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Load & install packages

library(curl)

package 㤼㸱curl㤼㸲 was built under R version 3.3.3

library(devtools)
install_github("weatherData", "Ram-N")

Username parameter is deprecated. Please use Ram-N/weatherDataSkipping install of 'weatherData' from a github remote, the SHA1 (76401386) has not changed since last install.
  Use `force = TRUE` to force installation

library(weatherData)
#install.packages("weatherData") - the personal weather station data is only available via github version
#package info at https://ram-n.github.io/weatherData/
library(ggplot2)

Stackoverflow is a great place to get help: http://stackoverflow.com/tags/ggplot2.

library(psych)


Attaching package: 㤼㸱psych㤼㸲

The following objects are masked from 㤼㸱package:ggplot2㤼㸲:

    %+%, alpha

library(reshape2)
library(knitr)
library(lattice) #just to illustrate another histogram function

package 㤼㸱lattice㤼㸲 was built under R version 3.3.3

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checkDataAvailability("SYD", "2017-02-15", station_type = "airportCode")

Getting data from:
 https://www.wunderground.com/history/airport/SYD/2017/2/15/DailyHistory.html?format=1

[1] 72

The following columns are available for:2017-02-15

 [1] "TimeAEDT"              "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1] 36 14
        V1 V2 V3 V4   V5 V6   V7   V8 V9 V10  V11                V12 V13                 V14
1 12:00 AM 19 17 86 1018 15 West  7.4        Rain Light Rain Showers 260 2017-02-14 13:00:00
2 12:30 AM 19 17 88 1017 10  SSW 14.8  - N/A Rain Light Rain Showers 200 2017-02-14 13:30:00
3  1:00 AM 19 16 83 1017 10  WSW 11.1  - N/A           Mostly Cloudy 240 2017-02-14 14:00:00
4  2:00 AM 19 16 81 1017 NA West 11.1                                260 2017-02-14 15:00:00
5  2:30 AM 19 16 83 1016 10   NW 11.1  - N/A Rain Light Rain Showers 310 2017-02-14 15:30:00
6  3:00 AM 18 15 78 1017  7  NNW 11.1        Rain Light Rain Showers 340 2017-02-14 16:00:00

Checking Data Availability For SYD
Found Records for 2017-02-15
Data is Available

[1] 1

getWeatherForDate("SYD", "2017-02-15", opt_detailed=TRUE)

Getting data from:
 https://www.wunderground.com/history/airport/SYD/2017/2/15/DailyHistory.html?format=1

[1] 72

The following columns are available for:2017-02-15

 [1] "TimeAEDT"              "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1] 36 14
        V1 V2 V3 V4   V5 V6   V7   V8 V9 V10  V11                V12 V13                 V14
1 12:00 AM 19 17 86 1018 15 West  7.4        Rain Light Rain Showers 260 2017-02-14 13:00:00
2 12:30 AM 19 17 88 1017 10  SSW 14.8  - N/A Rain Light Rain Showers 200 2017-02-14 13:30:00
3  1:00 AM 19 16 83 1017 10  WSW 11.1  - N/A           Mostly Cloudy 240 2017-02-14 14:00:00
4  2:00 AM 19 16 81 1017 NA West 11.1                                260 2017-02-14 15:00:00
5  2:30 AM 19 16 83 1016 10   NW 11.1  - N/A Rain Light Rain Showers 310 2017-02-14 15:30:00
6  3:00 AM 18 15 78 1017  7  NNW 11.1        Rain Light Rain Showers 340 2017-02-14 16:00:00

Checking Data Availability For SYD
Found Records for 2017-02-15
Data is Available
Will be fetching these Columns:

[1] "Time"         "TemperatureC"

Begin getting Daily Data for SYD
SYD 1 2017-02-15 : Fetching 71 Rows with 2 Column(s)

getWeatherForDate("SYD", "2017-02-10", opt_detailed=TRUE)

Getting data from:
 https://www.wunderground.com/history/airport/SYD/2017/2/10/DailyHistory.html?format=1

