From 87df6dafa07cd8394667ea4cfaa532b98de47acc Mon Sep 17 00:00:00 2001 From: Michael Clark Date: Mon, 13 Jul 2020 16:32:18 -0400 Subject: [PATCH] add row_min, row_max, minor cleanup elsewhere --- DESCRIPTION | 5 +- NAMESPACE | 7 +- NEWS.md | 6 +- R/combn_2_col.R | 108 +-- R/gather_multi.R | 4 +- R/globals.R | 2 +- R/head_tail.R | 4 +- R/num_summary.R | 2 +- R/pre_process.R | 11 +- R/row_sums.R | 29 +- R/spread2.R | 3 +- R/utils.R | 10 +- _pkgdown.yml | 4 +- docs/404.html | 33 +- docs/LICENSE-text.html | 33 +- docs/articles/index.html | 34 +- docs/articles/introduction.html | 806 +++++++----------- .../empty-anchor.js | 15 + .../figure-html/describe_cat-1.svg | 74 +- docs/authors.html | 27 +- docs/bootstrap-toc.css | 60 ++ docs/bootstrap-toc.js | 159 ++++ docs/index.html | 35 +- docs/news/index.html | 61 +- docs/pkgdown.css | 121 ++- docs/pkgdown.js | 5 - docs/pkgdown.yml | 3 +- docs/reference/combn_2_col.html | 122 ++- docs/reference/create_prediction_data.html | 40 +- docs/reference/describe_all-1.svg | 76 +- docs/reference/describe_all.html | 103 +-- docs/reference/gather_multi.html | 138 +-- docs/reference/head_tail.html | 44 +- docs/reference/index.html | 73 +- docs/reference/num_by.html | 119 ++- docs/reference/num_summary.html | 51 +- docs/reference/onehot.html | 63 +- docs/reference/pre_process.html | 59 +- docs/reference/rnd.html | 40 +- docs/reference/row_sums.html | 55 +- docs/reference/select_not.html | 55 +- docs/reference/spread2.html | 152 +--- docs/reference/sum_NA.html | 40 +- docs/reference/tidyext.html | 37 +- man/combn_2_col.Rd | 67 +- man/head_tail.Rd | 4 +- man/pre_process.Rd | 9 +- man/reexports.Rd | 19 - man/row_sums.Rd | 11 +- tests/testthat/test_cat_by.R | 2 +- tests/testthat/test_combn_2_col.R | 2 +- tests/testthat/test_describe_all.R | 2 +- tests/testthat/test_num_by.R | 2 +- tests/testthat/test_num_summary.R | 2 +- tests/testthat/test_pre_process.R | 3 +- tests/testthat/test_row_sums.R | 42 +- tests/testthat/test_sum_NA.R | 2 +- vignettes/introduction.Rmd | 99 +-- 58 files changed, 1617 insertions(+), 1577 deletions(-) create mode 100644 docs/articles/introduction_files/accessible-code-block-0.0.1/empty-anchor.js create mode 100644 docs/bootstrap-toc.css create mode 100644 docs/bootstrap-toc.js delete mode 100644 man/reexports.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 49db96c..e2501da 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: tidyext Type: Package Title: Tidy Extensions for Data Processing -Version: 0.3.5 +Version: 0.3.6 Authors@R: person("Michael", "Clark", role = c("aut", "cre"), email = "micl@umich.edu") Maintainer: Michael Clark Description: Common data processing and summary functions to extend your tidy ways. @@ -16,10 +16,8 @@ Depends: Imports: dplyr (>= 1.0.0), purrr, - magrittr, rlang, scales, - tibble, tidyr (>= 1.0.0) Suggests: ggplot2, @@ -28,5 +26,6 @@ Suggests: stringi, stringr, testthat, + tibble, covr RoxygenNote: 7.1.1 diff --git a/NAMESPACE b/NAMESPACE index 41e569b..4ebf8ef 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,5 @@ # Generated by roxygen2: do not edit by hand -export("%>%") export(cat_by) export(combn_2_col) export(create_prediction_data) @@ -15,17 +14,16 @@ export(onehot) export(pre_process) export(rnd) export(row_apply) +export(row_max) export(row_means) +export(row_min) export(row_sums) export(spread2) export(sum_NA) export(sum_NaN) export(sum_blank) -export(vars) import(dplyr) importFrom(dplyr,quo_name) -importFrom(dplyr,vars) -importFrom(magrittr,"%>%") importFrom(purrr,map) importFrom(purrr,map_df) importFrom(purrr,map_dfr) @@ -39,7 +37,6 @@ importFrom(stats,model.matrix) importFrom(stats,na.omit) importFrom(stats,quantile) importFrom(stats,sd) -importFrom(tibble,rowid_to_column) importFrom(tidyr,gather) importFrom(tidyr,nest) importFrom(tidyr,unnest) diff --git a/NEWS.md b/NEWS.md index 4820c4a..5528188 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,10 @@ +# tidyext 0.3.6 + +Added `row_min` and `row_max`. Removed some dependencies. + # tidyext 0.3.5 -Cleanup and update for R 4.0 and dplyr 1.0. Deprecate gather_multi and spread2. +Cleanup and update for R 4.0 and dplyr 1.0. Deprecate `gather_multi` and `spread2`. # tidyext 0.3.1 diff --git a/R/combn_2_col.R b/R/combn_2_col.R index b6d8fee..2aa6f42 100644 --- a/R/combn_2_col.R +++ b/R/combn_2_col.R @@ -47,75 +47,38 @@ #' only the indicator columns. #' @examples #' library(tidyext) -#' d = data.frame(id = 1:4, -#' labs = c('A/B', 'B/C/D/E', 'A/E', 'D/E')) -#' test = combn_2_col(data=d, var='labs', max_m=3) +#' +#' d = data.frame(id = 1:4, labs = c('A/B', 'B/C/D/E', 'A/E', 'D/E')) +#' test = combn_2_col(data = d, var = 'labs', max_m = 3) #' test #' str(test) +#' #' d$labs = c('A B', 'B C D E', 'A E', 'D E') -#' combn_2_col(data=d, var='labs', max_m=1) +#' combn_2_col(data = d, var = 'labs', max_m = 1) +#' #' d$labs = c('Tom, Dick & Harriet', "J'Sean", "OBG, Andreas", NA) -#' combn_2_col(data=d, var='labs', sep=',', max_m=2, collapse='-') -#' -#' \dontrun{ -#' # requires at least tidytext -#' tidy_dtm <- function(data, var, sep='-', max_m=3) { -#' init = stringr::str_split(data[[var]], pattern = sep) # creates a list of separated letters -#' -#' # the following gets the combos with a dot separating drugs in a given combo -#' # this first lapply could be parallelized if need be and is probably slowest -#' # probably want to change to m = min(c(4, m)) so as to only limit to 4 -#' # see also, combinat::combn which is slightly faster than base R below -#' observation_combos = init %>% -#' lapply(function(x) -#' sapply(seq_along(x), function(m) -#' utils::combn(x, min(max_m, m), FUN=paste, collapse = '_'))) -#' -#' # now we have a standard text analysis problem in need of a document term -#' matrix -#' documents = observation_combos %>% lapply(unlist) -#' -#' # create a 'tidy' form of documents and terms; each term (i.e. combo) only -#' occurs once in a document -#' doc_df = data.frame(id=rep(data$id, sapply(documents, length)), -#' combos=unlist(documents), -#' count=1) # each term only occurs once in the document -#' doc_df %>% -#' tidytext::cast_dfm(document=id, term=combos, value=count) -#' } -#' -#' # requires at least text2vec -#' ttv <- function(data, var, sep='-', max_m=3) { -#' docs = sapply(stringr::str_split(data[[var]], pattern=sep), -#' function(str_vec) -#' sapply(seq_along(str_vec), -#' function(m) -#' combn(str_vec, -#' m = min(max_m, m), -#' FUN = paste, -#' collapse = '_') -#' ) %>% unlist() -#' ) -#' -#' toks = itoken(docs, progressbar = FALSE) -#' vocab = create_vocabulary(toks) -#' create_dtm(toks, vectorizer = vocab_vectorizer(vocab), progressbar = FALSE) %>% -#' as.matrix() %>% -#' cbind(data,.) -#' } -#' -#' } +#' +#' combn_2_col( +#' data = d, +#' var = 'labs', +#' sep = ',', +#' max_m = 2, +#' collapse = '-' +#' ) +#' #' #' #' #' @export -combn_2_col <- function(data, - var, - sep='[^[:alnum:]]+', - max_m=1, - collapse = '_', - toInteger=FALSE, - sparse=FALSE) { +combn_2_col <- function( + data, + var, + sep = '[^[:alnum:]]+', + max_m = 1, + collapse = '_', + toInteger = FALSE, + sparse = FALSE +) { if (is.null(data) | is.null(var)) stop('Need data and variable name to continue.') @@ -123,16 +86,18 @@ combn_2_col <- function(data, if (max_m < 1) stop('Need positive value for max_m.') data$combo <- - map(stringr::str_split(data[[var]], pattern=sep), - function(str_vec) - map(seq_along(str_vec), - function(m) - combn(str_vec, - m = min(max_m, m), - FUN = paste, - collapse = collapse) - ) %>% unlist() + map(stringr::str_split(data[[var]], pattern = sep), + function(str_vec) + map(seq_along(str_vec), + function(m) + combn(str_vec, + m = min(max_m, m), + FUN = paste, + collapse = collapse) + ) %>% + unlist() ) + combo_cols <- unique(unlist(data$combo)) if (sparse) { @@ -142,7 +107,7 @@ combn_2_col <- function(data, do.call(rbind,.) %>% Matrix::Matrix(sparse = TRUE, dimnames = list(rownames(data), combo_cols)) - ) + ) } if (toInteger) { @@ -156,6 +121,7 @@ combn_2_col <- function(data, map(function(x) combo_cols %in% x) %>% do.call(rbind,.) } + data } diff --git a/R/gather_multi.R b/R/gather_multi.R index c9de413..b5d925b 100644 --- a/R/gather_multi.R +++ b/R/gather_multi.R @@ -132,7 +132,7 @@ gather_multi <- function(data, na.rm = na.rm, convert = convert, factor_key = factor_key) %>% - rowid_to_column() + mutate(rowid = 1:nrow(.)) %>% # changed to get rid of tibble requirement while deprecated for (i in 2:length(varlist)) { data_long <- data %>% @@ -144,7 +144,7 @@ gather_multi <- function(data, na.rm = na.rm, convert = convert, factor_key = factor_key) %>% - rowid_to_column()%>% + mutate(rowid = 1:nrow(.)) %>% # changed to get rid of tibble requirement while deprecated select(rowid, !!values[[i]]) %>% left_join(data_long, ., by='rowid') } diff --git a/R/globals.R b/R/globals.R index c685973..78877f1 100644 --- a/R/globals.R +++ b/R/globals.R @@ -3,4 +3,4 @@ utils::globalVariables(c(".", 'Max', 'Mean', 'Median', 'Min', 'Missing', 'N', 'Q1', 'Q3', 'SD', 'Variable', 'X1st.Qu.', 'X3rd.Qu.', 'perc', 'result', 'results', 'target', 'value', 'x.Freq', 'x.x', 'y.Freq', 'y.x', '%', 'rowid', - 'Group', 'Frequency')) + 'Group', 'Frequency', 'rn')) diff --git a/R/head_tail.R b/R/head_tail.R index 62646ac..0b5100a 100644 --- a/R/head_tail.R +++ b/R/head_tail.R @@ -11,8 +11,8 @@ #' @examples #' #' library(tidyext) -#' as.matrix(mtcars) %>% -#' head_tail(6) +#' +#' head_tail(mtcars) #' head_tail = function(data, n_slice = 6) { # initial checks diff --git a/R/num_summary.R b/R/num_summary.R index bdfa26c..901903c 100644 --- a/R/num_summary.R +++ b/R/num_summary.R @@ -35,7 +35,7 @@ num_summary <- function(x, digits = 1, extra = FALSE) { x <- as.numeric(x) - d <- tibble( + d <- dplyr::tibble( N = length(na.omit(x)), data.frame(t(c(summary(x)))), SD = sd(x, na.rm = TRUE), diff --git a/R/pre_process.R b/R/pre_process.R index 03f5d55..df22c9a 100644 --- a/R/pre_process.R +++ b/R/pre_process.R @@ -33,14 +33,17 @@ #' them. #' @importFrom scales rescale #' @return A data frame that has been processed -#' @export #' #' @examples #' library(tidyext) +#' library(dplyr) +#' #' pre_process(mtcars) -#' pre_process(mtcars, log_vars=vars(mpg, wt)) -#' pre_process(mtcars, zero_start=vars(cyl, gear)) -#' pre_process(mtcars, zero_one=vars(mpg)) +#' pre_process(mtcars, log_vars = vars(mpg, wt)) +#' pre_process(mtcars, zero_start = vars(cyl, gear)) +#' pre_process(mtcars, zero_one = vars(mpg)) +#' +#' @export pre_process <- function( data, std = TRUE, diff --git a/R/row_sums.R b/R/row_sums.R index 33e7789..ae0a17f 100644 --- a/R/row_sums.R +++ b/R/row_sums.R @@ -7,7 +7,7 @@ #' @param ... The columns to sum, take the mean of, etc. \emph{Required}. #' @param .fun The function to apply. #' @param na_rm Whether to remove \code{NA} values or not. Default is \code{FALSE}. -#' @param varname The column name of the sums means etc. +#' @param varname The column name of the sums means etc. as a character string. #' #' @details Simple wrappers for applying rowwise operations only for selected #' columns within the tidyverse approach to data processing. The @@ -28,6 +28,9 @@ #' d %>% #' row_means(matches('x|z')) #' +#' d %>% +#' row_max(matches('x|y')) +#' #' row_apply( #' d , #' everything(), @@ -37,6 +40,8 @@ #' #' @export row_sums <- function(data, ..., na_rm = FALSE, varname = 'sum') { + # note: dplyr 1.0 included rowwise operations, but it wasn't obvious what + # advantage there would be for these functions except for min and max dplyr::mutate(data, !!varname := rowSums(select(data, ...), na.rm = na_rm)) } @@ -46,6 +51,28 @@ row_means <- function(data, ..., na_rm = FALSE, varname = 'mean') { dplyr::mutate(data, !!varname := rowMeans(select(data, ...), na.rm = na_rm)) } +#' @export +#' @rdname row_sums +row_min <- function(data, ..., na_rm = FALSE, varname = 'min') { + dplyr::select(data, ...) %>% + dplyr::mutate(rn = 1:nrow(.)) %>% + dplyr::rowwise(rn) %>% + dplyr::mutate(!!varname := min(dplyr::c_across(...), na.rm = na_rm)) %>% + dplyr::ungroup() %>% + dplyr::select(-rn) +} + +#' @export +#' @rdname row_sums +row_max <- function(data, ..., na_rm = FALSE, varname = 'max') { + dplyr::select(data, ...) %>% + dplyr::mutate(rn = 1:nrow(.)) %>% + dplyr::rowwise(rn) %>% + dplyr::mutate(!!varname := max(dplyr::c_across(...), na.rm = na_rm)) %>% + dplyr::ungroup() %>% + dplyr::select(-rn) +} + #' @export #' @rdname row_sums row_apply <- function(data, ..., .fun, varname = 'var') { diff --git a/R/spread2.R b/R/spread2.R index c89c4b3..789f11b 100644 --- a/R/spread2.R +++ b/R/spread2.R @@ -29,7 +29,6 @@ #' #' @return A data frame with 'wide' format. #' @seealso \code{\link[tidyr]{spread}} -#' @importFrom tibble rowid_to_column #' @examples #' \dontrun{ #' library(tidyext); library(tidyr) @@ -90,7 +89,7 @@ spread2 <- function(data, } else { data <- data %>% bind_cols(data %>% - tibble::rowid_to_column() %>% + mutate(rowid = 1:nrow(.)) %>% # changed to get rid of tibble requirement while deprecated select(rowid) ) } diff --git a/R/utils.R b/R/utils.R index 4a897c6..259c729 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,9 +1 @@ -#' @importFrom magrittr %>% -#' @export -magrittr::`%>%` - - - -#' @importFrom dplyr vars -#' @export -dplyr::vars +#' @importFrom dplyr vars `%>%` tibble diff --git a/_pkgdown.yml b/_pkgdown.yml index 243efc7..e757c8c 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -40,7 +40,7 @@ reference: - create_prediction_data - gather_multi - onehot - # - pre_process + - pre_process # - spread2 - row_sums - title: "Miscellaneous" @@ -48,6 +48,8 @@ reference: Miscellaneous functions. contents: - select_not + - head_tail + - rnd - tidyext figures: dev: svglite::svglite diff --git a/docs/404.html b/docs/404.html index 28d862e..c957034 100644 --- a/docs/404.html +++ b/docs/404.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,7 +50,6 @@ - @@ -64,7 +67,7 @@ - +
@@ -136,6 +139,12 @@

