How to merge rows that have the same number on a column?

[RGabriel]

Hi! I'm trying to merge subjects from a dataframe that have the same number on a column.

> ID CONSENTIMIENTO EDAD GENERO    FECHA.NAC     JPC_VI_GRUPO JPC_VI_COND JPC_VI_COLOR JPC_VI_DECISION1 JPC_VI_DECISION2
> 1   1              1   11      2 30/05/2006           11           D            N                1                0
> 2   7              1   43      1 10/03/1974           11           D            V                3                3
> 3   3              1   11      2 24/05/2006           33           D         <NA>                4                5
> 4   6              1   41      2 05/08/1976           33           D         <NA>                4                4
> 5   8              1    5      1 28/04/2012            1           D            V                1                2
> 6  11              1   38      1 27/07/1979            1           D            N                3                3
> 7   9              1    8      2 08/05/2009           18           D            V                1                3
> 8  10              1   36      2 24/06/1981           18           D            N                3                4
> 9  16              1    8      1 03/02/2009           19           D            V                1                0
> 10 17              1   36      2 12/05/1981           19           D            N                2                4

in this case i need to merge rows that have the same JPC_VI_GRUPO number to make a grid.table with JPC_VI_COND and JPC_VI_DECISION1

> grid.table(table(var_datosgrupal$JPC_VI_COND,var_datosgrupal$JPG_VG_VINCULO))

Here i have the total amount of subjects but i want to make a grid using only the peer that have the same group number.
How could i get this done?

[williaml]

Can you provide a reproducible example?

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[CALUM_POLWART]

Have you tried adding a third variable to table

table (a, b, c)

Should effectively give you a

A= ...

BxC

As a table?

You may find it's easier to use ftable

[RGabriel]

Sure,

> df <- data.frame(stringsAsFactors = FALSE,
>                  ID = c(1,2,3,4,5,6,7,8),
>                  JPC_VI_GRUPO = c(1,1,2,2,3,3,4,4)
>                  JPC_VI_COND=c(D,D,D,I,I,D,I,I)
>                  JPC_VI_DECISION1=c(1,3,4,2,1,4,3,1)

here i have an example of the df im trying to use

i used grid.table to represent the data for every subject in my original df but now i need to use JPC_VI_GRUPO.
> grid.table(table(var_datosgrupal$JPC_VI_COND,var_datosgrupal$JPG_VG_VINCULO))
Hope this is a reproducible example

[williaml]

What exactly do you want the output to look like?

I have this, but I am not sure if that is what you want:

library(dplyr)

df <- data.frame(stringsAsFactors = FALSE,
                 ID = c(1,2,3,4,5,6,7,8),
                 JPC_VI_GRUPO = c(1,1,2,2,3,3,4,4),
                 JPC_VI_COND=c("D","D","D","I","I","D","I","I"),
                 JPC_VI_DECISION1=c(1,3,4,2,1,4,3, 1))

df %>% 
  group_by(JPC_VI_GRUPO, JPC_VI_COND) %>% 
  count() %>% 
  pivot_wider(JPC_VI_COND, names_from = c(JPC_VI_GRUPO), values_from = n)

# A tibble: 2 x 5
# Groups:   JPC_VI_COND [2]
  JPC_VI_COND   `1`   `2`   `3`   `4`
  <chr>       <int> <int> <int> <int>
1 D               2     1     1    NA
2 I              NA     1     1     2

[RGabriel]

Please use this as an example.

> df <- data.frame(stringsAsFactors = FALSE,
>                  ID = c(1,2,3,4,5,6,7,8),
>                  JPC_VI_GRUPO = c(1,1,2,2,3,3,4,4),
>                  JPC_VI_COND=c("D","D","I","I","D","D","I","I"),
>                  JPC_VI_DECISION1=c(1,3,4,2,1,4,3,1))

What i want is to show how many dyads of "D" and "I" conditions have responded 1, 2, 3 or 4 in decision.
So if my n was 8 (because i had 8 subjects) now my n should be 4 (because they where in peer).

[williaml]

I don't understand what you want the output to be.

Can you create it?

[RGabriel]

> df <- data.frame(stringsAsFactors = FALSE,
>                  ID = c(1,2,3,4,5,6,7,8),
>                  JPC_VI_GRUPO = c(1,1,2,2,3,3,4,4),
>                  JPC_VI_COND=c("D","D","I","I","D","D","I","I"),
>                  JPC_VI_DECISION1=c(1,1,4,4,2,2,3,3))

i would need to count the amount of JPC_VI_GRUPO that meet both condition JPC_VI_COND and JPC_VI_DECISION1

so in this table we should see "1" in every slot
i hope this makes sense

[williaml]

But with that data, there isn't a 1 in every slot. for example, in group 1, they are both D.

df %>% 
  group_by(JPC_VI_GRUPO, JPC_VI_COND) %>% 
  count() %>% 
  pivot_wider(JPC_VI_COND, names_from = c(JPC_VI_GRUPO), values_from = n)

# A tibble: 2 x 5
# Groups:   JPC_VI_COND [2]
  JPC_VI_COND   `1`   `2`   `3`   `4`
  <chr>       <int> <int> <int> <int>
1 D               2    NA     2    NA
2 I              NA     2    NA     2

[RGabriel]

i think my point is that i need some code to merge the 2 rows (i.e. 1 and 2) that represent the same group so i can count how many groups i have that meet JPC_VI_COND and JPC_VI_DECISION1