[1] 75

The following columns are available for:2017-02-10

 [1] "TimeAEDT"              "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1] 37 14
        V1 V2 V3 V4   V5    V6  V7   V8 V9 V10 V11   V12 V13                 V14
1 12:00 AM 25 21 72 1011    25 NNE 13.0         NA  Haze  30 2017-02-09 13:00:00
2 12:30 AM 25 21 78 1010 -9999  NE  9.3  - N/A  NA Clear  40 2017-02-09 13:30:00
3  1:00 AM 25 21 78 1010 -9999  NE 11.1  - N/A  NA Clear  50 2017-02-09 14:00:00
4  2:00 AM 24 22 83 1010    NA  NE 16.7         NA        40 2017-02-09 15:00:00
5  2:30 AM 24 22 89 1010 -9999  NE 13.0  - N/A  NA Clear  50 2017-02-09 15:30:00
6  3:00 AM 24 21 83 1010 -9999  NE  9.3  - N/A  NA Clear  40 2017-02-09 16:00:00

Checking Data Availability For SYD
Found Records for 2017-02-10
Data is Available
Will be fetching these Columns:

[1] "Time"         "TemperatureC"

Begin getting Daily Data for SYD
SYD 1 2017-02-10 : Fetching 74 Rows with 2 Column(s)

getSummarizedWeather("SYD", "2017-02-10")

Grab some data

SYD_weather <- getWeatherForDate("SYD", "2017-02-15", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T)

Getting data from:
 https://www.wunderground.com/history/airport/SYD/2017/2/15/DailyHistory.html?format=1

[1] 72

The following columns are available for:2017-02-15

 [1] "TimeAEDT"              "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1] 36 14
        V1 V2 V3 V4   V5 V6   V7   V8 V9 V10  V11                V12 V13                 V14
1 12:00 AM 19 17 86 1018 15 West  7.4        Rain Light Rain Showers 260 2017-02-14 13:00:00
2 12:30 AM 19 17 88 1017 10  SSW 14.8  - N/A Rain Light Rain Showers 200 2017-02-14 13:30:00
3  1:00 AM 19 16 83 1017 10  WSW 11.1  - N/A           Mostly Cloudy 240 2017-02-14 14:00:00
4  2:00 AM 19 16 81 1017 NA West 11.1                                260 2017-02-14 15:00:00
5  2:30 AM 19 16 83 1016 10   NW 11.1  - N/A Rain Light Rain Showers 310 2017-02-14 15:30:00
6  3:00 AM 18 15 78 1017  7  NNW 11.1        Rain Light Rain Showers 340 2017-02-14 16:00:00

Getting data from:
 https://www.wunderground.com/history/airport/SYD/2017/4/4/DailyHistory.html?format=1

[1] 77

The following columns are available for:2017-04-04

 [1] "TimeAEST"              "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1] 38 14
        V1 V2 V3 V4   V5 V6  V7   V8   V9 V10  V11                V12 V13                 V14
1 12:00 AM 17 13 67 1028 15  SE 35.2          Rain Light Rain Showers 130 2017-04-03 14:00:00
2 12:30 AM 18 14 77 1027 10 ESE 29.6    - N/A        Scattered Clouds 120 2017-04-03 14:30:00
3  1:00 AM 18 14 77 1027 10  SE 25.9    - N/A        Scattered Clouds 130 2017-04-03 15:00:00
4  2:00 AM 17 14 78 1027 NA ESE 27.8                                  110 2017-04-03 16:00:00
5  2:30 AM 17 13 77 1026  8 ESE 35.2 53.7 N/A Rain Light Rain Showers 120 2017-04-03 16:30:00
6  3:00 AM 18 15 83 1026 10 SSE 25.9    - N/A           Mostly Cloudy 150 2017-04-03 17:00:00

Checking Data Availability For SYD
Found 36 records for 2017-02-15
Found 38 records for 2017-04-04

Data is Available for the interval.

Will be fetching these Columns:

 [1] "Time"                  "TimeAEDT"              "TemperatureC"         
 [4] "Dew_PointC"            "Humidity"              "Sea_Level_PressurehPa"
 [7] "VisibilityKm"          "Wind_Direction"        "Wind_SpeedKm_h"       
[10] "Gust_SpeedKm_h"        "Precipitationmm"       "Events"               
[13] "Conditions"            "WindDirDegrees"        "DateUTC"

