This notebook compares the data from rh-1 with the meteorological data obtained from wow metoffice.
require(dplyr)
require(ggplot2)
require(plotly)
require(lubridate)
require(stringr)
rh_1 <- readRDS(file="C:/Github/lorawan-analysis/data/rh-1.Rds")
wow_met <- readRDS("C:/Github/lorawan-analysis/data/wow_metoffice.Rds")
rh_1_day <- rh_1 %>%
filter(time<=as.POSIXct("2019-11-16",tz="UTC")) %>%
group_by(date=as.Date(time, tz="UTC")) %>%
summarise(ok=n(), transmit= 60*24) %>%
mutate(device = "rh-1", percentage=ok/transmit*100)
rh_1_day <- rh_1 %>%
filter(time<=as.POSIXct("2019-11-16",tz="UTC")) %>%
group_by(date=as.Date(time, tz="UTC")) %>%
summarise(ok=n(), transmit=60*24) %>%
mutate(device="rh-1", percentage=ok/transmit*100)
wow_day <- wow_met %>%
group_by(date=as.Date(time,tz="Europe/London")) %>%
summarise(rainfall=sum(Rainfall.Accumulation),
rh=mean(Relative.Humidity, na.rm=TRUE),
temperature=mean(Air.Temperature, na.rm=TRUE),
dew_point = mean(Dew.Point, na.rm=TRUE))comp<-inner_join(rh_1_day, wow_day, by = c("date", "date"))
require(ggplot2)
require(plotly)
p <- ggplot(data=comp, aes(x = rainfall, y = percentage)) + geom_point() + ggtitle("Impact of rainfall on Percentage received - Daily")
ggplotly(p)p <- ggplot(data=comp, aes(x = rh, y = percentage)) + geom_point() + ggtitle("Impact of RH on Percentage received - Daily")
ggplotly(p)p <- ggplot(data=comp, aes(x = temperature, y = percentage)) + geom_point() + ggtitle("Impact of temperature on Percentage received - Daily")
ggplotly(p)p <- ggplot(data=comp, aes(x = dew_point, y = percentage)) + geom_point() + ggtitle("Impact of dew point on Percentage received - Daily")
ggplotly(p)We have very few data points and there is no obvious trend in the data. Look at hourly data instead.
rh_1_hour <- rh_1 %>%
filter(time<=as.POSIXct("2019-11-16",tz="UTC")) %>%
group_by(hour=make_datetime(year = year(time), month = month(time), day = day(time), hour = hour(time))) %>%
summarise(ok=n(), transmit=60) %>%
mutate(device="rh-1", percentage=ok/transmit*100)
wow_hour <- wow_met %>%
group_by(hour=make_datetime(year = year(time), month = month(time), day = day(time), hour = hour(time))) %>%
summarise(rainfall=sum(Rainfall.Accumulation),
rh=mean(Relative.Humidity, na.rm=TRUE),
temperature=mean(Air.Temperature, na.rm=TRUE),
dew_point = mean(Dew.Point, na.rm=TRUE))comp<-inner_join(rh_1_hour, wow_hour, by = c("hour", "hour"))
require(ggplot2)
require(plotly)
p <- ggplot(data=comp, aes(x = rainfall, y = percentage)) + geom_point() + ggtitle("Impact of rainfall on Percentage received - Hourly")
ggplotly(p)p <- ggplot(data=comp, aes(x = rh, y = percentage)) + geom_point() + ggtitle("Impact of RH on Percentage received - Hourly")
ggplotly(p)p <- ggplot(data=comp, aes(x = temperature, y = percentage)) + geom_point() + ggtitle("Impact of temperature on Percentage received - Hourly")
ggplotly(p)p <- ggplot(data=comp, aes(x = dew_point, y = percentage)) + geom_point() + ggtitle("Impact of dew point on Percentage received - Hourly")
ggplotly(p)No pattern appearing but again, the dataset is very small.
##
## Call:
## lm(formula = percentage ~ temperature + rh + dew_point + rainfall,
## data = comp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.395 -11.260 2.625 12.735 41.981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -84.2442 184.6866 -0.456 0.649
## temperature 6.3050 10.3482 0.609 0.543
## rh 1.6272 1.8967 0.858 0.391
## dew_point -7.2689 10.6275 -0.684 0.494
## rainfall -0.2703 0.1953 -1.384 0.167
##
## Residual standard error: 18.41 on 388 degrees of freedom
## Multiple R-squared: 0.03592, Adjusted R-squared: 0.02598
## F-statistic: 3.614 on 4 and 388 DF, p-value: 0.006601rh_1_hour <- rh_1 %>%
filter(time<=as.POSIXct("2019-11-16",tz="UTC")) %>%
group_by(hour=make_datetime(year = year(time), month = month(time), day = day(time), hour = hour(time))) %>%
summarise(ok=n(), transmit=60,
temperature = mean(payload_fields.temperature, na.rm=TRUE),
rh = mean(payload_fields.humidity, na.rm=TRUE)) %>%
mutate(device="rh-1", percentage=ok/transmit*100)
p <- rh_1_hour %>%
ggplot(aes(x=temperature, y = percentage))+geom_point()+ggtitle("Temperature recorded by device")
ggplotly(p)p<-rh_1_hour %>%
ggplot(aes(x=hour(hour),y=percentage))+geom_boxplot()+ggtitle("Percentage per hour of the day")
