data-analyst-certification-case-study.ipynb

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    "## Introduction\n",
    "\n",
    "This case study is part of Google's Data Analyst Certificate at Coursera, completed on September 2022.\n",
    "\n",
    "On this case study, we'll go over each step of the data analysis process: **ask, prepare, process, analyze, share,** and **act**. I chose to work with R and Kaggle for data manipulation and presentation, and Tableau for the visualizations.\n",
    "\n",
    "### Summary\n",
    "\n",
    "#### Scenario\n",
    "\n",
    "You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing, Lily Moreno, believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.\n",
    "\n",
    "#### About the company\n",
    "\n",
    "In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.\n",
    "\n",
    "Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.\n",
    "\n",
    "Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.\n",
    "\n",
    "Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.\n",
    "\n",
    "## The Analysis Process\n",
    "\n",
    "### Ask\n",
    "\n",
    "**Business task:** Analyze data from annual members and casual riders to understand which marketing efforts can turn more casual riders into members.\n",
    "\n",
    "### Prepare\n",
    "\n",
    "For this case study it was used Cyclistic’s historical trip data to analyze and identify trends. Keep in mind that Cyclistic is a fictional company, the data has been made available by Motivate International Inc. under [this license](http://https://www.divvybikes.com/data-license-agreement).\n",
    "\n",
    "Datasets:\n",
    "12 .csv files with bike trip data from **Sep 2021 to Aug 2022**.\n",
    "* 202109-divvy-tripdata.csv\n",
    "* 202110-divvy-tripdata.csv\n",
    "* 202111-divvy-tripdata.csv\n",
    "* 202112-divvy-tripdata.csv\n",
    "* 202201-divvy-tripdata.csv\n",
    "* 202202-divvy-tripdata.csv\n",
    "* 202203-divvy-tripdata.csv\n",
    "* 202204-divvy-tripdata.csv\n",
    "* 202205-divvy-tripdata.csv\n",
    "* 202206-divvy-tripdata.csv\n",
    "* 202207-divvy-tripdata.csv\n",
    "* 202208-divvy-tripdata.csv\n",
    "\n",
    "Defining metrics:\n",
    "* Number of rides\n",
    "* Length of ride\n",
    "* Day of the week\n",
    "* Hour of the day\n",
    "* Yearly seasons\n",
    "* Busiest stations\n",
    "\n",
    "Tag along and go with me on each step of this process, on the code chunks below you will find details of what's being done."
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      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6      \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8      \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.10\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.1      \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.1 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2      \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "Attaching package: ‘lubridate’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    date, intersect, setdiff, union\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘data.table’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:lubridate’:\n",
      "\n",
      "    hour, isoweek, mday, minute, month, quarter, second, wday, week,\n",
      "    yday, year\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    transpose\n",
      "\n",
      "\n"
     ]
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    "# Installing and loading R packages\n",
    "install.packages(\"tidyverse\")\n",
    "library(tidyverse)\n",
    "library(lubridate)\n",
    "library(data.table)"
   ]
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       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'ride_id'</li><li>'rideable_type'</li><li>'started_at'</li><li>'ended_at'</li><li>'start_station_name'</li><li>'start_station_id'</li><li>'end_station_name'</li><li>'end_station_id'</li><li>'start_lat'</li><li>'start_lng'</li><li>'end_lat'</li><li>'end_lng'</li><li>'member_casual'</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 'ride\\_id'\n",
       "\\item 'rideable\\_type'\n",
       "\\item 'started\\_at'\n",
       "\\item 'ended\\_at'\n",
       "\\item 'start\\_station\\_name'\n",
       "\\item 'start\\_station\\_id'\n",
       "\\item 'end\\_station\\_name'\n",
       "\\item 'end\\_station\\_id'\n",
       "\\item 'start\\_lat'\n",
       "\\item 'start\\_lng'\n",
       "\\item 'end\\_lat'\n",
       "\\item 'end\\_lng'\n",
       "\\item 'member\\_casual'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ride_id'\n",
       "2. 'rideable_type'\n",
       "3. 'started_at'\n",
       "4. 'ended_at'\n",
       "5. 'start_station_name'\n",
       "6. 'start_station_id'\n",
       "7. 'end_station_name'\n",
       "8. 'end_station_id'\n",
       "9. 'start_lat'\n",
       "10. 'start_lng'\n",
       "11. 'end_lat'\n",
       "12. 'end_lng'\n",
       "13. 'member_casual'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ride_id\"            \"rideable_type\"      \"started_at\"        \n",
       " [4] \"ended_at\"           \"start_station_name\" \"start_station_id\"  \n",
       " [7] \"end_station_name\"   \"end_station_id\"     \"start_lat\"         \n",
       "[10] \"start_lng\"          \"end_lat\"            \"end_lng\"           \n",
       "[13] \"member_casual\"     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading first dataset into a dataframe and checking its columns/attributes\n",
    "tripdata_202109 <- read.csv(\"../input/divvy-tripdata/202109-divvy-tripdata.csv\")\n",
    "colnames(tripdata_202109)"
   ]
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     "text": [
      "Ride types: electric_bike classic_bike docked_bike\n",
      "User types: casual member"
     ]
    }
   ],
   "source": [
    "# Understanding data on specific columns\n",
    "cat(\"Ride types:\",unique(tripdata_202109$rideable_type))\n",
    "cat(\"\\nUser types:\",unique(tripdata_202109$member_casual))"
   ]
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   "source": [
    "# Loading the rest of datasets into dataframes\n",
    "tripdata_202110 <- read.csv(\"../input/divvy-tripdata/202110-divvy-tripdata.csv\")\n",
    "tripdata_202111 <- read.csv(\"../input/divvy-tripdata/202111-divvy-tripdata.csv\")\n",
    "tripdata_202112 <- read.csv(\"../input/divvy-tripdata/202112-divvy-tripdata.csv\")\n",
    "tripdata_202201 <- read.csv(\"../input/divvy-tripdata/202201-divvy-tripdata.csv\")\n",
    "tripdata_202202 <- read.csv(\"../input/divvy-tripdata/202202-divvy-tripdata.csv\")\n",
    "tripdata_202203 <- read.csv(\"../input/divvy-tripdata/202203-divvy-tripdata.csv\")\n",
    "tripdata_202204 <- read.csv(\"../input/divvy-tripdata/202204-divvy-tripdata.csv\")\n",
    "tripdata_202205 <- read.csv(\"../input/divvy-tripdata/202205-divvy-tripdata.csv\")\n",
    "tripdata_202206 <- read.csv(\"../input/divvy-tripdata/202206-divvy-tripdata.csv\")\n",
    "tripdata_202207 <- read.csv(\"../input/divvy-tripdata/202207-divvy-tripdata.csv\")\n",
    "tripdata_202208 <- read.csv(\"../input/divvy-tripdata/202208-divvy-tripdata.csv\")"
   ]
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     "text": [
      "Total of data points/bike rides (12 months):  5,883,043\n",
      "Average data points/bike rides per month:  490,253.58\n",
      "Number of data points/bike rides in 2021 (Sep-Dec):  1,994,891\n",
      "Number of data points/bike rides in 2022 (Jan-Aug):  3,888,152"
     ]
    }
   ],
   "source": [
    "# Printing some data from the dataframes\n",
    "rows_2021 <- nrow(tripdata_202109) + nrow(tripdata_202110) + nrow(tripdata_202111) + nrow(tripdata_202112)\n",
    "rows_2022 <- nrow(tripdata_202201) + nrow(tripdata_202202) + nrow(tripdata_202203) + nrow(tripdata_202204) + nrow(tripdata_202205) + nrow(tripdata_202206) + nrow(tripdata_202207) + nrow(tripdata_202208)\n",
    "total_rows <- rows_2021 + rows_2022\n",
    "cat(\"Total of data points/bike rides (12 months): \", format(total_rows, big.mark=\",\"))\n",
    "cat(\"\\nAverage data points/bike rides per month: \", format((total_rows)/12, nsmall=2, big.mark=\",\"))\n",
    "cat(\"\\nNumber of data points/bike rides in 2021 (Sep-Dec): \", format(rows_2021, big.mark=\",\"))\n",