Begin getting Daily Data for SYD
SYD 1 2017-02-15 : Fetching 71 Rows with 15 Column(s)
SYD 2 2017-02-16 : Fetching 69 Rows with 15 Column(s)
SYD 3 2017-02-17 : Fetching 77 Rows with 15 Column(s)
SYD 4 2017-02-18 : Fetching 71 Rows with 15 Column(s)
SYD 5 2017-02-19 : Fetching 74 Rows with 15 Column(s)
SYD 6 2017-02-20 : Fetching 70 Rows with 15 Column(s)
SYD 7 2017-02-21 : Fetching 66 Rows with 15 Column(s)
SYD 8 2017-02-22 : Fetching 67 Rows with 15 Column(s)
SYD 9 2017-02-23 : Fetching 67 Rows with 15 Column(s)
SYD 10 2017-02-24 : Fetching 69 Rows with 15 Column(s)
SYD 11 2017-02-25 : Fetching 90 Rows with 15 Column(s)
SYD 12 2017-02-26 : Fetching 82 Rows with 15 Column(s)
SYD 13 2017-02-27 : Fetching 79 Rows with 15 Column(s)
SYD 14 2017-02-28 : Fetching 69 Rows with 15 Column(s)
SYD 15 2017-03-01 : Fetching 78 Rows with 15 Column(s)
SYD 16 2017-03-02 : Fetching 72 Rows with 15 Column(s)
SYD 17 2017-03-03 : Fetching 78 Rows with 15 Column(s)
SYD 18 2017-03-04 : Fetching 76 Rows with 15 Column(s)
SYD 19 2017-03-05 : Fetching 77 Rows with 15 Column(s)
SYD 20 2017-03-06 : Fetching 69 Rows with 15 Column(s)
SYD 21 2017-03-07 : Fetching 75 Rows with 15 Column(s)
SYD 22 2017-03-08 : Fetching 69 Rows with 15 Column(s)
SYD 23 2017-03-09 : Fetching 66 Rows with 15 Column(s)
SYD 24 2017-03-10 : Fetching 70 Rows with 15 Column(s)
SYD 25 2017-03-11 : Fetching 68 Rows with 15 Column(s)
SYD 26 2017-03-12 : Fetching 72 Rows with 15 Column(s)
SYD 27 2017-03-13 : Fetching 69 Rows with 15 Column(s)
SYD 28 2017-03-14 : Fetching 72 Rows with 15 Column(s)
SYD 29 2017-03-15 : Fetching 72 Rows with 15 Column(s)
SYD 30 2017-03-16 : Fetching 78 Rows with 15 Column(s)
SYD 31 2017-03-17 : Fetching 88 Rows with 15 Column(s)
SYD 32 2017-03-18 : Fetching 80 Rows with 15 Column(s)
SYD 33 2017-03-19 : Fetching 81 Rows with 15 Column(s)
SYD 34 2017-03-20 : Fetching 67 Rows with 15 Column(s)
SYD 35 2017-03-21 : Fetching 72 Rows with 15 Column(s)
SYD 36 2017-03-22 : Fetching 76 Rows with 15 Column(s)
SYD 37 2017-03-23 : Fetching 72 Rows with 15 Column(s)
SYD 38 2017-03-24 : Fetching 67 Rows with 15 Column(s)
SYD 39 2017-03-25 : Fetching 67 Rows with 15 Column(s)
SYD 40 2017-03-26 : Fetching 64 Rows with 15 Column(s)
SYD 41 2017-03-27 : Fetching 71 Rows with 15 Column(s)
SYD 42 2017-03-28 : Fetching 70 Rows with 15 Column(s)
SYD 43 2017-03-29 : Fetching 67 Rows with 15 Column(s)
SYD 44 2017-03-30 : Fetching 81 Rows with 15 Column(s)
SYD 45 2017-03-31 : Fetching 70 Rows with 15 Column(s)
SYD 46 2017-04-01 : Fetching 68 Rows with 15 Column(s)
SYD 47 2017-04-02 : Fetching 76 Rows with 15 Column(s)
SYD 48 2017-04-03 : Fetching 73 Rows with 15 Column(s)
SYD 49 2017-04-04 : Fetching 76 Rows with 15 Column(s)

MEL_weather <- getWeatherForDate("MEL", "2017-02-15", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T)

Getting data from:
 https://www.wunderground.com/history/airport/MEL/2017/2/15/DailyHistory.html?format=1

