Merge pull request #224 from KristinaRiemer/convert_timezones

Add time zone conversion and accompanying notes to all website tutorials

Author: KristinaRiemer

sensors/01-meteorological-data.Rmd

@@ -185,7 +185,7 @@ end of the time period from the stream.
 
 ```{r met-datapoints2}
 weather_data <- weather_all$properties %>% 
-  mutate(time = ymd_hms(weather_all$end_time))
+  mutate(time = with_tz(ymd_hms(weather_all$end_time), "America/Phoenix"))
 ```
 
 ## Weather Plots

traits/03-access-r-traits.Rmd

@@ -27,6 +27,7 @@ library(traits)
 library(ggplot2)
 library(ggthemes)
 library(dplyr)
+library(lubridate)
 ```
 
 ```{r}
@@ -94,18 +95,18 @@ canopy_height <- betydb_query(table     = 'search',
                                limit     = 'none')
 ```
 
-First let's fix the `raw_date` column so that it is represented as a actual date object using `lubridate::ymd_hms`.
+First let's fix the `raw_date` column so that it is represented as an actual date object using `lubridate::ymd_hms`. It is also converted to the correct time zone with `with_tz`, another `lubridate` function. 
 
 ```{r}
 canopy_height <- canopy_height %>% 
-  mutate(raw_date = ymd_hms(raw_date))
+  mutate(trans_date = with_tz(ymd_hms(raw_date), "America/Phoenix"))
 ```
 
 
 ```{r plot_height}
 
 ggplot(data = canopy_height,
-       aes(x = raw_date, y = mean)) +
+       aes(x = trans_date, y = mean)) +
   geom_point(size = 0.5, position = position_jitter(width = 0.1)) +
   xlab("Date") + ylab("Plant Height") +
   guides(color = guide_legend(title = 'Genotype')) +

vignettes/01-get-trait-data-R.Rmd

@@ -59,12 +59,12 @@ season_4 <- betydb_query(sitename  = "~Season 4",
 
 The return value for the `betydb_query` function is just a `data.frame` so we can work with it like any other `data.frame` in R.
 
-Let's plot a time series of all traits returned. First you might notice that the relevant date columns in the `season_4` data.frame are returned as characters instead of a date format. Before plotting, let's get our `raw_date` column into a proper date format using functions from `dplyr` and `lubridate`.
+Let's plot a time series of all traits returned. First you might notice that the relevant date columns in the `season_4` data.frame are returned as characters instead of a date format. Before plotting, let's get our `raw_date` column into a proper date format and time zone using functions from `dplyr` and `lubridate`.
 
 
 ```{r traits-vig-s4-summary-reformat-date, message = FALSE}
 season_4 <- season_4 %>%
-  mutate(raw_date = ymd_hms(raw_date))
+  mutate(trans_date = with_tz(ymd_hms(raw_date), "America/Phoenix"))
 ```
 
 ### Plot season 4 summary
@@ -73,8 +73,8 @@ Now we can create a plot of all of the trait data collected during season 4, inc
 
 ```{r traits-vig-s4-summary-plot-data, fig.height = 15, fig.width = 7}
 ggplot(data = season_4) +
-  geom_point(aes(x = raw_date, y = mean, color = method_name), shape = '.') +
-  geom_line(aes(x = raw_date, y = mean, group = cultivar, color = method_name)) +
+  geom_point(aes(x = trans_date, y = mean, color = method_name), shape = '.') +
+  geom_line(aes(x = trans_date, y = mean, group = cultivar, color = method_name)) +
   facet_wrap(~trait, ncol = 4, scales = "free_y") +
   xlab("Date") + 
   ylab("Mean trait value") + 
@@ -116,7 +116,7 @@ As before, we need to reformat the raw date column.
 
 ```{r traits-vig-fix-date}
 canopy_height <- canopy_height %>% 
-  mutate(raw_date = ymd_hms(raw_date))
+  mutate(trans_date = with_tz(ymd_hms(raw_date), "America/Phoenix"))
 ```
 
 And we can generate a time series plot of just the canopy height data.
@@ -125,7 +125,7 @@ And we can generate a time series plot of just the canopy height data.
 
 #plot a time series of canopy height 
 ggplot(data = canopy_height,
-       aes(x = raw_date, y = mean)) +
+       aes(x = trans_date, y = mean)) +
   geom_point(size = 0.5, position = position_jitter(width = 0.1)) +
   xlab("Date") +
   ylab("Plant height (cm)") +

vignettes/02-get-weather-data-R.Rmd

@@ -26,11 +26,11 @@ weather_all <- fromJSON('https://terraref.ncsa.illinois.edu/clowder/api/geostrea
 ```
 
 The `geometries` dataframe is then pulled out from these data, which contains the datapoints from this stream. 
-This is combined with a transformed version of the end of the time period from the stream. 
+This is combined with a transformed version of the end of the time period in the correct time zone from the stream. 
 
 ```{r met-datapoints}
 weather_data <- weather_all$properties %>% 
-  mutate(time = ymd_hms(weather_all$end_time))
+  mutate(time = with_tz(ymd_hms(weather_all$end_time), "America/Phoenix"))
 ```
 
 The temperature data, which is five minute averages for the entire month of January 2017, is used to calculate the growing degree days for each day. 

vignettes/04-synthesis-data.Rmd

@@ -32,13 +32,14 @@ trait_canopy_cover <- betydb_query(table     = "search",
                       date      = "~2018",
                       limit     =  "none")
 trait_canopy_cover_day = trait_canopy_cover %>% 
-  mutate(day = as.Date(raw_date))
+  mutate(trans_date = with_tz(ymd_hms(raw_date), "America/Phoenix"), 
+         day = as.Date(raw_date))
 ```
 
 ```{r get_weather_data}
 weather <- fromJSON('https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?stream_id=46431&since=2018-01-01&until=2018-12-31', flatten = FALSE)
 weather <- weather$properties %>% 
-  mutate(time = ymd_hms(weather$end_time))
+  mutate(time = with_tz(ymd_hms(weather$end_time), "America/Phoenix"))
 daily_values = weather %>% 
   mutate(day = as.Date(time), 
          air_temp_converted = air_temperature - 273.15) %>% 
@@ -256,7 +257,8 @@ trait_canopy_cover <- betydb_query(table     = "search",
                                    limit     =  "none")
                        
 trait_canopy_cover_day <- trait_canopy_cover %>% 
-  mutate(day = as.Date(raw_date))
+  mutate(trans_date = with_tz(ymd_hms(raw_date), "America/Phoenix"), 
+         day = as.Date(raw_date))
 ```
 
 We now need to add the height data to the data set to plot.