Page not found (404)

+ + @@ -146,7 +155,7 @@

Page not found (404)

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 2027b5d..8c85440 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,7 +50,6 @@ - @@ -64,7 +67,7 @@ - +
@@ -138,6 +141,12 @@

License

+ + @@ -148,7 +157,7 @@

License

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/index.html b/docs/articles/index.html index 9841c59..e15d098 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,7 +50,6 @@ - @@ -64,7 +67,7 @@ - +
@@ -136,9 +139,10 @@

Articles

All vignettes

- +
+
Demonstration of tidyext
+
+
@@ -150,7 +154,7 @@

All vignettes

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index a54a909..6d3c596 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -12,21 +12,21 @@ - - - - + + + + + - + - - +
+
-

Demonstration of tidyext

+

Demonstration of tidyext

Michel Clark

-

2020-02-17

+

2020-07-13

Source: vignettes/introduction.Rmd @@ -109,510 +109,342 @@

Data Summaries

To begin, we can load up the tidyverse and this package. I’ll also create some data that will be useful for demonstration.

-
library(tidyverse); library(tidyext)
-set.seed(8675309)
-df1 <- tibble(
-  g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')),
-  g2 = sample(1:4, 50, replace = TRUE),
-  a = rnorm(50),
-  b = rpois(50, 10),
-  c = sample(letters, 50, replace=TRUE),
-  d = sample(c(T,F), 50, replace=TRUE)
-)
-
-df_miss = df1
-df_miss[sample(1:nrow(df1), 10), sample(1:ncol(df1), 3)] = NA
+
library(tidyverse)
+library(tidyext)
+
+set.seed(8675309)
+
+df1 <- tibble(
+  g1 = factor(sample(1:2, 50, replace = TRUE), labels = c('a', 'b')),
+  g2 = sample(1:4, 50, replace = TRUE),
+  a = rnorm(50),
+  b = rpois(50, 10),
+  c = sample(letters, 50, replace = TRUE),
+  d = sample(c(T, F), 50, replace = TRUE)
+)
+
+df_miss = df1
+df_miss[sample(1:nrow(df1), 10), sample(1:ncol(df1), 3)] = NA

We can start by getting a quick numerical summary for a single column. As the name suggests, this will only work with numeric data.

-
num_summary(mtcars$mpg)
-   N Mean SD  Min   Q1 Median   Q3  Max Missing
-1 32 20.1  6 10.4 15.4   19.2 22.8 33.9       0
-
-num_summary(df_miss$a, extra = T)
-   N Mean SD  Min   Q1 Median  Q3 Max Missing Distinct Zeros
-1 40    0  1 -2.6 -0.9    0.1 0.6 1.7      10       41     0
+
num_summary(mtcars$mpg)
+# A tibble: 1 x 9
+      N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+1    32  20.1     6  10.4  15.4   19.2  22.8  33.9           0
+
+num_summary(df_miss$a, extra = T)
+# A tibble: 1 x 11
+      N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing` Distinct Zeros
+  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>    <dbl> <dbl>
+1    40   0.1   1.1  -2.6  -0.9    0.2   0.7     2          20       40     0

Note that the result’s class is a data.frame, which makes it easy to work with.

-
x = num_summary(mtcars$mpg)
-glimpse(x)
-Observations: 1
-Variables: 9
-$ N       <dbl> 32
-$ Mean    <dbl> 20.1
-$ SD      <dbl> 6
-$ Min     <dbl> 10.4
-$ Q1      <dbl> 15.4
-$ Median  <dbl> 19.2
-$ Q3      <dbl> 22.8
-$ Max     <dbl> 33.9
-$ Missing <dbl> 0
-
-mtcars %>% 
-  map_dfr(num_summary, .id = 'Variable')
-   Variable  N  Mean    SD  Min    Q1 Median    Q3   Max Missing
-1       mpg 32  20.1   6.0 10.4  15.4   19.2  22.8  33.9       0
-2       cyl 32   6.2   1.8  4.0   4.0    6.0   8.0   8.0       0
-3      disp 32 230.7 123.9 71.1 120.8  196.3 326.0 472.0       0
-4        hp 32 146.7  68.6 52.0  96.5  123.0 180.0 335.0       0
-5      drat 32   3.6   0.5  2.8   3.1    3.7   3.9   4.9       0
-6        wt 32   3.2   1.0  1.5   2.6    3.3   3.6   5.4       0
-7      qsec 32  17.8   1.8 14.5  16.9   17.7  18.9  22.9       0
-8        vs 32   0.4   0.5  0.0   0.0    0.0   1.0   1.0       0
-9        am 32   0.4   0.5  0.0   0.0    0.0   1.0   1.0       0
-10     gear 32   3.7   0.7  3.0   3.0    4.0   4.0   5.0       0
-11     carb 32   2.8   1.6  1.0   2.0    2.0   4.0   8.0       0
+
x = num_summary(mtcars$mpg)
+glimpse(x)
+Rows: 1
+Columns: 9
+$ N           <dbl> 32
+$ Mean        <dbl> 20.1
+$ SD          <dbl> 6
+$ Min         <dbl> 10.4
+$ Q1          <dbl> 15.4
+$ Median      <dbl> 19.2
+$ Q3          <dbl> 22.8
+$ Max         <dbl> 33.9
+$ `% Missing` <dbl> 0
+
+mtcars %>% 
+  map_dfr(num_summary, .id = 'Variable')
+# A tibble: 11 x 10
+   Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+ 1 mpg         32  20.1   6    10.4  15.4   19.2  22.8  33.9           0
+ 2 cyl         32   6.2   1.8   4     4      6     8     8             0
+ 3 disp        32 231.  124.   71.1 121.   196.  326   472             0
+ 4 hp          32 147.   68.6  52    96.5  123   180   335             0
+ 5 drat        32   3.6   0.5   2.8   3.1    3.7   3.9   4.9           0
+ 6 wt          32   3.2   1     1.5   2.6    3.3   3.6   5.4           0
+ 7 qsec        32  17.8   1.8  14.5  16.9   17.7  18.9  22.9           0
+ 8 vs          32   0.4   0.5   0     0      0     1     1             0
+ 9 am          32   0.4   0.5   0     0      0     1     1             0
+10 gear        32   3.7   0.7   3     3      4     4     5             0
+11 carb        32   2.8   1.6   1     2      2     4     8             0

There are also functions for summarizing missingness.

-
sum_NA(df_miss$a)
-[1] 10
-
-sum_blank(c(letters, '', '   '))
-[1] 2
-
-sum_NaN(c(1, NaN, 2))
-[1] 1
+
sum_NA(df_miss$a)
+[1] 10
+
+sum_blank(c(letters, '', '   '))
+[1] 2
+
+sum_NaN(c(1, NaN, 2))
+[1] 1

When dealing with a data frame of mixed types we can use the describe_* functions.

-
describe_all(df1)
-$`Numeric Variables`
-  Variable  N Mean   SD  Min    Q1 Median    Q3   Max Missing
-1       g2 50 2.54 1.13  1.0  2.00   3.00  3.75  4.00       0
-2        a 50 0.00 1.00 -2.6 -0.84   0.11  0.69  2.02       0
-3        b 50 9.98 3.07  4.0  7.25  10.00 12.00 17.00       0
-
-$`Categorical Variables`
-# A tibble: 29 x 4
-   Variable Group Frequency   `%`
-   <chr>    <chr>     <int> <dbl>
- 1 g1       a            25    50
- 2 g1       b            25    50
- 3 c        n             5    10
- 4 c        v             4     8
- 5 c        e             3     6
- 6 c        i             3     6
- 7 c        j             3     6
- 8 c        m             3     6
- 9 c        r             3     6
-10 c        c             2     4
-# … with 19 more rows
-
-describe_all_cat(df1)
-# A tibble: 29 x 4
-   Variable Group Frequency   `%`
-   <chr>    <chr>     <int> <dbl>
- 1 g1       a            25    50
- 2 g1       b            25    50
- 3 c        n             5    10
- 4 c        v             4     8
- 5 c        e             3     6
- 6 c        i             3     6
- 7 c        j             3     6
- 8 c        m             3     6
- 9 c        r             3     6
-10 c        c             2     4
-# … with 19 more rows
-
-describe_all_num(df1, digits = 1)
-  Variable  N Mean  SD  Min   Q1 Median   Q3 Max Missing
-1       g2 50  2.5 1.1  1.0  2.0    3.0  3.8   4       0
-2        a 50  0.0 1.0 -2.6 -0.8    0.1  0.7   2       0
-3        b 50 10.0 3.1  4.0  7.2   10.0 12.0  17       0
+
describe_all(df1)
+$`Numeric Variables`
+# A tibble: 3 x 10
+  Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+  <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+1 g2          50  2.54  1.13   1    2      3     3.75  4              0
+2 a           50  0     1     -2.6 -0.84   0.11  0.69  2.02           0
+3 b           50  9.98  3.07   4    7.25  10    12    17              0
+
+$`Categorical Variables`
+# A tibble: 29 x 4
+   Variable Group Frequency   `%`
+   <chr>    <fct>     <int> <dbl>
+ 1 g1       a            25    50
+ 2 g1       b            25    50
+ 3 c        n             5    10
+ 4 c        v             4     8
+ 5 c        e             3     6
+ 6 c        i             3     6
+ 7 c        j             3     6
+ 8 c        m             3     6
+ 9 c        r             3     6
+10 c        c             2     4
+# … with 19 more rows
+
+describe_all_cat(df1)
+# A tibble: 29 x 4
+   Variable Group Frequency   `%`
+   <chr>    <fct>     <int> <dbl>
+ 1 g1       a            25    50
+ 2 g1       b            25    50
+ 3 c        n             5    10
+ 4 c        v             4     8
+ 5 c        e             3     6
+ 6 c        i             3     6
+ 7 c        j             3     6
+ 8 c        m             3     6
+ 9 c        r             3     6
+10 c        c             2     4
+# … with 19 more rows
+
+describe_all_num(df1, digits = 1)
+# A tibble: 3 x 10
+  Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+  <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+1 g2          50   2.5   1.1   1     2      3     3.8     4           0
+2 a           50   0     1    -2.6  -0.8    0.1   0.7     2           0
+3 b           50  10     3.1   4     7.2   10    12      17           0

Note how the categorical data result is just as ready for visualization as the numeric, as it can be filtered by the Variable column. It also has an option to deal with NA and some other stuff.

-
describe_all_cat(df_miss, include_NAcat = TRUE, sort_by_freq = TRUE) %>% 
-  filter(Variable == 'g1') %>% 
-  ggplot(aes(x=Group, y=`%`)) +
-  geom_col(width = .25)
+
describe_all_cat(df_miss, include_NAcat = TRUE, sort_by_freq = TRUE) %>%
+  filter(Variable == 'g1') %>%
+  ggplot(aes(x=Group, y=`%`)) +
+  geom_col(width = .25)

Typically during data processing, we are performing grouped operations. As such there is a corresponding num_by and cat_by to provide the same information by some grouping variable. This basically is saving you from doing group_by %>% summarize() and creating variables for all these values. It can also take a set of variables to summarize using vars.

