Title:	Browse Over Longitudinal Data Graphically and Analytically in R
Version:	1.0.1
Description:	Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.
License:	MIT + file LICENSE
URL:	https://github.com/njtierney/brolgar, https://brolgar.njtierney.com/
BugReports:	https://github.com/njtierney/brolgar/issues
Depends:	R (≥ 3.5.0)
Imports:	dplyr (≥ 0.8.3), fabletools, ggplot2 (≥ 3.2.0), glue (≥ 1.3.1), magrittr (≥ 1.5), purrr (≥ 0.3.2), rlang (≥ 0.4.0), stats, tibble (≥ 2.1.3), tidyr (≥ 0.8.3), tsibble (≥ 0.8.2), vctrs
Suggests:	gapminder, gghighlight (≥ 0.1.0), knitr (≥ 1.23), Matrix (≥ 1.6-5), lme4, modelr, rmarkdown (≥ 1.14), spelling (≥ 2.1), testthat (≥ 3.0.0), tsibbledata, vdiffr (≥ 0.3.1)
VignetteBuilder:	knitr
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.1
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-05-10 07:57:20 UTC; nick
Author:	Nicholas Tierney [aut, cre], Di Cook [aut], Tania Prvan [aut], Stuart Lee [ctb], Earo Wang [ctb]
Maintainer:	Nicholas Tierney <nicholas.tierney@gmail.com>
Repository:	CRAN
Date/Publication:	2024-05-10 14:50:34 UTC

brolgar: Browse Over Longitudinal Data Graphically and Analytically in R

Description

Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) arXiv:2012.01619.

Details

brolgar stands for: BRowse over Longitudinal data Graphically and Analytically in R.

Author(s)

Maintainer: Nicholas Tierney nicholas.tierney@gmail.com (ORCID)

Authors:

• Di Cook dicook@monash.edu (ORCID)

• Tania Prvan tania.prvan@mq.edu.au

Other contributors:

• Stuart Lee [contributor]

• Earo Wang [contributor]

See Also

Useful links:

• https://github.com/njtierney/brolgar

• https://brolgar.njtierney.com/

• Report bugs at https://github.com/njtierney/brolgar/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Add the number of observations for each key in a tsibble

Description

Here, we are not counting the number of rows in the dataset, but rather we are counting the number observations for each keys in the data.

Usage

add_n_obs(.data, ...)

Arguments

Value

tsibble with n_obs, the number of observations per key added.

Examples

library(dplyr)
# you can explore the data to see those cases that have exactly two 
 # observations:
heights %>% 
  add_n_obs() %>% 
  filter(n_obs == 2)

Brolgar summaries (b_summaries)

Description

Customised summaries of vectors with appropriate defaults for longitudinal data. The functions are prefixed with b_ to assist with autocomplete. It uses na.rm = TRUE for all, and for calculations involving quantiles, type = 8 and names = FALSE. Summaries include: * b_min: The minimum * b_max: The maximum * b_median: The median * b_mean: The mean * b_q25: The 25th quantile * b_q75: The 75th quantile * b_range: The range * b_range_diff: difference in range (max - min) * b_sd: The standard deviation * b_var: The variance * b_mad: The mean absolute deviation * b_iqr: The Inter-quartile range * b_diff_var: The variance diff() * b_diff_sd: The standard deviation of diff() * b_diff_mean: The mean of diff() * b_diff_median: The median of diff() * b_diff_q25: The q25 of diff() * b_diff_q75: The q75 of diff()

Usage

b_min(x, ...)

b_max(x, ...)

b_median(x, ...)

b_mean(x, ...)

b_q25(x, ...)

b_q75(x, ...)

b_range(x, ...)

b_range_diff(x, ...)

b_sd(x, ...)

b_var(x, ...)

b_mad(x, ...)

b_iqr(x, ...)

b_diff_var(x, ...)

b_diff_sd(x, ...)

b_diff_mean(x, ...)

b_diff_median(x, ...)

b_diff_q25(x, ...)

b_diff_q75(x, ...)

b_diff_max(x, ...)

b_diff_min(x, ...)

b_diff_iqr(x, ...)

