qacBase: Functions to Facilitate Exploratory Data Analysis

Description

Functions for descriptive statistics, data management, and data visualization.

Author(s)

Maintainer: Kabacoff Robert rkabacoff@wesleyan.edu

Other contributors:

• Barich Griffen [contributor]

• Jamrog Kelly [contributor]

• Kravchenko Elizaveta [contributor]

• Kuruvilla Jacob [contributor]

• Liu Lex [contributor]

• Nakamura Shota [contributor]

• Pham Kim [contributor]

• Rodriguez Belen [contributor]

• Ross Shane [contributor]

• Russo Chris [contributor]

• Corpuz Frederick [contributor]

• Juradat Nurah [contributor]

• Karp Harrison [contributor]

• Koech Kevin [contributor]

• Peters Anna [contributor]

• Shah Dhhyey [contributor]

• Stevenson Kenneth [contributor]

• Thomas-Franz Kaitlyn [contributor]

• Zheng Jiner [contributor]

• Aldarmaki Ahmed [contributor]

• Alneyadi Mohammed [contributor]

• Altai Chossis [contributor]

• Colorado Sofia [contributor]

• Northrop Blake [contributor]

• Peretz Shea [contributor]

• Qin Cher [contributor]

• Tuhabonye Emma [contributor]

• Wong Phillip [contributor]

See Also

Useful links:

• https://github.com/rkabacoff/qacBase

• Report bugs at https://github.com/rkabacoff/qacBase/issues

Barcharts

Description

Create barcharts for all categorical variables in a data frame.

Usage

barcharts(
  data,
  fill = "deepskyblue2",
  color = "grey30",
  labels = TRUE,
  sort = TRUE,
  maxcat = 20,
  abbrev = 20
)

Arguments

Value

a ggplot graph

Examples

barcharts(cars74)

Automobile characteristics

Description

Cars dataset with features including make, model, year, engine, and other properties of the car used to predict its price.

Usage

cardata

Format

A data frame with 11914 rows and 16 variables. The variables are as follows:

make

car brand

model

model given by its brand

year

year of manufacture

engine_fuel_type

type of fuel required by its manufacturer

engine_hp

engine horse power

engine_cylinders

number of cylinders

transmission_type

automatic vs. manual

driven_wheels

AWD, FWD, AWD

number_of_doors

Number of Doors

market_category

Luxury, Performance, Hatchback, etc.

vehicle_size

Compact, Midsize, Large

vehicle_style

Type of Vehicle: Sedan, SUV, Coupe, etc.

highway_mpg

highway miles per gallon

city_mpg

city miles per gallon

popularity

Popularity index

msrp

manufacturer's suggested retail price

Details

This package contains a detailed car dataset.

Source

Taken from Kaggle https://www.kaggle.com/CooperUnion/cardataset.

Examples

summary(cardata)

Motor Trend car road tests

Description

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models).

Usage

cars74

Format

A data frame with 32 rows and 11 variables. The variables are as follows:

auto

highway miles per gallon

mpg

Miles/(US) gallon

cyl

Number of cylinders

disp

Displacement (cu.in.)

hp

Gross horsepower

drat

Rear axle ratio

wt

Weight (1000 lbs)

qsec

1/4 mile time

vs

Engine cylinder configuration

am

Transmission type

gear

Number of forward gears

carb

Number of carburetors

Details

This dataset is the mtcars dataset that comes with base R. However, cyl, vs, am, gear and carb have been converted to factors and rownames have been converted to the variable auto. A description of the variables by Soren Heitmann can be found here.

Source

Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391-411.

Examples

summary(cars74)

Detailed description of a data frame

Description

contents provides a comprehensive description of a data frame, including summary statistics for both quantitative and categorical variables

Usage

contents(data, digits = 2, maxcat = 10, label_length = 20)

Arguments

Details

Prints a comprehensive description of a data frame via several tables, a general summary table and tables that provide a breakdown of quantitative and categorical variables.

Value

a list with 6 components:

dfname

name of data frame

nrow

number of rows

ncol

number of columns

overall

data frame of overall dataset characteristics

qvars

data frame with summary statistics for quantitative variables

cvars

data frame with summary statistics for categorical variables

Examples

contents(cars74)

Correlation matrix plot

Description

Create a correlation matrix for all quantitative variables in a data frame.