-
df_miss %>% 
-  num_by(a, group_var = g2)
-# A tibble: 5 x 11
-# Groups:   g2 [5]
-     g2 Variable     N  Mean    SD   Min    Q1 Median    Q3   Max Missing
-  <int> <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>
-1     2 a            9  -0.2   0.9  -2.1  -0.8    0.1   0.5   0.6       0
-2     3 a           11  -0.2   1.1  -2.6  -0.8   -0.2   0.4   1.7       0
-3    NA a            0 NaN    NA    NA    NA     NA    NA    NA        10
-4     1 a           10   0.1   1.1  -1.3  -0.9    0.4   1     1.6       0
-5     4 a           10   0.3   0.9  -1.1  -0.3    0.2   1     1.5       0
-
-df_miss %>% 
-  num_by(vars(a, b), group_var = g2)
-# A tibble: 10 x 11
-# Groups:   g2 [5]
-      g2 Variable     N  Mean    SD   Min    Q1 Median    Q3   Max Missing
-   <int> <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>
- 1     2 a            9  -0.2   0.9  -2.1  -0.8    0.1   0.5   0.6       0
- 2     2 b            9  10.2   2.6   6     9     10    12    14         0
- 3     3 a           11  -0.2   1.1  -2.6  -0.8   -0.2   0.4   1.7       0
- 4     3 b           11   9.9   3.1   5     7     10    12    14         0
- 5    NA a            0 NaN    NA    NA    NA     NA    NA    NA        10
- 6    NA b           10   9.2   3.7   4     7      9.5  11.8  15         0
- 7     1 a           10   0.1   1.1  -1.3  -0.9    0.4   1     1.6       0
- 8     1 b           10  11.6   2.6   8    10.2   11    12.8  17         0
- 9     4 a           10   0.3   0.9  -1.1  -0.3    0.2   1     1.5       0
-10     4 b           10   9     3.1   5     7.2    8    11.2  15         0
+
df_miss %>% 
+  num_by(a, group_var = g2)
+# A tibble: 5 x 11
+# Groups:   g2 [5]
+     g2 Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+  <int> <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+1     2 a           11   0.1   1.1  -2.1  -0.5    0.2   0.6   2             0
+2     3 a           11  -0.2   1.1  -2.6  -0.8   -0.2   0.4   1.7           0
+3    NA a            0 NaN    NA    NA    NA     NA    NA    NA           100
+4     1 a           10   0.1   1.1  -1.3  -0.9    0.4   1     1.6           0
+5     4 a            8   0.4   1    -1.1  -0.2    0.6   1.1   1.5           0
+
+df_miss %>% 
+  num_by(vars(a, b), group_var = g2)
+# A tibble: 10 x 11
+# Groups:   g2 [5]
+      g2 Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+   <int> <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+ 1     2 a           11   0.1   1.1  -2.1  -0.5    0.2   0.6   2             0
+ 2     2 b           11   9.9   2.5   6     8     10    12    14             0
+ 3     3 a           11  -0.2   1.1  -2.6  -0.8   -0.2   0.4   1.7           0
+ 4     3 b           11   9.9   3.1   5     7     10    12    14             0
+ 5    NA a            0 NaN    NA    NA    NA     NA    NA    NA           100
+ 6    NA b           10   8.7   3.9   4     5.5    8    11.8  15             0
+ 7     1 a           10   0.1   1.1  -1.3  -0.9    0.4   1     1.6           0
+ 8     1 b           10  11.6   2.6   8    10.2   11    12.8  17             0
+ 9     4 a            8   0.4   1    -1.1  -0.2    0.6   1.1   1.5           0
+10     4 b            8   9.8   3     6     8      8.5  12    15             0

For categorical variables summarized by group, you can select whether the resulting percentage is irrespective of the grouping.

-
df1 %>% 
-  cat_by(d, 
-         group_var = g1, 
-         perc_by_group = TRUE)
-# A tibble: 4 x 5
-# Groups:   g1 [2]
-  g1    d         N `% of Total` `% of g1`
-  <fct> <lgl> <int>        <dbl>     <dbl>
-1 a     FALSE    10           20        40
-2 a     TRUE     15           30        60
-3 b     FALSE    10           20        40
-4 b     TRUE     15           30        60
-
-df1 %>% 
-  cat_by(d, 
-         group_var = g1, 
-         perc_by_group = FALSE, 
-         sort_by_group = FALSE)
-# A tibble: 4 x 4
-  g1    d         N `% of Total`
-  <fct> <lgl> <int>        <dbl>
-1 a     FALSE    10           20
-2 a     TRUE     15           30
-3 b     FALSE    10           20
-4 b     TRUE     15           30
+
df1 %>% 
+  cat_by(d, 
+         group_var = g1, 
+         perc_by_group = TRUE)
+# A tibble: 4 x 5
+# Groups:   g1 [2]
+  g1    d         N `% of Total` `% of g1`
+  <fct> <lgl> <int>        <dbl>     <dbl>
+1 a     FALSE    10           20        40
+2 a     TRUE     15           30        60
+3 b     FALSE    10           20        40
+4 b     TRUE     15           30        60
+
+df1 %>% 
+  cat_by(d, 
+         group_var = g1, 
+         perc_by_group = FALSE, 
+         sort_by_group = FALSE)
+# A tibble: 4 x 4
+  g1    d         N `% of Total`
+  <fct> <lgl> <int>        <dbl>
+1 a     FALSE    10           20
+2 a     TRUE     15           30
+3 b     FALSE    10           20
+4 b     TRUE     15           30

Data Processing

In addition there are some functions for data processing. We can start with the simple one-hot encoding function.

-
onehot(iris) %>% 
-  slice(c(1:2, 51:52, 101:102))
-  Sepal.Length Sepal.Width Petal.Length Petal.Width Species_setosa Species_versicolor Species_virginica
-1          5.1         3.5          1.4         0.2              1                  0                 0
-2          4.9         3.0          1.4         0.2              1                  0                 0
-3          7.0         3.2          4.7         1.4              0                  1                 0
-4          6.4         3.2          4.5         1.5              0                  1                 0
-5          6.3         3.3          6.0         2.5              0                  0                 1
-6          5.8         2.7          5.1         1.9              0                  0                 1
+
onehot(iris) %>% 
+  slice(c(1:2, 51:52, 101:102))
+    Sepal.Length Sepal.Width Petal.Length Petal.Width Species_setosa Species_versicolor Species_virginica
+1            5.1         3.5          1.4         0.2              1                  0                 0
+2            4.9         3.0          1.4         0.2              1                  0                 0
+51           7.0         3.2          4.7         1.4              0                  1                 0
+52           6.4         3.2          4.5         1.5              0                  1                 0
+101          6.3         3.3          6.0         2.5              0                  0                 1
+102          5.8         2.7          5.1         1.9              0                  0                 1

It can do it sparsely.

-
iris %>% 
-  slice(c(1:2, 51:52, 101:102)) %>% 
-  onehot(sparse = TRUE)
-6 x 3 sparse Matrix of class "dgCMatrix"
-  Species_xsetosa Species_xversicolor Species_xvirginica
-1               1                   .                  .
-2               1                   .                  .
-3               .                   1                  .
-4               .                   1                  .
-5               .                   .                  1
-6               .                   .                  1
+
iris %>% 
+  slice(c(1:2, 51:52, 101:102)) %>% 
+  onehot(sparse = TRUE)
+6 x 3 sparse Matrix of class "dgCMatrix"
+  Species_xsetosa Species_xversicolor Species_xvirginica
+1               1                   .                  .
+2               1                   .                  .
+3               .                   1                  .
+4               .                   1                  .
+5               .                   .                  1
+6               .                   .                  1

Choose a specific variable, whether you want to keep the others, and how to deal with NA.

-
df_miss %>%
-  onehot(var = c('g1', 'g2'), nas = 'na.omit', keep.original = FALSE) %>%
-  head()
-           a  b c     d g1_a g1_b g2_1 g2_2 g2_3 g2_4
-1  0.1348980 12 o FALSE    0    1    0    1    0    0
-2  0.1175821  5 r  TRUE    1    0    0    0    1    0
-3 -0.8255891 14 p FALSE    0    1    0    1    0    0
-4 -2.1352363  7 l FALSE    1    0    0    1    0    0
-5  0.2142085 10 n  TRUE    0    1    0    1    0    0
-6 -0.9470681 11 s FALSE    1    0    0    0    1    0
+
df_miss %>%
+  onehot(var = c('g1', 'g2'), nas = 'na.omit', keep.original = FALSE) %>%
+  head()
+           a  b c     d g1_a g1_b g2_1 g2_2 g2_3 g2_4
+1  0.1348980 12 o FALSE    0    1    0    1    0    0
+2  0.1175821  5 r  TRUE    1    0    0    0    1    0
+3 -0.8255891 14 p FALSE    0    1    0    1    0    0
+4 -2.1352363  7 l FALSE    1    0    0    1    0    0
+5  0.2142085 10 n  TRUE    0    1    0    1    0    0
+6 -0.9470681 11 s FALSE    1    0    0    0    1    0

With create_prediction_data, we can quickly create data for use with predict after a model. By default it will put numeric variables at their mean, and categorical variables at their most common category.

-
create_prediction_data(iris)
-  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-1     5.843333    3.057333        3.758    1.199333  setosa
-
-create_prediction_data(iris, num = function(x) quantile(x, p=.25))
-  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-1          5.1         2.8          1.6         0.3  setosa
+
create_prediction_data(iris)
+  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+1     5.843333    3.057333        3.758    1.199333  setosa
+
+create_prediction_data(iris, num = function(x) quantile(x, p=.25))
+  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+1          5.1         2.8          1.6         0.3  setosa

We can also supply specific values.

-
cd = data.frame(cyl=4, hp=100)
-create_prediction_data(mtcars, conditional_data = cd)
-  cyl  hp      mpg     disp     drat      wt     qsec     vs      am   gear   carb
-1   4 100 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125
+
cd = data.frame(cyl=4, hp=100)
+create_prediction_data(mtcars, conditional_data = cd)
+  cyl  hp      mpg     disp     drat      wt     qsec     vs      am   gear   carb
+1   4 100 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125

For modeling purposes, we often want to center or scale the data, take logs etc. The pre_process function will standardize numeric data by default.

-
pre_process(df1)
-# A tibble: 50 x 6
-   g1        g2      a        b c     d    
-   <fct>  <dbl>  <dbl>    <dbl> <chr> <lgl>
- 1 b     -0.478  0.133  0.659   o     FALSE
- 2 a      0.408  0.116 -1.62    r     TRUE 
- 3 b     -0.478 -0.828  1.31    p     FALSE
- 4 a     -0.478 -2.14  -0.972   l     FALSE
- 5 b     -0.478  0.212  0.00652 n     TRUE 
- 6 a      0.408 -0.949  0.333   s     FALSE
- 7 a      0.408 -0.654 -0.972   i     FALSE
- 8 a     -0.478  0.727 -0.972   n     FALSE
- 9 b     -1.36   1.05  -1.95    s     TRUE 
-10 a      0.408  0.100 -1.95    j     TRUE 
-# … with 40 more rows
+
pre_process(df1)
+# A tibble: 50 x 6
+   g1        g2      a        b c     d    
+   <fct>  <dbl>  <dbl>    <dbl> <chr> <lgl>
+ 1 b     -0.478  0.133  0.659   o     FALSE
+ 2 a      0.408  0.116 -1.62    r     TRUE 
+ 3 b     -0.478 -0.828  1.31    p     FALSE
+ 4 a     -0.478 -2.14  -0.972   l     FALSE
+ 5 b     -0.478  0.212  0.00652 n     TRUE 
+ 6 a      0.408 -0.949  0.333   s     FALSE
+ 7 a      0.408 -0.654 -0.972   i     FALSE
+ 8 a     -0.478  0.727 -0.972   n     FALSE
+ 9 b     -1.36   1.05  -1.95    s     TRUE 
+10 a      0.408  0.100 -1.95    j     TRUE 
+# … with 40 more rows

Other options are to simply center the data (scale_by = 0), start some variables at zero (e.g. time indicators), log some variables (with chosen base), and scale some to range from zero to one.

-
pre_process(mtcars, 
-            scale_by = 0, 
-            log_vars = vars(mpg, wt), 
-            zero_start = vars(cyl), 
-            zero_one = vars(hp, starts_with('d'))) %>% 
-  describe_all_num()
-   Variable  N Mean   SD   Min    Q1 Median   Q3  Max Missing
-1       mpg 32 2.96 0.30  2.34  2.74   2.95 3.13 3.52       0
-2       cyl 32 2.19 1.79  0.00  0.00   2.00 4.00 4.00       0
-3      disp 32 0.40 0.31  0.00  0.12   0.31 0.64 1.00       0
-4        hp 32 0.33 0.24  0.00  0.16   0.25 0.45 1.00       0
-5      drat 32 0.39 0.25  0.00  0.15   0.43 0.53 1.00       0
-6        wt 32 1.12 0.32  0.41  0.95   1.20 1.28 1.69       0
-7      qsec 32 0.00 1.79 -3.35 -0.96  -0.14 1.05 5.05       0
-8        vs 32 0.00 0.50 -0.44 -0.44  -0.44 0.56 0.56       0
-9        am 32 0.00 0.50 -0.41 -0.41  -0.41 0.59 0.59       0
-10     gear 32 0.00 0.74 -0.69 -0.69   0.31 0.31 1.31       0
-11     carb 32 0.00 1.62 -1.81 -0.81  -0.81 1.19 5.19       0
+
pre_process(mtcars, 
+            scale_by = 0, 
+            log_vars = vars(mpg, wt), 
+            zero_start = vars(cyl), 
+            zero_one = vars(hp, starts_with('d'))) %>% 
+  describe_all_num()
+# A tibble: 11 x 10
+   Variable     N  Mean    SD   Min    Q1 Median    Q3   Max `% Missing`
+   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>       <dbl>
+ 1 mpg         32  2.96  0.3   2.34  2.74   2.95  3.13  3.52           0
+ 2 cyl         32  2.19  1.79  0     0      2     4     4              0
+ 3 disp        32  0.4   0.31  0     0.12   0.31  0.64  1              0
+ 4 hp          32  0.33  0.24  0     0.16   0.25  0.45  1              0
+ 5 drat        32  0.39  0.25  0     0.15   0.43  0.53  1              0
+ 6 wt          32  1.12  0.32  0.41  0.95   1.2   1.28  1.69           0
+ 7 qsec        32  0     1.79 -3.35 -0.96  -0.14  1.05  5.05           0
+ 8 vs          32  0     0.5  -0.44 -0.44  -0.44  0.56  0.56           0
+ 9 am          32  0     0.5  -0.41 -0.41  -0.41  0.59  0.59           0
+10 gear        32  0     0.74 -0.69 -0.69   0.31  0.31  1.31           0
+11 carb        32  0     1.62 -1.81 -0.81  -0.81  1.19  5.19           0

Note that center/standardizing is done to any numeric variables not chosen for log, zero_start, and zero_one.

Here’s a specific function you will probably never need, but will be glad to have if you do. Some data columns have multiple entries for each observation/cell. While it’s understandable why someone would do this, it’s not very good practice. This will split out the entries, or any particular combination of them, into their own indicator column.

-
d = data.frame(id = 1:4,
-               labs = c('A-B', 'B-C-D-E', 'A-E', 'D-E'))
-combn_2_col(data=d, var='labs', max_m=2, sep = '-', collapse = ':', toInteger = T)
-  id    labs                                                                                                combo A B A:B C D E B:C B:D B:E C:D C:E D:E A:E
-1  1     A-B                                                                                            A, B, A:B 1 1   1 0 0 0   0   0   0   0   0   0   0
-2  2 B-C-D-E B, C, D, E, B:C, B:D, B:E, C:D, C:E, D:E, B:C, B:D, B:E, C:D, C:E, D:E, B:C, B:D, B:E, C:D, C:E, D:E 0 1   0 1 1 1   1   1   1   1   1   1   0
-3  3     A-E                                                                                            A, E, A:E 1 0   0 0 0 1   0   0   0   0   0   0   1
-4  4     D-E                                                                                            D, E, D:E 0 0   0 0 1 1   0   0   0   0   0   1   0
-
-combn_2_col(data=d, var='labs', max_m=2, sparse = T)
-4 x 13 sparse Matrix of class "dgCMatrix"
-   [[ suppressing 13 column names 'A', 'B', 'A_B' ... ]]
-                           
-1 1 1 1 . . . . . . . . . .
-2 . 1 . 1 1 1 1 1 1 1 1 1 .
-3 1 . . . . 1 . . . . . . 1
-4 . . . . 1 1 . . . . . 1 .
-

In addition, there is a function that makes tidyr’s spread work like it should when you don’t have unique identifiers. I spent a lot of time coming up with the name for this one.

