library(dslabs)
data(murders)$ operator to access the population size
data and store it as the object pop. Then use the sort
function to redefine pop so that it is sorted. Finally, use
the [ operator to report the smallest population sizepop <- murders$population
pop <- sort(pop)
pop[1] # The smallest population size## [1] 563626order
instead of sort.smallest_index <- order(pop)[1]
smallest_index## [1] 1which.min. Write one line of
code that does this# Find the index of the smallest population size using which.min
smallest_index <- which.min(pop)
smallest_index## [1] 1states
to be the state names from the murders data frame. Report
the name of the state with the smallest population.# Define a variable 'states' to store the state names
states <- murders$state
# Report the name of the state with the smallest population
states[smallest_index]## [1] "Alabama"data.frame
function. Here is a quick example:temp <- c(35, 88, 42, 84, 81, 30)
city <- c("Beijing", "Lagos", "Paris", "Rio de Janeiro", "San Juan", "Toronto")
city_temps <- data.frame(name = city, temperature = temp)
Use the rank function to determine the population rank of
each state from smallest population size to biggest. Save these ranks in
an object called ranks, then create a data frame with the
state name and its rank. Call the data frame my_df
# Create a data frame with state names and their populations
state_populations <- data.frame(state = murders$state, population = murders$population)
# Use the 'rank' function to determine the population rank
ranks <- rank(state_populations$population)
# Create a data frame with state names and their ranks
my_df <- data.frame(state = state_populations$state, rank = ranks)
# View the resulting data frame
my_df## state rank
## 1 Alabama 29
## 2 Alaska 5
## 3 Arizona 36
## 4 Arkansas 20
## 5 California 51
## 6 Colorado 30
## 7 Connecticut 23
## 8 Delaware 7
## 9 District of Columbia 2
## 10 Florida 49
## 11 Georgia 44
## 12 Hawaii 12
## 13 Idaho 13
## 14 Illinois 47
## 15 Indiana 37
## 16 Iowa 22
## 17 Kansas 19
## 18 Kentucky 26
## 19 Louisiana 27
## 20 Maine 11
## 21 Maryland 33
## 22 Massachusetts 38
## 23 Michigan 43
## 24 Minnesota 31
## 25 Mississippi 21
## 26 Missouri 34
## 27 Montana 8
## 28 Nebraska 14
## 29 Nevada 17
## 30 New Hampshire 10
## 31 New Jersey 41
## 32 New Mexico 16
## 33 New York 48
## 34 North Carolina 42
## 35 North Dakota 4
## 36 Ohio 45
## 37 Oklahoma 24
## 38 Oregon 25
## 39 Pennsylvania 46
## 40 Rhode Island 9
## 41 South Carolina 28
## 42 South Dakota 6
## 43 Tennessee 35
## 44 Texas 50
## 45 Utah 18
## 46 Vermont 3
## 47 Virginia 40
## 48 Washington 39
## 49 West Virginia 15
## 50 Wisconsin 32
## 51 Wyoming 1my_df so that the states are ordered from least populous to
most populous. Hint: create an object ind that stores the
