Wie berechnet man den Mittelwert in einem Datenrahmen?

Question

Ich habe diesen Datenrahmen für ein Beispiel:

key_date Particles   PM timestamp    date airport ws wd tempi humidity 
1 2017-04-25 0.0000000 0.000000 1.493132e+12 2017-04-25 15:45:53 <NA> NA NA NA  NA 
2 2017-04-25 0.0000000 0.000000 1.493132e+12 2017-04-25 15:46:23 <NA> NA NA NA  NA 
3 2017-04-25 0.0000000 0.000000 1.493132e+12 2017-04-25 15:46:53 <NA> NA NA NA  NA 
4 2017-04-25 1.5333300 91.269643 1.493132e+12 2017-04-25 15:47:23 <NA> NA NA NA  NA 
5 2017-04-25 1.7733300 105.555357 1.493132e+12 2017-04-25 15:47:53 <NA> NA NA NA  NA 
6 2017-04-25 0.0000000 0.000000 1.493132e+12 2017-04-25 15:48:23 <NA> NA NA NA  NA 
7 2017-04-25 0.4100000 24.404762 1.493132e+12 2017-04-25 15:48:53 <NA> NA NA NA  NA 
8 2017-04-25 0.0933333 5.555554 1.493132e+12 2017-04-25 15:49:23 <NA> NA NA NA  NA 
9 2017-04-25 0.2166670 12.896845 1.493132e+12 2017-04-25 15:49:53 <NA> NA NA NA  NA 
10 2017-04-25 0.0000000 0.000000 1.493132e+12 2017-04-25 15:50:23 <NA> NA NA NA  NA 

Normalerweise wende ich mean mein Grundstück über openair, zB:

timePlot(mergedDf, pollutant = c("Particles"), group = TRUE, avg.time = "1 min") 

Aber wie kann ich mean meine mergedDf Ebene anwenden, statt von openair zu verwenden?

Ich habe versucht:

mergedDf <- mergedDf[,list(avg=mean(Particles)),by='1 min'] 

ich diesen Fehler:

Error in [.data.frame (mergedDf, , list(avg = mean(Particles)), by = "1 min") : unused argument (by = "1 min")

Irgendwelche Ideen, wie soll ich es richtig machen?

EDIT:

Beispieldaten:

> dput(mergedDf[1:20, ]) 
structure(list(key_date = c("2017-04-25", "2017-04-25", "2017-04-25", 
"2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", 
"2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", 
"2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", "2017-04-25", 
"2017-04-25", "2017-04-25"), Particles = c(0, 0, 0, 1.53333, 
1.77333, 0, 0.41, 0.0933333, 0.216667, 0, 0, 0, 0.126667, 0.226667, 
0.103333, 0.26, 0.206667, 0.473333, 0, 0), PM = c(0, 0, 0, 91.2696428571429, 
105.555357142857, 0, 24.4047619047619, 5.55555357142857, 12.8968452380952, 
0, 0, 0, 7.53970238095238, 13.4920833333333, 6.15077380952381, 
15.4761904761905, 12.3016071428571, 28.1745833333333, 0, 0), 
    timestamp = c(1493131553332, 1493131583376, 1493131613410, 
    1493131643467, 1493131673527, 1493131703573, 1493131733617, 
    1493131763676, 1493131793730, 1493131823777, 1493131853791, 
    1493131883866, 1493131913922, 1493131943948, 1493131973986, 
    1493132004055, 1493132034084, 1493132064145, 1493132094211, 
    1493132124236), date = structure(c(1493131553.332, 1493131583.376, 
    1493131613.41, 1493131643.467, 1493131673.527, 1493131703.573, 
    1493131733.617, 1493131763.676, 1493131793.73, 1493131823.777, 
    1493131853.791, 1493131883.866, 1493131913.922, 1493131943.948, 
    1493131973.986, 1493132004.055, 1493132034.084, 1493132064.145, 
    1493132094.211, 1493132124.236), class = c("POSIXct", "POSIXt" 
    ), tzone = "UTC-1"), airport = c(NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_, NA_character_, NA_character_, 
    NA_character_, NA_character_), ws = c(NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ 
    ), wd = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_), tempi = c(NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ 
    ), humidity = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
    NA_real_, NA_real_, NA_real_)), .Names = c("key_date", "Particles", 
"PM", "timestamp", "date", "airport", "ws", "wd", "tempi", "humidity" 
), row.names = c(NA, 20L), class = "data.frame") 

Answer 1

Sie haben einen Fehler in mergedDf <- mergedDf[,list(avg=mean(Particles)),by='1 min']: Es ist etwas wie list(avg=mean(Particles),by='1 min') sein sollte. Wie jedoch die Fehlermeldung besagt: Es gibt kein Argument by.

Ich hatte einen Blick und ich denke, es gibt keinen gleitenden Durchschnitt in Base-R. Was ich fand, ist die folgende Lösung (Sie nicht angeben, die Ausgabe - so kann ich nur vermuten):

df <- dput(...) 
df$mean <- zoo::rollapply(df$Particles, width=60, mean, fill=NA, align="right") 

Hinweis:

• können Sie konvertieren POSIXct-numeric (Sekunden in Bezug auf die Ursprung). Dann ist 1 Minute 60 Sekunden.
• Ich fürchte, die Originalversion ist nur einfacher ...

Ich hoffe, dass