README.md

numform

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numform contains tools to assist in the formatting of numbers and plots for publication. Tools include the removal of leading zeros, standardization of number of digits, addition of affixes, and a p-value formatter. These tools combine the functionality of several 'base' functions such as paste(), format(), and sprintf() into specific use case functions that are named in a way that is consistent with usage, making their names easy to remember and easy to deploy.

Installation

To download the development version of numform:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the pacman package to install the development version:

if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh("trinker/numform")
pacman::p_load(tidyverse, gridExtra)

Table of Contents

Contact

You are welcome to: - submit suggestions and bug-reports at: https://github.com/trinker/numform/issues - send a pull request on: https://github.com/trinker/numform/ - compose a friendly e-mail to: tyler.rinker@gmail.com

Available Functions

Below is a table of available numform functions. Note that f_ is read as "format" whereas fv_ is read as "format vector". The former formats individual values in the vector while the latter uses the vector to compute a calculation on each of the values and then formats them. Additionally, all numform f_ functions have a closure, function retuning, version that is prefixed with an additional f (read "format function"). For example, f_num has ff_num which has the same arguments but returns a function instead. This is useful for passing in to ggplot2 scale_x/y_type functions (see Plotting for usage).

alignment f_byte f_latitude f_peta f_weekday_abbreviation as_factor f_celcius f_list f_pp f_weekday_name collapse f_comma f_list_amp f_prefix f_wrap constant_months f_data f_logical f_prop2percent f_year constant_months_abbreviation f_data_abbreviation f_longitude f_pval f_yotta constant_quarters f_date f_mean_sd f_quarter f_zetta constant_weekdays f_degree f_mega f_replace fv_num_percent constant_weekdays_abbreviation f_denom f_mills f_response fv_percent f_12_hour f_dollar f_month f_sign fv_percent_diff f_abbreviation f_exa f_month_abbreviation f_state fv_percent_diff_fixed_relative f_affirm f_fahrenheit f_month_name f_suffix fv_percent_lead f_affix f_giga f_num f_tera fv_percent_lead_fixed_relative f_bills f_interval f_num_percent f_text_bar fv_runs f_bin f_interval_right f_ordinal f_thous glue f_bin_right f_interval_text f_pad_zero f_title highlight_cells f_bin_text f_interval_text_right f_parenthesis f_trills f_bin_text_right f_kilo f_percent f_weekday

Available Formatting Functions

Demonstration

Load Packages

if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/numform")
pacman::p_load(dplyr)

Numbers

f_num(c(0.0, 0, .2, -00.02, 1.122222, pi, "A"))

## [1] ".0"  ".0"  ".2"  "-.0" "1.1" "3.1" NA

Abbreviated Numbers

f_thous(1234)

## [1] "1K"

f_thous(12345)

## [1] "12K"

f_thous(123456)

## [1] "123K"

f_mills(1234567)

## [1] "1M"

f_mills(12345678)

## [1] "12M"

f_mills(123456789)

## [1] "123M"

f_bills(1234567891)

## [1] "1B"

f_bills(12345678912)

## [1] "12B"

f_bills(123456789123)

## [1] "123B"

...or auto-detect:

f_denom(1234)

## [1] "1K"

f_denom(12345)

## [1] "12K"

f_denom(123456)

## [1] "123K"

f_denom(1234567)

## [1] "1M"

f_denom(12345678)

## [1] "12M"

f_denom(123456789)

## [1] "123M"

f_denom(1234567891)

## [1] "1B"

f_denom(12345678912)

## [1] "12B"

f_denom(123456789123)

## [1] "123B"

Commas

f_comma(c(1234.12345, 1234567890, .000034034, 123000000000, -1234567))

## [1] "1,234.123"       "1,234,567,890"   ".000034034"      "123,000,000,000"
## [5] "-1,234,567"

Percents

f_percent(c(30, 33.45, .1), digits = 1)

## [1] "30.0%" "33.5%" ".1%"

f_percent(c(0.0, 0, .2, -00.02, 1.122222, pi))

## [1] ".0%"  ".0%"  ".2%"  "-.0%" "1.1%" "3.1%"

f_prop2percent(c(.30, 1, 1.01, .33, .222, .01))