    "cat(\"\\nNumber of data points/bike rides in 2022 (Jan-Aug): \", format(rows_2022, big.mark=\",\"))"
   ]
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     "text": [
      "[1] \"tripdata_202109 structure\"\n",
      "Rows: 756,147\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"9DC7B962304CBFD8\", \"F930E2C6872D6B32\", \"6EF7213790…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-09-28 16:07:10\", \"2021-09-28 14:24:51\", \"2021…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-09-28 16:09:54\", \"2021-09-28 14:40:05\", \"2021…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"Clark St &…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"TA13070001…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.89000, 41.94000, 41.81000, 41.80000, 41.88000, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.68000, -87.64000, -87.72000, -87.72000, -87.740…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.89, 41.98, 41.80, 41.81, 41.88, 41.88, 41.74, 41…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.67, -87.67, -87.72, -87.72, -87.71, -87.74, -87…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"casual\", \"casual\", \"casual\", \"casual\", \"casual\", \"…\n",
      "[1] \"tripdata_202110 structure\"\n",
      "Rows: 631,226\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"620BC6107255BF4C\", \"4471C70731AB2E45\", \"26CA69D43D…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-10-22 12:46:42\", \"2021-10-21 09:12:37\", \"2021…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-10-22 12:49:50\", \"2021-10-21 09:14:14\", \"2021…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Kingsbury St & Kinzie St\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"KA1503000043\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.88919, 41.93000, 41.92000, 41.92000, 41.89000, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.63850, -87.70000, -87.70000, -87.69000, -87.710…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.89000, 41.93000, 41.94000, 41.92000, 41.89000, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.63000, -87.71000, -87.72000, -87.69000, -87.690…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"member\", \"member\", \"member\", \"…\n",
      "[1] \"tripdata_202111 structure\"\n",
      "Rows: 359,978\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"7C00A93E10556E47\", \"90854840DFD508BA\", \"0A7D10CDD1…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-11-27 13:27:38\", \"2021-11-27 13:38:25\", \"2021…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-11-27 13:46:38\", \"2021-11-27 13:56:10\", \"2021…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"Michigan Ave & Oak St\", \"\", \"\"…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"13042\", \"\", \"\", \"\", \"\", \"\", \"\"…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.93000, 41.96000, 41.96000, 41.94000, 41.90000, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.72000, -87.70000, -87.70000, -87.79000, -87.630…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.96, 41.92, 41.96, 41.93, 41.88, 41.90, 41.80, 41…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.73, -87.70, -87.70, -87.79, -87.62, -87.63, -87…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"casual\", \"casual\", \"casual\", \"casual\", \"casual\", \"…\n",
      "[1] \"tripdata_202112 structure\"\n",
      "Rows: 247,540\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"46F8167220E4431F\", \"73A77762838B32FD\", \"4CF4245205…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-12-07 15:06:07\", \"2021-12-11 03:43:29\", \"2021…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2021-12-07 15:13:42\", \"2021-12-11 04:10:23\", \"2021…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Laflin St & Cullerton St\", \"LaSalle Dr & Huron St\"…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"13307\", \"KP1705001026\", \"KA1504000117\", \"KA1504000…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Morgan St & Polk St\", \"Clarendon Ave & Leland Ave\"…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1307000130\", \"TA1307000119\", \"13137\", \"KP1705001…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.85483, 41.89441, 41.89936, 41.89939, 41.89558, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.66366, -87.63233, -87.64852, -87.64854, -87.682…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.87197, 41.96797, 41.93758, 41.89488, 41.93125, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.65097, -87.65000, -87.64410, -87.63233, -87.644…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"casual\", \"member\", \"member\", \"member\", \"…\n",
      "[1] \"tripdata_202201 structure\"\n",
      "Rows: 103,770\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"C2F7DD78E82EC875\", \"A6CF8980A652D272\", \"BD0F91DFF7…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"classic_bike\", \"…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-01-13 11:59:47\", \"2022-01-10 08:41:56\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-01-13 12:02:44\", \"2022-01-10 08:46:17\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Glenwood Ave & Touhy Ave\", \"Glenwood Ave & Touhy A…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"525\", \"525\", \"TA1306000016\", \"KA1504000151\", \"TA13…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Clark St & Touhy Ave\", \"Clark St & Touhy Ave\", \"Gr…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"RP-007\", \"RP-007\", \"TA1307000001\", \"TA1309000021\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 42.01280, 42.01276, 41.92560, 41.98359, 41.87785, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.66591, -87.66597, -87.65371, -87.66915, -87.624…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 42.01256, 42.01256, 41.92533, 41.96151, 41.88462, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.67437, -87.67437, -87.66580, -87.67139, -87.627…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"casual\", \"casual\", \"member\", \"casual\", \"member\", \"…\n",
      "[1] \"tripdata_202202 structure\"\n",
      "Rows: 115,609\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"E1E065E7ED285C02\", \"1602DCDC5B30FFE3\", \"BE7DD2AF4B…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"classic_bike\", \"classic_bike\", \"cl…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-02-19 18:08:41\", \"2022-02-20 17:41:30\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-02-19 18:23:56\", \"2022-02-20 17:45:56\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"State St & Randolph St\", \"Halsted St & Wrightwood …\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1305000029\", \"TA1309000061\", \"TA1305000029\", \"13…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Clark St & Lincoln Ave\", \"Southport Ave & Wrightwo…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"13179\", \"TA1307000113\", \"13011\", \"13323\", \"TA13070…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.88462, 41.92914, 41.88462, 41.94815, 41.88462, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.62783, -87.64908, -87.62783, -87.66394, -87.627…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.91569, 41.92877, 41.87926, 41.95283, 41.88584, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.63460, -87.66391, -87.63990, -87.64999, -87.635…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"member\", \"member\", \"member\", \"…\n",
      "[1] \"tripdata_202203 structure\"\n",
      "Rows: 284,042\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"47EC0A7F82E65D52\", \"8494861979B0F477\", \"EFE527AF80…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"electric_bike\", \"classic_bike\", \"c…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-03-21 13:45:01\", \"2022-03-16 09:37:16\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-03-21 13:51:18\", \"2022-03-16 09:43:34\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Wabash Ave & Wacker Pl\", \"Michigan Ave & Oak St\", …\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1307000131\", \"13042\", \"13109\", \"TA1307000131\", \"…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Kingsbury St & Kinzie St\", \"Orleans St & Chestnut …\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"KA1503000043\", \"620\", \"15578\", \"TA1305000025\", \"13…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.88688, 41.90100, 41.97835, 41.88688, 41.91172, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.62603, -87.62375, -87.65975, -87.62603, -87.626…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.88918, 41.89820, 41.98404, 41.87771, 41.87794, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.63851, -87.63754, -87.66027, -87.63532, -87.662…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"member\", \"member\", \"member\", \"…\n",
