weathercan

class: title-slide, nologo

### Dr. Steffi LaZerte

# `weathercan`

## An R package for accessing<br>Environment and Climate Change Canada weather data

.align-bottom-left[<img src = "./Figures/twitter_black.jpg" style = "height: 40px; vertical-align:middle">  @steffilazerte <img src = "./Figures/github.png" style = "height: 40px; vertical-align:middle; margin-left: 25px"> steffilazerte <img src = "./Figures/web.png" style = "height: 40px; vertical-align:middle; margin-left: 25px"> steffilazerte.ca <img src = "./Figures/email.svg" style = "height: 25px; vertical-align:middle; margin-left: 25px"> sel@steffilazerte.ca]

.align-bottom-right[July 2018]

---
# A bit of background

#### Dr. Steffi LaZerte

.spacer[]

**Biologist (Animal Behaviour)**

- BSc University of Toronto
- MSc McGill University
- PhD University of Northern BC

.spacer[]

**Working with R since 2007**

- Analysis requires a lot of data prep!

---
# A bit of background

#### Dr. Steffi LaZerte

.spacer[]

**Independent biological consultant and R programmer**

- Data cleaning, summarizing, and analysis
- Custom R packages and scripts
- R tutoring and workshops

<div class = "redbox" style = "right: 100px; top: 250px">Solving data problems</div>

---
# Historical weather data

- Available from Environment and Climate Change Canada (ECCC)
- 1840 to Present
- Hourly, daily, monthly intervals
- \> 8,000 stations (past and present)

> **Lots of Data!**

---
background-image: url("./Figures/ECCC_historical.png")
background-size: 85%
background-position: 50% 100%

# Accessing data from ECCC website

---
background-image: url("./Figures/ECCC_historical2.png")
background-size: 90%
background-position: 50% 50%

---
background-image: url("./Figures/ECCC_historical3.png")
background-size: 60%
background-position: 50% 50%

---
# Data good but not ready
.center[<img src = "./Figures/ECCC_historical_data.png" style = "width:100%">]

---
background-image: url("./Figures/weathercan_logo.png")
background-size: 450px
background-position: 50% 70%
class: center, nologo

# .red[`weathercan`]: An R package

---
background-image: url("./Figures/R_sm.png")
background-size: 200px
background-position: 50% 80%
class: center, nologo

.spacer[ ]

# .center.Rblue[What's R?]

.spacer[ ]

### An open source, programming language, and software environment

---
class: center, nologo

.spacer[ ]

# .center.Rblue[What's R?]

.spacer[ ]

### An open source, programming language, and software environment

Often used with RStudio IDE <img src = "./Figures/RStudio-Ball.png" style = "height:75px;vertical-align:middle;">

---
background-image: url("./Figures/Rexample.png")
background-size: cover
class: nologo

---
# Why use `weathercan`?

#### Free
- Free *and* open-source software (FOSS)

#### Fast and Easy
- One line of code to download data from many stations, over many years
- Instantly usable

#### Customizable
- Data is trimmed to start and end times
- You can specify stations, time intervals, timezones, etc.

---
# Why use `weathercan`?

.spacer[ ]

#### **Reproducible!**

> - Scripts provide a record of actions  
> - Make a note of the `weathercan` version (`packageVersion(weathercan)`)
> - Hard to document mouse clicks or website searches

---

# Getting started with .red[`weathercan`]

.spacer[ ]

#### Latest stable version (v0.2.7):

```r
install.packages("weathercan")
```

.spacer[ ]

#### Developmental version (v0.2.7.9000):

```r
library(devtools)
install_github("ropensci/weathercan", build_vignettes = TRUE)
```

---
# Basic usage

#### Code

```r
library(weathercan)
w <- weather_dl(station_ids = c(50821, 51097), start = "2017-09-01", interval = "hour")
```

