| Title: | Recreates Some 'SAS®' Procedures in 'R' |
|---|---|
| Description: | Contains functions to simulate the most commonly used 'SAS®' procedures. Specifically, the package aims to simulate the functionality of 'proc freq', 'proc means', 'proc ttest', 'proc reg', 'proc transpose', 'proc sort', and 'proc print'. The simulation will include recreating all statistics with the highest fidelity possible. |
| Authors: | David Bosak [aut, cre], Kevin Kramer [ctb], Brian Varney [ctb], Duong Tran [ctb], Yifei Chen [ctb] |
| Maintainer: | David Bosak <[email protected]> |
| License: | CC0 |
| Version: | 1.0.7 |
| Built: | 2025-02-20 04:15:23 UTC |
| Source: | https://github.com/dbosak01/procs |
The proc_freq function generates frequency statistics.
It is both an interactive function that can be used for data exploration,
and can produce dataset output for further analysis.
The function can perform one and two-way frequencies. Two-way
frequencies are produced as a cross-tabulation by default. There
are many options to control the generated tables. The function will return
requested tables in a named list.
proc_freq( data, tables = NULL, output = NULL, by = NULL, weight = NULL, options = NULL, titles = NULL )proc_freq( data, tables = NULL, output = NULL, by = NULL, weight = NULL, options = NULL, titles = NULL )
data |
The input data frame to perform frequency calculations on. Input data as the first parameter makes this function pipe-friendly. |
tables |
The variable or variables to perform frequency counts on. The table specifications are passed as a vector of strings. For one-way frequencies, simply pass the variable name. For two-way tables, pass the desired combination of variables separated by a star (*) operator. The parameter does not accept SAS® style grouping syntax. All cross combinations should be listed explicitly. If the table request is named, the name will be used as the list item name on the return list of tables. See "Example 3" for an illustration on how to name an output table. |
output |
Whether or not to return datasets from the function. Valid
values are "out", "none", and "report". Default is "out". This parameter
also accepts the data shaping options "long", "stacked", and "wide". See
the Data Shaping section for a description of these options. Multiple
output keywords may be passed on a character vector. For example,
to produce both a report dataset and a "long" output dataset,
use the parameter |
by |
An optional by group. Parameter accepts a vector of one or more variable names. When this parameter is set, data will be subset for each by group, and tables will be generated for each subset. |
weight |
An optional weight parameter. This parameter is passed as a variable name to use for the weight. If a weight variable is indicated, the weighted value will be summed to calculate the frequency counts. |
options |
The options desired for the function.
Options are passed to the parameter as a vector of quoted strings. You may
also use the |
titles |
A vector of titles to assign to the interactive report. |
The proc_freq function generates frequency statistics
for one-way and two-way tables. Data is passed in on the data
parameter. The desired frequencies are specified on the tables
parameter.
The function will return all requested datasets by default. This is
equivalent to the output = "out" option. To return the datasets
as created for the interactive report, pass the "report" output option. If
no output datasets are desired, pass the "none" output option. If a
single dataset is requested, the function
will return a single dataset. If multiple datasets are requested, the function
will return a list of datasets. The type of data frame returned will
correspond to the type of data frame passed in on the data parameter.
If the input data is a tibble, the output data will be a
tibble. If the input data is a Base R data frame, the output data will be
a Base R data frame.
By default, proc_freq results will
be immediately sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data with very little
effort. To turn off the interactive report, pass the "noprint" keyword
to the options parameter or set options("procs.print" = FALSE).
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
If the frequency variables have a label assigned, that label will be used in the report output. This feature gives you some control over the column headers in the final report.
The exact datasets used for the interactive output can be returned as a list.
To return these datasets as a list, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
The proc_freq function returns output datasets.
If you are requesting only one table, a single
data frame will be returned. If you request multiple tables, a list of data
frames will be returned.
By default, the list items are named according to the
strings specified on the tables parameter. You may control
the names of the returned results by using a named vector on the
tables parameter.
The standard output datasets are optimized for data manipulation. Column names have been standardized, and additional variables may be present to help with data manipulation. For instance, the by variable will always be named "BY", and the frequency category will always be named "CAT". In addition, data values in the output datasets are not rounded or formatted to give you the most accurate statistical results.
Normally the proc_freq function counts each row in the
input data equally. In some cases, however, each row in the data
can represent multiple observations, and rows should not be treated
equally. In these cases, use the weight parameter. The parameter
accepts a variable/column name to use as the weighted value. If the
weight parameter is used, the function will sum the weighted values
instead of counting rows.
You may request that frequencies be separated into by groups using the
by parameter. The parameter accepts one or more variable names
from the input dataset. When this parameter is assigned, the data
will be subset by the "by" variable(s) before frequency counts are
calculated. On the interactive report, the by groups will appear in
separate tables. On the output dataset, the by groups will be identified
by additional columns.
The options parameter accepts a vector of options. Normally, these
options must be quoted. But you may pass them unquoted using the v()
function. For example, you can request the number of category levels
and the Chi-Square statistic like this: options = v(nlevels, chisq).
Below are all the available options and a description of each:
crosstab: Two-way output tables are a list style by default. If you want a crosstab style, pass the "crosstab" option.
list: Two-way interactive tables are a crosstab style by default. If you want a list style two-way table, pass the "list" option.
missing: Normally, missing values are not counted and not shown on frequency tables. The "missing" option allows you to treat missing (NA) values as normal values, so that they are counted and shown on the frequency table. Missing levels will appear on the table as a single dot (".").
nlevels: The "nlevels" option will display the number of unique values for each variable in the frequency table. These levels are generated as a separate table that appears on the report, and will also be output from the function as a separate dataset.
nocol: Two-way cross tabulation tables include column percents by default. To turn them off, pass the "nocol" option.
nocum: Whether to include the cumulative frequency and percent columns on one-way, interactive tables. These columns are included by default. To turn them off, pass the "nocum" option.
nofreq: The "nofreq" option will remove the frequency column from one-way and two-way tables.
nopercent: The "nopercent" option will remove the percent column from one-way and two-way tables.
noprint: Whether to print the interactive report to the viewer. By default, the report is printed to the viewer. The "noprint" option will inhibit printing.
nonobs: Whether to include the number of observations "N" column on the output and interactive tables. By default, the N column will be included. The "nonobs" option turns it off.
norow: Whether to include the row percentages on two-way crosstab tables. The "norow" option will turn them off.
nosparse/sparse: Whether to include categories for which there are no frequency counts. Zero-count categories will be included by default, which is the "sparse" option. If the "nosparse" option is present, zero-count categories will be removed.
notable: Whether to include the frequency table in the output dataset list. Normally, the frequency table is included. You may want to exclude the frequency table in some cases, for instance, if you only want the Chi-Square statistic.
outcum: Whether to include the cumulative frequency and percent on output frequency tables. By default, these columns are not included. The "outcum" option will include them.
In addition to the above options, the options parameter accepts
some statistics options. The following keywords will generate
an additional tables of specialized statistics. These statistics
options are only available on two-way tables:
chisq: Requests that the Chi-square statistics be produced.
fisher: Requests that the Fisher's exact statistics be produced.
There are some occasions when you may want to define the tables variable
or by variables as a factor. One occasion is for sorting/ordering,
and the other is for obtaining zero-counts on sparse data.
To order the frequency categories in the frequency output, define the
tables variable as a factor in the desired order. The function will
then retain that order for the frequency categories in the output dataset
and report.
You may also wish to
define the tables variable as a factor if you are dealing with sparse data
and some of the frequency categories are not present in the data. To ensure
these categories are displayed with zero-counts, define the tables variable
or by variable
as a factor and use the "sparse" option. Note
that the "sparse" option is actually the default.
If you do not want to
show the zero-count categories on a variable that is defined as a factor,
pass the "nosparse" keyword on the options parameter.
By default, the proc_freq function returns an output dataset of
frequency results. If running interactively, the function also prints
the frequency results to the viewer. As described above, the output
dataset can be somewhat different than the dataset sent to the viewer.
The output parameter allows you to choose which datasets to return.
There are three choices:
"out", "report", and "none". The "out" keyword returns the default output
dataset. The "report" keyword returns the dataset(s) sent to the viewer. You
may also pass "none" if you don't want any datasets returned from the function.
In addition, the output dataset produced by the "out" keyword can be shaped in different ways. These shaping options allow you to decide whether the data should be returned long and skinny, or short and wide. The shaping options can reduce the amount of data manipulation necessary to get the frequencies into the desired form. The shaping options are as follows:
long: Transposes the output datasets so that statistics are in rows and frequency categories are in columns.
stacked: Requests that output datasets be returned in "stacked" form, such that both statistics and frequency categories are in rows.
wide: Requests that output datasets be returned in "wide" form, such that statistics are across the top in columns, and frequency categories are in rows. This shaping option is the default.
For summary statistics, see proc_means. To pivot
or transpose the data coming from proc_freq,
see proc_transpose.
