Welcome to the PSYC3361 coding W2 self test. The test assesses your ability to use the coding skills covered in the Week 2 online coding modules.

In particular, it assesses your ability to…

choose packages/functions
read in data
make a scatter plot
use facet_wrap
customise your plot with themes, colours and labels
use group_by and summarise

It is IMPORTANT to document the code that you write so that someone who is looking at your code can understand what it is doing. Above each chunk, write a few sentences outlining which packages/functions you have chosen to use and what the function is doing to your data. Where relevant, also write a sentence that interprets the output of your code.

Your notes should also document the troubleshooting process you went through to arrive at the code that worked.

For each of the challenges below, the documentation is JUST AS IMPORTANT as the code.

Good luck!!

Jenny

PS- if you get stuck have a look in the /images folder for inspiration

load the packages you need

library(tidyverse)

read in the dino data

dino = read_csv("data/dino.csv")
print(dino)

## # A tibble: 1,846 × 3
##    dataset     x     y
##    <chr>   <dbl> <dbl>
##  1 dino     55.4  97.2
##  2 dino     51.5  96.0
##  3 dino     46.2  94.5
##  4 dino     42.8  91.4
##  5 dino     40.8  88.3
##  6 dino     38.7  84.9
##  7 dino     35.6  79.9
##  8 dino     33.1  77.6
##  9 dino     29.0  74.5
## 10 dino     26.2  71.4
## # ℹ 1,836 more rows

reproduce this plot

The dino dataset comes from a paper illustrating the importance of plotting your data. In each of these datasets, the mean and variance of x and y are identical and the two variables are correlated in the same way (R = -0.06). When plotted, however, each reveals a very different pattern

dino = read_csv("data/dino.csv")

## Rows: 1846 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): dataset
## dbl (2): x, y
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

picture <- ggplot(dino, aes (x = x, y = y)) + 
  geom_point() +               # Plotting the Data
  geom_smooth(method ='lm') +                    # Adding Regression Line
  
  scale_x_continuous(
    name = NULL,
    labels = NULL
  ) +
  scale_y_continuous(
    limits = c(0,100)
  ) +
  facet_wrap((vars(dataset))) 
  print(picture)

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

what can you do to make it prettier

HINT: add some colour, play with palettes, try a different theme, add a title, subtitle, caption

dino = read_csv("data/dino.csv")

## Rows: 1846 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): dataset
## dbl (2): x, y
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

picture <- ggplot(dino, aes (x, y)) + 
  geom_point(colour = "deepskyblue1") +               # Plotting the Data
  geom_smooth(method = 'lm', colour = "coral1") +                    # Adding Regression Line
  labs(title = "Plotted Data",                        # Title, Subtitle, Caption
              subtitle = "that's colourful",
              caption = "Brought to you by me"
       ) +
  scale_x_continuous(
    name = NULL,
    labels = NULL
  ) +
  scale_y_continuous(
    limits = c(0, 100)
  ) +
  facet_wrap((vars(dataset))) +
  theme_minimal()

print(picture)

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

extra challenge

Can you write code to show that the mean, variance, and correlation between x and y is the same for each of the datasets?? HINT: this is a group_by and summarise problem

dino = read_csv("data/dino.csv")

## Rows: 1846 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): dataset
## dbl (2): x, y
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

proof_dino <- dino %>%
  group_by(dataset) %>%
  summarise(
    mean_x = mean(x),
    mean_y = mean(y),
    var_x = var(x),
    var_y = var(y),
    correlation = cor(x,y)
  )

picture <- ggplot(dino, aes(x = x, y = y, colour = x)) + 
  geom_point() +               # Plotting the Data
  geom_smooth(method = 'lm') +                    # Adding Regression Line
  labs(title = "Plotted Data",                        # Title, Subtitle, Caption
              subtitle = "that's colourful",
              caption = "Brought to you by me"
       ) +
  scale_x_discrete(
    name = NULL,
    labels = NULL
  ) +
  scale_y_continuous(
    limits = c (0,100)
  ) +
  facet_wrap((vars(dataset))) +
  theme_minimal() +
  scale_colour_gradientn(colours = rainbow(8))
print(picture)

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

## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

w2 self test

Witon Yee

14 June, 2024

load the packages you need

read in the dino data

reproduce this plot

what can you do to make it prettier

extra challenge

knit your document to pdf