Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing 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.

Ask

How do annual members and casual riders use Cyclistic bikes differently?

Guiding Question:

What is the problem you are trying to solve?
  How do annual members and casual riders use Cyclistic bikes differently?
How can your insights drive business decisions?
  The insight will help the marketing team to make a strategy for casual riders

Prepare

Guiding Question:

Where is your data located?
  Data located in Cyclistic organization data.

How is data organized?
  Dataset are in csv format for each month wise from Financial year 22.

Are there issues with bias or credibility in this data? Does your data ROCCC? 
  It is good it is ROCCC because data collected in from Cyclistic organization.

How are you addressing licensing, privacy, security, and accessibility?
  The company has their own license over the dataset. Dataset does not have any personal information about the riders.

How did you verify the data’s integrity?
  All the files have consistent columns and each column has the correct type of data.

How does it help you answer your questions?
  Insights always hidden in the data. We have the interpret with data to find the insights.

Are there any problems with the data?
  Yes, starting station names, ending station names have null values.

Process

Guiding Question:

What tools are you choosing and why?
  I used R studio for the cleaning and transforming the data for analysis phase because of large dataset and to gather experience in the language.

Have you ensured the data’s integrity?
 Yes, the data is consistent throughout the columns.

What steps have you taken to ensure that your data is clean?
  First duplicates, null values are removed then added new columns for analysis.

How can you verify that your data is clean and ready to analyze? 
 Make sure the column names are consistent thorough out all data sets by using the “bind row” function.

Make sure column data types are consistent throughout all the dataset by using the “compare_df_col” from the “janitor” package.
Combine the all dataset into single data frame to make consistent throught the analysis.
Removed the column start_lat, start_lng, end_lat, end_lng from the dataframe because those columns not required for analysis.
Create new columns day, date, month, year, from the started_at column this will provide additional opportunities to aggregate the data
Create the “ride_length” column from the started_at and ended_at column to find the average duration of the ride by the riders.
Removed the null rows from the dataset by using the “na.omit function”
Have you documented your cleaning process so you can review and share those results? 
  Yes, the cleaning process is documented clearly.

Analyze Phase:

Guiding Questions:

How should you organize your data to perform analysis on it? The data has been organized in one single dataframe by using the read csv function in R Has your data been properly formatted? Yes, all the columns have their correct data type.

What surprises did you discover in the data?
  Casual member ride duration is higher than the annual members
  Causal member widely uses docked bike than the annual members
What trends or relationships did you find in the data?
  Annual members are used mainly for commute purpose
  Casual member are preferred the docked bikes
  Annual members are preferred the electric or classic bikes
How will these insights help answer your business questions?
  This insights helps to build a profile for members

Share

Guiding Quesions:

Were you able to answer the question of how ...

• Main objective : To forecast the page visits of a website
• Tool : Time Series Forecasting using Prophet in R.
• Steps:
• Read the data
• Data Cleaning: Checking data types, date formats and missing data https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F56d7b1edf4f51157804e81b02c032e4d%2FPicture1.png?generation=1690271521103777&alt=media" alt="">
• Run libraries (dplyr, ggplot2, tidyverse, lubridate, prophet, forecast)
• Change the Date column from character vector to date and change data format using lubridate package
• Rename the column "Date" to "ds" and "Visits" to "y".
• Treat "Christmas" and "Black.Friday" as holiday events. As the data ranges from 2016 to 2020, there will be 5 Christmas and 5 Black Friday days.
• We will look at the impact of Christmas 3 days prior and 3 days later from Christmas date on "Visits" and 3 days prior and 1 day later for Black Friday
• We create two data frames called Christmas and Black.Friday and merge the two into a data frame called "holidays". https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fd07b366be2050fefe6a62563b6abac0c%2FPicture2.png?generation=1690272066356516&alt=media" alt="">
• We create train and test data. In train data & test data, we select only 3 variables namely ds, y , Easter. In train data, ds contains data before 2020-12-01 and test data contains data equal to and after 2020-12-01 (31 days) data
• Train Data
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F8f3f58fe40b29b276bb7103cb1dfdde1%2FPicture3.png?generation=1690272272038405&alt=media" alt="">
• Test Data
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fb4362117f46aeb210dad23f07d3ecb39%2FPicture4.png?generation=1690272400355824&alt=media" alt="">
• Use prophet model which will include multiple parameter. We are going with the default parameters. Thereafter, we add the external regressor "Easter".
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F7325be63d887372cc5764ddf29a94310%2FPicture5.png?generation=1690272892963939&alt=media" alt="">
• We create the future data frame for forecasting and name the data frame "future". It will include "m" and 31 days of the test data. We then predict this future data frame and create a new data frame called "forecast".
• Forecast data frame consists of 1827 rows and 34 variables. This shows the external Regressor (Easter) value is 0 through the entire time period. This shows that "Easter" has no impact or effect on "Visits".
• yhat stands for the predicted value (predicted visits).
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fae5c9414d1b1bbb2670b372a326970a5%2FPicture6.png?generation=1690273558489681&alt=media" alt="">
• We try to understand the impact of Holiday events "Christmas" and "Black.Friday"
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F5a36cc5308f9e46f0b63fa8e37c4b932%2FPicture7.png?generation=1690273814760538&alt=media" alt="">
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F8cc3dd0581db1e8b9d542d9a524abd39%2FPicture8.png?generation=1690273879506571&alt=media" alt="">
• We plot the forecast.
• plot(m,forecast) https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fa7968ff05abdd5b4e789f3723b41c4ed%2FPicture9.png?generation=1690274020880594&alt=media" alt="">
• blue is predicted value(yhat) and black is actual value(y) and blue shaded regions are the yhat_upper and yhat_lower values
• prophet_plot_components(m,forecast) https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F52408afb8c71118ef6729420085875e8%2FPicture10.png?generation=1690274184325240&alt=media" alt="">
• Trend indicates that the page visits remained constant from Jan'16 to Mid'17 and thereafter there was an upswing from Mid'19 to End of 2020
• From Holidays, we can make out that Christmas had a negative effect on page visits whereas Black Friday had a positive effect on page visits
• Weekly seasonality indicates that page visits tend to remain the highest from Monday to Thursday and starts going down thereafter
• Yearly seasonality indicates that page visits are the highest in Apr and then starts going down thereafter with
• Oct having reaching the bottom point
• External regressor "Easter" has no impact on page visits
• plot(m,forecast) + add_changepoints_to_plot(m)
• https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F1253a0e381ae04d3156a4b098dafb2ca%2FPicture11.png?generation=1690274373570449&alt=media" alt="">
• Trend which is indicated by the red line starts moving upwards from Mid 2019 to 2020 onwards
• We check for acc...

File List seedData.csv (MD5: 1046a13d7dc78e8de7ff15ed1569c743) es.csv (MD5: 83f92d3db02c919782c024c23f02476c) analyses.r (MD5: c2975e34a5540cf99e285a98d83bf817)

  Description
    This supplement provides the data and the R code to perform the analyses described in the methods section. The data is contained in seedData.csv and es.csv. The file seedData.csv contains data on seed production, es.csv contains data on emergence and survival. analyses.r is the code to perform analyses. For clarity, we divided the code in five sections: (1) models of seed production in unplanted quadrats; (2) create complete demographic data sets; (3) lambda models; (4) vital rates models; (6) LTRE analyses. In the first section, we use data from seedData.csv to create treatment specific means of seed production in unplanted quadrats. In the second section, we subtract these means to the seed production of planted quadrats. We use the resulting data set in sections three, four, and five to fit the mixed-effect models and to perform the LTRE analyses.
    Here we present the number, name and description of each column in the two data frames, seedData.csv and es.csv.


     Metadata seedData.csv

    1.Plot
    Definition: Number identifying each experimental plots.
    2. RemovalDefinition: A letter indicating whether the plot was subjected to vegetation removal ("R") or not ("N").
    3. WarmingDefinition: A letter indicating whether the plot was subjected to warming ("W") or not ("C").
    4. EcotypeDefinition: A word indicating whether the quadrat was planted with cheatgrass seeds collected at low ("low"), mid ("mid") or ("high") elevations. Moreover, every plot also contains a quadrat that has not been planted ("unplanted").
    5. SiteDefinition: A word indicating whether the plots are located at the low ("low"), mid ("mid") or ("high") elevation site.
    6. YearDefinition: A number indicating whether the data refers to seed collected in June 2010 or 2011.
    7. SeedsDefinition: A number reporting the estimated number of seeds produced in each quadrat.


     Metadata es.csv


   Numbers refer to the column number.
    1.PlotDefinition: Number identifying each experimental plots.
    2. RemovalDefinition: A letter indicating whether the plot was subjected to vegetation removal ("R") or not ("N").
    3. WarmingDefinition: A letter indicating whether the plot was subjected to warming ("W") or not ("C").
    4. EcotypeDefinition: A word indicating whether the quadrat was planted with cheatgrass seeds collected at low ("low"), mid ("mid") or ("high") elevations. Moreover, every plot also has a quadrat that has not been planted ("unplanted").
    5. SiteDefinition: A word indicating whether the plots are located at the low ("low"), mid ("mid") or ("high") elevation site.
    6. YearDefinition: A number indicating whether the data refers to the growing season 2009-2010 ("2010") or 2010-2011 ("2011").
    7. eDefinition: A number reporting the proportion of planted cheatgrass that emerged during the growing season.
    8. sDefinition: A number reporting the proportion of emerged cheatgrass that survived to seed set.