library(procs) # Turn off printing for CRAN checks options("procs.print" = FALSE) # Create sample data df <- as.data.frame(HairEyeColor, stringsAsFactors = FALSE) # Assign labels labels(df) <- list(Hair = "Hair Color", Eye = "Eye Color", Sex = "Sex at Birth") # Example #1: One way frequencies on Hair and Eye color with weight option. res <- proc_freq(df, tables = v(Hair, Eye), options = outcum, weight = Freq) # View result data res # $Hair # VAR CAT N CNT PCT CUMSUM CUMPCT # 1 Hair Black 592 108 18.24324 108 18.24324 # 2 Hair Blond 592 127 21.45270 235 39.69595 # 3 Hair Brown 592 286 48.31081 521 88.00676 # 4 Hair Red 592 71 11.99324 592 100.00000 # # $Eye # VAR CAT N CNT PCT CUMSUM CUMPCT # 1 Eye Blue 592 215 36.31757 215 36.31757 # 2 Eye Brown 592 220 37.16216 435 73.47973 # 3 Eye Green 592 64 10.81081 499 84.29054 # 4 Eye Hazel 592 93 15.70946 592 100.00000 # Example #2: 2 x 2 Crosstabulation table with Chi-Square statistic res <- proc_freq(df, tables = Hair * Eye, weight = Freq, options = v(crosstab, chisq)) # View result data res #$`Hair * Eye` # Category Statistic Blue Brown Green Hazel Total #1 Black Frequency 20.000000 68.000000 5.0000000 15.000000 108.00000 #2 Black Percent 3.378378 11.486486 0.8445946 2.533784 18.24324 #3 Black Row Pct 18.518519 62.962963 4.6296296 13.888889 NA #4 Black Col Pct 9.302326 30.909091 7.8125000 16.129032 NA #5 Blond Frequency 94.000000 7.000000 16.0000000 10.000000 127.00000 #6 Blond Percent 15.878378 1.182432 2.7027027 1.689189 21.45270 #7 Blond Row Pct 74.015748 5.511811 12.5984252 7.874016 NA #8 Blond Col Pct 43.720930 3.181818 25.0000000 10.752688 NA #9 Brown Frequency 84.000000 119.000000 29.0000000 54.000000 286.00000 #10 Brown Percent 14.189189 20.101351 4.8986486 9.121622 48.31081 #11 Brown Row Pct 29.370629 41.608392 10.1398601 18.881119 NA #12 Brown Col Pct 39.069767 54.090909 45.3125000 58.064516 NA #13 Red Frequency 17.000000 26.000000 14.0000000 14.000000 71.00000 #14 Red Percent 2.871622 4.391892 2.3648649 2.364865 11.99324 #15 Red Row Pct 23.943662 36.619718 19.7183099 19.718310 NA #16 Red Col Pct 7.906977 11.818182 21.8750000 15.053763 NA #17 Total Frequency 215.000000 220.000000 64.0000000 93.000000 592.00000 #18 Total Percent 36.317568 37.162162 10.8108108 15.709459 100.00000 # $`chisq:Hair * Eye` # CHISQ CHISQ.DF CHISQ.P # 1 138.2898 9 2.325287e-25 #' # Example #3: By variable with named table request res <- proc_freq(df, tables = v(Hair, Eye, Cross = Hair * Eye), by = Sex, weight = Freq) # View result data res # $Hair # BY VAR CAT N CNT PCT # 1 Female Hair Black 313 52 16.61342 # 2 Female Hair Blond 313 81 25.87859 # 3 Female Hair Brown 313 143 45.68690 # 4 Female Hair Red 313 37 11.82109 # 5 Male Hair Black 279 56 20.07168 # 6 Male Hair Blond 279 46 16.48746 # 7 Male Hair Brown 279 143 51.25448 # 8 Male Hair Red 279 34 12.18638 # # $Eye # BY VAR CAT N CNT PCT # 1 Female Eye Blue 313 114 36.421725 # 2 Female Eye Brown 313 122 38.977636 # 3 Female Eye Green 313 31 9.904153 # 4 Female Eye Hazel 313 46 14.696486 # 5 Male Eye Blue 279 101 36.200717 # 6 Male Eye Brown 279 98 35.125448 # 7 Male Eye Green 279 33 11.827957 # 8 Male Eye Hazel 279 47 16.845878 # # $Cross # BY VAR1 VAR2 CAT1 CAT2 N CNT PCT # 1 Female Hair Eye Black Blue 313 9 2.8753994 # 2 Female Hair Eye Black Brown 313 36 11.5015974 # 3 Female Hair Eye Black Green 313 2 0.6389776 # 4 Female Hair Eye Black Hazel 313 5 1.5974441 # 5 Female Hair Eye Blond Blue 313 64 20.4472843 # 6 Female Hair Eye Blond Brown 313 4 1.2779553 # 7 Female Hair Eye Blond Green 313 8 2.5559105 # 8 Female Hair Eye Blond Hazel 313 5 1.5974441 # 9 Female Hair Eye Brown Blue 313 34 10.8626198 # 10 Female Hair Eye Brown Brown 313 66 21.0862620 # 11 Female Hair Eye Brown Green 313 14 4.4728435 # 12 Female Hair Eye Brown Hazel 313 29 9.2651757 # 13 Female Hair Eye Red Blue 313 7 2.2364217 # 14 Female Hair Eye Red Brown 313 16 5.1118211 # 15 Female Hair Eye Red Green 313 7 2.2364217 # 16 Female Hair Eye Red Hazel 313 7 2.2364217 # 17 Male Hair Eye Black Blue 279 11 3.9426523 # 18 Male Hair Eye Black Brown 279 32 11.4695341 # 19 Male Hair Eye Black Green 279 3 1.0752688 # 20 Male Hair Eye Black Hazel 279 10 3.5842294 # 21 Male Hair Eye Blond Blue 279 30 10.7526882 # 22 Male Hair Eye Blond Brown 279 3 1.0752688 # 23 Male Hair Eye Blond Green 279 8 2.8673835 # 24 Male Hair Eye Blond Hazel 279 5 1.7921147 # 25 Male Hair Eye Brown Blue 279 50 17.9211470 # 26 Male Hair Eye Brown Brown 279 53 18.9964158 # 27 Male Hair Eye Brown Green 279 15 5.3763441 # 28 Male Hair Eye Brown Hazel 279 25 8.9605735 # 29 Male Hair Eye Red Blue 279 10 3.5842294 # 30 Male Hair Eye Red Brown 279 10 3.5842294 # 31 Male Hair Eye Red Green 279 7 2.5089606 # 32 Male Hair Eye Red Hazel 279 7 2.5089606library(procs) # Turn off printing for CRAN checks options("procs.print" = FALSE) # Create sample data df <- as.data.frame(HairEyeColor, stringsAsFactors = FALSE) # Assign labels labels(df) <- list(Hair = "Hair Color", Eye = "Eye Color", Sex = "Sex at Birth") # Example #1: One way frequencies on Hair and Eye color with weight option. res <- proc_freq(df, tables = v(Hair, Eye), options = outcum, weight = Freq) # View result data res # $Hair # VAR CAT N CNT PCT CUMSUM CUMPCT # 1 Hair Black 592 108 18.24324 108 18.24324 # 2 Hair Blond 592 127 21.45270 235 39.69595 # 3 Hair Brown 592 286 48.31081 521 88.00676 # 4 Hair Red 592 71 11.99324 592 100.00000 # # $Eye # VAR CAT N CNT PCT CUMSUM CUMPCT # 1 Eye Blue 592 215 36.31757 215 36.31757 # 2 Eye Brown 592 220 37.16216 435 73.47973 # 3 Eye Green 592 64 10.81081 499 84.29054 # 4 Eye Hazel 592 93 15.70946 592 100.00000 # Example #2: 2 x 2 Crosstabulation table with Chi-Square statistic res <- proc_freq(df, tables = Hair * Eye, weight = Freq, options = v(crosstab, chisq)) # View result data res #$`Hair * Eye` # Category Statistic Blue Brown Green Hazel Total #1 Black Frequency 20.000000 68.000000 5.0000000 15.000000 108.00000 #2 Black Percent 3.378378 11.486486 0.8445946 2.533784 18.24324 #3 Black Row Pct 18.518519 62.962963 4.6296296 13.888889 NA #4 Black Col Pct 9.302326 30.909091 7.8125000 16.129032 NA #5 Blond Frequency 94.000000 7.000000 16.0000000 10.000000 127.00000 #6 Blond Percent 15.878378 1.182432 2.7027027 1.689189 21.45270 #7 Blond Row Pct 74.015748 5.511811 12.5984252 7.874016 NA #8 Blond Col Pct 43.720930 3.181818 25.0000000 10.752688 NA #9 Brown Frequency 84.000000 119.000000 29.0000000 54.000000 286.00000 #10 Brown Percent 14.189189 20.101351 4.8986486 9.121622 48.31081 #11 Brown Row Pct 29.370629 41.608392 10.1398601 18.881119 NA #12 Brown Col Pct 39.069767 54.090909 45.3125000 58.064516 NA #13 Red Frequency 17.000000 26.000000 14.0000000 14.000000 71.00000 #14 Red Percent 2.871622 4.391892 2.3648649 2.364865 11.99324 #15 Red Row Pct 23.943662 36.619718 19.7183099 19.718310 NA #16 Red Col Pct 7.906977 11.818182 21.8750000 15.053763 NA #17 Total Frequency 215.000000 220.000000 64.0000000 93.000000 592.00000 #18 Total Percent 36.317568 37.162162 10.8108108 15.709459 100.00000 # $`chisq:Hair * Eye` # CHISQ CHISQ.DF CHISQ.P # 1 138.2898 9 2.325287e-25 #' # Example #3: By variable with named table request res <- proc_freq(df, tables = v(Hair, Eye, Cross = Hair * Eye), by = Sex, weight = Freq) # View result data res # $Hair # BY VAR CAT N CNT PCT # 1 Female Hair Black 313 52 16.61342 # 2 Female Hair Blond 313 81 25.87859 # 3 Female Hair Brown 313 143 45.68690 # 4 Female Hair Red 313 37 11.82109 # 5 Male Hair Black 279 56 20.07168 # 6 Male Hair Blond 279 46 16.48746 # 7 Male Hair Brown 279 143 51.25448 # 8 Male Hair Red 279 34 12.18638 # # $Eye # BY VAR CAT N CNT PCT # 1 Female Eye Blue 313 114 36.421725 # 2 Female Eye Brown 313 122 38.977636 # 3 Female Eye Green 313 31 9.904153 # 4 Female Eye Hazel 313 46 14.696486 # 5 Male Eye Blue 279 101 36.200717 # 6 Male Eye Brown 279 98 35.125448 # 7 Male Eye Green 279 33 11.827957 # 8 Male Eye Hazel 279 47 16.845878 # # $Cross # BY VAR1 VAR2 CAT1 CAT2 N CNT PCT # 1 Female Hair Eye Black Blue 313 9 2.8753994 # 2 Female Hair Eye Black Brown 313 36 11.5015974 # 3 Female Hair Eye Black Green 313 2 0.6389776 # 4 Female Hair Eye Black Hazel 313 5 1.5974441 # 5 Female Hair Eye Blond Blue 313 64 20.4472843 # 6 Female Hair Eye Blond Brown 313 4 1.2779553 # 7 Female Hair Eye Blond Green 313 8 2.5559105 # 8 Female Hair Eye Blond Hazel 313 5 1.5974441 # 9 Female Hair Eye Brown Blue 313 34 10.8626198 # 10 Female Hair Eye Brown Brown 313 66 21.0862620 # 11 Female Hair Eye Brown Green 313 14 4.4728435 # 12 Female Hair Eye Brown Hazel 313 29 9.2651757 # 13 Female Hair Eye Red Blue 313 7 2.2364217 # 14 Female Hair Eye Red Brown 313 16 5.1118211 # 15 Female Hair Eye Red Green 313 7 2.2364217 # 16 Female Hair Eye Red Hazel 313 7 2.2364217 # 17 Male Hair Eye Black Blue 279 11 3.9426523 # 18 Male Hair Eye Black Brown 279 32 11.4695341 # 19 Male Hair Eye Black Green 279 3 1.0752688 # 20 Male Hair Eye Black Hazel 279 10 3.5842294 # 21 Male Hair Eye Blond Blue 279 30 10.7526882 # 22 Male Hair Eye Blond Brown 279 3 1.0752688 # 23 Male Hair Eye Blond Green 279 8 2.8673835 # 24 Male Hair Eye Blond Hazel 279 5 1.7921147 # 25 Male Hair Eye Brown Blue 279 50 17.9211470 # 26 Male Hair Eye Brown Brown 279 53 18.9964158 # 27 Male Hair Eye Brown Green 279 15 5.3763441 # 28 Male Hair Eye Brown Hazel 279 25 8.9605735 # 29 Male Hair Eye Red Blue 279 10 3.5842294 # 30 Male Hair Eye Red Brown 279 10 3.5842294 # 31 Male Hair Eye Red Green 279 7 2.5089606 # 32 Male Hair Eye Red Hazel 279 7 2.5089606
The proc_means function generates summary statistics
for selected variables on the input dataset. The variables are identified
on the var parameter. The statistics to perform are identified
on the stats parameter. Results are displayed in
the viewer interactively and returned from the function.
