README.md
In kabhatia7/temperaturr: Learn more about the weather
temperaturr
The temperaturr package uses the climacell and google maps geocoding
APIs to tell you about the weather by providing data frames and plots of
the weather from certain locations.
Setup & Installation
First things first we’re gonna need to install the development version
of the package from Github with:
# install.packages("devtools")
devtools::install_github("kabhatia7/temperaturr")
library(temperaturr)
Next we need to get setup with API Keys. The first one for climacell is
very easy to get set up just click this
link to sign up for a free
climacell key. Then to store your key as in your global environment to
be used for all functions in the temperaturr package
I will be reading in our keys from a JSON file as they should not be
used by more than one person.
register_climacell_key(apikey = jsonlite::read_json("keys.json")$climacell)
The free version gives us 1000 calls a day and which for the purposes of
this package should be more than enough. The next key you’re going to
want to get is the Google API for geocoding. This key will make it so
you don’t have to always look up your location of interest by simply
passing the name of the location of interest. We will use the ggmap
package to register our google key with the register_google()
function. Instructions for a google key can be found
here.
#install.packages(ggmap)
library(ggmap)
#> Loading required package: ggplot2
#> Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
#> Please cite ggmap if you use it! See citation("ggmap") for details.
register_google(key = jsonlite::read_json("keys.json")$google)
Then you can set your location through the find_loc() function so you
wont have to pass any of the lat and lon arguments for the functions.
Try to be as specific as possible by adding state names or zip codes
since google will always pick the first result it searches for if there
are conflicts.
find_loc("San Luis Obispo, CA")
#> Source : https://maps.googleapis.com/maps/api/geocode/json?address=San+Luis+Obispo,+CA&key=xxx
Now we’re all set to use the package!
Retrieving a temperaturr data frame
get_prev_temp() %>%
head()
#> # A tibble: 6 x 6
#> observation_time year month day temp_in_F temp_in_C
#> <dttm> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 2020-04-25 03:05:00 2020 4 25 74.0 23.3
#> 2 2020-04-25 03:15:00 2020 4 25 72 22.2
#> 3 2020-04-25 03:25:00 2020 4 25 71.0 21.7
#> 4 2020-04-25 03:35:00 2020 4 25 69.0 20.6
#> 5 2020-04-25 03:45:00 2020 4 25 69.0 20.6
#> 6 2020-04-25 03:55:00 2020 4 25 68 20
Hourly comparison of today and day before in a dataframe
weather_comparison()
#> Day1_Times Day1_Temp_in_F Day1_Temp_in_C Day2_Times
#> 1 2020-05-03 03:00:00 54.70 12.61 2020-05-04 03:00:00
#> 2 2020-05-03 04:00:00 54.71 12.62 2020-05-04 04:00:00
#> 3 2020-05-03 05:00:00 54.78 12.66 2020-05-04 05:00:00
#> 4 2020-05-03 06:00:00 54.13 12.29 2020-05-04 06:00:00
#> 5 2020-05-03 07:00:00 54.71 12.62 2020-05-04 07:00:00
#> 6 2020-05-03 08:00:00 60.02 15.57 2020-05-04 08:00:00
#> 7 2020-05-03 09:00:00 65.86 18.81 2020-05-04 09:00:00
#> 8 2020-05-03 10:00:00 68.36 20.20 2020-05-04 10:00:00
#> 9 2020-05-03 11:00:00 68.85 20.47 2020-05-04 11:00:00
#> 10 2020-05-03 12:00:00 68.89 20.49 2020-05-04 12:00:00
#> 11 2020-05-03 13:00:00 68.42 20.23 2020-05-04 13:00:00
#> 12 2020-05-03 14:00:00 67.27 19.59 2020-05-04 14:00:00
#> 13 2020-05-03 15:00:00 66.04 18.91 2020-05-04 15:00:00
#> 14 2020-05-03 16:00:00 64.99 18.33 2020-05-04 16:00:00