[1] 5

The following columns are available for:2017-02-15

 [1] "Time_0545"             "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1]  2 14
       V1 V2 V3 V4 V5 V6    V7   V8 V9 V10 V11 V12 V13                 V14
1 8:45 AM  3 -4 50 NA  8  Calm Calm NA  NA  NA  NA  NA 2017-02-15 03:00:00
2 2:45 PM 21 -7  7 NA 12 South 13.0 NA  NA  NA  NA 190 2017-02-15 09:00:00

Getting data from:
 https://www.wunderground.com/history/airport/MEL/2017/4/4/DailyHistory.html?format=1

[1] 6

The following columns are available for:2017-04-04

 [1] "Time_0545"             "TemperatureC"          "Dew_PointC"           
 [4] "Humidity"              "Sea_Level_PressurehPa" "VisibilityKm"         
 [7] "Wind_Direction"        "Wind_SpeedKm_h"        "Gust_SpeedKm_h"       
[10] "Precipitationmm"       "Events"                "Conditions"           
[13] "WindDirDegrees"        "DateUTC"              
[1]  3 14
        V1 V2 V3 V4 V5 V6    V7  V8 V9 V10 V11              V12 V13                 V14
1  5:45 AM  6  4 81 NA  8 South 3.7 NA  NA  NA                  190 2017-04-04 00:00:00
2 11:45 AM 22  5 23 NA 10    SW 1.9 NA  NA  NA Scattered Clouds 220 2017-04-04 06:00:00
3  5:45 PM 19  2 21 NA  8   WSW 3.7 NA  NA  NA    Partly Cloudy 250 2017-04-04 12:00:00

Checking Data Availability For MEL
Found 2 records for 2017-02-15
Found 3 records for 2017-04-04

Data is Available for the interval.

Will be fetching these Columns:

 [1] "Time"                  "Time_0545"             "TemperatureC"         
 [4] "Dew_PointC"            "Humidity"              "Sea_Level_PressurehPa"
 [7] "VisibilityKm"          "Wind_Direction"        "Wind_SpeedKm_h"       
[10] "Gust_SpeedKm_h"        "Precipitationmm"       "Events"               
[13] "Conditions"            "WindDirDegrees"        "DateUTC"

Begin getting Daily Data for MEL
MEL 1 2017-02-15 : Fetching 4 Rows with 15 Column(s)
MEL 2 2017-02-16 : Fetching 5 Rows with 15 Column(s)
MEL 3 2017-02-17 : Fetching 5 Rows with 15 Column(s)
MEL 4 2017-02-18 : Fetching 5 Rows with 15 Column(s)
MEL 5 2017-02-19 : Fetching 5 Rows with 15 Column(s)
MEL 6 2017-02-20 : Fetching 5 Rows with 15 Column(s)
MEL 7 2017-02-21 : Fetching 5 Rows with 15 Column(s)
MEL 8 2017-02-22 : Fetching 5 Rows with 15 Column(s)
MEL 9 2017-02-23 : Fetching 4 Rows with 15 Column(s)
MEL 10 2017-02-24 : Fetching 5 Rows with 15 Column(s)
MEL 11 2017-02-25 : Fetching 4 Rows with 15 Column(s)
MEL 12 2017-02-26 : Fetching 5 Rows with 15 Column(s)
MEL 13 2017-02-27 : Fetching 5 Rows with 15 Column(s)
MEL 14 2017-02-28 : Fetching 5 Rows with 15 Column(s)
MEL 15 2017-03-01 : Fetching 5 Rows with 15 Column(s)
MEL 16 2017-03-02 : Fetching 4 Rows with 15 Column(s)
MEL 17 2017-03-03 : Fetching 4 Rows with 15 Column(s)
MEL 18 2017-03-04 : Fetching 5 Rows with 15 Column(s)
MEL 19 2017-03-05 : Fetching 4 Rows with 15 Column(s)
MEL 20 2017-03-06 : Fetching 5 Rows with 15 Column(s)
MEL 21 2017-03-07 : Fetching 4 Rows with 15 Column(s)
MEL 22 2017-03-08 : Fetching 4 Rows with 15 Column(s)
MEL 23 2017-03-09 : Fetching 5 Rows with 15 Column(s)
MEL 24 2017-03-10 : Fetching 3 Rows with 15 Column(s)
MEL 25 2017-03-11 : Fetching 4 Rows with 15 Column(s)
MEL 26 2017-03-12 : Fetching 4 Rows with 15 Column(s)
MEL 27 2017-03-13 : Fetching 5 Rows with 15 Column(s)
MEL 28 2017-03-14 : Fetching 3 Rows with 15 Column(s)
MEL 29 2017-03-15 : Fetching 4 Rows with 15 Column(s)
MEL 30 2017-03-16 : Fetching 4 Rows with 15 Column(s)
MEL 31 2017-03-17 : Fetching 5 Rows with 15 Column(s)
MEL 32 2017-03-18 : Fetching 5 Rows with 15 Column(s)
MEL 33 2017-03-19 : Fetching 4 Rows with 15 Column(s)
MEL 34 2017-03-20 : Fetching 5 Rows with 15 Column(s)
MEL 35 2017-03-21 : Fetching 4 Rows with 15 Column(s)
MEL 36 2017-03-22 : Fetching 4 Rows with 15 Column(s)
MEL 37 2017-03-23 : Fetching 5 Rows with 15 Column(s)
MEL 38 2017-03-24 : Fetching 5 Rows with 15 Column(s)
MEL 39 2017-03-25 : Fetching 4 Rows with 15 Column(s)
MEL 40 2017-03-26 : Fetching 5 Rows with 15 Column(s)
MEL 41 2017-03-27 : Fetching 5 Rows with 15 Column(s)
MEL 42 2017-03-28 : Fetching 4 Rows with 15 Column(s)
MEL 43 2017-03-29 : Fetching 5 Rows with 15 Column(s)
MEL 44 2017-03-30 : Fetching 4 Rows with 15 Column(s)
MEL 45 2017-03-31 : Fetching 5 Rows with 15 Column(s)
MEL 46 2017-04-01 : Fetching 4 Rows with 15 Column(s)
MEL 47 2017-04-02 : Fetching 5 Rows with 15 Column(s)
MEL 48 2017-04-03 : Fetching 5 Rows with 15 Column(s)
MEL 49 2017-04-04 : Fetching 5 Rows with 15 Column(s)

