Package 'GenderInfer'

Assign gender by first name

Description

This function use the data source based on combined US/UK censor data to assign gender based on first name.

Usage

assign_gender(data_df, first_name_col)

Arguments

data_df	input dataframe containing the first name
first_name_col	first name column's name to assign gender to

Value

The input data frame with the gender column:

gender - assigned gender (F/M/U)

Examples

gender <- assign_gender(authors, "first_name")

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names dataset

Description

This data sets contains all the name fro UK and US social security

Usage

authors

Format

a data frame with 1000 rows of four variables:

first_name

first name

last_name

last lame

country_code

country

publication_years

publication year

---

Function to create the balloon plot for gender first name

Description

Function to create the balloon plot for gender first name

Usage

balloon_plot(data_df, gender_var, cutoff)

Arguments

data_df	data frame containing 'first name' and 'gender' columns from assign_gender
gender_var	gender possible values are F for female, M for male and U for unknown
cutoff	numerical value indicating where to cut the counting data

Value

The output is a gg object from ggplot2 which shows the most frequent names as a balloon plot.

Examples

gender <- assign_gender(authors, "first_name")
bp <- balloon_plot(gender, "M", cutoff = 5)

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Function to create a bar chart of the total number by gender

Description

Function to create a bar chart of the total number by gender

Usage

bar_chart(data_df, x_label, y_label)

Arguments

data_df	dataframe from total_gender_df
x_label	label for x axis.
y_label	label for y axis.

Value

A bar chart as ggplot2 object showing on the y axis the total number per gender and on the x axis the level previously defined in total_gender_df.

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Calculate the female baseline

Description

baseline calculate the female baseline giving a dataframe containing the gender information.

Usage

baseline(data_df, gender_col)

Arguments

data_df	dataframe containing the gender column.
gender_col	the name of the column containing the gender information.

Value

The function returns a numeric vector containing the baseline values

Examples

## df is the dataframe in output from the function assign_gender
df <- data.frame(first_name = c("anna", "john", "ernest", "colin", "aileen"),
                 gender = c("F", "M",  "M", "M", "F"),
                 stringsAsFactors = FALSE)
baseline <- baseline(df, gender_col = "gender")

---

Create a bullet chart with significance bars to compare different baselines in percentage for gender analysis

Description

Create a bullet chart with significance bars to compare different baselines in percentage for gender analysis

Usage

bullet_chart(data_df, baseline_female, x_label, y_label, baseline_label)

Arguments

data_df	dataframe in output from percent_df
baseline_female	numeric vector containing the baseline for each level
x_label	label for x axis
y_label	label for y axis
baseline_label	label used to define the baseline name.

Value

This function create a bullet chart containing the percentage of submission with the corresponding baseline for the level defined in percent_df.

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Function to create a bullet chart with a line chart in the same graphical frame; to compare different baselines for gender analysis.

Description

Function to create a bullet chart with a line chart in the same graphical frame; to compare different baselines for gender analysis.

Usage

bullet_line_chart(
  data_df,
  baseline_female,
  x_label,
  y_bullet_chart_label,
  baseline_label,
  line_chart_df,
  line_chart_scaling,
  y_line_chart_label,
  line_label
)

Arguments

data_df	dataframe in output from percent_df
baseline_female	numeric vector containing the baseline for each level
x_label	label for x axis for both charts
y_bullet_chart_label	label for y axis of the bullet chart
baseline_label	label used to define the baseline name.
line_chart_df	data frame containing the total number of submissions
line_chart_scaling	factor of conversion for second y-axis
y_line_chart_label	label the y-axis of the line chart
line_label	label used to define the line chart.

Value

The function create a bullet chart containing the percentage of male and female with the corresponding baseline for the level defined in percent_df. The total number of submissions are displayed on the top of the bullet chart.

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Calculate binomials and significance for multiple baselines.

Description

Function to calculate the lower CI, upper CI, percentages and counts, and significance of difference from one or multiple baseline percentages, given supplied confidence level using

Usage

calculate_binom_baseline(data_df, baseline_female, confidence_level = 0.95)

Arguments

data_df	dataframe in output from reshape_for_binomials containing the columns: female, male, which contain the integer counts of males and females respectively and must be a numeric vector greater than 0.
baseline_female	female baseline in percentage from baseline.
confidence_level	confidence level to use for significance calculation, default is 0.95

Value

This function returns a dataframe with additional columns than the input one:

lower_CI = lower confidence level of confidence interval expressed as a percentage

upper_CI = upper confidence level of confidence interval expressed as a percentage

lower_CI_count = lower confidence level of confidence interval expressed as a count

upper_CI_count = upper confidence level of confidence interval expressed as a count

significance = flag indicating whether difference of female percentage with baseline percentage is significant for the row in consideration. It has values "significant" or "" if not.

---

Gender names dataset

Description

This data sets contains all the name fro UK and US social security

Usage

gender_names

Format

a data frame of two variables:

Name

First name

UKUS_Gender

Gender of the first name

---

Create a dataframe that will be the input to generate stacked bar chart and bullet chart that show percentage to compare proportions among gender.

Description

Create a dataframe that will be the input to generate stacked bar chart and bullet chart that show percentage to compare proportions among gender.

Usage

percent_df(data_df)

Arguments

data_df

dataframe containing level, lower_CI, upper_CI, significance and female and male percentages from calculate_binom_baseline

Value

The output dataframe contains the columns x_values, y_values, gender, labels

---

Reshape the dataframe to make it easier to carry out binomial calculations.

Description

reshape dataframe from long format to wide format.

Usage

reshape_for_binomials(data_df, gender_col, level)

Arguments

data_df	dataframe containing the columns gender and counts
gender_col	the name of the column containing the gender values.
level	variable to compare for the baseline.

Value

The output is a dataframe containing more columns than the input one, such as:

level : the variable used to perform the binomials total_for_level: the total amount of each gender including unknowns total_female_male: the total amount of male and female female_percentage: the percentage of female in the total_female_male male_percentage: the percentage of male in the total_female_male

Examples

authors_df <- assign_gender(data_df = authors, first_name_col = "first_name")
female_count <- dplyr::count(authors_df, gender)

## create a new data frame to be used for the binomial calculation.
df_gender <- reshape_for_binomials(data = female_count, gender_col = "gender",
                                  level = 2020)

---

Create a stacked bar chart with significance bars to compare with the female baseline for gender analysis.

Description

Create a stacked bar chart with significance bars to compare with the female baseline for gender analysis.

Usage

stacked_bar_chart(data_df, baseline_female, x_label, y_label, baseline_label)

Arguments

data_df	is the output dataframe from percent_df
baseline_female	female baseline in percentage from baseline
x_label	label for x axis
y_label	label for y axis
baseline_label	label used to define the baseline name.

Value

This function create a bar chart containing the percentage of submission with the corresponding baseline.

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This function create a gender diversity theme for chart based on ggplot2

Description

This function create a gender diversity theme for chart based on ggplot2

Usage

theme_gd()

Value

an object of the class theme defined in ggplot2 own class system.

Examples

require(ggplot2)
 ggplot(authors, aes(x = publication_years)) + geom_bar() + theme_gd()

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Create a dataframe that will be the input to generate the bar chart of the full amount of female and male

Description

Create a dataframe that will be the input to generate the bar chart of the full amount of female and male

Usage

total_gender_df(data_df, level)

Arguments

data_df	dataframe from calculate_binom_baseline containing Level, LCI, UCI, Significance and Male and Female percentages
level	name of level

Value

The output is a dataframe with the columns x_values, total_female_male, gender, y_values. This data frame is the input to create the bar chart for bar_chart

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