Finding the Weekday Average from numerical values in R - r

Question

So I have values like
Mon 162 Tue 123 Wed 29
and so on. I need to find the average for all weekdays in R. I have tried filter and group_by but cannot get an answer.
Time Day Count Speed
1 00:00 Sun 169 60.2
2 00:00 Mon 71 58.5
3 00:00 Tue 70 57.2
4 00:00 Wed 68 58.5
5 00:00 Thu 91 58.8
6 00:00 Fri 94 58.7
7 00:00 Sat 135 58.5
8 01:00 Sun 111 60.0
9 01:00 Mon 45 59.2
10 01:00 Tue 50 57.6
I need the out come to be Weekday Average = ####

Answer 1

Let's say your df is
> df
# A tibble: 14 x 2
Day Count
<chr> <dbl>
1 Sun 31
2 Mon 51
3 Tue 21
4 Wed 61
5 Thu 31
6 Fri 51
7 Sat 65
8 Sun 31
9 Mon 13
10 Tue 61
11 Wed 72
12 Thu 46
13 Fri 62
14 Sat 13
You can use
df %>%
filter(!Day %in% c('Sun', 'Sat')) %>%
group_by(Day) %>%
summarize(mean(Count))
To get
# A tibble: 5 x 2
Day `mean(Count)`
<chr> <dbl>
1 Fri 56.5
2 Mon 32
3 Thu 38.5
4 Tue 41
5 Wed 66.5
For the average of all filtered values
df %>%
filter(!Day %in% c("Sun", "Sat")) %>%
summarize("Average of all Weekday counts" = mean(Count))
Output
# A tibble: 1 x 1
`Average of all Weekday counts`
<dbl>
1 46.9
To get a numeric value instead of a tibble
df %>%
filter(!Day %in% c("Sun", "Sat")) %>%
summarize("Average of all Weekday counts" = mean(Count)) %>%
as.numeric()
Output
[1] 46.9

Answer 2

This might do the trick
days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
d.f <- data.frame(Day = rep(days, 3), Speed = rnorm(21))
# split dataframe by days then take the mean over the speed
lapply(split(d.f, f=days), function(d) mean(d$Speed))

Answer 3

If you're looking for the single mean for just the weekdays, you could do something like this:
dat = data.frame(Time = rep(c("00:00","01:00"),c(7,3)),
Day = c("Sun","Mon","Tue","Wed","Thu","Fri","Sat","Sun","Mon","Tue"),
Count = c(169,71,70,68,91,94,135,111,45,50),
Speed = c(60.2,58.5,57.2,58.5,58.8,58.7,58.5,60.0,59.2,57.6))
mean(dat$Count[dat$Day %in% c("Mon","Tue","Wed","Thu","Fri")])
# [1] 69.85714
If, on the other hand, you're looking for the mean across each individual day then you could do this using base R:
aggregate(dat$Count, by=list(dat$Day), FUN = mean)
# Group.1 x
# 1 Fri 94
# 2 Mon 58
# 3 Sat 135
# 4 Sun 140
# 5 Thu 91
# 6 Tue 60
# 7 Wed 68
It looks like you've tried dplyr, so the syntax for that same operation in dplyr would be:
library(dplyr)
dat %>% group_by(Day) %>% summarize(mean_count = mean(Count))
# Day mean_count
# <chr> <dbl>
# 1 Fri 94
# 2 Mon 58
# 3 Sat 135
# 4 Sun 140
# 5 Thu 91
# 6 Tue 60
# 7 Wed 68
And if you want to do the same thing in data.table you would do this:
library(data.table)
as.data.table(dat)[,.(mean_count = mean(Count)), by = Day]
# Day mean_count
# 1: Sun 140
# 2: Mon 58
# 3: Tue 60
# 4: Wed 68
# 5: Thu 91
# 6: Fri 94
# 7: Sat 135