mean across all columns

kunal.bali9 · March 16, 2022, 11:07pm

Hi,

I am trying to calculate the daily mean of multiple columns using

neeraj[neeraj == 0] <- NA
neeraj$date <- as.POSIXct(as.character(paste(neeraj$date)), format="%m-%d-%Y %H:%M")

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%    ##### hOURLY MEAN
  summarize_if(is.numeric, mean, na.rm = TRUE)

But, I am facing some issue like

Error in `summarize_if()`:
! `.funs` must be a one sided formula, a function, or a function name.
Run `rlang::last_error()` to see where the error occurred.

But, if I do the mean of one columns via

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%    ##### hOURLY MEAN
  summarise(pm = mean(pm2.5, na.rm = TRUE))

then it's working.
So, could please let me know how to do the mean across all columns.
The data can be found on

andresrcs · March 17, 2022, 12:17am

Here is an example of how to do it using the built-in iris data set

library(dplyr)

iris %>% 
    summarise(across(.cols = where(is.numeric),
                     .fns = mean,
                     rm.na = TRUE,
                     .names = "{.col}_mean"))
#>   Sepal.Length_mean Sepal.Width_mean Petal.Length_mean Petal.Width_mean
#> 1          5.843333         3.057333             3.758         1.199333

^{Created on 2022-03-16 by the reprex package (v2.0.1)}

If you need more specific help, please provide a proper REPRoducible EXample (reprex) illustrating your issue.

kunal.bali9 · March 17, 2022, 1:17pm

Hi,

Initial data is given in hours like

# Groups:   Datetime [275]
   date                pm2.5    no   no2   nox   so2    co    o3 benzene toulene Temp     rh    ws    wd    sr    bp
   <dttm>              <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 2020-04-01 00:00:00 120.  33.2   140. 102.   13.4  1.99    NA    0.47    0.78 NA     87     0.3  302    6    760 
 2 2020-04-01 01:00:00 104.  33.4   142. 103.   12.5  1.9     NA    0.53    0.65 NA     87.5   0.3  262.   6    760.
 3 2020-04-01 02:00:00  99.5 36.0   139. 103.   14.4  1.87    NA    0.5     0.7  NA     88.8   0.3  288    6    762.
 4 2020-04-01 03:00:00  99.5 41.3   145. 111.   16.0  1.7     NA    0.53    0.6  NA     89.2   0.3  172    6    762 
 5 2020-04-01 04:00:00 108.  47.1   138. 111.   17.2  2.14    NA    1.42    1.97 NA     89.5   0.3   NA    6    763.
 6 2020-04-01 05:00:00 122.  46.1   133. 108.   17.2  2.17    NA    2.05    3.15 NA     90     0.3   NA    6    762.
 7 2020-04-01 06:00:00 118.  41.9   135. 106.   15    2.35    NA    1.8     1.95 NA     88.5   0.3   NA   16.8  760.
 8 2020-04-01 07:00:00 115   30.2   139.  98.7  15.1  2.41    NA    1.67    1.85 NA     84.5   0.3   NA   51.5  756.
 9 2020-04-01 08:00:00 120.  17.9   144.  91.0  16.2  2.29    NA    1.52    1.88 NA     79.8   0.3   NA   93.2  752.

I need the output in daily mean like given below, but for each column

# A tibble: 275 × 2
   Datetime               pm
   <dttm>              <dbl>
 1 2020-04-01 00:00:00  88.1
 2 2020-04-02 00:00:00  65.5
 3 2020-04-03 00:00:00  50.1
 4 2020-04-04 00:00:00  63.8
 5 2020-04-05 00:00:00  70.3
 6 2020-04-06 00:00:00  59.4
 7 2020-04-07 00:00:00  58.0
 8 2020-04-08 00:00:00  67.6
 9 2020-04-09 00:00:00  69.1
10 2020-04-10 00:00:00  83.1

I am able to do it for one column via

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%    ##### hOURLY MEAN
  summarise(pm = mean(pm2.5, na.rm = TRUE))

But, FOR multiple columns I am trying

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%    ##### hOURLY MEAN
  summarize_if(is.numeric, mean, na.rm = TRUE)

and it's not working and giving an error (previous post).

andresrcs · March 17, 2022, 1:52pm

Have you tried my proposed solution?

