Creating a long format data frame from several single-case data frames (scdf).
Source:R/as.data.frame.scdf.R
as.data.frame.scdf.RdThe as.data.frame function transposes an scdf into one long data frame. Additionally, a data frame can be merged that includes level 2 data of the subjects. This might be helpful to prepare data to be used with other packages than scan.
Usage
# S3 method for scdf
as.data.frame(x, ..., l2 = NULL, id = "case")Arguments
- x
An scdf object
- ...
Not implemented
- l2
A data frame providing additional variables at Level 2. The scdf has to have names for all cases and the Level 2 data frame has to have a column with corresponding case names.
- id
Variable name of the Level 2 data frame that contains the case names.
See also
Other data manipulation functions:
add_l2(),
as_scdf(),
fill_missing(),
moving_median(),
outlier(),
ranks(),
rescale(),
scdf(),
select_cases(),
set_vars(),
shift(),
smooth_cases(),
standardize(),
truncate_phase()
Examples
## Convert the list of three single-case data frames from Grosche (2011)
### into one long data frame
Grosche2011
#> #A single-case data frame with three cases
#>
#> Eva: values mt phase | Georg: values mt phase | Olaf: values mt phase |
#> 3.39 0 A | 13 0 A | 7.69 0 A |
#> 2.09 1 A | 17.4 1 A | 7.27 1 A |
#> 1.46 7 A | 13 6 A | 7.02 7 A |
#> 3.23 8 A | 7.74 10 A | 5.37 8 A |
#> 3.06 13 A | 8.82 13 A | 4.57 14 A |
#> 2.83 15 A | 7.5 15 A | 3.02 15 A |
#> 2.44 38 B | 5.82 36 A | 4.68 38 A |
#> 1.86 42 B | 6.26 38 B | 4.97 42 A |
#> 2.17 45 B | 5.6 41 B | 5.73 43 A |
#> 2.59 49 B | 12.79 43 B | 7.63 49 A |
#> 3.92 50 B | 9.17 48 B | 6.98 50 A |
#> 4.14 56 B | 10.59 50 B | 7.5 56 A |
#> 3.31 63 B | 9.23 55 B | 6.91 59 B |
#> 2.69 64 B | 10.71 59 B | 4.86 63 B |
#> 4.27 70 B | 7.61 62 B | 6.71 64 B |
#> # ... up to nine more rows
Grosche2011_long <- as.data.frame(Grosche2011)
Grosche2011_long
#> case values mt phase
#> 1 Eva 3.39 0 A
#> 2 Eva 2.09 1 A
#> 3 Eva 1.46 7 A
#> 4 Eva 3.23 8 A
#> 5 Eva 3.06 13 A
#> 6 Eva 2.83 15 A
#> 7 Eva 2.44 38 B
#> 8 Eva 1.86 42 B
#> 9 Eva 2.17 45 B
#> 10 Eva 2.59 49 B
#> 11 Eva 3.92 50 B
#> 12 Eva 4.14 56 B
#> 13 Eva 3.31 63 B
#> 14 Eva 2.69 64 B
#> 15 Eva 4.27 70 B
#> 16 Eva 4.66 77 B
#> 17 Eva 3.20 78 B
#> 18 Eva 4.98 91 B
#> 19 Eva 4.42 92 B
#> 20 Georg 13.00 0 A
#> 21 Georg 17.40 1 A
#> 22 Georg 13.00 6 A
#> 23 Georg 7.74 10 A
#> 24 Georg 8.82 13 A
#> 25 Georg 7.50 15 A
#> 26 Georg 5.82 36 A
#> 27 Georg 6.26 38 B
#> 28 Georg 5.60 41 B
#> 29 Georg 12.79 43 B
#> 30 Georg 9.17 48 B
#> 31 Georg 10.59 50 B
#> 32 Georg 9.23 55 B
#> 33 Georg 10.71 59 B
#> 34 Georg 7.61 62 B
