Tutoring_data

# Are there seasonal trends in enrollment, by type of program, by onsite vs offsite, that we could use to drive our marketing efforts?

seasonal_trends_stu <- FWDT %>%
  ggplot(aes(x = Qtr, y = New_Students_Enrolled, group = 1, colour = year)) +
  geom_line() +
  labs(title = "Student Enrollment per quarter",
       x = "Quarter",
       y = "Students",
       color = "Year") +
  facet_wrap(year ~ .)

seasonal_trends_stu

## Warning: Removed 3 row(s) containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

seasonal_trends_hrs <- FWDT %>%
  ggplot(aes(x = Qtr, y = `Total_of_all_Tut'g_Hrs._delivered_at_all_locations`, group = 1, colour = `Total_of_all_Tut'g_Hrs._delivered_at_all_locations`)) +
  geom_line() +
  labs(title = "Hours per quarter",
       x = "Quarter",
       y = "Hours",
       color = "Total Hours") +
  facet_wrap(year ~ .)

seasonal_trends_hrs

# If we had only the choice of doubling enrollment at our learning center, or doubling enrollment at our offsite locations, which would have a more positive effect on income? 


current_enroll <- FWDT %>%
  select(Qtr,
         year,
         `Total_Tut'g_Hrs._delivered_at_Learning_Center`,
         `Core_program_Tut'g_Hrs._delivered_at_OFFSITE_locations`) %>%
  mutate(Double_LC_stu = `Total_Tut'g_Hrs._delivered_at_Learning_Center` * 2) %>%
  mutate(Double_LC_income = (`Total_Tut'g_Hrs._delivered_at_Learning_Center` * 2) + `Core_program_Tut'g_Hrs._delivered_at_OFFSITE_locations`) %>%
  mutate(Double_OSL_stu = `Core_program_Tut'g_Hrs._delivered_at_OFFSITE_locations` * 2) %>%
  mutate(Double_OSL_income = (`Core_program_Tut'g_Hrs._delivered_at_OFFSITE_locations` * 2) + `Total_Tut'g_Hrs._delivered_at_Learning_Center`) %>%
  arrange(year)


current_enroll

## # A tibble: 28 x 8
##    Qtr    year `Total_Tut'g_Hr… `Core_program_T… Double_LC_stu Double_LC_income
##    <chr> <dbl>            <dbl>            <dbl>         <dbl>            <dbl>
##  1 Q1     2016              971              328          1942             2270
##  2 Q2     2016             1130              325          2260             2585
##  3 Q3     2016              959              262          1918             2180
##  4 Q4     2016             1197              382          2394             2776
##  5 Q1     2017             1044              357          2088             2445
##  6 Q2     2017             1240              451          2480             2931
##  7 Q3     2017             1093              355          2186             2541
##  8 Q4     2017             1224              364          2448             2812
##  9 Q1     2018              942              289          1884             2173
## 10 Q2     2018             1250              400          2500             2900
## # … with 18 more rows, and 2 more variables: Double_OSL_stu <dbl>,
## #   Double_OSL_income <dbl>