Data Visualization and Analysis

Project instructions

Make use of the file child.csv available to you and load it into RStudio. It is a modified dataset (adapted from the autism dataset at https://www.kaggle.com) which contains attributes/variables for a number of children who were tested for autism.

You are required to conduct the following tasks using R where coding is required. If, for a specific task, you are unable to undertake data preparation using R but you are able prepare your data by other means, explain how you have achieved this and complete the rest of the task in R.

Autism data

To follow along this tutorial, you can download the dataset below:

Import packages

# Please make sure your computer is connected to the internet!


packages_needed <- c(
  "dplyr", "tidyr", "readr", "ggplot2", "vtable", "stringr", "inspectdf", "forcats", "knitr", "kableExtra", "gt",
  "patchwork", "treemap"
)

if (!require(install.load)) {
  install.packages("install.load")
}

install.load::install_load(packages_needed)


theme_set(theme_bw())

Import dataset

child_data <- read_csv("data/child.csv")

Data preprocessing

This removes white spaces and ' found in any variable in the children autism dataset.

clean_data <- child_data %>%
  mutate_if(~ is.character(.), ~ str_squish(str_remove_all(string = ., pattern = "'"))) %>%
  mutate_at(vars(5:11), as.factor)

label <- data.frame(
  score = "A numeric value obtained from the standard tests for autism",
  score2 = "A numeric value obtained using alternative (non-standard) tests for autism",
  age = "The child’s age.",
  cost = "Total cost of testing the child in pounds.",
  gender = "m or f (male or female)",
  ethnicity = "Child’s ethnicity",
  jaundice = "Whether the child was born with jaundice",
  autismFH = "Whether there is family history of autism",
  residence = "Country of residence",
  relation = "Who completed the test for the child",
  autism = "Whether the child has autism (YES or NO)"
)

vtable(clean_data, labels = label, factor.limit = 5)

