changed T and F to TRUE and FALSE

.lintr

@@ -1,4 +1,4 @@
-exclusions: list.files(path = 'inst/doc', full.names = T)
+exclusions: list.files(path = 'inst/doc', full.names = TRUE)
 linters: with_defaults(
     # we use snake case
     camel_case_linter = NULL,

R/api.R

@@ -94,7 +94,7 @@ get_stats_ = function (endpoint, cache) {
 #' @param cache Optional path to a directory were responses will be cached. If not NA, no requests will be made when a request for the given is already cached.
 #' @return Result of a Request to openSenseMap API
 #' @noRd
-osem_get_resource = function (host, path, ..., type = 'parsed', progress = T, cache = NA) {
+osem_get_resource = function (host, path, ..., type = 'parsed', progress = TRUE, cache = NA) {
   query = list(...)
   if (!is.na(cache)) {
     filename = osem_cache_filename(path, query, host) %>% paste(cache, ., sep = '/')

R/archive.R

@@ -64,7 +64,7 @@ osem_measurements_archive.default = function (x, ...) {
 #'     sensorFilter = sensors
 #'   )
 #' }
-osem_measurements_archive.sensebox = function (x, fromDate, toDate = fromDate, sensorFilter = ~ T, ..., progress = T) {
+osem_measurements_archive.sensebox = function (x, fromDate, toDate = fromDate, sensorFilter = ~ TRUE, ..., progress = TRUE) {
   if (nrow(x) != 1)
     stop('this function only works for exactly one senseBox!')
 

R/box.R

@@ -159,7 +159,7 @@ parse_senseboxdata = function (boxdata) {
   boxdata[c(
     'loc', 'locations', 'currentLocation', 'sensors', 'image', 'boxType', 'lastMeasurementAt', 'grouptag'
   )] = NULL
-  thebox = as.data.frame(boxdata, stringsAsFactors = F)
+  thebox = as.data.frame(boxdata, stringsAsFactors = FALSE)
 
   # parse timestamps (updatedAt might be not defined)
   thebox$createdAt = isostring_as_date(thebox$createdAt)
@@ -176,7 +176,7 @@ parse_senseboxdata = function (boxdata) {
   # create a dataframe of sensors
   thebox$sensors = sensors %>%
     recursive_lapply(function (x) if (is.null(x)) NA else x) %>% # replace NULLs with NA
-    lapply(as.data.frame, stringsAsFactors = F) %>%
+    lapply(as.data.frame, stringsAsFactors = FALSE) %>%
     dplyr::bind_rows(.) %>%
     dplyr::select(phenomenon = title, id = X_id, unit, sensor = sensorType) %>%
     list

R/box_utils.R

@@ -22,8 +22,8 @@ plot.sensebox = function (x, ..., mar = c(2, 2, 1, 1)) {
 
   oldpar = par()
   par(mar = mar)
-  plot(world, col = 'gray', xlim = bbox[c(1, 3)], ylim = bbox[c(2, 4)], axes = T, ...)
+  plot(world, col = 'gray', xlim = bbox[c(1, 3)], ylim = bbox[c(2, 4)], axes = TRUE, ...)
-  plot(geom, add = T, col = x$exposure, ...)
+  plot(geom, add = TRUE, col = x$exposure, ...)
   legend('left', legend = levels(x$exposure), col = 1:length(x$exposure), pch = 1)
   par(mar = oldpar$mar)
 
@@ -39,7 +39,7 @@ print.sensebox = function(x, columns = c('name', 'exposure', 'lastMeasurement',
 
 #' @export
 summary.sensebox = function(object, ...) {
-  cat('boxes total:', nrow(object), fill = T)
+  cat('boxes total:', nrow(object), fill = TRUE)
   cat('\nboxes by exposure:')
   table(object$exposure) %>% print()
   cat('\nboxes by model:')
@@ -59,10 +59,10 @@ summary.sensebox = function(object, ...) {
 
   oldest = object[object$createdAt == min(object$createdAt), ]
   newest = object[object$createdAt == max(object$createdAt), ]
-  cat('oldest box:', format(oldest$createdAt, '%F %T'), paste0('(', oldest$name, ')'), fill = T)
+  cat('oldest box:', format(oldest$createdAt, '%F %T'), paste0('(', oldest$name, ')'), fill = TRUE)
-  cat('newest box:', format(newest$createdAt, '%F %T'), paste0('(', newest$name, ')'), fill = T)
+  cat('newest box:', format(newest$createdAt, '%F %T'), paste0('(', newest$name, ')'), fill = TRUE)
 
