Efficiently summarizing and transforming a table of data using tidyverse functions

-1

I have a relatively large data file that looks like (a), and need create a structure like (b). Thus I need to calculate the sum of Amount times Coeficient for each ID and each year.

enter image description here

I quickly hacked something together using nested for loops, but thats of course terribly inefficient:

library(tidyverse)



data  <- tibble(

    id=c("A", "B", "C", "A", "A", "B", "C"),

    year=c(2002,2002,2004,2002,2003,2003,2005),

    amount=c(1000,1500,1000,500,1000,1000,500),

    coef=rep(0.5,7)

)



years  <- sort(unique(data$year))

ids  <- unique(data$id)



result  <- matrix(0,length(ids),length(years)) %>%

    as.tibble() %>% setNames(., years)



for (i in seq_along(ids)){

    for (j in seq_along(years)){

        d  <- filter(data, id==ids[i] & year== years[j])

        if (nrow(d)!=0){

            result[i,j]  <- sum(d$amount*d$coef)

        }

    }

}

result  <- add_column(result, ID=ids, .before = 1)

I was wondering how one could solve this efficiently using map(), group_by() or any other tidyverse functions.

Thanks in advance for helpful suggestions.

asked Dec 31 '18 at 21:20

Florian

255

4

look up "long to wide [r] [[tidyverse]".

– 42-
Dec 31 '18 at 21:21

2

?spread will get you there

– svenhalvorson
Dec 31 '18 at 21:23

add a comment |

-1

I have a relatively large data file that looks like (a), and need create a structure like (b). Thus I need to calculate the sum of Amount times Coeficient for each ID and each year.

enter image description here

I quickly hacked something together using nested for loops, but thats of course terribly inefficient:

library(tidyverse)



data  <- tibble(

    id=c("A", "B", "C", "A", "A", "B", "C"),

    year=c(2002,2002,2004,2002,2003,2003,2005),

    amount=c(1000,1500,1000,500,1000,1000,500),

    coef=rep(0.5,7)

)



years  <- sort(unique(data$year))

ids  <- unique(data$id)



result  <- matrix(0,length(ids),length(years)) %>%

    as.tibble() %>% setNames(., years)



for (i in seq_along(ids)){

    for (j in seq_along(years)){

        d  <- filter(data, id==ids[i] & year== years[j])

        if (nrow(d)!=0){

            result[i,j]  <- sum(d$amount*d$coef)

        }

    }

}

result  <- add_column(result, ID=ids, .before = 1)

I was wondering how one could solve this efficiently using map(), group_by() or any other tidyverse functions.

Thanks in advance for helpful suggestions.

asked Dec 31 '18 at 21:20

Florian

255

4

look up "long to wide [r] [[tidyverse]".

– 42-
Dec 31 '18 at 21:21

2

?spread will get you there

– svenhalvorson
Dec 31 '18 at 21:23

add a comment |

-1

I have a relatively large data file that looks like (a), and need create a structure like (b). Thus I need to calculate the sum of Amount times Coeficient for each ID and each year.

enter image description here

I quickly hacked something together using nested for loops, but thats of course terribly inefficient:

library(tidyverse)



data  <- tibble(

    id=c("A", "B", "C", "A", "A", "B", "C"),

    year=c(2002,2002,2004,2002,2003,2003,2005),

    amount=c(1000,1500,1000,500,1000,1000,500),

    coef=rep(0.5,7)

)



years  <- sort(unique(data$year))

ids  <- unique(data$id)



result  <- matrix(0,length(ids),length(years)) %>%

    as.tibble() %>% setNames(., years)



for (i in seq_along(ids)){

    for (j in seq_along(years)){

        d  <- filter(data, id==ids[i] & year== years[j])

        if (nrow(d)!=0){

            result[i,j]  <- sum(d$amount*d$coef)

        }

    }

}

result  <- add_column(result, ID=ids, .before = 1)

I was wondering how one could solve this efficiently using map(), group_by() or any other tidyverse functions.

Thanks in advance for helpful suggestions.

asked Dec 31 '18 at 21:20

Florian

255

I have a relatively large data file that looks like (a), and need create a structure like (b). Thus I need to calculate the sum of Amount times Coeficient for each ID and each year.

enter image description here

I quickly hacked something together using nested for loops, but thats of course terribly inefficient:

library(tidyverse)



data  <- tibble(

    id=c("A", "B", "C", "A", "A", "B", "C"),

    year=c(2002,2002,2004,2002,2003,2003,2005),

    amount=c(1000,1500,1000,500,1000,1000,500),

    coef=rep(0.5,7)

)



years  <- sort(unique(data$year))

ids  <- unique(data$id)



result  <- matrix(0,length(ids),length(years)) %>%

    as.tibble() %>% setNames(., years)



for (i in seq_along(ids)){

    for (j in seq_along(years)){

        d  <- filter(data, id==ids[i] & year== years[j])

        if (nrow(d)!=0){

            result[i,j]  <- sum(d$amount*d$coef)

        }

    }

}

result  <- add_column(result, ID=ids, .before = 1)

I was wondering how one could solve this efficiently using map(), group_by() or any other tidyverse functions.

Thanks in advance for helpful suggestions.

r tidyverse purrr

asked Dec 31 '18 at 21:20

Florian

255

asked Dec 31 '18 at 21:20

Florian

255

asked Dec 31 '18 at 21:20

Florian

255

asked Dec 31 '18 at 21:20

Florian

255

asked Dec 31 '18 at 21:20

Florian

255

4

look up "long to wide [r] [[tidyverse]".