#> 35 Georg 9.78 64 B
#> 36 Georg 8.33 69 B
#> 37 Georg 12.00 71 B
#> 38 Georg 12.19 77 B
#> 39 Georg 10.70 78 B
#> 40 Georg 11.36 85 B
#> 41 Georg 7.89 91 B
#> 42 Georg 10.82 94 B
#> 43 Georg 11.56 101 B
#> 44 Olaf 7.69 0 A
#> 45 Olaf 7.27 1 A
#> 46 Olaf 7.02 7 A
#> 47 Olaf 5.37 8 A
#> 48 Olaf 4.57 14 A
#> 49 Olaf 3.02 15 A
#> 50 Olaf 4.68 38 A
#> 51 Olaf 4.97 42 A
#> 52 Olaf 5.73 43 A
#> 53 Olaf 7.63 49 A
#> 54 Olaf 6.98 50 A
#> 55 Olaf 7.50 56 A
#> 56 Olaf 6.91 59 B
#> 57 Olaf 4.86 63 B
#> 58 Olaf 6.71 64 B
#> 59 Olaf 5.67 70 B
#> 60 Olaf 5.00 71 B
#> 61 Olaf 6.14 80 B
#> 62 Olaf 8.14 91 B
#> 63 Olaf 4.53 97 B
## Combine an scdf with data for l2
Leidig2018_long <- as.data.frame(Leidig2018, l2 = Leidig2018_l2)
names(Leidig2018_long)
#> [1] "case" "mt" "academic_engagement"
#> [4] "classID" "weekday" "disruptive_behavior"
#> [7] "phase" "class" "gender"
#> [10] "migration" "first_language_german" "SDQ_TOTAL"
#> [13] "SDQ_EXTERNALIZING" "SDQ_INTERNALIZING" "ITRF_TOTAL"
#> [16] "ITRF_ACADEMIC" "ITRF_BEHAVIOR"
summary(Leidig2018_long)
#> case mt academic_engagement classID
#> 1a1 : 108 Min. : 1.00 Min. :0.0 Length:3780
#> 1a2 : 108 1st Qu.: 27.75 1st Qu.:3.0 Class :character
#> 1a3 : 108 Median : 54.50 Median :4.0 Mode :character
#> 1a4 : 108 Mean : 54.50 Mean :3.7
#> 1a5 : 108 3rd Qu.: 81.25 3rd Qu.:5.0
#> 2a1 : 108 Max. :108.00 Max. :5.0
#> (Other):3132 NA's :1366
#> weekday disruptive_behavior phase class
#> Min. :1.000 Min. :0.0000 A: 730 Length:3780
#> 1st Qu.:2.000 1st Qu.:0.0000 B:3050 Class :character
#> Median :3.000 Median :0.0000 Mode :character
#> Mean :3.028 Mean :0.6192
#> 3rd Qu.:4.000 3rd Qu.:1.0000
#> Max. :5.000 Max. :4.0000
#> NA's :1393
#> gender migration first_language_german SDQ_TOTAL
#> Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 4.00
#> 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 8.00
#> Median :0.0000 Median :1.0000 Median :1.0000 Median :11.00
#> Mean :0.1429 Mean :0.5429 Mean :0.7429 Mean :11.46
#> 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:14.00
#> Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :22.00
#>
#> SDQ_EXTERNALIZING SDQ_INTERNALIZING ITRF_TOTAL ITRF_ACADEMIC
#> Min. : 3.000 Min. : 0.0 Min. : 3.0 Min. : 0.000
#> 1st Qu.: 6.000 1st Qu.: 1.0 1st Qu.:12.0 1st Qu.: 2.000
#> Median : 9.000 Median : 2.0 Median :16.0 Median : 9.000
#> Mean : 8.857 Mean : 2.6 Mean :17.4 Mean : 9.286
#> 3rd Qu.:11.000 3rd Qu.: 3.0 3rd Qu.:23.0 3rd Qu.:16.000
#> Max. :18.000 Max. :10.0 Max. :35.0 Max. :24.000
#>
#> ITRF_BEHAVIOR
#> Min. : 0.000
#> 1st Qu.: 4.000
#> Median : 7.000
#> Mean : 8.114
#> 3rd Qu.:11.000
#> Max. :20.000
#>