What Does the Error "Arguments Imply Differing Number of Rows: X, Y" Mean

What does the error arguments imply differing number of rows: x, y mean?

Your data.frame mat is rectangular (n_rows!= n_cols).

Therefore, you cannot make a data.frame out of the column- and rownames, because each column in a data.frame must be the same length.

Maybe this suffices your needs:

require(reshape2)
mat$id <- rownames(mat) 
melt(mat)

Error in data.frame() arguments imply differing number of rows: 1, 0

All,

A lot of you have been getting this error with #3/ ggradar:

Error in data.frame(group = i, x = pathData[,j] * sin(angles[j-1]), : arguments imply differing number of rows: 1, 0

If you are getting this, it's likely because your dataframe isn't coverting to a tibble correctly.

To resolve it, try to read in the files with the command read_csv() instead of read.csv

read_csv reads it in as a tibble, which prevents conversion issues later. read.csv reads it in as a dataframe.

Of note, you need to call library(readr) before using read_csv

map2 error arguments imply differing number of rows

The issue is coming from the

augment(model, newdata = assessment(splits)) 

because in the previous step

model <- glm(egalit_scale ~ cut(income06, cc),
          data = analysis(splits))

we do the analysis on the 'splits' instead of the assessment and this results in getting different number of rows, e.g.

out <- tidyr::expand(training_df, id, cc = 2:15) %>%
         left_join(training_df)

tmp <- out$splits[[1]]

analysis(tmp)
# A tibble: 1,332 x 2
#   egalit_scale income06
#          <dbl>    <dbl>
# 1        27.3      9.69
# 2         7.71     8.48
# 3        34.3     21.3 
# 4         7.85    15.8 
# 5        13.3     24.6 
# 6        26.2      8.67
# 7        34.3      4.78
# 8        17.9     16.8 
# 9         1.45    21.2 
#10         9.84    15.7 
# … with 1,322 more rows

assessment(tmp)
# A tibble: 149 x 2
#   egalit_scale income06
#          <dbl>    <dbl>
# 1        28.6     14.8 
# 2        17.8      2.47
# 3         5.03    24.3 
# 4        31.5      5.79
# 5        18.4     18.0 
# 6         4.05     8.06
# 7         2.28     8.16
# 8        28.6     16.8 
# 9        21.1      7.03
#10         3.67    14.2 
# … with 139 more rows

---

So, if we change the model statement with assessment

mse_df <- function(splits, cc){
  model <- glm(egalit_scale ~ cut(income06, cc),
              data = assessment(splits))
  model_mse <- augment(model, newdata = assessment(splits)) %>%
    mse(truth = egalit_scale, estimate = round(.fitted))
  model_mse$.estimate
}

library(yardstick)
out1 <- tidyr::expand(training_df, id, cc = 2:15) %>%
   left_join(training_df) %>%
   mutate(mse = map2_dbl(splits, cc, mse_df))
out1
# A tibble: 140 x 4
#   id        cc splits               mse
#   <chr>  <int> <named list>       <dbl>
# 1 Fold01     2 <split [1.3K/149]>  94.9
# 2 Fold01     3 <split [1.3K/149]>  94.6
# 3 Fold01     4 <split [1.3K/149]>  93.8
# 4 Fold01     5 <split [1.3K/149]>  94.5
# 5 Fold01     6 <split [1.3K/149]>  94.0
# 6 Fold01     7 <split [1.3K/149]>  92.0
# 7 Fold01     8 <split [1.3K/149]>  88.9
# 8 Fold01     9 <split [1.3K/149]>  91.2
# 9 Fold01    10 <split [1.3K/149]>  92.8
#10 Fold01    11 <split [1.3K/149]>  86.0
# … with 130 more rows

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