Title:	Hospital Data Analysis Workflow Tools
Version:	0.2.2
Description:	Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
URL:	https://github.com/spsanderson/healthyR
BugReports:	https://github.com/spsanderson/healthyR/issues
Imports:	magrittr, rlang (≥ 0.1.2), tibble, timetk, ggplot2, dplyr, lubridate, graphics, purrr, stringr, writexl, cowplot, scales, sqldf, plotly
Suggests:	knitr, rmarkdown, pacman, healthyR.data, broom, tidyselect
VignetteBuilder:	knitr
Depends:	R (≥ 3.3)
NeedsCompilation:	no
Packaged:	2024-07-01 12:58:29 UTC; steve
Author:	Steven Sanderson [aut, cre, cph]
Maintainer:	Steven Sanderson <spsanderson@gmail.com>
Repository:	CRAN
Date/Publication:	2024-07-01 13:20:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Counts by Category

Description

Get the counts of a column by a particular grouping if supplied, otherwise just get counts of a column.

Usage

category_counts_tbl(.data, .count_col, .arrange_value = TRUE, ...)

Arguments

Details

• Requires a data.frame/tibble.

• Requires a value column, a column that is going to counted.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: los_ra_index_summary_tbl(), named_item_list(), top_n_tbl(), ts_census_los_daily_tbl(), ts_signature_tbl()

Examples

library(healthyR.data)
library(dplyr)

healthyR_data %>%
  category_counts_tbl(
    .count_col = payer_grouping
    , .arrange = TRUE
    , ip_op_flag
  )

healthyR_data %>%
  category_counts_tbl(
    .count_col = ip_op_flag
    , .arrange_value = TRUE
    , service_line
  )

Provide Colorblind Compliant Colors

Description

8 Hex RGB color definitions suitable for charts for colorblind people.

Usage

color_blind()

Details

This function is used in others in order to help render plots for those that are color blind.

Value

A vector of 8 Hex RGB definitions.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Color Blind: hr_scale_color_colorblind(), hr_scale_fill_colorblind()

Examples

color_blind()

Diverging Bar Chart

Description

Diverging Bars is a bar chart that can handle both negative and positive values. This can be implemented by a smart tweak with geom_bar(). But the usage of geom_bar() can be quite confusing. That's because, it can be used to make a bar chart as well as a histogram. Let me explain.

By default, geom_bar() has the stat set to count. That means, when you provide just a continuous X variable (and no Y variable), it tries to make a histogram out of the data.

In order to make a bar chart create bars instead of histogram, you need to do two things. Set stat = identity and provide both x and y inside aes() where, x is either character or factor and y is numeric. In order to make sure you get diverging bars instead of just bars, make sure, your categorical variable has 2 categories that changes values at a certain threshold of the continuous variable. In below example, the mpg from mtcars data set is normalized by computing the z score. Those vehicles with mpg above zero are marked green and those below are marked red.

Usage

diverging_bar_plt(
  .data,
  .x_axis,
  .y_axis,
  .fill_col,
  .plot_title = NULL,
  .plot_subtitle = NULL,
  .plot_caption = NULL,
  .interactive = FALSE
)

Arguments

Details

This function takes only a few arguments and returns a ggplot2 object.

Value

A plotly plot or a ggplot2 static plot

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

suppressPackageStartupMessages(library(ggplot2))

data("mtcars")
mtcars$car_name <- rownames(mtcars)
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$car_name <- factor(mtcars$car_name, levels = mtcars$car_name)

diverging_bar_plt(
  .data          = mtcars
  , .x_axis      = car_name
  , .y_axis      = mpg_z
  , .fill_col    = mpg_type
  , .interactive = FALSE
)

Diverging Lollipop Chart

Description

This is a diverging lollipop function. Lollipop chart conveys the same information as bar chart and diverging bar. Except that it looks more modern. Instead of geom_bar, I use geom_point and geom_segment to get the lollipops right. Let’s draw a lollipop using the same data I prepared in the previous example of diverging bars.

Usage

diverging_lollipop_plt(
  .data,
  .x_axis,
  .y_axis,
  .plot_title = NULL,
  .plot_subtitle = NULL,
  .plot_caption = NULL,
  .interactive = FALSE
)

Arguments

Details

This function takes only a few arguments and returns a ggplot2 object.