proc_means( data, var = NULL, stats = c("n", "mean", "std", "min", "max"), output = NULL, by = NULL, class = NULL, options = NULL, titles = NULL )proc_means( data, var = NULL, stats = c("n", "mean", "std", "min", "max"), output = NULL, by = NULL, class = NULL, options = NULL, titles = NULL )
data |
The input data frame for which to calculate summary statistics. This parameter is required. |
var |
The variable(s) to calculate summary statistics for. If no variables are specified, summary statistics will be generated for all numeric variables on the input data frame. |
stats |
A vector of summary statistics keywords. Valid keywords are: "css", "clm", "cv", "kurt", "kurtosis", "lclm", "mean", "median", "mode", "min", "max", "n", "nmiss", "nobs", "p1", "p5", "p10", "p20", "p25", "p30", "p40", "p50", "p60", "p70", "p75", "p80", "p90", "p95", "p99", "q1", "q3", "qrange", "range", "skew", "skewness", "std", "stddev", "stderr", "sum", "uclm", "uss", and "vari". For hypothesis testing, the function supports "t", "prt", "probt", and "df". Default statistics are: "n", "mean", "std", "min", and "max". |
output |
Whether or not to return datasets from the function. Valid
values are "out", "none", and "report". Default is "out", and will
produce dataset output specifically designed for programmatic use. The "none"
option will return a NULL instead of a dataset or list of datasets.
The "report" keyword returns the datasets from the interactive report, which
may be different from the standard output. The output parameter also accepts
data shaping keywords "long, "stacked", and "wide".
The shaping keywords control the structure of the output data. See the
Data Shaping section for additional details. Note that
multiple output keywords may be passed on a
character vector. For example,
to produce both a report dataset and a "long" output dataset,
use the parameter |
by |
An optional by group. If you specify a by group, the input data will be subset on the by variable(s) prior to performing any statistics. |
class |
The |
options |
A vector of optional keywords. Valid values are: "alpha =", "completetypes", "maxdec =", "noprint", "notype", "nofreq", "nonobs", "nway". The "notype", "nofreq", and "nonobs" keywords will turn off columns on the output datasets. The "alpha = " option will set the alpha value for confidence limit statistics. The default is 95% (alpha = 0.05). The "maxdec = " option sets the maximum number of decimal places displayed on report output. The "nway" option returns only the highest type values. |
titles |
A vector of one or more titles to use for the report output. |
The proc_means function is for analysis of continuous variables.
Data is passed in on the data
parameter. The desired statistics are specified using keywords
on the stats parameter. The function can segregate data into
groups using the by and class parameters. There are also
options to determine whether and what results are returned.
Normally, the requested summary statistics are shown interactively
in the viewer, and output results are returned as a data frame.
If the request produces multiple data frames, they will be returned in a list.
You may then access individual datasets from the list.
The interactive report can be turned off using the "noprint" option, and
the output datasets can be turned off using the "none" keyword on the
output parameter.
By default, proc_freq results will
be immediately sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data. To turn off the
interactive report, pass the "noprint" keyword
to the options parameter.
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
The exact datasets used for the interactive report can be returned as a list.
To return these datasets as a list, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
Dataset results are also returned from the function by default. If the results are a single dataset, a single data frame will be returned. If there are multiple results, a list of data frames will be returned.
The output datasets generated are optimized for data manipulation. The column names have been standardized, and additional variables may be present to help with data manipulation. For example, the by variable will always be named "BY", and the class variable will always be named "CLASS". In addition, data values in the output datasets are intentionally not rounded or formatted to give you the most accurate statistical results.
The following statistics keywords can be passed on the stats
parameter. Normally, each statistic
will be contained in a separate column and the column name will be
the same as the statistic keyword. You may pass statistic keywords as a
quoted vector of strings, or an unquoted vector using the v() function.
css: Corrected Sum of Squares.
clm, lclm, uclm: Upper and lower confidence limits.
cv: Coefficient of Variation.
kurt/kurtosis: The Kurtosis is a description of the distribution tails. It requires at least 4 complete observations.
mean: The arithmetic mean.
median: The median.
mode: The mode of the target variable.
min, max: The minimum and maximum values of the target variable.
n: The number of non-missing observations.
nmiss: The number of missing observations.
nobs: The number of observations, whether missing or non-missing.
p1 - p99: Percentile ranges from p1 to p99, in increments of 5.
qrange, q1, q3: Quantile ranges for the first and third quantiles.
range: Difference between the minimum and maximum values.
skew/skewness: A measure of distribution skewness. It requires at least 3 complete observations.
std/stddev: Standard deviation.
stderr: Standard error.
sum: The sum of variable values.
uss: Uncorrected sum of squares.
vari: The variance.
The function supports the following keywords to perform hypothesis testing:
t: Student's t statistic.
prt/probt: A two-tailed p-value for the Student's t statistic.
df: Degrees of freedom for the Student's t statistic.
The proc_means function recognizes the following options. Options may
be passed as a quoted vector of strings, or an unquoted vector using the
v() function.
alpha = : The "alpha = " option will set the alpha
value for confidence limit statistics. Set the alpha as a decimal value
between 0 and 1. For example, you can set a 90% confidence limit as
alpha = 0.1.
completetypes: The "completetypes" option will generate all combinations of the class variable, even if there is no data present for a particular level. Combinations will be distinguished by the TYPE variable. To use the "completetypes" option, define the class variable(s) as a factor.
maxdec = : The "maxdec = " option will set the maximum
of decimal places displayed on report output. For example, you can set 4 decimal
places as follows: maxdec = 4. Default is 7 decimal places.
This option will not round any values on the output dataset.
nofreq, nonobs: Turns off the FREQ column on the output datasets.
noprint: Whether to print the interactive report to the viewer. By default, the report is printed to the viewer. The "noprint" option will inhibit printing.
notype: Turns off the TYPE column on the output dataset.
nway: Returns only the highest level TYPE combination. By default, the function returns all TYPE combinations.
The TYPE and FREQ variables appear on the output dataset by default.
The FREQ variable contains a count of the number of input rows/observations that were included in the statistics for that output row. The FREQ count can be different from the N statistic. The FREQ count is a count of the number of rows/observations, while the N statistic is a count of non-missing values. These counts can be different if you have missing values in your data. If you want to remove the FREQ column from the output dataset, use the "nofreq" option.
The TYPE variable identifies combinations of class categories, and produces summary statistics for each of those combinations. For example, the output dataset normally produces statistics for TYPE 0, which is all class categories, and a TYPE 1 which is each class category. If there are multiple classes, there will be multiple TYPE values for each level of class combinations. If you do no want to show the various type combinations, use the "nway" option. If you want to remove the TYPE column from the output dataset, use the "notype" option.
There are some occasions when you may want to define the class variable(s)
as a factor. One occasion is for sorting/ordering,
and the other is for obtaining zero-counts on sparse data.
To order the class categories in the means output, define the
class variable as a factor in the desired order. The function will
then retain that order for the class categories in the output dataset
and report.
You may also wish to
define the class variable as a factor if you are dealing with sparse data
and some of the class categories are not present in the data. To ensure
these categories are displayed with zero-counts, define the class variable
as a factor and use the "completetypes" option.
The output dataset produced by the "out" keyword can be shaped in different ways. These shaping options allow you to decide whether the data should be returned long and skinny, or short and wide. The shaping options can reduce the amount of data manipulation necessary to get the frequencies into the desired form. The shaping options are as follows:
long: Transposes the output datasets so that statistics are in rows and variables are in columns.
stacked: Requests that output datasets be returned in "stacked" form, such that both statistics and variables are in rows.
wide: Requests that output datasets be returned in "wide" form, such that statistics are across the top in columns, and variables are in rows. This shaping option is the default.
