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

E.g., this could be a column name, or just some value inside a cell of your data

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?

There are few columns in the data set that are unclear until reading the documentation.Here are four such columns:

temp_min and temp_max: These two columns represent the maximum and minimum temperatures of different places on respective dates. The specific temperature units are not specified in the data. Without documentation, it would be challenging to determine the temperature scale is Celsius or Fahrenheit.

humidity_min and humidity_max: This columns represents the maximum and minimum humidity level recorded on a given date. However, the data does not specify whether humidity is measured in percentage, relative humidity, or another unit. Without documentation, it will be difficult for interpreting the humidity values accurately.

Why they chose to encode the data this way:

It’s possible that the data may have been generated and recorded by automated weather monitoring systems, which might follow standardized naming conventions that are widely understood in meteorology.

What could have happened if you didn’t read the documentation:

If I couldn’t read the documentation of the data set, it can lead to misinterpretation, and ambiguity in data analysis and decision-making processes.

At least one element or your data that is unclear even after reading the documentation

You may need to do some digging, but is there anything about the data that your documentation does not explain?

After reading the documentation of above data set, one element that is still be unclear even is the precise geographical coordinates or location information of all the places.

For instance, considering a location from the data set i.e., “Moulali” which comes under “Uppal” mandal and “Medchal-Malkajgiri” district. The documentation does not provided the specific latitude and longitude coordinates of the exact geographical boundaries of the “Moulali” location within the Mandal. Without this additional geographical information, it could be challenging to precisely pinpoint the location on a map or to conduct spatial analysis related to this area.

# Importing the packages and reading the data set
library(readr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ purrr     1.0.2
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
my_data <- read_delim("C:/Users/user/Documents/Statistics/Telangana_2018_complete_weather_data.csv",delim=",")
## Rows: 230384 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): District, Mandal, Location,  Date
## dbl (6): row_id, temp_min, temp_max, humidity_min, humidity_max, wind_speed
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#I extracted 25 rows of data from  date,location,and temp_max columns from my data set


dates <- c(
    "01-01-2018", "02-01-2018", "03-01-2018", "04-01-2018", "05-01-2018",
    "06-01-2018", "07-01-2018", "08-01-2018", "09-01-2018", "10-01-2018",
    "11-01-2018", "12-01-2018", "13-01-2018", "14-01-2018", "15-01-2018",
    "16-01-2018", "17-01-2018", "18-01-2018", "19-01-2018", "20-01-2018",
    "21-01-2018", "22-01-2018", "23-01-2018", "24-01-2018", "25-01-2018"
    
)

temp_max <- c(
    32.6, 32.6, 33.0, 31.7, 31.0, 31.8, 31.6, 31.59, 30.4, 30.0,
    32.4, 34.5, 34.6, 34.9, 34.4, 35.5, 35.3, 33.7, 34.1, 34.0,
    32.6, 33.1, 32.1, 31.8, 31.9
)

locations <- c(
    "Location 1", "Location 2", "Location 3", "Location 4", "Location 5",
    "Location 6", "Location 7", "Location 8", "Location 9", "Location 10",
    "Location 11", "Location 12", "Location 13", "Location 14", "Location 15",
    "Location 16", "Location 17", "Location 18", "Location 19", "Location 20",
    "Location 21", "Location 22", "Location 23", "Moulali", "Location 25" 
    
)

data <- data.frame(Date = dates, Max_Temperature = temp_max, Location = locations)

plot <- ggplot(data, aes(x = Date, y = Max_Temperature, color = Max_Temperature)) +
    geom_point(size = 3, alpha = 0.7) +
    scale_color_gradient(low = "blue", high = "red") +
    labs(x = "Date", y = "Max Temperature (°C)", title = "Max Temperature in Various Locations (Including Moulali)") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    geom_text(data = subset(data, Location == "Moulali"), aes(label = Location), vjust = -0.5, size = 4)
   

print(plot)

Build a visualization which uses a column of data that is affected by the issue you brought up in bullet #2, above. In this visualization, find a way to highlight the issue, and explain what is unclear and why it might be unclear.

From the above visualization

1.Each data point represents the temp_max recorded on a specific date in various locations.

2.The color of each data point indicates the temperature range, with warmer temperatures shown in warmer colors.

3.The “Moulali” data point is annotated to highlight the issue of unclear geographical location.

Why it might be unclear:

The issue is unclear because the data point for “Moulali” is shown among other locations, but there is no additional geographical information or coordinates to precisely identify where “Moulali” is located on the map.

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

Without precise geographical coordinates for “Moulali,” it is challenging to correlate the temperature data with the exact location. This ambiguity could lead to misinterpretation or incorrect conclusions.

To reduce the negative consequences of this issue, additional documentation could be needed to specify the exact geographical boundaries or coordinates of “Moulali.”