Help on Looping

[srini]

Please find below the data and the reprex. The output still remains the same
sai <- data.frame(
Id = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L),
nos = c(4L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 5L, 2L, 1L, 2L, 1L, 1L),
grade = c(3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 4L, 3L, 3L, 4L),
Date_Created = c("2016-03-01", "2016-03-15", "2016-03-21", "2016-05-25",
"2016-07-29", "2016-07-29", "2016-07-29", "2016-08-04",
"2016-10-14", "2016-10-31", "2017-04-13", "2017-04-13",
"2017-04-13", "2017-04-13", "2017-06-29"),
location = as.factor(c("Aus", "Aus", "Aus", "Ind", "Ind", "Ind", "ML",
"ML", "Aus", "Ind", "Ind", "PA", "KA", "LA",
"LA"))
)

library(tidyverse)
library(lubridate)

sai$Date_Created = ymd(sai$Date_Created)

sai %>%
mutate(earliest_date = Date_Created %m-% months(6),
sum_nos = map2_dbl(.x = earliest_date,
.y = Date_Created,
~ sai %>%
filter(Date_Created >= .x & Date_Created <= .y) %>%
summarise(sum_nos = sum(nos)) %>%
.$sum_nos
))

[srini]

Thank you for the book. Sure will read it.

[andresrcs]

That works normally for me, what happens if you run this code through the reprex package?

reprex::reprex({
    sai <- data.frame(
        Id = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
               14L, 15L),
        nos = c(4L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 5L, 2L, 1L, 2L, 1L, 1L),
        grade = c(3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 4L, 3L, 3L, 4L),
        Date_Created = c("2016-03-01", "2016-03-15", "2016-03-21", "2016-05-25",
                         "2016-07-29", "2016-07-29", "2016-07-29", "2016-08-04",
                         "2016-10-14", "2016-10-31", "2017-04-13", "2017-04-13",
                         "2017-04-13", "2017-04-13", "2017-06-29"),
        location = as.factor(c("Aus", "Aus", "Aus", "Ind", "Ind", "Ind", "ML",
                               "ML", "Aus", "Ind", "Ind", "PA", "KA", "LA",
                               "LA"))
    )
    
    library(tidyverse)
    library(lubridate)
    
    sai$Date_Created = ymd(sai$Date_Created)
    
    sai %>%
        mutate(earliest_date = Date_Created %m-% months(6),
               sum_nos = map2_dbl(.x = earliest_date,
                                  .y = Date_Created,
                                  ~ sai %>%
                                      filter(Date_Created >= .x & Date_Created <= .y) %>%
                                      summarise(sum_nos = sum(nos)) %>%
                                      .$sum_nos
               ))
})

[srini]

Its gives the below error if i run with reprex

[srini]

[quote="srini, post:47, topic:33553"]
Need one more help how can i transform this for loop into the vectorize form

ibrary(lubridate)
data$Date_Created <- as.Date(data$Date_Created, "%m/%d/%Y") # convert to date
data$less6month <- data$Date_Created %m-% months(6) # subtract 6 months

data$count <- 0 # initialise counter
i <- 1 # for testing
for (i in 1:nrow(data)){ # loop through rows

compare all rows to row i

compare <- (datanew1$location==datanew1$location[i]) &
(data$grade==data$grade[i]) &
(data$Date_Created >=data$less6month[i])&
(data$Date_Created <= data$Date_Created[i])

count number of TRUE results (subtract 1 for row i)

data$count[i] <- sum(compare)

}

[andresrcs]

I'm going to give you the solution, but I want to ask you for something in return, please read and follow this guides before writing your next post/topic

FAQ: How to make your code look nice? Markdown Formatting

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library(tidyverse)
library(lubridate)

data <- data.frame(
    Id = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
           14L, 15L),
    nos = c(4L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 5L, 2L, 1L, 2L, 1L, 1L),
    grade = c(3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 4L, 3L, 3L, 4L),
    Date_Created = c("2016-03-01", "2016-03-15", "2016-03-21", "2016-05-25",
                     "2016-07-29", "2016-07-29", "2016-07-29", "2016-08-04",
                     "2016-10-14", "2016-10-31", "2017-04-13", "2017-04-13",
                     "2017-04-13", "2017-04-13", "2017-06-29"),
    location = as.factor(c("Aus", "Aus", "Aus", "Ind", "Ind", "Ind", "ML",
                           "ML", "Aus", "Ind", "Ind", "PA", "KA", "LA",
                           "LA"))
)

data$Date_Created = ymd(data$Date_Created)

