(x=sleep_rem,y= sleep_cycle))
ggplot(data=msleep)+ geom_point(mapping=aes(y=sleep_rem,x= sleep_cycle))
newSleep<-transmute(msleep,
name,
sleep_total,
percentSleeping=sleep_total/24,
bodyBrainRation=bodywt/brainwt
)
newSleep
newSleep<-transmute(msleep,
name,
sleep_total,
percentSleeping=sleep_total/24,
bodyBrainRatio=bodywt/brainwt
)
newSleep
ggplot(data=newSleep)+ geom_point(mapping=aes(y=sleepTotal,x= bodyBrainRatio))
ggplot(data=newSleep)+ geom_point(mapping=aes(y=sleep_total,x= bodyBrainRatio))
transmute(flights,
dep_time,
hour = dep_time %/% 100,
minute = dep_time %% 100
)
library(nycflights13)
library(tidyverse)
flights
?flights
transmute(flights,
dep_time,
hour = dep_time %/% 100,
minute = dep_time %% 100
)
#dep_delay should be equal to dep_time-sched_dep_time
time<-mutate(flights,
dep_time = dep_time%/%100*60+dep_time%%100,
sched_dep_time= sched_dep_time%/%100*60+sched_dep_time%%100
)
newFlights <- transmute(time,
dep_delay,
new_delay=dep_time-sched_dep_time)
arrange(newFlights,desc(new_delay))
#my hypothesis is that having a longer sleep cycle will result in having less rem sleep.
#using the plot created below you can see there is a correlation between the two as the trend is sloping down
ggplot(data=msleep)+ geom_point(mapping=aes(y=sleep_rem,x= sleep_cycle))
newFlights
view(newFlights)
View(newFlights)
### Looking at summarize() and group_by() ##################
nycflights13::flights
by_day <- group_by(flights, year, month, day)
View(by_day)
summarize(by_day, delay = mean(dep_delay, na.rm = TRUE))
by_dest <- group_by(flights, dest)
delay <- summarize(by_dest,count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
delay
delay <- filter(delay, count >20, dest != "HNL")
ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
geom_point(aes(size = count)) +
geom_smooth(se = FALSE)
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(flights, dep_delay != "NA")
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(flights, dep_delay is.na())
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(flights, is.na(dep_time))
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(flights, is.na(dep_time)==FALSE)
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(is.na(dep_time)==FALSE)
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(is.na(dep_time)==TRUE)
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(is.na(dep_time)==FALSE)
## Let's make a dataframe that represents only flights that actually flew"
not_cancelled <- flights%>%
filter(!is.na(dep_time))
not_cancelled  %>%
group_by(dest) %>%
summarize(carriers = n_distinct(carrier)) %>%
arrange(desc(carriers))
## 1.) Find all destinations that are flown by at least two carriers.
not_cancelled  %>%
group_by(dest) %>%
summarize(carriers = n_distinct(carrier)) %>%
arrange(desc(carriers))
not_cancelled %>%
count(dest)
## 1.) Find all destinations that are flown by at least two carriers.
not_cancelled  %>%
group_by(dest) %>%
summarize(carriers = n_distinct(carrier)) %>%
arrange(desc(carriers)) %>%
filter(carriers>1)
View(not_cancelled)
not_cancelled %>%
group_by(flight) %<%
summarize(min_delay= min(arr_delay)) %<%
filter(min_delay == 10)
not_cancelled %>%
group_by(flight) %>%
summarize(min_delay= min(arr_delay)) %>%
filter(min_delay == 10)
not_cancelled %>%
group_by(flight) %>%
summarize(min_delay= min(arr_delay)) %>%
summarize(max_delay = max(arr_delay)) %>%
filter(min_delay == 10& max_delay==10)
not_cancelled %>%
group_by(flight) %>%
summarize(min_delay= min(arr_delay)) %>%
summarize(max_delay = max(arr_delay)) %>%
filter(min_delay == 10 & max_delay==10)
not_cancelled %>%
group_by(flight) %>%
summarize(min_delay= min(arr_delay)) %>%
filter(min_delay >= 10)
## 1.) Find all destinations that are flown by at least two carriers.
not_cancelled  %>%
group_by(dest) %>%
summarize(carriers = n_distinct(carrier)) %>%
arrange(desc(carriers)) %>%
filter(carriers>1)
install.packages(knitr)
install.packages("knitr")
library(knitr)
knitr::opts_chunk$set(echo = TRUE)
ggplot(data=mpg) +
stat_summary(
mapping = aes(x = class, y = displ),
fun.ymin = min,
fun.ymax = max,
fun.y = median
)
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color=class))
library(ggplot)
library(tidyverse)
library(ggplot2)
ggplot(data = mpg) + geom_point(mapping =aes(x = displ, y = hwy), position = "jitter")
library(tidyverse)
1
123
packages.install('varhandle')
install.packages(varhandle)
install.packages('varhandle')
install.packages('gridExtra')
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', color='bw')#Get the map from Google Maps
library(tidyverse)
library(ggplot2)
library(lubridate)
library(ggplot2)
library(ggmap)
library(dplyr)
library(data.table)
library(ggrepel)
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', api_key='AIzaSyBOnSvd9M9waO2CKqKaq9pB794slabDTjc',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', api_key='AIzaSyBOnSvd9M9waO2CKqKaq9pB794slabDTjc',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013)key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_map(location=c(lon=-87.645167,lat=41.808013),api_key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
map <- get_googlemap(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
##1) Create a map with all of the crime locations plotted.
p <- ggmap(get_googlemap(center = c(lon = -87.645167, lat = 41.808013),
zoom = 11, scale = 2,
maptype ='terrain',
color = 'color',
key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA'
))
map <- get_googlemap(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
download.file('https://maps.googleapis.com/maps/api/staticmap?center=29.763284,-95.363271&zoom=11&size=640x640&scale=2&maptype=roadmap',destfile=destfile,mode ="wb")
map <- 0
download.file('https://maps.googleapis.com/maps/api/staticmap?center=29.763284,-95.363271&zoom=11&size=640x640&scale=2&maptype=roadmap',destfile=map,mode ="wb")
download.file('https://maps.googleapis.com/maps/api/staticmap?center=29.763284,-95.363271&zoom=11&size=640x640&scale=2&maptype=roadmap',destfile='map',mode ="wb")
download.file('https://maps.googleapis.com/maps/api/staticmap?center=29.763284,-95.363271&zoom=11&key=AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA&size=640x640&scale=2&maptype=roadmap',destfile='map',mode ="wb")
setwd("~/Desktop/DS/Project1")
map <- get_googlemap(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
download.file('https://maps.googleapis.com/maps/api/staticmap?center=29.763284,-95.363271&zoom=11&key=AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA&size=640x640&scale=2&maptype=roadmap',destfile='map',mode ="wb")
map <- get_googlemap(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
register_google(key = "AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA")
map <- get_googlemap(location=c(lon=-87.645167,lat=41.808013),key='AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA', zoom=11, maptype='roadmap',color='bw')#Get the map from Google Maps
knitr::opts_chunk$set(echo = TRUE)
register_google(key = "AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA")
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', color='bw')#Get the map from Google Maps
register_google(key = "AIzaSyC9rrtr993vzkQlEF3HfYdzwcj0ojOnLzA")
map <- get_map(location=c(lon=-87.645167,lat=41.808013), zoom=11, maptype='roadmap', color='bw')#Get the map from Google Maps
install.packages("XQuartz")