Arguments

Examples

x <- c(1:5, NA, 5:1)
min(x)
b_min(x)
max(x)
b_max(x)
median(x)
b_median(x)
mean(x)
b_mean(x)
range(x)
b_range(x)
var(x)
b_var(x)
sd(x)
b_sd(x)

Calculate features of a tsibble object in conjunction with features()

Description

You can calculate a series of summary statistics (features) of a given variable for a dataset. For example, a three number summary, the minimum, median, and maximum, can be calculated for a given variable. This is designed to work with the features() function shown in the examples. Other available features in brolgar include:

• feat_three_num() - minimum, median, maximum

• feat_five_num() - minimum, q25, median, q75, maximum.

• feat_ranges() - min, max, range difference, interquartile range.

• feat_spread() - variance, standard deviation, median absolute distance, and interquartile range

• feat_monotonic() - is it always increasing, decreasing, or unvarying?

• feat_diff_summary() - the summary statistics of the differences amongst a value, including the five number summary, as well as the standard deviation and variance. Returns NA if there is only one observation, as we can't take the difference of one observation, and a difference of 0 in these cases would be misleading.

• feat_brolgar() all features in brolgar.

Usage

feat_three_num(x, ...)

feat_five_num(x, ...)

feat_ranges(x, ...)

feat_spread(x, ...)

feat_monotonic(x, ...)

feat_brolgar(x, ...)

feat_diff_summary(x, ...)

Arguments

Examples

# You can use any of the features `feat_*` in conjunction with `features` 
# like so:
heights %>%
  features(height_cm, # variable you want to explore
           feat_three_num) # the feature summarisation you want to perform

Facet data into groups to facilitate exploration

Description

This function requires a tbl_ts object, which can be created with tsibble::as_tsibble(). Under the hood, facet_strata is powered by stratify_keys() and sample_n_keys().

Usage

facet_sample(
  n_per_facet = 3,
  n_facets = 12,
  nrow = NULL,
  ncol = NULL,
  scales = "fixed",
  shrink = TRUE,
  strip.position = "top"
)

Arguments

Value

a ggplot object

Examples

library(ggplot2)
ggplot(heights,
aes(x = year,
    y = height_cm,
    group = country)) +
  geom_line() +
  facet_sample()

ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_sample(n_per_facet = 1,
               n_facets = 12)

Facet data into groups to facilitate exploration

Description

This function requires a tbl_ts object, which can be created with tsibble::as_tsibble(). Under the hood, facet_strata is powered by stratify_keys().

Usage

facet_strata(
  n_strata = 12,
  along = NULL,
  fun = mean,
  nrow = NULL,
  ncol = NULL,
  scales = "fixed",
  shrink = TRUE,
  strip.position = "top"
)

Arguments

Value

a ggplot object

Examples

library(ggplot2)
ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_strata()
  

ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_wrap(~continent)

ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_strata(along = year)


library(dplyr)
heights %>%
  key_slope(height_cm ~ year) %>%
  right_join(heights, ., by = "country") %>%
  ggplot(aes(x = year,
             y = height_cm)) +
  geom_line(aes(group = country)) +
  geom_smooth(method = "lm") + 
  facet_strata(along = .slope_year)

World Height Data

Description

Average male heights in 144 countries from 1810-1989, with a smaller number of countries from 1500-1800. Data has been filtered to only include countries with more than one observation.

Usage

heights

Format

An object of class tbl_ts (inherits from tbl_df, tbl, data.frame) with 1490 rows and 4 columns.

Details

heights is stored as a time series tsibble object. It contains the variables:

• country: The Country. This forms the identifying key.

• year: Year. This forms the time index.

• height_cm: Average male height in centimeters.

• continent: continent extracted from country name using countrycode package (https://joss.theoj.org/papers/10.21105/joss.00848).