Value

A plotly plot or a ggplot2 static plot

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

suppressPackageStartupMessages(library(ggplot2))

data("mtcars")
mtcars$car_name <- rownames(mtcars)
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$car_name <- factor(mtcars$car_name, levels = mtcars$car_name)

diverging_lollipop_plt(.data = mtcars, .x_axis = car_name
, .y_axis = mpg_z)

Diagnosis to Condition Code Mapping file

Description

Diagnosis to Condition Code Mapping file

Usage

data(dx_cc_mapping)

Format

A data frame with 86852 rows and 5 variables

See Also

Other AHRQ: px_cc_mapping

Gartner Magic Chart - Plotting of two continuous variables

Description

Plot a Gartner Magic Chart of two continuous variables.

Usage

gartner_magic_chart_plt(
  .data,
  .x_col,
  .y_col,
  .point_size_col = NULL,
  .y_lab = "",
  .x_lab = "",
  .plot_title = "",
  .top_left_label = "",
  .top_right_label = "",
  .bottom_right_label = "",
  .bottom_left_label = ""
)

Arguments

Value

A ggplot plot.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), los_ra_index_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

library(dplyr)
library(ggplot2)

data_tbl <- tibble(
  x = rnorm(100, 0, 1),
  y = rnorm(100, 0, 1),
  z = abs(x) + abs(y)
)

gartner_magic_chart_plt(
  .data = data_tbl,
  .x_col = x,
  .y_col = y,
  .point_size_col = z,
  .x_lab = "los",
  .y_lab = "ra",
  .plot_title = "tst",
  .top_right_label = "High RA-LOS",
  .top_left_label = "High RA",
  .bottom_left_label = "Leader",
  .bottom_right_label = "High LOS"
)

gartner_magic_chart_plt(
  .data = data_tbl,
  .x_col = x,
  .y_col = y,
  .point_size_col = NULL,
  .x_lab = "los",
  .y_lab = "ra",
  .plot_title = "tst",
  .top_right_label = "High RA-LOS",
  .top_left_label = "High RA",
  .bottom_left_label = "Leader",
  .bottom_right_label = "High LOS"
)

Provide Colorblind Compliant Colors

Description

8 Hex RGB color definitions suitable for charts for colorblind people.

Usage

hr_scale_color_colorblind(..., theme = "hr")

Arguments

Details

This function is used in others in order to help render plots for those that are color blind.

Value

A gggplot layer

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Color Blind: color_blind(), hr_scale_fill_colorblind()

Provide Colorblind Compliant Colors

Description

8 Hex RGB color definitions suitable for charts for colorblind people.

Usage

hr_scale_fill_colorblind(..., theme = "hr")

Arguments

Details

This function is used in others in order to help render plots for those that are color blind.

Value

A gggplot layer

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Color Blind: color_blind(), hr_scale_color_colorblind()

Plot LOS and Readmit Index with Variance

Description

Plot the index of the length of stay and readmit rate against each other along with the variance

Usage

los_ra_index_plt(.data)

Arguments

Details

• Expects a tibble

• Expects a Length of Stay and Readmit column, must be numeric

• Uses cowplot to stack plots

Value

A patchwork ggplot2 plot

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

suppressPackageStartupMessages(library(dplyr))

data_tbl <- tibble(
  "alos"                 = runif(186, 1, 20)
  , "elos"               = runif(186, 1, 17)
  , "readmit_rate"       = runif(186, 0, .25)
  , "readmit_rate_bench" = runif(186, 0, .2)
)

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 15
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_rate_bench
) %>%
  los_ra_index_plt()

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 10
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_rate_bench
) %>%
  los_ra_index_plt()

Make LOS and Readmit Index Summary Tibble

Description

Create the length of stay and readmit index summary tibble

Usage

los_ra_index_summary_tbl(
  .data,
  .max_los = 15,
  .alos_col,
  .elos_col,
  .readmit_rate,
  .readmit_bench
)

Arguments

Details

• Expects a tibble

• Expects the following columns and there should only be these 4

  • Length Of Stay Actual - Should be an integer

  • Length Of Stacy Benchmark - Should be an integer

  • Readmit Rate Actual - Should be 0/1 for each record, 1 = readmitted, 0 did not.