Autism dataset

After data cleaning

score	score2	age	cost	gender	ethnicity	jaundice	autismFH	residence	relation	autism
4.6	4.4	5	1170.0	m	Others	no	no	Jordan	Parent	NO
4.4	4.4	5	1090.0	m	Middle Eastern	no	no	Jordan	Parent	NO
4.8	4.3	5	1130.0	m	NA	no	no	Jordan	NA	NO
3.6	3.6	4	980.0	f	NA	yes	no	Jordan	NA	NO
9.7	9.5	4	2475.0	m	Others	yes	no	United States	Parent	YES
4.9	4.5	3	1315.0	m	NA	no	yes	Egypt	NA	NO
7.3	7.1	4	1815.0	m	White-European	no	no	United Kingdom	Parent	YES
7.5	7.1	4	1955.0	f	Middle Eastern	no	no	Bahrain	Parent	YES
6.7	6.4	2	1665.0	f	Middle Eastern	no	no	Bahrain	Parent	YES
5.9	6.2	2	1445.0	f	NA	no	yes	Austria	NA	NO
6.8	7.0	1	1760.0	m	White-European	yes	no	United Kingdom	Self	YES
3.6	3.5	4	880.0	f	NA	no	no	Kuwait	NA	NO
9.0	9.4	3	2300.0	m	White-European	yes	no	United States	Parent	YES
1.6	2.1	3	370.0	f	Black	no	no	United Arab Emirates	Parent	NO
15.0	14.0	5	3840.0	m	White-European	no	no	Europe	Parent	YES
10.0	10.0	7	3267.5	m	White-European	no	no	Malta	Parent	YES
8.5	8.7	3	2205.0	m	South Asian	no	no	Bulgaria	Parent	YES
0.5	0.0	6	2557.5	m	Others	no	no	United States	Parent	NO
7.5	8.0	2	1915.0	m	White-European	no	yes	United States	Parent	YES
7.7	8.1	4	1865.0	m	NA	no	no	Egypt	NA	YES
7.5	7.6	4	1875.0	m	White-European	yes	no	South Africa	Parent	YES
4.7	5.1	8	2830.0	f	NA	no	no	Egypt	NA	NO
2.4	1.9	3	660.0	m	Asian	no	no	India	Parent	NO
5.6	5.7	5	1470.0	f	South Asian	no	no	India	Parent	NO
7.8	8.0	2	1970.0	m	NA	no	no	Egypt	NA	YES
6.6	6.7	5	1620.0	m	White-European	no	yes	United Kingdom	Relative	NO
6.3	6.7	5	1515.0	f	Middle Eastern	no	no	Afghanistan	Self	NO
10.0	10.0	4	2560.0	m	White-European	yes	no	United States	Parent	YES
4.4	4.6	5	1190.0	m	NA	no	yes	United Arab Emirates	NA	NO
3.4	3.9	3	850.0	f	Others	yes	yes	Georgia	Parent	NO
10.0	10.0	2	2450.0	m	White-European	no	no	United Kingdom	Parent	YES
3.6	3.1	5	980.0	m	Pasifika	yes	no	New Zealand	Parent	NO
7.0	6.9	9	3025.0	m	NA	no	no	Egypt	NA	YES
5.8	6.0	4	1540.0	m	South Asian	yes	no	India	Care professional	NO
5.3	5.4	5	1325.0	m	South Asian	yes	no	India	Parent	NO
1.5	1.8	6	2625.0	f	Middle Eastern	yes	no	Syria	Parent	NO
3.0	3.4	3	780.0	f	NA	no	no	Syria	NA	NO
1.4	1.7	6	2627.5	m	Asian	no	no	New Zealand	Parent	NO
10.0	10.0	3	2510.0	m	White-European	yes	no	United Kingdom	Parent	YES
8.1	7.6	3	1965.0	m	Asian	no	no	India	Parent	YES
6.9	7.2	4	1725.0	m	NA	yes	no	Jordan	NA	NO
0.4	0.2	3	30.0	m	Middle Eastern	no	no	Afghanistan	Parent	NO
4.3	4.8	5	1045.0	f	Middle Eastern	no	no	Jordan	Parent	NO
7.6	7.5	3	1890.0	f	NA	no	no	Jordan	NA	YES
2.7	3.2	1	605.0	m	Middle Eastern	no	no	Jordan	Parent	NO
5.9	5.9	3	1535.0	f	Middle Eastern	yes	no	Iraq	Relative	NO
4.0	3.5	3	920.0	f	Middle Eastern	yes	no	Iraq	Relative	NO
6.5	6.0	5	1565.0	m	NA	no	no	Jordan	NA	YES
7.9	7.4	5	2055.0	f	White-European	yes	no	New Zealand	Parent	YES
2.0	1.5	6	2640.0	m	Middle Eastern	no	yes	Jordan	Parent	NO
3.8	3.4	6	2790.0	m	NA	yes	no	Jordan	NA	NO
6.1	6.5	3	1475.0	m	Asian	no	no	India	Parent	NO
5.6	6.0	5	1360.0	m	NA	no	no	Jordan	NA	NO
9.4	9.7	6	3192.5	m	White-European	yes	no	United States	Parent	YES
4.8	4.7	4	1280.0	m	NA	no	no	United Arab Emirates	NA	NO
1.9	2.3	4	485.0	m	White-European	no	no	Australia	Parent	NO
1.5	1.0	5	435.0	m	NA	no	no	Saudi Arabia	NA	NO
9.9	9.5	3	2505.0	f	White-European	no	no	Georgia	Parent	YES
8.3	8.6	8	3125.0	f	Middle Eastern	no	no	Armenia	Care professional	YES
7.3	6.8	3	1915.0	m	Hispanic	no	yes	United States	Parent	YES
3.7	3.5	3	925.0	m	Turkish	no	no	Turkey	Relative	NO
9.7	10.0	8	3227.5	m	White-European	no	no	United States	Parent	YES
6.5	6.0	3	1565.0	f	White-European	yes	no	Australia	Parent	NO
9.3	9.7	8	3175.0	m	Asian	yes	no	Pakistan	Parent	YES
4.4	4.2	7	2850.0	m	Middle Eastern	no	no	United States	Parent	NO
1.4	1.0	9	2587.5	m	Middle Eastern	no	no	Jordan	Parent	NO
3.0	3.0	3	710.0	m	White-European	no	no	United Kingdom	Parent	NO
4.1	4.3	3	1075.0	m	White-European	no	no	United Kingdom	Parent	NO
5.8	5.7	3	1410.0	f	NA	no	yes	Pakistan	NA	NO
9.7	9.8	3	2365.0	m	White-European	no	yes	Canada	Parent	YES
4.8	4.3	6	2867.5	m	Asian	no	no	Oman	Parent	NO
4.6	4.8	6	2822.5	f	White-European	yes	no	United Kingdom	Parent	NO
7.5	7.1	5	1825.0	m	South Asian	no	no	India	Parent	YES
8.3	8.0	4	2045.0	f	Middle Eastern	no	no	Canada	Parent	YES
