Selecting Specific Months

[Craigdux]

Hello again!

I have a large dataset (subset here) and I need to make three histograms:

1. all of the data
2. Data from June through September (all years)
   3 Data from October through May

I am able to use filter (dpylr) to select years, but I have not been able to figure out filtering ranges of months.

I have copied a subset of my data here. "Result" is obviously the value. I apologize if data are not in correct format. Please advise if not.

Thank you.

Date	Parameter	Result
15-Nov-17	nitrogen	7.97
5-Apr-18	nitrogen	7.47
4-Apr-19	nitrogen	11.63
16-Nov-17	nitrogen	1.14
6-Apr-18	nitrogen	1
21-Jun-18	nitrogen	0.992
30-Aug-18	nitrogen	1.1
6-Dec-18	nitrogen	1.2
27-Mar-19	nitrogen	1.12
4-Apr-19	nitrogen	1.124
25-Jul-19	nitrogen	1.24
17-Oct-19	nitrogen	1.25
19-Nov-19	nitrogen	1.528
23-Oct-19	nitrogen	1.217
15-Nov-17	nitrogen	0.17
5-Apr-18	nitrogen	0.1
16-Nov-17	nitrogen	0.083
6-Apr-18	nitrogen	0.09
21-Jun-18	nitrogen	0.36
30-Aug-18	nitrogen	0.17
6-Dec-18	nitrogen	0.09
27-Mar-19	nitrogen	0.09
25-Jul-19	nitrogen	0.09
17-Oct-19	nitrogen	0.12
15-Nov-17	nitrogen	8.14
5-Apr-18	nitrogen	7.56
4-Apr-19	nitrogen	11.785
16-Nov-17	nitrogen	1.14
6-Apr-18	nitrogen	1
21-Jun-18	nitrogen	0.99
30-Aug-18	nitrogen	1.27
6-Dec-18	nitrogen	1.2
27-Mar-19	nitrogen	1.12
4-Apr-19	nitrogen	1.173
25-Jul-19	nitrogen	1.24
17-Oct-19	nitrogen	1.36
19-Nov-19	nitrogen	1.765
23-Oct-19	nitrogen	1.381

[Craigdux]

This is my simplified R code. Having a problem with the date selections...

plot1 <- Data1 %>%
filter(Data1$Date == months= )%>%
ggplot(x=Result)+
geom_histogram()
plot1

[Erica]

I'm sure this isn't the most concise way to do it but the way I've approached problems like this in the past is by creating dummy variables for example

Data1$Summer <- ifelse(grepl("Jun", Data1$Date) | grepl("Jul", Data1$Date) |
                         grepl("Aug", Data1$Date) | grepl("Sep", Data1$Date), 1, 0)

Then do the same for Data1$Winter

Then you can use filter() to create three separate dataframes to create histograms of.

I'll be keeping an eye on the replies for more efficient ways of doing this.

[andresrcs]

This is simple if you transform your Date variable into an actual date variable instead of a character variable.

library(dplyr)
library(lubridate)

# Sample data on a copy/paste friendly format
sample_df <- data.frame(
  stringsAsFactors = FALSE,
              Date = c("15-Nov-17","5-Apr-18",
                       "4-Apr-19","16-Nov-17","6-Apr-18","21-Jun-18","30-Aug-18",
                       "6-Dec-18","27-Mar-19","4-Apr-19","25-Jul-19",
                       "17-Oct-19","19-Nov-19","23-Oct-19","15-Nov-17","5-Apr-18",
                       "16-Nov-17","6-Apr-18","21-Jun-18","30-Aug-18",
                       "6-Dec-18","27-Mar-19","25-Jul-19","17-Oct-19","15-Nov-17",
                       "5-Apr-18","4-Apr-19","16-Nov-17","6-Apr-18",
                       "21-Jun-18","30-Aug-18","6-Dec-18","27-Mar-19","4-Apr-19",
                       "25-Jul-19","17-Oct-19","19-Nov-19","23-Oct-19"),
         Parameter = c("nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen","nitrogen","nitrogen","nitrogen",
                       "nitrogen","nitrogen"),
            Result = c(7.97,7.47,11.63,1.14,1,
                       0.992,1.1,1.2,1.12,1.124,1.24,1.25,1.528,1.217,0.17,
                       0.1,0.083,0.09,0.36,0.17,0.09,0.09,0.09,0.12,
                       8.14,7.56,11.785,1.14,1,0.99,1.27,1.2,1.12,1.173,
                       1.24,1.36,1.765,1.381)
)

sample_df %>% 
    mutate(Date = dmy(Date)) %>% 
    filter(month(Date) %in% 6:9)
#>         Date Parameter Result
#> 1 2018-06-21  nitrogen  0.992
#> 2 2018-08-30  nitrogen  1.100
#> 3 2019-07-25  nitrogen  1.240
#> 4 2018-06-21  nitrogen  0.360
#> 5 2018-08-30  nitrogen  0.170
#> 6 2019-07-25  nitrogen  0.090
#> 7 2018-06-21  nitrogen  0.990
#> 8 2018-08-30  nitrogen  1.270
#> 9 2019-07-25  nitrogen  1.240

^{Created on 2020-05-15 by the reprex package (v0.3.0)}

[Craigdux]

@Erica and @andresrcs

Thanks for you help! I looking forward to diving into this.

Craig