Chapter 4 Midterm assignment

4.1 Dr. Walker’s Lab Report

I have just ran an experiment that measures the effect of exercise on sleep habits. I was able to measure participants average hours of sleep before and after the experiment. I was also able to measure participants sleep efficiency at the end of the experiment. Since the experiment is over, I needed to identify which, if any, of the exercise methods best impacts sleep.

The packages we will be using for this assignment include: readxl, dplyr, tibble, tidyverse, mosaic, ggplot2, supernova, and stats.

Here is my report.

4.2 DATA

In this section, I ran some code to load the two data sets for the analysis below. I am using glimpse and head to preview my two data sets to ensure they correctly have been loaded.

participant_info_midterm <- read_excel("data/participant_info_midterm.xlsx")
glimpse(participant_info_midterm)

## Rows: 100
## Columns: 4
## $ ID             <chr> "P001", "P002", "P003", "P004", "P005", "P006", "P007", "P008", "P009", "P010", "P…
## $ Exercise_Group <chr> "NONE", "Nonee", "None", "None", "None", "None", "None", "None", "None", "None", "…
## $ Sex            <chr> "Male", "Malee", "Female", "Female", "Male", "Female", "Male", "Female", "Male", "…
## $ Age            <dbl> 35, 57, 26, 29, 33, 33, 32, 30, 37, 28, 30, 20, 42, 31, 33, 26, 41, 18, 28, 37, 48…

head(participant_info_midterm)

## # A tibble: 6 × 4
##   ID    Exercise_Group Sex      Age
##   <chr> <chr>          <chr>  <dbl>
## 1 P001  NONE           Male      35
## 2 P002  Nonee          Malee     57
## 3 P003  None           Female    26
## 4 P004  None           Female    29
## 5 P005  None           Male      33
## 6 P006  None           Female    33

sleep_data_midterm <- read_excel("data/sleep_data_midterm.xlsx")
glimpse(sleep_data_midterm)

## Rows: 100
## Columns: 4
## $ ID               <chr> "P001", "P002", "P003", "P004", "P005", "P006", "P007", "P008", "P009", "P010", …
## $ Pre_Sleep        <chr> "zzz-5.8", "Sleep-6.6", NA, "SLEEP-7.2", "score-7.4", "Sleep-6.6", "Sleep-6", "z…
## $ Post_Sleep       <dbl> 4.7, 7.4, 6.2, 7.3, 7.4, 7.1, 6.7, 9.0, 5.1, 6.3, 6.2, 4.6, 7.6, 7.2, 4.6, 8.2, …
## $ Sleep_Efficiency <dbl> 81.6, 75.7, 82.9, 83.6, 83.5, 88.5, 83.6, 73.4, 88.2, 80.4, 85.2, 82.9, 74.0, 92…

head(sleep_data_midterm)

## # A tibble: 6 × 4
##   ID    Pre_Sleep Post_Sleep Sleep_Efficiency
##   <chr> <chr>          <dbl>            <dbl>
## 1 P001  zzz-5.8          4.7             81.6
## 2 P002  Sleep-6.6        7.4             75.7
## 3 P003  <NA>             6.2             82.9
## 4 P004  SLEEP-7.2        7.3             83.6
## 5 P005  score-7.4        7.4             83.5
## 6 P006  Sleep-6.6        7.1             88.5

4.3 DATA CLEANING

In this section, we will be cleaning the data. The column names thankfully do not have to be cleaned.

In this piece of code below, we are standardizing the column titled Exercise_Group.

participant_info_midterm<-participant_info_midterm %>%
  mutate(Exercise_Group =case_when(
    Exercise_Group=="NONE" ~"None",
    Exercise_Group=="Nonee" ~ "None",
    Exercise_Group=="N"~"None",
    Exercise_Group=="C"~"Cardio",
    Exercise_Group=="WEIGHTS"~"Weights",
    Exercise_Group=="WEIGHTZ"~"Weights",
    Exercise_Group=="WEIGHTSSS"~"Weights",
    Exercise_Group=="CW"~"Cardio+Weights",
    Exercise_Group=="C+W"~"Cardio+Weights",
    TRUE~Exercise_Group
  ))

In this piece of code below, we are standardizing the column titled Sex.

participant_info_midterm<-participant_info_midterm %>%
  mutate(Sex=case_when(
    Sex=="Malee"~"Male",
    Sex=="MALE"~"Male",
    Sex=="M"~"Male",
    Sex=="Mal"~"Male",
    Sex=="Femalee"~"Female",
    Sex=="F"~"Female",
    TRUE~Sex
  ))

Since our data is now all clean, the last step was to merge our two data sets.