#> 15 2020-05-03 17:00:00 63.22 17.34 2020-05-04 17:00:00
#> 16 2020-05-03 18:00:00 61.43 16.35 2020-05-04 18:00:00
#> 17 2020-05-03 19:00:00 59.00 15.00 2020-05-04 19:00:00
#> 18 2020-05-03 20:00:00 55.40 13.00 2020-05-04 20:00:00
#> 19 2020-05-03 21:00:00 55.40 13.00 2020-05-04 21:00:00
#> 20 2020-05-03 22:00:00 55.40 13.00 2020-05-04 22:00:00
#> 21 2020-05-03 23:00:00 55.40 13.00 2020-05-04 23:00:00
#> 22 2020-05-04 00:00:00 55.40 13.00 2020-05-05 00:00:00
#> 23 2020-05-04 01:00:00 53.60 12.00 2020-05-05 01:00:00
#> 24 2020-05-04 02:00:00 53.60 12.00 2020-05-05 02:00:00
#> Day2_Temp_in_F Day2_Temp_in_C Diffrence_in_F Diffrence_in_C
#> 1 51.8 11 2.90 1.61
#> 2 51.8 11 2.91 1.62
#> 3 51.8 11 2.98 1.66
#> 4 51.8 11 2.33 1.29
#> 5 51.8 11 2.91 1.62
#> 6 57.2 14 2.82 1.57
#> 7 62.6 17 3.26 1.81
#> 8 66.2 19 2.16 1.20
#> 9 69.8 21 -0.95 -0.53
#> 10 71.6 22 -2.71 -1.51
#> 11 71.6 22 -3.18 -1.77
#> 12 69.8 21 -2.53 -1.41
#> 13 69.8 21 -3.76 -2.09
#> 14 66.2 19 -1.21 -0.67
#> 15 66.2 19 -2.98 -1.66
#> 16 64.4 18 -2.97 -1.65
#> 17 60.8 16 -1.80 -1.00
#> 18 57.2 14 -1.80 -1.00
#> 19 57.2 14 -1.80 -1.00
#> 20 59.0 15 -3.60 -2.00
#> 21 59.0 15 -3.60 -2.00
#> 22 57.2 14 -1.80 -1.00
#> 23 57.2 14 -3.60 -2.00
#> 24 57.2 14 -3.60 -2.00
Plot the weather patterns for the past week
plot_weather(CF = "F", num_days = 7)
kabhatia7/temperaturr documentation built on May 12, 2020, 4:54 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
temperaturr
The temperaturr package uses the climacell and google maps geocoding APIs to tell you about the weather by providing data frames and plots of the weather from certain locations.
Setup & Installation
First things first we’re gonna need to install the development version of the package from Github with:
# install.packages("devtools")
devtools::install_github("kabhatia7/temperaturr")
library(temperaturr)
Next we need to get setup with API Keys. The first one for climacell is
very easy to get set up just click this
link to sign up for a free
climacell key. Then to store your key as in your global environment to
be used for all functions in the temperaturr package
I will be reading in our keys from a JSON file as they should not be used by more than one person.
register_climacell_key(apikey = jsonlite::read_json("keys.json")$climacell)
The free version gives us 1000 calls a day and which for the purposes of
this package should be more than enough. The next key you’re going to
want to get is the Google API for geocoding. This key will make it so
you don’t have to always look up your location of interest by simply
passing the name of the location of interest. We will use the ggmap
package to register our google key with the register_google()
function. Instructions for a google key can be found
here.
#install.packages(ggmap)
library(ggmap)
#> Loading required package: ggplot2
#> Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
#> Please cite ggmap if you use it! See citation("ggmap") for details.
register_google(key = jsonlite::read_json("keys.json")$google)
Then you can set your location through the find_loc() function so you
wont have to pass any of the lat and lon arguments for the functions.
Try to be as specific as possible by adding state names or zip codes
since google will always pick the first result it searches for if there
are conflicts.
find_loc("San Luis Obispo, CA")
#> Source : https://maps.googleapis.com/maps/api/geocode/json?address=San+Luis+Obispo,+CA&key=xxx
Now we’re all set to use the package!