# Turn off printing for CRAN checks options("procs.print" = FALSE) # Default statistics on iris res1 <- proc_means(iris) # View results res1 # TYPE FREQ VAR N MEAN STD MIN MAX # 1 0 150 Sepal.Length 150 5.843333 0.8280661 4.3 7.9 # 2 0 150 Sepal.Width 150 3.057333 0.4358663 2.0 4.4 # 3 0 150 Petal.Length 150 3.758000 1.7652982 1.0 6.9 # 4 0 150 Petal.Width 150 1.199333 0.7622377 0.1 2.5 # Defaults statistics with by res2 <- proc_means(iris, by = Species) # View results res2 # BY TYPE FREQ VAR N MEAN STD MIN MAX # 1 setosa 0 50 Sepal.Length 50 5.006 0.3524897 4.3 5.8 # 2 setosa 0 50 Sepal.Width 50 3.428 0.3790644 2.3 4.4 # 3 setosa 0 50 Petal.Length 50 1.462 0.1736640 1.0 1.9 # 4 setosa 0 50 Petal.Width 50 0.246 0.1053856 0.1 0.6 # 5 versicolor 0 50 Sepal.Length 50 5.936 0.5161711 4.9 7.0 # 6 versicolor 0 50 Sepal.Width 50 2.770 0.3137983 2.0 3.4 # 7 versicolor 0 50 Petal.Length 50 4.260 0.4699110 3.0 5.1 # 8 versicolor 0 50 Petal.Width 50 1.326 0.1977527 1.0 1.8 # 9 virginica 0 50 Sepal.Length 50 6.588 0.6358796 4.9 7.9 # 10 virginica 0 50 Sepal.Width 50 2.974 0.3224966 2.2 3.8 # 11 virginica 0 50 Petal.Length 50 5.552 0.5518947 4.5 6.9 # 12 virginica 0 50 Petal.Width 50 2.026 0.2746501 1.4 2.5 # Specified variables, statistics, and options res3 <- proc_means(iris, var = v(Petal.Length, Petal.Width), class = Species, stats = v(n, mean, std, median, qrange, clm), options = nofreq, output = long) # View results res3 # CLASS TYPE STAT Petal.Length Petal.Width # 1 <NA> 0 N 150.0000000 150.0000000 # 2 <NA> 0 MEAN 3.7580000 1.1993333 # 3 <NA> 0 STD 1.7652982 0.7622377 # 4 <NA> 0 MEDIAN 4.3500000 1.3000000 # 5 <NA> 0 QRANGE 3.5000000 1.5000000 # 6 <NA> 0 LCLM 3.4731854 1.0763533 # 7 <NA> 0 UCLM 4.0428146 1.3223134 # 8 setosa 1 N 50.0000000 50.0000000 # 9 setosa 1 MEAN 1.4620000 0.2460000 # 10 setosa 1 STD 0.1736640 0.1053856 # 11 setosa 1 MEDIAN 1.5000000 0.2000000 # 12 setosa 1 QRANGE 0.2000000 0.1000000 # 13 setosa 1 LCLM 1.4126452 0.2160497 # 14 setosa 1 UCLM 1.5113548 0.2759503 # 15 versicolor 1 N 50.0000000 50.0000000 # 16 versicolor 1 MEAN 4.2600000 1.3260000 # 17 versicolor 1 STD 0.4699110 0.1977527 # 18 versicolor 1 MEDIAN 4.3500000 1.3000000 # 19 versicolor 1 QRANGE 0.6000000 0.3000000 # 20 versicolor 1 LCLM 4.1264528 1.2697993 # 21 versicolor 1 UCLM 4.3935472 1.3822007 # 22 virginica 1 N 50.0000000 50.0000000 # 23 virginica 1 MEAN 5.5520000 2.0260000 # 24 virginica 1 STD 0.5518947 0.2746501 # 25 virginica 1 MEDIAN 5.5500000 2.0000000 # 26 virginica 1 QRANGE 0.8000000 0.5000000 # 27 virginica 1 LCLM 5.3951533 1.9479453 # 28 virginica 1 UCLM 5.7088467 2.1040547# Turn off printing for CRAN checks options("procs.print" = FALSE) # Default statistics on iris res1 <- proc_means(iris) # View results res1 # TYPE FREQ VAR N MEAN STD MIN MAX # 1 0 150 Sepal.Length 150 5.843333 0.8280661 4.3 7.9 # 2 0 150 Sepal.Width 150 3.057333 0.4358663 2.0 4.4 # 3 0 150 Petal.Length 150 3.758000 1.7652982 1.0 6.9 # 4 0 150 Petal.Width 150 1.199333 0.7622377 0.1 2.5 # Defaults statistics with by res2 <- proc_means(iris, by = Species) # View results res2 # BY TYPE FREQ VAR N MEAN STD MIN MAX # 1 setosa 0 50 Sepal.Length 50 5.006 0.3524897 4.3 5.8 # 2 setosa 0 50 Sepal.Width 50 3.428 0.3790644 2.3 4.4 # 3 setosa 0 50 Petal.Length 50 1.462 0.1736640 1.0 1.9 # 4 setosa 0 50 Petal.Width 50 0.246 0.1053856 0.1 0.6 # 5 versicolor 0 50 Sepal.Length 50 5.936 0.5161711 4.9 7.0 # 6 versicolor 0 50 Sepal.Width 50 2.770 0.3137983 2.0 3.4 # 7 versicolor 0 50 Petal.Length 50 4.260 0.4699110 3.0 5.1 # 8 versicolor 0 50 Petal.Width 50 1.326 0.1977527 1.0 1.8 # 9 virginica 0 50 Sepal.Length 50 6.588 0.6358796 4.9 7.9 # 10 virginica 0 50 Sepal.Width 50 2.974 0.3224966 2.2 3.8 # 11 virginica 0 50 Petal.Length 50 5.552 0.5518947 4.5 6.9 # 12 virginica 0 50 Petal.Width 50 2.026 0.2746501 1.4 2.5 # Specified variables, statistics, and options res3 <- proc_means(iris, var = v(Petal.Length, Petal.Width), class = Species, stats = v(n, mean, std, median, qrange, clm), options = nofreq, output = long) # View results res3 # CLASS TYPE STAT Petal.Length Petal.Width # 1 <NA> 0 N 150.0000000 150.0000000 # 2 <NA> 0 MEAN 3.7580000 1.1993333 # 3 <NA> 0 STD 1.7652982 0.7622377 # 4 <NA> 0 MEDIAN 4.3500000 1.3000000 # 5 <NA> 0 QRANGE 3.5000000 1.5000000 # 6 <NA> 0 LCLM 3.4731854 1.0763533 # 7 <NA> 0 UCLM 4.0428146 1.3223134 # 8 setosa 1 N 50.0000000 50.0000000 # 9 setosa 1 MEAN 1.4620000 0.2460000 # 10 setosa 1 STD 0.1736640 0.1053856 # 11 setosa 1 MEDIAN 1.5000000 0.2000000 # 12 setosa 1 QRANGE 0.2000000 0.1000000 # 13 setosa 1 LCLM 1.4126452 0.2160497 # 14 setosa 1 UCLM 1.5113548 0.2759503 # 15 versicolor 1 N 50.0000000 50.0000000 # 16 versicolor 1 MEAN 4.2600000 1.3260000 # 17 versicolor 1 STD 0.4699110 0.1977527 # 18 versicolor 1 MEDIAN 4.3500000 1.3000000 # 19 versicolor 1 QRANGE 0.6000000 0.3000000 # 20 versicolor 1 LCLM 4.1264528 1.2697993 # 21 versicolor 1 UCLM 4.3935472 1.3822007 # 22 virginica 1 N 50.0000000 50.0000000 # 23 virginica 1 MEAN 5.5520000 2.0260000 # 24 virginica 1 STD 0.5518947 0.2746501 # 25 virginica 1 MEDIAN 5.5500000 2.0000000 # 26 virginica 1 QRANGE 0.8000000 0.5000000 # 27 virginica 1 LCLM 5.3951533 1.9479453 # 28 virginica 1 UCLM 5.7088467 2.1040547
The proc_print function is used to print a dataset
or datasets. To print multiple datasets, pass them to the data
parameter in a list.
By default, the function prints to the viewer. It may also be used
to print to the file system using the output_type and
file_path parameters. This print function has limited options,
and is meant to quickly view your data or dump it out to a file.
For more reporting options, use the reporter package.
proc_print( data, file_path = NULL, output_type = "HTML", titles = NULL, style = NULL, view = TRUE )proc_print( data, file_path = NULL, output_type = "HTML", titles = NULL, style = NULL, view = TRUE )
data |
The data to print. Can be either a single dataset, or a list of datasets. |
file_path |
The path of the report to print. |
output_type |
The type of report to create. Valid values are "TXT", "RTF", "PDF", "HTML", and "DOCX". Default is "HTML". |
titles |
A vector of titles. |
style |
A style object, as defined by the reporter package. See that package for details. |
view |
Whether to send the print output to the viewer. Valid values are TRUE and FALSE. Default is TRUE. |
If a file report was produced, the full path of the report. Otherwise, a NULL. In either case, the value will be returned invisibly.
# Turn off printing to pass CRAN checks options("procs.print" = FALSE) # Print mtcars to the viewer proc_print(mtcars) # Print mtcars to an RTF pth <- proc_print(mtcars, file_path = tempfile(fileext = ".rtf"), titles = "MTCARS Proc Print Example", output_type = "RTF", view = FALSE) # View file # file.show(pth)# Turn off printing to pass CRAN checks options("procs.print" = FALSE) # Print mtcars to the viewer proc_print(mtcars) # Print mtcars to an RTF pth <- proc_print(mtcars, file_path = tempfile(fileext = ".rtf"), titles = "MTCARS Proc Print Example", output_type = "RTF", view = FALSE) # View file # file.show(pth)
The proc_reg function performs a regression
for one or more models. The model(s) are passed on the model parameter,
and the input dataset is passed on the data parameter. The stats
parameter allows you to request additional statistics, similar to the
model options in SAS. The by
parameter allows you to subset the data into groups and run the model on each
group. The weight parameter let's you assign a weight to each observation
in the dataset. The output and options parameters provide
additional customization of the results.
proc_reg( data, model, by = NULL, stats = NULL, output = NULL, weight = NULL, options = NULL, titles = NULL )proc_reg( data, model, by = NULL, stats = NULL, output = NULL, weight = NULL, options = NULL, titles = NULL )
data |
The input data frame for which to perform the regression analysis. This parameter is required. |
model |
A model for the regression to be performed. The model can be
specified using either R syntax or SAS syntax. |
by |
An optional by group. If you specify a by group, the input
data will be subset on the by variable(s) prior to performing the regression.
For multiple by variables, pass them as a quoted vector of variable names.
You may also pass them unquoted using the |
stats |
Optional statistics keywords. Valid values are "adjrsq", "clb",
"est", "edf", "hcc", "hccmethod", "mse", "p", "press", "rsquare",
"sse", "spec", "seb", and "table". A single keyword may be passed with or
without quotes. Pass multiple keywords either as a quoted vector, or unquoted
vector using the |
output |
Whether or not to return datasets from the function. Valid
values are "out", "none", and "report". Default is "out", and will
produce dataset output specifically designed for programmatic use. The "none"
option will return a NULL instead of a dataset or list of datasets.
The "report" keyword returns the datasets from the interactive report, which
may be different from the standard output. Note that some statistics are only
available on the interactive report. The output parameter also accepts
data shaping keywords "long, "stacked", and "wide".