– 42-
Dec 31 '18 at 21:21

2

?spread will get you there

– svenhalvorson
Dec 31 '18 at 21:23

add a comment |

4

look up "long to wide [r] [[tidyverse]".

– 42-
Dec 31 '18 at 21:21

2

?spread will get you there

– svenhalvorson
Dec 31 '18 at 21:23

look up "long to wide [r] [[tidyverse]".

– 42-
Dec 31 '18 at 21:21

?spread will get you there

– svenhalvorson
Dec 31 '18 at 21:23

add a comment |

2 Answers
2

active

oldest

votes

Here's one way that seems to work. I'm sure there are others.

library(tidyverse)



id <- c("A", "B", "C", "A", "A", "B", "C")

year <- c(2002,2002,2004,2002,2003,2003,2005)

amount <- c(1000,1500,1000,500,1000,1000,500)

coef <- rep(0.5,7)



data <- tibble(id, year, amount, coef)



table <- data %>% 

  group_by(., id, year)  %>%  

  mutate(prod = amount*coef)%>%

  summarize(., sumprod = sum(prod)) %>%

  spread(., year, sumprod) %>% 

  replace(is.na(.), 0)

answered Dec 31 '18 at 23:12

Steph

415

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

add a comment |

Thanks for the hint, this really is just one line:

result  <- data %>% group_by(id, year) %>% summarise(S=sum(amount*coef)) %>% spread(year, S)

answered Dec 31 '18 at 22:15

Florian

255

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

Here's one way that seems to work. I'm sure there are others.

library(tidyverse)



id <- c("A", "B", "C", "A", "A", "B", "C")

year <- c(2002,2002,2004,2002,2003,2003,2005)

amount <- c(1000,1500,1000,500,1000,1000,500)

coef <- rep(0.5,7)



data <- tibble(id, year, amount, coef)



table <- data %>% 

  group_by(., id, year)  %>%  

  mutate(prod = amount*coef)%>%

  summarize(., sumprod = sum(prod)) %>%

  spread(., year, sumprod) %>% 

  replace(is.na(.), 0)

answered Dec 31 '18 at 23:12

Steph

415

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

add a comment |

Here's one way that seems to work. I'm sure there are others.

library(tidyverse)



id <- c("A", "B", "C", "A", "A", "B", "C")

year <- c(2002,2002,2004,2002,2003,2003,2005)

amount <- c(1000,1500,1000,500,1000,1000,500)

coef <- rep(0.5,7)



data <- tibble(id, year, amount, coef)



table <- data %>% 

  group_by(., id, year)  %>%  

  mutate(prod = amount*coef)%>%

  summarize(., sumprod = sum(prod)) %>%

  spread(., year, sumprod) %>% 

  replace(is.na(.), 0)

answered Dec 31 '18 at 23:12

Steph

415

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

add a comment |

Here's one way that seems to work. I'm sure there are others.

library(tidyverse)



id <- c("A", "B", "C", "A", "A", "B", "C")

year <- c(2002,2002,2004,2002,2003,2003,2005)

amount <- c(1000,1500,1000,500,1000,1000,500)

coef <- rep(0.5,7)



data <- tibble(id, year, amount, coef)



table <- data %>% 

  group_by(., id, year)  %>%  

  mutate(prod = amount*coef)%>%

  summarize(., sumprod = sum(prod)) %>%

  spread(., year, sumprod) %>% 

  replace(is.na(.), 0)

answered Dec 31 '18 at 23:12

Steph

415

Here's one way that seems to work. I'm sure there are others.

library(tidyverse)



id <- c("A", "B", "C", "A", "A", "B", "C")

year <- c(2002,2002,2004,2002,2003,2003,2005)

amount <- c(1000,1500,1000,500,1000,1000,500)

coef <- rep(0.5,7)



data <- tibble(id, year, amount, coef)



table <- data %>% 

  group_by(., id, year)  %>%  

  mutate(prod = amount*coef)%>%

  summarize(., sumprod = sum(prod)) %>%

  spread(., year, sumprod) %>% 

  replace(is.na(.), 0)

answered Dec 31 '18 at 23:12

Steph

415

answered Dec 31 '18 at 23:12

Steph

415

answered Dec 31 '18 at 23:12

Steph

415

answered Dec 31 '18 at 23:12

Steph

415

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

add a comment |

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

Ha ha...oops. I posted mine before I realized you'd cracked it. Good illustration that you can usually get to the same place in different ways. :-)

– Steph
Dec 31 '18 at 23:16

Thanks, I'm still new to the tidyverse so seeing a different way is helpful ;-)

– Florian
Jan 1 at 0:18

add a comment |

Thanks for the hint, this really is just one line:

result  <- data %>% group_by(id, year) %>% summarise(S=sum(amount*coef)) %>% spread(year, S)

answered Dec 31 '18 at 22:15

Florian

255

add a comment |

Thanks for the hint, this really is just one line:

result  <- data %>% group_by(id, year) %>% summarise(S=sum(amount*coef)) %>% spread(year, S)

answered Dec 31 '18 at 22:15

Florian

255

add a comment |

Thanks for the hint, this really is just one line:

result  <- data %>% group_by(id, year) %>% summarise(S=sum(amount*coef)) %>% spread(year, S)

answered Dec 31 '18 at 22:15

Florian

255

Thanks for the hint, this really is just one line:

result  <- data %>% group_by(id, year) %>% summarise(S=sum(amount*coef)) %>% spread(year, S)

answered Dec 31 '18 at 22:15

Florian

255

answered Dec 31 '18 at 22:15

Florian

255

answered Dec 31 '18 at 22:15

Florian

255

answered Dec 31 '18 at 22:15

Florian

255

add a comment |

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