We can now see that our data is cleaned and merged below.

Data_Midterm<- merge(participant_info_midterm, sleep_data_midterm, by="ID")

knitr::kable(Data_Midterm)

ID	Exercise_Group	Sex	Age	Pre_Sleep	Post_Sleep	Sleep_Efficiency
P001	None	Male	35	zzz-5.8	4.7	81.6
P002	None	Male	57	Sleep-6.6	7.4	75.7
P003	None	Female	26	NA	6.2	82.9
P004	None	Female	29	SLEEP-7.2	7.3	83.6
P005	None	Male	33	score-7.4	7.4	83.5
P006	None	Female	33	Sleep-6.6	7.1	88.5
P007	None	Male	32	Sleep-6	6.7	83.6
P008	None	Female	30	zzz-8.1	9.0	73.4
P009	None	Male	37	sleep-5.5	5.1	88.2
P010	None	Female	28	sleep-5.7	6.3	80.4
P011	None	Female	30	score-7	6.2	85.2
P012	None	Male	20	sleep-5.5	4.6	82.9
P013	None	Male	42	Sleep-8	7.6	74.0
P014	None	Male	31	NA	7.2	92.0
P015	None	Female	33	score-5.3	4.6	89.6
P016	None	Male	26	SLEEP-7.8	8.2	71.7
P017	None	Female	41	zzz-6.7	7.6	78.5
P018	None	Male	18	SLEEP-7.4	7.2	73.8
P019	None	Male	28	NA	5.7	69.1
P020	None	Female	37	score-7.1	6.8	81.5
P021	None	Male	48	zzz-6.8	7.1	78.7
P022	None	Female	37	Sleep-5.7	5.8	90.4
P023	None	Female	42	sleep-5	NA	81.3
P024	None	Female	39	score-6	5.6	76.6
P025	None	Male	20	Sleep-6.2	7.1	81.1
P026	Cardio	Female	34	score-5.2	6.7	84.5
P027	Cardio	Female	28	sleep-6.6	7.8	75.9
P028	Cardio	Female	29	SLEEP-5.6	6.3	79.0
P029	Cardio	Male	36	Sleep-6	6.3	87.6
P030	Cardio	Male	30	SLEEP-5.7	NA	88.0
P031	Cardio	Female	42	Sleep-6.9	7.6	88.5
P032	Cardio	Female	18	SLEEP-6.9	7.9	87.8
P033	Cardio	Female	38	zzz-5.9	6.6	80.9
P034	Cardio	Female	20	score-5.2	6.0	92.8
P035	Cardio	Male	30	SLEEP-6.7	8.0	86.4
P036	Cardio	Female	19	zzz-5.4	5.7	88.0
P037	Cardio	Male	35	NA	9.7	85.6
P038	Cardio	Female	34	zzz-7	8.1	79.6
P039	Cardio	Male	39	SLEEP-5.6	6.3	81.3
P040	Cardio	Female	39	zzz-6	8.1	79.7
P041	Cardio	Male	29	zzz-7	NA	83.2
P042	Cardio	Female	28	SLEEP-5.6	7.3	81.9
P043	Cardio	Female	43	zzz-6.9	8.2	81.3
P044	Cardio	Female	33	zzz-5.8	7.1	88.7
P045	Cardio	Male	31	score-4	5.1	86.0
P046	Cardio	Male	30	SLEEP-7.1	8.5	95.0
P047	Cardio	Female	42	SLEEP-5.8	7.0	85.5
P048	Cardio	Fem	31	NA	7.7	93.4
P049	Cardio	Male	40	sleep-6.7	8.2	82.5
P050	Cardio	Male	31	zzz-6.4	8.4	101.5
P051	Weights	Male	18	Sleep-6.5	6.8	80.4
P052	Weights	Male	23	Sleep-7.2	8.3	76.7
P053	Weights	Female	39	score-7.3	9.1	82.2
P054	Weights	Female	37	zzz-7	7.7	88.6
P055	Weights	Female	31	sleep-6.2	6.6	76.6
P056	Weights	Female	38	Sleep-6.2	NA	80.2
P057	Weights	Male	26	sleep-7.3	7.4	77.7
P058	Weights	Male	18	Sleep-6.2	6.3	85.3
P059	Weights	Female	38	SLEEP-6.2	7.3	80.5
P060	Weights	Male	39	SLEEP-6.6	6.9	80.2
P061	Weights	Female	27	SLEEP-6.1	6.6	77.9
P062	Weights	Male	35	score-6.7	7.2	80.8
P063	Weights	Female	18	zzz-5.8	5.1	82.9
P064	Weights	Female	38	score-5.1	5.2	80.9
P065	Weights	Female	38	score-5.5	7.2	76.1
P066	Weights	Female	46	zzz-7	8.7	74.8
P067	Weights	Male	41	NA	6.7	92.8
P068	Weights	Female	22	zzz-5.4	5.7	88.0
P069	Weights	Female	45	sleep-6.4	7.2	79.1
P070	Weights	Female	25	score-7.5	8.7	89.5
P071	Weights	Female	27	zzz-6.6	7.4	87.4
P072	Weights	Female	32	NA	6.9	86.9
P073	Weights	Female	44	Sleep-5.6	6.3	83.6
P074	Weights	Female	42	SLEEP-5.1	NA	87.1
P075	Weights	Male	32	sleep-6.5	7.0	81.4
P076	Cardio+Weights	Male	37	sleep-5.7	6.5	83.9
P077	Cardio+Weights	Female	37	score-5.9	7.3	92.4
P078	Cardio+Weights	Female	24	score-6.6	7.2	74.5
P079	Cardio+Weights	Male	38	SLEEP-6.6	7.4	89.0
P080	Cardio+Weights	Male	42	Sleep-7.2	8.1	94.5
P081	Cardio+Weights	Female	38	Sleep-8	8.9	74.5
P082	Cardio+Weights	Male	46	score-6.5	6.4	88.7
P083	Cardio+Weights	Female	49	score-5.9	6.8	90.4
P084	Cardio+Weights	Female	31	sleep-8	8.4	80.2
P085	Cardio+Weights	Male	33	score-6.2	6.9	89.9
P086	Cardio+Weights	Female	46	SLEEP-6.2	7.4	83.1
P087	Cardio+Weights	Male	18	SLEEP-6.7	7.6	96.3
P088	Cardio+Weights	Male	34	score-7.5	8.9	81.2
P089	Cardio+Weights	Female	42	score-5.9	7.0	90.6
P090	Cardio+Weights	Female	41	Sleep-7.2	7.6	79.9
P091	Cardio+Weights	Male	46	zzz-7.3	7.9	87.5
P092	Cardio+Weights	Male	18	sleep-5.6	6.6	86.3
P093	Cardio+Weights	Female	40	Sleep-8	9.5	90.4
P094	Cardio+Weights	Male	35	zzz-6.1	7.2	84.4
P095	Cardio+Weights	Female	29	Sleep-5.7	NA	85.0
P096	Cardio+Weights	Female	37	sleep-5.9	7.1	93.8
P097	Cardio+Weights	Male	24	SLEEP-4.4	4.7	88.9
P098	Cardio+Weights	Female	35	score-5.9	6.9	92.6
P099	Cardio+Weights	Male	28	NA	8.8	88.5
P100	Cardio+Weights	Male	32	zzz-6.5	7.3	84.2