These shaping keywords control the structure of the output data. See the
Data Shaping section for additional details. Note that
multiple output keywords may be passed on a
character vector. For example,
to produce both a report dataset and a "long" output dataset,
use the parameter |
weight |
The name of a variable to use as a weight for each observation. The weight is commonly provided as the inverse of each variance. |
options |
A vector of optional keywords. Valid values are: "alpha =", "edf", "noprint", "outest", "outseb", "press", "rsquare", and "tableout". The "alpha = " option will set the alpha value for confidence limit statistics. The default is 95% (alpha = 0.05). The "noprint" option turns off the interactive report. For other options, see the Options section for explanations of each. |
titles |
A vector of one or more titles to use for the report output. |
The proc_reg function is a general purpose regression function. It
produces a dataset output by default, and, when working in RStudio,
also produces an interactive report. The function has many convenient options
for what statistics are produced and how the analysis is performed. All
statistical output from proc_reg matches SAS.
A model may be specified using R model syntax or SAS model syntax. To use
SAS syntax, the model statement must be quoted. To pass multiple models using
R syntax, pass them to the model parameter in a list. To pass multiple
models using SAS syntax, pass them to the model parameter as a vector
of strings.
Normally, the requested regression statistics are shown interactively
in the viewer, and output results are returned as a data frame.
If you request "report" datasets, they will be returned as a list.
You may then access individual datasets from the list using dollar sign
($) syntax.
The interactive report can be turned off using the "noprint" option.
The output dataset can be turned off using the "none" keyword on the
output parameter. If the output dataset is turned off, the function
will return a NULL.
By default, proc_reg results will
be sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data. To turn off the
interactive report, pass the "noprint" keyword
to the options parameter.
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
The exact datasets used for the interactive report can be returned as a list.
To return these datasets, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
Dataset results are also returned from the function by default.
proc_reg typically returns a single dataset. The columns and rows
on this dataset can change depending on the keywords passed
to the stats and options parameters.
The default output dataset is optimized for data manipulation. The column names have been standardized, and additional variables may be present to help with data manipulation. The data values in the output dataset are intentionally not rounded or formatted to give you the most accurate numeric results.
You may also request
to return the datasets used in the interactive report. To request these
datasets, pass the "report" option to the output parameter. Each report
dataset will be named according to the category of statistical
results. There are four standard categories: "NObs",
"ANOVA", "FitStatistics", and "ParameterEstimates". When the "spec" statistics option
is passed, the function will also return a "SpecTest" dataset containing
the White's test results. If the "p" option is present, the "OutputStatistics"
and "ResidualStatistics" tables will be included.
If you don't want any datasets returned, pass the "none" option on the
output parameter.
The following statistics keywords can be passed on the stats
parameter. You may pass statistic keywords as a
quoted vector of strings, or an unquoted vector using the v() function.
An individual statistics keyword can be passed without quoting.
adjrsq: Adds adjusted r-square value to the output dataset.
clb: Requests confidence limits be added to the interactive report.
edf: Includes the number of regressors, the error degress of freedom, and the model r-square to the output dataset.
est: Request an output dataset of parameter estimate and optional model fit summary statistics. This statistics option is the default.
hcc: The "hcc" statistics keyword requests that heteroscedasticity-consistent standard errors of the parameter estimates be sent to the interactive report.
hccmethod=: When the "hcc" option is present, the "hccmethod=" option specifies the type of method to use. Valid values are 0 and 3.
mse: Computes the mean squared error for each model and adds to the output dataset.
p: Computes predicted and residual values and sends to a separate table on the interactive report.
press: Includes the predicted residual sum of squares (PRESS) statistic in the output dataset.
rsquare: Include the r-square statistic in the output dataset. The "rsquare" option has the same effect as the "edf" option.
seb: Outputs the standard errors of the parameter estimates to the output dataset. These values will be identified as type "SEB".
spec: Adds the "White's test" table to the interactive output. This test determines whether the first and second moments of the model are correctly specified.
sse: Adds the error sum of squares to the output dataset.
table: The "table" keyword is used to send standard
errors, t-statistics, p-values, and confidence limits to the output
dataset. These additional statistics are identified by types "STDERR",
"T", and "PVALUE". The confidence limits are identified by "LxxB" and "UxxB",
where "xx" is the alpha value in percentage terms. The "table" keyword
on the stats parameter performs the same functions as the "tableout"
option on the options parameter.
The proc_reg function recognizes the following options. Options may
be passed as a quoted vector of strings, or an unquoted vector using the
v() function.
alpha = : The "alpha = " option will set the alpha
value for confidence limit statistics. Set the alpha as a decimal value
between 0 and 1. For example, you can set a 90% confidence limit as
alpha = 0.1.
edf: Includes the number of regressors, the error degress of freedom, and the model r-square to the output dataset.
noprint: Whether to print the interactive report to the
viewer. By default, the report is printed to the viewer. The "noprint"
option will inhibit printing. You may inhibit printing globally by
setting the package print option to false:
options("procs.print" = FALSE).
outest: The "outest" option is used to request the parameter estimates and model fit summary statistic be sent to the output dataset. The parameter estimates are identified as type "PARMS" on the output dataset. The "outest" dataset is the default output dataset, and this option does not normally need to be passed.
outseb: The "outseb" option is used to request the standard
errors be sent to the output dataset. The standard errors will be added
as a new row identified by type "SEB". This request
can also be made by passing the "seb" keyword to the stats parameter.
press: Includes the predicted residual sum of squares (PRESS) statistic in the output dataset.
rsquare: Include the r-square statistic in the output dataset. The "rsquare" option has the same effect as the "edf" option.
tableout: The "tableout" option is used to send standard
errors, t-statistics, p-values, and confidence limits to the output
dataset. These additional statistics are identified by types "STDERR",
"T", and "PVALUE". The confidence limits are identified by "LxxB" and "UxxB",
where "xx" is the alpha value in percentage terms. The "tableout" option
on the options parameter performs the same functions as the "table"
keyword on the stats parameter.
The output datasets produced by the function can be shaped in different ways. These shaping options allow you to decide whether the data should be returned long and skinny, or short and wide. The shaping options can reduce the amount of data manipulation necessary to get the data into the desired form. The shaping options are as follows:
long: Transposes the output datasets so that statistics are in rows and variables are in columns.
stacked: Requests that output datasets be returned in "stacked" form, such that both statistics and variables are in rows.
wide: Requests that output datasets be returned in "wide" form, such that statistics are across the top in columns, and variables are in rows. This shaping option is the default.
These shaping options are passed on the output parameter. For example,
to return the data in "long" form, use output = "long".
# Turn off printing for CRAN checks options("procs.print" = FALSE) # Prepare sample data set.seed(123) dat <- cars samplecar <- sample(c(TRUE, FALSE), nrow(cars), replace=TRUE, prob=c(0.6, 0.4)) dat$group <- ifelse(samplecar %in% seq(1, nrow(cars)), "Group A", "Group B") # Example 1: R Model Syntax res1 <- proc_reg(dat, model = dist ~ speed) # View Results res1 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909 3.932409 -1 # Example 2: SAS Model Syntax res2 <- proc_reg(dat, model = "dist = speed") # View Results res2 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909 3.932409 -1 # Example 3: Report Output res3 <- proc_reg(dat, model = dist ~ speed, output = report) # View Results res3 # $NObs # LABEL NOBS # 1 Number of Observations Read 50 # 2 Number of Observations Used 50 # # $ANOVA # LABEL DF SUMSQ MEANSQ FVAL PROBF # 1 Model 1 21185.46 21185.4589 89.56711 1.489919e-12 # 2 Error 48 11353.52 236.5317 NA NA # 3 Corrected Total 49 32538.98 NA NA NA # # $FitStatistics # RMSE DEPMEAN COEFVAR RSQ ADJRSQ # 1 15.37959 42.98 35.78312 0.6510794 0.6438102 # # $ParameterEstimates # PARM DF EST STDERR T PROBT # 1 Intercept 1 -17.579095 6.7584402 -2.601058 1.231882e-02 # 2 speed 1 3.932409 0.4155128 9.463990 1.489919e-12 # Example 4: By variable res4 <- proc_reg(dat, model = dist ~ speed, by = group) # View Results res4 # BY MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 Group A MODEL1 PARMS dist 15.35049 -24.888326 4.275357 -1 # 2 Group B MODEL1 PARMS dist 15.53676 -8.705547 3.484381 -1 # Example 5: "tableout" Option res5 <- proc_reg(dat, model = dist ~ speed, options = tableout) # View Results res5 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909489 3.932409e+00 -1 # 2 MODEL1 STDERR dist 15.37959 6.75844017 4.155128e-01 NA # 3 MODEL1 T dist 15.37959 -2.60105800 9.463990e+00 NA # 4 MODEL1 PVALUE dist 15.37959 0.01231882 1.489919e-12 NA # 5 MODEL1 L95B dist 15.37959 -31.16784960 3.096964e+00 NA # 6 MODEL1 U95B dist 15.37959 -3.99034018 4.767853e+00 NA # Example 6: Multiple Models plus Statistics Keywords res6 <- proc_reg(dat, model = list(mod1 = dist ~ speed, mod2 = speed ~ dist), stats = v(press, seb)) # View Results res6 # MODEL TYPE DEPVAR RMSE PRESS Intercept speed dist # 1 mod1 PARMS dist 15.379587 12320.2708 -17.5790949 3.9324088 -1.00000000 # 2 mod1 SEB dist 15.379587 NA 6.7584402 0.4155128 -1.00000000 # 3 mod2 PARMS speed 3.155753 526.2665 8.2839056 -1.0000000 0.16556757 # 4 mod2 SEB speed 3.155753 NA 0.8743845 -1.0000000 0.01749448# Turn off printing for CRAN checks options("procs.print" = FALSE) # Prepare sample data set.seed(123) dat <- cars samplecar <- sample(c(TRUE, FALSE), nrow(cars), replace=TRUE, prob=c(0.6, 0.4)) dat$group <- ifelse(samplecar %in% seq(1, nrow(cars)), "Group A", "Group B") # Example 1: R Model Syntax res1 <- proc_reg(dat, model = dist ~ speed) # View Results res1 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909 3.932409 -1 # Example 2: SAS Model Syntax res2 <- proc_reg(dat, model = "dist = speed") # View Results res2 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909 3.932409 -1 # Example 3: Report Output res3 <- proc_reg(dat, model = dist ~ speed, output = report) # View Results res3 # $NObs # LABEL NOBS # 1 Number of Observations Read 50 # 2 Number of Observations Used 50 # # $ANOVA # LABEL DF SUMSQ MEANSQ FVAL PROBF # 1 Model 1 21185.46 21185.4589 89.56711 1.489919e-12 # 2 Error 48 11353.52 236.5317 NA NA # 3 Corrected Total 49 32538.98 NA NA NA # # $FitStatistics # RMSE DEPMEAN COEFVAR RSQ ADJRSQ # 1 15.37959 42.98 35.78312 0.6510794 0.6438102 # # $ParameterEstimates # PARM DF EST STDERR T PROBT # 1 Intercept 1 -17.579095 6.7584402 -2.601058 1.231882e-02 # 2 speed 1 3.932409 0.4155128 9.463990 1.489919e-12 # Example 4: By variable res4 <- proc_reg(dat, model = dist ~ speed, by = group) # View Results res4 # BY MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 Group A MODEL1 PARMS dist 15.35049 -24.888326 4.275357 -1 # 2 Group B MODEL1 PARMS dist 15.53676 -8.705547 3.484381 -1 # Example 5: "tableout" Option res5 <- proc_reg(dat, model = dist ~ speed, options = tableout) # View Results res5 # MODEL TYPE DEPVAR RMSE Intercept speed dist # 1 MODEL1 PARMS dist 15.37959 -17.57909489 3.932409e+00 -1 # 2 MODEL1 STDERR dist 15.37959 6.75844017 4.155128e-01 NA # 3 MODEL1 T dist 15.37959 -2.60105800 9.463990e+00 NA # 4 MODEL1 PVALUE dist 15.37959 0.01231882 1.489919e-12 NA # 5 MODEL1 L95B dist 15.37959 -31.16784960 3.096964e+00 NA # 6 MODEL1 U95B dist 15.37959 -3.99034018 4.767853e+00 NA # Example 6: Multiple Models plus Statistics Keywords res6 <- proc_reg(dat, model = list(mod1 = dist ~ speed, mod2 = speed ~ dist), stats = v(press, seb)) # View Results res6 # MODEL TYPE DEPVAR RMSE PRESS Intercept speed dist # 1 mod1 PARMS dist 15.379587 12320.2708 -17.5790949 3.9324088 -1.00000000 # 2 mod1 SEB dist 15.379587 NA 6.7584402 0.4155128 -1.00000000 # 3 mod2 PARMS speed 3.155753 526.2665 8.2839056 -1.0000000 0.16556757 # 4 mod2 SEB speed 3.155753 NA 0.8743845 -1.0000000 0.01749448
The proc_sort function sorts a dataset according to
the variables passed on the by parameter. If no parameters are
passed on the by parameter, it will sort by all variables. The
direction of the sort is controlled with the order parameter.