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%
  summarise(across(.cols = where(is.numeric),
                     .fns = mean,
                     rm.na = TRUE,
                     .names = "{.col}_mean"))

As I said, if you need help with your specific data, we need a proper reproducible example, the sample data you are sharing is not on a copy/paste friendly format and it only have records for one day so it is not big enough to illustrate your problem.

In the future, please try to provide a proper reproducible example as explained on the link I gave you before.

kunal.bali9 · March 17, 2022, 2:13pm

andresrcs:

neeraj %>% 
  group_by(Datetime = floor_date(date, "day")) %>%
  summarise(across(.cols = where(is.numeric),
                     .fns = mean,
                     rm.na = TRUE,
                     .names = "{.col}_mean"))

Yes, I tried, But I m getting an error from this,

Error in `summarise()`:
! Problem while computing `..1 = across(...)`.
ℹ The error occurred in group 1: Datetime = 2020-04-01.
Caused by error in `across()`:
! Names must be unique.
x These names are duplicated:
  * "pm2.5_mean" at locations 1, 2, 3, 4, 5, etc.
  * "no_mean" at locations 7, 8, 9, 10, 11, etc.
  * "no2_mean" at locations 13, 14, 15, 16, 17, etc.
  * "nox_mean" at locations 19, 20, 21, 22, 23, etc.
  * "so2_mean" at locations 25, 26, 27, 28, 29, etc.
  * ...
Run `rlang::last_error()` to see where the error occurred.

