`table1.Rd`

Produces a descriptive table, stratified by an optional categorical variable, providing means/frequencies and standard deviations/percentages. It is well-formatted for easy transition to academic article or report. Can be used within the piping framework [see library(magrittr)].

table1( .data, ..., splitby = NULL, FUN = NULL, FUN2 = NULL, total = FALSE, second = NULL, row_wise = FALSE, test = FALSE, param = TRUE, header_labels = NULL, type = "pvalues", output = "text", rounding_perc = 1, digits = 1, var_names = NULL, format_number = FALSE, NAkeep = NULL, na.rm = TRUE, booktabs = TRUE, caption = NULL, align = NULL, float = "ht", export = NULL, label = NULL )

.data | the data.frame that is to be summarized |
---|---|

... | variables in the data set that are to be summarized; unquoted names separated by commas (e.g. age, gender, race) or indices. If indices, it needs to be a single vector (e.g. c(1:5, 8, 9:20) instead of 1:5, 8, 9:20). As it is currently, it CANNOT handle both indices and unquoted names simultaneously. Finally, any empty rows (where the row is NA for each variable selected) will be removed for an accurate n count. |

splitby | the categorical variable to stratify (in formula form |

FUN | the function to be applied to summarize the numeric data; default is to report the means and standard deviations |

FUN2 | a secondary function to be applied to summarize the numeric data; default is to report the medians and 25% and 75% quartiles |

total | whether a total (not stratified with the |

second | a vector or list of quoted continuous variables for which the |

row_wise | how to calculate percentages for factor variables when |

test | logical; if set to |

param | logical; if set to |

header_labels | a character vector that renames the header labels (e.g., the blank above the variables, the p-value label, and test value label). |

type | what is displayed in the table; a string or a vector of strings. Two main sections can be inputted: 1. if test = TRUE, can write "pvalues", "full", or "stars" and 2. can state "simple" and/or "condense". These are discussed in more depth in the details section below. |

output | how the table is output; can be "text" or "text2" for regular console output or any of |

rounding_perc | the number of digits after the decimal for percentages; default is 1 |

digits | the number of significant digits for the numerical variables (if using default functions); default is 1. |

var_names | custom variable names to be printed in the table. Variable names can be applied directly in the list of variables. |

format_number | default is FALSE; if TRUE, then the numbers are formatted with commas (e.g., 20,000 instead of 20000) |

NAkeep | when set to |

na.rm | when set to |

booktabs | when |

caption | when |

align | when |

float | the float applied to the table in Latex when output is |

export | character; when given, it exports the table to a CSV file to folder named "table1" in the working directory with the name of the given string (e.g., "myfile" will save to "myfile.csv") |

label | for |

A table with the number of observations, means/frequencies and standard deviations/percentages is returned. The object is a `table1`

class object with a print method. Can be printed in `LaTex`

form.

In defining `type`

, 1. options are "pvalues" that display the p-values of the tests, "full" which also shows the test statistics, or "stars" which only displays stars to highlight significance with *** < .001 ** .01 * .05; and
2. "simple" then only percentages are shown for categorical variable and
"condense" then continuous variables' means and SD's will be on the same line as the variable name and dichotomous variables only show counts and percentages for the reference category.

#> #>#>#> #>#>#> #>x <- runif(1000) y <- rnorm(1000) z <- factor(sample(c(0,1), 1000, replace=TRUE)) a <- factor(sample(c(1,2), 1000, replace=TRUE)) df <- data.frame(x, y, z, a) ## Simple table1(df, x, y, z, a)#>#> #> ──────────────────────── #> Mean/Count (SD/%) #> n = 1000 #> x #> 0.5 (0.3) #> y #> 0.1 (1.0) #> z #> 0 500 (50%) #> 1 500 (50%) #> a #> 1 478 (47.8%) #> 2 522 (52.2%) #> ────────────────────────## Stratified ## all three below are the same table1(df, x, y, z, splitby = ~ a)#>#> #> ────────────────────────────── #> a #> 1 2 #> n = 478 n = 522 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.1 (1.0) 0.1 (1.0) #> z #> 0 247 (51.7%) 253 (48.5%) #> 1 231 (48.3%) 269 (51.5%) #> ──────────────────────────────table1(df, x, y, z, splitby = "a")#>#> #> ────────────────────────────── #> a #> 1 2 #> n = 478 n = 522 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.1 (1.0) 0.1 (1.0) #> z #> 0 247 (51.7%) 253 (48.5%) #> 1 231 (48.3%) 269 (51.5%) #> ──────────────────────────────## With Piping df %>% table1(x, y, z, splitby = ~a)#>#> #> ────────────────────────────── #> a #> 1 2 #> n = 478 n = 522 #> x #> 0.5 (0.3) 0.5 (0.3) #> y #> 0.1 (1.0) 0.1 (1.0) #> z #> 0 247 (51.7%) 253 (48.5%) #> 1 231 (48.3%) 269 (51.5%) #> ──────────────────────────────#>#>#>#> #> ──────────────────────── #> Mean/Count (SD/%) #> n = 1000 #> x2 #> 1.0 (0.0) #> z #> 0 500 (50%) #> 1 500 (50%) #> ────────────────────────