How to assign mutate and distinct to another variable in R? - r

Question

enter image description hereI have a huge data set which has data for every 30 seconds . First I get the mean to take hourly data , then sum it for daily data and again sum it for monthly data . I need to assign the mutate function to a new data set / variable called mE_131 . for plotting monthly value .I'm new to this Please Help!
library(dplyr)
library(ggplot2)
attach(data)
data%>% #filtering 131 and 132
select(time,Column3,m_Pm) %>%
filter(data,Column3=="131")
filter(data,Column3=="132")
data_131<-filter(data,Column3=="131")
data_132<-filter(data,Column3=="132")
data_131%>%
mutate(datehour= format(time,"%Y-%m-%d %H"), date1= format(time,"%Y-%m-%d"), month=format(time,"%Y-%m")) %>%
group_by(datehour) %>% mutate(hourlyP=mean(m_Pm)) %>% distinct(datehour, .keep_all = TRUE) %>%
group_by(date1) %>% mutate(dailyP=sum(hourlyP)) %>% distinct(date1, .keep_all = TRUE) %>%
group_by(month) %>% summarise(monthlyP=sum(dailyP))

Answer 1

If your goal is to compare monthly data between column3 == 131 and column3 == 132 then you don't necessarily need to create a separate dataset for each of them although I will show you how to do it in the end.
First, let's create the required summary for both 131 and 132 :
data <- data %>%
filter(column3 == "131" | column3 == "132") %>% # filtering the required data only
mutate(datehour= format(time,"%Y-%m-%d %H"), # calculate the required stats
date1= format(time,"%Y-%m-%d"),
month=format(time,"%Y-%m")) %>%
group_by(datehour) %>%
mutate(hourlyP=mean(m_Pm)) %>%
distinct(datehour, .keep_all = TRUE) %>%
group_by(date1) %>%
mutate(dailyP=sum(hourlyP)) %>%
distinct(date1, .keep_all = TRUE) %>%
group_by(month) %>%
summarise(monthlyP=sum(dailyP))
Note: I have written every part of code in separate line to enhance readability but it is basically the same as your code shown above.
Now, let's do the plotting:
data %>%
ggplot(aes(x=month, y=monthlyP, fill=column3)) +
geom_bar(position="dodge") # this will produce similar plot as in your example
If you insist on having a separate dataset for each value in column3 then you can simply use the assignment operator <- to create a new dataframe as follows
mE_131 <- data_131 %>%
mutate(datehour= format(time,"%Y-%m-%d %H"),
date1= format(time,"%Y-%m-%d"),
month=format(time,"%Y-%m")) %>%
group_by(datehour) %>%
mutate(hourlyP=mean(m_Pm)) %>%
distinct(datehour, .keep_all = TRUE) %>%
group_by(date1) %>%
mutate(dailyP=sum(hourlyP)) %>%
distinct(date1, .keep_all = TRUE) %>%
group_by(month) %>%
summarise(monthlyP=sum(dailyP))
Then do the same thing to create mE_132. However, I don't recommend this because it would be harder to plot them.