kunal.bali9 · March 17, 2022, 2:21pm

the data would be like this

date	pm2.5	no	no2	nox	so2	co	o3	benzene	toulene	Temp	rh	ws	wd	sr	bp	vws	xylene	at
04-01-2020 0:00	119.5	33.25	140.22	101.62	13.45	1.99	NA	0.47	0.78	NA	87	0.3	302	6	760	-1.08	0	30.73
04-01-2020 1:00	104.5	33.42	142.25	102.83	12.47	1.9	NA	0.53	0.65	NA	87.5	0.3	261.75	6	760.25	-1.18	0	30.9
04-01-2020 2:00	99.5	35.95	138.93	103.12	14.38	1.87	NA	0.5	0.7	NA	88.75	0.3	288	6	761.5	-1.39	0	31.23
04-01-2020 3:00	99.5	41.32	145.05	110.72	16.02	1.7	NA	0.53	0.6	NA	89.25	0.3	172	6	762	-1.62	0	31.48
04-01-2020 4:00	108.5	47.08	137.52	111.45	17.2	2.14	NA	1.42	1.97	NA	89.5	0.3	NA	6	762.75	-1.48	0	31.55
04-01-2020 5:00	121.75	46.1	133.05	108.25	17.15	2.17	NA	2.05	3.15	NA	90	0.3	NA	6	761.75	-1.52	0	31.77
04-01-2020 6:00	118.5	41.9	134.78	105.75	15	2.35	NA	1.8	1.95	NA	88.5	0.3	NA	16.75	760.25	-1.42	0	31.1
04-01-2020 7:00	115	30.2	139.35	98.67	15.07	2.41	NA	1.67	1.85	NA	84.5	0.3	NA	51.5	756.5	-1.49	0	29.82
04-01-2020 8:00	119.75	17.9	143.6	90.95	16.15	2.29	NA	1.52	1.88	NA	79.75	0.3	NA	93.25	752.5	-1.81	0	28.35
04-01-2020 9:00	87.25	9.17	146.75	85.53	16.33	2.17	NA	1.2	1.23	NA	77.5	0.3	NA	131.5	750	-1.88	0	27.4
04-01-2020 10:00	56.25	9.53	142.52	83.53	15.52	2.17	NA	1.05	1.3	NA	75.5	0.3	NA	160.5	749	-1.78	0	26.82
04-01-2020 11:00	57.5	8.75	139.95	81.55	14.48	1.93	NA	0.83	0.93	NA	75	0.3	NA	179	748.25	-1.97	0	26.52
04-01-2020 12:00	45.25	4.1	128.97	71	14.32	1.06	NA	0.5	0.53	NA	74	0.3	NA	181.5	748	-1.77	0	26.25
04-01-2020 13:00	31.75	8.57	127.55	74.83	14.02	0.95	NA	0.28	0	NA	73	0.3	NA	161.75	747.5	-1.84	0	25.88
04-01-2020 14:00	36.25	8.03	130.25	75.8	14.7	0.9	NA	0.23	0	NA	73	0.3	NA	136.75	747.25	-1.8	0	25.88
04-01-2020 15:00	41.75	6.62	133.5	76.38	15.18	0.9	NA	0.2	0	NA	73	0.3	NA	94.25	747	-2	0	25.8
04-01-2020 16:00	39.75	6.78	138.9	79.38	13.62	0.9	NA	0.28	0	NA	73.5	0.3	NA	48.25	747.75	-2.12	0	26.05
04-01-2020 17:00	50	7.55	139.95	80.57	12.6	1.02	NA	0.3	0	NA	75.5	0.3	NA	12.5	748.5	-2.04	0	26.77
04-01-2020 18:00	66.75	8.03	125.35	73.17	13.12	1.12	NA	0.28	0.25	NA	78	0.3	NA	6	750	-1.77	0	27.65
04-01-2020 19:00	95.25	11.4	95.42	60.05	14.2	1.77	NA	0.53	0.6	NA	80	0.3	NA	6	751.75	-1.46	0	28.38
04-01-2020 20:00	156.5	18.35	128.02	83.03	14.12	2.19	NA	1.38	1.53	NA	81.5	0.3	NA	6	753	-1.41	0	28.82
04-01-2020 21:00	128.75	20.85	137.4	90.03	14.62	2.29	NA	1.68	1.88	NA	82.75	0.3	NA	6	754	-1.31	0	29.25
04-01-2020 22:00	117.25	20.65	138.53	90.5	14.85	2.19	NA	1.55	1.9	NA	83.25	0.3	267.5	6	754.75	-1.17	0	29.48