#getWeatherForDate(station_id = "ISYDNEY143", start_date = "2017-02-10", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T, station_type = "ID")

How might we explore some of this data?

kable(rbind(describe(SYD_weather$TemperatureC),describe(MEL_weather$TemperatureC)))

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
X1	1	3515	-356.99943	1912.3546	22	21.89264	2.9652	-9999	32	10031	-4.842284	21.45387	32.25565
X11	1	221	-78.54751	950.2152	13	11.91525	8.8956	-9999	30	10029	-10.297442	104.51675	63.91839

Let’s have a basic look at the distribution

hist(MEL_weather$TemperatureC)

hist(SYD_weather$TemperatureC)

Ah, there’s something wrong! Let’s fix that and get the melbourne and sydney data into the same frame

SYD_temp <- as.data.frame(as.numeric(unlist(subset(SYD_weather, TemperatureC >-300, select=c("TemperatureC"))))) #you could also replace these wiht NA, but here we're just going to exclude the missing data
colnames(SYD_temp)[1] <- "temp"
SYD_temp$loc <- "SYD"
MEL_temp <- as.data.frame(as.numeric(unlist(subset(MEL_weather, TemperatureC >-300, select=c("TemperatureC")))))
colnames(MEL_temp)[1] <- "temp"
MEL_temp$loc <- "MEL"
temps <- rbind(SYD_temp, MEL_temp)
temps$temp <- as.numeric(temps$temp)

Let’s look at it again

ggplot(temps, aes(x = temp, fill = loc)) + geom_histogram(alpha = .5, position = 'identity') #ah, what's the problem here?

Try that again

ggplot(temps, aes(x = temp, fill = loc)) + geom_histogram(alpha = .5, aes(y = ..density..), position = 'identity') #note use of 'density' because we have unequal temperature counts in each dataset. Alpha is the transparency level.

histogram(~ temp | loc, data=temps)

What’s wrong with this?

ggplot(temps) + 
  geom_bar(aes(x = loc, y = temp, fill = loc),
           position = "dodge", stat = "summary", fun.y = "mean")

More informative?

ggplot(temps, aes(x=loc, y=temp, fill=loc)) + geom_boxplot() +
    guides(fill=FALSE)+
    stat_summary(fun.y=mean, geom="point", shape=5, size=4)

Couple of useful things - let’s pull the date out to its own value, and this time we’ll replace missing values (-9999) with NA

ggplot(SYD_weather, aes(x=TemperatureC, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles

SYD_weather[SYD_weather == -9999] <- NA
SYD_weather$date <- as.Date(SYD_weather$DateUTC)
MEL_weather[MEL_weather == -9999] <- NA
MEL_weather$date <- as.Date(MEL_weather$DateUTC)
ggplot(SYD_weather, aes(x=TemperatureC, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles

What if we want to explore the relationship between Dew_PointC and other features https://support.office.com/en-us/article/Present-your-data-in-a-scatter-chart-or-a-line-chart-4570a80f-599a-4d6b-a155-104a9018b86e

One way you might be tempted to do this…

bad_example <- subset(SYD_weather, !is.na(Humidity), select=c("Humidity", "Dew_PointC","date"))
bad_example[c("Humidity","Dew_PointC")] <- lapply(bad_example[c("Humidity","Dew_PointC")],as.numeric)

NAs introduced by coercion

bad_example <- aggregate(. ~ date, bad_example, FUN=mean)
#convert to long
bad_example <- melt(bad_example, id.vars = c("date"))
ggplot(data=bad_example, aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line() +
    geom_point()

#Is date an important variable in this analysis? Does the scaling of the data gives us the best available insight into relationships of paired values? Is the use of a line to join datapoints appropriate given missing data?

A better way?

ggplot(SYD_weather, aes(x=Humidity, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles

cor.test(as.numeric(SYD_weather$Humidity),as.numeric(SYD_weather$Dew_PointC))

NAs introduced by coercion


    Pearson's product-moment correlation

data:  as.numeric(SYD_weather$Humidity) and as.numeric(SYD_weather$Dew_PointC)
t = 45.252, df = 3380, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5927923 0.6347879
sample estimates:
      cor 
0.6142248

Ok what if we want to look at how weather varies over time and place?

SYD_weather$loc <- "Sydney"
MEL_weather$loc <- "Melbourne"

weather <- rbind(SYD_weather[c("TemperatureC","Dew_PointC","Humidity","Wind_SpeedKm_h","Precipitationmm","Events","Conditions","DateUTC","loc")],MEL_weather[c("TemperatureC","Dew_PointC","Humidity","Wind_SpeedKm_h","Precipitationmm","Events","Conditions","DateUTC","loc")])
weather$month <- format(as.Date(weather$DateUTC), "%m")
ggplot(weather, aes(x=month, y=TemperatureC, fill=loc)) + geom_boxplot() +
    guides(fill=FALSE) +
    stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
    facet_wrap(~loc)

Or at how weather events vary by place

unique(weather$Events)

[1] "Rain"              ""                  NA                  "Thunderstorm"     
[5] "Rain-Thunderstorm" "Snow"              "Hail-Thunderstorm"

unique(weather$Conditions)

 [1] "Light Rain Showers"           ""                            
 [3] "Mostly Cloudy"                "Rain Showers"                
 [5] "Partly Cloudy"                "Scattered Clouds"            
 [7] "Clear"                        "Unknown"                     
 [9] "Haze"                         "Light Thunderstorms and Rain"
[11] "Thunderstorms and Rain"       "Light Rain"                  
[13] "Heavy Rain Showers"           "Heavy Thunderstorms and Rain"
[15] "Smoke"                        "Light Drizzle"               
[17] "Drizzle"                      "Thunderstorm"                
[19] "Heavy Rain"                   "Rain"                        
[21] "Overcast"                     "Heavy Drizzle"               
[23] NA                             "Heavy Snow"                  
[25] "Light Snow"                   "Thunderstorms with Hail"

table(weather$Conditions,weather$loc)

                              
                               Melbourne Sydney
                                      49    762
  Clear                                0    394
  Drizzle                              2      6
  Haze                                 1     42
  Heavy Drizzle                        0      1
  Heavy Rain                           0      2
  Heavy Rain Showers                   0     25
  Heavy Snow                           2      0
  Heavy Thunderstorms and Rain         0      5
  Light Drizzle                        4     66
  Light Rain                           1     56
  Light Rain Showers                   0    413
  Light Snow                           2      0
  Light Thunderstorms and Rain         0     15
  Mostly Cloudy                        5    977
  Overcast                             4      9
  Partly Cloudy                       33    428
  Rain                                 0     13
  Rain Showers                         0    108
  Scattered Clouds                    77    226
  Smoke                                0      1
  Thunderstorm                         0      4
  Thunderstorms and Rain               0      4
  Thunderstorms with Hail              1      0
  Unknown                              0     11