4.4 CREATE DERIVED VARIABLES

We now want to Create a column titled Sleep_Difference with Post_Sleep - Pre_Sleep.

First we got to check if Post_Sleep and Pre_Sleep columns are numeric below.

is.numeric(Data_Midterm$Post_Sleep)

## [1] TRUE

is.numeric(Data_Midterm$Pre_Sleep)

## [1] FALSE

Post_Sleep IS numeric but Pre_Sleep is NOT numeric. Before we use code to turn it into numeric, we first have to clean the column further since it has letters and numbers. I did this by separating the letters and numbers into separate columns below.

Data_Midterm<- Data_Midterm %>%
  separate(
    col= Pre_Sleep,
    into= c("Pre_Sleep_Letters", "Pre_Sleep"),
    sep= "-"
  )

I then turned the Pre_Sleep column into numeric and deleted Pre_Sleep_Letters column. You can see here that the Pre_Sleep_Letters column is gone and we are left with Pre_Sleep Column cleaned up. I also checked if Pre_Sleep column is numeric and it is. All of this can be seen below.

Data_Midterm<- Data_Midterm%>% 
  mutate(across(
    c("Pre_Sleep"), 
    ~as.numeric(.)
  ))

Data_Midterm<- Data_Midterm %>% 
  select(-"Pre_Sleep_Letters")

is.numeric(Data_Midterm$Pre_Sleep)

## [1] TRUE

head(Data_Midterm)

##     ID Exercise_Group    Sex Age Pre_Sleep Post_Sleep Sleep_Efficiency
## 1 P001           None   Male  35       5.8        4.7             81.6
## 2 P002           None   Male  57       6.6        7.4             75.7
## 3 P003           None Female  26        NA        6.2             82.9
## 4 P004           None Female  29       7.2        7.3             83.6
## 5 P005           None   Male  33       7.4        7.4             83.5
## 6 P006           None Female  33       6.6        7.1             88.5

Now we can create our new column titled Sleep_Difference.

Furthermore, I identified that there was 14 participants with no Sleep_Difference scores. I removed them from our data leaving us with 86 participants which you can view below.

Data_Midterm<-Data_Midterm%>%
  mutate(Sleep_Difference=Post_Sleep-Pre_Sleep)

Data_Midterm$Sleep_Difference %>% is.na() %>% which()

##  [1]  3 14 19 23 30 37 41 48 56 67 72 74 95 99

Data_Midterm<- Data_Midterm %>% filter(!is.na(Sleep_Difference))

knitr::kable(Data_Midterm)