indexes needed to order the population values. Then use the bracket
operator [ to re-order each column in the data frame# Create an object 'ind' to store the indexes needed to order the population values
ind <- order(ranks)
# Re-order each column in the data frame using the 'ind' vector
my_df <- my_df[ind, ]
my_df## state rank
## 51 Wyoming 1
## 9 District of Columbia 2
## 46 Vermont 3
## 35 North Dakota 4
## 2 Alaska 5
## 42 South Dakota 6
## 8 Delaware 7
## 27 Montana 8
## 40 Rhode Island 9
## 30 New Hampshire 10
## 20 Maine 11
## 12 Hawaii 12
## 13 Idaho 13
## 28 Nebraska 14
## 49 West Virginia 15
## 32 New Mexico 16
## 29 Nevada 17
## 45 Utah 18
## 17 Kansas 19
## 4 Arkansas 20
## 25 Mississippi 21
## 16 Iowa 22
## 7 Connecticut 23
## 37 Oklahoma 24
## 38 Oregon 25
## 18 Kentucky 26
## 19 Louisiana 27
## 41 South Carolina 28
## 1 Alabama 29
## 6 Colorado 30
## 24 Minnesota 31
## 50 Wisconsin 32
## 21 Maryland 33
## 26 Missouri 34
## 43 Tennessee 35
## 3 Arizona 36
## 15 Indiana 37
## 22 Massachusetts 38
## 48 Washington 39
## 47 Virginia 40
## 31 New Jersey 41
## 34 North Carolina 42
## 23 Michigan 43
## 11 Georgia 44
## 36 Ohio 45
## 39 Pennsylvania 46
## 14 Illinois 47
## 33 New York 48
## 10 Florida 49
## 44 Texas 50
## 5 California 51is.na function returns a logical vector that
tells us which entries are NA. Assign this logical vector
to an object called ind and determine how many
NAs does na_example havedata("na_example")
str(na_example)## int [1:1000] 2 1 3 2 1 3 1 4 3 2 ...na_example## [1] 2 1 3 2 1 3 1 4 3 2 2 NA 2 2 1 4 NA 1 1 2 1 2 2 1
## [25] 2 5 NA 2 2 3 1 2 4 1 1 1 4 5 2 3 4 1 2 4 1 1 2 1
## [49] 5 NA NA NA 1 1 5 1 3 1 NA 4 4 7 3 2 NA NA 1 NA 4 1 2 2
## [73] 3 2 1 2 2 4 3 4 2 3 1 3 2 1 1 1 3 1 NA 3 1 2 2 1
## [97] 2 2 1 1 4 1 1 2 3 3 2 2 3 3 3 4 1 1 1 2 NA 4 3 4
## [121] 3 1 2 1 NA NA NA NA 1 5 1 2 1 3 5 3 2 2 NA NA NA NA 3 5
## [145] 3 1 1 4 2 4 3 3 NA 2 3 2 6 NA 1 1 2 2 1 3 1 1 5 NA
## [169] NA 2 4 NA 2 5 1 4 3 3 NA 4 3 1 4 1 1 3 1 1 NA NA 3 5
## [193] 2 2 2 3 1 2 2 3 2 1 NA 2 NA 1 NA NA 2 1 1 NA 3 NA 1 2
## [217] 2 1 3 2 2 1 1 2 3 1 1 1 4 3 4 2 2 1 4 1 NA 5 1 4
## [241] NA 3 NA NA 1 1 5 2 3 3 2 4 NA 3 2 5 NA 2 3 4 6 2 2 2
## [265] NA 2 NA 2 NA 3 3 2 2 4 3 1 4 2 NA 2 4 NA 6 2 3 1 NA 2
## [289] 2 NA 1 1 3 2 3 3 1 NA 1 4 2 1 1 3 2 1 2 3 1 NA 2 3
## [313] 3 2 1 2 3 5 5 1 2 3 3 1 NA NA 1 2 4 NA 2 1 1 1 3 2
## [337] 1 1 3 4 NA 1 2 1 1 3 3 NA 1 1 3 5 3 2 3 4 1 4 3 1
## [361] NA 2 1 2 2 1 2 2 6 1 2 4 5 NA 3 4 2 1 1 4 2 1 1 1
## [385] 1 2 1 4 4 1 3 NA 3 3 NA 2 NA 1 2 1 1 4 2 1 4 4 NA 1
## [409] 2 NA 3 2 2 2 1 4 3 6 1 2 3 1 3 2 2 2 1 1 