## [1] "30.0%"  "100.0%" "101.0%" "33.0%"  "22.2%"  "1.0%"

f_prop2percent(c(.30, 1, 1.01, .33, .222, .01), digits = 0)

## [1] "30%"  "100%" "101%" "33%"  "22%"  "1%"

f_pp(c(.30, 1, 1.01, .33, .222, .01)) # same as f_prop2percent(digits = 0)

## [1] "30%"  "100%" "101%" "33%"  "22%"  "1%"

Dollars

f_dollar(c(0, 30, 33.45, .1))

## [1] "$0.00"  "$30.00" "$33.45" "$0.10"

f_dollar(c(0.0, 0, .2, -00.02, 1122222, pi)) %>% 
    f_comma()

## [1] "$0.00"         "$0.00"         "$0.20"         "$-.02"        
## [5] "$1,122,222.00" "$3.14"

Sometimes one wants to lop off digits of money in order to see the important digits, the real story. The f_denom family of functions can do job.

f_denom(c(12345267, 98765433, 658493021), prefix = '$')

## [1] "$ 12M" "$ 99M" "$658M"

f_denom(c(12345267, 98765433, 658493021), relative = 1, prefix = '$')

## [1] "$ 12.3M" "$ 98.8M" "$658.5M"

Tables

Notice the use of the alignment function to detect the column alignment.

pacman::p_load(dplyr, pander)

set.seed(10)
dat <- data_frame(
    Team = rep(c("West Coast", "East Coast"), each = 4),
    Year = rep(2012:2015, 2),
    YearStart = round(rnorm(8, 2e6, 1e6) + sample(1:10/100, 8, TRUE), 2),
    Won = round(rnorm(8, 4e5, 2e5) + sample(1:10/100, 8, TRUE), 2),
    Lost = round(rnorm(8, 4.4e5, 2e5) + sample(1:10/100, 8, TRUE), 2),
    WinLossRate = Won/Lost,
    PropWon = Won/YearStart,
    PropLost = Lost/YearStart
)


dat %>%
    group_by(Team) %>%
    mutate(
        `%&Delta;WinLoss` = fv_percent_diff(WinLossRate, 0),
        `&Delta;WinLoss` = f_sign(Won - Lost, '<b>+</b>', '<b>&ndash;</b>')

    ) %>%
    ungroup() %>%
    mutate_at(vars(Won:Lost), .funs = ff_denom(relative = -1, prefix = '$')) %>%
    mutate_at(vars(PropWon, PropLost), .funs = ff_prop2percent(digits = 0)) %>%
    mutate(
        YearStart = f_denom(YearStart, 1, prefix = '$'),
        Team = fv_runs(Team),
        WinLossRate = f_num(WinLossRate, 1)
    ) %>%
    data.frame(stringsAsFactors = FALSE, check.names = FALSE) %>%
    pander::pander(split.tables = Inf, justify = alignment(.), style = 'simple')
Team Year YearStart Won Lost WinLossRate PropWon PropLost %ΔWinLoss ΔWinLoss West Coast 2012 $2.0M $350K $190K 1.9 17% 9% 0% + 2013 $1.8M $600K $370K 1.6 33% 20% -13% + 2014 $ .6M $550K $300K 1.8 87% 48% 11% + 2015 $1.4M $420K $270K 1.6 30% 19% -13% + East Coast 2012 $2.3M $210K $420K .5 9% 18% 0% 2013 $2.4M $360K $390K .9 15% 16% 86% 2014 $ .8M $590K $ 70K 8.4 74% 9% 811% + 2015 $1.6M $500K $420K 1.2 30% 26% -86% +
pacman::p_load(dplyr, pander)

data_frame(
    Event = c('freezing water', 'room temp', 'body temp', 'steak\'s done', 'hamburger\'s done', 'boiling water', 'sun surface', 'lighting'),
    F = c(32, 70, 98.6, 145, 160, 212, 9941, 50000)
) %>%
    mutate(
        Event = f_title(Event),
        C = (F - 32) * (5/9)
    ) %>%
    mutate(
        F = f_degree(F, measure = 'F', type = 'string'),
        C = f_degree(C, measure = 'C', type = 'string', zero = '0.0')
    )  %>%
    data.frame(stringsAsFactors = FALSE, check.names = FALSE) %>%
    pander::pander(split.tables = Inf, justify = alignment(.), style = 'simple')
Event F C Freezing Water 32.0°F 0.0°C Room Temp 70.0°F 21.1°C Body Temp 98.6°F 37.0°C Steak's Done 145.0°F 62.8°C Hamburger's Done 160.0°F 71.1°C Boiling Water 212.0°F 100.0°C Sun Surface 9941.0°F 5505.0°C Lighting 50000.0°F 27760.0°C
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse)