6.8	6.7	7	3015.0	f	Middle Eastern	no	yes	Canada	Parent	YES
6.0	6.0	4	1490.0	m	White-European	no	no	United Kingdom	Parent	NO
9.7	10.0	2	2345.0	f	Others	no	no	Canada	Parent	YES
4.4	4.8	7	2820.0	m	White-European	yes	no	New Zealand	Parent	NO
9.1	8.9	3	2365.0	m	Latino	no	yes	Brazil	Parent	YES
5.9	6.0	6	2932.5	m	White-European	yes	no	New Zealand	Parent	NO
2.7	3.1	3	665.0	m	White-European	no	no	New Zealand	Parent	NO
9.2	9.3	6	3210.0	m	White-European	yes	yes	New Zealand	Parent	YES
8.3	8.0	7	3130.0	m	White-European	no	no	United States	Parent	YES
5.0	4.7	4	1210.0	m	Asian	no	no	South Korea	Parent	NO
6.0	5.8	3	1500.0	m	Asian	no	no	India	Parent	NO
9.2	9.4	2	2390.0	f	White-European	no	no	United Kingdom	Parent	YES
7.1	7.3	2	1735.0	f	White-European	no	no	United Kingdom	Parent	YES
10.0	10.0	3	2560.0	m	White-European	no	no	South Africa	Parent	YES
4.9	4.6	4	1135.0	m	Latino	no	yes	Costa Rica	Parent	NO
8.5	8.2	5	2105.0	m	Hispanic	no	no	United States	Parent	YES
8.3	8.7	3	2045.0	f	White-European	no	no	Australia	Parent	YES
5.2	5.4	2	1310.0	f	White-European	yes	yes	United Kingdom	Relative	NO
7.6	7.3	4	1850.0	m	Asian	no	no	India	Parent	YES
8.7	8.5	4	2255.0	m	Asian	no	no	India	Parent	YES
10.0	10.0	5	2530.0	m	Latino	no	no	United States	Parent	YES
7.1	7.3	6	3052.5	m	Hispanic	no	no	United States	Parent	YES
8.6	8.5	2	2200.0	m	White-European	no	no	Sweden	Parent	YES
8.5	8.1	3	2065.0	m	White-European	no	no	Australia	Parent	YES
8.3	8.8	3	2145.0	m	White-European	no	yes	United States	Parent	YES
1.9	1.4	6	2635.0	m	White-European	no	yes	United States	Parent	NO
6.3	6.3	2	1505.0	f	White-European	yes	yes	United Kingdom	Relative	NO
7.8	8.2	5	2030.0	f	Asian	no	no	Philippines	Parent	YES
2.9	2.9	8	2740.0	f	White-European	no	no	United Kingdom	Parent	NO
4.7	4.4	1	1105.0	m	Others	no	no	United States	Relative	NO
4.4	4.1	3	1110.0	m	Asian	no	yes	Malaysia	Parent	NO
8.2	8.6	3	2040.0	m	Asian	yes	no	Philippines	Parent	YES
8.2	8.7	1	2050.0	m	White-European	yes	no	United Kingdom	Parent	YES
2.4	2.5	3	640.0	f	White-European	yes	yes	United Kingdom	Parent	NO
4.8	4.6	2	1190.0	m	Asian	no	no	Argentina	Parent	NO
3.1	3.6	6	2732.5	m	Asian	no	no	Japan	Parent	NO
6.4	6.0	4	1520.0	m	NA	no	no	Syria	NA	YES
3.9	4.3	3	955.0	f	White-European	no	no	United States	Parent	NO
7.1	7.2	7	3012.5	m	White-European	no	no	United States	Parent	YES
9.7	9.8	3	2435.0	m	White-European	no	no	Australia	Parent	YES
6.1	6.0	3	1435.0	m	South Asian	no	no	India	Parent	NO
10.0	10.0	2	2500.0	m	Asian	yes	no	India	Relative	YES
9.6	9.6	1	2460.0	f	Asian	no	no	United States	Parent	YES
6.6	6.6	5	1600.0	f	White-European	no	no	United States	Parent	NO
6.5	6.8	3	1645.0	m	Asian	no	no	Bangladesh	Relative	NO
4.2	4.1	3	1110.0	m	Asian	no	yes	Bangladesh	Parent	NO
7.8	7.8	3	1910.0	m	Asian	no	no	Bangladesh	Relative	YES
4.5	4.1	1	1155.0	m	White-European	no	no	United States	Parent	NO
9.8	10.0	6	3230.0	m	White-European	no	no	United Kingdom	Relative	YES
5.1	5.2	3	1285.0	m	NA	yes	no	Qatar	NA	NO
8.8	9.0	5	2280.0	f	White-European	yes	no	Ireland	Parent	YES
8.4	8.0	4	2070.0	m	Asian	no	no	India	Parent	YES
6.8	7.2	9	3010.0	m	NA	yes	no	Jordan	NA	YES
7.9	8.3	3	1915.0	f	Asian	yes	no	United Kingdom	Parent	YES
6.5	6.9	8	2985.0	m	Asian	no	no	India	Parent	NO
7.6	7.5	3	1860.0	f	White-European	yes	no	United States	Parent	YES
9.2	9.2	1	2300.0	m	White-European	no	no	New Zealand	Parent	YES
4.0	3.6	8	2792.5	m	White-European	no	no	New Zealand	Parent	NO
5.9	6.3	4	1565.0	m	White-European	yes	yes	United Kingdom	Parent	NO
5.9	6.4	3	1475.0	f	Black	no	no	Canada	Parent	NO
9.8	10.0	3	2500.0	m	White-European	no	yes	United Kingdom	Parent	YES
4.4	4.8	5	1100.0	m	White-European	no	no	Romania	Parent	NO
6.9	7.1	3	1695.0	f	White-European	yes	no	United Kingdom	Parent	YES
0.0	0.2	4	-30.0	f	Hispanic	no	no	United States	Parent	NO
5.6	5.1	9	2927.5	m	NA	yes	no	Qatar	NA	NO
10.0	10.0	8	3250.0	m	White-European	yes	yes	United Kingdom	Parent	YES
7.6	7.3	6	3077.5	f	White-European	no	no	Australia	Parent	YES
6.5	6.4	1	1665.0	f	White-European	no	no	Netherlands	Parent	NO
6.5	6.9	3	1635.0	m	South Asian	no	no	India	Parent	YES
7.9	8.4	3	2065.0	m	Asian	no	no	India	Relative	YES
6.9	6.4	6	3010.0	f	White-European	no	no	United Kingdom	Parent	NO
