consistent pollutant scale for polarPlot

[on_a_meesh]

Hello,
I am brand new to R and openair. I am producing polar plots and want two non-consecutive days on the same graphic. I cannot figure out how to get the scale the same for the pollutant. Please help! Also although it is not an issue with these plots, how do I make sure the was is the same too? They both read to 7 but how would I fix it if one was to 7 and one was to 8?

The code is...

a <- polarPlot(selectByDate(Rata_street_data,start = "31/01/2020",end = "31/01/2020"),
pollutant = "pm10",
key.position="bottom",
key.header = "31/01/2020 PM10 (ug/m3)",
key.footer = NULL
)

b <- polarPlot(selectByDate(Rata_street_data,start = "17/03/2020",end = "17/03/2020"),
pollutant = "pm10",
key.position="bottom",
key.header = "17/03/2020 PM10 (ug/m3)",
key.footer = NULL
)

print(a, split = c(1, 1, 2, 1))
print(b, split = c(2, 1, 2, 1), newpage = FALSE)

Attached are the plots

[on_a_meesh]

sorry that should read how do I make sure the "ws" is on the same scale too

[technocrat]

@on_a_meesh

Rata_street_data

is missing. See the FAQ: How to do a minimal reproducible example reprex for beginners.

Are you aiming for a single plot showing the data for each day in the same or in contrasting continuous color schemes?

[on_a_meesh]

Thank you, I'll have a look at that info. I am wanting the plots to have the same continuous colour schemes

[on_a_meesh]

I'm sorry this is the best I can figure out how to do. I am very very beginner and it takes a bit to figure all of this out. I started using the program today

date long	date	time	ws	wd	pm10
31/01/2020 23:00	31/01/2020	23:00	3.61	254.195	9.489
31/01/2020 23:10	31/01/2020	23:10	3.262	254.037	5.243
31/01/2020 23:20	31/01/2020	23:20	2.831	260.564	4.386
31/01/2020 23:30	31/01/2020	23:30	2.567	254.344	4.062
31/01/2020 23:40	31/01/2020	23:40	2.648	246.959	3.578
31/01/2020 23:50	31/01/2020	23:50	2.634	253.559	4.532
17/03/2020 0:00	17/03/2020	0:00	2.312	123.102	11.375
17/03/2020 0:10	17/03/2020	0:10	1.925	128.463
17/03/2020 0:20	17/03/2020	0:20	2.23	132.018	13.474
17/03/2020 0:30	17/03/2020	0:30	2.039	131.712	8.101
17/03/2020 0:40	17/03/2020	0:40	1.74	133.473	-1.888
17/03/2020 0:50	17/03/2020	0:50	2.149	126.05	2.12

[technocrat]

Here's what a reprex looks like. Besides making the problem easier to work with, it helps in catching threshold problems shown below.

library(openair)

# cut and pasted from post; NA added to last field of row 6
# readr::read_csv("~/Desktop/grist.csv") -> Rata_street_data

# produced with dput(Rata_street_data)

Rata_street_data <- structure(list(
  date_long =
    c("31/01/2020 3:00", "31/01/2020 23:10", "31/01/2020 23:20", "31/01/2020 23:30", "31/01/2020 23:40", "31/01/2020 23:50", "17/03/2020 0:00", "17/03/2020 0:10", "17/03/2020 0:20", "17/03/2020 0:30", "17/03/2020 0:40", "17/03/2020 0:50"),
  date =
    c("31/01/2020", "31/01/2020", "31/01/2020", "31/01/2020", "31/01/2020", "31/01/2020", "17/03/2020", "17/03/2020", "17/03/2020", "17/03/2020", "17/03/2020", "17/03/2020"), time = structure(c(82800, 83400, 84000, 84600, 85200, 85800, 0, 600, 1200, 1800, 2400, 3000),
    class =
      c("hms", "difftime"), units = "secs"
  ),
  ws =
    c(3.61, 3.262, 2.831, 2.567, 2.648, 2.634, 2.312, 1.925, 2.23, 2.039, 1.74, 2.149),
  wd =
    c(254.195, 254.037, 260.564, 254.344, 246.959, 253.559, 123.102, 128.463, 132.018, 131.712, 133.473, 126.05),
  pm10 =
    c(9.489, 5.243, 4.386, 4.062, 3.578, 4.532, 11.375, NA, 13.474, 8.101, -1.888, 2.12)
),
class =
  c("spec_tbl_df", "tbl_df", "tbl", "data.frame"),
row.names =
  c(NA, -12L), spec = structure(list(cols = list(date_long = structure(list(),
  class =
    c("collector_character", "collector")
), date = structure(list(),
  class =
    c("collector_character", "collector")
), time = structure(list(format = ""),
  class =
    c("collector_time", "collector")
), ws = structure(list(),
  class =
    c("collector_double", "collector")
), wd = structure(list(),
  class =
    c("collector_double", "collector")
), pm10 = structure(list(),
  class =
    c("collector_double", "collector")
)), default = structure(list(),
  class =
    c("collector_guess", "collector")
), skip = 1L),
class = "col_spec"
)
)

