Merge pull request #4 from globeandmail/1.1

1.1

DESCRIPTION

@@ -1,12 +1,11 @@
 Package: startR
 Type: Package
 Title: A Template For Data Journalism Projects In R
-Version: 1.0.1
+Version: 1.1.0
 Author: Michael Pereira <mpereira@globeandmail.com> and Tom Cardoso <tcardoso@globeandmail.com>
 Maintainer: Michael Pereira <mpereira@globeandmail.com> and Tom Cardoso <tcardoso@globeandmail.com>
 Description: This project structures the data analysis process around an expected set of files and steps.
-    This lowers the upfront effort of starting and maintaining a project and supports easier verification by providing reviewers with an expected and logically organized project.
-    Think of it like Ruby on Rails or React, but for R analysis.
+    This lowers the upfront effort of starting and maintaining a project and supports easier verification by providing reviewers with an expected and logically organized project. Think of it like Ruby on Rails or React, but for R analysis.
 License: MIT
 Encoding: UTF-8
 LazyData: true

R/analyze.R

@@ -1,12 +1,9 @@
-############################################################
-# This file handles the primary analysis using the tidied
-# data as input. Should never read from `dir_data_raw`,
-# only `dir_data_processed`.
-#
-#
-############################################################
+# =======================================================================
+# This file handles the primary analysis using the tidied  data as input.
+# Should never read from `dir_data_raw()`, only `dir_data_processed()`.
+# =======================================================================
 
-# sample <- read_feather(here::here(dir_data_processed, 'sample.feather')) %>%
+# sample <- read_feather(dir_data_processed('sample.feather')) %>%
 #   group_by(cma) %>%
 #   arrange(desc(date)) %>%
 #   mutate(sale_avg_3mo = rollmean(sale_avg, k = 3, fill = NA)) %>%

R/functions.R

@@ -1,5 +1,3 @@
-############################################################
+# =======================================================================
 # Project-specific functions.
-#
-#
-############################################################
+# =======================================================================

R/process.R

@@ -1,14 +1,10 @@
-############################################################
-# This file is used to read in raw data, tidy, clean it,
-# and save a file to src *before* proceeding to analysis.
-# If you're using `mutate()` for any actual analysis you're
-# doing it wrong.
-#
-# Specify column types as required.
-#
-############################################################
-
-# begin_processing()
+# =======================================================================
+# Read raw data, clean it and save it out to `dir_data_processed()` here
+# before moving to analysis. If run from `run.R`, all variables generated
+# in this file will be wiped after completion to keep the environment
+# clean. If your process step is complex, you can break it into several
+# files like so: `source(dir_src('process_files', 'process_step_1.R'))`
+# =======================================================================
 
 # sample.raw <- read_csv(sample.raw.file) %>%
 #   rename(
@@ -23,6 +19,4 @@
 #   ) %>%
 #   arrange(cma, desc(date))
 #
-# write_feather(sample.raw, here::here(dir_data_processed, 'sample.feather'))
-#
-# end_processing()
+# write_feather(sample.raw, dir_data_processed('sample.feather'))

R/visualize.R

@@ -1,15 +1,11 @@
-############################################################
-# Charts, maps, etc. from your data
-#
-# Use the `write_plot` function to write the plot directly
-# to the `plots/` folder, using the variable name as
-# the filename.
-#
-############################################################
+# =======================================================================
+# Graphics. Use the `write_plot` function to write the plot directly
+# to the `plots/` folder, using the variable name as the filename.
+# =======================================================================
 
-# plot_house_price_change <- ggplot(sample %>%
-#   filter(cma != 'C11'),
-#   aes(x = reorder(cma, yoy), y = yoy)) +
+# plot_house_price_change <- sample %>%
+#   filter(cma != 'C11') %>%
+#   ggplot(aes(x = reorder(cma, yoy), y = yoy)) +
 #   geom_bar(colour = 'white', stat = 'identity') +
 #   scale_y_continuous(expand = c(0, 0), limits = c(0, 25)) +
 #   coord_flip() +
@@ -21,6 +17,6 @@
 #   ) +
 #   theme_classic()
 #
-# plot(plot_house_price_change)
+# plot_house_price_change
 #
 # write_plot(plot_house_price_change)