Usage

cor_plot(
  data,
  method = c("pearson", "kendall", "spearman"),
  sort = FALSE,
  axis_text_size = 12,
  number_text_size = 3,
  legend = FALSE
)

Arguments

Details

The cor_plot function will only select quantitative variables from a data frame. Categorical variables are ignored. The correlation matrix is presented as a lower triangle matrix. Missing values are deleted in listwise fashion.

Value

a ggplot graph

Note

This function is a wrapper for the ggcorrplot function.

Examples

cor_plot(cars74)
cor_plot(cars74, sort=TRUE)

Two-way frequency table

Description

This function creates a two way frequency table.

Usage

crosstab(
  data,
  rowvar,
  colvar,
  type = c("freq", "percent", "rowpercent", "colpercent"),
  total = TRUE,
  na.rm = TRUE,
  digits = 2,
  chisquare = FALSE,
  plot = FALSE
)

Arguments

Details

Given a data frame, a row factor, a column factor, and a type (frequencies, cell percents, row percents, or column percents) the function provides the requested cross-tabulation.

If na.rm = FALSE, a level labeled <NA> added. If total = TRUE, a level labeled Total is added. If chisquare = TRUE, a chi-square test of independence is performed.

Value

If plot=TRUE, return a ggplot2 graph. Otherwise the function return a list with 6 components:

• table (table). Table of frequencies or percents

• type (character). Type of table to print

• total (logical). If TRUE, print row and or column totals

• digits (numeric). number of digits to print

• rowname (character). Row variable name

• colname (character). Column variable name

• chisquare (character). If chisquare=TRUE, contains the results of the Chi-square test. NULL otherwise.

See Also

print.crosstab, plot.crosstab

Examples

# print frequencies
crosstab(mtcars, cyl, gear)

# print cell percents
crosstab(cardata, vehicle_size, driven_wheels)
crosstab(cardata, vehicle_size, driven_wheels,
plot=TRUE)
crosstab(cardata, driven_wheels, vehicle_size,
type="colpercent", plot=TRUE, chisquare=TRUE)

Density plots

Description

Create desnsity plots for all quantitative variables in a data frame.

Usage

densities(data, fill = "deepskyblue2", adjust = 1)

Arguments

Details

The densities function will only plot quantitative variables from a data frame. Categorical variables are ignored.

The adjust parameter mulitplies the smoothing parameter. For example adjust = 2 will make the density plots twice as smooth. The adjust = 1/2 will make the density plots half as smooth (i.e., twice as spiky).

Value

a ggplot graph

Examples

densities(cars74)

densities(cars74, adjust=2)

densities(cars74, adjust=1/2)

Visualize a data frame

Description

df_plot visualizes the variables in a data frame.

Usage

df_plot(data)

Arguments

Details

For each variable, the plot displays

• type (numeric, integer, factor, ordered factor, logical, or date)

• percent of available (and missing) cases

Variables are sorted by type and the total number of variables and cases are printed in the caption.

Value

a ggplot2 graph

See Also

For more descriptive statistics on a data frame see contents.

Examples

df_plot(cars74)

Test of group differences

Description

One-way analysis (ANOVA or Kruskal-Wallis Test) with post-hoc comparisons and plots

Usage

groupdiff(
  data,
  y,
  x,
  method = c("anova", "kw"),
  digits = 2,
  horizontal = FALSE,
  posthoc = FALSE
)

Arguments

Details

The groupdiff function performs one of two analyses:

anova

A one-way analysis of variance, with TukeyHSD post-hoc comparisons.

kw

A Kruskal Wallis Rank Sum Test, with Conover Test post-hoc comparisons.

In each case, summary statistics and a grouped boxplots are provided. In the parametric case, the statistics are n, mean, and standard deviation. In the nonparametric case the statistics are n, median, and median absolute deviation. If posthoc = TRUE, pairwise comparisons of superimposed on the boxplots. Groups that share a letter are not significantly different (p < .05), controlling for multiple comparisons.

Value

a list with 3 components:

result

omnibus test

summarystats

summary statistics

plot

ggplot2 graph

See Also

kwAllPairsConoverTest, multcompLetters.