-  cat('\nsensors per box:', fill = T)
+  cat('\nsensors per box:', fill = TRUE)
   lapply(object$phenomena, length) %>%
     as.numeric() %>%
     summary() %>%

man/osem_measurements_archive.Rd

@@ -11,9 +11,9 @@ osem_measurements_archive(x, ...)
   x,
   fromDate,
   toDate = fromDate,
-  sensorFilter = ~T,
+  sensorFilter = ~TRUE,
   ...,
-  progress = T
+  progress = TRUE
 )
 }
 \arguments{

tests/testthat/test_archive.R

@@ -46,6 +46,15 @@ test_that('osem_measurements_archive works for one box', {
   expect_s3_class(m, c('data.frame'))
 })
 
+test_that('osem_measurements_archive sensorFilter works for one box', {
+  check_api()
+  if (is.null(box)) skip('no box data could be fetched')
+  
+  m = osem_measurements_archive(box, as.POSIXlt('2018-08-08'), sensorFilter = ~ phenomenon == 'Temperatur')
+  expect_length(m, 2) # one column for Temperatur + createdAt
+  expect_s3_class(m, c('data.frame'))
+})
+
 test_that('osem_measurements_archive fails for multiple boxes', {
   check_api()
   if (is.null(boxes)) skip('no box data available')

vignettes/osem-history_revised.Rmd

@@ -66,7 +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=FALSE, warning=FALSE}
 if (!require('maps'))     install.packages('maps')
 if (!require('maptools')) install.packages('maptools')
 if (!require('rgeos'))    install.packages('rgeos')

vignettes/osem-intro.Rmd

@@ -28,7 +28,7 @@ Its main goals are to provide means for:
 Before we look at actual observations, lets get a grasp of the openSenseMap
 datasets' structure.
 
-```{r results = F}
+```{r results = FALSE}
 library(magrittr)
 library(opensensmapr)
 
@@ -48,7 +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=FALSE, warning=FALSE}
 if (!require('maps'))     install.packages('maps')
 if (!require('maptools')) install.packages('maptools')
 if (!require('rgeos'))    install.packages('rgeos')
@@ -82,7 +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, eval=FALSE}
+```{r results = FALSE, eval=FALSE}
 pm25_sensors = osem_boxes(
   exposure = 'outdoor',
   date = Sys.time(), # ±4 hours
@@ -104,7 +104,7 @@ 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, results=F, message=F}
+```{r, results=FALSE, message=FALSE}
 library(sf)
 library(units)
 library(lubridate)
@@ -113,7 +113,7 @@ 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}
+```{r bbox, results = FALSE, eval=FALSE}
 # construct a bounding box: 12 kilometers around Berlin
 berlin = st_point(c(13.4034, 52.5120)) %>%
   st_sfc(crs = 4326) %>%
@@ -138,9 +138,9 @@ plot(pm25)
 Now we can get started with actual spatiotemporal data analysis.
 First, lets mask the seemingly uncalibrated sensors:
 
-```{r, warning=F}
+```{r, warning=FALSE}
 outliers = filter(pm25, value > 100)$sensorId
-bad_sensors = outliers[, drop = T] %>% levels()
+bad_sensors = outliers[, drop = TRUE] %>% levels()
 
 pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 ```
@@ -148,7 +148,7 @@ pm25 = mutate(pm25, invalid = sensorId %in% bad_sensors)
 Then plot the measuring locations, flagging the outliers:
 
 ```{r}
-st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = T)
+st_as_sf(pm25) %>% st_geometry() %>% plot(col = factor(pm25$invalid), axes = TRUE)
 ```
 
 Removing these sensors yields a nicer time series plot: