Main Question
Does implementing late fines affect when parents pick up their child?
setwd("~/Documents/RStudio (DATA-101)")
Fines <- read.csv("daycare_fines.csv")Imagine if daycare centers started implemented late fees if parents are late to picking up their child? It does raise the question if late fees will make an impact on when parents will pick up their child? In this study, we will be taking a look at volunteer daycare centers by introducing a penalty for late pickups to observe if this new policy makes a difference. Using a dataset from OpenIntro, we will be analyzing specific periods of the study to see how different was the pickup rate before and after fines were implemented. By using specific commands, we can do just that and look at a specific period of time in the study, depending on which variables you choose.
Does implementing late fines affect when parents pick up their child?
For my analysis, I will be going over the study periods consisting of what the results were before the fine was implemented, and weeks after the implementation. So, if we want to see what the results were like 8 weeks after the fines were implemented in the study, we can use the select and filter commands to run only the variables we have included.
str(Fines)## 'data.frame': 200 obs. of 7 variables:
## $ center : int 1 1 1 1 1 1 1 1 1 1 ...
## $ group : chr "test" "test" "test" "test" ...
## $ children : int 37 37 37 37 37 37 37 37 37 37 ...
## $ week : int 1 2 3 4 5 6 7 8 9 10 ...
## $ late_pickups : int 8 8 7 6 8 9 9 12 13 13 ...
## $ study_period_4: chr "before fine" "before fine" "before fine" "before fine" ...
## $ study_period_3: chr "before fine" "before fine" "before fine" "before fine" ...dim(Fines)## [1] 200 7names(Fines) <- gsub("[(). \\-]", "_", names(Fines)) # replace ., (), space, with dash
names(Fines) <- gsub("_$", "", names(Fines)) # remove trailing underscore
names(Fines) <- tolower(names(Fines)) # lowercase
head(Fines)## center group children week late_pickups study_period_4
## 1 1 test 37 1 8 before fine
## 2 1 test 37 2 8 before fine
## 3 1 test 37 3 7 before fine
## 4 1 test 37 4 6 before fine
## 5 1 test 37 5 8 first 4 weeks with fine
## 6 1 test 37 6 9 first 4 weeks with fine
## study_period_3
## 1 before fine
## 2 before fine
## 3 before fine
## 4 before fine
## 5 with fine
## 6 with finestudy_period_4 <- Fines |>
select(week, late_pickups, study_period_4) |>
filter(study_period_4 == "before fine")
study_period_4## week late_pickups study_period_4
## 1 1 8 before fine
## 2 2 8 before fine
## 3 3 7 before fine
## 4 4 6 before fine
## 5 1 6 before fine
## 6 2 7 before fine
## 7 3 3 before fine
## 8 4 5 before fine
## 9 1 8 before fine
## 10 2 9 before fine
## 11 3 8 before fine
## 12 4 9 before fine
## 13 1 10 before fine
## 14 2 3 before fine
## 15 3 14 before fine
## 16 4 9 before fine
## 17 1 13 before fine
## 18 2 12 before fine
## 19 3 9 before fine
## 20 4 13 before fine
## 21 1 5 before fine
## 22 2 8 before fine
## 23 3 7 before fine
## 24 4 5 before fine
## 25 1 7 before fine
## 26 2 10 before fine
## 27 3 12 before fine
## 28 4 6 before fine
## 29 1 12 before fine
## 30 2 9 before fine
## 31 3 14 before fine
## 32 4 18 before fine
## 33 1 3 before fine
## 34 2 4 before fine
## 35 3 9 before fine
## 36 4 3 before fine
## 37 1 15 before fine
## 38 2 13 before fine
## 39 3 13 before fine
## 40 4 12 before finestudy_period_4 <- Fines |>
select(week, late_pickups, study_period_4) |>
filter(study_period_4 == "last 8 weeks with fine")
study_period_4## week late_pickups study_period_4