  • Readmit Rate Benchmark - Should be a percentage from the benchmark file.

• This will add a column called visits that will be the count of records per length of stay from 1 to .max_los

• The .max_los param can be left blank and the function will default to 15. If this is not a good default and you don't know what it should be then set it to 75 percentile from the stats::quantile() function using the defaults, like so .max_los = stats::quantile(data_tbl$alos)[[4]]

• Uses all data to compute variance, if you want it for a particular time frame you will have to filter the data that goes into the .data argument. It is suggested to use timetk::filter_by_time()

• The index is computed as the excess of the length of stay or readmit rates over their respective expectations.

Value

A tibble

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: category_counts_tbl(), named_item_list(), top_n_tbl(), ts_census_los_daily_tbl(), ts_signature_tbl()

Examples

suppressPackageStartupMessages(library(dplyr))

data_tbl <- tibble(
  "alos"            = runif(186, 1, 20)
  , "elos"          = runif(186, 1, 17)
  , "readmit_rate"  = runif(186, 0, .25)
  , "readmit_bench" = runif(186, 0, .2)
)

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 15
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_bench
)

los_ra_index_summary_tbl(
  .data = data_tbl
  , .max_los       = 10
  , .alos_col      = alos
  , .elos_col      = elos
  , .readmit_rate  = readmit_rate
  , .readmit_bench = readmit_bench
)

Tibble to named list

Description

Takes in a data.frame/tibble and creates a named list from a supplied grouping variable. Can be used in conjunction with save_to_excel() to create a new sheet for each group of data.

Usage

named_item_list(.data, .group_col)

Arguments

Details

• Requires a data.frame/tibble and a grouping column.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: category_counts_tbl(), los_ra_index_summary_tbl(), top_n_tbl(), ts_census_los_daily_tbl(), ts_signature_tbl()

Examples

library(healthyR.data)

df <- healthyR_data
df_list <- named_item_list(.data = df, .group_col = service_line)
df_list

Get the optimal binwidth for a histogram

Description

Gives the optimal binwidth for a histogram given a data set, it's value and the desired amount of bins

Usage

opt_bin(.data, .value_col, .iters = 30)

Arguments

Details

• Supply a data.frame/tibble with a value column. from this an optimal binwidth will be computed for the amount of binds desired

Value

A tibble of histogram breakpoints

Author(s)

Steven P. Sanderson II, MPH

Modified from Hideaki Shimazaki Department of Physics, Kyoto University shimazaki at ton.scphys.kyoto-u.ac.jp Feel free to modify/distribute this program.

See Also

Other Utilities: save_to_excel(), sql_left(), sql_mid(), sql_right()

Examples

suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(dplyr))

df_tbl <- rnorm(n = 1000, mean = 0, sd = 1)
df_tbl <- df_tbl %>%
  as_tibble() %>%
  set_names("value")

df_tbl %>%
  opt_bin(
    .value_col = value
    , .iters = 100
  )

Procedure to Condition Code Mapping file

Description

Procedure to Condition Code Mapping file

Usage

data(px_cc_mapping)

Format

A data frame with 79721 rows and 5 variables

See Also

Other AHRQ: dx_cc_mapping

Save a file to Excel

Description

Save a tibble/data.frame to an excel .xlsx file. The file will automatically with a save_dtime in the format of 20201109_132416 for November 11th, 2020 at 1:24:16PM.

Usage

save_to_excel(.data, .file_name)

Arguments

Details

• Requires a tibble/data.frame to be passed to it.

Value

A saved excel file

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utilities: opt_bin(), sql_left(), sql_mid(), sql_right()

Service Line Grouper Augment Function

Description

Takes a few arguments from a data.frame/tibble and returns a service line augmented to a data.frame/tibble for a set of patients.