Retrieving a temperaturr data frame
get_prev_temp() %>%
head()
#> # A tibble: 6 x 6
#> observation_time year month day temp_in_F temp_in_C
#> <dttm> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 2020-04-25 03:05:00 2020 4 25 74.0 23.3
#> 2 2020-04-25 03:15:00 2020 4 25 72 22.2
#> 3 2020-04-25 03:25:00 2020 4 25 71.0 21.7
#> 4 2020-04-25 03:35:00 2020 4 25 69.0 20.6
#> 5 2020-04-25 03:45:00 2020 4 25 69.0 20.6
#> 6 2020-04-25 03:55:00 2020 4 25 68 20
Hourly comparison of today and day before in a dataframe
weather_comparison()
#> Day1_Times Day1_Temp_in_F Day1_Temp_in_C Day2_Times
#> 1 2020-05-03 03:00:00 54.70 12.61 2020-05-04 03:00:00
#> 2 2020-05-03 04:00:00 54.71 12.62 2020-05-04 04:00:00
#> 3 2020-05-03 05:00:00 54.78 12.66 2020-05-04 05:00:00
#> 4 2020-05-03 06:00:00 54.13 12.29 2020-05-04 06:00:00
#> 5 2020-05-03 07:00:00 54.71 12.62 2020-05-04 07:00:00
#> 6 2020-05-03 08:00:00 60.02 15.57 2020-05-04 08:00:00
#> 7 2020-05-03 09:00:00 65.86 18.81 2020-05-04 09:00:00
#> 8 2020-05-03 10:00:00 68.36 20.20 2020-05-04 10:00:00
#> 9 2020-05-03 11:00:00 68.85 20.47 2020-05-04 11:00:00
#> 10 2020-05-03 12:00:00 68.89 20.49 2020-05-04 12:00:00
#> 11 2020-05-03 13:00:00 68.42 20.23 2020-05-04 13:00:00
#> 12 2020-05-03 14:00:00 67.27 19.59 2020-05-04 14:00:00
#> 13 2020-05-03 15:00:00 66.04 18.91 2020-05-04 15:00:00
#> 14 2020-05-03 16:00:00 64.99 18.33 2020-05-04 16:00:00
#> 15 2020-05-03 17:00:00 63.22 17.34 2020-05-04 17:00:00
#> 16 2020-05-03 18:00:00 61.43 16.35 2020-05-04 18:00:00
#> 17 2020-05-03 19:00:00 59.00 15.00 2020-05-04 19:00:00
#> 18 2020-05-03 20:00:00 55.40 13.00 2020-05-04 20:00:00
#> 19 2020-05-03 21:00:00 55.40 13.00 2020-05-04 21:00:00
#> 20 2020-05-03 22:00:00 55.40 13.00 2020-05-04 22:00:00
#> 21 2020-05-03 23:00:00 55.40 13.00 2020-05-04 23:00:00
#> 22 2020-05-04 00:00:00 55.40 13.00 2020-05-05 00:00:00
#> 23 2020-05-04 01:00:00 53.60 12.00 2020-05-05 01:00:00
#> 24 2020-05-04 02:00:00 53.60 12.00 2020-05-05 02:00:00
#> Day2_Temp_in_F Day2_Temp_in_C Diffrence_in_F Diffrence_in_C
#> 1 51.8 11 2.90 1.61
#> 2 51.8 11 2.91 1.62
#> 3 51.8 11 2.98 1.66
#> 4 51.8 11 2.33 1.29
#> 5 51.8 11 2.91 1.62
#> 6 57.2 14 2.82 1.57
#> 7 62.6 17 3.26 1.81
#> 8 66.2 19 2.16 1.20
#> 9 69.8 21 -0.95 -0.53
#> 10 71.6 22 -2.71 -1.51
#> 11 71.6 22 -3.18 -1.77
#> 12 69.8 21 -2.53 -1.41
#> 13 69.8 21 -3.76 -2.09
#> 14 66.2 19 -1.21 -0.67
#> 15 66.2 19 -2.98 -1.66
#> 16 64.4 18 -2.97 -1.65
#> 17 60.8 16 -1.80 -1.00
#> 18 57.2 14 -1.80 -1.00
#> 19 57.2 14 -1.80 -1.00
#> 20 59.0 15 -3.60 -2.00
#> 21 59.0 15 -3.60 -2.00
#> 22 57.2 14 -1.80 -1.00
#> 23 57.2 14 -3.60 -2.00
#> 24 57.2 14 -3.60 -2.00
Plot the weather patterns for the past week
plot_weather(CF = "F", num_days = 7)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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