--
#### Output

```
## # A tibble: 15,984 x 29
##   station_name station_id station_operator prov    lat    lon  elev climate_id WMO_id TC_id
## * <chr>             <dbl> <chr>            <fct> <dbl>  <dbl> <dbl> <chr>      <chr>  <chr>
## 1 BRANDON A         50821 NAV Canada       MB     49.9 -100.0  409. 5010481    71140  YBR  
## 2 BRANDON A         50821 NAV Canada       MB     49.9 -100.0  409. 5010481    71140  YBR  
## 3 BRANDON A         50821 NAV Canada       MB     49.9 -100.0  409. 5010481    71140  YBR  
## 4 BRANDON A         50821 NAV Canada       MB     49.9 -100.0  409. 5010481    71140  YBR  
## 5 BRANDON A         50821 NAV Canada       MB     49.9 -100.0  409. 5010481    71140  YBR  
## # ... with 1.598e+04 more rows, and 19 more variables
```

--
<div class = "redbox" style = "top: 27.25%; right: 17.5%; width: 150px; height: 0px;padding: 2px;"></div>
<div style = "position: absolute; top: 18%; right: 18.5%;"><h5><strong>"day" or "month"<strong></h5></div>

---
# Plotting

```r
library(ggplot2)
ggplot(data = w, aes(x = time, y = temp, colour = station_name)) +
  theme_bw() +
  geom_line() +
  scale_colour_viridis_d(end = 0.5) +
  labs(x = "Date", y = "Temperature C", colour = "Station")
```

---
# And done!

```r
library(weathercan)
w <- weather_dl(station_ids = c(50821, 51097), start = "2017-09-01", interval = "hour")

ggplot(data = w, aes(x = time, y = temp, colour = station_name)) +
  theme_bw() +
  geom_line() +
  scale_colour_viridis_d(end = 0.5) +
  labs(x = "Date", y = "Temperature C", colour = "Station")
```

---

# Hmmm...

```r
library(weathercan)
w <- weather_dl(station_ids = c(50821, 51097), start = "2017-09-01")

ggplot(data = w, aes(x = time, y = temp, colour = station_name)) +
  theme_bw() +
  geom_line() +
  scale_colour_viridis_d(end = 0.5) +
  labs(x = "Date", y = "Temperature C", colour = "Station")
```

.spacer[ ]

> How do we get station ids?

---

# Stations data set

```r
stations
```

```
## # A tibble: 26,208 x 12
##    prov  station_name     station_id climate_id WMO_id TC_id   lat   lon  elev interval start   end
##    <fct> <chr>            <fct>      <fct>      <fct>  <fct> <dbl> <dbl> <dbl> <chr>    <int> <int>
##  1 AB    ABEE AGDM        32232      3010010    71285  XAF    54.3 -113.   664 day       2002  2018
##  2 AB    ABEE AGDM        32232      3010010    71285  XAF    54.3 -113.   664 hour      1990  2018
##  3 AB    ABEE AGDM        32232      3010010    71285  XAF    54.3 -113.   664 month     2002  2007
##  4 AB    ACADIA VALLEY    2030       3020018    <NA>   <NA>   51.2 -110.   732 day       1980  1991
##  5 AB    ACADIA VALLEY    47748      3020035    71048  PAC    51.1 -110.   735 day       2009  2018
##  6 AB    ACADIA VALLEY    2030       3020018    <NA>   <NA>   51.2 -110.   732 hour        NA    NA
##  7 AB    ACADIA VALLEY    47748      3020035    71048  PAC    51.1 -110.   735 hour      2009  2018
##  8 AB    ACADIA VALLEY    2030       3020018    <NA>   <NA>   51.2 -110.   732 month     1980  1991
##  9 AB    ACADIA VALLEY    47748      3020035    71048  PAC    51.1 -110.   735 month       NA    NA
## 10 AB    ACADIA VALLEY C… 2032       3020022    <NA>   <NA>   51.1 -110.    NA day       1965  1965
## # ... with 2.62e+04 more rows
```