04-01-2020 23:00	96.75	23.12	133	89.58	14.32	1.99	NA	1.22	1.57	NA	84	0.3	305.75	6	755.25	-1.08	0	29.68
04-02-2020 0:00	77.5	25.88	136.05	93.38	17.02	1.72	NA	1.17	1.67	NA	85	0.3	246	6	756	-1.12	0	29.92
04-02-2020 1:00	75.25	28	134.47	94.25	18.8	1.71	NA	1.23	1.8	NA	85.75	0.3	NA	6	757.5	-1.57	0	30.25
04-02-2020 2:00	72	29.18	132.15	93.98	18.93	1.64	NA	1.2	1.8	NA	86	0.3	NA	6	758	-1.53	0	30.45
04-02-2020 3:00	67.25	29.3	132.12	94.12	18.15	1.56	NA	1.05	1.52	NA	87	0.3	326	6	758.75	-1.22	0	30.65
04-02-2020 4:00	59.75	28.17	122.27	87.95	17.8	1.46	NA	0.62	0.92	NA	87	0.3	NA	6	759	-1.53	0	30.67
04-02-2020 5:00	56.75	29.07	124.47	89.88	16.05	1.44	NA	0.38	0.55	NA	87	0.3	NA	6	759.5	-1.49	0	30.68
04-02-2020 6:00	73	26.68	127.07	89.3	17.62	1.62	NA	0.35	0.55	NA	86.5	0.3	NA	15.25	760.25	-1.4	0	30.55
04-02-2020 7:00	92.75	22.83	130.3	87.88	17.07	1.66	NA	0.35	0.35	NA	83.5	0.3	NA	49.5	756.75	-1.46	0	29.43
04-02-2020 8:00	105.5	14.85	132.4	82.5	15.88	1.55	NA	0.4	0.47	NA	79.25	0.3	NA	92	753.75	-1.45	0	28.07
04-02-2020 9:00	98.25	10.23	133.22	79.17	15.97	1.48	NA	0.43	0.47	NA	77.5	0.3	NA	130	752.75	-1.34	0	27.45
04-02-2020 10:00	80.25	8.22	124.78	73.05	12.7	1.23	NA	0.35	0	NA	74.75	0.3	NA	160.75	749.75	-1.54	0	26.42
04-02-2020 11:00	48.75	9.6	130.5	77.22	10.45	1.07	NA	0.3	0	NA	72.25	0.3	NA	174.5	748.25	-2.04	0	25.72
04-02-2020 12:00	47	12.65	138.97	84.2	15.1	1.75	NA	0.38	0.32	NA	72	0.3	NA	176	748	-2.19	0	25.52
04-02-2020 13:00	58.5	7.6	136.68	78.88	16.93	2.02	NA	0.55	0.88	NA	72	0.3	NA	155	747.5	-2.09	0	25.5
04-02-2020 14:00	44.5	7.82	135	78.2	15.55	2.05	NA	0.47	0.83	NA	72	0.3	NA	131.5	748	-2.12	0	25.62
04-02-2020 15:00	35.5	9.02	132.7	77.93	14.38	2.04	NA	0.38	0.47	NA	72.25	0.3	NA	90.25	748	-2.07	0	25.72
04-02-2020 16:00	40.5	9.38	91.1	56.08	15.57	2.1	NA	0.38	0.42	NA	73.5	0.3	NA	43.5	748.75	-2.13	0	26.1
04-02-2020 17:00	46.75	18.12	44.33	38.33	15.38	1.82	NA	0.5	0.6	NA	75.5	0.3	NA	11.75	749.75	-2.1	0	26.8
04-02-2020 18:00	59.75	14	61.97	34.6	14.57	2.07	NA	1.2	1.8	NA	77.75	0.3	NA	6	751	-1.96	0	27.52
04-02-2020 19:00	56.5	3.1	105.35	56.97	15.52	2.07	NA	1.48	2.17	NA	79.5	0.3	NA	6	752.5	-1.9	0	28.05
04-02-2020 20:00	58.5	5.28	115.9	65.92	17.33	1.26	NA	0.93	1.18	NA	81	0.3	280	6	753.75	-1.52	0	28.65
04-02-2020 21:00	60.75	13.38	126.2	78.02	18.05	1.5	NA	0.35	0.28	NA	82	0.3	264	6	753.75	-1.65	0	28.98
04-02-2020 22:00	68.25	15.8	130.38	82.2	13.1	1.63	NA	0.32	0	NA	82	0.3	NA	6	753	-1.85	0	29.02