-
# initial example from spread
-stocks_init <- data.frame(
-  time = as.Date('2009-01-01') + 0:9,
-  X = rnorm(10, 0, 1),
-  Y = rnorm(10, 0, 2),
-  Z = rnorm(10, 0, 4)
-)
-
-# a very common situation
-stocks <- data.frame(
-  X = rnorm(10, 0, 1),
-  Y = rnorm(10, 0, 2),
-  Z = rnorm(10, 0, 4)
-)
-
-
-stocksm_init <- stocks_init %>% 
-  gather(stock, price, -time)
-head(stocksm_init)
-        time stock       price
-1 2009-01-01     X  0.09786637
-2 2009-01-02     X -0.07268211
-3 2009-01-03     X -0.01375654
-4 2009-01-04     X -2.72281902
-5 2009-01-05     X  0.41191338
-6 2009-01-06     X  1.23162468
-
-stocksm_init %>% 
-  spread(stock, price)  %>% 
-  head()                                 # works fine
-        time           X          Y         Z
-1 2009-01-01  0.09786637 -3.1067803  4.115598
-2 2009-01-02 -0.07268211  2.7425990 -6.643963
-3 2009-01-03 -0.01375654 -1.2177186 -3.671878
-4 2009-01-04 -2.72281902  0.1097497 -5.486397
-5 2009-01-05  0.41191338 -2.2411176  3.706905
-6 2009-01-06  1.23162468  2.8402477 -2.990143
-
-# no time
-stocksm <- stocks %>% 
-  gather(stock, price)
-head(stocksm)
-  stock      price
-1     X  1.1563222
-2     X -0.9381941
-3     X  0.5240523
-4     X -0.1280867
-5     X -1.8263122
-6     X  0.5326658
-
-stocksm %>% 
-  spread(stock, price)         # annoying
-Error: Each row of output must be identified by a unique combination of keys.
-Keys are shared for 30 rows:
-* 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
-* 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
-* 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
-
-stocksm %>% 
-  spread2(stock, price)        # works fine
-             X           Y          Z
-1   1.15632219  3.20580570  3.4522024
-2  -0.93819413 -0.05906042 -3.9751471
-3   0.52405233 -0.79476986 -1.9407887
-4  -0.12808670 -0.19162662  2.0707290
-5  -1.82631218 -1.66025840 -1.4190455
-6   0.53266578  1.22780477 -0.7697497
-7   0.04044005  0.11893358  7.6421795
-8   1.01614077 -3.35639245  7.2477876
-9   0.27227199  0.93510359  1.3064328
-10  0.99972077 -3.72109524 -0.1234475
-

However, with unbalanced data the result can be interpreted in different ways, so you’ll have a choice to make.

-
stocksm$price[sample(1:nrow(stocksm), 5)] = NA
-stocksm %>% 
-  spread2(stock, price)
-             X           Y          Z
-1   1.15632219  3.20580570         NA
-2  -0.93819413 -0.05906042 -3.9751471
-3   0.52405233 -0.79476986 -1.9407887
-4           NA -0.19162662  2.0707290
-5           NA -1.66025840 -1.4190455
-6   0.53266578  1.22780477 -0.7697497
-7   0.04044005  0.11893358  7.6421795
-8           NA -3.35639245  7.2477876
-9   0.27227199  0.93510359  1.3064328
-10          NA -3.72109524 -0.1234475
-
-stocksm %>% 
-  spread2(stock, price, compact = FALSE)
-             X           Y          Z
-1   1.15632219          NA         NA
-2  -0.93819413          NA         NA
-3   0.52405233          NA         NA
-4           NA          NA         NA
-5           NA          NA         NA
-6   0.53266578          NA         NA
-7   0.04044005          NA         NA
-8           NA          NA         NA
-9   0.27227199          NA         NA
-10          NA          NA         NA
-11          NA  3.20580570         NA
-12          NA -0.05906042         NA
-13          NA -0.79476986         NA
-14          NA -0.19162662         NA
-15          NA -1.66025840         NA
-16          NA  1.22780477         NA
-17          NA  0.11893358         NA
-18          NA -3.35639245         NA
-19          NA  0.93510359         NA
-20          NA -3.72109524         NA
-21          NA          NA         NA
-22          NA          NA -3.9751471
-23          NA          NA -1.9407887
-24          NA          NA  2.0707290
-25          NA          NA -1.4190455
-26          NA          NA -0.7697497
-27          NA          NA  7.6421795
-28          NA          NA  7.2477876
-29          NA          NA  1.3064328
-30          NA          NA -0.1234475
-

I can speak from experience that having longitudinal data in the compact = FALSE format is about the worst way you could keep such data.

-

Use gather_multi to extend tidyr::gather to deal with multiple sets of variables.

-
demo_data_wide = data.frame(id = 1:10,
-                            X = matrix(rnorm(40), ncol = 4),
-                            Y = matrix(sample(0:1, 40, replace = T), ncol = 4),
-                            Z = matrix(rpois(40, 5), ncol = 4))
-head(demo_data_wide)
-  id         X.1        X.2        X.3        X.4 Y.1 Y.2 Y.3 Y.4 Z.1 Z.2 Z.3 Z.4
-1  1 -0.53445423  0.5065096 -1.3589762  0.7598957   0   1   1   0   2   7   6  11
-2  2 -0.09368249 -1.2568838  0.5249439  0.6650419   0   1   0   1   4   4   6   2
-3  3 -0.33135633 -1.0589727  2.5174225  0.9208250   1   0   1   1   2   3   9   6
-4  4 -1.05702864  0.6320716  0.8481803 -0.3679236   0   0   1   0   4   8   6   0
-5  5  0.18527863 -0.7138931 -2.1592811  0.1919997   0   0   0   1   3   7   4   6
-6  6  0.32825631  0.8307916 -0.4392953 -1.2894000   1   1   0   1   5   6   2  10
-
-gather_multi(demo_data_wide,
-             key = wave,
-             values  = vars(X, Y, Z),
-             varlist = vars(starts_with('X'),
-                            starts_with('Y'),
-                            starts_with('Z')),
-             -id)
-   id wave           X Y  Z
-1   1  X.1 -0.53445423 0  2
-2   2  X.1 -0.09368249 0  4
-3   3  X.1 -0.33135633 1  2
-4   4  X.1 -1.05702864 0  4
-5   5  X.1  0.18527863 0  3
-6   6  X.1  0.32825631 1  5
-7   7  X.1  1.55306164 1  4
-8   8  X.1  1.18040282 0  5
-9   9  X.1 -1.17539903 0  6
-10 10  X.1 -0.42584154 1  7
-11  1  X.2  0.50650964 1  7
-12  2  X.2 -1.25688378 1  4
-13  3  X.2 -1.05897267 0  3
-14  4  X.2  0.63207158 0  8
-15  5  X.2 -0.71389314 0  7
-16  6  X.2  0.83079161 1  6
-17  7  X.2 -0.14366319 0  4
-18  8  X.2  0.48591854 0  6
-19  9  X.2  0.66086652 1  5
-20 10  X.2  2.86171590 1  4
-21  1  X.3 -1.35897615 1  6
-22  2  X.3  0.52494388 0  6
-23  3  X.3  2.51742254 1  9
-24  4  X.3  0.84818031 1  6
-25  5  X.3 -2.15928107 0  4
-26  6  X.3 -0.43929526 0  2
-27  7  X.3  0.65278320 1  8
-28  8  X.3  0.07591408 1  2
-29  9  X.3 -0.55796017 1  8
-30 10  X.3 -0.25082981 0  6
-31  1  X.4  0.75989565 0 11
-32  2  X.4  0.66504187 1  2
-33  3  X.4  0.92082505 1  6
-34  4  X.4 -0.36792358 0  0
-35  5  X.4  0.19199969 1  6
-36  6  X.4 -1.28940004 1 10
-37  7  X.4 -1.69247051 1  7
-38  8  X.4  0.35244834 1  5
-39  9  X.4  0.54339917 1  3
-40 10  X.4  0.83661156 1  4
-

Note that this only makes sense with balanced data, though it will work if NAs are present. Also, you may want to change the key in some fashion.

+
d = data.frame(id = 1:4, labs = c('A-B', 'B-C-D-E', 'A-E', 'D-E'))
+
+combn_2_col(
+  data = d,
+  var = 'labs',
+  max_m = 2,
+  sep = '-',
+  collapse = ':',
+  toInteger = T
+)
+  id    labs                                                                                                combo A B A:B C D E B:C B:D B:E C:D C:E D:E A:E
+1  1     A-B                                                                                            A, B, A:B 1 1   1 0 0 0   0   0   0   0   0   0   0
+2  2 B-C-D-E B, C, D, E, B:C, B:D, B:E, C:D, C:E, D:E, B:C, B:D, B:E, C:D, C:E, D:E, B:C, B:D, B:E, C:D, C:E, D:E 0 1   0 1 1 1   1   1   1   1   1   1   0
+3  3     A-E                                                                                            A, E, A:E 1 0   0 0 0 1   0   0   0   0   0   0   1
+4  4     D-E                                                                                            D, E, D:E 0 0   0 0 1 1   0   0   0   0   0   1   0
+
+combn_2_col(
+  data = d,
+  var = 'labs',
+  max_m = 2,
+  sparse = T
+)
+4 x 13 sparse Matrix of class "dgCMatrix"
+   [[ suppressing 13 column names 'A', 'B', 'A_B' ... ]]
+                           
+1 1 1 1 . . . . . . . . . .
+2 . 1 . 1 1 1 1 1 1 1 1 1 .
+3 1 . . . . 1 . . . . . . 1
+4 . . . . 1 1 . . . . . 1 .

Oftentimes I need to create a column that represents the total scores or means of just a few columns. This is a slight annoyance in the tidyverse, and there isn’t much support behind the dplyr:rowwise function. As such, tidyext has a couple simple wrappers for row_sums, row_means, and row_apply.

-
d = data.frame(x = 1:3,
-               y = 4:6,
-               z = 7:9,
-               q = NA)
-
-d  %>%
- row_sums(x:y)
-  x y z  q sum
-1 1 4 7 NA   5
-2 2 5 8 NA   7
-3 3 6 9 NA   9
-
-d  %>%
- row_means(matches('x|z'))
-  x y z  q mean
-1 1 4 7 NA    4
-2 2 5 8 NA    5
-3 3 6 9 NA    6
-
-row_apply(
- d ,
- x:z,
- .fun = function(x)
-   apply(x, 1, paste, collapse = '')
-)
-  x y z  q var
-1 1 4 7 NA 147
-2 2 5 8 NA 258
-3 3 6 9 NA 369
+
d = data.frame(x = 1:3,
+               y = 4:6,
+               z = 7:9,
+               q = NA)
+
+d  %>%
+ row_sums(x:y)
+  x y z  q sum
+1 1 4 7 NA   5
+2 2 5 8 NA   7
+3 3 6 9 NA   9
+
+d  %>%
+ row_means(matches('x|z'))
+  x y z  q mean
+1 1 4 7 NA    4
+2 2 5 8 NA    5
+3 3 6 9 NA    6
+
+row_apply(
+ d ,
+ x:z,
+ .fun = function(x)
+   apply(x, 1, paste, collapse = '')
+)
+  x y z  q var
+1 1 4 7 NA 147
+2 2 5 8 NA 258
+3 3 6 9 NA 369







- @@ -623,7 +455,7 @@

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/introduction_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/introduction_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 0000000..ca349fd --- /dev/null +++ b/docs/articles/introduction_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/introduction_files/figure-html/describe_cat-1.svg b/docs/articles/introduction_files/figure-html/describe_cat-1.svg index 2619925..b8f80ff 100644 --- a/docs/articles/introduction_files/figure-html/describe_cat-1.svg +++ b/docs/articles/introduction_files/figure-html/describe_cat-1.svg @@ -12,49 +12,49 @@ ]]> - + - - + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + -0 -10 -20 -30 -40 -50 - - - - - - - - -a -b -Group -% +0 +10 +20 +30 +40 +50 + + + + + + + + +a +b +Group +% diff --git a/docs/authors.html b/docs/authors.html index a00586f..c93716a 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,7 +50,6 @@ - @@ -64,7 +67,7 @@ - +
@@ -151,7 +154,7 @@

Authors

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('
    '); + }, + + createChildNavList: function($parent) { + var $childList = this.createNavList(); + $parent.append($childList); + return $childList; + }, + + generateNavEl: function(anchor, text) { + var $a = $(''); + $a.attr('href', '#' + anchor); + $a.text(text); + var $li = $('
  • '); + $li.append($a); + return $li; + }, + + generateNavItem: function(headingEl) { + var anchor = this.generateAnchor(headingEl); + var $heading = $(headingEl); + var text = $heading.data('toc-text') || $heading.text(); + return this.generateNavEl(anchor, text); + }, + + // Find the first heading level (`

    `, then `

    `, etc.) that has more than one element. Defaults to 1 (for `

    `). + getTopLevel: function($scope) { + for (var i = 1; i <= 6; i++) { + var $headings = this.findOrFilter($scope, 'h' + i); + if ($headings.length > 1) { + return i; + } + } + + return 1; + }, + + // returns the elements for the top level, and the next below it + getHeadings: function($scope, topLevel) { + var topSelector = 'h' + topLevel; + + var secondaryLevel = topLevel + 1; + var secondarySelector = 'h' + secondaryLevel; + + return this.findOrFilter($scope, topSelector + ',' + secondarySelector); + }, + + getNavLevel: function(el) { + return parseInt(el.tagName.charAt(1), 10); + }, + + populateNav: function($topContext, topLevel, $headings) { + var $context = $topContext; + var $prevNav; + + var helpers = this; + $headings.each(function(i, el) { + var $newNav = helpers.generateNavItem(el); + var navLevel = helpers.getNavLevel(el); + + // determine the proper $context + if (navLevel === topLevel) { + // use top level + $context = $topContext; + } else if ($prevNav && $context === $topContext) { + // create a new level of the tree and switch to it + $context = helpers.createChildNavList($prevNav); + } // else use the current $context + + $context.append($newNav); + + $prevNav = $newNav; + }); + }, + + parseOps: function(arg) { + var opts; + if (arg.jquery) { + opts = { + $nav: arg + }; + } else { + opts = arg; + } + opts.$scope = opts.$scope || $(document.body); + return opts; + } + }, + + // accepts a jQuery object, or an options object + init: function(opts) { + opts = this.helpers.parseOps(opts); + + // ensure that the data attribute is in place for styling + opts.$nav.attr('data-toggle', 'toc'); + + var $topContext = this.helpers.createChildNavList(opts.$nav); + var topLevel = this.helpers.getTopLevel(opts.$scope); + var $headings = this.helpers.getHeadings(opts.$scope, topLevel); + this.helpers.populateNav($topContext, topLevel, $headings); + } + }; + + $(function() { + $('nav[data-toggle="toc"]').each(function(i, el) { + var $nav = $(el); + Toc.init($nav); + }); + }); +})(); diff --git a/docs/index.html b/docs/index.html index dbaab5c..76e61cf 100644 --- a/docs/index.html +++ b/docs/index.html @@ -12,21 +12,21 @@ - - - - + + + + + - - +

    Overview

    @@ -106,7 +106,7 @@

    Installation

    To install from GitHub the devtools package is required.