# code and data provided was not used to create the images posted

obj <- polarPlot(selectByDate(Rata_street_data, start = "31/01/2020", end = "31/01/2020"),
  pollutant = "pm10",
  key.position = "bottom",
  key.header = "31/01/2020 PM10 (ug/m3)",
  key.footer = NULL
)
#> Warning: Missing dates detected, removing 12 lines
#> Warning in min(mydata[[x]], na.rm = TRUE): no non-missing arguments to min;
#> returning Inf
#> Error in if (length(which(mydata[pollutant] < 0))/nrow(mydata) > 0.1 && : missing value where TRUE/FALSE needed

Using the built-in data, we see

library(openair)

obj <- polarPlot(openair::mydata, pollutant = "nox", type = "season")

str(obj)
#> List of 3
#>  $ plot:List of 45
#>   ..$ formula          :Class 'formula'  language z ~ u * v | season
#>   .. .. ..- attr(*, ".Environment")=<environment: 0x5588d2e348c8> 
#>   ..$ as.table         : logi TRUE
#>   ..$ aspect.fill      : logi FALSE
#>   ..$ legend           :List of 1
#>   .. ..$ right:List of 3
#>   .. .. ..$ fun :function (key, draw = FALSE, vp = NULL)  
#>   .. .. ..$ args:List of 1
#>   .. .. .. ..$ key:List of 10
#>   .. .. .. .. ..$ col      : chr [1:199] "#5E4EA2" "#5851A5" "#5253A9" "#4D56AC" ...
#>   .. .. .. .. ..$ at       : num [1:200] 7.25 8.98 10.71 12.44 14.18 ...
#>   .. .. .. .. ..$ labels   :List of 2
#>   .. .. .. .. .. ..$ labels: num [1:7] 50 100 150 200 250 300 350
#>   .. .. .. .. .. ..$ at    : num [1:7] 50 100 150 200 250 300 350
#>   .. .. .. .. ..$ space    : chr "right"
#>   .. .. .. .. ..$ auto.text: logi TRUE
#>   .. .. .. .. ..$ footer   : chr "nox"
#>   .. .. .. .. ..$ header   : chr "mean"
#>   .. .. .. .. ..$ height   : num 1
#>   .. .. .. .. ..$ width    : num 1.5
#>   .. .. .. .. ..$ fit      : chr "all"
#>   .. .. ..$ draw: logi FALSE
#>   ..$ panel            :function (x, y, z, subscripts, ...)  
#>   ..$ page             : NULL
#>   ..$ layout           : NULL
#>   ..$ skip             : logi FALSE
#>   ..$ strip            :function (...)  
#>   ..$ strip.left       : logi FALSE
#>   ..$ xscale.components:function (lim, packet.number = 0, packet.list = NULL, top = TRUE, ...)  
#>   ..$ yscale.components:function (lim, packet.number = 0, packet.list = NULL, right = TRUE, ...)  
#>   ..$ axis             :function (side = c("top", "bottom", "left", "right"), scales, components, 
#>     as.table, labels = c("default", "yes", "no"), ticks = c("default", 
#>         "yes", "no"), ..., prefix = lattice.getStatus("current.prefix"))  
#>   ..$ xlab             :  expression(paste())
#>   ..$ ylab             :  expression(paste())
#>   ..$ xlab.default     : chr "u"
#>   ..$ ylab.default     : chr "v"
#>   ..$ xlab.top         : NULL
#>   ..$ ylab.right       : NULL
#>   ..$ main             :  expression(paste())
#>   ..$ sub              : NULL
#>   ..$ x.between        : num 0
#>   ..$ y.between        : num 0
#>   ..$ par.settings     : NULL
#>   ..$ plot.args        : NULL
#>   ..$ lattice.options  : NULL