For more information, see the article: "Why are you tall while others are short? Agricultural production and other proximate determinants of global heights", Joerg Baten and Matthias Blum, European Review of Economic History 18 (2014), 144–165. Data available from https://datasets.iisg.amsterdam/dataset.xhtml?persistentId=hdl:10622/IAEKLA, accessed via the Clio Infra website.

Examples

# show the data
heights

# show the spaghetti plot (ugh!)
library(ggplot2)
ggplot(heights, 
       aes(x = year, 
           y = height_cm, 
           group = country)) + 
    geom_line()
    
# Explore all samples with `facet_strata()`
ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_strata()

# Explore the heights over each continent
ggplot(heights,
       aes(x = year,
           y = height_cm,
           group = country)) +
  geom_line() +
  facet_wrap(~continent)
  
# explore the five number summary of height_cm with `features`
heights %>% 
  features(height_cm, feat_five_num)

Index summaries

Description

These functions check if the index is regular (index_regular()), and summarise the index variable (index_summary()). This can be useful to check your index variables.

Usage

index_regular(.data, ...)

## S3 method for class 'tbl_ts'
index_regular(.data, ...)

## S3 method for class 'data.frame'
index_regular(.data, index, ...)

index_summary(.data, ...)

## S3 method for class 'tbl_ts'
index_summary(.data, ...)

## S3 method for class 'data.frame'
index_summary(.data, index, ...)

Arguments

Value

logical TRUE means it is regular, FALSE means not

Examples

# a tsibble
index_regular(heights)

# some data frames
index_regular(pisa, year)
index_regular(airquality, Month)

# a tsibble
index_summary(heights)
# some data frames
index_summary(pisa, year)
index_summary(airquality, Month)
index_summary(airquality, Day)

Fit linear model for each key

Description

Using key_slope you can fit a linear model to each key in the tsibble. add_key_slope adds this slope information back to the data, and returns the full dimension tsibble.

Usage

key_slope(.data, formula, ...)

add_key_slope(.data, formula)

add_key_slope.default(.data, formula)

Arguments

Value

tibble with coefficient information

Examples

key_slope(heights, height_cm ~ year)

Return keys nearest to a given statistics or summary.

Description

Return keys nearest to a given statistics or summary.

Usage

keys_near(.data, ...)

## Default S3 method:
keys_near(.data, ...)

Arguments

Value

data.frame containing keys closest to a given statistic.

Examples

keys_near(heights, height_cm)

Return keys nearest to a given statistics or summary.

Description

Return keys nearest to a given statistics or summary.

Usage

## S3 method for class 'data.frame'
keys_near(.data, key, var, top_n = 1, funs = l_five_num, ...)

Arguments

Examples

heights %>%
  key_slope(height_cm ~ year) %>%
  keys_near(key = country,
            var = .slope_year)
# Specify your own list of summaries
l_ranges <- list(min = b_min,
                 range_diff = b_range_diff,
                 max = b_max,
                 iqr = b_iqr)

heights %>%
  key_slope(formula = height_cm ~ year) %>%
  keys_near(key = country,
              var = .slope_year,
              funs = l_ranges)

Return keys nearest to a given statistics or summary.

Description

Return keys nearest to a given statistics or summary.

Usage

## S3 method for class 'tbl_ts'
keys_near(.data, var, top_n = 1, funs = l_five_num, stat_as_factor = TRUE, ...)

Arguments

Examples

               
# Return observations closest to the five number summary of height_cm
heights %>%
  keys_near(var = height_cm)
               

A named list of the five number summary

Description

Designed for use with the keys_near() function.

Usage

l_five_num

l_three_num

Format

An object of class list of length 5.

An object of class list of length 3.

Examples

# Specify your own list of summaries
l_ranges <- list(min = b_min,
                 range_diff = b_range_diff,
                 max = b_max,
                 iqr = b_iqr)

heights %>%
  key_slope(formula = height_cm ~ year) %>%
  keys_near(key = country,
              var = .slope_year,
              funs = l_ranges)

Are values monotonic? Always increasing, decreasing, or unvarying?