Use the nodupkey option to eliminate duplicate rows from
the dataset, and the keep parameter to subset columns.
The parameters will accept either quoted or unquoted values.
proc_sort( data, by = NULL, keep = NULL, order = "ascending", options = NULL, as.character = FALSE )proc_sort( data, by = NULL, keep = NULL, order = "ascending", options = NULL, as.character = FALSE )
data |
The input data to sort. |
by |
A vector of variables to sort by. |
keep |
A vector of variables on the output data to keep. All other variables will be dropped. |
order |
The sort order of the variables on the by parameter. Valid values are 'ascending' or 'descending'. These values may also be abbreviated to 'asc', 'desc', 'a', or 'd'. You may pass a vector of order values equal to the number of variables on the by parameter. Default is 'ascending' for all by variables. |
options |
Any options desired for the sort. Available options are 'dupkey' and 'nodupkey'. The 'nodupkey' option removes duplicate rows from the sorted dataset. The 'dupkey' option removes unique rows from the sorted dataset. |
as.character |
If TRUE, will cast any factors in the 'by' parameter to character. Default is FALSE. This parameter is included because it is common to use factors for sorting in R, but you may not want to keep the variable as a factor. This parameter therefore allows you to use the factor for the sort, but then convert back to a character once the sort is complete. |
The sorted dataset. If a data frame was input, a data frame will be output. If a tibble was input, a tibble will be output.
Below are the available options for the proc_sort function:
dupkey: This option keeps duplicate rows and discards unique rows. Duplicate rows will be identified by the key variables listed on the by parameter if passed. This option is the opposite of 'nodupkey'.
nodupkey: Removes duplicate rows following the sort. Duplicate rows will be identified by the key variables listed on the by parameter if passed. Otherwise, the function will dedupe on all variables returned.
# Prepare data subset dat <- data.frame(HairEyeColor, stringsAsFactors = FALSE)[1:32 %% 4 == 1, ] # View data dat # Hair Eye Sex Freq # 1 Black Brown Male 32 # 5 Black Blue Male 11 # 9 Black Hazel Male 10 # 13 Black Green Male 3 # 17 Black Brown Female 36 # 21 Black Blue Female 9 # 25 Black Hazel Female 5 # 29 Black Green Female 2 # Sort by Frequency res1 <- proc_sort(dat, by = Freq) # View results res1 # Hair Eye Sex Freq # 29 Black Green Female 2 # 13 Black Green Male 3 # 25 Black Hazel Female 5 # 21 Black Blue Female 9 # 9 Black Hazel Male 10 # 5 Black Blue Male 11 # 1 Black Brown Male 32 # 17 Black Brown Female 36 # Sort by Frequency descending res2 <- proc_sort(dat, by = Freq, order = d) # View results res2 # Hair Eye Sex Freq # 17 Black Brown Female 36 # 1 Black Brown Male 32 # 5 Black Blue Male 11 # 9 Black Hazel Male 10 # 21 Black Blue Female 9 # 25 Black Hazel Female 5 # 13 Black Green Male 3 # 29 Black Green Female 2 # Get unique combinations of Eye and Sex res3 <- proc_sort(dat, keep = v(Eye, Sex), options = nodupkey) # View results res3 # Eye Sex # 1 Brown Male # 17 Brown Female # 5 Blue Male # 21 Blue Female # 9 Hazel Male # 25 Hazel Female # 13 Green Male # 29 Green Female# Prepare data subset dat <- data.frame(HairEyeColor, stringsAsFactors = FALSE)[1:32 %% 4 == 1, ] # View data dat # Hair Eye Sex Freq # 1 Black Brown Male 32 # 5 Black Blue Male 11 # 9 Black Hazel Male 10 # 13 Black Green Male 3 # 17 Black Brown Female 36 # 21 Black Blue Female 9 # 25 Black Hazel Female 5 # 29 Black Green Female 2 # Sort by Frequency res1 <- proc_sort(dat, by = Freq) # View results res1 # Hair Eye Sex Freq # 29 Black Green Female 2 # 13 Black Green Male 3 # 25 Black Hazel Female 5 # 21 Black Blue Female 9 # 9 Black Hazel Male 10 # 5 Black Blue Male 11 # 1 Black Brown Male 32 # 17 Black Brown Female 36 # Sort by Frequency descending res2 <- proc_sort(dat, by = Freq, order = d) # View results res2 # Hair Eye Sex Freq # 17 Black Brown Female 36 # 1 Black Brown Male 32 # 5 Black Blue Male 11 # 9 Black Hazel Male 10 # 21 Black Blue Female 9 # 25 Black Hazel Female 5 # 13 Black Green Male 3 # 29 Black Green Female 2 # Get unique combinations of Eye and Sex res3 <- proc_sort(dat, keep = v(Eye, Sex), options = nodupkey) # View results res3 # Eye Sex # 1 Brown Male # 17 Brown Female # 5 Blue Male # 21 Blue Female # 9 Hazel Male # 25 Hazel Female # 13 Green Male # 29 Green Female
A function to pivot or transpose a data frame. In the default
usage, the variables identified by the parameter var are transposed
to become rows. The variable values in the parameter id become
the new columns. The function has several more parameters to control
how variables are named in the transposed data set. Parameters will
accept quoted or unquoted values.
proc_transpose( data, by = NULL, var = NULL, id = NULL, idlabel = NULL, copy = NULL, name = "NAME", namelabel = NULL, prefix = NULL, delimiter = ".", suffix = NULL, where = NULL, options = NULL, log = TRUE )proc_transpose( data, by = NULL, var = NULL, id = NULL, idlabel = NULL, copy = NULL, name = "NAME", namelabel = NULL, prefix = NULL, delimiter = ".", suffix = NULL, where = NULL, options = NULL, log = TRUE )
data |
The input data to transpose. |
by |
An optional by group. Parameter accepts a vector of one or more quoted variable names. If the by group is requested, the data will be subset by that variable and the transpose function will transpose each group and stack them together in a single table. |
var |
The variable or variables to transpose. Parameter accepts a vector of variable names. By default, all numeric variables will be transposed. |
id |
The variable or variables to use for the transposed column names. |
idlabel |
The variable to use for the transposed column labels. |
copy |
A vector of variables to retain in the output data without transposition. Values will be truncated or recycled to fit the number of output rows. |
name |
Specifies the name of the variable to be used for the var values. |
namelabel |
The label to use for the name variable. |
prefix |
Contains a prefix to be used in the construction of column names. |
delimiter |
Specifies a delimiter to be used in the construction of column names. |
suffix |
Contains a suffix to be used in the construction of column names. |
where |
An expression to filter the rows after the transform
is complete. Use the |
options |
Optional keywords that affect the transpose. Default is NULL. Available option is "noname" which drops the name column from the output dataset. |
log |
Whether or not to log the procedure. Default is TRUE. This parameter is used internally. |
The proc_tranpose function takes an input data frame or tibble
and transposes the columns and rows. If no parameters are specified,
the function will assign all numeric variables to the var parameter.
These variables will become rows, and generic column names
("COL1", "COL2", etc.) will be generated.
Other variables will be dropped.
There are several parameters to control how new column names are constructed.
If the desired column names already exist in your data, identify them on the
id parameter. The function will then use those data values
unaltered. The label for these new columns can also be constructed from
data values using the idlabel parameter.
The name and namelabel parameter are used to control the
name of the column created for the var values. If this parameter
is not passed, the column will be called "NAME", and no label will be assigned.
You may group the transposed values using the by parameter. This
parameter accepts one or more variable names to use for grouping. If this
parameter is used, the function will first subset the data by the unique
combination of by variables, transpose each subset, and then
combine the result into a single output dataset. The by group variables
will be named on the output dataset with a generic name ("BY1", "BY2", etc.).