04-02-2020 23:00	88.25	14.85	132.95	82.78	11.22	1.6	NA	0.23	0	NA	83	0.3	NA	6	754	-2.12	0	29.32
04-03-2020 0:00	99.5	13.92	134.08	82.6	13.22	1.31	NA	0.3	0	NA	83.25	0.3	NA	6	753.75	-2.02	0	29.45
04-03-2020 1:00	88.75	13.77	136.05	83.55	13.7	1.61	NA	0.3	0	NA	84	0.3	NA	6	754	-2.08	0	29.62
04-03-2020 2:00	69.75	11.95	133.15	80.53	12.72	1.61	NA	0.2	0	NA	85	0.3	NA	6	755	-2.13	0	30
04-03-2020 3:00	65.25	11.6	132.95	80.12	11.43	1.33	NA	0.23	0	NA	86	0.3	NA	6	755.25	-2.14	0	30.3
04-03-2020 4:00	55	16.07	133.38	83.97	15.68	1.63	NA	0.35	0.17	NA	87	0.3	NA	6	756	-2.13	0	30.6
04-03-2020 5:00	57.25	12.78	126.78	77.85	18.15	2.12	NA	0.88	0.93	NA	87	0.3	NA	6	756	-2.17	0	30.65
04-03-2020 6:00	53.25	7.7	127.05	73.82	17.35	2.15	NA	0.8	0.95	NA	85.5	0.3	NA	19	755	-2.12	0	30.05
04-03-2020 7:00	48.5	13.22	128.55	79.12	16.75	2.19	NA	0.8	0.95	NA	82.5	0.3	NA	60	753	-2.04	0	29.1
04-03-2020 8:00	48.75	7.9	134.62	78.05	18.05	2.17	NA	0.72	1.02	NA	79.75	0.3	NA	105.25	750.75	-1.92	0	28.1
04-03-2020 9:00	44.25	11.18	133.2	79.95	17.38	2.15	NA	0.65	0.88	NA	77.5	0.3	NA	144	749	-2.02	0	27.48
04-03-2020 10:00	41.5	8.85	137.18	80.15	15.93	2.1	NA	0.57	0.7	NA	76.5	0.3	NA	175	748	-1.94	0	27.03
04-03-2020 11:00	38.25	10.58	132.65	79.12	15.15	2.03	NA	0.47	0.83	NA	75.25	0.3	NA	190.75	746	-2.13	0	26.73
04-03-2020 12:00	30.75	3.4	125.78	69.65	14.43	1.36	NA	0.35	0.65	NA	75	0.3	NA	190.5	746	-2	0	26.55
04-03-2020 13:00	24.75	9.4	125.7	74.5	14.3	1.23	NA	0.08	0	NA	74.25	0.3	NA	167.5	745.75	-2.03	0	26.3
04-03-2020 14:00	26.25	6.9	129.1	74.3	12.5	1.2	NA	0	0	NA	74.5	0.3	NA	142.5	745.5	-2.09	0	26.4
04-03-2020 15:00	23.75	7.77	129.57	75.25	12.93	1.19	NA	0	0	NA	75	0.3	NA	100.25	745.25	-2.14	0	26.5
04-03-2020 16:00	21.5	7.72	131.5	76.25	11.72	1.21	NA	0	0	NA	75.25	0.3	NA	56.75	745.75	-2.07	0	26.73
04-03-2020 17:00	18.75	9.03	137.15	80.28	12.27	1.39	NA	0	0	NA	77.5	0.3	NA	15.5	746.5	-2.08	0	27.4
04-03-2020 18:00	25.75	9.3	139.78	81.92	14.85	1.51	NA	0	0	NA	80	0.3	NA	6	747.75	-2.15	0	28.33
04-03-2020 19:00	33.5	9.9	147.22	86.35	17.02	1.82	NA	0.15	0	NA	81.5	0.3	NA	6	748	-1.95	0	28.82
04-03-2020 20:00	47.25	15.72	157.35	96.47	17.58	1.8	NA	0.25	0.25	NA	82.25	0.3	NA	6	748.75	-1.65	0	29.1
04-03-2020 21:00	72.5	16.18	150.07	93.03	17	2.54	NA	0.95	1.18	NA	84	0.3	NA	6	751	-1.38	0	29.7
04-03-2020 22:00	89.5	18.23	150.77	95	14.15	2.36	NA	1.02	1	NA	85	0.3	355	6	751.75	-1.44	0	29.95
04-03-2020 23:00	78	18.05	151.5	95.28	12.65	2.18	NA	0.78	0.83	NA	85	0.3	NA	6	751	-1.7	0	29.9