3 2 1 1
## [433] 1 3 2 2 NA 4 4 4 1 1 NA 4 3 NA 1 3 1 3 2 4 2 2 2 3
## [457] 2 1 4 3 NA 1 4 3 1 3 2 NA 3 NA 1 3 1 4 1 1 1 2 4 3
## [481] 1 2 2 2 3 2 3 1 1 NA 3 2 1 1 2 NA 2 2 2 3 3 1 1 2
## [505] NA 1 2 1 1 3 3 1 3 1 1 1 1 1 2 5 1 1 2 2 1 1 NA 1
## [529] 4 1 2 4 1 3 2 NA 1 1 NA 2 1 1 4 2 3 3 1 5 3 1 1 2
## [553] NA 1 1 3 1 3 2 4 NA 2 3 2 1 2 1 1 1 2 2 3 1 5 2 NA
## [577] 2 NA 3 2 2 2 1 5 3 2 3 1 NA 3 1 2 2 2 1 2 2 4 NA 6
## [601] 1 2 NA 1 1 2 2 3 NA 3 2 3 3 4 2 NA 2 NA 4 NA 1 1 2 2
## [625] 3 1 1 1 3 NA 2 5 NA 7 1 NA 4 3 3 1 NA 1 1 1 1 3 2 4
## [649] 2 2 3 NA NA 1 4 3 2 2 2 3 2 4 2 2 4 NA NA NA 6 3 3 1
## [673] 4 4 2 1 NA 1 6 NA 3 3 2 1 1 6 NA 1 5 1 NA 2 6 2 NA 4
## [697] 1 3 1 2 NA 1 1 3 1 2 4 2 1 3 2 4 3 2 2 1 1 5 6 4
## [721] 2 2 2 2 4 NA 1 2 2 2 2 4 5 NA NA NA 4 3 3 3 2 4 2 4
## [745] NA NA NA NA 2 1 NA 2 4 3 2 NA 2 3 1 3 4 NA 1 2 1 2 NA 3
## [769] 1 2 1 2 1 2 1 2 2 2 2 1 1 3 3 1 3 4 3 NA NA 4 2 3
## [793] 2 1 3 2 4 2 2 3 1 2 4 3 3 4 NA 1 4 2 1 1 1 3 1 5
## [817] 2 2 4 2 NA 1 3 1 2 NA 1 2 1 2 1 NA 1 3 2 3 2 NA 2 1
## [841] 4 2 NA NA NA 2 4 2 NA NA 3 1 NA 5 5 2 2 2 NA 2 1 3 1 3
## [865] 2 4 2 4 NA 4 1 2 3 2 3 3 2 3 2 2 2 1 3 2 4 2 NA 3
## [889] 3 2 2 NA NA 3 2 1 2 4 1 1 1 1 4 3 2 NA 3 2 NA 1 NA 3
## [913] 2 1 1 1 2 NA 2 2 3 3 2 NA NA 4 5 2 2 2 1 2 3 1 3 3
## [937] 4 3 NA 1 1 1 NA 4 3 5 1 1 2 NA 2 2 2 2 5 2 2 3 1 2
## [961] 3 NA 1 2 NA NA 2 NA 3 1 1 2 5 3 5 1 1 4 NA 2 1 3 1 1
## [985] 2 4 3 3 3 NA 1 1 2 2 1 1 2 2 NA 2# Find the number of NAs
ind <- is.na(na_example)
sum(ind)## [1] 145NA. Hint: remember the ! operator# Compute the average for non-NA entries
sum(na_example[!ind])## [1] 1968mean(na_example[!ind])## [1] 2.301754# Temperature data in Fahrenheit
temp <- c(35, 88, 42, 84, 81, 30)
city <- c("Beijing", "Lagos", "Paris", "Rio de Janeiro", "San Juan", "Toronto")
# Convert Fahrenheit to Celsius
temp_celsius <- (5/9) * (temp - 32)
# Create the data frame with city names and temperatures in Celsius
city_temps <- data.frame(name = city, temperature_celsius = temp_celsius)
# View the resulting data frame
city_temps## name temperature_celsius
## 1 Beijing 1.666667
## 2 Lagos 31.111111
## 3 Paris 5.555556
## 4 Rio de Janeiro 28.888889
## 5 San Juan 27.222222
## 6 Toronto -1.111111# Calculate the sum of the series
result <- sum(1 / (1:21)^2)
# Display the result
result## [1] 1.598431murder_rate. Then compute the average murder
rate for the US using the function mean. What is the
average?# Calculate the murder rate per 100,000 for each state
murder_rate <- (murders$murder / murders$population) * 100000
# Compute the average murder rate for the US
average_murder_rate <- mean(murder_rate)
# Display the average murder rate
average_murder_rate## [1] NaN