data %>%
    mutate(earliest_date = Date_Created %m-% months(6),
           count = pmap_dbl(.l = list(location,
                                      grade,
                                      earliest_date,
                                      Date_Created),
                            ~ data %>%
                                filter(location == ..1, grade == ..2, Date_Created >= ..3 & Date_Created <= ..4) %>%
                                summarise(count = n()) %>% 
                                .$count
           ),
           sum_nos = pmap_dbl(.l = list(location,
                                        grade,
                                        earliest_date,
                                        Date_Created),
                              ~ data %>%
                                  filter(location == ..1, grade == ..2, Date_Created >= ..3 & Date_Created <= ..4) %>%
                                  summarise(sum_nos = sum(nos)) %>% 
                                  .$sum_nos
                              )
           )
#>    Id nos grade Date_Created location earliest_date count sum_nos
#> 1   1   4     3   2016-03-01      Aus    2015-09-01     1       4
#> 2   2   2     3   2016-03-15      Aus    2015-09-15     2       6
#> 3   3   3     3   2016-03-21      Aus    2015-09-21     3       9
#> 4   4   2     3   2016-05-25      Ind    2015-11-25     1       2
#> 5   5   3     3   2016-07-29      Ind    2016-01-29     2       5
#> 6   6   2     2   2016-07-29      Ind    2016-01-29     1       2
#> 7   7   2     2   2016-07-29       ML    2016-01-29     1       2
#> 8   8   3     4   2016-08-04       ML    2016-02-04     1       3
#> 9   9   1     4   2016-10-14      Aus    2016-04-14     1       1
#> 10 10   5     3   2016-10-31      Ind    2016-04-30     3      10
#> 11 11   2     3   2017-04-13      Ind    2016-10-13     2       7
#> 12 12   1     4   2017-04-13       PA    2016-10-13     1       1
#> 13 13   2     3   2017-04-13       KA    2016-10-13     1       2
#> 14 14   1     3   2017-04-13       LA    2016-10-13     1       1
#> 15 15   1     4   2017-06-29       LA    2016-12-29     1       1

[srini]

Thank you so much for all the help. This is perfect what i was looking for. Kudos I make sure that next time i post i will follow the guidelines.

[srini]

One more help if you can suggest the resources in R to improve my coding skills. Thank you

[andresrcs]

These are my favorite books for getting anybody up and running with R (and they are free)

R Programming for Data Science

R for Data Science

I also recommend this online course

Data Science | Coursera

[srini]

Thank you so much.!!

[srini]

Thank you for the solution. This was the exactly what i was working through. I replicated the exact code and the data. Unfortunately the code is not giving the exact result you posted. I cleared the R session and ran again.

[srini]

I am getting the following messages while running the package. is this could be a problem?

[andresrcs]

That is just telling you that another package has a function with the same name, so if you want to use for example the plyr version of arrange() then you would have to specify the source like this plyr::arrange()

[srini]

ok. Thank you. But still not sure why the code is not executing. There are two problems.
If the code executes it gives two problems

1. The output has the same values across all the rows.
2. The code is not running.

[srini]

data$Date_Created = ymd(data$Date_Created)
chk <- data %>%
mutate(earliest_date = Date_Created %m-% months(6),
count = pmap_dbl(.l = list(location,
grade,
earliest_date,
Date_Created),
~ data %>%
filter(location == ..1, grade == ..2, Date_Created >= ..3 & Date_Created <= ..4)%>%
dplyr::summarise_(count=n()) %>%
.$count
))

[srini]

Data is
structure(list(Id = 1:15, nos = c(4L, 2L, 3L, 2L, 3L, 2L, 2L,
3L, 1L, 5L, 2L, 1L, 2L, 1L, 1L), grade = c(3L, 3L, 3L, 3L, 3L,
2L, 2L, 4L, 4L, 3L, 3L, 4L, 3L, 3L, 4L), Date_Created = structure(c(16861,
16875, 16881, 16946, 17011, 17011, 17011, 17017, 17088, 17105,
17269, 17269, 17269, 17269, 17346), class = "Date"), location = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 5L, 5L, 1L, 2L, 2L, 6L, 3L, 4L, 4L), .Label = c("Aus",
"Ind", "KA", "LA", "ML", "PA"), class = "factor")), row.names = c(NA,
-15L), class = "data.frame")

[srini]

Error 1: No output

Error 2: Output has same values for all the rows

[andresrcs]

In your first image you are using summarise_() instead of summarise(), it is not the same.

For your second image we don't know what the structure of data is, any chance you have grouped data? That could be causing the problem.

Also, I want to point out that you are not honoring your part of the deal, have you read the guides about how to use this forum? Please follow our guides when posting, using unformatted text and screenshots is considered a bad practice here.

[srini]

Thank you. I have actually used the same data and the code which you shared. There is no change in the structure of the data.
I am sorry i am on deal and i tried to put it in the format which you shared. However it didn't work out. I will try again.

[srini]

library(tidyverse)
library(lubridate)
library(plyr)
library(dplyr)
library(purrr)
library(magrittr)
data$Date_Created = ymd(data$Date_Created)
chk <- data %>% 
    mutate(earliest_date = Date_Created %m-% months(6),
         count = pmap_dbl(.l = list(location,
                                    grade,
                                    earliest_date,
                                    Date_Created),
                          ~ data %>%
                            filter(location == ..1, grade == ..2, Date_Created >= ..3 & Date_Created <= ..4)%>%
                            summarise(count=n()) %>%
                            .$count
                          ),
           sum_nos = pmap_dbl(.l = list(location,
                                        grade,
                                        earliest_date,
                                        Date_Created),
                              ~ data %>%
                                  filter(location == ..1, grade == ..2, Date_Created >= ..3 & Date_Created <= ..4) %>%
                                  summarise(sum_nos = sum(nos)) %>% 
                                  .$sum_nos
                              )
           )