Examples

# parametric analysis
groupdiff(cars74, hp, gear)

# nonparametric analysis
groupdiff(cardata, popularity, vehicle_style, posthoc=TRUE,
          method="kw", horizontal=TRUE)

Histograms

Description

Create histograms for all quantitative variables in a data frame.

Usage

histograms(data, fill = "deepskyblue2", color = "white", bins = 30)

Arguments

Details

The histograms function will only plot quantitative variables from a data frame. Categorical variables are ignored.

Value

a ggplot graph

Examples

histograms(cars74)
histograms(cars74, bins=15, fill="darkred")

List object sizes and types

Description

lso lists object sizes and types.

Usage

lso(
  pos = 1,
  pattern,
  order.by = "Size",
  decreasing = TRUE,
  head = TRUE,
  n = 10
)

Arguments

Details

This function list the sizes and types of all objects in an environment. By default, the list describes the objects in the current environment, presented in descending order by object size and reported in megabytes (Mb).

Value

a data.frame with four columns (Type, Size, Rows, Columns) and object names as row names.

Author(s)

Based on based on postings by Petr Pikal and David Hinds to the r-help list in 2004 and modified Dirk Eddelbuettel, Patrick McCann, and Rob Kabacoff.

References

https://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session/.

Examples

data(cardata)
data(cars74)
lso()

Normalize numeric variables

Description

Normalize the numeric variables in a data frame

Usage

normalize(data, new_min = 0, new_max = 1)

Arguments

Details

normalize transforms all the numeric variables in a data frame to have the same minimum and maximum values. By default, this will be a minimum of 0 and maximum of 1. Character variables and factors are left unchanged.

Value

a data frame

Note

Use this function to be transform variables into a given range. The default is [0, 1], but [-1, 1], [0, 100], or any other range is permissible.

Examples

head(cars74)

cars74_st <- normalize(cars74)
head(cars74_st)

Get help on a package

Description

phelp provides help on an installed package.

Usage

phelp(pckg)

Arguments

Details

This function provides help on an installed package. The package does not have to be loaded. The package name does not need to be entered with quotes.

Value

No return value, called for side effects.

Examples

phelp(stats)

Plot a crosstab object

Description

This function plots the results of a calculated two-way frequency table.

Usage

## S3 method for class 'crosstab'
plot(x, size = 3.5, ...)

Arguments

Value

a ggplot2 graph

Examples

tbl <- crosstab(cars74, cyl, gear, type = "freq")
plot(tbl)

tbl <- crosstab(cars74, cyl, gear, type = "colpercent")
plot(tbl)

Plot a tab object

Description

Plot a frequency or cumulative frequency table

Usage

## S3 method for class 'tab'
plot(x, fill = "deepskyblue2", size = 3.5, ...)

Arguments

Value

a ggplot2 graph

Examples

tbl1 <- tab(cars74, carb)
plot(tbl1)

tbl2 <- tab(cars74, carb, sort = TRUE)
plot(tbl2)

tbl3 <- tab(cars74, carb, cum=TRUE)
plot(tbl3)

Print a contents object

Description

print.contents prints the results of the content function.

Usage

## S3 method for class 'contents'
print(x, ...)

Arguments

Value

No return value, called for side effects.

Examples

testdata <- data.frame(height=c(4, 5, 3, 2, 100),
                       weight=c(39, 88, NA, 15, -2),
                       names=c("Bill","Dean", "Sam", NA, "Jane"),
                       race=c('b', 'w', 'w', 'o', 'b'))

x <- contents(testdata)
print(x)

Print a crosstab object

Description

This function prints the results of a calculated two-way frequency table.

Usage

## S3 method for class 'crosstab'
print(x, ...)

Arguments

Value

No return value, called for side effects

Examples

mycrosstab <- crosstab(mtcars, cyl, gear, type = "freq", digits = 2)
print(mycrosstab)

mycrosstab <- crosstab(mtcars, cyl, gear, type = "rowpercent", digits = 3)
print(mycrosstab)

Print a tab object

Description

Print the results of calculating a frequency table

Usage

## S3 method for class 'tab'
print(x, ...)

Arguments

Value

No return value, called for side effects

Examples

frequency <- tab(cardata, make, sort = TRUE, na.rm = FALSE)
print(frequency)

Summary statistics for a quantitative variable

Description

This function provides descriptive statistics for a quantitative variable alone or separately by groups. Any function that returns a single numeric value can bue used.