The copy parameter is used to simply copy columns from the input
dataset to the transposed dataset. If necessary, these values will be
recycled or truncated to fit the number of output rows. Any input
variables not included in the var, id, or copy parameter
will be dropped.
Once the transpose is complete, you may wish to filter the output data.
Filtering can be accomplished using the where parameter. This parameter
takes an expression using the expression function. The expression
is constructed using standard R logical operators. Variable names do
not need to be quoted.
The prefix, delimiter, and suffix parameter are used
to control how generic column names are constructed. These parameters
are especially useful when there are multiple var variables.
The transposed dataset. If a data frame is input, a data frame will be output. If a tibble is input, a tibble will be output.
# Prepare data dat <- data.frame(CAT = rownames(USPersonalExpenditure), USPersonalExpenditure, stringsAsFactors = FALSE, row.names = NULL)[1:4, ] # View data dat # CAT X1940 X1945 X1950 X1955 X1960 # 1 Food and Tobacco 22.20 44.50 59.60 73.2 86.8 # 2 Household Operation 10.50 15.50 29.00 36.5 46.2 # 3 Medical and Health 3.53 5.76 9.71 14.0 21.1 # 4 Personal Care 1.04 1.98 2.45 3.4 5.4 # Default transpose tdat1 <- proc_transpose(dat) # View results tdat1 # NAME COL1 COL2 COL3 COL4 # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1945 44.5 15.5 5.76 1.98 # 3 X1950 59.6 29.0 9.71 2.45 # 4 X1955 73.2 36.5 14.00 3.40 # 5 X1960 86.8 46.2 21.10 5.40 # Transpose with ID and Name tdat2 <- proc_transpose(dat, id = CAT, name = Year) # View results tdat2 # Year Food and Tobacco Household Operation Medical and Health Personal Care # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1945 44.5 15.5 5.76 1.98 # 3 X1950 59.6 29.0 9.71 2.45 # 4 X1955 73.2 36.5 14.00 3.40 # 5 X1960 86.8 46.2 21.10 5.40 # Transpose only some of the variables tdat3 <- proc_transpose(dat, var = v(X1940, X1950, X1960), id = CAT, name = Year) # View results tdat3 # Year Food and Tobacco Household Operation Medical and Health Personal Care # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1950 59.6 29.0 9.71 2.45 # 3 X1960 86.8 46.2 21.10 5.40 # By with a where clause tdat4 <- proc_transpose(dat, by = CAT, name = Year, where = expression(Year %in% c("X1940", "X1950", "X1960"))) # View Results tdat4 # CAT Year COL1 # 1 Food and Tobacco X1940 22.20 # 2 Food and Tobacco X1950 59.60 # 3 Food and Tobacco X1960 86.80 # 4 Household Operation X1940 10.50 # 5 Household Operation X1950 29.00 # 6 Household Operation X1960 46.20 # 7 Medical and Health X1940 3.53 # 8 Medical and Health X1950 9.71 # 9 Medical and Health X1960 21.10 # 10 Personal Care X1940 1.04 # 11 Personal Care X1950 2.45 # 12 Personal Care X1960 5.40# Prepare data dat <- data.frame(CAT = rownames(USPersonalExpenditure), USPersonalExpenditure, stringsAsFactors = FALSE, row.names = NULL)[1:4, ] # View data dat # CAT X1940 X1945 X1950 X1955 X1960 # 1 Food and Tobacco 22.20 44.50 59.60 73.2 86.8 # 2 Household Operation 10.50 15.50 29.00 36.5 46.2 # 3 Medical and Health 3.53 5.76 9.71 14.0 21.1 # 4 Personal Care 1.04 1.98 2.45 3.4 5.4 # Default transpose tdat1 <- proc_transpose(dat) # View results tdat1 # NAME COL1 COL2 COL3 COL4 # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1945 44.5 15.5 5.76 1.98 # 3 X1950 59.6 29.0 9.71 2.45 # 4 X1955 73.2 36.5 14.00 3.40 # 5 X1960 86.8 46.2 21.10 5.40 # Transpose with ID and Name tdat2 <- proc_transpose(dat, id = CAT, name = Year) # View results tdat2 # Year Food and Tobacco Household Operation Medical and Health Personal Care # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1945 44.5 15.5 5.76 1.98 # 3 X1950 59.6 29.0 9.71 2.45 # 4 X1955 73.2 36.5 14.00 3.40 # 5 X1960 86.8 46.2 21.10 5.40 # Transpose only some of the variables tdat3 <- proc_transpose(dat, var = v(X1940, X1950, X1960), id = CAT, name = Year) # View results tdat3 # Year Food and Tobacco Household Operation Medical and Health Personal Care # 1 X1940 22.2 10.5 3.53 1.04 # 2 X1950 59.6 29.0 9.71 2.45 # 3 X1960 86.8 46.2 21.10 5.40 # By with a where clause tdat4 <- proc_transpose(dat, by = CAT, name = Year, where = expression(Year %in% c("X1940", "X1950", "X1960"))) # View Results tdat4 # CAT Year COL1 # 1 Food and Tobacco X1940 22.20 # 2 Food and Tobacco X1950 59.60 # 3 Food and Tobacco X1960 86.80 # 4 Household Operation X1940 10.50 # 5 Household Operation X1950 29.00 # 6 Household Operation X1960 46.20 # 7 Medical and Health X1940 3.53 # 8 Medical and Health X1950 9.71 # 9 Medical and Health X1960 21.10 # 10 Personal Care X1940 1.04 # 11 Personal Care X1950 2.45 # 12 Personal Care X1960 5.40
The proc_ttest function generates T-Test statistics
for selected variables on the input dataset.
The variables are identified on the var parameter or the paired
parameter. The function will calculate a standard set of T-Test statistics.
Results are displayed in
the viewer interactively and returned from the function.
proc_ttest( data, var = NULL, paired = NULL, output = NULL, by = NULL, class = NULL, options = NULL, titles = NULL )proc_ttest( data, var = NULL, paired = NULL, output = NULL, by = NULL, class = NULL, options = NULL, titles = NULL )
data |
The input data frame for which to calculate summary statistics. This parameter is required. |
var |
The variable or variables to be used for hypothesis testing.
Pass the variable names in a quoted vector,
or an unquoted vector using the
|
paired |
A vector of paired variables to perform a paired T-Test on.
Variables should
be separated by a star (*). The entire string should be quoted, for example,
|
output |
Whether or not to return datasets from the function. Valid
values are "out", "none", and "report". Default is "out", and will
produce dataset output specifically designed for programmatic use. The "none"
option will return a NULL instead of a dataset or list of datasets.
The "report" keyword returns the datasets from the interactive report, which
may be different from the standard output. The output parameter also accepts
data shaping keywords "long, "stacked", and "wide".
These shaping keywords control the structure of the output data. See the
Data Shaping section for additional details. Note that
multiple output keywords may be passed on a
character vector. For example,
to produce both a report dataset and a "long" output dataset,
use the parameter |
by |
An optional by group. If you specify a by group, the input data will be subset on the by variable(s) prior to performing any statistics. |
class |
The |
options |
A vector of optional keywords. Valid values are: "alpha =", "h0 =", and "noprint". The "alpha = " option will set the alpha value for confidence limit statistics. The default is 95% (alpha = 0.05). The "h0 = " option sets the baseline hypothesis value for single-variable hypothesis testing. The "noprint" option turns off the interactive report. |
titles |
A vector of one or more titles to use for the report output. |
The proc_ttest function is for performing hypothesis testing.
Data is passed in on the data
parameter. The function can segregate data into
groups using the by parameter. There are also
options to determine whether and what results are returned.
The proc_ttest function allows for three types of analysis:
One Sample: The one sample test allows you to perform
significance testing of a single variable against a known baseline
value or null hypothesis. To perform this test, pass the variable name
on the var parameter and the baseline on the h0= option. The
one sample T-Test performs a classic Student's T-Test and assumes your
data has a normal distribution.
Paired Comparison: The paired comparison is for tests of
two variables with a natural pairing and the same number of observations
for both measures.
For instance, if you are checking for a change in blood pressure for
the same group of patients at different time points. To perform a paired
comparison, use the paired parameter with the two variables
separated by a star (*). The paired T-Test performs a classic Student's T-Test
and assumes your data has a normal distribution.
Two Independant Samples: The analysis of two independent
samples is used when there is no natural pairing, and there may be a different
number of observations in each group. This method is used, for example,
if you are comparing the effectiveness of a treatment between two different
groups of patients. The function assumes that there is
a single variable that contains the analysis values for both groups, and
another variable to identify the groups. To perform this analysis,
pass the target variable name on the var parameter, and the
grouping variable on the class parameter. The Two Sample T-Test
provides both a Student's T-Test and a Welch-Satterthwaite T-Test. Select
the appropriate T-Test results for your data based on the known normality.
Normally, the requested T-Test statistics are shown interactively
in the viewer, and output results are returned as a list of data frames.
You may then access individual datasets from the list using dollar sign ($)
syntax.
The interactive report can be turned off using the "noprint" option, and
the output datasets can be turned off using the "none" keyword on the
output parameter.
By default, proc_ttest results will
be sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data. To turn off the
interactive report, pass the "noprint" keyword
to the options parameter.
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
The exact datasets used for the interactive report can be returned as a list.
To return these datasets, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
Dataset results are also returned from the function by default.
proc_ttest typically returns multiple datasets in a list. Each
dataset will be named according to the category of statistical
results. There are three standard categories: "Statistics",
"ConfLimits", and "TTests". For the class style analysis, the function
also returns a dataset called "Equality" that shows the Folded F analysis.
The output datasets generated are optimized for data manipulation. The column names have been standardized, and additional variables may be present to help with data manipulation. For example, the by variable will always be named "BY". In addition, data values in the output datasets are intentionally not rounded or formatted to give you the most accurate numeric results.
The proc_ttest function recognizes the following options. Options may
be passed as a quoted vector of strings, or an unquoted vector using the
v() function.
alpha = : The "alpha = " option will set the alpha
value for confidence limit statistics. Set the alpha as a decimal value
between 0 and 1. For example, you can set a 90% confidence limit as
alpha = 0.1.
h0: The "h0 =" option is used to set the baseline mean value
for testing a single variable. Pass the option as a name/value pair,
such as h0 = 95.
noprint: Whether to print the interactive report to the
viewer. By default, the report is printed to the viewer. The "noprint"
option will inhibit printing. You may inhibit printing globally by
setting the package print option to false:
options("procs.print" = FALSE).
