-
Notifications
You must be signed in to change notification settings - Fork 2
/
ggplot2-exercises1.Rmd
121 lines (59 loc) · 2.31 KB
/
ggplot2-exercises1.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
---
title: "Exercise Set 1 — Geoms and Aesthetics"
author: "Name"
date: '`r format(Sys.time(), "Last modified: %d %b %Y")`'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T,eval=T)
```
## Exercise 1
These first few exercises will run through some of the simple principles of creating a ggplot2 object, assigning aesthetics mappings and geoms.
1. Read in the cleaned patients dataset as we saw in ggplot2 course earlier ("patients_clean_ggplot2.txt")
```{r exerciseReadin, echo=T}
patients_clean <- read.delim("patient-data-cleaned.txt",sep="\t")
```
### Scatterplots
2. Using the patient dataset generate a scatter plot of BMI versus Weight.
```{r exercise1}
```
3. Extending the plot from exercise 2, add a colour scale to the scatterplot based on the Height variable.
```{r exercise2}
```
4. Following from exercise 3, split the BMI vs Weight plot into a grid of plots separated by Smoking status and Sex .
```{r exercise3}
```
5. Using an additional geom, add an extra layer of a fit line to the solution from exercise 3.
```{r exercise4}
```
6. Does the fit in question 5 look good? Look at the description for ?geom_smooth() and adjust the method for a better fit.
```{r exercise5}
```
###Boxplots and Violin plots
7. Generate a boxplot of BMIs comparing smokers and non-smokers.
```{r exercise6}
```
8. Following from the boxplot comparing smokers and non-smokers in exercise 7, colour boxplot edges by Sex.
```{r exercise7}
```
9. Now reproduce the boxplots in exercise 8 (grouped by smoker, coloured by sex) but now include a separate facet for people of different age (using Age column).
```{r exercise8}
```
10. Produce a similar boxplot of BMIs but this time group data by Sex, colour by Age and facet by Smoking status.
HINT - Discrete values such as in factors are used for categorical data.
```{r exercise9}
```
11. Regenerate the solution to exercise 10 but this time using a violin plot.
```{r exercise10}
```
###Histogram and Density plots
12. Generate a histogram of BMIs with each bar coloured blue.
```{r exercise11}
```
13. Generate density plots of BMIs coloured by Sex.
HINT: alpha can be used to control transparancy.
```{r exercise12}
```
14. Generate a separate density plot of BMI coloured by sex for each Grade,
```{r exercise13}
```