## 1 9 13 last 8 weeks with fine
## 2 10 13 last 8 weeks with fine
## 3 11 15 last 8 weeks with fine
## 4 12 13 last 8 weeks with fine
## 5 13 14 last 8 weeks with fine
## 6 14 16 last 8 weeks with fine
## 7 15 14 last 8 weeks with fine
## 8 16 15 last 8 weeks with fine
## 9 9 16 last 8 weeks with fine
## 10 10 12 last 8 weeks with fine
## 11 11 10 last 8 weeks with fine
## 12 12 14 last 8 weeks with fine
## 13 13 14 last 8 weeks with fine
## 14 14 16 last 8 weeks with fine
## 15 15 12 last 8 weeks with fine
## 16 16 17 last 8 weeks with fine
## 17 9 16 last 8 weeks with fine
## 18 10 14 last 8 weeks with fine
## 19 11 20 last 8 weeks with fine
## 20 12 18 last 8 weeks with fine
## 21 13 25 last 8 weeks with fine
## 22 14 22 last 8 weeks with fine
## 23 15 27 last 8 weeks with fine
## 24 16 19 last 8 weeks with fine
## 25 9 22 last 8 weeks with fine
## 26 10 19 last 8 weeks with fine
## 27 11 25 last 8 weeks with fine
## 28 12 18 last 8 weeks with fine
## 29 13 23 last 8 weeks with fine
## 30 14 22 last 8 weeks with fine
## 31 15 24 last 8 weeks with fine
## 32 16 17 last 8 weeks with fine
## 33 9 35 last 8 weeks with fine
## 34 10 10 last 8 weeks with fine
## 35 11 24 last 8 weeks with fine
## 36 12 32 last 8 weeks with fine
## 37 13 29 last 8 weeks with fine
## 38 14 29 last 8 weeks with fine
## 39 15 26 last 8 weeks with fine
## 40 16 31 last 8 weeks with fine
## 41 9 19 last 8 weeks with fine
## 42 10 17 last 8 weeks with fine
## 43 11 14 last 8 weeks with fine
## 44 12 13 last 8 weeks with fine
## 45 13 10 last 8 weeks with fine
## 46 14 15 last 8 weeks with fine
## 47 15 14 last 8 weeks with fine
## 48 16 16 last 8 weeks with fine
## 49 9 5 last 8 weeks with fine
## 50 10 12 last 8 weeks with fine
## 51 11 3 last 8 weeks with fine
## 52 12 5 last 8 weeks with fine
## 53 13 6 last 8 weeks with fine
## 54 14 13 last 8 weeks with fine
## 55 15 7 last 8 weeks with fine
## 56 16 4 last 8 weeks with fine
## 57 9 14 last 8 weeks with fine
## 58 10 13 last 8 weeks with fine
## 59 11 7 last 8 weeks with fine
## 60 12 12 last 8 weeks with fine
## 61 13 9 last 8 weeks with fine
## 62 14 9 last 8 weeks with fine
## 63 15 17 last 8 weeks with fine
## 64 16 8 last 8 weeks with fine
## 65 9 2 last 8 weeks with fine
## 66 10 7 last 8 weeks with fine
## 67 11 6 last 8 weeks with fine
## 68 12 6 last 8 weeks with fine
## 69 13 9 last 8 weeks with fine
## 70 14 4 last 8 weeks with fine
## 71 15 9 last 8 weeks with fine
## 72 16 2 last 8 weeks with fine
## 73 9 15 last 8 weeks with fine
## 74 10 10 last 8 weeks with fine
## 75 11 17 last 8 weeks with fine
## 76 12 12 last 8 weeks with fine
## 77 13 13 last 8 weeks with fine
## 78 14 11 last 8 weeks with fine
## 79 15 14 last 8 weeks with fine
## 80 16 17 last 8 weeks with fine study_period_4 %>%
arrange(desc(week)) %>%
slice(1:5) ## week late_pickups study_period_4
## 1 16 15 last 8 weeks with fine
## 2 16 17 last 8 weeks with fine
## 3 16 19 last 8 weeks with fine
## 4 16 17 last 8 weeks with fine
## 5 16 31 last 8 weeks with finestudy_period_4 <- Fines |>
select(week, late_pickups, study_period_4) |>
filter(week == "16")
study_period_4## week late_pickups study_period_4
## 1 16 15 last 8 weeks with fine
## 2 16 17 last 8 weeks with fine
## 3 16 19 last 8 weeks with fine
## 4 16 17 last 8 weeks with fine
## 5 16 31 last 8 weeks with fine
## 6 16 16 last 8 weeks with fine
## 7 16 4 last 8 weeks with fine
## 8 16 8 last 8 weeks with fine
## 9 16 2 last 8 weeks with fine
## 10 16 17 last 8 weeks with fineBased on the data presented, the highest late pickup rate peaked at 31 children which took place at center 5, 16 weeks into the study after the fine was implemented based on the dataset. But do these factors affect the results in any way? According to the study, the highest pick up rate of 31 children occurred 8 weeks after the daycare centers started handing out fines. When we arranged our code to show the top 5 weeks with late pickup rates, they’ve all occurred at 8 weeks after the fines were handed out. The late pickup rate increased significantly since the fines were implemented a few weeks afterwards. Other factors that can affect the data shown also includes the centers, which each contains a different amount of children per center. If this study could have included the locations of the center and a bit more on their conditions, it would be nice for some added context to what led to these results. Otherwise, we can imply based on the analysis of the dataset that the late pickup rates increased when fines were implemented more than when there weren’t any fines yet.
Dataset is by OpenIntro.org: https://www.openintro.org/data/index.php?data=daycare_fines