Usage

qstats(data, x, ..., stats = c("n", "mean", "sd"), na.rm = TRUE, digits = 2)

Arguments

Value

a data frame, where columns are grouping variables (optional) and statistics

Examples

# If no keyword arguments are provided, default values are used
qstats(mtcars, mpg, am, gear)

# You can supply as many (or no) grouping variables as needed
qstats(mtcars, mpg)

qstats(mtcars, mpg, am, cyl)

# You can specify your own functions (e.g., median,
# median absolute deviation, minimum, maximum))
qstats(mtcars, mpg, am, gear,
       stats = c("median", "mad", "min", "max"))

R Colors

Description

Plot a grid of R colors and their associated names

Usage

rcolors(color = NULL, cex = 0.6)

Arguments

Details

By default rcolors plots the basic 502 distinct colors provided by the colors function. If a color name or part of a name is provided, only colors with matching names are plotted.

Value

No return value, called for side effects

References

This function is adapted from code published by Karl W. Broman.

See Also

colors

Examples

rcolors()
rcolors("blue")
rcolors("red")
rcolors("dark")

Recode one or more variables

Description

recodes recodes the values of one or more variables in a data frame

Usage

recodes(data, vars, from, to)

Arguments

Details

• For each variable in the vars parameter, values are checked against the list of values in the from vector. If a value matches, it is replaced with the corresponding entry in the to vector.

• Once a given observation's value matches a from value, it is recoded. That particular observation will not be recoded again by that recodes() statement (i.e., no chaining).

• One or more values in the from vector can be an expression, using the dollar sign ($) to represent the variable being recoded. If the expression evaluates to TRUE, the corresponding to value is returned.

• If the number of values in the to vector is less than the from vector, the values are recycled. This lets you convert several values to a single outcome value (e.g., NA).

• If the to values are numeric, the resulting recoded variable will be numeric. If the variable being recoded is a factor and the to values are character values, the resulting variable will remain a factor. If the variable being recoded is a character variable and the to values are character values, the resulting variable will remain a character variable.

Value

a data frame

Note

See the vignette for detailed examples.

Examples

df <- data.frame(x = c(1, 5, 7, 3, 0),
                 y = c(9, 0, 5, 9, 2),
                 z = c(1, 1, 2, 2, 1)
                 )
df <- recodes(df, 
              vars = c("x", "y"), 
              from = 0, to = NA)
df <- recodes(df, 
              vars = "z", 
              from = c(1, 2), to = c("pass", "fail"))

Scatterplot

Description

Create a scatter plot between two quantitative variables.

Usage

scatter(
  data,
  x,
  y,
  outlier = 3,
  alpha = 1,
  digits = 3,
  title,
  margin = "none",
  stats = TRUE,
  point_color = "deepskyblue2",
  outlier_color = "violetred1",
  line_color = "grey30",
  margin_color = "deepskyblue2"
)

Arguments

Details

The scatter function generates a scatterplot between two quantitative variables, along with a line of best fit and a 95% confidence interval. By default, regression statistics (b, r, r2, p) are printed and outliers (observations with studentized residuals > 3) are flagged. Optionally, variable distributions (histograms, boxplots, violin plots, density plots) can be added to the plot margins.

Value

a ggplot2 graph

Note

Variable names do not have to be quoted.

Examples

scatter(cars74, hp, mpg)
scatter(cars74, wt, hp)
p <- scatter(ggplot2::mpg, displ, hwy,
        margin="histogram",
        title="Engine Displacement vs. Highway Mileage")
plot(p)

Skewness

Description

Calculate the skewness of a numeric variable

Usage

skewness(x, na.rm = TRUE)

Arguments

Value

a number

Examples

skewness(mtcars$mpg)

Standardize numeric variables

Description

Standardize the numeric variables in a data frame

Usage

standardize(data, mean = 0, sd = 1, include_dummy = FALSE)

Arguments

Details

standardize transforms all the numeric variables in a data frame to have the same mean and standard deviation. By default, this will be a mean of 0 and standard deviation of 1. Character variables and factors are left unchanged. By default, dummy coded variables are also left unchanged. Use include_dummy=TRUE to transform these variables as well.

