STAT100 Written Assessment 1

Lillie Ridgley

220254110

Research Question

Methodology

Data

Water Salinity

Table 1

Town	House_no	Water_salin	House average	Town average	house Varience	town varience
Talu	223	573 mg/L
Talu	223	553 mg/L
Talu	223	562 mg/L
Talu	223	559 mg/L
Talu	223	570 mg/L
Talu	223	573 mg/L	573 mg/L		68.4
Talu	49	555 mg/L
Talu	49	552 mg/L	555 mg/L		4.5
Talu	128	552 mg/L
Talu	84	554 mg/L
Talu	84	562 mg/L	554 mg/L		32
Talu	228	548 mg/L		573 mg/L		74.99
Maeva	546	545 mg/L
Maeva	546	551 mg/L
Maeva	546	537 mg/L
Maeva	546	559 mg/L
Maeva	546	547 mg/L	545 mg/L		65.2
Maeva	493	544 mg/L
Maeva	96	544 mg/L
Maeva	96	545 mg/L
Maeva	96	537 mg/L	544 mg/L		13.67
Maeva	325	539 mg/L
Maeva	214	545 mg/L		545 mg/L		40.16
Kinsale	260	504 mg/L
Kinsale	116	524 mg/L
Kinsale	80	516 mg/L
Kinsale	330	525 mg/L
Kinsale	214	509 mg/L		504 mg/L		224.7

Other Variables

Table 2

Town	Water_body_Type	Heart_Disease	Oncology	Antenatal_Clinic	Stroke_Clinic	Primary_Education	Secondary_Education	Average_Water_Salinity
Maeva	in_land	0	5	14	6	98	135	545 mg/L
Talu	coastal	2	1	9	4	66	140	573 mg/L
Kinsale	none	2	2	2	5	70	107	504 mg/L

Results

Percentage of variable prominence within entire population/s: includes water salinity population variance.

Table 3

Town	Population	PopHD %	PopONC%	PopANTE%	PopSTRO%	PopPE%	PopSE%	Water_Salinity PopVar
Maeva	1199	0.00%	0.42%	1.17%	0.5%	8.17%	11.26%	36.51
Talu	850	0.24%	0.12%	1.06%	0.47%	7.76%	16.47%	68.74
Kinsale	753	0.27%	0.27%	0.27%	0.66%	9.3%	14.21%	179.76

Figure 1

Visualization of Water Salinity data by town.

Figure 2

Visualization of data summary (health)

( Data Collected from the island of Bonne Santé. Please note: Columns in grey account for total count across all towns. )

Figure 3

Visualization of data summary (education)

Data Description

Summary and Discussion

RStudio Script

> round(0.417014178482068, digits = 2)

[1] 0.42

> round(1.167639699749791, digits = 2)

[1] 1.17

> round(0.500417014178482, digits = 2)

[1] 0.5

> round(8.17347789824854, digits = 2)

[1] 8.17

> round(11.259382819015847, digits = 2)

[1] 11.26

> x <- c(545, 551, 537, 559, 547, 544, 544, 545, 537, 539, 545)

> var_pop <- function(x) {mean((x - mean(x))^2)}

> var_pop(x)

[1] 36.5124

> round(36.5124, digits = 2)

[1] 36.51

> x <- c(504, 542, 516, 525, 509)

> var_pop(x)

[1] 179.76

> x <- c(573, 553, 562, 559, 570, 573, 555, 552, 552, 554, 562, 548)

> var_pop(x)

[1] 68.74306

> round(68.74306, digits = 2)

[1] 68.74

> round(0.23529417647059, digits = 2)

[1] 0.24

> round(0.265604249667995, digits = 2)

[1] 0.27

> round(0.117647058823529, digits = 2)

[1] 0.12

> round(1.058823529411765, digits = 2) [

1] 1.06

> round(0.470588235294118, digits = 2) [1] 0.47

> roundfunction (x, digits = 0) .Primitive("round")

> round(0.664010624169987, digits = 2)

[1] 0.66

> round(7.764705882352941, digits = 2) [1] 7.76 > round(9.296148738379814, digits = 2) [1] 9.3 > round(16.470588235294118, digits = 2) [1] 16.47 > round function (x, digits = 0) .Primitive("round") > round(14.209827357237716, digits = 2) [1] 14.21

> library(readxl)

> theislands_watersal <- read_excel("~/Desktop/STAT100 assessmet 1/theislands.watersal.xlsx")

> View(theislands_watersal) > library(ggplot2)

> ggplot(data = theislands_watersal, aes(x=Town, y=Water_salin)) + geom_point()

>library(readxl)

> theislands_summary <- read_excel("~/Desktop/STAT100 assessmet 1/theislands.summary.xlsx")

> View(theislands_summary)

> library(tidyr)

> theislands_summary<-pivot_longer(theislands_summary, cols = c("Heart_Disease", "Oncology", "Antenatal_Clinic", "Stroke_Clinic"), names_to = "variables", values_to = "Number")

>library(ggplot2)

> barchart + geom_col(data = theislands_summary, aes(x = Town, y = Number, fill = variables), position = "dodge")

> library(tidyr)

> theislands_summary<-pivot_longer(theislands_summary, cols = c("Primary_Education", "Secomdary_Education"), names_to = "Education", values_to = "Count")

Error in `pivot_longer()`: ! Can't subset columns that don't exist. ✖ Column `Secomdary_Education` doesn't exist. Run `rlang::last_trace()` to see where the error occurred.

> theislands_summary<-pivot_longer(theislands_summary, cols = c("Primary_Education", "Secondary_Education"), names_to = "Education", values_to = "Count")

> library(ggplot2)

> barchart + geom_col(data = theislands_summary, aes(x = Town, y = Count, fill = Education), position = "dodge")

> theislands_summary<-theislands_summary[, -c(3,4,5,6)] > barchart + geom_col(data = theislands_summary, aes(x = Town, y = Count, fill = Education), position = "dodge")

> library(ggplot2) > barchart + geom_col(data = theislands_summary, aes(x = Town, y = Count, fill = Education), position = "dodge")

> barchart + geom_col(data = theislands_summary, aes(x = Town, y = Count, fill = Education), position = "dodge")