8.4	8.5	3	2060.0	m	Black	yes	no	United States	Parent	YES
5.8	6.0	3	1460.0	m	NA	yes	no	Lebanon	NA	NO
8.5	8.3	1	2165.0	m	White-European	no	no	Germany	Care professional	YES
5.8	6.2	3	1520.0	m	Asian	no	no	India	Parent	NO
3.5	3.6	3	865.0	m	NA	no	no	Latvia	NA	NO
6.9	6.4	3	1635.0	m	South Asian	no	yes	Saudi Arabia	Parent	YES
7.6	7.2	3	1910.0	m	Black	no	yes	United States	Parent	YES
6.5	6.1	6	3000.0	m	White-European	yes	no	United States	Parent	NO
9.3	9.3	3	2255.0	m	White-European	no	no	United States	Parent	YES
8.8	9.3	4	2200.0	f	White-European	no	no	New Zealand	Parent	YES
9.8	9.5	5	2480.0	m	Others	yes	no	United Kingdom	Parent	YES
6.3	6.4	5	1585.0	f	Asian	no	no	United Kingdom	Parent	NO
7.6	7.9	5	1870.0	f	White-European	no	yes	United Kingdom	Parent	YES
7.2	7.4	8	3052.5	m	White-European	no	yes	United Kingdom	Parent	YES
10.0	10.0	7	3265.0	m	White-European	no	no	United Kingdom	Parent	YES
7.5	7.2	2	1955.0	m	NA	no	no	Jordan	NA	YES
7.0	7.4	8	3030.0	m	White-European	no	no	Australia	Parent	YES
2.7	3.1	8	2692.5	f	White-European	no	yes	United Kingdom	Parent	NO
7.1	7.1	6	3052.5	m	Black	no	no	United States	Parent	YES
4.3	3.9	3	1025.0	m	Asian	no	no	India	Relative	NO
5.1	5.5	1	1235.0	f	Others	no	no	Australia	Self	NO
3.4	3.3	7	2770.0	m	Middle Eastern	no	no	United Arab Emirates	Parent	NO
8.5	8.3	5	2145.0	m	Others	no	no	Australia	Parent	YES
8.3	7.8	7	3120.0	m	NA	yes	no	Russia	NA	YES
9.1	8.9	2	2285.0	f	White-European	no	no	Austria	Parent	YES
5.1	5.3	4	1265.0	f	White-European	no	no	Italy	Parent	NO
3.5	3.6	3	945.0	f	White-European	no	yes	Australia	Relative	NO
10.0	10.0	2	2550.0	m	White-European	no	no	Australia	Parent	YES
5.5	5.9	2	1285.0	f	Others	yes	no	United Kingdom	Self	NO
5.3	5.0	3	1315.0	m	NA	yes	no	Qatar	NA	NO
4.1	4.2	7	2805.0	m	White-European	no	no	United Kingdom	Parent	NO
3.4	2.9	7	2735.0	m	White-European	no	no	United Kingdom	Parent	NO
9.7	9.5	8	3212.5	m	White-European	no	no	United Kingdom	Parent	YES
3.8	4.3	3	990.0	m	Asian	no	no	Bangladesh	Parent	NO
6.9	6.6	3	1785.0	m	Asian	no	no	Bangladesh	Parent	YES
8.3	8.4	3	2095.0	f	NA	yes	no	China	NA	YES
3.8	3.5	3	1020.0	f	NA	no	no	Pakistan	NA	NO
8.5	8.5	2	2125.0	m	Hispanic	no	no	United States	Self	YES
5.7	5.8	1	1335.0	f	Asian	no	no	Australia	Parent	NO
7.9	8.1	2	1985.0	m	Asian	no	no	India	Parent	YES
5.5	5.6	3	1455.0	m	Black	yes	no	United Kingdom	Parent	NO
7.9	7.4	3	1935.0	m	White-European	yes	no	United States	Parent	YES
9.5	9.4	5	2395.0	f	Black	no	no	Nigeria	Parent	YES
5.5	5.0	7	2905.0	m	White-European	no	no	United Kingdom	Parent	NO
9.1	9.5	3	2335.0	f	White-European	no	yes	Australia	Parent	YES
7.9	7.5	3	1975.0	m	NA	no	no	Lebanon	NA	YES
7.7	7.9	7	3070.0	m	White-European	no	no	Armenia	Parent	YES
6.8	7.1	2	1660.0	m	Hispanic	no	no	United States	Relative	YES
6.6	6.8	3	1650.0	f	White-European	yes	yes	United Kingdom	Parent	NO
4.9	4.7	4	1255.0	m	NA	no	no	Iraq	NA	NO
2.6	2.7	3	620.0	m	White-European	no	no	U.S. Outlying Islands	Parent	NO
7.4	7.2	7	3047.5	m	Black	no	no	Australia	Parent	YES
6.6	6.5	3	1600.0	m	Pasifika	no	no	New Zealand	Care professional	YES
9.5	9.9	3	2385.0	m	South Asian	no	no	India	Parent	YES
3.9	3.8	8	2787.5	m	White-European	no	yes	Australia	Parent	NO
3.4	3.6	8	2772.5	m	White-European	yes	yes	Australia	Parent	NO
8.5	8.1	3	2065.0	f	White-European	no	yes	United Kingdom	Parent	YES
4.4	4.7	4	1020.0	m	South Asian	no	no	India	Relative	NO
7.4	7.5	6	3052.5	f	Asian	yes	no	India	Parent	YES
7.7	7.4	3	1975.0	f	White-European	yes	no	United Kingdom	Parent	YES
5.1	5.2	4	1185.0	f	White-European	no	no	United Kingdom	Parent	NO
8.7	9.2	5	2185.0	m	Asian	no	no	Nepal	Care professional	YES
9.9	10.0	7	3252.5	m	White-European	no	no	United Kingdom	Parent	YES
3.9	4.4	3	925.0	m	Asian	no	no	Bangladesh	Parent	NO
6.5	6.1	4	1665.0	m	Latino	no	yes	Mexico	Parent	NO
6.9	7.1	4	1785.0	m	Latino	no	yes	Mexico	Parent	YES
6.1	5.6	5	1445.0	m	White-European	yes	no	United States	Parent	NO
5.8	6.2	3	1400.0	m	NA	yes	no	Malaysia	NA	NO
6.7	6.4	7	3015.0	m	White-European	no	no	United Kingdom	Parent	NO
5.5	5.8	4	1375.0	m	South Asian	no	no	India	Parent	NO
9.8	9.5	3	2440.0	f	Asian	no	yes	United States	Parent	YES
8.7	9.1	5	2165.0	m	White-European	no	no	Australia	Parent	YES
0.4	0.0	2	150.0	m	Turkish	no	yes	Turkey	Parent	NO