    -
    devtools::install_github('m-clark/tidyext')
    +
    devtools::install_github('m-clark/tidyext')

    Note that this package more or less assumes your are working within the tidyverse, especially dplyr. As such you should have the tidyverse packages installed.

    @@ -117,12 +117,10 @@

  • combn_2_col: Takes a column with multiple entries per cell and creates indicator columns of all possible combinations of the cell values up to m combinations.

  • create_prediction_data: Straightforward way to quickly create data to make model predictions.

  • describe_all: A summary function for mixed data types that provides the information I usually want. Saves one from doing a group_by %>% summarize operation to create multiple results for multiple types of variables. Has corresponding describe_all_num and describe_all_cat for numeric-only and categorical-only data respectively.

    • -

  • gather_multi: Gather multiple sets of variables.

  • num_by: A quick summarize, possibly with dplyr::group_by, that provides things like mean, sd, etc. See num_summary.

  • num_summary: A little better than the base R summary, gives the info one typically wants as well as options for rounding and other statistics.

  • onehot: A function for one-hot encoding with a few helpful options for dealing with missing data, using sparse matrices, and more.

  • pre_process: Easily pre-process a data set with common operations like standardization, logging, etc.

    • -

  • spread2: The tidyr spread without the duplicate row id problem.

  • sum_NA, sum_NaN, sum_blank: Understand your nothingness.

  • row_sums, row_means, row_apply: Simple (intuitive) rowwise calculations.

    • @@ -149,11 +147,11 @@

    -
    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/describe_all-1.svg b/docs/reference/describe_all-1.svg index 1991084..57f10e4 100644 --- a/docs/reference/describe_all-1.svg +++ b/docs/reference/describe_all-1.svg @@ -12,50 +12,50 @@ ]]> - + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + -25 -30 -35 -40 -45 - - - - - - - - -8 -4 -6 -Group -% +25 +30 +35 +40 +45 + + + + + + + + +8 +4 +6 +Group +% diff --git a/docs/reference/describe_all.html b/docs/reference/describe_all.html index 396b27d..9f1d305 100644 --- a/docs/reference/describe_all.html +++ b/docs/reference/describe_all.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -171,7 +174,7 @@

    Arg digits -

    See round. Default is 2, which for +

    See [base::round()]. Default is 2, which for categorical is applied to the proportion (i.e. before converting to percentage).

    @@ -252,20 +255,22 @@

    Examp #> intersect, setdiff, setequal, union

    X = data.frame(f1 =gl(2, 1, 20, labels=c('A', 'B')), f2=gl(2, 2, 20, labels=c('X', 'Q'))) X = X %>% mutate(bin1 = rbinom(20, 1, p=.5), - logic1 = sample(c(TRUE, FALSE), 20, replace = TRUE), + logic1 = sample(c(TRUE, FALSE), 20, replace = TRUE), num1 = rnorm(20), num2 = rpois(20, 5), - char1 = sample(letters, 20, replace = TRUE)) + char1 = sample(letters, 20, replace = TRUE)) describe_all(X)
    #> $`Numeric Variables` -#> Variable N Mean SD Min Q1 Median Q3 Max Missing -#> 1 bin1 20 0.30 0.47 0.00 0.00 0.00 1.00 1.00 0 -#> 2 num1 20 0.01 0.99 -1.91 -0.44 0.05 0.54 1.89 0 -#> 3 num2 20 4.95 2.42 1.00 3.00 5.00 6.00 12.00 0 +#> # A tibble: 3 x 10 +#> Variable N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 bin1 20 0.3 0.47 0 0 0 1 1 0 +#> 2 num1 20 0.01 0.99 -1.91 -0.44 0.05 0.54 1.89 0 +#> 3 num2 20 4.95 2.42 1 3 5 6 12 0 #> #> $`Categorical Variables` -#> # A tibble: 19 x 4 +#> # A tibble: 19 x 4 #> Variable Group Frequency `%` -#> <chr> <chr> <int> <dbl> +#> <chr> <fct> <int> <dbl> #> 1 f1 A 10 50 #> 2 f1 B 10 50 #> 3 f2 Q 10 50 @@ -301,35 +306,25 @@

    Examp #> 6 x 6 1 14.3 #> 7 x 7 1 14.3 #>

    describe_all(mtcars, digits=5, include_numeric=TRUE, max_levels=3)
    #> $`Numeric Variables` -#> Variable N Mean SD Min Q1 Median Q3 Max -#> 1 mpg 32 20.09062 6.02695 10.400 15.42500 19.200 22.80 33.900 -#> 2 cyl 32 6.18750 1.78592 4.000 4.00000 6.000 8.00 8.000 -#> 3 disp 32 230.72188 123.93869 71.100 120.82500 196.300 326.00 472.000 -#> 4 hp 32 146.68750 68.56287 52.000 96.50000 123.000 180.00 335.000 -#> 5 drat 32 3.59656 0.53468 2.760 3.08000 3.695 3.92 4.930 -#> 6 wt 32 3.21725 0.97846 1.513 2.58125 3.325 3.61 5.424 -#> 7 qsec 32 17.84875 1.78694 14.500 16.89250 17.710 18.90 22.900 -#> 8 vs 32 0.43750 0.50402 0.000 0.00000 0.000 1.00 1.000 -#> 9 am 32 0.40625 0.49899 0.000 0.00000 0.000 1.00 1.000 -#> 10 gear 32 3.68750 0.73780 3.000 3.00000 4.000 4.00 5.000 -#> 11 carb 32 2.81250 1.61520 1.000 2.00000 2.000 4.00 8.000 -#> Missing -#> 1 0 -#> 2 0 -#> 3 0 -#> 4 0 -#> 5 0 -#> 6 0 -#> 7 0 -#> 8 0 -#> 9 0 -#> 10 0 -#> 11 0 +#> # A tibble: 11 x 10 +#> Variable N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 mpg 32 20.1 6.03 10.4 15.4 19.2 22.8 33.9 0 +#> 2 cyl 32 6.19 1.79 4 4 6 8 8 0 +#> 3 disp 32 231. 124. 71.1 121. 196. 326 472 0 +#> 4 hp 32 147. 68.6 52 96.5 123 180 335 0 +#> 5 drat 32 3.60 0.535 2.76 3.08 3.70 3.92 4.93 0 +#> 6 wt 32 3.22 0.978 1.51 2.58 3.32 3.61 5.42 0 +#> 7 qsec 32 17.8 1.79 14.5 16.9 17.7 18.9 22.9 0 +#> 8 vs 32 0.438 0.504 0 0 0 1 1 0 +#> 9 am 32 0.406 0.499 0 0 0 1 1 0 +#> 10 gear 32 3.69 0.738 3 3 4 4 5 0 +#> 11 carb 32 2.81 1.62 1 2 2 4 8 0 #> #> $`Categorical Variables` #> # A tibble: 10 x 4 #> Variable Group Frequency `%` -#> <chr> <chr> <int> <dbl> +#> <chr> <fct> <int> <dbl> #> 1 cyl 8 14 43.8 #> 2 cyl 4 11 34.4 #> 3 cyl 6 7 21.9 @@ -350,20 +345,14 @@

    Examp filter(Variable == 'cyl') %>% tidyr::unnest() %>% ggplot(aes(x=Group, y=`%`)) + - geom_point(size = 10)

    #> Warning: `cols` is now required. + geom_point(size = 10)
    #> Warning: `cols` is now required when using unnest(). #> Please use `cols = c(data)`
    - @@ -374,7 +363,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/gather_multi.html b/docs/reference/gather_multi.html index 9d4002c..4e4c5ea 100644 --- a/docs/reference/gather_multi.html +++ b/docs/reference/gather_multi.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,9 +50,9 @@ + columns. It is now deprecated and no longer needed due to advancements in + `tidyr`." /> - @@ -66,7 +70,7 @@ - +
    @@ -138,7 +142,8 @@

    Extend tidyr gather to multiple sets of variables

    This function extends gather to work on multiple sets of - columns.

    + columns. It is now deprecated and no longer needed due to advancements in + `tidyr`.

    gather_multi(
@@ -233,12 +238,13 @@ 
    Note
    
   You can use the key_func argument to pretty it up.
    
 
     
    Examples
    
-    
    library(tidyext); library(dplyr)
+    
    if (FALSE) {
+library(tidyext); library(dplyr)
 
 # example of longitudinal data with 4 waves
 demo_data_wide = data.frame(id = 1:10,
                             X = matrix(rnorm(40), ncol = 4),
-                            Y = matrix(sample(0:1, 40, replace = TRUE),
+                            Y = matrix(sample(0:1, 40, replace = TRUE),
                             ncol = 4),
                             Z = matrix(rpois(40, 5), ncol = 4))
 
@@ -248,47 +254,8 @@ 
    Examp
                      varlist = vars(starts_with('X'),
                                     starts_with('Y'),
                                     starts_with('Z')))
-test
    #>    id wave          X Y  Z
-#> 1   1  X.1  0.1684728 1  1
-#> 2   2  X.1  1.3970665 0  6
-#> 3   3  X.1 -0.6790954 1  4
-#> 4   4  X.1  0.7376294 1  8
-#> 5   5  X.1 -0.8607243 0  6
-#> 6   6  X.1  0.4212304 0  6
-#> 7   7  X.1  1.4505432 0  6
-#> 8   8  X.1  0.1943924 0  5
-#> 9   9  X.1 -0.6912054 0  3
-#> 10 10  X.1  1.3398599 1  5
-#> 11  1  X.2  2.7361084 1  4
-#> 12  2  X.2 -0.9441017 1  6
-#> 13  3  X.2 -1.7810619 1  6
-#> 14  4  X.2 -0.7160587 0  4
-#> 15  5  X.2  0.9110785 0  9
-#> 16  6  X.2 -0.7721921 1  4
-#> 17  7  X.2 -0.7820777 0  2
-#> 18  8  X.2 -0.4321952 1  5
-#> 19  9  X.2 -0.6675648 0  3
-#> 20 10  X.2  1.3895059 0  3
-#> 21  1  X.3  0.9118739 0  9
-#> 22  2  X.3  0.2053894 0  7
-#> 23  3  X.3  2.5844322 0  6
-#> 24  4  X.3 -0.7893881 0  5
-#> 25  5  X.3  0.5880771 0  5
-#> 26  6  X.3 -0.7112873 0  2
-#> 27  7  X.3  1.5849968 0  4
-#> 28  8  X.3  0.6763896 0  3
-#> 29  9  X.3 -0.2327618 0  6
-#> 30 10  X.3  0.6374729 0  6
-#> 31  1  X.4 -1.3707612 1  9
-#> 32  2  X.4 -1.4256595 0  3
-#> 33  3  X.4 -1.2461920 0 10
-#> 34  4  X.4 -0.6832669 0  4
-#> 35  5  X.4 -0.9796754 0  6
-#> 36  6  X.4 -0.4625191 1  7
-#> 37  7  X.4  1.2145097 1  2
-#> 38  8  X.4 -1.2778199 1  5
-#> 39  9  X.4  0.7478688 0  7
-#> 40 10  X.4  3.3915088 0  4
    
+test
+
 test <- gather_multi(demo_data_wide,
                      key = wave,
                      values = c('X', 'Y', 'Z'),
@@ -296,58 +263,13 @@ 
    Examp
                                     starts_with('Y'),
                                     starts_with('Z')),
                      key_func = function(x) substr(x, start=3, stop=3))
-test
    #>    id wave          X Y  Z
-#> 1   1    1  0.1684728 1  1
-#> 2   2    1  1.3970665 0  6
-#> 3   3    1 -0.6790954 1  4
-#> 4   4    1  0.7376294 1  8
-#> 5   5    1 -0.8607243 0  6
-#> 6   6    1  0.4212304 0  6
-#> 7   7    1  1.4505432 0  6
-#> 8   8    1  0.1943924 0  5
-#> 9   9    1 -0.6912054 0  3
-#> 10 10    1  1.3398599 1  5
-#> 11  1    2  2.7361084 1  4
-#> 12  2    2 -0.9441017 1  6
-#> 13  3    2 -1.7810619 1  6
-#> 14  4    2 -0.7160587 0  4
-#> 15  5    2  0.9110785 0  9
-#> 16  6    2 -0.7721921 1  4
-#> 17  7    2 -0.7820777 0  2
-#> 18  8    2 -0.4321952 1  5
-#> 19  9    2 -0.6675648 0  3
-#> 20 10    2  1.3895059 0  3
-#> 21  1    3  0.9118739 0  9
-#> 22  2    3  0.2053894 0  7
-#> 23  3    3  2.5844322 0  6
-#> 24  4    3 -0.7893881 0  5
-#> 25  5    3  0.5880771 0  5
-#> 26  6    3 -0.7112873 0  2
-#> 27  7    3  1.5849968 0  4
-#> 28  8    3  0.6763896 0  3
-#> 29  9    3 -0.2327618 0  6
-#> 30 10    3  0.6374729 0  6
-#> 31  1    4 -1.3707612 1  9
-#> 32  2    4 -1.4256595 0  3
-#> 33  3    4 -1.2461920 0 10
-#> 34  4    4 -0.6832669 0  4
-#> 35  5    4 -0.9796754 0  6
-#> 36  6    4 -0.4625191 1  7
-#> 37  7    4  1.2145097 1  2
-#> 38  8    4 -1.2778199 1  5
-#> 39  9    4  0.7478688 0  7
-#> 40 10    4  3.3915088 0  4
    
+test
+}
    - @@ -358,7 +280,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/head_tail.html b/docs/reference/head_tail.html index 2bfe6e2..fc5f73a 100644 --- a/docs/reference/head_tail.html +++ b/docs/reference/head_tail.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -164,8 +167,8 @@

    Details

    Examples

    
 library(tidyext)
-as.matrix(mtcars) %>%
-head_tail(6)
    #>    index  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
+
+head_tail(mtcars)
    #>    index  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
 #> 1      1 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
 #> 2      2 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
 #> 3      3 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
@@ -180,15 +183,10 @@ 
    Examp
 #> 12    32 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
    - @@ -199,7 +197,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/index.html b/docs/reference/index.html index 35195e8..66a7775 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -46,7 +50,6 @@ - @@ -64,7 +67,7 @@ - +
    @@ -147,6 +150,11 @@

    Functions for summarizing data.

