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Use R dplyr/purrr To Get Chi-square Output Matrices By Group

Create Multiple 2-dimensional Tables from Multiple Columns in R Using dplyrdata.table vs dplyr: can one do something well the other can't or does poorly?r cumsum per group in dplyrpass grouped dataframe to own function in dplyrChi-square statistic across multiple columns of a dataframe using dplyr or reshape2add results of chi square test to each rowGet `chisq.test()$p.value` for several groups using `dplyr::group_by()`Extracting unique values of repeating subset of data frameUse Dplyr::Bind_Rows and Purrr to Selectively Bind Different Dataframes In a List of DataframesChi -Square test with grouped data in dplyrdplyr group by: add null groups

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0

I'd like to get chi-square output matrices (e.g., standardized residuals, expected values) by group using elements of the tidyverse. Using the mtcars data set, here's where I've started:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) 

Which produces the chi-square test statistic. In order to get standardized residuals, for example, my only successful code is this:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) -> chi.out

chi.out$stdres

 vs am Freq
1 0 0 0.9523038
2 1 0 -0.9523038
3 0 1 -0.9523038
4 1 1 0.9523038

Ideally, I'd like to get the observed values and the standardized residuals into a dataframe format. Something like this:

cbind(as.data.frame(chi.out$observed),as.data.frame(chi.out$stdres))

 vs am Freq vs am Freq
1 0 0 12 0 0 0.9523038
2 1 0 7 1 0 -0.9523038
3 0 1 6 0 1 -0.9523038
4 1 1 7 1 1 0.9523038

Finally, I'd like to do this by group, for example over the cyl column in the mtcars data set. Seems dplyr and some some version of purrr's map with map_dfr or map_dfc would do the trick but I can't quite pull it together. Thanks in advance.

r dplyr purrr

share|improve this question

asked Mar 25 at 0:54

JoeJoe

566

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  "I'd like to get chi-square output matrices by group" Which group? You're not grouping by any variable. Can you add your expected output for the case where you are grouping by cyl?
  
  – Maurits Evers
  Mar 25 at 1:32
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Should have provided an example of what the output should look like. See Humpelstielzchen's response/solution below.
  
  – Joe
  Mar 25 at 18:20
  

  
  
  
  

add a comment | 

0

I'd like to get chi-square output matrices (e.g., standardized residuals, expected values) by group using elements of the tidyverse. Using the mtcars data set, here's where I've started:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) 

Which produces the chi-square test statistic. In order to get standardized residuals, for example, my only successful code is this:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) -> chi.out

chi.out$stdres

 vs am Freq
1 0 0 0.9523038
2 1 0 -0.9523038
3 0 1 -0.9523038
4 1 1 0.9523038

Ideally, I'd like to get the observed values and the standardized residuals into a dataframe format. Something like this:

cbind(as.data.frame(chi.out$observed),as.data.frame(chi.out$stdres))

 vs am Freq vs am Freq
1 0 0 12 0 0 0.9523038
2 1 0 7 1 0 -0.9523038
3 0 1 6 0 1 -0.9523038
4 1 1 7 1 1 0.9523038

Finally, I'd like to do this by group, for example over the cyl column in the mtcars data set. Seems dplyr and some some version of purrr's map with map_dfr or map_dfc would do the trick but I can't quite pull it together. Thanks in advance.

r dplyr purrr

share|improve this question

asked Mar 25 at 0:54

JoeJoe

566

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  "I'd like to get chi-square output matrices by group" Which group? You're not grouping by any variable. Can you add your expected output for the case where you are grouping by cyl?
  
  – Maurits Evers
  Mar 25 at 1:32
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Should have provided an example of what the output should look like. See Humpelstielzchen's response/solution below.
  
  – Joe
  Mar 25 at 18:20
  

  
  
  
  

add a comment | 

I'd like to get chi-square output matrices (e.g., standardized residuals, expected values) by group using elements of the tidyverse. Using the mtcars data set, here's where I've started:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) 

Which produces the chi-square test statistic. In order to get standardized residuals, for example, my only successful code is this:

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) -> chi.out

chi.out$stdres

 vs am Freq
1 0 0 0.9523038
2 1 0 -0.9523038
3 0 1 -0.9523038
4 1 1 0.9523038

Ideally, I'd like to get the observed values and the standardized residuals into a dataframe format. Something like this:

cbind(as.data.frame(chi.out$observed),as.data.frame(chi.out$stdres))

 vs am Freq vs am Freq
1 0 0 12 0 0 0.9523038
2 1 0 7 1 0 -0.9523038
3 0 1 6 0 1 -0.9523038
4 1 1 7 1 1 0.9523038