2.5	2.5	5	665.0	m	Others	no	no	United Kingdom	Parent	NO
9.0	8.6	3	2160.0	f	Hispanic	no	no	United States	Parent	YES
10.0	9.8	7	3262.5	m	White-European	no	no	United States	Parent	YES
7.8	7.6	3	1970.0	m	White-European	no	yes	United States	Parent	YES
7.6	7.8	3	1870.0	m	White-European	no	no	United States	Parent	YES
8.2	8.1	5	1990.0	m	White-European	no	no	United States	Parent	YES
3.5	4.0	7	2775.0	m	White-European	yes	no	Canada	Care professional	NO
10.0	9.8	8	3260.0	m	Black	yes	no	United Kingdom	Parent	YES
4.2	3.7	7	2795.0	f	South Asian	no	no	India	Parent	NO
7.5	7.9	4	1825.0	m	South Asian	no	no	India	Parent	YES
3.7	4.2	4	885.0	m	Middle Eastern	no	no	Jordan	Care professional	NO
8.1	8.4	3	2005.0	m	Asian	yes	no	Isle of Man	Care professional	YES
8.7	9.0	1	2185.0	m	White-European	no	no	United States	Parent	YES
5.7	6.1	3	1475.0	m	NA	yes	no	Libya	NA	NO
6.9	7.3	3	1735.0	m	NA	yes	no	Libya	NA	YES
8.2	8.4	4	1990.0	m	NA	no	no	Russia	NA	YES
5.3	5.0	3	1385.0	m	Others	yes	no	Libya	Parent	NO
6.0	6.0	4	1520.0	m	NA	no	no	Russia	NA	NO
8.6	8.6	6	3160.0	f	Asian	no	no	Philippines	Parent	YES
5.5	5.5	2	1345.0	f	Latino	yes	no	Philippines	Care professional	NO
5.0	5.2	2	1260.0	m	Asian	no	no	India	Parent	NO
5.9	5.9	2	1515.0	f	White-European	no	yes	Australia	Parent	NO
6.9	6.6	8	3005.0	m	South Asian	no	no	New Zealand	Parent	NO
5.9	6.3	5	1485.0	m	South Asian	yes	no	India	Parent	NO
2.5	2.2	5	625.0	m	NA	yes	no	Saudi Arabia	NA	NO
2.7	2.7	8	2712.5	f	NA	yes	no	Saudi Arabia	NA	NO
6.0	5.9	6	2967.5	m	NA	yes	no	Jordan	NA	NO
5.7	6.2	4	1515.0	m	Middle Eastern	no	no	Jordan	Parent	NO
4.6	4.6	4	1110.0	m	Middle Eastern	yes	no	United Arab Emirates	Parent	NO
1.8	2.0	1	370.0	m	Middle Eastern	no	yes	United Arab Emirates	Parent	NO
3.6	4.1	6	2787.5	m	Middle Eastern	no	no	Jordan	Parent	NO
6.1	6.1	8	2947.5	m	NA	yes	no	Egypt	NA	NO
6.5	6.0	6	2990.0	m	Middle Eastern	yes	no	Egypt	Parent	YES
8.0	8.0	6	3082.5	m	NA	yes	no	Egypt	NA	YES
7.9	7.4	6	3092.5	m	Middle Eastern	yes	no	Jordan	Parent	YES
8.8	9.1	8	3177.5	f	Middle Eastern	no	no	Egypt	Parent	YES
4.9	5.4	4	1145.0	m	Middle Eastern	yes	no	United Arab Emirates	Parent	NO
4.6	4.3	3	1090.0	m	South Asian	no	no	India	Parent	NO
4.2	4.0	4	1090.0	m	Asian	no	yes	India	Parent	NO
10.0	10.0	3	2580.0	f	South Asian	no	no	Armenia	Care professional	YES
4.4	4.2	4	1140.0	m	South Asian	no	no	India	Parent	NO
8.3	8.4	4	2145.0	m	Black	no	no	United States	Parent	YES
5.7	5.4	3	1415.0	m	White-European	no	no	Italy	Parent	NO
4.9	4.7	5	1205.0	m	Asian	no	no	India	Parent	NO
10.0	10.0	5	2490.0	f	Black	no	no	Canada	Parent	YES
6.0	6.3	4	1580.0	m	Asian	no	no	India	Relative	NO
7.5	7.2	2	1855.0	m	White-European	no	no	United Kingdom	Parent	YES
8.8	9.1	1	2180.0	m	Black	yes	no	India	Parent	YES
5.2	4.8	3	1360.0	m	South Asian	no	no	India	Parent	NO
8.5	8.1	4	2175.0	m	Others	no	no	United Kingdom	Parent	YES
7.0	6.9	1	1740.0	m	NA	yes	no	Pakistan	NA	YES
3.6	4.0	8	2780.0	m	Middle Eastern	yes	no	New Zealand	Parent	NO
7.5	7.1	3	1925.0	m	Asian	no	no	India	Parent	YES
2.7	2.6	3	685.0	f	White-European	no	yes	United Kingdom	Parent	NO
3.6	3.9	4	990.0	m	Black	no	no	Ghana	Parent	NO
9.0	8.8	7	3195.0	m	White-European	no	yes	Australia	Parent	YES
9.2	9.6	7	3175.0	m	White-European	yes	no	United States	Parent	YES
4.0	4.4	5	990.0	m	Asian	no	no	India	Care professional	NO
6.0	6.5	2	1490.0	m	Asian	no	no	India	Parent	NO
7.5	7.0	1	1895.0	f	Asian	no	no	India	Parent	YES
4.7	4.8	5	1175.0	f	White-European	no	no	United Kingdom	Parent	NO
9.3	9.3	5	2405.0	m	Asian	no	yes	India	Parent	YES
2.5	2.4	3	535.0	m	Black	no	yes	India	Parent	NO
6.6	6.5	3	1600.0	m	White-European	no	no	Australia	Parent	NO
6.5	6.1	3	1635.0	f	White-European	yes	no	United Kingdom	Parent	NO
4.9	5.1	4	1305.0	m	Others	no	no	United States	Parent	NO
6.0	5.7	1	1530.0	f	White-European	no	no	Australia	Care professional	NO
8.9	9.0	1	2135.0	f	White-European	no	no	Australia	Care professional	YES
9.0	8.8	4	2340.0	f	Latino	yes	no	Bhutan	Parent	YES
9.7	10.0	6	3230.0	f	White-European	yes	yes	United Kingdom	Parent	YES
3.6	4.1	6	2747.5	f	White-European	yes	yes	Australia	Parent	NO
7.0	7.5	3	1840.0	m	Latino	no	no	Brazil	Parent	YES
9.6	9.9	3	2490.0	m	South Asian	no	no	India	Parent	YES
3.7	4.1	3	925.0	f	South Asian	no	no	India	Parent	NO

