Readings

R for Data Science https://r4ds.had.co.nz

Hands-on practice

On RStudio Cloud, you will have a new project called HW1, which has a R Markdown document called HW1.Rmd. Please open it, follow the instructions within it, answer the questions, and then compile to HTML (see instructions)

Exercises

Question 1

Run the following code, either in your console or from the R Markdown document

living_wage <- 15

l1ving_wage

Does this code work? Why or why not? How would you correct it? Put the corrected code in a R code chunk in your answer, with option eval = T to show that the code works

Answer:

No; because the object spelling is incorrect (not matching)

Question 2

Complete the code below by repalcing the ___, following the instructions in the comments (lines starting with #). Change eval = F to eval = T, so that the answers show in the HTML file. You can learn more about the dataset by typing ?mtcars in the console.

library(tidyverse)

data(mtcars)

# Show me the first 3 rows of this dataset

head(mtcars, n = 3)
##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
# What is the average mpg of all the cars?

mean(mtcars$mpg)
## [1] 20.09062
# Find the average mpg for 4 cylinder cars

mean(mtcars$mpg[mtcars$mpg == 4])
## [1] NaN
# Find the average mpg for 4, 6 and 8 cylinder cars, in one statement

mtcars %>% 
  group_by(cyl) %>% 
  summarize(avg_mpg = mean(mpg))
## # A tibble: 3 x 2
##     cyl avg_mpg
##   <dbl>   <dbl>
## 1     4    26.7
## 2     6    19.7
## 3     8    15.1
# Convert mpg to km/l (1 mpg = 1.6 km/g = 0.42 km / l) and compute avg mpg & km/l by cylinder

mtcars %>% 
  mutate( kml = mpg * 1.6) %>% 
  group_by(cyl) %>% 
  summarize(avg_US = mean(mpg), avg_metric = mean(kml))
## # A tibble: 3 x 3
##     cyl avg_US avg_metric
##   <dbl>  <dbl>      <dbl>
## 1     4   26.7       42.7
## 2     6   19.7       31.6
## 3     8   15.1       24.2

Question 3: A first pass at graphs

There is some template ggplot2 code below. Once again fill in the blanks and change eval = F to eval = T to run the code during knitting.

library(ggplot2)

# Draw a scatter plot showing the relationship between displacement and mpg

mtcars
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point()

# Draw a scatterplot like above, but color points based on cylinders
# 

ggplot(mtcars, aes(x = disp, y = mpg)) + 
  geom_point(aes(  color = cyl),  group=1)

Extra credit:
Is this quite right? Can you fix it by changing the data type of cyl?