    + + + + + @@ -179,6 +187,11 @@

    Functions for creating data.

    + + + + + @@ -212,13 +225,7 @@

    spread2()

    - -

    Spread without frustration

    - - - -

    row_sums() row_means() row_apply()

    +

    row_sums() row_means() row_min() row_max() row_apply()

    Apply simple rowwise functions

    @@ -229,6 +236,11 @@

    Miscellaneous functions.

    + + + + + @@ -237,6 +249,18 @@

    head_tail()

    + +

    Show head and tail simultaneously

    + + + +

    rnd()

    + +

    Sensible rounding for printing.

    + +

    tidyext

    @@ -246,13 +270,10 @@

    -

    Contents

    -     +

    @@ -263,7 +284,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/num_by.html b/docs/reference/num_by.html index 375c0ed..d40fbfb 100644 --- a/docs/reference/num_by.html +++ b/docs/reference/num_by.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -182,7 +185,7 @@

    Arg sort_by_group

    when supplied a grouping variable for cat_by, do you -want result sorted on the grouping variable? Default is TRUE.

    +want the result sorted on the grouping variable? Default is TRUE.

    @@ -196,7 +199,7 @@

    Details median, third quartile, max, and number of missing values, possibly over a grouping variable.

    It works in the dplyr style using unquoted (bare) variable names, using the - vars() function if there is more than one variable. If using a + vars() function if there is more than one variable. If using a grouping variable, it will treat missing values as a separate group.

    For cat_by, frequencies and percentage (out of total or group_var) are returned. Warnings are given if any of the main @@ -217,70 +220,66 @@

    Examp g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), b = rpois(50, 10), - c = sample(letters, 50, replace=TRUE), - d = sample(c(TRUE, FALSE), 50, replace=TRUE) + c = sample(letters, 50, replace = TRUE), + d = sample(c(TRUE, FALSE), 50, replace = TRUE) ) -num_by(df1, main_var = a)

    #> Variable N Mean SD Min Q1 Median Q3 Max Missing -#> 1 a 50 -0.1 1.1 -3.7 -0.6 -0.1 0.7 1.9 0
    num_by(df1, main_var = a, group_var = g2, digits=2)
    #> Adding missing grouping variables: `g2`
    #> Adding missing grouping variables: `g2`
    #> # A tibble: 4 x 11 +num_by(df1, main_var = a)
    #> # A tibble: 1 x 10 +#> Variable N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 a 50 -0.1 1.1 -2.2 -0.9 -0.2 0.6 2 0
    num_by(df1, main_var = a, group_var = g2, digits = 2)
    #> Adding missing grouping variables: `g2`
    #> Adding missing grouping variables: `g2`
    #> # A tibble: 4 x 11 #> # Groups: g2 [4] -#> g2 Variable N Mean SD Min Q1 Median Q3 Max Missing -#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 a 13 -0.74 1.27 -3.71 -0.78 -0.61 -0.290 1.06 0 -#> 2 2 a 13 0.37 1.26 -2.29 -0.31 0.68 1.57 1.86 0 -#> 3 3 a 11 -0.14 0.87 -1.77 -0.71 -0.07 0.6 1.18 0 -#> 4 4 a 13 0.1 0.67 -1.29 -0.25 0.01 0.38 1.2 0
    +#> g2 Variable N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 4 a 14 -0.17 0.87 -1.64 -0.82 -0.28 0.51 1.2 0 +#> 2 3 a 10 -0.24 1.35 -2.18 -1.23 -0.1 0.4 1.74 0 +#> 3 2 a 13 0.15 1.29 -2.12 -0.78 0.35 0.94 2.04 0 +#> 4 1 a 13 -0.21 1.04 -1.87 -0.78 -0.22 0.39 1.96 0
    num_by(df1, main_var = dplyr::vars(a,b), group_var = g1, digits=1)
    #> Adding missing grouping variables: `g1`
    #> Adding missing grouping variables: `g1`
    #> # A tibble: 4 x 11 #> # Groups: g1 [2] -#> g1 Variable N Mean SD Min Q1 Median Q3 Max Missing -#> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 a a 20 -0.3 0.9 -2.3 -0.8 -0.3 0.4 1.2 0 -#> 2 a b 20 9.8 3.1 6 7 10 11.5 16 0 -#> 3 b a 30 0 1.2 -3.7 -0.5 0 0.8 1.9 0 -#> 4 b b 30 10.2 3.9 2 8 10 13 17 0
    +#> g1 Variable N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 b a 19 -0.6 1.1 -2.2 -1.6 -0.8 0.1 1.6 0 +#> 2 b b 19 9.9 2.8 5 8.5 10 13 13 0 +#> 3 a a 31 0.2 1 -1.6 -0.7 0.3 0.7 2 0 +#> 4 a b 31 11.2 3.5 6 9 11 14.5 18 0
    cat_by(df1, main_var = g1, group_var = g2, digits=1)
    #> `mutate_if()` ignored the following grouping variables: #> Column `g2`
    #> # A tibble: 8 x 5 #> # Groups: g2 [4] #> g2 g1 N `% of Total` `% of g2` #> <int> <fct> <dbl> <dbl> <dbl> -#> 1 1 a 4 8 30.8 -#> 2 1 b 9 18 69.2 -#> 3 2 a 5 10 38.5 -#> 4 2 b 8 16 61.5 -#> 5 3 a 7 14 63.6 -#> 6 3 b 4 8 36.4 -#> 7 4 a 4 8 30.8 -#> 8 4 b 9 18 69.2
    cat_by(df1, main_var = dplyr::vars(g1,d), group_var = g2, perc_by_group=FALSE)
    #> # A tibble: 15 x 5 +#> 1 1 a 8 16 61.5 +#> 2 1 b 5 10 38.5 +#> 3 2 a 9 18 69.2 +#> 4 2 b 4 8 30.8 +#> 5 3 a 5 10 50 +#> 6 3 b 5 10 50 +#> 7 4 a 9 18 64.3 +#> 8 4 b 5 10 35.7
    cat_by(df1, main_var = dplyr::vars(g1,d), group_var = g2, perc_by_group = FALSE)
    #> # A tibble: 15 x 5 #> g2 g1 d N `% of Total` #> <int> <fct> <lgl> <int> <dbl> -#> 1 1 a FALSE 1 2 +#> 1 1 a FALSE 5 10 #> 2 1 a TRUE 3 6 -#> 3 1 b FALSE 2 4 -#> 4 1 b TRUE 7 14 -#> 5 2 a FALSE 3 6 -#> 6 2 a TRUE 2 4 -#> 7 2 b FALSE 2 4 -#> 8 2 b TRUE 6 12 -#> 9 3 a FALSE 3 6 -#> 10 3 a TRUE 4 8 -#> 11 3 b FALSE 3 6 -#> 12 3 b TRUE 1 2 +#> 3 1 b FALSE 1 2 +#> 4 1 b TRUE 4 8 +#> 5 2 a FALSE 5 10 +#> 6 2 a TRUE 4 8 +#> 7 2 b TRUE 4 8 +#> 8 3 a FALSE 1 2 +#> 9 3 a TRUE 4 8 +#> 10 3 b FALSE 4 8 +#> 11 3 b TRUE 1 2 +#> 12 4 a FALSE 5 10 #> 13 4 a TRUE 4 8 -#> 14 4 b FALSE 5 10 +#> 14 4 b FALSE 1 2 #> 15 4 b TRUE 4 8
    - @@ -291,7 +290,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/num_summary.html b/docs/reference/num_summary.html index 0793ad7..1a091f1 100644 --- a/docs/reference/num_summary.html +++ b/docs/reference/num_summary.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -171,19 +174,19 @@

    Details

    Examples

    library(tidyext)
-num_summary(c(1:10, NA))
    #>    N Mean SD Min  Q1 Median  Q3 Max Missing
-#> 1 10  5.5  3   1 3.2    5.5 7.8  10       1
    num_summary(c('1','2','3'))
    #>   N Mean SD Min  Q1 Median  Q3 Max Missing
-#> 1 3    2  1   1 1.5      2 2.5   3       0
    +num_summary(c(1:10, NA))
    #> # A tibble: 1 x 9 +#> N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 10 5.5 3 1 3.2 5.5 7.8 10 9
    num_summary(c('1','2','3'))
    #> # A tibble: 1 x 9 +#> N Mean SD Min Q1 Median Q3 Max `% Missing` +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 3 2 1 1 1.5 2 2.5 3 0
    +
    - @@ -194,7 +197,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/onehot.html b/docs/reference/onehot.html index 919bc90..217f666 100644 --- a/docs/reference/onehot.html +++ b/docs/reference/onehot.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -233,31 +236,25 @@

    Examp iris2 = iris iris2[sample(1:150, 25),] = NA str(onehot(iris2))

    #> 'data.frame': 150 obs. of 7 variables: -#> $ Sepal.Length : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... -#> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... -#> $ Petal.Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... -#> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... -#> $ Species_setosa : num 1 1 1 1 1 1 1 1 1 1 ... -#> $ Species_versicolor: num 0 0 0 0 0 0 0 0 0 0 ... -#> $ Species_virginica : num 0 0 0 0 0 0 0 0 0 0 ...
    str(onehot(iris2, nas = 'na.omit'))
    #> 'data.frame': 125 obs. of 7 variables: -#> $ Sepal.Length : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... -#> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... -#> $ Petal.Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... -#> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... +#> $ Sepal.Length : num 5.1 4.9 NA NA 5 NA 4.6 NA 4.4 4.9 ... +#> $ Sepal.Width : num 3.5 3 NA NA 3.6 NA 3.4 NA 2.9 3.1 ... +#> $ Petal.Length : num 1.4 1.4 NA NA 1.4 NA 1.4 NA 1.4 1.5 ... +#> $ Petal.Width : num 0.2 0.2 NA NA 0.2 NA 0.3 NA 0.2 0.1 ... +#> $ Species_setosa : num 1 1 NA NA 1 NA 1 NA 1 1 ... +#> $ Species_versicolor: num 0 0 NA NA 0 NA 0 NA 0 0 ... +#> $ Species_virginica : num 0 0 NA NA 0 NA 0 NA 0 0 ...
    str(onehot(iris2, nas = 'na.omit'))
    #> 'data.frame': 125 obs. of 7 variables: +#> $ Sepal.Length : num 5.1 4.9 5 4.6 4.4 4.9 4.8 4.8 4.3 5.8 ... +#> $ Sepal.Width : num 3.5 3 3.6 3.4 2.9 3.1 3.4 3 3 4 ... +#> $ Petal.Length : num 1.4 1.4 1.4 1.4 1.4 1.5 1.6 1.4 1.1 1.2 ... +#> $ Petal.Width : num 0.2 0.2 0.2 0.3 0.2 0.1 0.2 0.1 0.1 0.2 ... #> $ Species_setosa : num 1 1 1 1 1 1 1 1 1 1 ... #> $ Species_versicolor: num 0 0 0 0 0 0 0 0 0 0 ... #> $ Species_virginica : num 0 0 0 0 0 0 0 0 0 0 ...
    - @@ -268,7 +265,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/pre_process.html b/docs/reference/pre_process.html index 1c2efe1..6915b59 100644 --- a/docs/reference/pre_process.html +++ b/docs/reference/pre_process.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -166,19 +169,19 @@

    Arg log_vars -

    Which variables to log. Requires vars().

    +

    Which variables to log. Requires `vars()`.

    log_base -

    Log base. Default is exp(1).

    +

    Log base. Default is `exp(1)`.

    zero_start -

    Which variables to start by zero. Requires vars().

    +

    Which variables to start by zero. Requires `vars()`.

    zero_one -

    Which variables to rescale from 0 to 1. Requires vars().

    +

    Which variables to rescale from 0 to 1. Requires `vars()`.