ID	Exercise_Group	Sex	Age	Pre_Sleep	Post_Sleep	Sleep_Efficiency	Sleep_Difference
P001	None	Male	35	5.8	4.7	81.6	-1.1
P002	None	Male	57	6.6	7.4	75.7	0.8
P004	None	Female	29	7.2	7.3	83.6	0.1
P005	None	Male	33	7.4	7.4	83.5	0.0
P006	None	Female	33	6.6	7.1	88.5	0.5
P007	None	Male	32	6.0	6.7	83.6	0.7
P008	None	Female	30	8.1	9.0	73.4	0.9
P009	None	Male	37	5.5	5.1	88.2	-0.4
P010	None	Female	28	5.7	6.3	80.4	0.6
P011	None	Female	30	7.0	6.2	85.2	-0.8
P012	None	Male	20	5.5	4.6	82.9	-0.9
P013	None	Male	42	8.0	7.6	74.0	-0.4
P015	None	Female	33	5.3	4.6	89.6	-0.7
P016	None	Male	26	7.8	8.2	71.7	0.4
P017	None	Female	41	6.7	7.6	78.5	0.9
P018	None	Male	18	7.4	7.2	73.8	-0.2
P020	None	Female	37	7.1	6.8	81.5	-0.3
P021	None	Male	48	6.8	7.1	78.7	0.3
P022	None	Female	37	5.7	5.8	90.4	0.1
P024	None	Female	39	6.0	5.6	76.6	-0.4
P025	None	Male	20	6.2	7.1	81.1	0.9
P026	Cardio	Female	34	5.2	6.7	84.5	1.5
P027	Cardio	Female	28	6.6	7.8	75.9	1.2
P028	Cardio	Female	29	5.6	6.3	79.0	0.7
P029	Cardio	Male	36	6.0	6.3	87.6	0.3
P031	Cardio	Female	42	6.9	7.6	88.5	0.7
P032	Cardio	Female	18	6.9	7.9	87.8	1.0
P033	Cardio	Female	38	5.9	6.6	80.9	0.7
P034	Cardio	Female	20	5.2	6.0	92.8	0.8
P035	Cardio	Male	30	6.7	8.0	86.4	1.3
P036	Cardio	Female	19	5.4	5.7	88.0	0.3
P038	Cardio	Female	34	7.0	8.1	79.6	1.1
P039	Cardio	Male	39	5.6	6.3	81.3	0.7
P040	Cardio	Female	39	6.0	8.1	79.7	2.1
P042	Cardio	Female	28	5.6	7.3	81.9	1.7
P043	Cardio	Female	43	6.9	8.2	81.3	1.3
P044	Cardio	Female	33	5.8	7.1	88.7	1.3
P045	Cardio	Male	31	4.0	5.1	86.0	1.1
P046	Cardio	Male	30	7.1	8.5	95.0	1.4
P047	Cardio	Female	42	5.8	7.0	85.5	1.2
P049	Cardio	Male	40	6.7	8.2	82.5	1.5
P050	Cardio	Male	31	6.4	8.4	101.5	2.0
P051	Weights	Male	18	6.5	6.8	80.4	0.3
P052	Weights	Male	23	7.2	8.3	76.7	1.1
P053	Weights	Female	39	7.3	9.1	82.2	1.8
P054	Weights	Female	37	7.0	7.7	88.6	0.7
P055	Weights	Female	31	6.2	6.6	76.6	0.4
P057	Weights	Male	26	7.3	7.4	77.7	0.1
P058	Weights	Male	18	6.2	6.3	85.3	0.1
P059	Weights	Female	38	6.2	7.3	80.5	1.1
P060	Weights	Male	39	6.6	6.9	80.2	0.3
P061	Weights	Female	27	6.1	6.6	77.9	0.5
P062	Weights	Male	35	6.7	7.2	80.8	0.5
P063	Weights	Female	18	5.8	5.1	82.9	-0.7
P064	Weights	Female	38	5.1	5.2	80.9	0.1
P065	Weights	Female	38	5.5	7.2	76.1	1.7
P066	Weights	Female	46	7.0	8.7	74.8	1.7
P068	Weights	Female	22	5.4	5.7	88.0	0.3
P069	Weights	Female	45	6.4	7.2	79.1	0.8