      "[1] \"tripdata_202204 structure\"\n",
      "Rows: 371,249\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"3564070EEFD12711\", \"0B820C7FCF22F489\", \"89EEEE3229…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"classic_bike\", \"classic_bike\", \"c…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-04-06 17:42:48\", \"2022-04-24 19:23:07\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-04-06 17:54:36\", \"2022-04-24 19:43:17\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Paulina St & Howard St\", \"Wentworth Ave & Cermak R…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"515\", \"13075\", \"TA1307000121\", \"13075\", \"TA1307000…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"University Library (NU)\", \"Green St & Madison St\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"605\", \"TA1307000120\", \"TA1307000120\", \"KA170600500…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 42.01913, 41.85308, 41.87184, 41.85308, 41.87181, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.67353, -87.63193, -87.64664, -87.63193, -87.646…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 42.05294, 41.88189, 41.88189, 41.86749, 41.88224, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.67345, -87.64879, -87.64879, -87.63219, -87.641…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"member\", \"casual\", \"member\", \"…\n",
      "[1] \"tripdata_202205 structure\"\n",
      "Rows: 634,858\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"EC2DE40644C6B0F4\", \"1C31AD03897EE385\", \"1542FBEC83…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"classic_bike\", \"classic_bike\", \"cl…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-05-23 23:06:58\", \"2022-05-11 08:53:28\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-05-23 23:40:19\", \"2022-05-11 09:31:22\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Wabash Ave & Grand Ave\", \"DuSable Lake Shore Dr & …\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1307000117\", \"13300\", \"TA1305000032\", \"TA1305000…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Halsted St & Roscoe St\", \"Field Blvd & South Water…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1309000025\", \"15534\", \"13221\", \"TA1305000030\", \"…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.89147, 41.88096, 41.88224, 41.88224, 41.88224, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.62676, -87.61674, -87.64107, -87.64107, -87.641…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.94367, 41.88635, 41.90765, 41.88458, 41.88578, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.64895, -87.61752, -87.67255, -87.63189, -87.651…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"member\", \"member\", \"member\", \"…\n",
      "[1] \"tripdata_202206 structure\"\n",
      "Rows: 769,204\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"600CFD130D0FD2A4\", \"F5E6B5C1682C6464\", \"B6EB6D27BA…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-06-30 17:27:53\", \"2022-06-30 18:39:52\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-06-30 17:35:15\", \"2022-06-30 18:47:28\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.89, 41.91, 41.91, 41.80, 41.91, 42.03, 41.91, 41…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.62, -87.62, -87.65, -87.66, -87.63, -87.71, -87…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.91, 41.93, 41.89, 41.80, 41.93, 42.06, 41.92, 41…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.62, -87.63, -87.61, -87.65, -87.64, -87.73, -87…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"casual\", \"casual\", \"casual\", \"casual\", \"casual\", \"…\n",
      "[1] \"tripdata_202207 structure\"\n",
      "Rows: 823,488\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"954144C2F67B1932\", \"292E027607D218B6\", \"5776585258…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"classic_bike\", \"classic_bike\", \"cl…\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-07-05 08:12:47\", \"2022-07-26 12:53:38\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-07-05 08:24:32\", \"2022-07-26 12:55:31\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Ashland Ave & Blackhawk St\", \"Buckingham Fountain …\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"13224\", \"15541\", \"15541\", \"15541\", \"TA1307000117\",…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Kingsbury St & Kinzie St\", \"Michigan Ave & 8th St\"…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"KA1503000043\", \"623\", \"623\", \"TA1307000164\", \"TA13…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.90707, 41.86962, 41.86962, 41.86962, 41.89147, 4…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.66725, -87.62398, -87.62398, -87.62398, -87.626…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.88918, 41.87277, 41.87277, 41.79526, 41.93625, 4…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.63851, -87.62398, -87.62398, -87.59647, -87.652…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"casual\", \"casual\", \"casual\", \"member\", \"…\n",
      "[1] \"tripdata_202208 structure\"\n",
      "Rows: 785,932\n",
      "Columns: 13\n",
      "$ ride_id            \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"550CF7EFEAE0C618\", \"DAD198F405F9C5F5\", \"E6F2BC47B6…\n",
      "$ rideable_type      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n",
      "$ started_at         \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-08-07 21:34:15\", \"2022-08-08 14:39:21\", \"2022…\n",
      "$ ended_at           \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"2022-08-07 21:41:46\", \"2022-08-08 14:53:23\", \"2022…\n",
      "$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_station_id   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_name   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ end_station_id     \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\",…\n",
      "$ start_lat          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.93, 41.89, 41.97, 41.94, 41.85, 41.79, 41.89, 41…\n",
      "$ start_lng          \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.69, -87.64, -87.69, -87.65, -87.65, -87.72, -87…\n",
      "$ end_lat            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.94, 41.92, 41.97, 41.97, 41.84, 41.82, 41.89, 41…\n",
      "$ end_lng            \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.72, -87.64, -87.66, -87.69, -87.66, -87.69, -87…\n",
      "$ member_casual      \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"casual\", \"casual\", \"casual\", \"casual\", \"casual\", \"…\n"
     ]
    }
   ],
   "source": [
    "# Checking the structure of our dataframes\n",
    "print(\"tripdata_202109 structure\")\n",
    "glimpse(tripdata_202109)\n",
    "print(\"tripdata_202110 structure\")\n",
    "glimpse(tripdata_202110)\n",
    "print(\"tripdata_202111 structure\")\n",
    "glimpse(tripdata_202111)\n",
    "print(\"tripdata_202112 structure\")\n",
    "glimpse(tripdata_202112)\n",
    "print(\"tripdata_202201 structure\")\n",
    "glimpse(tripdata_202201)\n",
    "print(\"tripdata_202202 structure\")\n",
    "glimpse(tripdata_202202)\n",
    "print(\"tripdata_202203 structure\")\n",
    "glimpse(tripdata_202203)\n",
    "print(\"tripdata_202204 structure\")\n",
    "glimpse(tripdata_202204)\n",
    "print(\"tripdata_202205 structure\")\n",
    "glimpse(tripdata_202205)\n",
    "print(\"tripdata_202206 structure\")\n",
    "glimpse(tripdata_202206)\n",
    "print(\"tripdata_202207 structure\")\n",
    "glimpse(tripdata_202207)\n",
    "print(\"tripdata_202208 structure\")\n",