set.seed(11)
data_frame(
    date = sample(seq(as.Date("1990/1/1"), by = "day", length.out = 2e4), 12)
) %>%
    mutate(
        year_4 = f_year(date, 4),
        year_2 = f_year(date, 2),
        quarter = f_quarter(date),
        month_name = f_month_name(date) %>%
            numform::as_factor(),
        month_abbreviation = f_month_abbreviation(date) %>%
            numform::as_factor(),
        month_short = f_month(date),
        weekday_name = f_weekday_name(date),
        weekday_abbreviation = f_weekday_abbreviation(date),
       weekday_short = f_weekday(date),
        weekday_short_distinct = f_weekday(date, distinct = TRUE)
    ) %>%
    data.frame(stringsAsFactors = FALSE, check.names = FALSE) %>%
    pander::pander(split.tables = Inf, justify = alignment(.), style = 'simple')
date year_4 year_2 quarter month_name month_abbreviation month_short weekday_name weekday_abbreviation weekday_short weekday_short_distinct 2005-03-07 2005 05 Q1 March Mar M Monday Mon M M 1990-01-11 1990 90 Q1 January Jan J Thursday Thu T Th 2017-12-16 2017 17 Q4 December Dec D Saturday Sat S S 1990-10-08 1990 90 Q4 October Oct O Monday Mon M M 1993-07-17 1993 93 Q3 July Jul J Saturday Sat S S 2042-04-10 2042 42 Q2 April Apr A Thursday Thu T Th 1994-09-26 1994 94 Q3 September Sep S Monday Mon M M 2005-11-15 2005 05 Q4 November Nov N Tuesday Tue T T 2038-03-16 2038 38 Q1 March Mar M Tuesday Tue T T 1996-09-29 1996 96 Q3 September Sep S Sunday Sun S Su 1999-08-02 1999 99 Q3 August Aug A Monday Mon M M 2014-02-14 2014 14 Q1 February Feb F Friday Fri F F
mtcars %>%
    count(cyl, gear) %>%
    group_by(cyl) %>%
    mutate(
        p = numform::f_pp(n/sum(n))
    ) %>%
    ungroup() %>%
    mutate(
        cyl = numform::fv_runs(cyl),
        ` ` = f_text_bar(n)  ## Overall
    ) %>%
    as.data.frame()

  cyl gear  n   p          
1   4    3  1  9% _        
2        4  8 73% ______   
3        5  2 18% __       
4   6    3  2 29% __       
5        4  4 57% ___      
6        5  1 14% _        
7   8    3 12 86% _________
8        5  2 14% __

Plotting

library(tidyverse); library(viridis)

set.seed(10)
data_frame(
    revenue = rnorm(10000, 500000, 50000),
    date = sample(seq(as.Date('1999/01/01'), as.Date('2000/01/01'), by="day"), 10000, TRUE),
    site = sample(paste("Site", 1:5), 10000, TRUE)
) %>%
    mutate(
        dollar = f_comma(f_dollar(revenue, digits = -3)),
        thous = f_denom(revenue),
        thous_dollars = f_denom(revenue, prefix = '$'),
        abb_month = f_month(date),
        abb_week = numform::as_factor(f_weekday(date, distinct = TRUE))
    ) %>%
    group_by(site, abb_week) %>%
    mutate(revenue = {if(sample(0:1, 1) == 0) `-` else `+`}(revenue, sample(1e2:1e5, 1))) %>%
    ungroup() %T>%
    print() %>%
    ggplot(aes(abb_week, revenue)) +
        geom_jitter(width = .2, height = 0, alpha = .2, aes(color = revenue)) +
        scale_y_continuous(label = ff_denom(prefix = '$'))+
        facet_wrap(~site) +
        theme_bw() +
        scale_color_viridis() +
        theme(
            strip.text.x = element_text(hjust = 0, color = 'grey45'),
            strip.background = element_rect(fill = NA, color = NA),
            panel.border = element_rect(fill = NA, color = 'grey75'),
            panel.grid = element_line(linetype = 'dotted'),
            axis.ticks = element_line(color = 'grey55'),
            axis.text = element_text(color = 'grey55'),
            axis.title.x = element_text(color = 'grey55', margin = margin(t = 10)),            
            axis.title.y = element_text(color = 'grey55', angle = 0, margin = margin(r = 10)),
            legend.position = 'none'
        ) +
        labs(
            x = 'Day of Week',
            y = 'Revenue',
            title = 'Site Revenue by Day of Week',
            subtitle = f_wrap(c(
                'This faceted dot plot shows the distribution of revenues within sites',
                'across days of the week.  Notice the consistently increasing revenues for',
                'Site 2 across the week.'
            ), width = 85, collapse = TRUE)
        )

