Created on Skills Network Labs

conditional_piping_tidyverse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional piping in the `tidyverse`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lib\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible that you will need to conditionally in- or exclude certain parts of a `tidyverse` pipeline. This may be especially true if you are using\n",
    "pipelines in functions which should be as generic as possible. The code below briefly demonstrates how this can be achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume you would like a function which allows you to return the median 1/4 mile time (i.e. `qsec`) and Miles/(US) gallon (i.e. `mpg`)\n",
    "per cylinder count (i.e. `cyl`) for the `mtcars` dataset. However, you would like to do this for the different engine shapes (i.e. `vs`) individually\n",
    "and for the entire dataset. Instead of rewriting the pipeline three times with minor variations you can write a function which includes a conditional\n",
    "part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cat will return useful results for 0, 1 or \"all\"\n",
    "mtcars_averaging <- function(cat = \"all\") {\n",
    "  mtcars %>%\n",
    "    { if (cat != \"all\") filter(., vs == cat) else . } %>%\n",
    "    group_by(cyl) %>%\n",
    "    summarize(median_qsec = median(qsec),\n",
    "              median_mpg = median(mpg))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cyl</th><th scope=col>median_qsec</th><th scope=col>median_mpg</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4</td><td>18.900</td><td>26.0</td></tr>\n",
       "\t<tr><td>6</td><td>18.300</td><td>19.7</td></tr>\n",
       "\t<tr><td>8</td><td>17.175</td><td>15.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " cyl & median\\_qsec & median\\_mpg\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 4 & 18.900 & 26.0\\\\\n",
       "\t 6 & 18.300 & 19.7\\\\\n",
       "\t 8 & 17.175 & 15.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| cyl &lt;dbl&gt; | median_qsec &lt;dbl&gt; | median_mpg &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 4 | 18.900 | 26.0 |\n",
       "| 6 | 18.300 | 19.7 |\n",
       "| 8 | 17.175 | 15.2 |\n",
       "\n"
      ],
      "text/plain": [
       "  cyl median_qsec median_mpg\n",
       "1 4   18.900      26.0      \n",
       "2 6   18.300      19.7      \n",
       "3 8   17.175      15.2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars_averaging(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cyl</th><th scope=col>median_qsec</th><th scope=col>median_mpg</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4</td><td>19.185</td><td>25.85</td></tr>\n",
       "\t<tr><td>6</td><td>19.170</td><td>18.65</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " cyl & median\\_qsec & median\\_mpg\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 4 & 19.185 & 25.85\\\\\n",
       "\t 6 & 19.170 & 18.65\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| cyl &lt;dbl&gt; | median_qsec &lt;dbl&gt; | median_mpg &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 4 | 19.185 | 25.85 |\n",
       "| 6 | 19.170 | 18.65 |\n",
       "\n"
      ],
      "text/plain": [
       "  cyl median_qsec median_mpg\n",
       "1 4   19.185      25.85     \n",
       "2 6   19.170      18.65     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars_averaging(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cyl</th><th scope=col>median_qsec</th><th scope=col>median_mpg</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>4</td><td>16.700</td><td>26.0</td></tr>\n",
       "\t<tr><td>6</td><td>16.460</td><td>21.0</td></tr>\n",
       "\t<tr><td>8</td><td>17.175</td><td>15.2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " cyl & median\\_qsec & median\\_mpg\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 4 & 16.700 & 26.0\\\\\n",
       "\t 6 & 16.460 & 21.0\\\\\n",
       "\t 8 & 17.175 & 15.2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| cyl &lt;dbl&gt; | median_qsec &lt;dbl&gt; | median_mpg &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 4 | 16.700 | 26.0 |\n",
       "| 6 | 16.460 | 21.0 |\n",
       "| 8 | 17.175 | 15.2 |\n",
       "\n"
      ],
      "text/plain": [
       "  cyl median_qsec median_mpg\n",
       "1 4   16.700      26.0      \n",
       "2 6   16.460      21.0      \n",
       "3 8   17.175      15.2      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars_averaging(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# manual tests (should all evaluate to TRUE)\n",
    "median(mtcars$mpg[which(mtcars$cyl == 6)]) == 19.7\n",
    "median(mtcars$qsec[which(mtcars$vs == 1 & mtcars$cyl == 4)]) == 19.185\n",
    "median(mtcars$qsec[which(mtcars$vs == 0 & mtcars$cyl == 6)]) == 16.46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.1 (2018-07-02)\n",
       "Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 10 (buster)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /home/jupyterlab/conda/envs/r/lib/R/lib/libRlapack.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n",
       " [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n",
       " [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n",
       "[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] forcats_0.5.0   stringr_1.4.0   dplyr_0.8.5     purrr_0.3.4    \n",
       "[5] readr_1.3.1     tidyr_1.0.2     tibble_3.0.1    ggplot2_3.3.0  \n",
       "[9] tidyverse_1.3.0\n",
       "\n",
       "loaded via a namespace (and not attached):\n",
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       "[13] withr_2.2.0      glue_1.4.0       DBI_1.1.0        dbplyr_1.4.3    \n",
       "[17] modelr_0.1.7     readxl_1.3.1     uuid_0.1-4       lifecycle_0.2.0 \n",
       "[21] munsell_0.5.0    gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5     \n",
       "[25] evaluate_0.14    fansi_0.4.1      broom_0.5.6      IRdisplay_0.7.0 \n",
       "[29] Rcpp_1.0.4.6     backports_1.1.6  scales_1.1.0     IRkernel_0.8.12 \n",
       "[33] jsonlite_1.6.1   fs_1.4.1         hms_0.5.3        digest_0.6.25   \n",
       "[37] stringi_1.4.6    grid_3.5.1       cli_2.0.2        tools_3.5.1     \n",
       "[41] magrittr_1.5     crayon_1.3.4     pkgconfig_2.0.3  ellipsis_0.3.0  \n",
       "[45] xml2_1.3.2       reprex_0.3.0     lubridate_1.7.8  assertthat_0.2.1\n",
       "[49] httr_1.4.1       rstudioapi_0.11  R6_2.4.1         nlme_3.1-147    \n",
       "[53] compiler_3.5.1  "
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   "source": [
    "sessionInfo()"
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