Analyse des ouvertures de commerces

Jul 23, 2024, 6:49 PM

occupants_ouvertures.RDS

Missing file

Je voudrais effectuer une analyse sur les ouvertures de commerces

The dataset occupants_ouvertures contains 228 observations and 37 variables. Some of the key variables include:
  • id: Unique identifier for each record
  • ouverture: Year of opening
  • no_civique: Civic number
  • rue: Street name
  • no_local: Local number
  • code_postal: Postal code
  • etage: Floor
  • entreprise: Name of the business
  • ratio: Some ratio value associated with the business
  • enfant: Logical value indicating if children are involved
  • modification_autre: Logical value indicating other modifications
  • geometry: Geospatial data (latitude and longitude)
What specific analysis would you like to perform on the openings of businesses? For example, you might want to analyze the number of openings per year, the distribution of businesses by street, or visualize the locations of the businesses on a map.

Can you show the number of business openings per year?

Pourrais-tu faire la même chose mais en empilant les catégories usage_1?

Sur l'horizontal

Continue the analysis

Let's continue our analysis by exploring other aspects of the data. Here are a few potential analyses we can perform:
  1. Distribution of Business Types: Analyze the distribution of different business types (usage_1) across the dataset.
  2. Geospatial Analysis: Visualize the locations of the businesses on a map to see the geographical distribution.
  3. Ratio Analysis: Investigate the ratio variable to understand its distribution and any potential correlations with other variables.
  4. Temporal Trends: Examine trends over time, such as changes in the types of businesses opening over the years.
Which analysis would you like to perform next? Feel free to suggest any other specific analysis you have in mind.

L'option 4 svp

We analyzed the trends over time for different types of businesses opening each year. The area plot shows the proportion of each business type (usage_1) over the years, providing a clear visual representation of how the distribution of business types has evolved.
Would you like to explore any other aspects of the data or perform a different analysis? Here are a few suggestions:
  1. Distribution of Business Types: Analyze the distribution of different business types (usage_1) across the dataset.
  2. Geospatial Analysis: Visualize the locations of the businesses on a map to see the geographical distribution.
  3. Ratio Analysis: Investigate the ratio variable to understand its distribution and any potential correlations with other variables.
Feel free to suggest any other specific analysis you have in mind.

Can you analyze the ratio variable and its correlations?

We analyzed the distribution of the ratio variable and its correlations with other numerical variables in the dataset.

Distribution of the ratio Variable

  • The histogram shows the distribution of the ratio variable, indicating how frequently different ratio values occur.

Correlation Analysis

  • The correlation matrix reveals the relationships between numerical variables:
    • ratio has a moderate positive correlation with employe_n (number of employees) and local_superficie (local area).
    • ratio also shows a positive correlation with ouverture (year of opening) and no_civique (civic number).
These insights can help us understand how the ratio variable interacts with other factors in the dataset.
Would you like to explore any other aspects of the data or perform a different analysis? Here are a few suggestions:
  1. Distribution of Business Types: Analyze the distribution of different business types (usage_1) across the dataset.
  2. Geospatial Analysis: Visualize the locations of the businesses on a map to see the geographical distribution.
Feel free to suggest any other specific analysis you have in mind.
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