## # A tibble: 10,000 x 8
##    revenue date       site   dollar  thous thous_dollars abb_month abb_week
##      <dbl> <date>     <chr>  <chr>   <chr> <chr>         <chr>     <fct>   
##  1 449648. 1999-11-29 Site 1 $501,0~ 501K  $501K         N         M       
##  2 560514. 1999-07-07 Site 4 $491,0~ 491K  $491K         J         W       
##  3 438891. 1999-08-06 Site 2 $431,0~ 431K  $431K         A         F       
##  4 528543. 1999-05-04 Site 3 $470,0~ 470K  $470K         M         T       
##  5 462758. 1999-07-08 Site 4 $515,0~ 515K  $515K         J         Th      
##  6 553879. 1999-07-22 Site 2 $519,0~ 519K  $519K         J         Th      
##  7 473985. 1999-05-20 Site 2 $440,0~ 440K  $440K         M         Th      
##  8 533825. 1999-05-28 Site 5 $482,0~ 482K  $482K         M         F       
##  9 426124. 1999-01-15 Site 2 $419,0~ 419K  $419K         J         F       
## 10 406613. 1999-08-19 Site 3 $487,0~ 487K  $487K         A         Th      
## # ... with 9,990 more rows

library(tidyverse); library(viridis)

set.seed(10)
dat <- data_frame(
    revenue = rnorm(144, 500000, 10000),
    date = seq(as.Date('2005/01/01'), as.Date('2016/12/01'), by="month")
) %>%
    mutate(
        quarter = f_quarter(date),
        year = f_year(date, 4)
    ) %>%
    group_by(year, quarter) %>%
    summarize(revenue = sum(revenue)) %>%
    ungroup() %>%
    mutate(quarter = as.integer(gsub('Q', '', quarter)))

year_average <- dat %>%
    group_by(year) %>%
    summarize(revenue = mean(revenue)) %>%
    mutate(x1 = .8, x2 = 4.2)

dat %>%
    ggplot(aes(quarter, revenue, group = year)) +
        geom_segment(
            linetype = 'dashed', 
            data = year_average, color = 'grey70', size = 1,
            aes(x = x1, y = revenue, xend = x2, yend = revenue)
        ) +
        geom_line(size = .85, color = '#009ACD') +
        geom_point(size = 1.5, color = '#009ACD') +
        facet_wrap(~year, nrow = 2)  +
        scale_y_continuous(label = ff_denom(relative = 2)) +
        scale_x_continuous(breaks = 1:4, label = f_quarter) +
        theme_bw() +
        theme(
            strip.text.x = element_text(hjust = 0, color = 'grey45'),
            strip.background = element_rect(fill = NA, color = NA),
            panel.border = element_rect(fill = NA, color = 'grey75'),
            panel.grid.minor = element_blank(),
            panel.grid.major = element_line(linetype = 'dotted'),
            axis.ticks = element_line(color = 'grey55'),
            axis.text = element_text(color = 'grey55'),
            axis.title.x = element_text(color = 'grey55', margin = margin(t = 10)),            
            axis.title.y = element_text(color = 'grey55', angle = 0, margin = margin(r = 10)),
            legend.position = 'none'
        ) +
        labs(
            x = 'Quarter',
            y = 'Revenue ($)',
            title = 'Quarterly Revenue Across Years',
            subtitle = f_wrap(c(
                'This faceted line plot shows the change in quarterly revenue across', 
                'years.'
            ), width = 85, collapse = TRUE)
        )

