A stable release of prepdat is now available on CRAN https://cran.r-project.org/package=prepdat. To install prepdat use:
install.packages("prepdat")To install the latest version of prepdat (i.e., the development version of next release), install devtools, and then install directly from GitHub by using:
# install devtools
install.packages("devtools")
# install prepdat from GitHub
devtools::install_github("ayalaallon/prepdat")prepdat is an R package that enables the user to merge files containing data tables in a long format into a single large dataset and go form one single large dataset in a long format to one finalized aggregated table ready for statistical analysis. This pacakge is very useful for merging and aggregating raw data files of individual subjects in an experiment (in which each line corresponds to a single observation in the experiment) to one finalized table in which each line corresponds to the averaged performance of each subject according to specified dependent and independent variables. prepdat also includes several other possibilities for the aggregated values such as medians of the dependent variable and trimming procedures for reaction-times according to Van Selst & Jolicoeur (1994).
The two major functions you need to know in order to use
prepdat are file_merge() and prep().
### file_merge() The file_merge() function vertically
concatenates files containing data tables in a long format into a single
large dataset. In order for the function to work, all files should be in
the same format (either txt or csv). This function is very useful for
concatenating raw data files of individual subjects in an experiment (in
which each line corresponds to a single observation in the experiment)
to one raw data file that includes all subjects.
After you merged the raw data files using file_merge()
(or any other function that results in a merged raw data file in a long
format), you are ready to continue implementing prepdat by
using the prep() function, which is the main function of
prepdat.
prep() takes the raw data table created in
file_merge() (or by other functions) and creates one
finalized table ready for statistical analysis. The finalized table
contains for each subject (i.e., id) the averaged or aggregated values
(e.g., medians) of several possible dependent variables (e.g.,
reaction-time and accuracy) according to specified independent variables
(i.e., grouping variables), which can be any combination of
within-subject (a.k.a repeated measures) and between-subject independent
variables. The possibilities for dependent measures include: - mdvc:
Mean of the dependent variable. - sdvc: Standard deviation of the
dependent variable. - meddvc: Median of the dependent variable. - tdvc:
Mean/s of the dependent variable after rejecting observations above
standard deviation criteria you specify. - ntr: Number of observations
of the dependent variable that were rejected for each standard deviation
criteria. - ndvc: Number of observations of the dependent variable
before rejection. - ptr: Proportion of observations of the dependent
variable that were rejected for each standard deviation criteria. -
rminv: Harmonic mean of the dependent variable. - prt: Percentiles of
the dependent variable according to any percentile (default is 0.05,
0.25, 0.75, 0.95). - mdvd: Mean of a second dependent variable (e.g.,
accuracy). - merr: error rate (i.e., suitable when the second dependnet
variable is accuracy). - nrmc: Mean according to non-recursive procedure
with moving criterion (Van Selst & Jolicoeur, 1994). - nnrmc: Number
of observations of the dependent variable that were rejected for the
non-recursive procedure. - pnrmc: Proportion of observations of the
dependent variable that were rejected for the non-recursive procedure. -
tnrmc: Total number of observations upon which the non-recursive
procedure was applied. - mrmc: Mean according to modified-recursive
procedure with moving criterion (Van Selst & Jolicoeur, 1994). -
nmrmc: Number of observations of the dependent variable that were
rejected for the modified-recursive procedure. - pmrmc: Proportion of
observations of the dependent variable that were rejected for the
modified-recursive procedure. - tmrmc: Total number of observations upon
which the modified-recursive procedure was applied. - hrmc: Mean
according to hybrid-recursive procedure with moving criterion (Van Selst
& Jolicoeur, 1994). - nhrmc: Number of observations of the dependent
variable that were rejected for the hybrid-recursive procedure. - thrmc:
Total number of observations upon which the hybrid-recursive procedure
was applied.
In the example below, we use prep() to go from one table
containing data (after already merging the individuals raw data files)
from 15 participants (5400 trials in total) to a finalized table showing
all the possibilities for the dependent variable (e.g., means and
medians) for each participant according to specified within-subject and
between-subject independent variables, including the modified recursive
procedure of Van Selst & Jolicoeur (1994).