    "glimpse(tripdata_202208)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693321a8",
   "metadata": {
    "papermill": {
     "duration": 0.016744,
     "end_time": "2022-09-24T10:43:28.478195",
     "exception": false,
     "start_time": "2022-09-24T10:43:28.461451",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Process\n",
    "\n",
    "In the previous step, I loaded R packages and looked at the data types in our datasets.\n",
    "\n",
    "Now, we're going to sort, filter, and convert the data, to clean it and process it.\n",
    "\n",
    "Here's a list of data cleaning and manipulation actions taken:\n",
    "1. Merged all datasets into one dataframe\n",
    "2. Filtered consolidated dataframe to remove visible null data points\n",
    "3. Checked for more null data points for each column/attribute\n",
    "4. Changed _start_ and _end_ trip time to DateTime type\n",
    "5. Created new columns for the metrics\n",
    "6. Created a new column to calculate trip length\n",
    "7. Filtered out trips under 1 minute, to remove any possible mistakes\n",
    "8. Checked if there were duplicated ride IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07659447",
   "metadata": {
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2022-09-24T10:43:28.514688Z",
     "iopub.status.busy": "2022-09-24T10:43:28.512987Z",
     "iopub.status.idle": "2022-09-24T10:43:38.811281Z",
     "shell.execute_reply": "2022-09-24T10:43:38.808928Z"
    },
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     "duration": 10.320077,
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     "exception": false,
     "start_time": "2022-09-24T10:43:28.494640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points on all_tripdata:  5,883,043"
     ]
    }
   ],
   "source": [
    "# Merging all datasets into one dataframe\n",
    "all_tripdata <- bind_rows(tripdata_202109,\n",
    "                          tripdata_202110,\n",
    "                          tripdata_202111,\n",
    "                          tripdata_202112,\n",
    "                          tripdata_202201,\n",
    "                          tripdata_202202,\n",
    "                          tripdata_202203,\n",
    "                          tripdata_202204,\n",
    "                          tripdata_202205,\n",
    "                          tripdata_202206,\n",
    "                          tripdata_202207,\n",
    "                          tripdata_202208\n",
    "                         )\n",
    "all_tripdata_total <- nrow(all_tripdata)\n",
    "cat(\"Data points on all_tripdata: \", format(all_tripdata_total, big.mark=\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "110dd14a",
   "metadata": {
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2022-09-24T10:43:38.859187Z",
     "iopub.status.busy": "2022-09-24T10:43:38.857340Z",
     "iopub.status.idle": "2022-09-24T10:43:59.041635Z",
     "shell.execute_reply": "2022-09-24T10:43:59.038806Z"
    },
    "papermill": {
     "duration": 20.214436,
     "end_time": "2022-09-24T10:43:59.045064",
     "exception": false,
     "start_time": "2022-09-24T10:43:38.830628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows cleaned:  1,322,897\n",
      "Data points on cleaned_tripdata:  4,560,146"
     ]
    }
   ],
   "source": [
    "# Filtering consolidated dataframe to remove null data points\n",
    "cleaned_tripdata <- filter(all_tripdata,start_station_name!=\"\")\n",
    "cleaned_tripdata <- filter(cleaned_tripdata,end_station_name!=\"\")\n",
    "\n",
    "cleaned_tripdata_total <- nrow(cleaned_tripdata)\n",
    "cat(\"Rows cleaned: \", format(all_tripdata_total - cleaned_tripdata_total, big.mark=\",\"))\n",
    "cat(\"\\nData points on cleaned_tripdata: \", format(cleaned_tripdata_total, big.mark=\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f389055",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:43:59.081605Z",
     "iopub.status.busy": "2022-09-24T10:43:59.080079Z",
     "iopub.status.idle": "2022-09-24T10:43:59.094250Z",
     "shell.execute_reply": "2022-09-24T10:43:59.092234Z"
    },
    "papermill": {
     "duration": 0.036228,
     "end_time": "2022-09-24T10:43:59.097723",
     "exception": false,
     "start_time": "2022-09-24T10:43:59.061495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Checked for more null data points for each column/attribute - used the following code for each column:\n",
    "# sorted_tripdata <- arrange(cleaned_tripdata,ride_id)\n",
    "# glimpse(sorted_tripdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1b3f614",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:43:59.135293Z",
     "iopub.status.busy": "2022-09-24T10:43:59.133576Z",
     "iopub.status.idle": "2022-09-24T10:44:35.977248Z",
     "shell.execute_reply": "2022-09-24T10:44:35.974488Z"
    },
    "papermill": {
     "duration": 36.881719,
     "end_time": "2022-09-24T10:44:35.996669",
     "exception": false,
     "start_time": "2022-09-24T10:43:59.114950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 4,560,146\n",
      "Columns: 2\n",
      "$ started_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-09-05 01:25:08, 2021-09-05 13:33:41, 2021-09-04 23:00…\n",
      "$ ended_at   \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-09-05 01:35:46, 2021-09-05 14:06:08, 2021-09-04 23:10…\n"
     ]
    }
   ],
   "source": [
    "# Changing start and end trip time to DateTime type\n",
    "cleaned_tripdata <- mutate(cleaned_tripdata, started_at = as.POSIXct(started_at))\n",
    "cleaned_tripdata <- mutate(cleaned_tripdata, ended_at = as.POSIXct(ended_at))\n",
    "glimpse(select(cleaned_tripdata,started_at,ended_at))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd4a6ff5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:44:36.033430Z",
     "iopub.status.busy": "2022-09-24T10:44:36.031870Z",
     "iopub.status.idle": "2022-09-24T10:45:21.812280Z",
     "shell.execute_reply": "2022-09-24T10:45:21.810353Z"
    },
    "papermill": {
     "duration": 45.819118,
     "end_time": "2022-09-24T10:45:21.832136",
     "exception": false,
     "start_time": "2022-09-24T10:44:36.013018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 4,560,146\n",
      "Columns: 5\n",
      "$ trip_year       \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, …\n",
      "$ trip_month      \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, …\n",
      "$ trip_year_month \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 202109, 202109, 202109, 202109, 202109, 202109, 202109…\n",
      "$ trip_hour_day   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1, 13, 23, 16, 22, 10, 13, 7, 7, 1, 18, 8, 15, 21, 17,…\n",
      "$ trip_day_week   \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Sunday\", \"Sunday\", \"Saturday\", \"Tuesday\", \"Monday\", \"…\n"
     ]
    }
   ],
   "source": [
    "# Creating new columns for the metrics: year, month, year_month, hour_of_day, and day_of_the_week\n",
    "cleaned_tripdata$trip_year <- as.numeric(format(as.Date(cleaned_tripdata$started_at), \"%Y\"))\n",
    "cleaned_tripdata$trip_month <- as.numeric(format(as.Date(cleaned_tripdata$started_at), \"%m\"))\n",
    "cleaned_tripdata$trip_year_month <- as.numeric(format(as.Date(cleaned_tripdata$started_at), \"%Y%m\"))\n",
    "cleaned_tripdata$trip_hour_day <- as.numeric(hour(ymd_hms(as.character(cleaned_tripdata$started_at))))\n",
    "cleaned_tripdata$trip_day_week <- format(as.Date(cleaned_tripdata$started_at), \"%A\")\n",
    "glimpse(select(cleaned_tripdata,trip_year,trip_month,trip_year_month,trip_hour_day,trip_day_week))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64f230a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:45:21.869608Z",
     "iopub.status.busy": "2022-09-24T10:45:21.867956Z",
     "iopub.status.idle": "2022-09-24T10:45:21.944216Z",
     "shell.execute_reply": "2022-09-24T10:45:21.942019Z"
    },
    "papermill": {
     "duration": 0.098553,
     "end_time": "2022-09-24T10:45:21.947524",
     "exception": false,
     "start_time": "2022-09-24T10:45:21.848971",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 4,560,146\n",
      "Columns: 1\n",
      "$ trip_length \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 10.63333333, 32.45000000, 9.65000000, 9.78333333, 0.066666…\n"