library(tidyverse); library(gridExtra)

set.seed(10)
dat <- data_frame(
    level = c("not_involved", "somewhat_involved_single_group",
        "somewhat_involved_multiple_groups", "very_involved_one_group",
        "very_involved_multiple_groups"
    ),
    n = sample(1:10, length(level))
) %>%
    mutate(
        level = factor(level, levels = unique(level)),
        `%` = n/sum(n)
    )

gridExtra::grid.arrange(

    gridExtra::arrangeGrob(

        dat %>%
            ggplot(aes(level, `%`)) +
                geom_col() +
                labs(title = 'Very Sad', y = NULL) +
                theme(
                    axis.text = element_text(size = 7),
                    title = element_text(size = 9)
                ),

       dat %>%
            ggplot(aes(level, `%`)) +
                geom_col() +
                scale_x_discrete(labels = function(x) f_replace(x, '_', '\n')) +
                scale_y_continuous(labels = ff_prop2percent(digits = 0))  +
                labs(title = 'Underscore Split (Readable)', y = NULL) +
                theme(
                    axis.text = element_text(size = 7),
                    title = element_text(size = 9)
                ),

        ncol = 2

    ),
    gridExtra::arrangeGrob(

       dat %>%
            ggplot(aes(level, `%`)) +
                geom_col() +
                scale_x_discrete(labels = function(x) f_title(f_replace(x))) +
                scale_y_continuous(labels = ff_prop2percent(digits = 0))  +
                labs(title = 'Underscore Replaced & Title (Capitalized Sadness)', y = NULL) +
                theme(
                    axis.text = element_text(size = 7),
                    title = element_text(size = 9)
                ),

        dat %>%
            ggplot(aes(level, `%`)) +
                geom_col() +
                scale_x_discrete(labels = function(x) f_wrap(f_title(f_replace(x)))) +
                scale_y_continuous(labels = ff_prop2percent(digits = 0))  +
                labs(title = 'Underscore Replaced, Title, & Wrapped (Happy)', y = NULL) +
                theme(
                    axis.text = element_text(size = 7),
                    title = element_text(size = 9)
                ),

        ncol = 2

    ), ncol = 1

)

set.seed(10)
dat <- data_frame(
    state = sample(state.name, 10),
    value = sample(10:20, 10) ^ (7),
    cols = sample(colors()[1:150], 10)
) %>%
    arrange(desc(value)) %>%
    mutate(state = factor(state, levels = unique(state)))

dat %>%
    ggplot(aes(state, value, fill = cols)) +
        geom_col() +
        scale_x_discrete(labels = f_state) +
        scale_fill_identity() +
        scale_y_continuous(labels = ff_denom(prefix = '$'), expand = c(0, 0), 
            limits = c(0, max(dat$value) * 1.05)
        ) +
        theme_minimal() +
        theme(
            panel.grid.major.x = element_blank(),
            axis.title.y = element_text(angle = 0)
        ) +
        labs(x = 'State', y = 'Cash\nFlow', 
            title = f_title("look at how professional i look"),
            subtitle = 'Subtitles: For that extra professional look.'
        )

library(tidyverse); library(viridis)

data_frame(
    Event = c('freezing water', 'room temp', 'body temp', 'steak\'s done', 'hamburger\'s done', 'boiling water'),
    F = c(32, 70, 98.6, 145, 160, 212)
) %>%
    mutate(
        C = (F - 32) * (5/9),
        Event = f_title(Event),
        Event = factor(Event, levels = unique(Event))
    ) %>%
    ggplot(aes(Event, F, fill = F)) +
        geom_col() +
        geom_text(aes(y = F + 4, label = f_fahrenheit(F, digits = 1, type = 'text')), parse = TRUE, color = 'grey60') +
        scale_y_continuous(
            labels = f_fahrenheit, limits = c(0, 220), expand = c(0, 0),
            sec.axis = sec_axis(trans = ~(. - 32) * (5/9), labels = f_celcius, name = f_celcius(prefix = 'Temperature ', type = 'title'))
        ) +
        scale_x_discrete(labels = ff_replace(pattern = ' ', replacement = '\n')) +
        scale_fill_viridis(option =  "magma", labels = f_fahrenheit, name = NULL) +
        theme_bw() +
        labs(
            y = f_fahrenheit(prefix = 'Temperature ', type = 'title'),
            title = f_fahrenheit(prefix = 'Temperature of Common Events ', type = 'title')
        ) +
        theme(
            axis.ticks.x = element_blank(),
            panel.border = element_rect(fill = NA, color = 'grey80'),
            panel.grid.minor.x = element_blank(),
            panel.grid.major.x = element_blank()
        )