Value

a data frame

Examples

head(cars74)

cars74_st <- standardize(cars74)
head(cars74_st)

Frequency distribution for a categorical variable

Description

Function to calculate frequency distributions for categorical variables

Usage

tab(
  data,
  x,
  sort = FALSE,
  maxcat = NULL,
  minp = NULL,
  na.rm = FALSE,
  total = FALSE,
  digits = 2,
  cum = FALSE,
  plot = FALSE
)

Arguments

Details

The function tab will calculate the frequency distribution for a categorical variable and output a data frame with three columns: level, n, percent.

Value

If plot = TRUE return a ggplot2 bar chart. Otherwise return a data frame.

Examples

tab(cars74, carb)
tab(cars74, carb, plot=TRUE)
tab(cars74, carb, sort=TRUE)
tab(cars74, carb, sort=TRUE, plot=TRUE)
tab(cars74, carb, cum=TRUE)
tab(cars74, carb, cum=TRUE, plot=TRUE)

Time spent watching television - 2017

Description

This is a data set detailing TV usage on days surveyed as determined by the 2017 American Time Use Survey. The data set includes demographic information, as well as details regarding employment and family makeup, where applicable. Information on days surveyed, as well as whether the day is a holiday, is also included.

Usage

tv

Format

A data frame with 10,223 rows and 21 variables. The variables are as follows:

id

ID of respondent

weight

ATUS final weight

youngest_child

Age of the youngest child in the household that is less than 18 years old (if applicable). Range: 1-17; if no child in household: NA

age

Age of respondent

sex

Sex of respondent

job

Status of employment of the respondent. Direct transcription from original codebook: 1 = Employed, at work, 2 = Employed, absent, 3 = Unemployed, on layoff, 4 = Unemployed, looking, 5 = Not in the labor force.

m_job

The response to question, “in the last seven days did you have more than one job?” Returns NA if no job.

f_job

Does the respondent have a full time job or a part time job? (NA if no job)

educ

Are you enrolled in high school, college, or university? (NA if not currently enrolled)

educ2

If yes to educ, are you enrolled in high school or upper schooling? (NA if not currently enrolled)

partner

Presence of the respondent's spouse or unmarried partner in the household with 1 = Spouse present 2 = Unmarried partner present 3 = No spouse/unmarried partner present

pr_job

Answer to the question, “does your partner have a job?” (NA if not applicable)

salary

Weekly earnings at the respondent’s main job, two decimals implied

children

Number of children under 18 in the household

pr_job_f

Part time/full time job status of partner, if applicable (NA if partner unemployed or no partner)

job_hours

Total hours usually worked per week (-4: Hours vary)

day

Day of the week about which the respondent was interviewed (Monday thorugh Friday)

holiday

Notes if the respondent was interviewed on a holiday

elder_care

Total time spent providing elder care that day by the respondent, in minutes

child_time

Total time spent during diary day providing secondary childcare for household children younger than 13, in minutes

tv

Minutes spent watching TV

Details

For more information regarding the key visit https://www.bls.gov/tus/atusintcodebk17.pdf. This data is retrieved from the American Time Use Survey, made available through the Bureau of Labor Statistics https://www.bls.gov/tus/datafiles_2017.htm.

Examples

summary(tv)

hist(tv$tv, col="skyblue")

Univariate plot

Description

Generates a descriptive graph for a quantitative variable.

Usage

univariate_plot(
  data,
  x,
  bins = 30,
  fill = "deepskyblue",
  pointcolor = "black",
  density = TRUE,
  densitycolor = "grey",
  alpha = 0.2,
  seed = 1234
)

Arguments

Details

univariate_plot generates a plot containing three graphs: a histogram (with an optional density curve), a horizontal jittered point plot, and a horizontal box plot. The subtitle contains descriptive statistics, including the mean, standard deviation, median, minimum, maximum, and skew.

Value

a ggplot2 graph

Note

The graphs are created with ggplot2 and then assembled into a single plot through the patchwork package. Missing values are deleted.

Examples

univariate_plot(mtcars, mpg)
univariate_plot(cardata, city_mpg, fill="lightsteelblue",
                pointcolor="lightsteelblue", densitycolor="lightpink",
                alpha=.6)