Usage

service_line_augment(.data, .dx_col, .px_col, .drg_col)

Arguments

Details

This is an augment function in that appends a vector to an data.frame/tibble that is passed to the .data parameter. A data.frame/tibble is required, along with a principal diagnosis column, a principal procedure column, and a column for the DRG number. These are needed so that the function can join the dx_cc_mapping and px_cc_mapping columns to provide the service line. This function only works on visits that are coded using ICD Version 10 only.

Lets take an example discharge, the DRG is 896 and the Principal Diagnosis code maps to DX_660, then this visit would get grouped to alcohol_abuse

DRG 896: ALCOHOL, DRUG ABUSE OR DEPENDENCE WITHOUT REHABILITATION THERAPY WITH MAJOR COMPLICATION OR COMORBIDITY (MCC)

DX_660 Maps to the following ICD-10 Codes ie F1010 Alcohol abuse, uncomplicated:

library(healthyR)
dx_cc_mapping %>%
  filter(CC_Code == "DX_660", ICD_Ver_Flag == "10")

Value

An augmented data.frame/tibble with the service line appended as a new column.

Author(s)

Steven P. Sanderson II, MPH

Examples

df <- data.frame(
  dx_col = "F10.10",
  px_col = NA,
  drg_col = "896"
)

service_line_augment(
  .data = df,
  .dx_col = dx_col,
  .px_col = px_col,
  .drg_col = drg_col
)

Service Line Grouper Vectorized Function

Description

Takes a few arguments from a data.frame/tibble and returns a service line vector for a set of patients.

Usage

service_line_vec(.data, .dx_col, .px_col, .drg_col)

Arguments

Details

This is a vectorized function in that it returns a vector. It can be applied inside of a mutate statement when using dplyr if desired. A data.frame/tibble is required, along with a principal diagnosis column, a principal procedure column, and a column for the DRG number. These are needed so that the function can join the dx_cc_mapping and px_cc_mapping columns to provide the service line. This function only works on visits that are coded using ICD Version 10 only.

Lets take an example discharge, the DRG is 896 and the Principal Diagnosis code maps to DX_660, then this visit would get grouped to alcohol_abuse

DRG 896: ALCOHOL, DRUG ABUSE OR DEPENDENCE WITHOUT REHABILITATION THERAPY WITH MAJOR COMPLICATION OR COMORBIDITY (MCC)

DX_660 Maps to the following ICD-10 Codes ie F1010 Alcohol abuse, uncomplicated:

library(healthyR)
dx_cc_mapping %>%
  filter(CC_Code == "DX_660", ICD_Ver_Flag == "10")

Value

A vector of service line assignments.

Author(s)

Steven P. Sanderson II, MPH

Examples

df <- data.frame(
  dx_col = "F10.10",
  px_col = NA,
  drg_col = "896"
)

service_line_vec(
  .data = df,
  .dx_col = dx_col,
  .px_col = px_col,
  .drg_col = drg_col
)

Use SQL LEFT type function

Description

Perform an SQL LEFT() type function on a piece of text

Usage

sql_left(.text, .num_char)

Arguments

Details

• You must supply data that you want to manipulate.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utilities: opt_bin(), save_to_excel(), sql_mid(), sql_right()

Examples

sql_left("text", 3)

Use SQL MID type function

Description

Perform an SQL SUBSTRING type function

Usage

sql_mid(.text, .start_num, .num_char)

Arguments

Details

• You must supply data that you want to manipulate.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utilities: opt_bin(), save_to_excel(), sql_left(), sql_right()

Examples

sql_mid("this is some text", 6, 2)

Use SQL RIGHT type functions

Description

Perform an SQL RIGHT type function

Usage

sql_right(.text, .num_char)

Arguments

Details

• You must supply data that you want to manipulate.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Utilities: opt_bin(), save_to_excel(), sql_left(), sql_mid()

Examples

sql_right("this is some more text", 3)

Tidy eval helpers

Description

• sym() creates a symbol from a string and syms() creates a list of symbols from a character vector.

• enquo() and enquos() delay the execution of one or several function arguments. enquo() returns a single quoted expression, which is like a blueprint for the delayed computation. enquos() returns a list of such quoted expressions.

• expr() quotes a new expression locally. It is mostly useful to build new expressions around arguments captured with enquo() or enquos(): expr(mean(!!enquo(arg), na.rm = TRUE)).