Description

These provides three families of functions to tell you if values are always increasing, decreasing, or unvarying, with the functions, increasing(), decreasing(), or unvarying(). Under the hood it uses diff to find differences, so if you like you can pass extra arguments to diff.

Usage

increasing(x, ...)

decreasing(x, ...)

unvarying(x, ...)

monotonic(x, ...)

Arguments

Value

logical TRUE or FALSE

Examples

vec_inc <- c(1:10)
vec_dec<- c(10:1)
vec_ran <- c(sample(1:10))
vec_flat <- rep.int(1,10)

increasing(vec_inc)
increasing(vec_dec)
increasing(vec_ran)
increasing(vec_flat)

decreasing(vec_inc)
decreasing(vec_dec)
decreasing(vec_ran)
decreasing(vec_flat)

unvarying(vec_inc)
unvarying(vec_dec)
unvarying(vec_ran)
unvarying(vec_flat)

library(ggplot2)
library(gghighlight)
library(dplyr)

heights_mono <- heights %>%
  features(height_cm, feat_monotonic) %>%
  left_join(heights, by = "country")
  
  ggplot(heights_mono,
         aes(x = year,
             y = height_cm,
             group = country)) +
  geom_line() + 
  gghighlight(increase)

 ggplot(heights_mono,
        aes(x = year,
            y = height_cm,
             group = country)) +
  geom_line() + 
  gghighlight(decrease)

heights_mono %>%
filter(monotonic) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line()
  
heights_mono %>%
  filter(increase) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line()
  

Return the number of observations

Description

Returns the number of observations of a vector or data.frame. It uses vctrs::vec_size() under the hood.

Usage

n_obs(x, names = TRUE)

Arguments

Value

number of observations

Note

You cannot use n_obs with features counting the key variable like so - features(heights, country, n_obs). Instead, use any other variable.

Examples

n_obs(iris)
n_obs(1:10)
add_n_obs(heights)
heights %>%
  features(height_cm, n_obs) # can be any variable except id, the key.

Return x percent to y percent of values

Description

Return x percent to y percent of values

Usage

near_between(x, from, to)

Arguments

Value

logical vector

Examples

x <- runif(20)

near_middle(x = x,
            middle = 0.5,
            within = 0.2)

library(dplyr)
heights %>% features(height_cm, list(min = min)) %>%
  filter(near_between(min, 0.1, 0.9))

near_quantile(x = x,
              probs = 0.5, 
              tol = 0.01)

near_quantile(x, c(0.25, 0.5, 0.75), 0.05)

heights %>%
  features(height_cm, l_five_num) %>%
  mutate_at(vars(min:max),
            .funs = near_quantile,
            0.5, 
            0.01) %>%
  filter(min)

heights %>%
  features(height_cm, list(min = min)) %>%
  mutate(min_near_q3 = near_quantile(min, c(0.25, 0.5, 0.75), 0.01)) %>%
  filter(min_near_q3)

heights %>%
  features(height_cm, list(min = min)) %>%
  filter(near_between(min, 0.1, 0.9))

heights %>%
  features(height_cm, list(min = min)) %>%
  filter(near_middle(min, 0.5, 0.1))

Return the middle x percent of values

Description

Return the middle x percent of values

Usage

near_middle(x, middle, within)

Arguments

Value

logical vector

Examples

x <- runif(20)
near_middle(x = x,
            middle = 0.5,
            within = 0.2)
            
library(dplyr)
heights %>% features(height_cm, list(min = min)) %>%
  filter(near_middle(min, 0.5, 0.1))
            

Which values are nearest to any given quantiles

Description

Which values are nearest to any given quantiles

Usage

near_quantile(x, probs, tol = 0.01)

Arguments

Value

logical vector of TRUE/FALSE if number is close to a quantile

Examples

x <- runif(20)
near_quantile(x, 0.5, 0.05)
near_quantile(x, c(0.25, 0.5, 0.75), 0.05)

library(dplyr)
heights %>% 
  features(height_cm, list(min = min)) %>% 
  mutate(min_near_median = near_quantile(min, 0.5, 0.01)) %>%
  filter(min_near_median)
heights %>% 
  features(height_cm, list(min = min)) %>% 
  mutate(min_near_q3 = near_quantile(min, c(0.25, 0.5, 0.75), 0.01)) %>%
  filter(min_near_q3)

Is x nearest to y?