    @@ -207,6 +210,8 @@

    Details

    Examples

    library(tidyext)
+library(dplyr)
+
 pre_process(mtcars)
    #>            mpg        cyl        disp          hp        drat           wt
 #> 1   0.15088482 -0.1049878 -0.57061982 -0.53509284  0.56751369 -0.610399567
 #> 2   0.15088482 -0.1049878 -0.57061982 -0.53509284  0.56751369 -0.349785269
@@ -272,7 +277,7 @@ 
    Examp
 #> 29 -1.87401028 -0.8680278  1.1899014  1.7789276  0.7352031
 #> 30 -1.31439542 -0.8680278  1.1899014  1.7789276  1.9734398
 #> 31 -1.81804880 -0.8680278  1.1899014  1.7789276  3.2116766
-#> 32  0.42041067  1.1160357  1.1899014  0.4235542 -0.5030337
    pre_process(mtcars, log_vars=vars(mpg, wt))
    #>         mpg        cyl        disp          hp        drat        wt
+#> 32  0.42041067  1.1160357  1.1899014  0.4235542 -0.5030337
    pre_process(mtcars, log_vars = vars(mpg, wt))
    #>         mpg        cyl        disp          hp        drat        wt
 #> 1  3.044522 -0.1049878 -0.57061982 -0.53509284  0.56751369 0.9631743
 #> 2  3.044522 -0.1049878 -0.57061982 -0.53509284  0.56751369 1.0560527
 #> 3  3.126761 -1.2248578 -0.99018209 -0.78304046  0.47399959 0.8415672
@@ -337,7 +342,7 @@ 
    Examp
 #> 29 -1.87401028 -0.8680278  1.1899014  1.7789276  0.7352031
 #> 30 -1.31439542 -0.8680278  1.1899014  1.7789276  1.9734398
 #> 31 -1.81804880 -0.8680278  1.1899014  1.7789276  3.2116766
-#> 32  0.42041067  1.1160357  1.1899014  0.4235542 -0.5030337
    pre_process(mtcars, zero_start=vars(cyl, gear))
    #>            mpg cyl        disp          hp        drat           wt        qsec
+#> 32  0.42041067  1.1160357  1.1899014  0.4235542 -0.5030337
    pre_process(mtcars, zero_start = vars(cyl, gear))
    #>            mpg cyl        disp          hp        drat           wt        qsec
 #> 1   0.15088482   2 -0.57061982 -0.53509284  0.56751369 -0.610399567 -0.77716515
 #> 2   0.15088482   2 -0.57061982 -0.53509284  0.56751369 -0.349785269 -0.46378082
 #> 3   0.44954345   0 -0.99018209 -0.78304046  0.47399959 -0.917004624  0.42600682
@@ -402,7 +407,7 @@ 
    Examp
 #> 29 -0.8680278  1.1899014    2  0.7352031
 #> 30 -0.8680278  1.1899014    2  1.9734398
 #> 31 -0.8680278  1.1899014    2  3.2116766
-#> 32  1.1160357  1.1899014    1 -0.5030337
    pre_process(mtcars, zero_one=vars(mpg))
    #>          mpg        cyl        disp          hp        drat           wt
+#> 32  1.1160357  1.1899014    1 -0.5030337
    pre_process(mtcars, zero_one = vars(mpg))
    #>          mpg        cyl        disp          hp        drat           wt
 #> 1  0.4510638 -0.1049878 -0.57061982 -0.53509284  0.56751369 -0.610399567
 #> 2  0.4510638 -0.1049878 -0.57061982 -0.53509284  0.56751369 -0.349785269
 #> 3  0.5276596 -1.2248578 -0.99018209 -0.78304046  0.47399959 -0.917004624
@@ -467,17 +472,13 @@ 
    Examp
 #> 29 -1.87401028 -0.8680278  1.1899014  1.7789276  0.7352031
 #> 30 -1.31439542 -0.8680278  1.1899014  1.7789276  1.9734398
 #> 31 -1.81804880 -0.8680278  1.1899014  1.7789276  3.2116766
-#> 32  0.42041067  1.1160357  1.1899014  0.4235542 -0.5030337
    +#> 32 0.42041067 1.1160357 1.1899014 0.4235542 -0.5030337
    +
    - @@ -488,7 +489,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/rnd.html b/docs/reference/rnd.html index f588b66..0be4572 100644 --- a/docs/reference/rnd.html +++ b/docs/reference/rnd.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -169,15 +172,10 @@

    Examp rnd(rnorm(10))

    #> [1] "-0.560" "-0.230" " 1.559" " 0.071" " 0.129" " 1.715" " 0.461" "-1.265" #> [9] "-0.687" "-0.446"
    rnd(1)
    #> [1] "1.000"
    - @@ -188,7 +186,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/row_sums.html b/docs/reference/row_sums.html index 5f04252..cb0f65e 100644 --- a/docs/reference/row_sums.html +++ b/docs/reference/row_sums.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -48,7 +52,6 @@ - @@ -66,7 +69,7 @@ - +
    @@ -145,6 +148,10 @@

    Apply simple rowwise functions

    row_means(data, ..., na_rm = FALSE, varname = "mean") +row_min(data, ..., na_rm = FALSE, varname = "min") + +row_max(data, ..., na_rm = FALSE, varname = "max") + row_apply(data, ..., .fun, varname = "var")

    Arguments

    @@ -164,7 +171,7 @@

    Arg varname -

    The column name of the sums means etc.

    +

    The column name of the sums means etc. as a character string.

    .fun @@ -198,6 +205,13 @@

    Examp #> 1 1 4 7 4 #> 2 2 5 8 5 #> 3 3 6 9 6

    +d %>% + row_max(matches('x|y'))
    #> # A tibble: 3 x 3 +#> x y max +#> <int> <int> <int> +#> 1 1 4 4 +#> 2 2 5 5 +#> 3 3 6 6
    row_apply( d , everything(), @@ -207,17 +221,12 @@

    Examp #> 1 1 4 7 147 #> 2 2 5 8 258 #> 3 3 6 9 369

    -
    + - @@ -228,7 +237,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/select_not.html b/docs/reference/select_not.html index f53da84..ce8c952 100644 --- a/docs/reference/select_not.html +++ b/docs/reference/select_not.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -47,7 +51,6 @@ - @@ -65,7 +68,7 @@ - +
    @@ -170,27 +173,27 @@

    Details get `invalid argument` errors. I needed this functionality because an operation was to be performed on some columns, but not if some operations had been performed on others, and those others were passed as unquoted - variables via vars(). See pre_process.

    + variables via vars(). See pre_process.

    In the end, it was far easier to write a two line function than figure out the tidy eval approach that would have worked with less code. It literally just does:

    not_these = names(select(data, ...))

    -

    data %>%
-  select(-one_of(not_these))
    +

    data %&gt;%
+  select(-one_of(not_these))

    Lionel's answer on SO shows how one could do it. He is a developer for RStudio, so one can assume it's a fairly spot on suggestion. Yet even that would need a bit of modification to work with a - vars(), which I needed for multiple column entries to a single + vars(), which I needed for multiple column entries to a single argument. Here is what ultimately would work with tidyeval using that approach.

    -

    x = vars_input %>%
-      map(function(sym) call("-", sym))
    +

    x = vars_input %&gt;%
+      map(function(sym) call("-", sym))
    -

    data %>%
-  select(!!!x)
    +

    data %&gt;%
+  select(!!!x)

    You then would need an 'x' for every argument's input. And no one reading the @@ -202,14 +205,10 @@

    Details is essentially the same solution.

    - @@ -220,7 +219,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/spread2.html b/docs/reference/spread2.html index df56190..a928683 100644 --- a/docs/reference/spread2.html +++ b/docs/reference/spread2.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -45,9 +49,9 @@ - + - @@ -65,7 +69,7 @@ - +
    @@ -136,7 +140,8 @@

    Spread without frustration

    -

    Spread without frustration

    +

    #' @description This is now deprecated and no longer needed due to +advancements in `tidyr`.

    spread2(
@@ -211,7 +216,8 @@ 
    See a
     
    spread
    
 
     
    Examples
    
-    
    library(tidyext); library(tidyr)
+    
    if (FALSE) {
+library(tidyext); library(tidyr)
 
 # initial example from spread
 stocks_init <- data.frame(
@@ -230,114 +236,22 @@ 
    Examp
 
 
 stocksm_init <- stocks_init %>% gather(stock, price, -time)
-stocksm_init %>% spread(stock, price)    # works fine
    #>          time          X          Y          Z
-#> 1  2009-01-01  1.2240818 -2.1356474  1.7058569
-#> 2  2009-01-02  0.3598138 -0.4359498 -1.1802859
-#> 3  2009-01-03  0.4007715 -2.0520089  3.5805026
-#> 4  2009-01-04  0.1106827 -1.4577825  3.5125340
-#> 5  2009-01-05 -0.5558411 -1.2500785  3.2863243
-#> 6  2009-01-06  1.7869131 -3.3733866  2.7545610
-#> 7  2009-01-07  0.4978505  1.6755741  2.2156706
-#> 8  2009-01-08 -1.9666172  0.3067462 -0.2476468
-#> 9  2009-01-09  0.7013559 -2.2762739 -1.2238507
-#> 10 2009-01-10 -0.4727914  2.5076298 -1.5218840
    stocksm <- stocks %>% gather(stock, price)
+stocksm_init %>% spread(stock, price)    # works fine
+stocksm <- stocks %>% gather(stock, price)
 # stocksm %>% spread(stock, price)       # annoying
-stocksm %>% spread2(stock, price)        # works fine
    #>              X           Y          Z
-#> 1  -0.69470698  0.50663703  1.5185579
-#> 2  -0.20791728 -0.05709351 -2.0092938
-#> 3  -1.26539635 -0.08574091 -1.3328295
-#> 4   2.16895597  2.73720457 -4.0743015
-#> 5   1.20796200 -0.45154197 -4.2871649
-#> 6  -1.12310858  3.03294121  1.2141146
-#> 7  -0.40288484 -3.09750561  1.7928391
-#> 8  -0.46665535  1.16922750  0.2120169
-#> 9   0.77996512  0.24770849  3.6890699
-#> 10 -0.08336907  0.43188314  8.2003387
    stocksm %>% spread2(stock, price, compact = FALSE)
    #>              X           Y          Z
-#> 1  -0.69470698          NA         NA
-#> 2  -0.20791728          NA         NA
-#> 3  -1.26539635          NA         NA
-#> 4   2.16895597          NA         NA
-#> 5   1.20796200          NA         NA
-#> 6  -1.12310858          NA         NA
-#> 7  -0.40288484          NA         NA
-#> 8  -0.46665535          NA         NA
-#> 9   0.77996512          NA         NA
-#> 10 -0.08336907          NA         NA
-#> 11          NA  0.50663703         NA
-#> 12          NA -0.05709351         NA
-#> 13          NA -0.08574091         NA
-#> 14          NA  2.73720457         NA
-#> 15          NA -0.45154197         NA
-#> 16          NA  3.03294121         NA
-#> 17          NA -3.09750561         NA
-#> 18          NA  1.16922750         NA
-#> 19          NA  0.24770849         NA
-#> 20          NA  0.43188314         NA
-#> 21          NA          NA  1.5185579
-#> 22          NA          NA -2.0092938
-#> 23          NA          NA -1.3328295
-#> 24          NA          NA -4.0743015
-#> 25          NA          NA -4.2871649
-#> 26          NA          NA  1.2141146
-#> 27          NA          NA  1.7928391
-#> 28          NA          NA  0.2120169
-#> 29          NA          NA  3.6890699
-#> 30          NA          NA  8.2003387
    
+stocksm %>% spread2(stock, price)        # works fine
+stocksm %>% spread2(stock, price, compact = FALSE)
+
 # works with NA
 stocksm$price[sample(1:nrow(stocksm), 5)] = NA
-stocksm %>% spread2(stock, price)
    #>              X           Y         Z
-#> 1  -0.69470698  0.50663703  1.518558
-#> 2  -0.20791728          NA -2.009294
-#> 3  -1.26539635 -0.08574091 -1.332830
-#> 4   2.16895597          NA -4.074302
-#> 5   1.20796200 -0.45154197 -4.287165
-#> 6  -1.12310858  3.03294121  1.214115
-#> 7  -0.40288484          NA  1.792839
-#> 8  -0.46665535  1.16922750        NA
-#> 9   0.77996512  0.24770849  3.689070
-#> 10 -0.08336907          NA  8.200339
    stocksm %>% spread2(stock, price, compact = FALSE)
    #>              X           Y         Z
-#> 1  -0.69470698          NA        NA
-#> 2  -0.20791728          NA        NA
-#> 3  -1.26539635          NA        NA
-#> 4   2.16895597          NA        NA
-#> 5   1.20796200          NA        NA
-#> 6  -1.12310858          NA        NA
-#> 7  -0.40288484          NA        NA
-#> 8  -0.46665535          NA        NA
-#> 9   0.77996512          NA        NA
-#> 10 -0.08336907          NA        NA
-#> 11          NA  0.50663703        NA
-#> 12          NA          NA        NA
-#> 13          NA -0.08574091        NA
-#> 14          NA          NA        NA
-#> 15          NA -0.45154197        NA
-#> 16          NA  3.03294121        NA
-#> 17          NA          NA        NA
-#> 18          NA  1.16922750        NA
-#> 19          NA  0.24770849        NA
-#> 20          NA          NA        NA
-#> 21          NA          NA  1.518558
-#> 22          NA          NA -2.009294
-#> 23          NA          NA -1.332830
-#> 24          NA          NA -4.074302
-#> 25          NA          NA -4.287165
-#> 26          NA          NA  1.214115
-#> 27          NA          NA  1.792839
-#> 28          NA          NA        NA
-#> 29          NA          NA  3.689070
-#> 30          NA          NA  8.200339
    
-
    
+stocksm %>% spread2(stock, price)
+stocksm %>% spread2(stock, price, compact = FALSE)
+}
    
   
-  
 
@@ -348,7 +262,7 @@ 
    Contents
    
 
 
 
    
-  
    Site built with pkgdown 1.4.1.
    
+  
    Site built with pkgdown 1.5.1.
    
 
    
 
       
diff --git a/docs/reference/sum_NA.html b/docs/reference/sum_NA.html
index b75075f..1769534 100644
--- a/docs/reference/sum_NA.html
+++ b/docs/reference/sum_NA.html
@@ -17,23 +17,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -47,7 +51,6 @@
 
 
 
-
 
 
 
@@ -65,7 +68,7 @@
 
   
 
-  
+  
     
    
       
    
       
 
@@ -174,15 +177,10 @@ 
    Examp
    - @@ -193,7 +191,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/tidyext.html b/docs/reference/tidyext.html index 8cc3b9f..796b5dc 100644 --- a/docs/reference/tidyext.html +++ b/docs/reference/tidyext.html @@ -17,23 +17,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -49,7 +53,6 @@ to do with data. I work with data for myself and others everyday, and use these functions quite often. The goal here is to not have any dependencies beyond the tidyverse." /> - @@ -67,7 +70,7 @@ - +
    @@ -151,12 +154,10 @@

    Details get created some day: browseVignettes(package = "tidyext")