• as_name() transforms a quoted variable name into a string. Supplying something else than a quoted variable name is an error.

  That's unlike as_label() which also returns a single string but supports any kind of R object as input, including quoted function calls and vectors. Its purpose is to summarise that object into a single label. That label is often suitable as a default name.

  If you don't know what a quoted expression contains (for instance expressions captured with enquo() could be a variable name, a call to a function, or an unquoted constant), then use as_label(). If you know you have quoted a simple variable name, or would like to enforce this, use as_name().

To learn more about tidy eval and how to use these tools, visit Metaprogramming section of Advanced R.

Top N tibble

Description

Get a tibble returned with n records sorted either by descending order (default) or ascending order.

Usage

top_n_tbl(.data, .n_records, .arrange_value = TRUE, ...)

Arguments

Details

• Requires a data.frame/tibble

• Requires at least one column to be chosen inside of the ...

• Will return the tibble in sorted order that is chosen with descending as the default

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: category_counts_tbl(), los_ra_index_summary_tbl(), named_item_list(), ts_census_los_daily_tbl(), ts_signature_tbl()

Examples

library(healthyR.data)

df <- healthyR_data

df_tbl <- top_n_tbl(
  .data = df
  , .n_records = 3
  , .arrange_value = TRUE
  , service_line
  , payer_grouping
)

print(df_tbl)

Plot ALOS - Average Length of Stay

Description

Plot ALOS - Average Length of Stay

Usage

ts_alos_plt(.data, .date_col, .value_col, .by_grouping, .interactive)

Arguments

Details

• Expects a tibble with a date time column and a value column

• Uses timetk for underlying sumarization and plot

• If .by_grouping is missing it will default to "day"

• A static ggplot2 object is return if the .interactive function is FALSE otherwise a plotly plot is returned.

Value

A timetk time series plot

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

library(healthyR)
library(healthyR.data)
library(timetk)
library(dplyr)
library(purrr)

# Make A Series of Dates ----
data_tbl <- healthyR_data

df_tbl <- data_tbl %>%
    filter(ip_op_flag == "I") %>%
    select(visit_end_date_time, length_of_stay) %>%
    summarise_by_time(
        .date_var = visit_end_date_time
        , .by     = "day"
        , visits  = mean(length_of_stay, na.rm = TRUE)
    ) %>%
    filter_by_time(
        .date_var     = visit_end_date_time
        , .start_date = "2012"
        , .end_date   = "2019"
    ) %>%
    set_names("Date","Values")

ts_alos_plt(
  .data = df_tbl
  , .date_col = Date
  , .value_col = Values
  , .by = "month"
  , .interactive = FALSE
)

Time Series - Census and LOS by Day

Description

Sometimes it is important to know what the census was on any given day, or what the average length of stay is on given day, including for those patients that are not yet discharged. This can be easily achieved. This will return one record for every account so the data will still need to be summarized. If there are multiple entries per day then those records will show up and you will therefore have multiple entries in the column date in the resulting tibble. If you want to aggregate from there you should be able to do so easily.

If you have a record where the .start_date_col is filled in but the corresponding end_date is null then the end date will be set equal to Sys.Date()

If a record has a start_date that is NA then it will be discarded.

This function can take a little bit of time to run while the join comparison runs.

Usage

ts_census_los_daily_tbl(
  .data,
  .keep_nulls_only = FALSE,
  .start_date_col,
  .end_date_col,
  .by_time = "day"
)

Arguments

Details

• Requires a dataset that has at least a start date column and an end date column

• Takes a single boolean parameter

Value

A tibble object

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: category_counts_tbl(), los_ra_index_summary_tbl(), named_item_list(), top_n_tbl(), ts_signature_tbl()

Examples

library(healthyR)
library(healthyR.data)
library(dplyr)

df <- healthyR_data

df_tbl <- df %>%
  filter(ip_op_flag == "I") %>%
  select(visit_start_date_time, visit_end_date_time) %>%
  timetk::filter_by_time(.date_var = visit_start_date_time, .start_date = "2020")

ts_census_los_daily_tbl(
   .data              = df_tbl
   , .keep_nulls_only = FALSE
   , .start_date_col  = visit_start_date_time
   , .end_date_col    = visit_end_date_time
)

Create a plot showing the excess of the median value

Description

Plot out the excess +/- of the median value grouped by certain time parameters.