     ]
    }
   ],
   "source": [
    "# Creating a new column to calculate trip length \n",
    "cleaned_tripdata$trip_length <- as.numeric(difftime(cleaned_tripdata$ended_at, cleaned_tripdata$started_at, units = \"mins\")) \n",
    "glimpse(select(cleaned_tripdata,trip_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5d5ec01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:45:21.985647Z",
     "iopub.status.busy": "2022-09-24T10:45:21.983970Z",
     "iopub.status.idle": "2022-09-24T10:45:22.543002Z",
     "shell.execute_reply": "2022-09-24T10:45:22.540835Z"
    },
    "papermill": {
     "duration": 0.582016,
     "end_time": "2022-09-24T10:45:22.546173",
     "exception": false,
     "start_time": "2022-09-24T10:45:21.964157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows cleaned:  71,813\n",
      "Data points on cleaned_tripdata:  4,488,333"
     ]
    }
   ],
   "source": [
    "# Filtering for trips above 1 minute, to remove any possible mistakes, like negative length trips\n",
    "cleaned_tripdata = filter(cleaned_tripdata,trip_length>=1)\n",
    "\n",
    "cat(\"Rows cleaned: \", format(cleaned_tripdata_total - nrow(cleaned_tripdata), big.mark=\",\"))\n",
    "cleaned_tripdata_total <- nrow(cleaned_tripdata)\n",
    "cat(\"\\nData points on cleaned_tripdata: \", format(cleaned_tripdata_total, big.mark=\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "76862464",
   "metadata": {
    "_kg_hide-output": false,
    "execution": {
     "iopub.execute_input": "2022-09-24T10:45:22.584751Z",
     "iopub.status.busy": "2022-09-24T10:45:22.583156Z",
     "iopub.status.idle": "2022-09-24T10:49:10.214516Z",
     "shell.execute_reply": "2022-09-24T10:49:10.211735Z"
    },
    "papermill": {
     "duration": 227.671044,
     "end_time": "2022-09-24T10:49:10.235182",
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     "start_time": "2022-09-24T10:45:22.564138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Resulted on an empty dataframe, so no duplicates. Yay!\"\n"
     ]
    }
   ],
   "source": [
    "# Checking if there are duplicated ride IDs\n",
    "ride_ids <- cleaned_tripdata %>%\n",
    "  count(ride_id) %>%\n",
    "  filter(n > 1)\n",
    "\n",
    "if (nrow(ride_ids) == 0) {\n",
    "        print(\"Resulted on an empty dataframe, so no duplicates. Yay!\")\n",
    "    } else {\n",
    "        print(\"It seems we found some duplicates!\")\n",
    "    } "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c70aea",
   "metadata": {
    "papermill": {
     "duration": 0.016702,
     "end_time": "2022-09-24T10:49:10.269081",
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     "start_time": "2022-09-24T10:49:10.252379",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Analyze\n",
    "\n",
    "Our data is clean now! Time to move to the analysis step.\n",
    "\n",
    "Let's start organizing the data, sort and filter our cleaned dataset, convert, format, and combine data, and then proceed with some calculations. We'll now look for patterns!\n",
    "\n",
    "Identifying patterns:\n",
    "* Average ride length\n",
    "* Busiest day of the week\n",
    "* Members vs casual user behavior\n",
    "* Rides during different seasons\n",
    "* Busiest ride stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6323c3eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:49:10.306244Z",
     "iopub.status.busy": "2022-09-24T10:49:10.304514Z",
     "iopub.status.idle": "2022-09-24T10:49:14.448004Z",
     "shell.execute_reply": "2022-09-24T10:49:14.445620Z"
    },
    "papermill": {
     "duration": 4.165016,
     "end_time": "2022-09-24T10:49:14.450910",
     "exception": false,
     "start_time": "2022-09-24T10:49:10.285894",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member Riders:\n",
      "Trips: 2,635,450 trips.\n",
      "Avg Trip Length: 12.76 minutes.\n",
      "\n",
      "Casual Riders:\n",
      "Trips: 1,852,883 trips.\n",
      "Avg Trip Length: 26.13 minutes."
     ]
    }
   ],
   "source": [
    "# Checking the average trip length and number of trips for members and casual riders\n",
    "###\n",
    "# Members Stats\n",
    "###\n",
    "member_stats <- cleaned_tripdata %>%\n",
    "    filter(member_casual==\"member\") %>%\n",
    "    summarize(avg_trip_length = mean(trip_length))\n",
    "\n",
    "member_trip_length <- member_stats[1,1]\n",
    "\n",
    "member_stats <- cleaned_tripdata %>%\n",
    "    filter(member_casual==\"member\") %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n)\n",
    "\n",
    "member_trips <- member_stats[1,2]\n",
    "\n",
    "cat(\"Member Riders:\")\n",
    "cat(\"\\nTrips:\", format(member_trips, big.mark=\",\"), \"trips.\")\n",
    "cat(\"\\nAvg Trip Length:\", format(round(member_trip_length, 2), nsmall = 2), \"minutes.\")\n",
    "\n",
    "###\n",
    "# Casual Stats\n",
    "###\n",
    "casual_stats <- cleaned_tripdata %>%\n",
    "    filter(member_casual==\"casual\") %>%\n",
    "    summarize(avg_trip_length = mean(trip_length))\n",
    "\n",
    "casual_trip_length <- casual_stats[1,1]\n",
    "\n",
    "casual_stats <- cleaned_tripdata %>%\n",
    "    filter(member_casual==\"casual\") %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n)\n",
    "\n",
    "casual_trips <- casual_stats[1,2]\n",
    "\n",
    "cat(\"\\n\\nCasual Riders:\")\n",
    "cat(\"\\nTrips:\", format(casual_trips, big.mark=\",\"), \"trips.\")\n",
    "cat(\"\\nAvg Trip Length:\", format(round(casual_trip_length, 2), nsmall = 2), \"minutes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2e0c35d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:49:14.489449Z",
     "iopub.status.busy": "2022-09-24T10:49:14.487921Z",
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     "shell.execute_reply": "2022-09-24T10:49:15.169377Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 7 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_day_week</th><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Wednesday</td><td>member</td><td>427381</td></tr>\n",
       "\t<tr><td>Tuesday  </td><td>member</td><td>419690</td></tr>\n",
       "\t<tr><td>Thursday </td><td>member</td><td>406041</td></tr>\n",
       "\t<tr><td>Monday   </td><td>member</td><td>370279</td></tr>\n",
       "\t<tr><td>Friday   </td><td>member</td><td>359259</td></tr>\n",
       "\t<tr><td>Saturday </td><td>member</td><td>344897</td></tr>\n",
       "\t<tr><td>Sunday   </td><td>member</td><td>307903</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 7 × 3\n",
       "\\begin{tabular}{lll}\n",
       " trip\\_day\\_week & member\\_casual & count\\_trips\\\\\n",
       " <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Wednesday & member & 427381\\\\\n",
       "\t Tuesday   & member & 419690\\\\\n",
       "\t Thursday  & member & 406041\\\\\n",
       "\t Monday    & member & 370279\\\\\n",
       "\t Friday    & member & 359259\\\\\n",
       "\t Saturday  & member & 344897\\\\\n",
       "\t Sunday    & member & 307903\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 7 × 3\n",
       "\n",
       "| trip_day_week &lt;chr&gt; | member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| Wednesday | member | 427381 |\n",
       "| Tuesday   | member | 419690 |\n",
       "| Thursday  | member | 406041 |\n",
       "| Monday    | member | 370279 |\n",
       "| Friday    | member | 359259 |\n",
       "| Saturday  | member | 344897 |\n",
       "| Sunday    | member | 307903 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_day_week member_casual count_trips\n",
       "1 Wednesday     member        427381     \n",
       "2 Tuesday       member        419690     \n",
       "3 Thursday      member        406041     \n",
       "4 Monday        member        370279     \n",
       "5 Friday        member        359259     \n",
       "6 Saturday      member        344897     \n",
       "7 Sunday        member        307903     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casual Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 7 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_day_week</th><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Saturday </td><td>casual</td><td>396081</td></tr>\n",