    - @@ -167,7 +168,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/man/combn_2_col.Rd b/man/combn_2_col.Rd index 47b12a6..7f9f4a5 100644 --- a/man/combn_2_col.Rd +++ b/man/combn_2_col.Rd @@ -72,64 +72,25 @@ This comes up every once in a while. Someone has for whatever } \examples{ library(tidyext) -d = data.frame(id = 1:4, - labs = c('A/B', 'B/C/D/E', 'A/E', 'D/E')) -test = combn_2_col(data=d, var='labs', max_m=3) + +d = data.frame(id = 1:4, labs = c('A/B', 'B/C/D/E', 'A/E', 'D/E')) +test = combn_2_col(data = d, var = 'labs', max_m = 3) test str(test) + d$labs = c('A B', 'B C D E', 'A E', 'D E') -combn_2_col(data=d, var='labs', max_m=1) +combn_2_col(data = d, var = 'labs', max_m = 1) + d$labs = c('Tom, Dick & Harriet', "J'Sean", "OBG, Andreas", NA) -combn_2_col(data=d, var='labs', sep=',', max_m=2, collapse='-') - -\dontrun{ -# requires at least tidytext -tidy_dtm <- function(data, var, sep='-', max_m=3) { - init = stringr::str_split(data[[var]], pattern = sep) # creates a list of separated letters - - # the following gets the combos with a dot separating drugs in a given combo - # this first lapply could be parallelized if need be and is probably slowest - # probably want to change to m = min(c(4, m)) so as to only limit to 4 - # see also, combinat::combn which is slightly faster than base R below - observation_combos = init \%>\% - lapply(function(x) - sapply(seq_along(x), function(m) - utils::combn(x, min(max_m, m), FUN=paste, collapse = '_'))) - - # now we have a standard text analysis problem in need of a document term - matrix - documents = observation_combos \%>\% lapply(unlist) - - # create a 'tidy' form of documents and terms; each term (i.e. combo) only - occurs once in a document - doc_df = data.frame(id=rep(data$id, sapply(documents, length)), - combos=unlist(documents), - count=1) # each term only occurs once in the document - doc_df \%>\% - tidytext::cast_dfm(document=id, term=combos, value=count) - } - -# requires at least text2vec -ttv <- function(data, var, sep='-', max_m=3) { - docs = sapply(stringr::str_split(data[[var]], pattern=sep), - function(str_vec) - sapply(seq_along(str_vec), - function(m) - combn(str_vec, - m = min(max_m, m), - FUN = paste, - collapse = '_') - ) \%>\% unlist() - ) - - toks = itoken(docs, progressbar = FALSE) - vocab = create_vocabulary(toks) - create_dtm(toks, vectorizer = vocab_vectorizer(vocab), progressbar = FALSE) \%>\% - as.matrix() \%>\% - cbind(data,.) -} -} +combn_2_col( +data = d, +var = 'labs', +sep = ',', +max_m = 2, +collapse = '-' +) + diff --git a/man/head_tail.Rd b/man/head_tail.Rd index 16f3aac..9b17456 100644 --- a/man/head_tail.Rd +++ b/man/head_tail.Rd @@ -23,7 +23,7 @@ This will work on matrices and also not be limited to default tibble display. \examples{ library(tidyext) -as.matrix(mtcars) \%>\% -head_tail(6) + +head_tail(mtcars) } diff --git a/man/pre_process.Rd b/man/pre_process.Rd index e04a6cc..ebb53d3 100644 --- a/man/pre_process.Rd +++ b/man/pre_process.Rd @@ -60,8 +60,11 @@ At a minimum, by default, this function will standardize } \examples{ library(tidyext) +library(dplyr) + pre_process(mtcars) -pre_process(mtcars, log_vars=vars(mpg, wt)) -pre_process(mtcars, zero_start=vars(cyl, gear)) -pre_process(mtcars, zero_one=vars(mpg)) +pre_process(mtcars, log_vars = vars(mpg, wt)) +pre_process(mtcars, zero_start = vars(cyl, gear)) +pre_process(mtcars, zero_one = vars(mpg)) + } diff --git a/man/reexports.Rd b/man/reexports.Rd deleted file mode 100644 index 8faef7d..0000000 --- a/man/reexports.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils.R -\docType{import} -\name{reexports} -\alias{reexports} -\alias{\%>\%} -\alias{vars} -\title{Objects exported from other packages} -\keyword{internal} -\description{ -These objects are imported from other packages. Follow the links -below to see their documentation. - -\describe{ - \item{dplyr}{\code{\link[dplyr]{vars}}} - - \item{magrittr}{\code{\link[magrittr:pipe]{\%>\%}}} -}} - diff --git a/man/row_sums.Rd b/man/row_sums.Rd index 732b053..0eab0c1 100644 --- a/man/row_sums.Rd +++ b/man/row_sums.Rd @@ -3,6 +3,8 @@ \name{row_sums} \alias{row_sums} \alias{row_means} +\alias{row_min} +\alias{row_max} \alias{row_apply} \title{Apply simple rowwise functions} \usage{ @@ -10,6 +12,10 @@ row_sums(data, ..., na_rm = FALSE, varname = "sum") row_means(data, ..., na_rm = FALSE, varname = "mean") +row_min(data, ..., na_rm = FALSE, varname = "min") + +row_max(data, ..., na_rm = FALSE, varname = "max") + row_apply(data, ..., .fun, varname = "var") } \arguments{ @@ -19,7 +25,7 @@ row_apply(data, ..., .fun, varname = "var") \item{na_rm}{Whether to remove \code{NA} values or not. Default is \code{FALSE}.} -\item{varname}{The column name of the sums means etc.} +\item{varname}{The column name of the sums means etc. as a character string.} \item{.fun}{The function to apply.} } @@ -48,6 +54,9 @@ d \%>\% d \%>\% row_means(matches('x|z')) +d \%>\% + row_max(matches('x|y')) + row_apply( d , everything(), diff --git a/tests/testthat/test_cat_by.R b/tests/testthat/test_cat_by.R index 2dafa3b..db8de68 100644 --- a/tests/testthat/test_cat_by.R +++ b/tests/testthat/test_cat_by.R @@ -1,7 +1,7 @@ context('test cat_by') set.seed(1) -df1 <- tibble( +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), diff --git a/tests/testthat/test_combn_2_col.R b/tests/testthat/test_combn_2_col.R index ab555d6..1d66cbd 100644 --- a/tests/testthat/test_combn_2_col.R +++ b/tests/testthat/test_combn_2_col.R @@ -22,7 +22,7 @@ test_that('combn_2_col will fail with no data', { }) test_that('combn_2_col handles factors, NAs, other separators', { - init <- tibble(id = 1:5, + init <- dplyr::tibble(id = 1:5, labs = factor(c('AB', 'B/C/D/E', 'A/E', 'D/E', NA))) expect_s3_class(combn_2_col(data=d, var='labs', sep='/', max_m=3), 'data.frame') diff --git a/tests/testthat/test_describe_all.R b/tests/testthat/test_describe_all.R index 2cb55ff..51947ee 100644 --- a/tests/testthat/test_describe_all.R +++ b/tests/testthat/test_describe_all.R @@ -2,7 +2,7 @@ context('test describe_all') set.seed(1234) -df1 <- tibble( +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')), g2 = sample(1:4, 50, replace = TRUE), g3 = ordered(sample(letters[1:3], 50, replace = TRUE)), diff --git a/tests/testthat/test_num_by.R b/tests/testthat/test_num_by.R index d79afd2..17df04d 100644 --- a/tests/testthat/test_num_by.R +++ b/tests/testthat/test_num_by.R @@ -1,6 +1,6 @@ context('test num_by') -df1 <- tibble( +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels = c('a', 'b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), diff --git a/tests/testthat/test_num_summary.R b/tests/testthat/test_num_summary.R index 69fe49f..df05e52 100644 --- a/tests/testthat/test_num_summary.R +++ b/tests/testthat/test_num_summary.R @@ -1,6 +1,6 @@ context('test num_summary') -df1 <- tibble( +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels = c('a', 'b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), diff --git a/tests/testthat/test_pre_process.R b/tests/testthat/test_pre_process.R index 453fe1b..68fd23f 100644 --- a/tests/testthat/test_pre_process.R +++ b/tests/testthat/test_pre_process.R @@ -2,7 +2,8 @@ context('test pre_process') set.seed(1234) -df1 <- tibble( + +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), diff --git a/tests/testthat/test_row_sums.R b/tests/testthat/test_row_sums.R index 2b860de..c08f4d8 100644 --- a/tests/testthat/test_row_sums.R +++ b/tests/testthat/test_row_sums.R @@ -52,25 +52,49 @@ test_that('row_means returns a data frame', { }) test_that('row_means returns correct values', { - expect_equal(d %>% row_means(x:z) %>% pull(mean), c(4, 5, 6)) + expect_equal(d %>% row_means(x:z) %>% pull(mean), 4:6) }) test_that('row_means handles NA', { - expect_equal(d %>% row_means(x:z, na_rm = TRUE) %>% pull(mean), c(4, 5, 6)) + expect_equal(d %>% row_means(x:q, na_rm = TRUE) %>% pull(mean), 4:6) }) test_that('row_means takes varname', { - expect_equal(d %>% row_means(x:z, varname = 'blah') %>% pull(blah), - c(4, 5, 6)) + expect_equal(d %>% row_means(x:z, varname = 'blah') %>% pull(blah), 4:6) }) test_that('row_means handles select helpers', { - expect_equal(d %>% row_means(dplyr::matches('x|z')) %>% pull(mean), - c(4, 5, 6)) + expect_equal(d %>% row_means(dplyr::matches('x|z')) %>% pull(mean), 4:6) }) +# row_min_max ------------------------------------------------------------- + +test_that('row_max returns a data frame', { + expect_s3_class(d %>% row_max(x:z), 'data.frame') + expect_s3_class(d %>% row_min(x:z), 'data.frame') +}) + +test_that('row_max returns correct values', { + expect_equal(d %>% row_max(x:z) %>% pull(max), 7:9) + expect_equal(d %>% row_min(x:z) %>% pull(min), 1:3) +}) + +test_that('row_max handles NA', { + expect_equal(d %>% row_max(x:q, na_rm = TRUE) %>% pull(max), 7:9) + expect_equal(d %>% row_min(x:q, na_rm = TRUE) %>% pull(min), 1:3) +}) + +test_that('row_max takes varname', { + expect_equal(d %>% row_max(x:z, varname = 'blah') %>% pull(blah), 7:9) + expect_equal(d %>% row_min(x:z, varname = 'blah') %>% pull(blah), 1:3) +}) + +test_that('row_max handles select helpers', { + expect_equal(d %>% row_max(dplyr::matches('x|z')) %>% pull(max), 7:9) + expect_equal(d %>% row_min(dplyr::matches('x|z')) %>% pull(min), 1:3) +}) # row_apply --------------------------------------------------------------- @@ -83,7 +107,7 @@ test_that('row_apply returns a data frame', { }) test_that('row_apply returns correct values', { - expect_equal(d %>% row_apply(x:z, .fun = fun_med) %>% pull(var), c(4, 5, 6)) + expect_equal(d %>% row_apply(x:z, .fun = fun_med) %>% pull(var), 4:6) }) test_that('row_apply returns correct values', { @@ -95,7 +119,7 @@ test_that('row_means takes varname', { expect_equal(d %>% row_apply(x:z, .fun = fun_med, varname = 'blah') %>% pull(blah), - c(4, 5, 6)) + 4:6) }) @@ -103,5 +127,5 @@ test_that('row_means handles select helpers', { expect_equal(d %>% row_apply(dplyr::matches('x|z'), .fun = fun_med) %>% pull(var), - c(4, 5, 6)) + 4:6) }) diff --git a/tests/testthat/test_sum_NA.R b/tests/testthat/test_sum_NA.R index e6d07d7..6ae5c0b 100644 --- a/tests/testthat/test_sum_NA.R +++ b/tests/testthat/test_sum_NA.R @@ -1,6 +1,6 @@ context('test sum_na') -df1 <- tibble( +df1 <- dplyr::tibble( g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd index 2a815d4..ab84c53 100644 --- a/vignettes/introduction.Rmd +++ b/vignettes/introduction.Rmd @@ -31,15 +31,18 @@ knitr::opts_chunk$set(echo = T, message=F, warning=F, error=F, collapse = TRUE, To begin, we can load up the tidyverse and this package. I'll also create some data that will be useful for demonstration. ```{r packages} -library(tidyverse); library(tidyext) +library(tidyverse) +library(tidyext) + set.seed(8675309) + df1 <- tibble( - g1 = factor(sample(1:2, 50, replace = TRUE), labels=c('a','b')), + g1 = factor(sample(1:2, 50, replace = TRUE), labels = c('a', 'b')), g2 = sample(1:4, 50, replace = TRUE), a = rnorm(50), b = rpois(50, 10), - c = sample(letters, 50, replace=TRUE), - d = sample(c(T,F), 50, replace=TRUE) + c = sample(letters, 50, replace = TRUE), + d = sample(c(T, F), 50, replace = TRUE) ) df_miss = df1 @@ -189,88 +192,30 @@ Note that center/standardizing is done to any numeric variables *not* chosen for Here's a specific function you will probably never need, but will be glad to have if you do. Some data columns have multiple entries for each observation/cell. While it's understandable why someone would do this, it's not very good practice. This will split out the entries, or any particular combination of them, into their own indicator column. ```{r combn, message=TRUE} -d = data.frame(id = 1:4, - labs = c('A-B', 'B-C-D-E', 'A-E', 'D-E')) -combn_2_col(data=d, var='labs', max_m=2, sep = '-', collapse = ':', toInteger = T) - -combn_2_col(data=d, var='labs', max_m=2, sparse = T) -``` - -In addition, there is a function that makes tidyr's spread work like it should when you don't have unique identifiers. I spent a lot of time coming up with the name for this one. - -```{r spread2, error=TRUE} -# initial example from spread -stocks_init <- data.frame( - time = as.Date('2009-01-01') + 0:9, - X = rnorm(10, 0, 1), - Y = rnorm(10, 0, 2), - Z = rnorm(10, 0, 4) +d = data.frame(id = 1:4, labs = c('A-B', 'B-C-D-E', 'A-E', 'D-E')) + +combn_2_col( + data = d, + var = 'labs', + max_m = 2, + sep = '-', + collapse = ':', + toInteger = T ) -# a very common situation -stocks <- data.frame( - X = rnorm(10, 0, 1), - Y = rnorm(10, 0, 2), - Z = rnorm(10, 0, 4) +combn_2_col( + data = d, + var = 'labs', + max_m = 2, + sparse = T ) - - -stocksm_init <- stocks_init %>% - gather(stock, price, -time) -head(stocksm_init) - -stocksm_init %>% - spread(stock, price) %>% - head() # works fine - -# no time -stocksm <- stocks %>% - gather(stock, price) -head(stocksm) - -stocksm %>% - spread(stock, price) # annoying - -stocksm %>% - spread2(stock, price) # works fine -``` - -However, with unbalanced data the result can be interpreted in different ways, so you'll have a choice to make. - -```{r spread2compact} -stocksm$price[sample(1:nrow(stocksm), 5)] = NA -stocksm %>% - spread2(stock, price) - -stocksm %>% - spread2(stock, price, compact = FALSE) ``` -I can speak from experience that having longitudinal data in the `compact = FALSE` format is about the worst way you could keep such data. - -Use gather_multi to extend `tidyr::gather` to deal with multiple sets of variables. - -```{r gather_multi} -demo_data_wide = data.frame(id = 1:10, - X = matrix(rnorm(40), ncol = 4), - Y = matrix(sample(0:1, 40, replace = T), ncol = 4), - Z = matrix(rpois(40, 5), ncol = 4)) -head(demo_data_wide) - -gather_multi(demo_data_wide, - key = wave, - values = vars(X, Y, Z), - varlist = vars(starts_with('X'), - starts_with('Y'), - starts_with('Z')), - -id) -``` -Note that this only makes sense with balanced data, though it will work if NAs are present. Also, you may want to change the key in some fashion. Oftentimes I need to create a column that represents the total scores or means of just a few columns. This is a slight annoyance in the tidyverse, and there isn't much support behind the dplyr:rowwise function. As such, tidyext has a couple simple wrappers for row_sums, row_means, and row_apply. -```{r} +```{r rowapply} d = data.frame(x = 1:3, y = 4:6, z = 7:9,