nirgrahamuk · March 17, 2022, 2:31pm

this is not an appropriate way to share example data.
please reread the guide you were linked to, pay particular attention for the key word 'dput'

andresrcs · March 17, 2022, 3:06pm

Even if I make the extra effort of reading the sample data from what you have posted into a copy/paste friendly version, your problem is not reproducible with the information you are providing, see this reproducible example:

library(dplyr)
library(lubridate)

# Sample data on a copy/paste friendly format
sample_data <- data.frame(
  stringsAsFactors = FALSE,
              date = c("04-01-2020 0:00",
                       "04-01-2020 1:00","04-01-2020 2:00","04-01-2020 3:00",
                       "04-01-2020 4:00","04-01-2020 5:00","04-01-2020 6:00",
                       "04-01-2020 7:00","04-01-2020 8:00","04-01-2020 9:00",
                       "04-01-2020 10:00","04-01-2020 11:00","04-01-2020 12:00",
                       "04-01-2020 13:00","04-01-2020 14:00","04-01-2020 15:00",
                       "04-01-2020 16:00","04-01-2020 17:00","04-01-2020 18:00",
                       "04-01-2020 19:00","04-01-2020 20:00","04-01-2020 21:00",
                       "04-01-2020 22:00","04-01-2020 23:00","04-02-2020 0:00",
                       "04-02-2020 1:00","04-02-2020 2:00","04-02-2020 3:00",
                       "04-02-2020 4:00","04-02-2020 5:00","04-02-2020 6:00",
                       "04-02-2020 7:00","04-02-2020 8:00","04-02-2020 9:00",
                       "04-02-2020 10:00","04-02-2020 11:00",
                       "04-02-2020 12:00","04-02-2020 13:00","04-02-2020 14:00",
                       "04-02-2020 15:00","04-02-2020 16:00","04-02-2020 17:00",
                       "04-02-2020 18:00","04-02-2020 19:00","04-02-2020 20:00",
                       "04-02-2020 21:00","04-02-2020 22:00","04-02-2020 23:00",
                       "04-03-2020 0:00","04-03-2020 1:00","04-03-2020 2:00",
                       "04-03-2020 3:00","04-03-2020 4:00","04-03-2020 5:00",
                       "04-03-2020 6:00","04-03-2020 7:00","04-03-2020 8:00",
                       "04-03-2020 9:00","04-03-2020 10:00","04-03-2020 11:00",
                       "04-03-2020 12:00","04-03-2020 13:00","04-03-2020 14:00",
                       "04-03-2020 15:00","04-03-2020 16:00",
                       "04-03-2020 17:00","04-03-2020 18:00","04-03-2020 19:00",
                       "04-03-2020 20:00","04-03-2020 21:00","04-03-2020 22:00",
                       "04-03-2020 23:00"),
             pm2.5 = c(119.5,104.5,99.5,99.5,108.5,
                       121.75,118.5,115,119.75,87.25,56.25,57.5,45.25,
                       31.75,36.25,41.75,39.75,50,66.75,95.25,156.5,
                       128.75,117.25,96.75,77.5,75.25,72,67.25,59.75,56.75,
                       73,92.75,105.5,98.25,80.25,48.75,47,58.5,44.5,
                       35.5,40.5,46.75,59.75,56.5,58.5,60.75,68.25,88.25,
                       99.5,88.75,69.75,65.25,55,57.25,53.25,48.5,48.75,
                       44.25,41.5,38.25,30.75,24.75,26.25,23.75,21.5,
                       18.75,25.75,33.5,47.25,72.5,89.5,78),
                no = c(33.25,33.42,35.95,41.32,
                       47.08,46.1,41.9,30.2,17.9,9.17,9.53,8.75,4.1,8.57,
                       8.03,6.62,6.78,7.55,8.03,11.4,18.35,20.85,20.65,
                       23.12,25.88,28,29.18,29.3,28.17,29.07,26.68,22.83,
                       14.85,10.23,8.22,9.6,12.65,7.6,7.82,9.02,9.38,
                       18.12,14,3.1,5.28,13.38,15.8,14.85,13.92,13.77,
                       11.95,11.6,16.07,12.78,7.7,13.22,7.9,11.18,8.85,
                       10.58,3.4,9.4,6.9,7.77,7.72,9.03,9.3,9.9,15.72,
                       16.18,18.23,18.05),
               no2 = c(140.22,142.25,138.93,145.05,
                       137.52,133.05,134.78,139.35,143.6,146.75,142.52,
                       139.95,128.97,127.55,130.25,133.5,138.9,139.95,
                       125.35,95.42,128.02,137.4,138.53,133,136.05,134.47,
                       132.15,132.12,122.27,124.47,127.07,130.3,132.4,
                       133.22,124.78,130.5,138.97,136.68,135,132.7,91.1,44.33,
                       61.97,105.35,115.9,126.2,130.38,132.95,134.08,
                       136.05,133.15,132.95,133.38,126.78,127.05,128.55,
                       134.62,133.2,137.18,132.65,125.78,125.7,129.1,129.57,
                       131.5,137.15,139.78,147.22,157.35,150.07,150.77,
                       151.5),
               nox = c(101.62,102.83,103.12,110.72,
                       111.45,108.25,105.75,98.67,90.95,85.53,83.53,
                       81.55,71,74.83,75.8,76.38,79.38,80.57,73.17,60.05,
                       83.03,90.03,90.5,89.58,93.38,94.25,93.98,94.12,
                       87.95,89.88,89.3,87.88,82.5,79.17,73.05,77.22,84.2,
                       78.88,78.2,77.93,56.08,38.33,34.6,56.97,65.92,
                       78.02,82.2,82.78,82.6,83.55,80.53,80.12,83.97,77.85,
                       73.82,79.12,78.05,79.95,80.15,79.12,69.65,74.5,
                       74.3,75.25,76.25,80.28,81.92,86.35,96.47,93.03,95,
                       95.28),
               so2 = c(13.45,12.47,14.38,16.02,
                       17.2,17.15,15,15.07,16.15,16.33,15.52,14.48,14.32,
                       14.02,14.7,15.18,13.62,12.6,13.12,14.2,14.12,14.62,
                       14.85,14.32,17.02,18.8,18.93,18.15,17.8,16.05,
                       17.62,17.07,15.88,15.97,12.7,10.45,15.1,16.93,
                       15.55,14.38,15.57,15.38,14.57,15.52,17.33,18.05,13.1,
