Slicing rows is similar to filtering rows, except that slicing is performed based on row numbers rather tha filter criteria. In TidierData.jl, slicing works similarly to R's tidyverse in that both positive (which rows to keep) and negative (which rows to remove) slicing is supported. For @slice(), any valid UnitRange of integers is considered valid; this is not the case for @select() or across().
Remember: Just like every other TidierData.jl top-level macro, @slice() respects group. This means that in a grouped data frame, @slice(1:2) will select the first 2 rows from each group.
using TidierData
df = DataFrame(row_num = 1:10,
a = string.(repeat('a':'e', inner = 2)),
b = [1,1,1,2,2,2,3,3,3,4])
| 1 | 1 | a | 1 |
| 2 | 2 | a | 1 |
| 3 | 3 | b | 1 |
| 4 | 4 | b | 2 |
| 5 | 5 | c | 2 |
| 6 | 6 | c | 2 |
| 7 | 7 | d | 3 |
| 8 | 8 | d | 3 |
| 9 | 9 | e | 3 |
| 10 | 10 | e | 4 |
Slicing using a range of numbers
This is an easy way of retrieving 5 consecutive rows.
@chain df begin
@slice(1:5)
end
Slicing using a more complex UnitRange of numbers
How would we obtain every other from 1 to 7 (counting up by 2)? Note that range() is similar to seq() in R.
@chain df begin
@slice(range(start = 1, step = 2, stop = 7))
end
This same code can also be written using Julia's shorthand syntax for unit ranges.
@chain df begin
@slice(1:2:7)
end
Separate multiple row selections with commas
If you have multiple different row selections, you can separate them with commas.
@chain df begin
@slice(1:5, 10)
end
| 1 | 1 | a | 1 |
| 2 | 2 | a | 1 |
| 3 | 3 | b | 1 |
| 4 | 4 | b | 2 |
| 5 | 5 | c | 2 |
| 6 | 10 | e | 4 |
Use n() as short-hand to indicate the number of rows
Select the last 2 rows.
@chain df begin
@slice(n()-1, n())
end
You can even use n() inside of UnitRanges, just like in R. Notice that the order of operations is slightly different in Julia as compared to R, so you don't have to wrap the n()-1 expression inside of parentheses.
@chain df begin
@slice(n()-1:n())
end
Inverted selection using negative numbers
This line selects all rows except the first 5 rows.
@chain df begin
@slice(-(1:5))
end
Sample 5 random rows in the data frame
@chain df begin
@slice_sample(n = 5)
end
Slice the min
This line selects all rows with the the minimum value of the desired column
@chain df begin
@slice_min(b)
end
This line will only show the first row.
@chain df begin
@slice_min(b, with_ties = false)
end
Slice the max
The optional prop arguement will slice a proportion of the full dataframe.
@chain df begin
@slice_max(b, prop = 0.5)
end
Slice the tail
@chain df begin
@slice_tail(prop = 0.5)
end
Slice the head
@chain df begin
@slice_head(n = 3)
end
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