Finally, I'd like to do this by group, for example over the cyl column in the mtcars data set. Seems dplyr and some some version of purrr's map with map_dfr or map_dfc would do the trick but I can't quite pull it together. Thanks in advance.

r dplyr purrr

share|improve this question

asked Mar 25 at 0:54

JoeJoe

566

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) 

mtcars %>% 
 dplyr::select(vs, am) %>%
 table() %>%
 chisq.test(.) -> chi.out

chi.out$stdres

 vs am Freq
1 0 0 0.9523038
2 1 0 -0.9523038
3 0 1 -0.9523038
4 1 1 0.9523038

cbind(as.data.frame(chi.out$observed),as.data.frame(chi.out$stdres))

 vs am Freq vs am Freq
1 0 0 12 0 0 0.9523038
2 1 0 7 1 0 -0.9523038
3 0 1 6 0 1 -0.9523038
4 1 1 7 1 1 0.9523038

share|improve this question

asked Mar 25 at 0:54

JoeJoe

566

share|improve this question

asked Mar 25 at 0:54

JoeJoe

566

asked Mar 25 at 0:54

JoeJoe

566

asked Mar 25 at 0:54

JoeJoe

566

1 Answer
1

active

oldest

votes

1

So this is my proposal for a solution.

library(dplyr)
library(reshape2)

mtcars %>% 
 select(vs, am, cyl) %>%
 table() %>%
 apply(3, chisq.test) %>%
 lapply(`[`, c(6,9)) %>%
 melt() %>%
 spread(key = L2, value = value) %>%
 rename(cyl = L1) %>%
 select(cyl, vs, am, observed, stdres) %>%
 arrange(cyl)


 cyl vs am observed stdres
1 4 0 0 0 -0.6422616
2 4 0 1 1 0.6422616
3 4 1 0 3 0.6422616
4 4 1 1 7 -0.6422616
5 6 0 0 0 -2.6457513
6 6 0 1 3 2.6457513
7 6 1 0 4 2.6457513
8 6 1 1 0 -2.6457513
9 8 0 0 12 NaN
10 8 0 1 2 NaN
11 8 1 0 0 NaN
12 8 1 1 0 NaN

This does a chi-square test for each group of cyl. The grouping is done implicitly in the select() statement. In the end you get the observed values and standardized residuals for every combination of cyl, vs, am. Should be applicable to any dataframe.

Hope this is what you were looking for.

share|improve this answer

edited Mar 25 at 8:22

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  That works perfectly! Thanks so much. Have to load tidyr (or tidyverse) so that the spread function works. The NaN's for 8-cylinder vehicles are the result of a matrix with zeros in the marginals.
  
  – Joe
  Mar 25 at 18:19
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Glad to help! Yes it wasn't able to calculate residuals this way and also gave a warning. But it was just a dummy example.
  
  – Humpelstielzchen
  Mar 25 at 18:23
  

  
  
  
  

add a comment | 

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1

So this is my proposal for a solution.

library(dplyr)
library(reshape2)

mtcars %>% 
 select(vs, am, cyl) %>%
 table() %>%
 apply(3, chisq.test) %>%
 lapply(`[`, c(6,9)) %>%
 melt() %>%
 spread(key = L2, value = value) %>%
 rename(cyl = L1) %>%
 select(cyl, vs, am, observed, stdres) %>%
 arrange(cyl)


 cyl vs am observed stdres
1 4 0 0 0 -0.6422616
2 4 0 1 1 0.6422616
3 4 1 0 3 0.6422616
4 4 1 1 7 -0.6422616
5 6 0 0 0 -2.6457513
6 6 0 1 3 2.6457513
7 6 1 0 4 2.6457513
8 6 1 1 0 -2.6457513
9 8 0 0 12 NaN
10 8 0 1 2 NaN
11 8 1 0 0 NaN
12 8 1 1 0 NaN

This does a chi-square test for each group of cyl. The grouping is done implicitly in the select() statement. In the end you get the observed values and standardized residuals for every combination of cyl, vs, am. Should be applicable to any dataframe.

