The main purpose of this task is to check that you can get it to load and run and knit it to html. Once that’s working, have a go at the embedded exercises. Exercise 7 is just bonus. At the end, knit it to html and upload both files (Rmd and html) on blackboard (not zipped).
R is a free software environment for statistical computing and graphics. We will use the RStudio IDE (Integrated Development Environment). This has a number of nice features which we’ll cover as we go through - identify the following elements:
Create variables for the following information: name (character): Your name age (numeric): Your age is_local (logical): Do you live in Leicester? (TRUE/FALSE)
# Define variables for personal information
name <- "Vittal"
age <- 23
is_local <- TRUE Is it possible to create a vector containing the values for your three variables? If not, create a named list containing the values for your three variables and print it.
# Vectors require same data type, so use a named list for mixed types
vittal_bio <- list(
name = name,
age = age,
is_local = is_local
)
# Print the list using pipe for consistency
print(vittal_bio)## $name
## [1] "Vittal"
##
## $age
## [1] 23
##
## $is_local
## [1] TRUECreate a data frame called “students” with the following columns: Name (character): Names of students (include your name and at least three more names) Age (numeric): Ages of students Grade (factor): Grades of students (with levels: A, B, C) Is_Local (logical): Whether each student lives in Leicester (TRUE/FALSE)
# Create students data frame with specified columns
students_list <- data.frame(
name = c("Vittal", "Mahi", "Abhi", "Jai"),
age = c(23, 24, 24, 22),
grade = factor(c("A", "B", "C", "A"), levels = c("A", "B", "C")),
is_local = c(TRUE, FALSE, TRUE, FALSE)
)
# Print the data frame
students_list## name age grade is_local
## 1 Vittal 23 A TRUE
## 2 Mahi 24 B FALSE
## 3 Abhi 24 C TRUE
## 4 Jai 22 A FALSELet R show how many times the different levels appear in the grade-column
# Count frequency of each grade level using dplyr
table(students_list$grade)##
## A B C
## 2 1 1Extract and print the Age vector from the students data frame.
# Extract and print the age column
students_list$age## [1] 23 24 24 22Look up the function “mean()” with the help function. Calculate the average age of the students from your dataframe and print it out. Use the pipe operator to do this calculation in one line of code.
Why does the following code not run? Fix the Code (in the code chunk below named r “Exercise 6, solution”). Note, before you run the code, you need to get rid of the “#”s. You can do this by selecting everything and using the shortcut ‘Ctrl Shift C’.” (Commenting it out again works the same way).
# df <- data.frame(Name = c("John", "Jane", "Doe"), Age = c(25, 30))
# print(df)
#
# print("The problem was )
#
# age_in_10_years <- df$age +10
# mean(age_in_10years)df <- data.frame(Name = c("John", "Jane", "Doe"), Age = c(25, 30, 24))
print(df)## Name Age
## 1 John 25
## 2 Jane 30
## 3 Doe 24#
print("The problem was fixed" )## [1] "The problem was fixed"#
age_in_10_years <- df$Age +10
print(age_in_10_years)## [1] 35 40 34mean_age_in_10_years <- mean(age_in_10_years)
print(mean_age_in_10_years)## [1] 36.33333R contains some built-in data sets (that means they are included with the R installation and can be accessed without the need to load any external libraries.) One of them is “mtcars”. Print it out and explore it using the functions head(), View(), summary(), glimpse(), str(). Try to understand what the functions tell you about the data set. Moreover, why should you not include the function “View()” when you try to knit your file?
# Explore mtcars dataset
print(mtcars) # Print full dataset## 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 2head(mtcars) # First 6 rows## 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
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# View(mtcars) # Commented out: View() is interactive and breaks knitting
summary(mtcars) # Summary statistics## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000#glimpse(mtcars) # Tidy overview of structure
str(mtcars) # Detailed structure ## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...