The output datasets produced by the function can be shaped in different ways. These shaping options allow you to decide whether the data should be returned long and skinny, or short and wide. The shaping options can reduce the amount of data manipulation necessary to get the data into the desired form. The shaping options are as follows:
long: Transposes the output datasets so that statistics are in rows and variables are in columns.
stacked: Requests that output datasets be returned in "stacked" form, such that both statistics and variables are in rows.
wide: Requests that output datasets be returned in "wide" form, such that statistics are across the top in columns, and variables are in rows. This shaping option is the default.
These shaping options are passed on the output parameter. For example,
to return the data in "long" form, use output = "long".
# Turn off printing for CRAN checks options("procs.print" = FALSE) # Prepare sample data dat1 <- subset(sleep, group == 1, c("ID", "extra")) dat2 <- subset(sleep, group == 2, c("ID", "extra")) dat <- data.frame(ID = dat1$ID, group1 = dat1$extra, group2 = dat2$extra) # View sample data dat # ID group1 group2 # 1 1 0.7 1.9 # 2 2 -1.6 0.8 # 3 3 -0.2 1.1 # 4 4 -1.2 0.1 # 5 5 -0.1 -0.1 # 6 6 3.4 4.4 # 7 7 3.7 5.5 # 8 8 0.8 1.6 # 9 9 0.0 4.6 # 10 10 2.0 3.4 # Example 1: T-Test using h0 option res1 <- proc_ttest(dat, var = "group1", options = c("h0" = 0)) # View results res1 # $Statistics # VAR N MEAN STD STDERR MIN MAX # 1 group1 10 0.75 1.78901 0.5657345 -1.6 3.7 # # $ConfLimits # VAR MEAN LCLM UCLM STD # 1 group1 0.75 -0.5297804 2.02978 1.78901 # # $TTests # VAR DF T PROBT # 1 group1 9 1.32571 0.2175978 # Example 2: T-Test using paired parameter res2 <- proc_ttest(dat, paired = "group2 * group1") # View results res2 # $Statistics # VAR1 VAR2 DIFF N MEAN STD STDERR MIN MAX # 1 group2 group1 group2-group1 10 1.58 1.229995 0.3889587 0 4.6 # # $ConfLimits # VAR1 VAR2 DIFF MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 group2 group1 group2-group1 1.58 0.7001142 2.459886 1.229995 0.8460342 2.245492 # # $TTests # VAR1 VAR2 DIFF DF T PROBT # 1 group2 group1 group2-group1 9 4.062128 0.00283289 # Example 3: T-Test using class parameter res3 <- proc_ttest(sleep, var = "extra", class = "group") # View results res3 # $Statistics # VAR CLASS METHOD N MEAN STD STDERR MIN MAX # 1 extra 1 <NA> 10 0.75 1.789010 0.5657345 -1.6 3.7 # 2 extra 2 <NA> 10 2.33 2.002249 0.6331666 -0.1 5.5 # 3 extra Diff (1-2) Pooled NA -1.58 NA 0.8490910 NA NA # 4 extra Diff (1-2) Satterthwaite NA -1.58 NA 0.8490910 NA NA # # $ConfLimits # VAR CLASS METHOD MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 extra 1 <NA> 0.75 -0.5297804 2.0297804 1.789010 1.230544 3.266034 # 2 extra 2 <NA> 2.33 0.8976775 3.7623225 2.002249 1.377217 3.655326 # 3 extra Diff (1-2) Pooled -1.58 -3.3638740 0.2038740 NA NA NA # 4 extra Diff (1-2) Satterthwaite -1.58 -3.3654832 0.2054832 NA NA NA # # $TTests # VAR METHOD VARIANCES DF T PROBT # 1 extra Pooled Equal 18.00000 -1.860813 0.07918671 # 2 extra Satterthwaite Unequal 17.77647 -1.860813 0.07939414 # # $Equality # VAR METHOD NDF DDF FVAL PROBF # 1 extra Folded F 9 9 1.252595 0.7427199 # Example 4: T-Test using alpha option and by variable res4 <- proc_ttest(sleep, var = "extra", by = "group", options = c(alpha = 0.1)) # View results res4 # $Statistics # BY VAR N MEAN STD STDERR MIN MAX # 1 1 extra 10 0.75 1.789010 0.5657345 -1.6 3.7 # 2 2 extra 10 2.33 2.002249 0.6331666 -0.1 5.5 # # $ConfLimits # BY VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 1 extra 0.75 -0.2870553 1.787055 1.789010 1.304809 2.943274 # 2 2 extra 2.33 1.1693340 3.490666 2.002249 1.460334 3.294095 # # $TTests # BY VAR DF T PROBT # 1 1 extra 9 1.325710 0.217597780 # 2 2 extra 9 3.679916 0.005076133 # Example 5: Single variable T-Test using "long" shaping option res5 <- proc_ttest(sleep, var = "extra", output = "long") # View results res5 # $Statistics # STAT extra # 1 N 20.0000000 # 2 MEAN 1.5400000 # 3 STD 2.0179197 # 4 STDERR 0.4512206 # 5 MIN -1.6000000 # 6 MAX 5.5000000 # # $ConfLimits # STAT extra # 1 MEAN 1.5400000 # 2 LCLM 0.5955845 # 3 UCLM 2.4844155 # 4 STD 2.0179197 # 5 LCLMSTD 1.5346086 # 6 UCLMSTD 2.9473163 # # $TTests # STAT extra # 1 DF 19.00000000 # 2 T 3.41296500 # 3 PROBT 0.00291762# Turn off printing for CRAN checks options("procs.print" = FALSE) # Prepare sample data dat1 <- subset(sleep, group == 1, c("ID", "extra")) dat2 <- subset(sleep, group == 2, c("ID", "extra")) dat <- data.frame(ID = dat1$ID, group1 = dat1$extra, group2 = dat2$extra) # View sample data dat # ID group1 group2 # 1 1 0.7 1.9 # 2 2 -1.6 0.8 # 3 3 -0.2 1.1 # 4 4 -1.2 0.1 # 5 5 -0.1 -0.1 # 6 6 3.4 4.4 # 7 7 3.7 5.5 # 8 8 0.8 1.6 # 9 9 0.0 4.6 # 10 10 2.0 3.4 # Example 1: T-Test using h0 option res1 <- proc_ttest(dat, var = "group1", options = c("h0" = 0)) # View results res1 # $Statistics # VAR N MEAN STD STDERR MIN MAX # 1 group1 10 0.75 1.78901 0.5657345 -1.6 3.7 # # $ConfLimits # VAR MEAN LCLM UCLM STD # 1 group1 0.75 -0.5297804 2.02978 1.78901 # # $TTests # VAR DF T PROBT # 1 group1 9 1.32571 0.2175978 # Example 2: T-Test using paired parameter res2 <- proc_ttest(dat, paired = "group2 * group1") # View results res2 # $Statistics # VAR1 VAR2 DIFF N MEAN STD STDERR MIN MAX # 1 group2 group1 group2-group1 10 1.58 1.229995 0.3889587 0 4.6 # # $ConfLimits # VAR1 VAR2 DIFF MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 group2 group1 group2-group1 1.58 0.7001142 2.459886 1.229995 0.8460342 2.245492 # # $TTests # VAR1 VAR2 DIFF DF T PROBT # 1 group2 group1 group2-group1 9 4.062128 0.00283289 # Example 3: T-Test using class parameter res3 <- proc_ttest(sleep, var = "extra", class = "group") # View results res3 # $Statistics # VAR CLASS METHOD N MEAN STD STDERR MIN MAX # 1 extra 1 <NA> 10 0.75 1.789010 0.5657345 -1.6 3.7 # 2 extra 2 <NA> 10 2.33 2.002249 0.6331666 -0.1 5.5 # 3 extra Diff (1-2) Pooled NA -1.58 NA 0.8490910 NA NA # 4 extra Diff (1-2) Satterthwaite NA -1.58 NA 0.8490910 NA NA # # $ConfLimits # VAR CLASS METHOD MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 extra 1 <NA> 0.75 -0.5297804 2.0297804 1.789010 1.230544 3.266034 # 2 extra 2 <NA> 2.33 0.8976775 3.7623225 2.002249 1.377217 3.655326 # 3 extra Diff (1-2) Pooled -1.58 -3.3638740 0.2038740 NA NA NA # 4 extra Diff (1-2) Satterthwaite -1.58 -3.3654832 0.2054832 NA NA NA # # $TTests # VAR METHOD VARIANCES DF T PROBT # 1 extra Pooled Equal 18.00000 -1.860813 0.07918671 # 2 extra Satterthwaite Unequal 17.77647 -1.860813 0.07939414 # # $Equality # VAR METHOD NDF DDF FVAL PROBF # 1 extra Folded F 9 9 1.252595 0.7427199 # Example 4: T-Test using alpha option and by variable res4 <- proc_ttest(sleep, var = "extra", by = "group", options = c(alpha = 0.1)) # View results res4 # $Statistics # BY VAR N MEAN STD STDERR MIN MAX # 1 1 extra 10 0.75 1.789010 0.5657345 -1.6 3.7 # 2 2 extra 10 2.33 2.002249 0.6331666 -0.1 5.5 # # $ConfLimits # BY VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD # 1 1 extra 0.75 -0.2870553 1.787055 1.789010 1.304809 2.943274 # 2 2 extra 2.33 1.1693340 3.490666 2.002249 1.460334 3.294095 # # $TTests # BY VAR DF T PROBT # 1 1 extra 9 1.325710 0.217597780 # 2 2 extra 9 3.679916 0.005076133 # Example 5: Single variable T-Test using "long" shaping option res5 <- proc_ttest(sleep, var = "extra", output = "long") # View results res5 # $Statistics # STAT extra # 1 N 20.0000000 # 2 MEAN 1.5400000 # 3 STD 2.0179197 # 4 STDERR 0.4512206 # 5 MIN -1.6000000 # 6 MAX 5.5000000 # # $ConfLimits # STAT extra # 1 MEAN 1.5400000 # 2 LCLM 0.5955845 # 3 UCLM 2.4844155 # 4 STD 2.0179197 # 5 LCLMSTD 1.5346086 # 6 UCLMSTD 2.9473163 # # $TTests # STAT extra # 1 DF 19.00000000 # 2 T 3.41296500 # 3 PROBT 0.00291762