P070	Weights	Female	25	7.5	8.7	89.5	1.2
P071	Weights	Female	27	6.6	7.4	87.4	0.8
P073	Weights	Female	44	5.6	6.3	83.6	0.7
P075	Weights	Male	32	6.5	7.0	81.4	0.5
P076	Cardio+Weights	Male	37	5.7	6.5	83.9	0.8
P077	Cardio+Weights	Female	37	5.9	7.3	92.4	1.4
P078	Cardio+Weights	Female	24	6.6	7.2	74.5	0.6
P079	Cardio+Weights	Male	38	6.6	7.4	89.0	0.8
P080	Cardio+Weights	Male	42	7.2	8.1	94.5	0.9
P081	Cardio+Weights	Female	38	8.0	8.9	74.5	0.9
P082	Cardio+Weights	Male	46	6.5	6.4	88.7	-0.1
P083	Cardio+Weights	Female	49	5.9	6.8	90.4	0.9
P084	Cardio+Weights	Female	31	8.0	8.4	80.2	0.4
P085	Cardio+Weights	Male	33	6.2	6.9	89.9	0.7
P086	Cardio+Weights	Female	46	6.2	7.4	83.1	1.2
P087	Cardio+Weights	Male	18	6.7	7.6	96.3	0.9
P088	Cardio+Weights	Male	34	7.5	8.9	81.2	1.4
P089	Cardio+Weights	Female	42	5.9	7.0	90.6	1.1
P090	Cardio+Weights	Female	41	7.2	7.6	79.9	0.4
P091	Cardio+Weights	Male	46	7.3	7.9	87.5	0.6
P092	Cardio+Weights	Male	18	5.6	6.6	86.3	1.0
P093	Cardio+Weights	Female	40	8.0	9.5	90.4	1.5
P094	Cardio+Weights	Male	35	6.1	7.2	84.4	1.1
P096	Cardio+Weights	Female	37	5.9	7.1	93.8	1.2
P097	Cardio+Weights	Male	24	4.4	4.7	88.9	0.3
P098	Cardio+Weights	Female	35	5.9	6.9	92.6	1.0
P100	Cardio+Weights	Male	32	6.5	7.3	84.2	0.8

Next we want to create a new column titled AgeGroup2 using via case_when. You can see below that we have successfully created this new column.

Data_Midterm<-Data_Midterm%>%
  mutate(AgeGroup2=case_when(
    Age <35~"Younger_Adult",
    Age >=35~"Older_Adult"
  ))

head(Data_Midterm)

##     ID Exercise_Group    Sex Age Pre_Sleep Post_Sleep Sleep_Efficiency Sleep_Difference     AgeGroup2
## 1 P001           None   Male  35       5.8        4.7             81.6             -1.1   Older_Adult
## 2 P002           None   Male  57       6.6        7.4             75.7              0.8   Older_Adult
## 3 P004           None Female  29       7.2        7.3             83.6              0.1 Younger_Adult
## 4 P005           None   Male  33       7.4        7.4             83.5              0.0 Younger_Adult
## 5 P006           None Female  33       6.6        7.1             88.5              0.5 Younger_Adult
## 6 P007           None   Male  32       6.0        6.7             83.6              0.7 Younger_Adult

4.5 DESCRIPTIVE STATISTICS

Below we are exploring the descriptive statistics for Sleep_Difference.