#>   ..$ par.strip.text   :List of 1
#>   .. ..$ cex: num 0.8
#>   ..$ index.cond       :List of 1
#>   .. ..$ : int [1:4] 1 2 3 4
#>   ..$ perm.cond        : int 1
#>   ..$ condlevels       :List of 1
#>   .. ..$ season: chr [1:4] "spring (MAM)" "summer (JJA)" "autumn (SON)" "winter (DJF)"
#>   ..$ call             : language levelplot(openair::mydata, pollutant = "nox", type = "season")
#>   ..$ x.scales         :List of 26
#>   .. ..$ draw          : logi FALSE
#>   .. ..$ axs           : chr "r"
#>   .. ..$ tck           : num [1:2] 1 1
#>   .. ..$ tick.number   : num 5
#>   .. ..$ at            : logi FALSE
#>   .. ..$ labels        : logi FALSE
#>   .. ..$ log           : logi FALSE
#>   .. ..$ alternating   : num [1:2] 1 2
#>   .. ..$ relation      : chr "same"
#>   .. ..$ abbreviate    : logi FALSE
#>   .. ..$ minlength     : num 4
#>   .. ..$ limits        : NULL
#>   .. ..$ format        : NULL
#>   .. ..$ equispaced.log: logi TRUE
#>   .. ..$ lty           : logi FALSE
#>   .. ..$ lwd           : logi FALSE
#>   .. ..$ cex           : logi [1:2] FALSE FALSE
#>   .. ..$ rot           : logi [1:2] FALSE FALSE
#>   .. ..$ col           : logi FALSE
#>   .. ..$ col.line      : logi FALSE
#>   .. ..$ alpha         : logi FALSE
#>   .. ..$ alpha.line    : logi FALSE
#>   .. ..$ font          : logi FALSE
#>   .. ..$ fontfamily    : logi FALSE
#>   .. ..$ fontface      : logi FALSE
#>   .. ..$ lineheight    : logi FALSE
#>   ..$ y.scales         :List of 26
#>   .. ..$ draw          : logi FALSE
#>   .. ..$ axs           : chr "r"
#>   .. ..$ tck           : num [1:2] 1 1
#>   .. ..$ tick.number   : num 5
#>   .. ..$ at            : logi FALSE
#>   .. ..$ labels        : logi FALSE
#>   .. ..$ log           : logi FALSE
#>   .. ..$ alternating   : num [1:2] 1 2
#>   .. ..$ relation      : chr "same"
#>   .. ..$ abbreviate    : logi FALSE
#>   .. ..$ minlength     : num 4
#>   .. ..$ limits        : NULL
#>   .. ..$ format        : NULL
#>   .. ..$ equispaced.log: logi TRUE
#>   .. ..$ lty           : logi FALSE
#>   .. ..$ lwd           : logi FALSE
#>   .. ..$ cex           : logi [1:2] FALSE FALSE
#>   .. ..$ rot           : logi [1:2] FALSE FALSE
#>   .. ..$ col           : logi FALSE
#>   .. ..$ col.line      : logi FALSE
#>   .. ..$ alpha         : logi FALSE
#>   .. ..$ alpha.line    : logi FALSE
#>   .. ..$ font          : logi FALSE
#>   .. ..$ fontfamily    : logi FALSE
#>   .. ..$ fontface      : logi FALSE
#>   .. ..$ lineheight    : logi FALSE
#>   ..$ panel.args.common:List of 7
#>   .. ..$ x          : num [1:161604] -20.2 -20 -19.8 -19.6 -19.4 ...
#>   .. ..$ y          : num [1:161604] -20.2 -20.2 -20.2 -20.2 -20.2 ...
#>   .. ..$ z          : num [1:161604] NA NA NA NA NA NA NA NA NA NA ...
#>   .. ..$ at         : num [1:17] -16.86 7.68 32.23 56.77 81.31 ...
#>   .. ..$ region     : logi TRUE
#>   .. ..$ axes       : logi FALSE
#>   .. ..$ col.regions: chr [1:199] "#5E4EA2" "#5851A5" "#5253A9" "#4D56AC" ...