# Load prepdat
library(prepdat)
# Load the example data that comes with prepdat
data(stroopdata)
# To get an overview of the example data
?stroopdata
# Look at the first few lines of the example data
head(stroopdata)
subject block age gender order font_size trial_num target_type rt ac
1 5020 1 24 2 1 12 1 1 677 1
2 5020 1 24 2 1 12 2 1 538 1
3 5020 1 24 2 1 12 3 1 507 1
4 5020 1 24 2 1 12 4 1 2818 1
5 5020 1 24 2 1 12 5 1 582 1
6 5020 1 24 2 1 12 6 1 498 1
# Perform prep
finalized_stroopdata <- prep(
dataset = stroopdata
, file_name = NULL
, file_path = NULL
, id = "subject"
, within_vars = c("block", "target_type")
, between_vars = c("order")
, dvc = "rt"
, dvd = "ac"
, keep_trials = NULL
, drop_vars = c()
, keep_trials_dvc = "raw_data$rt > 100 & raw_data$rt < 3000 & raw_data$ac == 1"
, keep_trials_dvd = "raw_data$rt > 100 & raw_data$rt < 3000"
, id_properties = c()
, sd_criterion = c(1, 1.5, 2)
, percentiles = c(0.05, 0.25, 0.75, 0.95)
, outlier_removal = 2
, keep_trials_outlier = "raw_data$ac == 1"
, decimal_places = 0
, notification = TRUE
, dm = c()
, save_results = FALSE
, results_name = "results.txt"
, results_path = NULL
, save_summary = FALSE
)
# Look at finalized_data:
# The hierarchical order for within_vars was first "block" (which has two levels- "1" and "2", and then
# "target_type" (which also has two levels- "1" and "2"). This means that for each of the dependent
# measures we will get four columns. For example mdvc1 is the mean for "block" 1 and "target_type" 2,
# mdvc2 is the mean for "block" 1 and "target_type" 2 etc.
> head(finalized_stroopdata)
subject order mdvc1 mdvc2 mdvc3 mdvc4 sdvc1 sdvc2 sdvc3 sdvc4 meddvc1
5013 5013 2 863 1038 1081 1103 328 214 417 321 758
5020 5020 1 707 781 637 713 410 362 305 328 586
5021 5021 2 655 742 559 653 162 170 121 144 633
5022 5022 1 604 725 580 650 108 153 128 135 594
5023 5023 2 747 827 909 963 265 200 347 243 726
5024 5024 1 616 793 667 764 125 157 182 180 600
meddvc2 meddvc3 meddvc4 t1dvc1 t1dvc2 t1dvc3 t1dvc4 t1.5dvc1 t1.5dvc2
5013 1036 1014 1037 777 1047 1033 1065 790 1013
5020 701 540 630 595 699 566 628 595 699
5021 780 540 630 632 760 536 625 630 748
5022 682 565 635 589 692 573 639 599 698
5023 834 821 900 724 825 858 923 718 851
5024 781 629 719 591 776 619 735 585 756
t1.5dvc3 t1.5dvc4 t2dvc1 t2dvc2 t2dvc3 t2dvc4 n1tr1 n1tr2 n1tr3 n1tr4
5013 1037 1054 809 1006 1001 1067 26 9 6 29
5020 566 626 595 699 566 632 11 2 2 12
5021 558 620 636 732 564 630 40 12 7 34
5022 569 627 602 725 563 638 25 11 5 44
5023 843 914 709 827 864 933 19 11 6 31
5024 619 745 591 756 635 751 30 9 5 21
n1.5tr1 n1.5tr2 n1.5tr3 n1.5tr4 n2tr1 n2tr2 n2tr3 n2tr4 ndvc1 ndvc2
5013 13 5 4 13 7 2 2 8 144 36