                       11.22,13.22,13.7,12.72,11.43,15.68,18.15,17.35,
                       16.75,18.05,17.38,15.93,15.15,14.43,14.3,12.5,
                       12.93,11.72,12.27,14.85,17.02,17.58,17,14.15,12.65),
                co = c(1.99,1.9,1.87,1.7,2.14,
                       2.17,2.35,2.41,2.29,2.17,2.17,1.93,1.06,0.95,0.9,
                       0.9,0.9,1.02,1.12,1.77,2.19,2.29,2.19,1.99,1.72,
                       1.71,1.64,1.56,1.46,1.44,1.62,1.66,1.55,1.48,
                       1.23,1.07,1.75,2.02,2.05,2.04,2.1,1.82,2.07,2.07,
                       1.26,1.5,1.63,1.6,1.31,1.61,1.61,1.33,1.63,2.12,
                       2.15,2.19,2.17,2.15,2.1,2.03,1.36,1.23,1.2,1.19,
                       1.21,1.39,1.51,1.82,1.8,2.54,2.36,2.18),
                o3 = c(NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA),
           benzene = c(0.47,0.53,0.5,0.53,1.42,
                       2.05,1.8,1.67,1.52,1.2,1.05,0.83,0.5,0.28,0.23,
                       0.2,0.28,0.3,0.28,0.53,1.38,1.68,1.55,1.22,1.17,
                       1.23,1.2,1.05,0.62,0.38,0.35,0.35,0.4,0.43,0.35,
                       0.3,0.38,0.55,0.47,0.38,0.38,0.5,1.2,1.48,0.93,
                       0.35,0.32,0.23,0.3,0.3,0.2,0.23,0.35,0.88,0.8,
                       0.8,0.72,0.65,0.57,0.47,0.35,0.08,0,0,0,0,0,
                       0.15,0.25,0.95,1.02,0.78),
           toulene = c(0.78,0.65,0.7,0.6,1.97,
                       3.15,1.95,1.85,1.88,1.23,1.3,0.93,0.53,0,0,0,0,0,
                       0.25,0.6,1.53,1.88,1.9,1.57,1.67,1.8,1.8,1.52,
                       0.92,0.55,0.55,0.35,0.47,0.47,0,0,0.32,0.88,
                       0.83,0.47,0.42,0.6,1.8,2.17,1.18,0.28,0,0,0,0,0,
                       0,0.17,0.93,0.95,0.95,1.02,0.88,0.7,0.83,0.65,
                       0,0,0,0,0,0,0,0.25,1.18,1,0.83),
              Temp = c(NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA),
                rh = c(87,87.5,88.75,89.25,89.5,
                       90,88.5,84.5,79.75,77.5,75.5,75,74,73,73,73,
                       73.5,75.5,78,80,81.5,82.75,83.25,84,85,85.75,86,
                       87,87,87,86.5,83.5,79.25,77.5,74.75,72.25,72,72,
                       72,72.25,73.5,75.5,77.75,79.5,81,82,82,83,
                       83.25,84,85,86,87,87,85.5,82.5,79.75,77.5,76.5,
                       75.25,75,74.25,74.5,75,75.25,77.5,80,81.5,82.25,84,
                       85,85),
                ws = c(0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,
                       0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3),
                wd = c(302,261.75,288,172,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,267.5,305.75,246,NA,NA,326,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,280,264,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
                       NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,355,NA),
                sr = c(6,6,6,6,6,6,16.75,51.5,
                       93.25,131.5,160.5,179,181.5,161.75,136.75,94.25,
                       48.25,12.5,6,6,6,6,6,6,6,6,6,6,6,6,15.25,
                       49.5,92,130,160.75,174.5,176,155,131.5,90.25,43.5,
                       11.75,6,6,6,6,6,6,6,6,6,6,6,6,19,60,
                       105.25,144,175,190.75,190.5,167.5,142.5,100.25,56.75,
                       15.5,6,6,6,6,6,6),
                bp = c(760,760.25,761.5,762,762.75,
                       761.75,760.25,756.5,752.5,750,749,748.25,748,
                       747.5,747.25,747,747.75,748.5,750,751.75,753,754,
                       754.75,755.25,756,757.5,758,758.75,759,759.5,
                       760.25,756.75,753.75,752.75,749.75,748.25,748,747.5,
                       748,748,748.75,749.75,751,752.5,753.75,753.75,753,
                       754,753.75,754,755,755.25,756,756,755,753,750.75,
                       749,748,746,746,745.75,745.5,745.25,745.75,
                       746.5,747.75,748,748.75,751,751.75,751),
               vws = c(-1.08,-1.18,-1.39,-1.62,
                       -1.48,-1.52,-1.42,-1.49,-1.81,-1.88,-1.78,-1.97,
                       -1.77,-1.84,-1.8,-2,-2.12,-2.04,-1.77,-1.46,-1.41,
                       -1.31,-1.17,-1.08,-1.12,-1.57,-1.53,-1.22,-1.53,
                       -1.49,-1.4,-1.46,-1.45,-1.34,-1.54,-2.04,-2.19,-2.09,
                       -2.12,-2.07,-2.13,-2.1,-1.96,-1.9,-1.52,-1.65,
                       -1.85,-2.12,-2.02,-2.08,-2.13,-2.14,-2.13,-2.17,
                       -2.12,-2.04,-1.92,-2.02,-1.94,-2.13,-2,-2.03,-2.09,
                       -2.14,-2.07,-2.08,-2.15,-1.95,-1.65,-1.38,-1.44,
                       -1.7),
            xylene = c(0L,0L,0L,0L,0L,0L,0L,0L,
                       0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,
                       0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,
                       0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,
                       0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,
                       0L,0L,0L,0L,0L,0L,0L),
                at = c(30.73,30.9,31.23,31.48,
                       31.55,31.77,31.1,29.82,28.35,27.4,26.82,26.52,26.25,
                       25.88,25.88,25.8,26.05,26.77,27.65,28.38,28.82,
                       29.25,29.48,29.68,29.92,30.25,30.45,30.65,30.67,
                       30.68,30.55,29.43,28.07,27.45,26.42,25.72,25.52,25.5,
                       25.62,25.72,26.1,26.8,27.52,28.05,28.65,28.98,
                       29.02,29.32,29.45,29.62,30,30.3,30.6,30.65,30.05,
                       29.1,28.1,27.48,27.03,26.73,26.55,26.3,26.4,26.5,
                       26.73,27.4,28.33,28.82,29.1,29.7,29.95,29.9)
)