Sleep_Diff_Stats<- favstats(~Sleep_Difference, data = Data_Midterm) 
Sleep_Diff_Stats

##   min  Q1 median  Q3 max      mean        sd  n missing
##  -1.1 0.3   0.75 1.1 2.1 0.6825581 0.6610494 86       0

For Sleep_Difference column, the statistics is as follows:

Mean: 0.68
SD: 0.66
Min: -1.1
Max: 2.1

Below we are exploring the descriptive statistics for Sleep_Efficiency.

Sleep_Effic_Stats<-favstats(~Sleep_Efficiency, data = Data_Midterm)
Sleep_Effic_Stats

##   min     Q1 median     Q3   max     mean       sd  n missing
##  71.7 79.975   83.3 88.425 101.5 83.77558 5.973804 86       0

For Sleep_Efficiency column, the statistics is as follows:

Mean: 83.78
SD: 5.98
Min: 71.7
Max: 101.5

Below we explore the pairwise means of Sleep_Difference by Exercise_Group.

Sleep_Diff_by_Exercise<-favstats(Sleep_Difference~Exercise_Group, data = Data_Midterm)
Sleep_Diff_by_Exercise

##   Exercise_Group  min    Q1 median  Q3 max       mean        sd  n missing
## 1         Cardio  0.3  0.70    1.2 1.4 2.1 1.13809524 0.4852589 21       0
## 2 Cardio+Weights -0.1  0.65    0.9 1.1 1.5 0.86086957 0.3822649 23       0
## 3           None -1.1 -0.40    0.1 0.6 0.9 0.04761905 0.6384505 21       0
## 4        Weights -0.7  0.30    0.5 1.1 1.8 0.66666667 0.6126445 21       0

For pairwise means, the means are as follows:

Cardio: 1.14
Cardio+Weights: 0.86
None: 0.048
Weights: 0.67

Below we explore the pairwise means of Sleep_Efficiency by Exercise_Group.

Sleep_Effic_by_Exercise<-favstats(Sleep_Efficiency~Exercise_Group, data = Data_Midterm)
Sleep_Effic_by_Exercise

##   Exercise_Group  min   Q1 median   Q3   max     mean       sd  n missing
## 1         Cardio 75.9 81.3   85.5 88.0 101.5 85.44762 5.991629 21       0
## 2 Cardio+Weights 74.5 83.5   88.7 90.5  96.3 86.83478 5.980317 23       0
## 3           None 71.7 76.6   81.5 83.6  90.4 81.07143 5.551499 21       0
## 4        Weights 74.8 77.9   80.8 83.6  89.5 81.45714 4.311331 21       0

For pairwise means, the means are as follows:

Cardio: 85.45
Cardio+Weights: 86.83
None: 81.07
Weights: 81.46

4.6 VISUALIZATIONS (PLOTS)

I proceeded to create plots to be able to visualize things better.

ggplot(Data_Midterm, aes(x=Exercise_Group, y=Sleep_Difference))+
  geom_boxplot(fill="orange", color="black")+
  coord_flip() +
  labs(
    title = "Exercise Group by Sleep Difference",
    x="Exercise Type",
    y="Sleep Difference")+
theme(plot.title = element_text(size=20, family="serif", face="bold"),
             axis.title = element_text(size=15, family ="serif"),
             axis.text = element_text(size = 10, family = "serif"))

Figure 4.1: Boxplot showing exercise group by sleep difference in order to look at it visually

Above a box plot was created to look at sleep difference by exercise group.

ggplot(Data_Midterm, aes(x=Exercise_Group, y=Sleep_Efficiency))+
  geom_boxplot(fill="orange", color="black")+
  coord_flip() +
  labs(
    title = "Exercise Group by Sleep Efficiency",
    x="Exercise Type",
    y="Sleep Efficiency")+
  theme(plot.title = element_text(size=20, family="serif", face="bold"),
        axis.title = element_text(size=15, family ="serif"),
        axis.text = element_text(size = 10, family = "serif"))

Figure 4.2: Boxplot showing sleep efficiency by exercise group in order to look at it visually

Above a box plot was created to look at sleep efficiency by exercise group.

ggplot(Data_Midterm, aes(x=Sleep_Difference, y=Sleep_Efficiency))+
  geom_point(alpha=1, size=2)+
  labs(
    title = "Sleep Difference by Sleep Efficiency",
    x="Sleep Difference",
    y="Sleep Efficiency"
  )+
  geom_smooth(method = "lm", se = FALSE, color="orange")+
  theme(plot.title = element_text(size=23, family="serif", face="bold"),
        axis.title = element_text(size=15, family ="serif"),
        axis.text = element_text(size = 10, family = "serif"))

## `geom_smooth()` using formula = 'y ~ x'

Figure 4.3: Graph with a trend line showing sleep difference by sleep efficiency in order to look at it visually

Above a graph was created with a trend line to look at sleep difference by sleep efficiency.