clean_data %>%
  inspect_na() %>%
  show_plot()

Question 1

Question

Produce a plot with the relative proportion of children residing in Australia, Germany, Italy and India. Comment on your visualisation and suggests one alternative to your plot, highlighting its advantages. There is no need to plot the alternative.

Solution

dat1 <- clean_data %>%
  filter(residence %in% c("Australia", "Germany", "Italy", "India")) %>%
  count(residence) %>%
  mutate(prop = n / sum(n))

dat1 %>%
  gt() %>%
  tab_spanner(
    label = "Statistics",
    columns = vars(n, prop)
  )

residence	Statistics
	n	prop
Australia	23	0.33823529
Germany	1	0.01470588
India	42	0.61764706
Italy	2	0.02941176

dat1 %>% ggplot(aes(x = reorder(residence, prop), y = prop, fill = residence)) +
  geom_col(width = 0.5, show.legend = FALSE) +
  theme_bw() +
  labs(x = "Residence", y = "Relative proportion") +
  scale_y_continuous(labels = scales::percent)

Among the four countries compared, it can be seen that most of the children resided in India.

Other chart that can be used to represent this data is a pie chart.

Advantages of a pie chart

• presents data as a simple and easy-to-understand picture

• visually simpler than other types of graphs

• excellent when few classes of data are involved

Question 2

Question

Use univariate statistics on at least the first 4 attributes to describe the data. Discuss the results obtained, highlighting any result which you consider particularly useful. Use visualisations if needed.