# Relevant code
sample_data %>% 
    mutate(date = mdy_hms(date)) %>% 
    group_by(Datetime = floor_date(date, "day")) %>%
    summarise(across(.cols = where(is.numeric),
                     .fns = mean,
                     rm.na = TRUE,
                     .names = "{.col}_mean"))
#> # A tibble: 3 × 17
#>   Datetime            pm2.5_mean no_mean no2_mean nox_mean so2_mean co_mean
#>   <dttm>                   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>   <dbl>
#> 1 2020-04-01 00:00:00       88.1    20.8     135.     88.7     14.7    1.77
#> 2 2020-04-02 00:00:00       65.5    16.4     121.     77.4     15.8    1.67
#> 3 2020-04-03 00:00:00       50.1    11.3     136.     81.5     14.9    1.76
#> # … with 10 more variables: benzene_mean <dbl>, toulene_mean <dbl>,
#> #   rh_mean <dbl>, ws_mean <dbl>, wd_mean <dbl>, sr_mean <dbl>, bp_mean <dbl>,
#> #   vws_mean <dbl>, xylene_mean <dbl>, at_mean <dbl>

^{Created on 2022-03-17 by the reprex package (v2.0.1)}

In order to help you we need to be able to reproduce your problem, that is why we ask you to provide a reproducible example as explained on the reprex guide I linked for you before.

system · April 7, 2022, 3:06pm

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.