Description

Returns TRUE if x is nearest to y. There are two implementations. nearest_lgl() returns a logical vector when an element of the first argument is nearest to an element of the second argument. nearest_qt_lgl() is similar to nearest_lgl(), but instead determines if an element of the first argument is nearest to some value of the given quantile probabilities. See example for more detail.

Usage

nearest_lgl(x, y)

nearest_qt_lgl(y, ...)

Arguments

Value

logical vector of length(y)

Examples

x <- 1:10
y <- 5:14
z <- 16:25
a <- -1:-5
b <- -1

nearest_lgl(x, y)
nearest_lgl(y, x)

nearest_lgl(x, z)
nearest_lgl(z, x)

nearest_lgl(x, a)
nearest_lgl(a, x)

nearest_lgl(x, b)
nearest_lgl(b, x)

library(dplyr)
heights_near_min <- heights %>%
  filter(nearest_lgl(min(height_cm), height_cm))
  
heights_near_fivenum <- heights %>%
  filter(nearest_lgl(fivenum(height_cm), height_cm))
  
heights_near_qt_1 <- heights %>%
  filter(nearest_qt_lgl(height_cm, c(0.5)))
  
heights_near_qt_3 <- heights %>%
  filter(nearest_qt_lgl(height_cm, c(0.1, 0.5, 0.9)))

Student data from 2000-2018 PISA OECD data

Description

A subset of PISA data, containing scores and other information from the triennial testing of 15 year olds around the globe. Original data available from https://www.oecd.org/pisa/data/. Data derived from https://github.com/kevinwang09/learningtower.

Usage

pisa

Format

A tibble of the following variables

• year the year of measurement

• country the three letter country code. This data contains Australia, New Zealand, and Indonesia. The full data from learningtower contains 99 countries.

• school_id The unique school identification number

• student_id The student identification number

• gender recorded gender - 1 female or 2 male or missing

• math Simulated score in mathematics

• read Simulated score in reading

• science Simulated score in science

• stu_wgt The final survey weight score for the student score

Understanding a bit more about the PISA data, the school_id and student_id are not unique across time. This means the longitudinal element is the country within a given year.

We can cast pisa as a tsibble, but we need to aggregate the data to each year and country. In doing so, it is important that we provide some summary statistics of each of the scores - we want to include the mean, and minimum and maximum of the math, reading, and science scores, so that we do not lose the information of the individuals.

The example code below does this, first grouping by year and country, then calculating the weighted mean for math, reading, and science. This can be done using the student weight variable stu_wgt, to get the survey weighted mean. The minimum and maximum are then calculated.

Examples

pisa

library(dplyr)
# Let's identify

#1.  The **key**, the individual, who would have repeated measurements. 
#2.  The **index**, the time component.
#3.  The **regularity** of the time interval (index). 

# Here it looks like the key is the student_id, which is nested within
# school_id #' and country,

# And the index is year, so we would write the following

as_tsibble(pisa, 
           key = country,
           index = year)

# We can assess the regularity of the year like so:

index_regular(pisa, year)
index_summary(pisa, year)

