Scorecards

Scorecards provide a snapshot of key performance indicators (KPIs) or metrics relevant to the project management. They typically display metrics such as resource utilization, quality metrics, and other relevant indicators. Scorecards help project managers and stakeholders quickly assess how well the project is performing against predefined targets or benchmarks. They can be presented in tabular format or visualized using charts and graphs.

# Provided values
values <- data.frame(A= c(-200,-78,-10),
                     B= c(100,0,-101),
                     C= c(-400,400,500))
                     

# Scorecard template
scorecard <- data.frame(Names = c("A","B","C"), 
                        "Score5" = c(-100,-200,-300), 
                        "Score3" = c(-50,-100,-150), 
                        "Score1" = c(-25,-50,-75))

# calculate scores based on scorecard template
calculate_score <- function(value, scorecard) {
  ifelse(value <= scorecard$Score5, 5,
         ifelse(value <= scorecard$Score3, 3,
                ifelse(value <= scorecard$Score1, 1, 0)))
}

# Apply calculate_score function to each column of values
scores <- lapply(values, calculate_score, scorecard)

# Convert list of scores to data frame
score_df <- data.frame(names(values), do.call(cbind, scores))
#names(score_df) <- c("Names", paste0("Score_", 1:ncol(score_df)))

# Print the scorecard
print(score_df)
##   names.values. A B C
## 1             A 5 0 5
## 2             B 1 0 0
## 3             C 0 1 0

Burn-down charts

In a burn-down chart: • The horizontal axis typically represents time, divided into iterations or sprints. • The vertical axis represents the amount of work remaining, often measured in story points or tasks. • A line or bar graph shows the ideal progress line, indicating how the work should ideally decrease over time. • The actual progress of the team is plotted against this ideal line, showing whether the team is ahead or behind schedule.

library(ggplot2)
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
# Sample Data
burn_down_data <- data.frame(
  Date = seq(as.Date("2024-01-01"), by = "days", length.out = 10),  # Example dates
  Work_Remaining = c(50, 45, 40, 35, 30, 25, 20, 15, 10, 5)  # Example work remaining for each date
)
# Create Burn Down Chart
ggplot(burn_down_data, aes(x = Date, y = Work_Remaining)) +
  geom_line() +  # Plot the line representing work remaining over time
  geom_point() +  # Add points for each data point
  labs(x = "Date", y = "Work Remaining", title = "Burn Down Chart") +
  theme_minimal()