       "\t<tr><td>Sunday   </td><td>casual</td><td>339345</td></tr>\n",
       "\t<tr><td>Friday   </td><td>casual</td><td>254639</td></tr>\n",
       "\t<tr><td>Thursday </td><td>casual</td><td>227962</td></tr>\n",
       "\t<tr><td>Monday   </td><td>casual</td><td>218043</td></tr>\n",
       "\t<tr><td>Wednesday</td><td>casual</td><td>214025</td></tr>\n",
       "\t<tr><td>Tuesday  </td><td>casual</td><td>202788</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 7 × 3\n",
       "\\begin{tabular}{lll}\n",
       " trip\\_day\\_week & member\\_casual & count\\_trips\\\\\n",
       " <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Saturday  & casual & 396081\\\\\n",
       "\t Sunday    & casual & 339345\\\\\n",
       "\t Friday    & casual & 254639\\\\\n",
       "\t Thursday  & casual & 227962\\\\\n",
       "\t Monday    & casual & 218043\\\\\n",
       "\t Wednesday & casual & 214025\\\\\n",
       "\t Tuesday   & casual & 202788\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 7 × 3\n",
       "\n",
       "| trip_day_week &lt;chr&gt; | member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| Saturday  | casual | 396081 |\n",
       "| Sunday    | casual | 339345 |\n",
       "| Friday    | casual | 254639 |\n",
       "| Thursday  | casual | 227962 |\n",
       "| Monday    | casual | 218043 |\n",
       "| Wednesday | casual | 214025 |\n",
       "| Tuesday   | casual | 202788 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_day_week member_casual count_trips\n",
       "1 Saturday      casual        396081     \n",
       "2 Sunday        casual        339345     \n",
       "3 Friday        casual        254639     \n",
       "4 Thursday      casual        227962     \n",
       "5 Monday        casual        218043     \n",
       "6 Wednesday     casual        214025     \n",
       "7 Tuesday       casual        202788     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking number of trips per day of the week, by casual vs member riders\n",
    "# Creating a subset with the needed data\n",
    "week_day_trips <- cleaned_tripdata %>%\n",
    "    select(member_casual, trip_day_week)\n",
    "\n",
    "# Checking stats for member riders\n",
    "cat(\"Member Riders:\")\n",
    "member_stats <- week_day_trips %>%\n",
    "    filter(member_casual==\"member\") %>%\n",
    "    group_by(trip_day_week) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(-count_trips)\n",
    "View(member_stats)\n",
    "\n",
    "# Checking stats for casual riders\n",
    "cat(\"Casual Riders:\")\n",
    "casual_stats <- week_day_trips %>%\n",
    "    filter(member_casual==\"casual\") %>%\n",
    "    group_by(trip_day_week) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(-count_trips)\n",
    "View(casual_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "693be3ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:49:15.213076Z",
     "iopub.status.busy": "2022-09-24T10:49:15.211598Z",
     "iopub.status.idle": "2022-09-24T10:49:15.814373Z",
     "shell.execute_reply": "2022-09-24T10:49:15.812301Z"
    },
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     "start_time": "2022-09-24T10:49:15.191756",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_day_week</th><th scope=col>avg_trip_length</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Sunday   </td><td>14.39859</td></tr>\n",
       "\t<tr><td>Saturday </td><td>14.34602</td></tr>\n",
       "\t<tr><td>Friday   </td><td>12.48092</td></tr>\n",
       "\t<tr><td>Monday   </td><td>12.35906</td></tr>\n",
       "\t<tr><td>Thursday </td><td>12.24109</td></tr>\n",
       "\t<tr><td>Wednesday</td><td>12.11787</td></tr>\n",
       "\t<tr><td>Tuesday  </td><td>12.00815</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{ll}\n",
       " trip\\_day\\_week & avg\\_trip\\_length\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Sunday    & 14.39859\\\\\n",
       "\t Saturday  & 14.34602\\\\\n",
       "\t Friday    & 12.48092\\\\\n",
       "\t Monday    & 12.35906\\\\\n",
       "\t Thursday  & 12.24109\\\\\n",
       "\t Wednesday & 12.11787\\\\\n",
       "\t Tuesday   & 12.00815\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| trip_day_week &lt;chr&gt; | avg_trip_length &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| Sunday    | 14.39859 |\n",
       "| Saturday  | 14.34602 |\n",
       "| Friday    | 12.48092 |\n",
       "| Monday    | 12.35906 |\n",
       "| Thursday  | 12.24109 |\n",
       "| Wednesday | 12.11787 |\n",
       "| Tuesday   | 12.00815 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_day_week avg_trip_length\n",
       "1 Sunday        14.39859       \n",
       "2 Saturday      14.34602       \n",
       "3 Friday        12.48092       \n",
       "4 Monday        12.35906       \n",
       "5 Thursday      12.24109       \n",
       "6 Wednesday     12.11787       \n",
       "7 Tuesday       12.00815       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casual Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_day_week</th><th scope=col>avg_trip_length</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Sunday   </td><td>30.12425</td></tr>\n",
       "\t<tr><td>Saturday </td><td>28.77001</td></tr>\n",
       "\t<tr><td>Monday   </td><td>27.14264</td></tr>\n",
       "\t<tr><td>Friday   </td><td>24.33936</td></tr>\n",
       "\t<tr><td>Thursday </td><td>23.06992</td></tr>\n",
       "\t<tr><td>Tuesday  </td><td>22.87172</td></tr>\n",
       "\t<tr><td>Wednesday</td><td>22.37314</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{ll}\n",
       " trip\\_day\\_week & avg\\_trip\\_length\\\\\n",
       " <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Sunday    & 30.12425\\\\\n",
       "\t Saturday  & 28.77001\\\\\n",
       "\t Monday    & 27.14264\\\\\n",
       "\t Friday    & 24.33936\\\\\n",
       "\t Thursday  & 23.06992\\\\\n",
       "\t Tuesday   & 22.87172\\\\\n",
       "\t Wednesday & 22.37314\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| trip_day_week &lt;chr&gt; | avg_trip_length &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| Sunday    | 30.12425 |\n",
       "| Saturday  | 28.77001 |\n",
       "| Monday    | 27.14264 |\n",
       "| Friday    | 24.33936 |\n",
       "| Thursday  | 23.06992 |\n",
       "| Tuesday   | 22.87172 |\n",
       "| Wednesday | 22.37314 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_day_week avg_trip_length\n",
       "1 Sunday        30.12425       \n",
       "2 Saturday      28.77001       \n",
       "3 Monday        27.14264       \n",
       "4 Friday        24.33936       \n",
       "5 Thursday      23.06992       \n",
       "6 Tuesday       22.87172       \n",
       "7 Wednesday     22.37314       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking length of trips per day of the week, by casual vs member riders\n",
    "# Creating a subset with the needed data\n",
    "week_day_trips <- cleaned_tripdata %>%\n",
    "    select(member_casual, trip_day_week, trip_length)\n",
    "\n",
    "# Checking stats for member riders\n",
    "cat(\"Member Riders:\")\n",
    "member_stats <- week_day_trips %>%\n",
    "    filter(member_casual==\"member\") %>%\n",
    "    group_by(trip_day_week) %>%\n",
    "    summarize(avg_trip_length = mean(trip_length)) %>%\n",
    "    arrange(-avg_trip_length)\n",
    "View(member_stats)\n",
    "\n",
    "# Checking stats for casual riders\n",
    "cat(\"Casual Riders:\")\n",
    "casual_stats <- week_day_trips %>%\n",
    "    filter(member_casual==\"casual\") %>%\n",
    "    group_by(trip_day_week) %>%\n",
    "    summarize(avg_trip_length = mean(trip_length)) %>%\n",
    "    arrange(-avg_trip_length)\n",
    "View(casual_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2db6243f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:49:15.860769Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>start_station_name</th><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Streeter Dr &amp; Grand Ave          </span></td><td>casual</td><td>26266</td></tr>\n",
       "\t<tr><td>DuSable Lake Shore Dr &amp; Monroe St</td><td>casual</td><td>15160</td></tr>\n",
       "\t<tr><td>Millennium Park                  </td><td>casual</td><td>11889</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " start\\_station\\_name & member\\_casual & count\\_trips\\\\\n",