---

# Searching by station name

```r
stations_search(name = "Brandon")
```

```
## # A tibble: 15 x 12
##    prov  station_name    station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end
##    <fct> <chr>           <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>
##  1 MB    BRANDON #1 WIN… 3474       5010498    <NA>   <NA>   49.8 -100.0  396  day       1987  2002
##  2 MB    BRANDON #1 WIN… 3474       5010498    <NA>   <NA>   49.8 -100.0  396  month     1987  2002
##  3 MB    BRANDON A       3471       5010480    71140  YBR    49.9 -100.0  409. day       1941  2012
##  4 MB    BRANDON A       3471       5010480    71140  YBR    49.9 -100.0  409. hour      1958  2012
##  5 MB    BRANDON A       3471       5010480    71140  YBR    49.9 -100.0  409. month     1941  2012
##  6 MB    BRANDON A       50821      5010481    71140  YBR    49.9 -100.0  409. day       2012  2018
##  7 MB    BRANDON A       50821      5010481    71140  YBR    49.9 -100.0  409. hour      2012  2018
##  8 MB    BRANDON CDA     3472       5010485    <NA>   <NA>   49.9 -100.0  363. day       1890  2010
##  9 MB    BRANDON CDA     3472       5010485    <NA>   <NA>   49.9 -100.0  363. month     1890  2007
## 10 MB    BRANDON RCS     49909      5010490    71136  PBO    49.9 -100.0  409. day       2012  2018
## # ... with 5 more rows
```

--
<div class = "highlightbox" style = "width: 90px; height: 380px; right: 15.5%; top: 32.5%"></div>

---
# Searching by station name

```r
stations_search(name = "Brandon", interval = "hour")
```

```
## # A tibble: 3 x 12
##   prov  station_name station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end
##   <fct> <chr>        <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>
## 1 MB    BRANDON A    3471       5010480    71140  YBR    49.9 -100.0  409. hour      1958  2012
## 2 MB    BRANDON A    50821      5010481    71140  YBR    49.9 -100.0  409. hour      2012  2018
## 3 MB    BRANDON RCS  49909      5010490    71136  PBO    49.9 -100.0  409. hour      2012  2018
```

---
# Searching by station name (fancy)

```r
stations_search(name = "Brandon|Winnipeg", interval = "hour")
```

```
## # A tibble: 8 x 12
##   prov  station_name     station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end
##   <fct> <chr>            <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>
## 1 MB    BRANDON A        3471       5010480    71140  YBR    49.9 -100.0  409. hour      1958  2012
## 2 MB    BRANDON A        50821      5010481    71140  YBR    49.9 -100.0  409. hour      2012  2018
## 3 MB    BRANDON RCS      49909      5010490    71136  PBO    49.9 -100.0  409. hour      2012  2018
## 4 MB    WINNIPEG A CS    27174      502S001    71849  XWG    49.9  -97.2  239. hour      2013  2018
## 5 MB    WINNIPEG INTL A  51097      5023227    <NA>   YWG    49.9  -97.2  239. hour      2013  2018
## 6 MB    WINNIPEG RICHAR… 47407      5023226    71852  YWG    49.9  -97.2  239. hour      2008  2013
## 7 MB    WINNIPEG RICHAR… 3698       5023222    71852  YWG    49.9  -97.2  239. hour      1953  2013
## 8 MB    WINNIPEG THE FO… 28051      5023262    71579  XWN    49.9  -97.1  230  hour      1999  2018
```

> Make a note: 50821 (Brandon) and 51097 (Winnipeg)

---
# Alternative: Searching by coordinates

- Search according to location: `c(latitude, longitude)`
- Search within 10km of this location: `dist = 10`

```r
stations_search(coords = c(49.84847, -99.95009), dist = 10, interval = "hour")
```

