prices <- Copy_of_FW_Tutoring_Data_Sheet1_2_ %>%
mutate(Price = Total_Revenue/Total_Hours)
prices
## # A tibble: 24 x 16
## Year Qtr Contact_of_Inte… New_Students `Inquiries_Enro… Avg_Tutors
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Q1 64 17 0.27 12
## 2 2016 Q2 47 24 0.51 11
## 3 2016 Q3 51 31 0.61 12
## 4 2016 Q4 40 20 0.5 11
## 5 2017 Q1 53 18 0.34 13
## 6 2017 Q2 56 32 0.570 13
## 7 2017 Q3 46 27 0.59 12
## 8 2017 Q4 41 15 0.37 14
## 9 2018 Q1 35 12 0.34 16
## 10 2018 Q2 53 29 0.55 15
## # … with 14 more rows, and 10 more variables: Avg_Enrollment <dbl>,
## # Learning_Center_Hours <dbl>, Homework_Program_Hours <dbl>,
## # Total_Learning_Center <dbl>, Offsite_Hours <dbl>, Total_Hours <dbl>,
## # Total_Revenue <dbl>, Avg_Finanncial_Aid_Students <dbl>,
## # Necessary_Finnancial_Aid_Funds <dbl>, Price <dbl>
cost <-prices %>%
filter(Year=="2021")
cost
## # A tibble: 4 x 16
## Year Qtr Contact_of_Inte… New_Students `Inquiries_Enro… Avg_Tutors
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2021 Q1 50 25 0.5 14
## 2 2021 Q2 80 42 0.53 16
## 3 2021 Q3 55 23 0.42 17
## 4 2021 Q4 43 10 0.23 20
## # … with 10 more variables: Avg_Enrollment <dbl>, Learning_Center_Hours <dbl>,
## # Homework_Program_Hours <dbl>, Total_Learning_Center <dbl>,
## # Offsite_Hours <dbl>, Total_Hours <dbl>, Total_Revenue <dbl>,
## # Avg_Finanncial_Aid_Students <dbl>, Necessary_Finnancial_Aid_Funds <dbl>,
## # Price <dbl>
#REVENUE: Quarter 4 makes the most, then Quarter 2, Quarter 1, and Quarter 3 respectively
#While Quarter 3 has the least revenue, Quarter 1 has the least amount of hours
Copy_of_FW_Tutoring_Data_Sheet1_2_ %>%
ggplot(data = Copy_of_FW_Tutoring_Data_Sheet1_2_,
mapping = aes(x = Year, y = Total_Revenue,
colour = Qtr)) +
geom_point() +
labs(title = "money",
x = "year", y = "revenue",
colour = "qtr") +
scale_colour_viridis_d()
Copy_of_FW_Tutoring_Data_Sheet1_2_
## # A tibble: 24 x 15
## Year Qtr Contact_of_Inte… New_Students `Inquiries_Enro… Avg_Tutors
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Q1 64 17 0.27 12
## 2 2016 Q2 47 24 0.51 11
## 3 2016 Q3 51 31 0.61 12
## 4 2016 Q4 40 20 0.5 11
## 5 2017 Q1 53 18 0.34 13
## 6 2017 Q2 56 32 0.570 13
## 7 2017 Q3 46 27 0.59 12
## 8 2017 Q4 41 15 0.37 14
## 9 2018 Q1 35 12 0.34 16
## 10 2018 Q2 53 29 0.55 15
## # … with 14 more rows, and 9 more variables: Avg_Enrollment <dbl>,
## # Learning_Center_Hours <dbl>, Homework_Program_Hours <dbl>,
## # Total_Learning_Center <dbl>, Offsite_Hours <dbl>, Total_Hours <dbl>,
## # Total_Revenue <dbl>, Avg_Finanncial_Aid_Students <dbl>,
## # Necessary_Finnancial_Aid_Funds <dbl>
#HOURS: Quarter 4 is consistently the best, second is Quarter 2, third is Quarter 3 and the worst is Quarter 1
Copy_of_FW_Tutoring_Data_Sheet1_2_ %>%
ggplot(data = Copy_of_FW_Tutoring_Data_Sheet1_2_,
mapping = aes(x = Year, y = Learning_Center_Hours,
colour = Qtr)) +
geom_point() +
labs(title = "hours",
x = "year", y = "hours",
colour = "qtr") +
scale_colour_viridis_d()
best <- Copy_of_FW_Tutoring_Data_Sheet1_2_ %>%
filter(Qtr=="Q4")
best
## # A tibble: 6 x 15
## Year Qtr Contact_of_Inte… New_Students `Inquiries_Enro… Avg_Tutors
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Q4 40 20 0.5 11
## 2 2017 Q4 41 15 0.37 14
## 3 2018 Q4 34 19 0.56 15
## 4 2019 Q4 40 20 0.5 16
## 5 2020 Q4 40 26 0.65 15
## 6 2021 Q4 43 10 0.23 20
## # … with 9 more variables: Avg_Enrollment <dbl>, Learning_Center_Hours <dbl>,
## # Homework_Program_Hours <dbl>, Total_Learning_Center <dbl>,
## # Offsite_Hours <dbl>, Total_Hours <dbl>, Total_Revenue <dbl>,
## # Avg_Finanncial_Aid_Students <dbl>, Necessary_Finnancial_Aid_Funds <dbl>
density2 <- ggplot(best, aes(x = Total_Hours, y = Total_Revenue)) +
geom_density_2d_filled()
density2