Solution: part 1

plot_box <- function(df, cols, col_x = "autism") {
  for (col in cols) {
    p <- ggplot(df, aes(x = .data[[col_x]], y = .data[[col]], fill = .data[[col_x]])) +
      geom_boxplot(
        show.legend = FALSE, width = 0.2, outlier.size = 1, outlier.shape = 5,
        outlier.colour = "purple"
      ) +
      scale_fill_manual(values = c("YES" = "red", "NO" = "green"), aesthetics = "fill") +
      labs(y = str_c(col), x = NULL, title = paste0("Boxplot of ", col, " by autism status")) +
      theme(
        axis.text.x = element_text(face = "bold"),
        axis.title.y = element_text(size = 12, face = "bold")
      )
    print(p)
  }
}

num_cols <-
  clean_data %>%
  select_if(is.numeric) %>%
  colnames()

plot_box(clean_data, num_cols)

Box plots are useful, since by construction we focused on the overlap (or not) of the quartiles of the distribution. In this case, we might ask the question like: is there sufficient differences in the quartiles for the feature to be useful in separating the label classes? It seems that all numerical features are useful in separating between children that have autism from those that did not.

As one might expect, the cost of testing the the children with autism is high compare to children without autism. Also, both standard test and alternative (non-standard) test scores are higher for children with autism than children without autism.

Solution: part 2

plot_bars <- function(df, cat_cols, facet_var) {
  for (col in cat_cols) {
    p <- ggplot(df, aes(x = .data[[col]], fill = .data[[col]])) +
      geom_bar(show.legend = F, width = 0.3) +
      labs(
        x = col, y = "Number of children",
        title = str_c("Bar plot of ", col), subtitle = paste0("faceted by autism status")
      ) +
      facet_wrap(vars({{ facet_var }}), scales = "free_y") +
      theme(
        axis.title.y = element_text(size = 12, face = "bold"),
        axis.title.x = element_text(size = 12, face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1, face = "bold")
      )

    print(p)
  }
}

cat_cols <-
  clean_data %>%
  select_if(is.factor) %>%
  colnames()

cat_cols <- cat_cols[-c(5, 7)] # removing the class label

plot_bars(clean_data, cat_cols, autism)

There is a lot of information in these plots. The key to interpretation of these plots is comparing the proportion of the categories for whether or not a child has autism. If these proportions are distinctly different for each label category, the feature is likely to be useful in separating the label.

There are several cases evident in these plots:

Some features such as relation, autismFH, jaundice, ethnicity and gender have significantly different distribution for autism.

Question 3

Apply data analysis techniques in order to answer each of the questions below, justifying the steps you have followed and the limitations (if any) of your analysis. If a question cannot be answered explain why.

Question 3a

• Is the score mean different for children with autism and children without autism using a significance value of 0.05?

• Is there a difference of at least 1 in mean scores between children with a family history of autism and those without a family history of autism?

Solution 3a

Part 1:

One of the assumptions of t-test of independence of means is homogeneity of variance (equal variance between groups).

The statistical hypotheses are:

Null hypothesis (\(H_0\)): the variances of the two groups are equal.

Alternative hypothesis (\(H_a\)): the variances are different.

clean_data <- clean_data %>% mutate(autism = fct_relevel(autism, "YES"))

car::leveneTest(score ~ autism, data = clean_data)

Levene's Test for Homogeneity of Variance (center = median)
       Df F value   Pr(>F)   
group   1  9.4429 0.002321 **
      290                    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation: The p-value is p = 0.002321 which is less than the significance level 0.05. In conclusion, there is a significant difference between the two variances.

t.test(score ~ autism, data = clean_data, alternative = "two.sided", var.equal = FALSE)

    Welch Two Sample t-test

data:  score by autism
t = 24.242, df = 280.24, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 3.584006 4.217497
sample estimates:
mean in group YES  mean in group NO 
         8.411348          4.510596 

There was a significance difference in the mean score for children with autism (M = 8.41, SD = 1.19) and children without autism (M = 4.51, SD = 1.54); t(280.24) = 24.242, p < 0.05.

Part 2:

clean_data <- clean_data %>% mutate(autismFH = fct_relevel(autismFH, "yes"))

car::leveneTest(score ~ autismFH, data = clean_data)

Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   1  1.5122 0.2198
      290               

Interpretation: The p-value is p = 0.2198 which is greater than the significance level 0.05. In conclusion, there is no significant difference between the two variances.

t.test(score ~ autismFH, data = clean_data, alternative = "two.sided", var.equal = TRUE)

    Two Sample t-test

data:  score by autismFH
t = -1.3311, df = 290, p-value = 0.1842
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.2348058  0.2384339
sample estimates:
mean in group yes  mean in group no 
         5.979592          6.477778 

There was no significance difference of at least 1 in the mean scores between children with a family history of autism (M = 5.98, SD = 2.60) and those without a family history of autism (M = 6.48, SD = 2.35); t(290) = -1.3311, p-value = 0.1842.

Question 3b

• What is the predicted value of the alternative score (score2) for a child with a standard score of 7?

• What is the predicted value of the alternative score (score2) for a child with a standard score of 12?

Solution 3b

Part 1:

This question cannot be answered directly without knowing the functional relationship between alternative score(score2) and standard score. Is alternative score(score2) a function of standard score?

If the answer is yes, then we can fit a simple linear regression such that alternative score(score2) will be a function of standard score.

model <- lm(score2 ~ score, data = clean_data)

predict(model, data.frame(score = 7))

       1 
7.007971 

\(score2 = 0.04645 + 0.99450(7)\)

\(score2 = 7.01\)

The predicted value of the alternative score (score2) for a child with a standard score of 7 is 7.01.

