PLR 2024-04-16
Load required packages first
# "tibble","tidyverse","here"
library(tibble)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ purrr 1.0.2
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ ggplot2 3.4.3 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(here)
## here() starts at /home/pablo/Documents/Pablo_zorin/Github_Pablo_source_zorin/targets-test
library(targets)This Markdown report is populated with the pipeline objects created in the pipeline file “_targets.R” and set of functions defined by the R script ” populate_markdown_with_targets_functions.R”. It shows an small example on how a Targets pipeline can be used to build a Markdown report.
As the “_targets.R” file is unique to each pipeline, I have saved the “_targets.R” file to setup and run the pipeline and also to replicate this specific report in the “Pipeline_01_populate_markdown_with_targets” folder.
All remaining files used to build and run this report can also be found in that folder The input data used to create the tables and charts in this report is also saved in the “data” folder at the project directory folder level.
We source required Target functions to create the output elements for this report. This functions are saved in the R sub-folder where Targets sources all scripts required to run the functions that populate the pipeline:
source(here("R","populate_markdown_with_targets_functions.R"))The next step is to run using_populate_markdown_with_targets_pipeline.R from at the project folder directory level we will run the set of commands required to build and inspect the pipeline:
- library(targets)
Then we source the script in the R folder with functions to populate the pipeline
- source(“R/populate_markdown_with_targets_functions.R”)
After that, we check for errors in the pipeline using tar_manifest() function
- tar_manifest(fields = command)
Next, check pipeline dependency graph using tar_visnetwork() function
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tar_visnetwork()
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Finally we run the pipeline we just built earlier using tar_make() function. This function runs the correct targets in the correct order and saves the results to files
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tar_make()
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Once all the objects from the pipeline have been created, we can Run this Markdown report pressing “Run” in the report_populated_targets_output.Rmd file and finally, by pressing the “knit” button, we will obtain the rendered document, in this instance is a github document, but it can also be rendered ad HTML, PDF or Word document.
We check the final data frame imported from Excel file with AE Attendances Type I, Type II and Type II data.
tar_read(clean_ATT_data)
## # A tibble: 164 × 4
## date AE_att_TypeI AE_att_TypeII AE_att_TypeIII
## <date> <int> <int> <int>
## 1 2010-08-01 1138652 54371 559358
## 2 2010-09-01 1150728 55181 550359
## 3 2010-10-01 1163143 54961 583244
## 4 2010-11-01 1111294 53727 486005
## 5 2010-12-01 1159203 45536 533000
## 6 2011-01-01 1133880 51584 542331
## 7 2011-02-01 1053707 51249 494407
## 8 2011-03-01 1225221 57900 580318
## 9 2011-04-01 1197212 54042 593119
## 10 2011-05-01 1221687 57066 594940
## # ℹ 154 more rowsThis section includes the plot for Type I A&E Attendances created in the pipeline
tar_read(line_chart_AE_att_TypeI)Total number of Type II A&E Attendances from 2011 to March 2024
tar_read(line_chart_AE_att_TypeII)Total number of Type III A&E Attendances from 2011 to March 2024
tar_read(line_chart_AE_att_TypeIII)