ggplotly(p)## Warning: Continuous x aesthetic -- did you forget aes(group=...)?| port | counter | payload_raw | dev_id | hardware_serial | app_id | metadata.data_rate | metadata.gateways | metadata.coding_rate | metadata.modulation | metadata.airtime | metadata.time | metadata.frequency | payload_fields.temperature | payload_fields.humidity | payload_fields.vdd | time |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 116 | AQDtAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(84, 90, 90), latitude = c(50.93692, 50.93672, 50.93672), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(4, 1, 17), timestamp = c(2031651523, 3536643083, 3536643083), rssi = c(-89, -86, -84), longitude = c(-1.40461, -1.40445, -1.40445), rf_chain = c(0, 0, 0), time = c(“2019-10-30T15:39:53.317749Z”, “2019-10-30T15:39:52.317751Z”, “2019-10-30T15:39:52.317751Z”), snr = c(6.2, 14, 14), fine_timestamp_encrypted = c(NA, “LCA1159QvlL8I6S1kgBe2Q==”, | |||||||||
| "zBRFox | Isqt6YEx7k | lJRSTQ=="), an | tenna = c | (NA, NA, 1)) | 4 | /5 LORA 51456000 2019-10-30T15:39:53.332484018Z 867.3 23.7 38 NA 1572449993 | ||||||||||
| 5 | 117 | AQDsAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(83, 96, 96), latitude = c(50.93691, 50.93674, 50.93674), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(5, 2, 18), timestamp = c(2091648492, 3596640027, 3596640027), rssi = c(-88, -86, -85), longitude = c(-1.40454, -1.40444, -1.40444), rf_chain = c(0, 0, 0), time = c(“2019-10-30T15:40:53.314708Z”, “2019-10-30T15:40:52.314708Z”, “2019-10-30T15:40:52.314708Z”), snr = c(0, 15, 14), fine_timestamp_encrypted = c(NA, “KCLzybnQKJrJFcFGR0eUAA==”, | |||||||||
| "vuPi4R | hspG4Tlf87 | hU7qZA=="), an | tenna = c | (NA, NA, 1)) | 4/5 LORA 51456000 2019-10-30T15:40:53.32512567Z 867.5 23.6 38 NA 1572450053 | |||||||||||
| 5 | 118 | AQDsAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(76, 96, 96), latitude = c(50.93696, 50.93675, 50.93675), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(2, 7, 23), timestamp = c(2151645451, 18209964, 18209964), rssi = c(-83, -85, -82), longitude = c(-1.40447, -1.40442, -1.40442), rf_chain = c(1, 0, 0), time = c(“2019-10-30T15:41:53.311655Z”, "“,”"), snr = c(6.5, 15, 15), antenna = c(NA, NA, 1)) | 4/5 | LORA | 51456000 | 2019-10-30T15:41:53.329615285Z | 868.5 | 23.6 | 38 | NA | 1572450113 |
| 5 | 119 | AQDsAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(75, 92, 92), latitude = c(50.93697, 50.9367, 50.9367), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(4, 1, 17), timestamp = c(2211642427, 78206907, 78206907), rssi = c(-95, -84, -84), longitude = c(-1.40447, -1.40445, -1.40445), rf_chain = c(0, 0, 0), time = c(“2019-10-30T15:42:53.308617Z”, “2019-10-30T15:42:52.308619Z”, “2019-10-30T15:42:52.308619Z”), snr = c(6.2, 15, 15), fine_timestamp_encrypted = c(NA, “dZh3OA0PPHe0cC2vd8ineg==”, | |||||||||
| "pcyxBX | jadbuQRo1S | 07bc7g=="), an | tenna = c | (NA, NA, 1)) | 4/5 LORA 51456000 2019-10-30T15:42:53.320970841Z 867.3 23.6 38 NA 1572450173 | |||||||||||
| 5 | 120 | AQDsAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(75, 89, 89), latitude = c(50.93696, 50.93667, 50.93667), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(1, 6, 22), timestamp = c(2271639379, 138203836, 138203836), rssi = c(-94, -84, -82), longitude = c(-1.40446, -1.40445, -1.40445), rf_chain = c(1, 0, 0), time = c(“2019-10-30T15:43:53.305558Z”, “2019-10-30T15:43:52.305561Z”, “2019-10-30T15:43:52.305561Z”), snr = c(8.5, 13, 14), fine_timestamp_encrypted = c(NA, “CY8fYjrWoSlEBukswVVCEA==”, | |||||||||
| "kscEEI | Y2Jp3lMmaD | OTiTHg=="), fi | ne_timest | amp = c(NA, NA, 305 | 183144), ante | nna = c(NA, NA, 1)) | 4/5 LORA 51456000 2019-10-30T15:43:53.324552859Z 868.3 23.6 38 NA 1572450233 | |||||||||
| 5 | 121 | AQDsAiY= | rht1 | A81758FFFE041CA6 | rhtsensors | SF7BW125 | list(altitude = c(75, 95, 95), latitude = c(50.93696, 50.93664, 50.93664), gtw_id = c(“eui-b827ebfffee36ef8”, “eui-7276fffffe0103ec”, “eui-7276fffffe0103ec”), channel = c(1, 6, 22), timestamp = c(2331636355, 198200788, 198200788), rssi = c(-94, -84, -82), longitude = c(-1.40447, -1.40452, -1.40452), rf_chain = c(1, 0, 0), time = c(“2019-10-30T15:44:53.302524Z”, “2019-10-30T15:44:52.302526Z”, “2019-10-30T15:44:52.302526Z”), snr = c(9, 13, 14), fine_timestamp_encrypted = c(NA, “BgbEUOM6+bvZ7UawKo043Q==”, | |||||||||
| "O6Ng29 | p8dIMUSY3s | qtW5bA=="), an | tenna = c | (NA, NA, 1)) | 4/5 LORA 51456000 2019-10-30T15:44:53.319938202Z 868.3 23.6 38 NA 1572450293 |
## [1] "7"rh_1$SF <-str_extract(rh_1$metadata.data_rate, "\\d+")
# grep("^SF[1-9]+", rh_1$metadata.data_rate[1])