4.7 T-TEST’S

I went ahead and conducted two t-tests.

Below I conducted a two sample t-test for sleep difference by sex (male vs female).

SleepDiffbySex_Ttest<-t.test(Sleep_Difference ~ Sex, data = Data_Midterm)
SleepDiffbySex_Ttest

## 
##  Welch Two Sample t-test
## 
## data:  Sleep_Difference by Sex
## t = 1.5801, df = 77.647, p-value = 0.1182
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
##  -0.05865017  0.50972574
## sample estimates:
## mean in group Female   mean in group Male 
##            0.7795918            0.5540541

Mean in Female Group: 0.78
Mean in Male Group: 0.55
P value: 0.12
The difference between males and females in their sleep differences is not significant.

Below I conducted a two sample t-test for sleep difference by age group (young vs older adults).

SleepDiffbyAge_Ttest<-t.test(Sleep_Difference ~ AgeGroup2, data = Data_Midterm)
SleepDiffbyAge_Ttest

## 
##  Welch Two Sample t-test
## 
## data:  Sleep_Difference by AgeGroup2
## t = 0.79148, df = 83.467, p-value = 0.4309
## alternative hypothesis: true difference in means between group Older_Adult and group Younger_Adult is not equal to 0
## 95 percent confidence interval:
##  -0.1712505  0.3976574
## sample estimates:
##   mean in group Older_Adult mean in group Younger_Adult 
##                   0.7404762                   0.6272727

Mean in Older Adult Group: 0.74
Mean in Younger Adult Group: 0.63
P value: 0.43
The difference between younger adult and older adult participants in their sleep difference is not significant.

4.8 ANOVA’S

I went ahead and conducted two ANOVA’s along with posthoc comparisons if the ANOVA was significant.

Below I conducted an ANOVA for sleep difference by exercise group and posthoc comparisons.

SleepDiffbyExercise_ANOVA<-aov(Sleep_Difference ~ Exercise_Group, data = Data_Midterm)

summary(SleepDiffbyExercise_ANOVA)

##                Df Sum Sq Mean Sq F value   Pr(>F)    
## Exercise_Group  3  13.56   4.520   15.72 3.67e-08 ***
## Residuals      82  23.58   0.288                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

supernova(SleepDiffbyExercise_ANOVA)

##  Analysis of Variance Table (Type III SS)
##  Model: Sleep_Difference ~ Exercise_Group
## 
##                              SS df    MS      F   PRE     p
##  ----- --------------- | ------ -- ----- ------ ----- -----
##  Model (error reduced) | 13.560  3 4.520 15.717 .3651 .0000
##  Error (from model)    | 23.583 82 0.288                   
##  ----- --------------- | ------ -- ----- ------ ----- -----
##  Total (empty model)   | 37.144 85 0.437

Important Information for above:

F = 15.717
df = 85
p = .0000
PRE = .37
effect size = Small

TukeyHSD(SleepDiffbyExercise_ANOVA)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Sleep_Difference ~ Exercise_Group, data = Data_Midterm)
## 
## $Exercise_Group
##                              diff        lwr         upr     p adj
## Cardio+Weights-Cardio  -0.2772257 -0.7017134  0.14726203 0.3237562
## None-Cardio            -1.0904762 -1.5245041 -0.65644825 0.0000000
## Weights-Cardio         -0.4714286 -0.9054565 -0.03740063 0.0278779
## None-Cardio+Weights    -0.8132505 -1.2377382 -0.38876282 0.0000171
## Weights-Cardio+Weights -0.1942029 -0.6186906  0.23028480 0.6287294
## Weights-None            0.6190476  0.1850197  1.05307556 0.0018927

TukeyHSD for above code:

TukeyHSD:

No exercise is statistically different to Cardio (P=0.0000). Cardio has a greater mean difference of 1.09 than no exercise, meaning Cardio has a higher sleep difference.
Weights is statistically different to Cardio (P=0.03). Cardio has a greater mean difference of .47 than weights, meaning Cardio has a higher sleep difference.
No exercise is statistically different to Cardio + Weights (P= 0.00002). Cardio+weights has a greater mean difference of .81 than no exercise, meaning Cardio+weights has a higher sleep difference.
Weights is statistically different to no exercise (P=0.001). Weights has a greater mean difference of 0.62 than no exercise, meaning weights has a higher sleep difference.
Overall, Cardio has the best sleep difference due to having the largest mean difference (1.09) and smallest p value (p=0.000).