Usage

ts_median_excess_plt(
  .data,
  .date_col,
  .value_col,
  .x_axis,
  .ggplot_group_var,
  .years_back
)

Arguments

Details

• Supply data that you want to view and you will see the excess +/- of the median values over a specified time series tibble.

Value

A ggplot2 plot

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_alos_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

suppressPackageStartupMessages(library(timetk))

ts_signature_tbl(
  .data       = m4_daily
  , .date_col = date
) %>%
ts_median_excess_plt(
  .date_col           = date
  , .value_col        = value
  , .x_axis           = month
  , .ggplot_group_var = year
  , .years_back       = 1
)

Time Series Plot

Description

This is a warpper function to the timetk::plot_time_series() function with a limited functionality parameter set. To see the full reference please visit the timetk package site.

Usage

ts_plt(
  .data,
  .date_col,
  .value_col,
  .color_col = NULL,
  .facet_col = NULL,
  .facet_ncol = NULL,
  .interactive = FALSE
)

Arguments

Details

This function takes only a few of the arguments in the function and presets others while choosing the defaults on others. The smoother functionality is turned off.

Value

A plotly plot or a ggplot2 static plot

Author(s)

Steven P. Sanderson II, MPH

See Also

https://business-science.github.io/timetk/reference/plot_time_series.html

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_readmit_rate_plt()

Examples

suppressPackageStartupMessages(library(dplyr))
library(timetk)
library(healthyR.data)

healthyR.data::healthyR_data %>%
  filter(ip_op_flag == "I") %>%
  select(visit_end_date_time, service_line) %>%
  filter_by_time(
    .date_var = visit_end_date_time
    , .start_date = "2020"
    ) %>%
  group_by(service_line) %>%
  summarize_by_time(
    .date_var = visit_end_date_time
    , .by = "month"
    , visits = n()
  ) %>%
 ungroup() %>%
 ts_plt(
   .date_col = visit_end_date_time
   , .value_col = visits
   , .color_col = service_line
 )

Plot Readmit Rate

Description

Plot Readmit Rate

Usage

ts_readmit_rate_plt(.data, .date_col, .value_col, .by_grouping, .interactive)

Arguments

Details

• Expects a tibble with a date time column and a value column

• Uses timetk for underlying sumarization and plot

• If .by_grouping is missing it will default to "day"

Value

A timetk time series plot that is interactive

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_alos_plt(), ts_median_excess_plt(), ts_plt()

Examples

set.seed(123)

suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(dplyr))

ts_tbl <- tk_make_timeseries(
  start = "2019-01-01"
  , by = "day"
  , length_out = "1 year 6 months"
)
values <- arima.sim(
  model = list(
    order = c(0, 1, 0))
    , n = 547
    , mean = 1
    , sd = 5
)

df_tbl <- tibble(
  x = ts_tbl
  , y = values
  ) %>%
  set_names("Date","Values")

ts_readmit_rate_plt(
  .data = df_tbl
  , .date_col = Date
  , .value_col = Values
  , .by = "month"
  , .interactive = FALSE
)

Make a Time Enhanced Tibble

Description

Returns a tibble that adds the time series signature from the timetk::tk_augment_timeseries_signature() function. All added from a chosen date column defined by the .date_col parameter.

Usage

ts_signature_tbl(.data, .date_col, .pad_time = TRUE, ...)

Arguments

Details

• Supply data with a date column and this will add the year, month, week, week day and hour to the tibble. The original date column is kept.

• Returns a time-series signature tibble.

• You must know the data going into the function and if certain columns should be dropped or kept when using further functions

Value

A tibble

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Table Functions: category_counts_tbl(), los_ra_index_summary_tbl(), named_item_list(), top_n_tbl(), ts_census_los_daily_tbl()

Examples

library(timetk)

ts_signature_tbl(
  .data       = m4_daily
  , .date_col = date
  , .pad_time = TRUE
  , id
)