.narrow[

```
## # A tibble: 3 x 13
##   prov  station_name station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end distance
##   <fct> <chr>        <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>    <dbl>
## 1 MB    BRANDON RCS  49909      5010490    71136  PBO    49.9 -100.0  409. hour      2012  2018     5.73
## 2 MB    BRANDON A    3471       5010480    71140  YBR    49.9 -100.0  409. hour      1958  2012     6.84
## 3 MB    BRANDON A    50821      5010481    71140  YBR    49.9 -100.0  409. hour      2012  2018     6.84
```

]

---
# Alternative: Searching with `tidyverse`

> Note here we're using the `stations` data frame directly

```r
library(tidyverse)
stations %>%
  filter(prov == "MB", start > 2000, interval == "day")
```

.narrow[

```
## # A tibble: 40 x 12
##    prov  station_name    station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end
##    <fct> <chr>           <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>
##  1 MB    BRANDON A       50821      5010481    71140  YBR    49.9 -100.0 409.  day       2012  2018
##  2 MB    BRANDON RCS     49909      5010490    71136  PBO    49.9 -100.0 409.  day       2012  2018
##  3 MB    CFB SHILO CCOC  43343      5012673    <NA>   <NA>   49.8  -99.6 372   day       2004  2005
##  4 MB    CHURCHILL       48969      5060595    71618  YYQ    58.7  -94.1  29.3 day       2010  2018
##  5 MB    CHURCHILL AWOS  47408      5060601    <NA>   <NA>   58.7  -94.1  28.7 day       2008  2010
##  6 MB    CHURCHILL CLIM… 44244      5060608    71696  PYQ    58.7  -94.1  28.5 day       2005  2018
##  7 MB    CROSS LAKE JEN… 54658      5060622    71106  PJP    54.5  -98.0 219.  day       2017  2018
##  8 MB    CYPRESS RIVER … 48128      5010641    71239  POY    49.6  -99.1 374   day       2009  2018
##  9 MB    DAUPHIN         48868      5040689    71036  YDN    51.1 -100.  304.  day       2010  2018
## 10 MB    EMERSON AUTO    48068      5020881    71560  WEX    49    -97.2 242   day       2009  2018
## # ... with 30 more rows
```

]

---
# Understanding the data

#### Flags

For each measurement there is a corresponding 'flag' column

.compact[

```r
w_month <- weather_dl(station_id = 5401, start =  "2017-01-01", interval = "month")
names(w_month)  # List all the column names
```

```
##  [1] "station_name"            "station_id"              "station_operator"       
##  [4] "prov"                    "lat"                     "lon"                    
##  [7] "elev"                    "climate_id"              "WMO_id"                 
## [10] "TC_id"                   "date"                    "year"                   
## [13] "month"                   "dir_max_gust"            "dir_max_gust_flag"      
## [16] "extr_max_temp"           "extr_max_temp_flag"      "extr_min_temp"          
## [19] "extr_min_temp_flag"      "mean_max_temp"           "mean_max_temp_flag"     
## [22] "mean_min_temp"           "mean_min_temp_flag"      "mean_temp"              
## [25] "mean_temp_flag"          "snow_grnd_last_day"      "snow_grnd_last_day_flag"
## [28] "spd_max_gust"            "spd_max_gust_flag"       "total_precip"           
## [31] "total_precip_flag"       "total_rain"              "total_rain_flag"        
## [34] "total_snow"              "total_snow_flag"
```
]

---
# Understanding the data

#### Flags

.compact[

```r
select(w_month, date, mean_temp, mean_temp_flag) # Pull out some columns
```

```
## # A tibble: 14 x 3
##    date       mean_temp mean_temp_flag
##  * <date>         <dbl> <chr>         
##  1 2017-01-01      -4.4 ""            
##  2 2017-02-01      -4.3 ""            
##  3 2017-03-01      -5.2 ""            
##  4 2017-04-01       7.9 ""            
##  5 2017-05-01      11.8 E             
##  6 2017-06-01      17.5 ""            
##  7 2017-07-01      19.3 ""            
##  8 2017-08-01      18.4 ""            
##  9 2017-09-01      17   ""            
## 10 2017-10-01      12.7 ""            
## # ... with 4 more rows
```
]