weather_con <- unique(subset(weather,select=c("Conditions","DateUTC","loc")))
ggplot(data=weather_con, aes(x=Conditions, fill = loc)) +
    geom_bar(position=position_dodge()) +
    theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1))

weather_event <- unique(subset(weather,select=c("Events","DateUTC","loc")))
ggplot(data=weather_event, aes(x=Events, fill = loc)) +
    geom_bar(position=position_dodge()) +
    #scale_y_continuous(labels=scales::percent) +
    theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1))

We’ve often seen students refer to ‘average mood’. Sometimes this might make sense, but this is an analogous example…

#let's take the weather event data, and code it from best ('no event') to worst ('snow')
weather_event$Event[weather_event$Event==""] <- 6
weather_event$Event[weather_event$Event=="Rain"] <- 5
weather_event$Event[weather_event$Event=="Thunderstorm"] <- 4
weather_event$Event[weather_event$Event=="Rain-Thunderstorm"] <- 3
weather_event$Event[weather_event$Event=="Hail-Thunderstorm"] <- 2
weather_event$Event[weather_event$Event=="Snow"] <- 1
weather_event$Event <- as.numeric(weather_event$Event)
ggplot(weather_event, aes(x = loc, y = Event, fill=loc)) + geom_boxplot() +
    stat_summary(fun.y=mean, geom="point", shape=5, size=4)

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

Load & install packages
===========================
```{r}
library(curl)
library(devtools)
install_github("weatherData", "Ram-N")
library(weatherData)
#install.packages("weatherData") - the personal weather station data is only available via github version
#package info at https://ram-n.github.io/weatherData/

library(ggplot2)
library(psych)
library(reshape2)
library(knitr)
library(lattice) #just to illustrate another histogram function

```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

```{r}
checkDataAvailability("SYD", "2017-02-15", station_type = "airportCode") 

getWeatherForDate("SYD", "2017-02-15", opt_detailed=TRUE)
getWeatherForDate("SYD", "2017-02-10", opt_detailed=TRUE)

getSummarizedWeather("SYD", "2017-02-10")
```

Grab some data
```{r}
SYD_weather <- getWeatherForDate("SYD", "2017-02-15", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T)
MEL_weather <- getWeatherForDate("MEL", "2017-02-15", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T)

#getWeatherForDate(station_id = "ISYDNEY143", start_date = "2017-02-10", end_date = "2017-04-04", opt_detailed=TRUE, opt_all_columns = T, station_type = "ID")
```


How might we explore some of this data?
```{r}
kable(rbind(describe(SYD_weather$TemperatureC),describe(MEL_weather$TemperatureC)))
```

Let's have a basic look at the distribution
```{r}
hist(MEL_weather$TemperatureC)
hist(SYD_weather$TemperatureC)
```

Ah, there's something wrong!  Let's fix that and get the melbourne and sydney data into the same frame
```{r}
SYD_temp <- as.data.frame(as.numeric(unlist(subset(SYD_weather, TemperatureC >-300, select=c("TemperatureC"))))) #you could also replace these wiht NA, but here we're just going to exclude the missing data
colnames(SYD_temp)[1] <- "temp"
SYD_temp$loc <- "SYD"

MEL_temp <- as.data.frame(as.numeric(unlist(subset(MEL_weather, TemperatureC >-300, select=c("TemperatureC")))))
colnames(MEL_temp)[1] <- "temp"
MEL_temp$loc <- "MEL"

temps <- rbind(SYD_temp, MEL_temp)
temps$temp <- as.numeric(temps$temp)
```

Let's look at it again
```{r}
ggplot(temps, aes(x = temp, fill = loc)) + geom_histogram(alpha = .5, position = 'identity') #ah, what's the problem here?
```

Try that again
```{r}
ggplot(temps, aes(x = temp, fill = loc)) + geom_histogram(alpha = .5, aes(y = ..density..), position = 'identity') #note use of 'density' because we have unequal temperature counts in each dataset. Alpha is the transparency level.

histogram(~ temp | loc, data=temps)

```

What's wrong with this?
```{r}
ggplot(temps) + 
  geom_bar(aes(x = loc, y = temp, fill = loc),
           position = "dodge", stat = "summary", fun.y = "mean")
```