       " <chr> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Streeter Dr \\& Grand Ave           & casual & 26266\\\\\n",
       "\t DuSable Lake Shore Dr \\& Monroe St & casual & 15160\\\\\n",
       "\t Millennium Park                   & casual & 11889\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 3 × 3\n",
       "\n",
       "| start_station_name &lt;chr&gt; | member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| Streeter Dr &amp; Grand Ave           | casual | 26266 |\n",
       "| DuSable Lake Shore Dr &amp; Monroe St | casual | 15160 |\n",
       "| Millennium Park                   | casual | 11889 |\n",
       "\n"
      ],
      "text/plain": [
       "  start_station_name                member_casual count_trips\n",
       "1 Streeter Dr & Grand Ave           casual        26266      \n",
       "2 DuSable Lake Shore Dr & Monroe St casual        15160      \n",
       "3 Millennium Park                   casual        11889      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking busiest 3 station over the weekend for casual riders\n",
    "casual_busiest_station <- cleaned_tripdata %>%\n",
    "    select(member_casual, trip_day_week, start_station_name) %>%\n",
    "    filter((trip_day_week == \"Saturday\" |\n",
    "            trip_day_week == \"Sunday\") &\n",
    "            member_casual == \"casual\") %>%\n",
    "    group_by(start_station_name) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(-count_trips)\n",
    "View(casual_busiest_station[1:3,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab2ff4e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-24T10:49:17.860758Z",
     "iopub.status.busy": "2022-09-24T10:49:17.859046Z",
     "iopub.status.idle": "2022-09-24T10:49:20.859209Z",
     "shell.execute_reply": "2022-09-24T10:49:20.857403Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trips during Spring: 1,219,139"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>casual</td><td>512119</td></tr>\n",
       "\t<tr><td>member</td><td>707020</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " member\\_casual & count\\_trips\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t casual & 512119\\\\\n",
       "\t member & 707020\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 2 × 2\n",
       "\n",
       "| member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|\n",
       "| casual | 512119 |\n",
       "| member | 707020 |\n",
       "\n"
      ],
      "text/plain": [
       "  member_casual count_trips\n",
       "1 casual        512119     \n",
       "2 member        707020     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trips during Fall: 1,020,886"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>casual</td><td>359653</td></tr>\n",
       "\t<tr><td>member</td><td>661233</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " member\\_casual & count\\_trips\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t casual & 359653\\\\\n",
       "\t member & 661233\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 2 × 2\n",
       "\n",
       "| member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|\n",
       "| casual | 359653 |\n",
       "| member | 661233 |\n",
       "\n"
      ],
      "text/plain": [
       "  member_casual count_trips\n",
       "1 casual        359653     \n",
       "2 member        661233     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trips during Summer: 1,846,480"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>casual</td><td>876109</td></tr>\n",
       "\t<tr><td>member</td><td>970371</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " member\\_casual & count\\_trips\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t casual & 876109\\\\\n",
       "\t member & 970371\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 2 × 2\n",
       "\n",
       "| member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|\n",
       "| casual | 876109 |\n",
       "| member | 970371 |\n",
       "\n"
      ],
      "text/plain": [
       "  member_casual count_trips\n",
       "1 casual        876109     \n",
       "2 member        970371     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trips during Winter: 336,816"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>casual</td><td> 79465</td></tr>\n",
       "\t<tr><td>member</td><td>257351</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " member\\_casual & count\\_trips\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t casual &  79465\\\\\n",
       "\t member & 257351\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 2 × 2\n",
       "\n",
       "| member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|\n",
       "| casual |  79465 |\n",
       "| member | 257351 |\n",
       "\n"
      ],
      "text/plain": [
       "  member_casual count_trips\n",
       "1 casual         79465     \n",
       "2 member        257351     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploring seasonal rides for member vs casual\n",
    "# Calculating trips during Spring\n",
    "spring_trips <- cleaned_tripdata %>%\n",
    "    filter(started_at>=ymd(\"2022-03-20\") & started_at<=ymd(\"2022-06-20\"))\n",
    "\n",
    "total_trips <- nrow(spring_trips)\n",
    "\n",
    "spring_trips <- spring_trips %>%\n",
    "    select(member_casual) %>%\n",
    "    group_by(member_casual) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(member_casual)\n",
    "\n",
    "cat(\"Trips during Spring:\", format(total_trips, big.mark=\",\"))\n",
    "View(spring_trips)\n",
    "\n",
    "# Calculating trips during Fall\n",
    "fall_trips <- cleaned_tripdata %>%\n",
    "    filter(started_at>=ymd(\"2021-09-22\") & started_at<=ymd(\"2021-12-20\"))\n",
    "\n",
    "total_trips <- nrow(fall_trips)\n",
    "\n",
    "fall_trips <- fall_trips %>%\n",
    "    select(member_casual) %>%\n",
    "    group_by(member_casual) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(member_casual)\n",
    "\n",
    "cat(\"Trips during Fall:\", format(total_trips, big.mark=\",\"))\n",
    "View(fall_trips)\n",
    "\n",
    "# Calculating trips during Summer\n",
    "summer_trips <- cleaned_tripdata %>%\n",
    "    filter((started_at>=ymd(\"2021-09-01\") & started_at<=ymd(\"2021-09-21\")) |\n",
    "          (started_at>=ymd(\"2022-06-21\") & started_at<=ymd(\"2022-08-31\")))\n",
    "\n",
    "total_trips <- nrow(summer_trips)\n",
    "\n",
    "summer_trips <- summer_trips %>%\n",
    "    select(member_casual) %>%\n",
    "    group_by(member_casual) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(member_casual)\n",
    "\n",
    "cat(\"Trips during Summer:\", format(total_trips, big.mark=\",\"))\n",
    "View(summer_trips)\n",
    "\n",
    "\n",
    "# Calculating trips during Winter\n",
    "winter_trips <- cleaned_tripdata %>%\n",
    "    filter(started_at>=ymd(\"2021-12-21\") & started_at<=ymd(\"2022-03-19\"))\n",
    "\n",
    "total_trips <- nrow(winter_trips)\n",
    "\n",
    "winter_trips <- winter_trips %>%\n",
    "    select(member_casual) %>%\n",
    "    group_by(member_casual) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(member_casual)\n",
    "\n",
    "cat(\"Trips during Winter:\", format(total_trips, big.mark=\",\"))\n",
    "View(winter_trips)"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_hour_day</th><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>17</td><td>member</td><td>284202</td></tr>\n",
       "\t<tr><td>16</td><td>member</td><td>231263</td></tr>\n",
       "\t<tr><td>18</td><td>member</td><td>228679</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " trip\\_hour\\_day & member\\_casual & count\\_trips\\\\\n",
       " <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t 17 & member & 284202\\\\\n",
       "\t 16 & member & 231263\\\\\n",
       "\t 18 & member & 228679\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 3 × 3\n",
       "\n",
       "| trip_hour_day &lt;dbl&gt; | member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| 17 | member | 284202 |\n",
       "| 16 | member | 231263 |\n",
       "| 18 | member | 228679 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_hour_day member_casual count_trips\n",
       "1 17            member        284202     \n",
       "2 16            member        231263     \n",
       "3 18            member        228679     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casual Riders:"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>trip_hour_day</th><th scope=col>member_casual</th><th scope=col>count_trips</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>17</td><td>casual</td><td>178050</td></tr>\n",
       "\t<tr><td>18</td><td>casual</td><td>159200</td></tr>\n",
       "\t<tr><td>16</td><td>casual</td><td>156443</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " trip\\_hour\\_day & member\\_casual & count\\_trips\\\\\n",
       " <dbl> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t 17 & casual & 178050\\\\\n",
       "\t 18 & casual & 159200\\\\\n",
       "\t 16 & casual & 156443\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 3 × 3\n",
       "\n",
       "| trip_hour_day &lt;dbl&gt; | member_casual &lt;chr&gt; | count_trips &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| 17 | casual | 178050 |\n",
       "| 18 | casual | 159200 |\n",
       "| 16 | casual | 156443 |\n",
       "\n"
      ],
      "text/plain": [
       "  trip_hour_day member_casual count_trips\n",
       "1 17            casual        178050     \n",
       "2 18            casual        159200     \n",
       "3 16            casual        156443     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking number of trips per hour of the day, by casual vs member riders\n",
    "# Creating a subset with the needed data\n",
    "hourly_trips <- cleaned_tripdata %>%\n",
    "    select(member_casual, trip_hour_day)\n",
    "\n",
    "# Checking stats for member riders\n",
    "cat(\"Member Riders:\")\n",
    "member_stats <- hourly_trips %>%\n",
    "    filter(member_casual==\"member\") %>%\n",
    "    group_by(trip_hour_day) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(-count_trips)\n",
    "View(member_stats[1:3,])\n",
    "\n",
    "# Checking stats for casual riders\n",
    "cat(\"Casual Riders:\")\n",
    "casual_stats <- hourly_trips %>%\n",
    "    filter(member_casual==\"casual\") %>%\n",
    "    group_by(trip_hour_day) %>%\n",
    "    count(member_casual) %>%\n",
    "    rename(count_trips = n) %>%\n",
    "    arrange(-count_trips)\n",
    "View(casual_stats[1:3,])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6aa8d2",
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   "source": [
    "#### Analysis Summary\n",
    "\n",
    "It's now time to summarize all the findings from the calculations done above. You can find all details below:\n",
    "\n",
    "* Member riders did a total of **2,635,450** trips, with an average length of **12.76** minutes.\n",
    "* Casual riders did a total of **1,852,883** trips, with an average length of **26.13** minutes.\n",
    "* Although members did **42.23%** more trips, the trips from casual riders are more than twice longer, an average of **204.78%**.\n",
    "* Busiest days for members are **Wednesday and Tuesday**, while for casual riders are the weekend days, **Saturday and Sunday**.\n",
    "* Longer trips happen on the weekend days, **Sunday and Saturday**, for both members and casual riders.\n",
    "* Preferred hours of the day to ride are **16, 17 and 18**, for both members and casual riders.\n",
    "* Preferred stations for members are **Kingsbury St & Kinzie St**, Clark St & Elm St, and Wells St & Concord Ln.\n",
    "* Preferred stations for casual riders are **Streeter Dr & Grand Ave**, DuSable Lake Shore Dr & Monroe St, and Millennium Park.\n",
    "* Preferred seasons for all riders are **Summer and Spring**, followed by Fall and Winter, the latter having only **18.24%** of the trips from the highest one."
   ]
  },
  {
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   "source": [
    "# Creating the summary file to create the vizzes on Tableau\n",
    "# fwrite(\n",
    "#  cleaned_tripdata, \n",
    "#  \"./summary_data.csv\", \n",
    "#  col.names = TRUE,\n",
    "#  row.names = FALSE\n",
    "#  )"
   ]
  },
  {
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"
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"
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"
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"
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    "### Share\n",
    "\n",
    "We have our analysis now and the summary .csv file, I moved to create the visualizations using Tableau, and now it's the time to use them to present my findings.\n",
    "\n",
    "#### Number of trips\n",
    "\n",
    "The graph below shows the difference between number of trips for member and casual riders. It's possible to spot that members did more trips than casual riders, a total of **42.23%** more.\n",
    "\n",
    "![viz_number_trips.png](attachment:a97a30b2-5cdb-4c45-ae40-e646bd2ba36a.png)\n",
    "\n",
    "#### Average trip length\n",
    "\n",
    "On this other graph you can see the difference in trip length by member and casual riders. So although members did **42.23%** more trips, the trips from casual riders are more than twice longer, an average of **204.78%**.\n",
    "\n",
    "![viz_trips_length.png](attachment:79e15f7d-ce93-4c7f-97cb-c2fe88e60d87.png)\n",
    "\n",
    "#### Trips by day of the week\n",
    "\n",
    "On the graphs below you can compare the number of trips and average length by each day of the week. You will be able to spot that busiest days for members are Wednesday and Tuesday, while for casual riders are the weekend days, Saturday and Sunday. And longer trips happen on the weekend days, Sunday and Saturday, for both members and casual riders.\n",
    "\n",
    "![viz_day_of_the_week_trips.png](attachment:b45dc43a-2ecd-411b-97d5-2cd5cec04f03.png)\n",
    "\n",
    "And here you can find the trips by hour, each day of the week, where we can spot that the preferred hours of the day to ride are **16, 17 and 18**, for both member and casual riders.\n",
    "\n",
    "![viz_trips_by_hour.png](attachment:46b3d933-eee3-4f45-bf9f-bc965e02038a.png)\n",
    "\n",
    "#### Busiest stations\n",
    "\n",
    "We previouslly discovered that the preferred days for casual riders are Saturday and Sunday, on this graph you can see their preferred stations.\n",
    "\n",
    "![viz_busiest_stations.png](attachment:5f8a5ce1-0384-4159-a9f1-c605d7621b7e.png)\n",
    "\n",
    "#### Trips by seasons\n",
    "\n",
    "On this graph, you can now see the number of trips each season of the year. It's easy to see that the busiest seasons are **Summer and Spring**, followed by Fall and Winter, the latter having only **18.24%** of the trips from the highest one. Both members and casual riders have similar behavior.\n",
    "\n",
    "![viz_seasonal_trips.png](attachment:84fe09e5-098d-43d0-ab70-58a76d14305f.png)"
   ]
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    },
    "tags": []
   },
   "source": [
    "### Act\n",
    "\n",
    "Based on the analysis shared above, my recommendations are the following:\n",
    "\n",
    "#### In app campaign during the weekends\n",
    "\n",
    "Casual riders prefer to ride on the weekends, the days they will access the app the most, the recommendation is to show a nudge in the app to get the annual membership. Preferably show the nudge on the busiest hours of the day, 16, 17, and 18 hours.\n",
    "\n",
    "#### Local campaign on busiest stations\n",
    "\n",
    "Run a local campaign with flyers/booths on the 3 busiest stations, over the weekend days. Casual riders prefer these 3 stations over the weekend: Streeter Dr & Grand Ave, DuSable Lake Shore Dr & Monroe St, and Millennium Park. The recommendation is to be on those stations with marketing material to engage with casual riders, show them all the benefits from the annual membership and motivate them to sign up.\n",
    "\n",
    "#### Seasonal campaign\n",
    "\n",
    "Create a campaign that focus on getting ready for summer, which is the season that casual riders like the most. Focus on health benefits of biking and the discounts they can get with an annual membership.\n"
   ]
  }
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