---
background-image: url("./Figures/flags_vignette.png")
background-position: 50% 90%
background-size: 65%

# Understanding the data

#### Flags

```r
vignette("flags", package = "weathercan")
```

---
# Understanding the data

#### Units and measurements

```
## # A tibble: 15,984 x 6
##    station_id time                 temp temp_dew rel_hum wind_dir
##  *      <dbl> <dttm>              <dbl>    <dbl>   <dbl>    <dbl>
##  1      50821 2017-09-01 00:00:00  20.8     17.3      80       18
##  2      50821 2017-09-01 01:00:00  20.8     17.2      80       17
##  3      50821 2017-09-01 02:00:00  20       16.9      83       17
##  4      50821 2017-09-01 03:00:00  19.4     16.9      85       16
##  5      50821 2017-09-01 04:00:00  19.2     17.2      88       19
##  6      50821 2017-09-01 05:00:00  18.8     17.8      93       17
##  7      50821 2017-09-01 06:00:00  18.9     17.9      94       16
##  8      50821 2017-09-01 07:00:00  18.3     17.7      96       18
##  9      50821 2017-09-01 08:00:00  19.9     17.8      88       20
## 10      50821 2017-09-01 09:00:00  20.6     18.2      86       23
## # ... with 1.597e+04 more rows
```

---
background-image: url("./Figures/glossary_vignette.png")
background-position: 50% 95%
background-size: 60%

# Understanding the data

#### Units and measurements

```r
vignette("glossary", package = "weathercan")
```

---
# Combining with other data

- Adding weather data to other data sets
- Times don't always line up

.pull-left[
#### Sediment data (for example)

```
## # A tibble: 1,392 x 2
##   time                amount
##   <dttm>               <dbl>
## 1 2017-09-01 00:05:34   168.
## 2 2017-09-01 00:35:34   157.
## 3 2017-09-01 01:05:34   176.
## 4 2017-09-01 01:35:34   185.
## 5 2017-09-01 02:05:34   163.
## 6 2017-09-01 02:35:34   169.
## 7 2017-09-01 03:05:34   168.
## # ... with 1,385 more rows
```
]

.pull-right[
#### Brandon weather data

```
## # A tibble: 7,992 x 3
##   time                 temp pressure
##   <dttm>              <dbl>    <dbl>
## 1 2017-09-01 00:00:00  20.8     96.2
## 2 2017-09-01 01:00:00  20.8     96.2
## 3 2017-09-01 02:00:00  20       96.1
## 4 2017-09-01 03:00:00  19.4     96.1
## 5 2017-09-01 04:00:00  19.2     96.1
## 6 2017-09-01 05:00:00  18.8     96.0
## 7 2017-09-01 06:00:00  18.9     96.0
## # ... with 7,985 more rows
```
]

---
# Interpolating

- Linear interpolation where possible
- Only a single weather station at a time

```r
w <- weather_dl(station_ids = 50821, start = "2017-09-01")
```

```r
sediment <- weather_interp(data = sediment, 
                           weather = w,
                           col = c("temp", "pressure"))
```

```
## temp is missing 7 out of 7992 data, interpolation may be less accurate as a result.
```

```
## pressure is missing 7 out of 7992 data, interpolation may be less accurate as a result.
```