Hope this is what you were looking for.

share|improve this answer

edited Mar 25 at 8:22

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  That works perfectly! Thanks so much. Have to load tidyr (or tidyverse) so that the spread function works. The NaN's for 8-cylinder vehicles are the result of a matrix with zeros in the marginals.
  
  – Joe
  Mar 25 at 18:19
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Glad to help! Yes it wasn't able to calculate residuals this way and also gave a warning. But it was just a dummy example.
  
  – Humpelstielzchen
  Mar 25 at 18:23
  

  
  
  
  

add a comment | 

1

So this is my proposal for a solution.

library(dplyr)
library(reshape2)

mtcars %>% 
 select(vs, am, cyl) %>%
 table() %>%
 apply(3, chisq.test) %>%
 lapply(`[`, c(6,9)) %>%
 melt() %>%
 spread(key = L2, value = value) %>%
 rename(cyl = L1) %>%
 select(cyl, vs, am, observed, stdres) %>%
 arrange(cyl)


 cyl vs am observed stdres
1 4 0 0 0 -0.6422616
2 4 0 1 1 0.6422616
3 4 1 0 3 0.6422616
4 4 1 1 7 -0.6422616
5 6 0 0 0 -2.6457513
6 6 0 1 3 2.6457513
7 6 1 0 4 2.6457513
8 6 1 1 0 -2.6457513
9 8 0 0 12 NaN
10 8 0 1 2 NaN
11 8 1 0 0 NaN
12 8 1 1 0 NaN

This does a chi-square test for each group of cyl. The grouping is done implicitly in the select() statement. In the end you get the observed values and standardized residuals for every combination of cyl, vs, am. Should be applicable to any dataframe.

Hope this is what you were looking for.

share|improve this answer

edited Mar 25 at 8:22

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  That works perfectly! Thanks so much. Have to load tidyr (or tidyverse) so that the spread function works. The NaN's for 8-cylinder vehicles are the result of a matrix with zeros in the marginals.
  
  – Joe
  Mar 25 at 18:19
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Glad to help! Yes it wasn't able to calculate residuals this way and also gave a warning. But it was just a dummy example.
  
  – Humpelstielzchen
  Mar 25 at 18:23
  

  
  
  
  

add a comment | 

So this is my proposal for a solution.

library(dplyr)
library(reshape2)

mtcars %>% 
 select(vs, am, cyl) %>%
 table() %>%
 apply(3, chisq.test) %>%
 lapply(`[`, c(6,9)) %>%
 melt() %>%
 spread(key = L2, value = value) %>%
 rename(cyl = L1) %>%
 select(cyl, vs, am, observed, stdres) %>%
 arrange(cyl)


 cyl vs am observed stdres
1 4 0 0 0 -0.6422616
2 4 0 1 1 0.6422616
3 4 1 0 3 0.6422616
4 4 1 1 7 -0.6422616
5 6 0 0 0 -2.6457513
6 6 0 1 3 2.6457513
7 6 1 0 4 2.6457513
8 6 1 1 0 -2.6457513
9 8 0 0 12 NaN
10 8 0 1 2 NaN
11 8 1 0 0 NaN
12 8 1 1 0 NaN

This does a chi-square test for each group of cyl. The grouping is done implicitly in the select() statement. In the end you get the observed values and standardized residuals for every combination of cyl, vs, am. Should be applicable to any dataframe.

Hope this is what you were looking for.

share|improve this answer

edited Mar 25 at 8:22

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

library(dplyr)
library(reshape2)

mtcars %>% 
 select(vs, am, cyl) %>%
 table() %>%
 apply(3, chisq.test) %>%
 lapply(`[`, c(6,9)) %>%
 melt() %>%
 spread(key = L2, value = value) %>%
 rename(cyl = L1) %>%
 select(cyl, vs, am, observed, stdres) %>%
 arrange(cyl)


 cyl vs am observed stdres
1 4 0 0 0 -0.6422616
2 4 0 1 1 0.6422616
3 4 1 0 3 0.6422616
4 4 1 1 7 -0.6422616
5 6 0 0 0 -2.6457513
6 6 0 1 3 2.6457513
7 6 1 0 4 2.6457513
8 6 1 1 0 -2.6457513
9 8 0 0 12 NaN
10 8 0 1 2 NaN
11 8 1 0 0 NaN
12 8 1 1 0 NaN

share|improve this answer

edited Mar 25 at 8:22

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523

answered Mar 25 at 8:17

HumpelstielzchenHumpelstielzchen

2,6131523