Part 2:

If alternative score(score2) is a function of standard score, then we can use R to fit the model such that:

predict(model, data.frame(score = 12))

       1 
11.98049 

\(score2 = 0.04645 + 0.99450(12)\)

\(score2 = 11. 98\)

The predicted value of the alternative score (score2) for a child with a standard score of 12 is 11.98.

Question 4

Question

Create a dataset which contains all the data in child plus a new column “ageGroup” with values “Five and under” and “6 and over”. Use one or more visualisations to compare the standard score against the cost for each age group. The visualisation(s) should also show whether there was a family history of autism. Comment on your visualisations.

Solution

clean_data <-
  clean_data %>% mutate(
    ageGroup =
      case_when(
        age >= 6 ~ "6 and over",
        TRUE ~ "Five and under"
      )
  )

clean_data %>% ggplot(aes(x = cost, y = score, color = ageGroup)) +
  geom_line() +
  facet_grid(ageGroup ~ autismFH, scales = "free")

Children whose age is five years and under and with family history of autism have a lower cost of standard test for autism.

Question 5

Critically discuss the following statement, using at most 3 plot examples to illustrate your explanations [ Word limit 300].

There are different methods of displaying data, with no method being suitable for the visualisation of all types of data. Some visualisations easily convey the information they are designed to communicate whereas others fail to adequately show the data. Data-ink ratio and lie factor also play a part in the quality of a visualisation.” Note: your plot examples must relate to the child dataset.

Solution: Part 1

p1 <- clean_data %>% ggplot(aes(x = score)) +
  geom_histogram(binwidth = 5, fill = "dark blue") +
  labs(title = "A histogram")

p2 <- clean_data %>% ggplot(aes(y = score)) +
  geom_boxplot(fill = "dark blue") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
  labs(title = "A boxplot")

p3 <- clean_data %>% ggplot(aes(x = score)) +
  geom_dotplot(binwidth = 0.23, stackratio = 1, fill = "blue", stroke = 2) +
  scale_y_continuous(NULL, breaks = NULL) +
  labs(title = "A dot plot")


p1 / (p2 + p3) + plot_annotation(title = "Different plots for the standard tests for autism")

A histogram and a boxplot are the most common charts for showing the distribution of a continuous variable. Another chart that can be used for continuous, quantitative, and univariate data is a dot plot. It is a simplest statistical plot that is suitable for small to moderate sized data set. A dot plot may become too cluttered when dataset involves more than 20 points. Other charts that can be more efficient are boxplot, histogram or violin plot. A dot plot is similar to a bar chart because the height of each “bar” of dots is equal to the number of items in a particular category.

Solution: Part 2

p1 <- clean_data %>%
  count(relation) %>%
  ggplot(aes(x = reorder(relation, n), y = n, fill = relation)) +
  geom_col(width = 0.4, show.legend = FALSE) +
  labs(title = "A bar chart", x = "")

p2 <- clean_data %>%
  select(relation) %>%
  count(relation) %>%
  ggplot(aes(x = reorder(relation, n, na.rm = T), y = n)) +
  geom_segment(aes(xend = relation, yend = 0)) +
  geom_point(size = 6, color = "orange") +
  theme_bw() +
  xlab("")

p3 <- clean_data %>%
  select(relation) %>%
  count(relation) %>%
  treemap(index = "relation", vSize = "n", title = "A treemap")

p4 <- pie(table(clean_data$relation), col = c(
  "purple", "violetred1", "green3",
  "cornsilk"
), radius = 0.9, main = "A pie chart")

Among the four charts used for displaying the distribution of residence, pie chart has the following disadvantages:

• pie chart becomes less effective if too many pieces of data are used

• One has to factor in angles and compare non-adjacent slices before one can understand a pie chart.

Question 6

Question

Assume that, in addition to the child dataset supplied with this coursework (dataset 1), you also have another 19 independent datasets with the same number of observations about children tested for autism. Load the dataset name independent_data.csv which have distribution for attribute autism and demonstrate that the size of the confidence intervals for the average percentage of positive cases of autism (autism = YES) increases as the confidence level increases. Use 90%, 95% and 98% confidence. Discuss any improvements which may enhance your demonstration.

Solution

another_dataset <- read_csv("data/independent_dataset.csv")

# This function returns size of confidence interval

conf.size <- function(dataset, level = 0.90) {
  t_test <- t.test(dataset [, 2] %>% pull(), conf.level = level)

  print(t_test$conf.int)
}

conf.size(another_dataset, level = 0.9)

[1] 48.41712 50.42498
attr(,"conf.level")
[1] 0.9

The 90 percent confidence interval for the average percentage of positive cases of autism is between 48.42 and 50.42.

conf.size(another_dataset, level = 0.95)

[1] 48.20473 50.63738
attr(,"conf.level")
[1] 0.95

The 95 percent confidence interval for the average percentage of positive cases of autism is between 48.20 and 50.64.

conf.size(another_dataset, level = 0.98)

[1] 47.94336 50.89875
attr(,"conf.level")
[1] 0.98

The 98 percent confidence interval for the average percentage of positive cases of autism is between 47.94 and 50.90.

Over all interpretation

This demonstrates that as the confidence level increases, the size of the confidence intervals becomes wider and we therefore, fail to reject the null hypothesis.

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