> Note messages regarding missing data. These are just a 'heads up', not errors

---
# Interpolating

.pull-left-55[
#### Sediment & weather data

```
## # A tibble: 1,392 x 4
##   time                amount  temp pressure
##   <dttm>               <dbl> <dbl>    <dbl>
## 1 2017-09-01 00:05:34   168.  20.8     96.2
## 2 2017-09-01 00:35:34   157.  20.8     96.2
## 3 2017-09-01 01:05:34   176.  20.7     96.1
## 4 2017-09-01 01:35:34   185.  20.3     96.1
## 5 2017-09-01 02:05:34   163.  19.9     96.1
## 6 2017-09-01 02:35:34   169.  19.6     96.1
## 7 2017-09-01 03:05:34   168.  19.4     96.1
## # ... with 1,385 more rows
```
]

.pull-right-45[
#### Original weather data

```
## # A tibble: 7,992 x 3
##   time                 temp pressure
## * <dttm>              <dbl>    <dbl>
## 1 2017-09-01 00:00:00  20.8     96.2
## 2 2017-09-01 01:00:00  20.8     96.2
## 3 2017-09-01 02:00:00  20       96.1
## 4 2017-09-01 03:00:00  19.4     96.1
## 5 2017-09-01 04:00:00  19.2     96.1
## 6 2017-09-01 05:00:00  18.8     96.0
## 7 2017-09-01 06:00:00  18.9     96.0
## # ... with 7,985 more rows
```
]

---
background-image: url("./Figures/weathercan_logo.png")
background-size: 450px
background-position: 50% 70%
class: center, nologo

# Taking .red[`weathercan`] to the next level...

---
# Getting the most out of `weathercan`

#### 1. Download from multiple stations (spatial)

.compact[

```r
mb <- filter(stations, prov == "MB", start <= "2018", end >= "2018", interval == "hour")
mb
```

```
## # A tibble: 60 x 12
##    prov  station_name    station_id climate_id WMO_id TC_id   lat    lon  elev interval start   end
##    <fct> <chr>           <fct>      <fct>      <fct>  <fct> <dbl>  <dbl> <dbl> <chr>    <int> <int>
##  1 MB    BERENS RIVER A  53321      5030201    <NA>   YBV    52.4  -97.0 222.  hour      2015  2018
##  2 MB    BERENS RIVER A  53318      5030204    <NA>   YBV    52.4  -97.0 222.  hour      2015  2018
##  3 MB    BERENS RIVER CS 3721       5030203    71158  WCF    52.4  -97.0 222.  hour      1985  2018
##  4 MB    BRANDON A       50821      5010481    71140  YBR    49.9 -100.0 409.  hour      2012  2018
##  5 MB    BRANDON RCS     49909      5010490    71136  PBO    49.9 -100.0 409.  hour      2012  2018
##  6 MB    CARBERRY CS     27741      5010547    71170  WZE    49.9  -99.4 384.  hour      1994  2018
##  7 MB    CARMAN U OF M … 26857      502I001    71147  WNK    49.5  -98.0 268.  hour      1995  2018
##  8 MB    CHURCHILL       48969      5060595    71618  YYQ    58.7  -94.1  29.3 hour      2010  2018
##  9 MB    CHURCHILL A     50148      5060605    71618  YYQ    58.7  -94.1  29.3 hour      2012  2018
## 10 MB    CHURCHILL CLIM… 44244      5060608    71696  PYQ    58.7  -94.1  28.5 hour      2005  2018
## # ... with 50 more rows
```
]

---
# Getting the most out of `weathercan`

```r
manitoba <- weather_dl(station_ids = mb$station_id, start = "2018-07-01")
```

> Big data sets can take time to download: save the output for later

```r
write_csv(manitoba, "./Data/manitoba_2018-07-01.csv")
```

---
# Getting the most out of `weathercan`

.compact[

```r
ggplot(manitoba, aes(x = date, y = temp)) +
  stat_summary(geom = "ribbon", fun.y = median, fun.ymax = max, fun.ymin = min, alpha = 0.5) +
  stat_summary(geom = "line", fun.y = median) +
  labs(title = "July 2018 Min/Median/Max Temperatures in Manitoba, Canada",
       x = "Date", y = "Temperature C")
```