library(tidyverse); library(maps)

world <- map_data(map="world")

ggplot(world, aes(map_id = region, x = long, y = lat)) +
    geom_map(map = world, aes(map_id = region), fill = "grey40", colour = "grey70", size = 0.25) +
    scale_y_continuous(labels = f_latitude) +
    scale_x_continuous(labels = f_longitude)

mtcars %>%
    mutate(mpg2 = cut(mpg, 10, right = FALSE)) %>%
    ggplot(aes(mpg2)) +
        geom_bar(fill = '#33A1DE') +
        scale_x_discrete(labels = function(x) f_wrap(f_bin_text_right(x, l = 'up to'), width = 8)) +
        scale_y_continuous(breaks = seq(0, 14, by = 2), limits = c(0, 7)) +
        theme_minimal() +
        theme(
            panel.grid.major.x = element_blank(),
            axis.text.x = element_text(size = 14, margin = margin(t = -12)),
            axis.text.y = element_text(size = 14),
            plot.title = element_text(hjust = .5)
        ) +
        labs(title = 'Histogram', x = NULL, y = NULL)

dat <- data_frame(
    Value = c(111, 2345, 34567, 456789, 1000001, 1000000001),
    Time = 1:6
)

gridExtra::grid.arrange(

    ggplot(dat, aes(Time, Value)) +
        geom_line() +
        scale_y_continuous(labels = ff_denom( prefix = '$')) +
        labs(title = "Single Denominational Unit"),

    ggplot(dat, aes(Time, Value)) +
        geom_line() +
        scale_y_continuous(
            labels = ff_denom(mix.denom = TRUE, prefix = '$', pad.char = '')
        ) +
        labs(title = "Mixed Denominational Unit"),

    ncol = 2
)

Modeling

We can see its use in actual model reporting as well:

mod1 <- t.test(1:10, y = c(7:20))

sprintf(
    "t = %s (%s)",
    f_num(mod1$statistic),
    f_pval(mod1$p.value)
)

## [1] "t = -5.4 (p < .05)"

mod2 <- t.test(1:10, y = c(7:20, 200))

sprintf(
    "t = %s (%s)",
    f_num(mod2$statistic, 2),
    f_pval(mod2$p.value, digits = 2)
)

## [1] "t = -1.63 (p = .12)"

We can build a function to report model statistics:

report <- function(mod, stat = NULL, digits = c(0, 2, 2)) {

    stat <- if (is.null(stat)) stat <- names(mod[["statistic"]])
    sprintf(
        "%s(%s) = %s, %s", 
        gsub('X-squared', '&Chi;<sup>2</sup>', stat),
        paste(f_num(mod[["parameter"]], digits[1]), collapse = ", "),
        f_num(mod[["statistic"]], digits[2]),
        f_pval(mod[["p.value"]], digits = digits[3])
    )

}

report(mod1)

## [1] "t(22) = -5.43, p < .05"

report(oneway.test(count ~ spray, InsectSprays))

## [1] "F(5, 30) = 36.07, p < .05"

report(chisq.test(matrix(c(12, 5, 7, 7), ncol = 2)))

## [1] "&Chi;<sup>2</sup>(1) = .64, p = .42"

This enables in-text usage as well. First set up the models in a code chunk:

mymod <- oneway.test(count ~ spray, InsectSprays)
mymod2 <- chisq.test(matrix(c(12, 5, 7, 7), ncol = 2))

And then use `r report(mymod)` resulting in a report that looks like this: F(5, 30) = 36.07, p < .05. For Χ2 using proper HTML leads to Χ2(1) = .64, p = .42.



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numform documentation built on Oct. 10, 2021, 1:10 a.m.