Below I conducted an ANOVA for sleep efficiency by exercise group and posthoc comparisons.

SleepEfficbyExercise_ANOVA<-aov(Sleep_Efficiency ~ Exercise_Group, data = Data_Midterm)

summary(SleepEfficbyExercise_ANOVA)

##                Df Sum Sq Mean Sq F value  Pr(>F)   
## Exercise_Group  3  540.4   180.1   5.925 0.00104 **
## Residuals      82 2492.9    30.4                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

supernova(SleepEfficbyExercise_ANOVA)

##  Analysis of Variance Table (Type III SS)
##  Model: Sleep_Efficiency ~ Exercise_Group
## 
##                                SS df      MS     F   PRE     p
##  ----- --------------- | -------- -- ------- ----- ----- -----
##  Model (error reduced) |  540.400  3 180.133 5.925 .1782 .0010
##  Error (from model)    | 2492.939 82  30.402                  
##  ----- --------------- | -------- -- ------- ----- ----- -----
##  Total (empty model)   | 3033.339 85  35.686

Important Information for above:

F = 5.93
df = 85
p = .001
PRE = .18
Effect size = Very Small

TukeyHSD(SleepEfficbyExercise_ANOVA)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Sleep_Efficiency ~ Exercise_Group, data = Data_Midterm)
## 
## $Exercise_Group
##                              diff        lwr         upr     p adj
## Cardio+Weights-Cardio   1.3871636  -2.977172  5.75149915 0.8383629
## None-Cardio            -4.3761905  -8.838613  0.08623232 0.0566544
## Weights-Cardio         -3.9904762  -8.452899  0.47194661 0.0962888
## None-Cardio+Weights    -5.7633540 -10.127690 -1.39901844 0.0046379
## Weights-Cardio+Weights -5.3776398  -9.741975 -1.01330416 0.0094267
## Weights-None            0.3857143  -4.076709  4.84813708 0.9958617

TukeyHSD for above code:

No exercise is not statistically different to Cardio but trending to significance (p= 0.057). Cardio has a greater mean difference of 4.38 than no exercise, meaning Cardio has a higher sleep efficiency.
No exercise is statistically significant to Cardio+Weights (p=0.005). Cardio+Weights has a greater mean difference of 5.76 than no exercise, meaning Cardio+Weights has a higher sleep efficiency.
Weights is statistically significant to Cardio+Weights (p=0.009). Cardio+Weights has a greater mean difference of 5.38 than weights, meaning Cardio+Weights has a higher sleep efficiency.
Overall Cardio+Weights has the best sleep efficiency due to having a greater mean (5.76 and 5.38) and smallest p value (0.005 and 0.009)

4.9 SYNTHESIS & RECOMMENDATION

If I had to pick one exercise regimen to recommend to improve overall sleep, I would recommend Weights+Cardio. Regardless of sex and age, Weights+Cardio was significant in improving sleep difference before and after exercise than no exercise (P= 0.00002) and improving sleep efficiency after this exercise rather than no exercise (p=0.005). Although Cardio had a smaller p value (P=0.0000) when compared to no exercise when it comes to sleep difference before and after exercise type, it was only trending to significance for sleep efficiency (p= 0.057). Cardio did improve sleep difference before and after more than Cardio+Weights, but did not significantly improve sleep efficiency. Visually, in graph 1 and 2, you can see that Cardio+Weights are close to each other, but Cardio+Weights exceeds Cardio in sleep efficiency. It is important to consider overall sleep, and therefore, I recommend Weights+Cardio.

4.10 REFLECTION

The most challenging part of this report was separating the column pre_sleep to have only the numbers for my analysis. I think I panicked, but after taking time to think and read over notes, I was able to figure it out and successfully run my code. I also felt that interpreting the pairwise comparisons was challenging as it has been a while doing posthoc, but I managed to finish! Overall, I felt confident in the rest of the task because I have been practicing my coding skills and always take my time when I do any assignment. It is like I relearn when I do assignments, which helps tremendously.