#>   ..$ panel.args       :List of 4
#>   .. ..$ :List of 1
#>   .. .. ..$ subscripts: int [1:40401] 1 2 3 4 5 6 7 8 9 10 ...
#>   .. ..$ :List of 1
#>   .. .. ..$ subscripts: int [1:40401] 40402 40403 40404 40405 40406 40407 40408 40409 40410 40411 ...
#>   .. ..$ :List of 1
#>   .. .. ..$ subscripts: int [1:40401] 80803 80804 80805 80806 80807 80808 80809 80810 80811 80812 ...
#>   .. ..$ :List of 1
#>   .. .. ..$ subscripts: int [1:40401] 121204 121205 121206 121207 121208 121209 121210 121211 121212 121213 ...
#>   ..$ packet.sizes     : num [1:4(1d)] 40401 40401 40401 40401
#>   .. ..- attr(*, "dimnames")=List of 1
#>   .. .. ..$ season: chr [1:4] "spring (MAM)" "summer (JJA)" "autumn (SON)" "winter (DJF)"
#>   ..$ x.limits         : num [1:2] -20.7 20.7
#>   ..$ y.limits         : num [1:2] -20.7 20.7
#>   ..$ x.used.at        : NULL
#>   ..$ y.used.at        : NULL
#>   ..$ x.num.limit      : NULL
#>   ..$ y.num.limit      : NULL
#>   ..$ aspect.ratio     : num 1
#>   ..$ prepanel.default : chr "prepanel.default.levelplot"
#>   ..$ prepanel         : NULL
#>   ..- attr(*, "class")= chr "trellis"
#>  $ data: grouped_df[,4] [161,604 × 4] (S3: grouped_df/tbl_df/tbl/data.frame)
#>   ..$ season: Ord.factor w/ 4 levels "spring (MAM)"<..: 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ u     : num [1:161604] -20.2 -20 -19.8 -19.6 -19.4 ...
#>   ..$ v     : num [1:161604] -20.2 -20.2 -20.2 -20.2 -20.2 ...
#>   ..$ z     : num [1:161604] NA NA NA NA NA NA NA NA NA NA ...
#>   ..- attr(*, "groups")= tibble[,2] [4 × 2] (S3: tbl_df/tbl/data.frame)
#>   .. ..$ season: Ord.factor w/ 4 levels "spring (MAM)"<..: 1 2 3 4
#>   .. ..$ .rows : list<int> [1:4] 
#>   .. .. ..$ : int [1:40401] 1 2 3 4 5 6 7 8 9 10 ...
#>   .. .. ..$ : int [1:40401] 40402 40403 40404 40405 40406 40407 40408 40409 40410 40411 ...
#>   .. .. ..$ : int [1:40401] 80803 80804 80805 80806 80807 80808 80809 80810 80811 80812 ...
#>   .. .. ..$ : int [1:40401] 121204 121205 121206 121207 121208 121209 121210 121211 121212 121213 ...
#>   .. .. ..@ ptype: int(0) 
#>   .. ..- attr(*, ".drop")= logi TRUE
#>  $ call: language polarPlot(mydata = openair::mydata, pollutant = "nox", type = "season")
#>  - attr(*, "class")= chr "openair"

This produces the same side-by-side representation as you showed. Although it won't be obvious without knowing a lot more about how the graphical representation is handled, there does not appear to be any provision for the overplotting treatment that is being sought.

For a dataset as small as Rata_street_data, however, openair is overkill. I can try this in {ggplot2} if that's of interest.

[on_a_meesh]

Thank you very much. The info I provided was only 5 rows out of 144 for each day.
I am pleased that I didn't miss anything as far as the solution to my problem is concerned. However, it is a little annoying that there is no way to do what I want. I may use the "type" option anyway as a work around.
Thank you for your time, I do appreciate it