library(readr)
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
data <- read.csv("C:\\Users\\Krishna\\Downloads\\productivity+prediction+of+garment+employees\\garments_worker_productivity.csv")
# Display the first few rows of the data
head(data)
## date quarter department day team targeted_productivity smv wip
## 1 01-01-2015 Quarter1 sweing Thursday 8 0.80 26.16 1108
## 2 01-01-2015 Quarter1 finishing Thursday 1 0.75 3.94 NA
## 3 01-01-2015 Quarter1 sweing Thursday 11 0.80 11.41 968
## 4 01-01-2015 Quarter1 sweing Thursday 12 0.80 11.41 968
## 5 01-01-2015 Quarter1 sweing Thursday 6 0.80 25.90 1170
## 6 01-01-2015 Quarter1 sweing Thursday 7 0.80 25.90 984
## over_time incentive idle_time idle_men no_of_style_change no_of_workers
## 1 7080 98 0 0 0 59.0
## 2 960 0 0 0 0 8.0
## 3 3660 50 0 0 0 30.5
## 4 3660 50 0 0 0 30.5
## 5 1920 50 0 0 0 56.0
## 6 6720 38 0 0 0 56.0
## actual_productivity
## 1 0.9407254
## 2 0.8865000
## 3 0.8005705
## 4 0.8005705
## 5 0.8003819
## 6 0.8001250
# Task 2: List of at least 3 unclear columns or values in the data
# 1. SMV (Standard Minute Value): It's unclear what this value represents without documentation. It could be a measure of expected time for a task or some other metric.
# 2. Incentive: It's not clear how the incentive is calculated or what it's based on.
# 3. Idle Time: It's unclear why there's idle time recorded and what it signifies in this context.
# Why the data might be encoded this way:
# The data might be encoded in this format to adhere to a specific data storage convention or to ensure compatibility with certain software or systems. Without documentation, it's hard to determine the exact reason for this encoding. Not reading the documentation could lead to misinterpretation of the data, incorrect analysis, or erroneous conclusions.
# At least one unclear element even after reading the documentation:
# The purpose of the "idle time" column is unclear. While it might indicate periods of inactivity, the reason for this inactivity and its significance in the context of productivity data is not explained in the provided documentation.
# Task 3: Build a visualization which uses a column of data that is affected by the issue you brought up in bullet #2, above.
library(ggplot2)
# Visualization using a column affected by the unclear element "idle time":
ggplot(data, aes(x = day, y = actual_productivity, color = idle_time)) +
geom_point() +
scale_color_gradient(low = "green", high = "red") +
labs(title = "Actual Productivity by Day with Idle Time",
x = "Day",
y = "Actual Productivity",
color = "Idle Time") +
theme_minimal()
# Explanation:
# In this visualization, actual productivity is plotted against the day of the week, with points colored according to the amount of idle time recorded. However, the meaning of "idle time" remains unclear, so it's challenging to interpret the color gradient accurately. This ambiguity could lead to misinterpretation of the data or incorrect conclusions about the relationship between productivity and idle time.
# Significant risks:
# One significant risk is misinterpretation of the data due to unclear column values such as "idle time." This could lead to incorrect conclusions about factors affecting productivity and potentially misguided decision-making based on the analysis.
# To reduce negative consequences:
# It's essential to seek clarification on unclear column values from the data provider or relevant documentation. Additionally, conducting exploratory data analysis and sensitivity analysis can help identify and mitigate risks associated with ambiguous data elements. It's also crucial to document any assumptions or interpretations made during the analysis to facilitate transparency and reproducibility.