5020 11 2 2 11 11 2 2 10 143 35
5021 18 5 3 12 8 1 2 7 143 34
5022 12 7 2 17 6 0 1 7 143 34
5023 8 6 2 17 5 2 1 8 143 34
5024 15 3 5 7 10 3 2 4 144 35
ndvc3 ndvc4 p1tr1 p1tr2 p1tr3 p1tr4 p1.5tr1 p1.5tr2 p1.5tr3 p1.5tr4
5013 36 143 0.181 0.250 0.167 0.203 0.090 0.139 0.111 0.091
5020 36 142 0.077 0.057 0.056 0.085 0.077 0.057 0.056 0.077
5021 36 140 0.280 0.353 0.194 0.243 0.126 0.147 0.083 0.086
5022 36 144 0.175 0.324 0.139 0.306 0.084 0.206 0.056 0.118
5023 35 142 0.133 0.324 0.171 0.218 0.056 0.176 0.057 0.120
5024 36 143 0.208 0.257 0.139 0.147 0.104 0.086 0.139 0.049
p2tr1 p2tr2 p2tr3 p2tr4 rminv1 rminv2 rminv3 rminv4 p0.05dvc1 p0.05dvc2
5013 0.049 0.056 0.056 0.056 777 997 951 1019 539 744
5020 0.077 0.057 0.056 0.070 612 710 575 648 474 532
5021 0.056 0.029 0.056 0.050 617 701 501 626 447 485
5022 0.042 0.000 0.028 0.049 586 694 559 623 498 507
5023 0.035 0.059 0.029 0.056 685 773 823 908 433 482
5024 0.069 0.086 0.056 0.028 596 767 630 732 484 595
p0.05dvc3 p0.05dvc4 p0.25dvc1 p0.25dvc2 p0.25dvc3 p0.25dvc4 p0.75dvc1
5013 575 704 666 890 858 910 958
5020 454 506 515 639 508 575 684
5021 457 484 552 595 502 550 735
5022 437 461 548 608 528 564 650
5023 549 668 641 722 706 794 820
5024 496 585 536 704 556 658 660
p0.75dvc2 p0.75dvc3 p0.75dvc4 p0.95dvc1 p0.95dvc2 p0.95dvc3 p0.95dvc4
5013 1150 1182 1245 1463 1440 1780 1649
5020 764 625 702 1857 1198 1035 1568
5021 866 607 699 959 990 744 941
5022 834 610 734 745 971 707 888
5023 953 1027 1096 1035 1140 1405 1439
5024 832 696 838 887 1120 1063 1027
mdvd1 mdvd2 mdvd3 mdvd4 merr1 merr2 merr3 merr4 mrmc1 mrmc2 mrmc3 mrmc4
5013 1.000 1.000 1 0.993 0.000 0.000 0 0.007 809 1038 1001 1058
5020 1.000 0.972 1 0.986 0.000 0.028 0 0.014 589 699 566 626
5021 1.000 0.944 1 0.972 0.000 0.056 0 0.028 655 742 572 642
5022 0.993 0.944 1 1.000 0.007 0.056 0 0.000 604 725 563 650
5023 1.000 0.944 1 0.986 0.000 0.056 0 0.014 709 827 843 955
5024 1.000 0.972 1 1.000 0.000 0.028 0 0.000 609 777 611 751
pmrmc1 pmrmc2 pmrmc3 pmrmc4 nmrmc1 nmrmc2 nmrmc3 nmrmc4 tmrmc1 tmrmc2
5013 4.861 0.000 5.556 4.196 7 0 2 6 144 36
5020 9.722 5.714 5.556 7.746 14 2 2 11 144 35
5021 0.000 0.000 2.778 2.143 0 0 1 3 143 34
5022 2.098 0.000 2.778 0.000 3 0 1 0 143 34
5023 4.167 0.000 8.333 0.704 6 0 3 1 144 34
5024 1.389 2.857 11.111 2.083 2 1 4 3 144 35
tmrmc3 tmrmc4
5013 36 143
5020 36 142
5021 36 140
5022 36 144
5023 36 142
5024 36 144Grange, J.A. (2015). trimr: An implementation of common response time trimming methods. R Package Version 1.0.1. https://cran.r-project.org/package=trimr
Van Selst, M., & Jolicoeur, P. (1994). A solution to the effect of sample size on outlier elimination. The quarterly journal of experimental psychology, 47 (3), 631-650.