Calculate change from a visit
reference_change.RdCalculate change from visit or proportion of change from a visit
Usage
change_from_reference(x, reference, point = "Baseline")
pchange_from_reference(x, reference, point = "Baseline")
prop_from_reference(x, reference, point = "Baseline")
get_reference_value(x, reference, point = "Baseline")
add_change_from_reference(
.data,
values = "value",
references = "VisitName",
point = "Baseline"
)
add_pchange_from_reference(
.data,
values = "value",
references = "VisitName",
point = "Baseline"
)
add_change_from_reference_wide(
.data,
point = "Baseline",
cols,
percent = FALSE,
name = c("CFB", "%CFB"),
sep = " ",
rearrange = c("end", "immediate", "after"),
collate = FALSE
)Arguments
- x
A vector of values or character name of column
- reference
A vector of references (i.e., such as visit names) or character name of column
- point
A scalar character of the reference (i.e., visit) (default: `"Baseline"`)
- .data
A `data.frame`
- values
The name of the value column
- references
A vector of column names to compute differences from
- cols
A character vector of column names
- percent
Logical, if `TRUE` will also calculate percent change
- name
A character vector to append to the new column names. The second element will be used if `percent = TRUE`.
- sep
A character separation for the new column names
- rearrange
A method to change the arrangement of the `data.frame` columns with the new columns added: `end` will do nothing and append columns at very end; `immediate` will append columns immediate after each `col`; `after` will append columns after the last `col` entered.
- collate
For `percent = TRUE` and `rearrange = "after"`; will show the change from reference for all `cols` then the percent change.
Details
These functions are vectorised so they may be used inside a (grouped) `data.frame` to calculate the change values.
`pchange_from_reference()` calculations a proportion of change from the reference, not a percentage, so it may need to be multiplied by `100` to achieve that.
`prop_from_reference()` is just an alias for `pchange_from_reference()`.
`get_reference_value()` returns the reference value but as a vector the same length as `x`. This may be useful for creating a new column based on the reference value in a `data.frame`.
`add_change_from_reference()` and `add_pchange_from_reference()` will take a `data.frame` and append reference change values.
Examples
visits <- c("Screening", "Baseline", "Week 2", "Week 4", "Week 6")
values <- c(3, 2, 3, 2, 4)
df <- data.frame(
values = values,
visits = visits,
cfb = change_from_reference(values, visits, "Baseline"),
pcfb = pchange_from_reference(values, visits, "Baseline"),
p_screening = pchange_from_reference(values, visits, "Week 2"),
bl_value = get_reference_value(values, visits)
)
df
#> values visits cfb pcfb p_screening bl_value
#> 1 3 Screening 1 0.5 0.0000000 2
#> 2 2 Baseline 0 0.0 -0.3333333 2
#> 3 3 Week 2 1 0.5 0.0000000 2
#> 4 2 Week 4 0 0.0 -0.3333333 2
#> 5 4 Week 6 2 1.0 0.3333333 2
add_change_from_reference(df, "values", "visits")
#> values visits cfb pcfb p_screening bl_value values_change
#> 1 3 Screening 1 0.5 0.0000000 2 1
#> 2 2 Baseline 0 0.0 -0.3333333 2 0
#> 3 3 Week 2 1 0.5 0.0000000 2 1
#> 4 2 Week 4 0 0.0 -0.3333333 2 0
#> 5 4 Week 6 2 1.0 0.3333333 2 2
add_pchange_from_reference(df, "values", "visits")
#> values visits cfb pcfb p_screening bl_value values_pchange
#> 1 3 Screening 1 0.5 0.0000000 2 0.5
#> 2 2 Baseline 0 0.0 -0.3333333 2 0.0
#> 3 3 Week 2 1 0.5 0.0000000 2 0.5
#> 4 2 Week 4 0 0.0 -0.3333333 2 0.0
#> 5 4 Week 6 2 1.0 0.3333333 2 1.0
# Appending to a wide data.frame
df <- data.frame(
Screening = c(1, 2, 3, 4),
Baseline = c(1, 2, 3, 4),
`Week 2` = c(1, 2, 3, 4),
`Week 4` = c(0, 1, 2, 1),
`Week 6` = c(0, -1, 0, 0),
end_col = letters[1:4],
end_col2 = letters[1:4],
check.names = FALSE
)
df
#> Screening Baseline Week 2 Week 4 Week 6 end_col end_col2
#> 1 1 1 1 0 0 a a
#> 2 2 2 2 1 -1 b b
#> 3 3 3 3 2 0 c c
#> 4 4 4 4 1 0 d d
add_change_from_reference_wide(df, "Baseline", paste("Week", c(2, 4, 6)))
#> Screening Baseline Week 2 Week 4 Week 6 end_col end_col2 Week 2 CFB
#> 1 1 1 1 0 0 a a 0
#> 2 2 2 2 1 -1 b b 0
#> 3 3 3 3 2 0 c c 0
#> 4 4 4 4 1 0 d d 0
#> Week 4 CFB Week 6 CFB
#> 1 -1 -1
#> 2 -1 -3
#> 3 -1 -3
#> 4 -3 -4
add_change_from_reference_wide(df, cols = paste("Week", c(2, 4, 6)),
rearrange = "after")
#> Screening Baseline Week 2 Week 4 Week 6 Week 2 CFB Week 4 CFB Week 6 CFB
#> 1 1 1 1 0 0 0 -1 -1
#> 2 2 2 2 1 -1 0 -1 -3
#> 3 3 3 3 2 0 0 -1 -3
#> 4 4 4 4 1 0 0 -3 -4
#> end_col end_col2
#> 1 a a
#> 2 b b
#> 3 c c
#> 4 d d
add_change_from_reference_wide(df, "Baseline", paste("Week", c(2, 4, 6)),
percent = TRUE, rearrange = "end")
#> Screening Baseline Week 2 Week 4 Week 6 end_col end_col2 Week 2 CFB
#> 1 1 1 1 0 0 a a 0
#> 2 2 2 2 1 -1 b b 0
#> 3 3 3 3 2 0 c c 0
#> 4 4 4 4 1 0 d d 0
#> Week 2 %CFB Week 4 CFB Week 4 %CFB Week 6 CFB Week 6 %CFB
#> 1 0 -1 -100.00000 -1 -100
#> 2 0 -1 -50.00000 -3 -150
#> 3 0 -1 -33.33333 -3 -100
#> 4 0 -3 -75.00000 -4 -100
add_change_from_reference_wide(
df, "Baseline", paste("Week", c(2, 4, 6)),
percent = TRUE, rearrange = "end", collate = TRUE
)
#> Screening Baseline Week 2 Week 4 Week 6 end_col end_col2 Week 2 CFB
#> 1 1 1 1 0 0 a a 0
#> 2 2 2 2 1 -1 b b 0
#> 3 3 3 3 2 0 c c 0
#> 4 4 4 4 1 0 d d 0
#> Week 4 CFB Week 6 CFB Week 2 %CFB Week 4 %CFB Week 6 %CFB
#> 1 -1 -1 0 -100.00000 -100
#> 2 -1 -3 0 -50.00000 -150
#> 3 -1 -3 0 -33.33333 -100
#> 4 -3 -4 0 -75.00000 -100