<img src="LaZerte_AGCAN_2018_weathercan_files/figure-html/unnamed-chunk-28-1.png" width="100%" />
]

---

# Getting the most out of `weathercan`

<img src="LaZerte_AGCAN_2018_weathercan_files/figure-html/unnamed-chunk-29-1.png" width="100%" />
  
  
---

# Getting the most out of `weathercan`

#### 2. Download from multiple years (temporal)

- no '`start`' means download until the '`end`' date
- no '`end`' means download from the '`start`' date to the present (or end of the data record)
- no '`start`' OR '`end`' means download all data

**Download ALL data:**

```r
w <- weather_dl(station_ids = c(3472, 3698), interval = "month")
write_csv(w, "./Data/brandon_winnipeg_historical.csv")
```

---

# Getting the most out of `weathercan`

```r
ggplot(data = w, aes(x = mean_temp, fill = station_name)) +
  theme_bw() +
  geom_density(alpha = 0.5) +
  scale_fill_viridis_d(end = 0.5) +
  labs(title = "Distribution of Monthly Mean Temperatures", 
       x = "Temperature C", y = "Density", fill = "Station")
```

---
# Recap!

#### 1. Load `weathercan` package

```r
library(weathercan)
```

#### 2. Find a station

```r
stations_search("Brandon")
```

#### 3. Download weather

```r
w <- weather_dl(station_ids = 50821, start = "2017-09-01")
```

#### 4. Add weather data to an existing data set

```r
sediment <- weather_interp(data = sediment, weather = w, cols = "temp")
```

---
# Last, but not least

#### 5. Cite `weathercan`

```r
citation("weathercan")
```

```
## 
## To cite 'weathercan' in publications, please use:
## 
##   LaZerte, Stefanie E and Sam Albers (2018). weathercan: Download and format weather data
##   from Environment and Climate Change Canada. The Journal of Open Source Software
##   3(22):571. doi:10.21105/joss.00571.
```

`weathercan` is

- Part of the [ropensci project](https://ropensci.org/)
- Published in [Journal of Open Source Software](https://joss.theoj.org/)

Both involve rigorous review of code, best-practices and usability
  
<img src = "./Figures/ropensci.svg", style = "width:150px; position:absolute; bottom: 150px; left: 650px">

---
# We invite contributions!

.spacer[ ]

#### Openly developed on GitHub <img src = "./Figures/github.png" style = "width: 35px; vertical-align: middle; margin-bottom: 5px;">

.spacer[ ]

Contribute what you can (**You don't have to be an R programmer!**):

- Ideas / Feature-requests
- Bugs
- Bug-fixes
- Development

.spacer[ ]

---
class: final-slide, nologo
# Get help with .red[`weathercan`]

**Tutorials and Reference:** <http://ropensci.github.io/weathercan>  
**This presentation:** <https://steffilazerte.github.io/Presentations/>

.spacer[ ]

**Contact Steffi:**  <img src = "./Figures/twitter_black.jpg" style = "height: 40px; vertical-align:middle; margin-left: 10px">  @steffilazerte <img src = "./Figures/github.png" style = "height: 40px; vertical-align:middle; margin-left: 20px"> steffilazerte <img src = "./Figures/web.png" style = "height: 40px; vertical-align:middle; margin-left: 20px"> steffilazerte.ca <img src = "./Figures/email.svg" style = "height: 25px; vertical-align:middle; margin-left: 20px"> sel@steffilazerte.ca

<div style = "position:absolute; bottom: 25%"> <h2>Thanks!</h2></div>
<img src = "./Figures/logo_eg.png",
style = "width: 400px; position:absolute; bottom: 19%; left: 40px">

.small[.align-bottom-left[Slides created with the R package [xaringan](https://github.com/yihui/xaringan), using [remark.js](https://remarkjs.com), [knitr](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com)<br> <code class = "remark-inline-code">Compiled on 2018-07-31 with weathercan v0.2.7</code>]]