From 97475dbbffb8d339e35bd08ef1576438511715e1 Mon Sep 17 00:00:00 2001 From: jan Date: Wed, 1 Mar 2023 16:35:27 +0100 Subject: [PATCH] using precomputed data for vignettes --- NEWS.md | 1 + vignettes/boxes_precomputed.rds | Bin 0 -> 1481239 bytes vignettes/osem-history.Rmd | 5 ++++- vignettes/osem-history_revised.Rmd | 7 ++++--- vignettes/osem-intro.Rmd | 29 ++++++++++++++++++----------- vignettes/pm25_berlin.rds | Bin 0 -> 961940 bytes vignettes/pm25_sensors.rds | Bin 0 -> 388086 bytes 7 files changed, 27 insertions(+), 15 deletions(-) create mode 100644 vignettes/boxes_precomputed.rds create mode 100644 vignettes/pm25_berlin.rds create mode 100644 vignettes/pm25_sensors.rds diff --git a/NEWS.md b/NEWS.md index 36973db..204e220 100644 --- a/NEWS.md +++ b/NEWS.md @@ -8,6 +8,7 @@ This project does its best to adhere to semantic versioning. - updated tests - updated maintainer - updated vignettes +- use precomputed data to create vignettes - new features: - added param bbox for osem_boxes function - support of multiple grouptags diff --git a/vignettes/boxes_precomputed.rds b/vignettes/boxes_precomputed.rds new file mode 100644 index 0000000000000000000000000000000000000000..9bb7771b711bc0abf4f8e3482f22e8bc975690d6 GIT binary patch literal 1481239 zcmW)ndpy(sAH^$)a!V@YvQW9jq>K9&5~V1T%4L;GQs$a1vniKV%92}hnL_ZOXw{9wt{%-& zjwO0LONsojzCLB6eZnWz`Vz*}-*)7FHZ9>*TP>&dVf2+KV!BW+2Ftl*u$+gLuR&z} z|2_z`X1RnfXUJs)894(hg$lalk5TdhluQVg5m2&8uv`l%Coq0KR-w->T~g`%1e)9* zr2?*ca_G`2P`D(h|6L7`NpeC-eg*93pPVHd+1N#${LA8J_1 zd^*~(G5PLMu9xs?NQ??NDZN(GV+awO+I96%M4atz*2`>XPtO9}5&olok_-7Hm8~25 zuC9D%BlS)q>oiXKqh9)CE*w)8$hfuUFg-3?sMwKY_-*t9Ca$^CKT9ig#li<>_}g3P z^VDcB-Hu#`m~~1kIMTCyx{ntEt2?`tIKdVxL+b;jvXi|)YSLdbUK_H)7Qnnxv6o4x zNxP6<6*m}3aQ<`CW3hR0aLE~e9(V#zuzShvg@BT+4s})|g%iPTH?xF`Sm3>QiY}k6 zO%7jWO!>etzpSV?zjasvezOWYs9zlVla3(ruL@2>5xF*7HhrdvGxXY7g%d{*aZ@#}wS1=g1~H;L~CnX^HZE%O;t0VUwHraP8gg^QlTOK>&n_&4@!vud-(xy1rgyOTL< zx-vJqC75?o{^TA@i#mLmD?3XZW4y{7+W~S{3xf3ph*7th&ADLLir}3{m0cmQ2my{N 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grouptag='ifgi' here -boxes = osem_boxes(cache = '.') + +# boxes = osem_boxes(cache = '.') +boxes = readRDS('boxes_precomputed.rds') # read precomputed file to save resources + ``` # Plot count of boxes by time {.tabset} diff --git a/vignettes/osem-history_revised.Rmd b/vignettes/osem-history_revised.Rmd index 8dbdc1b..284d593 100644 --- a/vignettes/osem-history_revised.Rmd +++ b/vignettes/osem-history_revised.Rmd @@ -45,8 +45,9 @@ So the first step is to retrieve *all the boxes*. ```{r download, results='hide', message=FALSE, warning=FALSE} # if you want to see results for a specific subset of boxes, # just specify a filter such as grouptag='ifgi' here -boxes_all = osem_boxes(cache = '.') -boxes = boxes_all + +# boxes = osem_boxes(cache = '.') +boxes = readRDS('boxes_precomputed.rds') # read precomputed file to save resources ``` # Introduction In the following we just want to have a look at the boxes created in 2022, so we filter for them. @@ -65,7 +66,7 @@ summary(boxes) -> summary.data.frame Another feature of interest is the spatial distribution of the boxes: `plot()` can help us out here. This function requires a bunch of optional dependencies though. -```{r message=F, warning=F} +```{r, message=F, warning=F} if (!require('maps')) install.packages('maps') if (!require('maptools')) install.packages('maptools') if (!require('rgeos')) install.packages('rgeos') diff --git a/vignettes/osem-intro.Rmd b/vignettes/osem-intro.Rmd index 22d6279..773810d 100644 --- a/vignettes/osem-intro.Rmd +++ b/vignettes/osem-intro.Rmd @@ -32,7 +32,8 @@ datasets' structure. library(magrittr) library(opensensmapr) -all_sensors = osem_boxes(cache = '.') +# all_sensors = osem_boxes(cache = '.') +all_sensors = readRDS('boxes_precomputed.rds') # read precomputed file to save resources ``` ```{r} summary(all_sensors) @@ -47,7 +48,7 @@ couple of minutes ago. Another feature of interest is the spatial distribution of the boxes: `plot()` can help us out here. This function requires a bunch of optional dependencies though. -```{r message=F, warning=F} +```{r, message=F, warning=F} if (!require('maps')) install.packages('maps') if (!require('maptools')) install.packages('maptools') if (!require('rgeos')) install.packages('rgeos') @@ -81,7 +82,7 @@ We should check how many sensor stations provide useful data: We want only those boxes with a PM2.5 sensor, that are placed outdoors and are currently submitting measurements: -```{r results = F} +```{r results = F, eval=FALSE} pm25_sensors = osem_boxes( exposure = 'outdoor', date = Sys.time(), # ±4 hours @@ -89,6 +90,8 @@ pm25_sensors = osem_boxes( ) ``` ```{r} +pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources + summary(pm25_sensors) plot(pm25_sensors) ``` @@ -101,12 +104,16 @@ We could call `osem_measurements(pm25_sensors)` now, however we are focusing on a restricted area of interest, the city of Berlin. Luckily we can get the measurements filtered by a bounding box: -```{r. eval = FALSE} +```{r, results=F, message=F} library(sf) library(units) library(lubridate) library(dplyr) +``` + +Since the API takes quite long to response measurements, especially filtered on space and time, we do not run the following chunks for publication of the package on CRAN. +```{r bbox, results = F, eval=FALSE} # construct a bounding box: 12 kilometers around Berlin berlin = st_point(c(13.4034, 52.5120)) %>% st_sfc(crs = 4326) %>% @@ -114,10 +121,6 @@ berlin = st_point(c(13.4034, 52.5120)) %>% st_buffer(set_units(12, km)) %>% st_transform(4326) %>% # the opensensemap expects WGS 84 st_bbox() -``` - -Since the API takes quite long to response measurements, especially filtered on space and time, we do not run the following chunks for publication of the package on CRAN. -```{r results = F, eval=FALSE} pm25 = osem_measurements( berlin, phenomenon = 'PM2.5', @@ -125,13 +128,17 @@ pm25 = osem_measurements( to = now() ) +``` + +```{r} +pm25 = readRDS('pm25_berlin.rds') # read precomputed file to save resources plot(pm25) ``` Now we can get started with actual spatiotemporal data analysis. First, lets mask the seemingly uncalibrated sensors: -```{r, eval=FALSE} +```{r, warning=F} outliers = filter(pm25, value > 100)$sensorId bad_sensors = outliers[, drop = T] %>% levels() @@ -140,13 +147,13 @@ pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors) Then plot the measuring locations, flagging the outliers: -```{r, eval=FALSE} +```{r} st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T) ``` Removing these sensors yields a nicer time series plot: -```{r, eval=FALSE} +```{r} pm25 %>% filter(invalid == FALSE) %>% plot() ``` diff --git a/vignettes/pm25_berlin.rds b/vignettes/pm25_berlin.rds new file mode 100644 index 0000000000000000000000000000000000000000..f3ccfb85260358559aba9f01bbff1124dd634bb1 GIT binary patch literal 961940 zcmW)Hi9^!&|NqJrwNh5Dn0c^r;V9m;HmU!d=!kf&MnHiax zGZiw|T$z$N^Wsp+D-SeFG!;-$P*Cps4Sf6kUjM@D`FcLi7d&sx2mk-9zGPe1|BsXi zPrG%fVBG$5}=YRO_g?HHgns1ZiAAHw%Wb37gfwUFB 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