data <- read.csv("/Users/yashuvaishu/Downloads/Spotify.csv")
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data1 = data %>% sample_n(10)

1) A list of at least 3 columns (or values) in your data which are unclear until you read the documentation?

The reason for encoding the data in this way could be to efficiently store and process the information in a structured format that allows for easy retrieval and analysis. By using specific column names and data types, the data can be organized and queried effectively.

  1. danceability: This column represents a measure of how suitable a track is for dancing based on a combination of musical elements. Without reading the documentation, it may be unclear how this metric is calculated and what specific factors contribute to the danceability score.

  2. loudness: This column represents the overall loudness of a track in decibels (dB). Without documentation, it might not be clear how the loudness value is measured and what range of values indicates loud or soft tracks.

  3. valence: This column represents the musical positiveness conveyed by a track. Without documentation, it may not be clear how valence is quantified and what range of values indicates positive or negative emotions in music.

Why do you think they chose to encode the data the way they did? What could have happened if you didn’t read the documentation? At least one element or data that is unclear even after reading the documentation !!!

The reason for encoding the data in this way could be to efficiently store and process the information in a structured format that allows for easy retrieval and analysis. By using specific column names and data types, the data can be organized and queried effectively.

If the documentation is not read, there can be several consequences. Firstly, there may be a misunderstanding of how to interpret and use the data correctly. For example, without understanding the calculation of danceability or valence, one might make incorrect assumptions about the characteristics of a track. Secondly, not reading the documentation can lead to misinterpretation of the data and potentially incorrect analysis or conclusions.

3) Building a visualization which uses a column of data that is affected by the issue you brought up in bullet #2, above. In this visualization, we will see the issue, and what is unclear and why it is unclear

To build a visualization highlighting the issue, let’s focus on the danceability column. We can create a scatter plot where the x-axis represents the track name, and the y-axis represents the danceability score. I used color-code the points based on the genre of each track. By doing this visually we see how danceability varies across different genres. We can annotate the plot to explain that the danceability score might be unclear without reading the documentation, as the specific factors and calculation methodology are not immediately apparent.

Note: Here I used only 15 rows of data set as my data have lot of unique values if not graph looks clumsy

library(ggplot2)
ggplot(data1, aes(x = trackName, y = danceability, color = genre)) +
  geom_count() +
  labs(title = "Danceability across Different Genres",
       x = "Track Name",
       y = "Danceability Score",
       color = "Genre") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  geom_text(aes(label = genre), vjust = -0.5, size =3)

Do you notice any significant risks? If so, what could you do to reduce negative consequences?

As for significant risks in the Spotify dataset, one risk is the presence of erroneous or inconsistent data. For example, there might be tracks with missing values or incorrect genre labels. To reduce negative consequences, it is important to implement data validation and cleaning processes. This includes checking for missing values, ensuring data consistency, and handling outliers. Additionally, cross-referencing the data with external sources or conducting data quality checks can help ensure the accuracy and reliability of the dataset.