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library(manymodelr)

As can be guessed from the name, this function provides an easy way to manipulate grouped data. We can for instance find the number of observations in the yields data set. The formula takes the form x~y where y is the grouping variable(in this case normal). One can supply a formula as shown next.

# Load the yields dataset
data("yields")
head(agg_by_group(yields,.~normal,length))
#> Grouped By[1]:   normal 
#> 
#>   normal height weight yield
#> 1     No    500    500   500
#> 2    Yes    500    500   500

head(agg_by_group(mtcars,cyl~hp+vs,sum))
#> Grouped By[2]:   hp vs 
#> 
#>    hp vs cyl
#> 1  91  0   4
#> 2 110  0  12
#> 3 150  0  16
#> 4 175  0  22
#> 5 180  0  24
#> 6 205  0   8

This is useful when trying to find differences between rows. The direction argument specifies how the subtractions are made while the exclude argument is used to specify classes that should be removed before calculations are made. Using direction="reverse" performs a subtraction akin to x-(x-1) where x is the row number.


head(rowdiff(yields,exclude = "factor",direction = "reverse"))
#>        height      weight      yield
#> 1          NA          NA         NA
#> 2 -0.04212634  0.24042659 -15.808303
#> 3  0.01516059  0.09649856  11.170825
#> 4  0.25961718  0.03008764   6.578424
#> 5 -0.11495811 -0.02971837 -19.584090
#> 6  0.57638627 -0.42979818   6.825719

This allows the user to conveniently replace missing values. Current options are ffill which replaces with the next non-missing value, samples that samples the data and does replacement, value that allows one to fill NAs with a specific value. Other common mathematical methods like min, max,get_mode, sd, etc are no longer supported. They are now available with more flexibility in standalone mde


head(na_replace(airquality, how="value", value="Missing"),8)
#>     Ozone Solar.R Wind Temp Month Day
#> 1      41     190  7.4   67     5   1
#> 2      36     118  8.0   72     5   2
#> 3      12     149 12.6   74     5   3
#> 4      18     313 11.5   62     5   4
#> 5 Missing Missing 14.3   56     5   5
#> 6      28 Missing 14.9   66     5   6
#> 7      23     299  8.6   65     5   7
#> 8      19      99 13.8   59     5   8

This provides a convenient way to replace values by group.

test_df <- data.frame(A=c(NA,1,2,3), B=c(1,5,6,NA),groups=c("A","A","B","B"))
# Replace NAs by group
# replace with the next non NA by group.
na_replace_grouped(df=test_df,group_by_cols = "groups",how="ffill")
#>   groups A B
#> 1      A 1 1
#> 2      A 1 5
#> 3      B 2 6
#> 4      B 3 6

The use of mean,sd,etc is no longer supported. Use mde instead which is focused on missingness.