# We can now convert this into a `tsibble`:

pisa_ts <- as_tsibble(pisa,
           key = country,
           index = year,
           regular = TRUE)

pisa_ts
pisa_ts_au_nz <- pisa_ts %>% filter(country %in% c("AUS", "NZL", "QAT"))

library(ggplot2)
ggplot(pisa_ts_au_nz, 
       aes(x = year, 
           y = math_mean,
           group = country,
           colour = country)) +
  geom_ribbon(aes(ymin = math_min, 
                  ymax = math_max), 
              fill = "grey70") +
  geom_line(size = 1) +
  lims(y = c(0, 1000)) +
  labs(y = "math") +
facet_wrap(~country)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

fabletools

features, features_all, features_at, features_if

tsibble

as_tsibble, n_keys

Sample a number or fraction of keys to explore

Description

Sample a number or fraction of keys to explore

Usage

sample_n_keys(.data, size)

sample_frac_keys(.data, size)

Arguments

Value

tsibble with fewer observations of key

Examples

library(ggplot2)
sample_n_keys(heights,
             size = 10) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line()
library(ggplot2)
sample_frac_keys(wages,
                0.1) %>%
  ggplot(aes(x = xp,
             y = unemploy_rate,
             group = id)) + 
  geom_line()

Stratify the keys into groups to facilitate exploration

Description

To look at as much of the raw data as possible, it can be helpful to stratify the data into groups for plotting. You can stratify the keys using the stratify_keys() function, which adds the column, .strata. This allows the user to create facetted plots showing a more of the raw data.

Usage

stratify_keys(.data, n_strata, along = NULL, fun = mean, ...)

Arguments

Value

data.frame with column, .strata containing n_strata groups

Examples

library(ggplot2)
library(brolgar)

heights %>%
  sample_frac_keys(size = 0.1) %>%
  stratify_keys(10) %>%
 ggplot(aes(x = height_cm,
            y = year,
            group = country)) + 
 geom_line() + 
 facet_wrap(~.strata)
 
 # now facet along some feature
library(dplyr)
 heights %>%
key_slope(height_cm ~ year) %>%
  right_join(heights, ., by = "country") %>%
  stratify_keys(n_strata = 12,
                along = .slope_year,
                fun = median) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line() + 
  facet_wrap(~.strata)


heights %>%
  stratify_keys(n_strata = 12,
                along = height_cm) %>%
  ggplot(aes(x = year,
             y = height_cm,
             group = country)) + 
  geom_line() + 
  facet_wrap(~.strata)

Wages data from National Longitudinal Survey of Youth (NLSY)

Description

This data contains measurements on hourly wages by years in the workforce, with education and race as covariates. The population measured was male high-school dropouts, aged between 14 and 17 years when first measured. wages is a time series tsibble. It comes from J. D. Singer and J. B. Willett. Applied Longitudinal Data Analysis. Oxford University Press, Oxford, UK, 2003. https://stats.idre.ucla.edu/stat/r/examples/alda/data/wages_pp.txt

Usage

wages

Format

A tsibble data frame with 6402 rows and 8 variables:

id

1–888, for each subject. This forms the key of the data

ln_wages

natural log of wages, adjusted for inflation, to 1990 dollars.

xp

Experience - the length of time in the workforce (in years). This is treated as the time variable, with t0 for each subject starting on their first day at work. The number of time points and values of time points for each subject can differ. This forms the index of the data

ged

when/if a graduate equivalency diploma is obtained.

xp_since_ged

change in experience since getting a ged (if they get one)

black

categorical indicator of race = black.

hispanic

categorical indicator of race = hispanic.

high_grade

highest grade completed

unemploy_rate

unemployment rates in the local geographic region at each measurement time

Examples

# show the data
wages
library(ggplot2)
# set seed so that the plots stay the same
set.seed(2019-7-15-1300)
# explore a sample of five individuals
wages %>%
  sample_n_keys(size = 5) %>%
  ggplot(aes(x = xp,
             y = ln_wages,
             group = id)) + 
  geom_line()

# Explore many samples with `facet_sample()`
  ggplot(wages, 
         aes(x = xp,
             y = ln_wages,
             group = id)) + 
  geom_line() + 
  facet_sample()

# explore the five number summary of ln_wages with `features`
wages %>% 
  features(ln_wages, feat_five_num)