More informative?
```{r}
ggplot(temps, aes(x=loc, y=temp, fill=loc)) + geom_boxplot() +
    guides(fill=FALSE)+
    stat_summary(fun.y=mean, geom="point", shape=5, size=4)
```

Couple of useful things - let's pull the date out to its own value, and this time we'll replace missing values (-9999) with NA
```{r}
ggplot(SYD_weather, aes(x=TemperatureC, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles

SYD_weather[SYD_weather == -9999] <- NA
SYD_weather$date <- as.Date(SYD_weather$DateUTC)

MEL_weather[MEL_weather == -9999] <- NA
MEL_weather$date <- as.Date(MEL_weather$DateUTC)

ggplot(SYD_weather, aes(x=TemperatureC, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles
```

What if we want to explore the relationship between Dew_PointC and other features
https://support.office.com/en-us/article/Present-your-data-in-a-scatter-chart-or-a-line-chart-4570a80f-599a-4d6b-a155-104a9018b86e

One way you might be tempted to do this...
```{r}
bad_example <- subset(SYD_weather, !is.na(Humidity), select=c("Humidity", "Dew_PointC","date"))
bad_example[c("Humidity","Dew_PointC")] <- lapply(bad_example[c("Humidity","Dew_PointC")],as.numeric)

bad_example <- aggregate(. ~ date, bad_example, FUN=mean)

#convert to long
bad_example <- melt(bad_example, id.vars = c("date"))

ggplot(data=bad_example, aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line() +
    geom_point()
#Is date an important variable in this analysis? Does the scaling of the data gives us the best available insight into relationships of paired values? Is the use of a line to join datapoints appropriate given missing data?
```

A better way?
```{r}
ggplot(SYD_weather, aes(x=Humidity, y=Dew_PointC)) +
    geom_point(shape=1)      # Use hollow circles

cor.test(as.numeric(SYD_weather$Humidity),as.numeric(SYD_weather$Dew_PointC))

```

Ok what if we want to look at how weather varies over time and place?
```{r}
SYD_weather$loc <- "Sydney"
MEL_weather$loc <- "Melbourne"
```

```{r}
weather <- rbind(SYD_weather[c("TemperatureC","Dew_PointC","Humidity","Wind_SpeedKm_h","Precipitationmm","Events","Conditions","DateUTC","loc")],MEL_weather[c("TemperatureC","Dew_PointC","Humidity","Wind_SpeedKm_h","Precipitationmm","Events","Conditions","DateUTC","loc")])
weather$month <- format(as.Date(weather$DateUTC), "%m")

ggplot(weather, aes(x=month, y=TemperatureC, fill=loc)) + geom_boxplot() +
    guides(fill=FALSE) +
    stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
    facet_wrap(~loc)

```

Or at how weather events vary by place
```{r}
unique(weather$Events)
unique(weather$Conditions)

table(weather$Conditions,weather$loc)

weather_con <- unique(subset(weather,select=c("Conditions","DateUTC","loc")))

ggplot(data=weather_con, aes(x=Conditions, fill = loc)) +
    geom_bar(position=position_dodge()) +
    theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1))

weather_event <- unique(subset(weather,select=c("Events","DateUTC","loc")))
ggplot(data=weather_event, aes(x=Events, fill = loc)) +
    geom_bar(position=position_dodge()) +
    #scale_y_continuous(labels=scales::percent) +
    theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1))

```

We've often seen students refer to 'average mood'. Sometimes this might make sense, but this is an analogous example...
```{r}
#let's take the weather event data, and code it from best ('no event') to worst ('snow')
weather_event$Event[weather_event$Event==""] <- 6
weather_event$Event[weather_event$Event=="Rain"] <- 5
weather_event$Event[weather_event$Event=="Thunderstorm"] <- 4
weather_event$Event[weather_event$Event=="Rain-Thunderstorm"] <- 3
weather_event$Event[weather_event$Event=="Hail-Thunderstorm"] <- 2
weather_event$Event[weather_event$Event=="Snow"] <- 1

weather_event$Event <- as.numeric(weather_event$Event)

ggplot(weather_event, aes(x = loc, y = Event, fill=loc)) + geom_boxplot() +
    stat_summary(fun.y=mean, geom="point", shape=5, size=4)
```


R Notebook

Load & install packages