library(dslabs)## Warning: package 'dslabs' was built under R version 4.2.3data(murders)##A.axemine
str(murders)## 'data.frame': 51 obs. of 5 variables:
## $ state : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ abb : chr "AL" "AK" "AZ" "AR" ...
## $ region : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
## $ population: num 4779736 710231 6392017 2915918 37253956 ...
## $ total : num 135 19 232 93 1257 ...##B.the 51 states
(murders$state)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"(murders$total)## [1] 135 19 232 93 1257 65 97 38 99 669 376 7 12 364 142
## [16] 21 63 116 351 11 293 118 413 53 120 321 12 32 84 5
## [31] 246 67 517 286 4 310 111 36 457 16 207 8 219 805 22
## [46] 2 250 93 27 97 5##c.state name and the obbrevation of the state name the stae region and state population and total numbers og murders for 2010
(murders$region)## [1] South West West South West
## [6] West Northeast South South South
## [11] South West West North Central North Central
## [16] North Central North Central South South Northeast
## [21] South Northeast North Central North Central South
## [26] North Central West North Central West Northeast
## [31] Northeast West Northeast South North Central
## [36] North Central South West Northeast Northeast
## [41] South North Central South South West
## [46] Northeast South West South North Central
## [51] West
## Levels: Northeast South North Central West(murders$total)## [1] 135 19 232 93 1257 65 97 38 99 669 376 7 12 364 142
## [16] 21 63 116 351 11 293 118 413 53 120 321 12 32 84 5
## [31] 246 67 517 286 4 310 111 36 457 16 207 8 219 805 22
## [46] 2 250 93 27 97 5(murders$population)## [1] 4779736 710231 6392017 2915918 37253956 5029196 3574097 897934
## [9] 601723 19687653 9920000 1360301 1567582 12830632 6483802 3046355
## [17] 2853118 4339367 4533372 1328361 5773552 6547629 9883640 5303925
## [25] 2967297 5988927 989415 1826341 2700551 1316470 8791894 2059179
## [33] 19378102 9535483 672591 11536504 3751351 3831074 12702379 1052567
## [41] 4625364 814180 6346105 25145561 2763885 625741 8001024 6724540
## [49] 1852994 5686986 563626(murders$abb)## [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"(murders$state)## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "Utah"
## [46] "Vermont" "Virginia" "Washington"
## [49] "West Virginia" "Wisconsin" "Wyoming"##D. str show no relevent information
str(murders)## 'data.frame': 51 obs. of 5 variables:
## $ state : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ abb : chr "AL" "AK" "AZ" "AR" ...
## $ region : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
## $ population: num 4779736 710231 6392017 2915918 37253956 ...
## $ total : num 135 19 232 93 1257 ...##2.what are the column name used by the data frame for these five variable
colnames(murders)## [1] "state" "abb" "region" "population" "total"##3.use the accessor $to extract the state abbrevation and assign them to the object a. what is the class of object
a<-(murders$abb)
a## [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"class(a)## [1] "character"##4.use the square brackets to extract the stat abbrevation and assign them to object b.
b<-murders[["abbrevation"]]
b## NULLa==b## logical(0)##5.use saw that the region column store s function you can cororate this by typing
class(murders$region)## [1] "factor"##6.The function table takes a vector and returns the frequency of each element. You can quickly see
table(murders$region)##
## Northeast South North Central West
## 9 17 12 13##Exercise 3.9 ## Use the function c to create a vector with the average high temperatures in January for Beijing, Lagos, Paris, Rio de Janeiro, San Juan and Toronto, which are 35, 88, 42, 84, 81, and 30 degrees Fahrenheit.Call the object temp.
temp <- c(35, 88, 42, 84, 81, 30)
temp## [1] 35 88 42 84 81 30city <- c("Beijing", "Lagos", "Paris", "Rio de Janeiro", "San Juan", "Toronto")
city## [1] "Beijing" "Lagos" "Paris" "Rio de Janeiro"
## [5] "San Juan" "Toronto"##3.Use the names function and the objects defined in the previous exercises to associate the temperature data with its corresponding city.
# Assuming 'temp' and 'city' are already defined
names(temp) <- city
names(temp)## [1] "Beijing" "Lagos" "Paris" "Rio de Janeiro"
## [5] "San Juan" "Toronto"##4.Use the [ and : operators to access the temperature of the first three cities on the list.
# Assuming 'temp' is already defined
first_three_temps <- temp[1:3]
first_three_temps## Beijing Lagos Paris
## 35 88 42##5.Use the [ operator to access the temperature of Paris and San Juan.
# Assuming 'temp' is already defined
paris_san_juan_temps <- temp[c("Paris", "San Juan")]
paris_san_juan_temps## Paris San Juan
## 42 81##6.Use the : operator to create a sequence of numbers 12,13,14,…,73
12:73## [1] 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
## [26] 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
## [51] 62 63 64 65 66 67 68 69 70 71 72 73##7.Create a vector containing all the positive odd numbers smaller than 100.
seq(1,99,2)## [1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
## [26] 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99##8.Create a vector of numbers that starts at 6, does not pass 55, and adds numbers in increments of 4/7: 6, 6+4/7, 6+8/7, etc.. How many numbers does the list have?
seq( 6, 4/7, length.out = 100)## [1] 6.0000000 5.9451659 5.8903319 5.8354978 5.7806638 5.7258297 5.6709957
## [8] 5.6161616 5.5613276 5.5064935 5.4516595 5.3968254 5.3419913 5.2871573
## [15] 5.2323232 5.1774892 5.1226551 5.0678211 5.0129870 4.9581530 4.9033189
## [22] 4.8484848 4.7936508 4.7388167 4.6839827 4.6291486 4.5743146 4.5194805
## [29] 4.4646465 4.4098124 4.3549784 4.3001443 4.2453102 4.1904762 4.1356421
## [36] 4.0808081 4.0259740 3.9711400 3.9163059 3.8614719 3.8066378 3.7518038
## [43] 3.6969697 3.6421356 3.5873016 3.5324675 3.4776335 3.4227994 3.3679654
## [50] 3.3131313 3.2582973 3.2034632 3.1486291 3.0937951 3.0389610 2.9841270
## [57] 2.9292929 2.8744589 2.8196248 2.7647908 2.7099567 2.6551227 2.6002886
## [64] 2.5454545 2.4906205 2.4357864 2.3809524 2.3261183 2.2712843 2.2164502
## [71] 2.1616162 2.1067821 2.0519481 1.9971140 1.9422799 1.8874459 1.8326118
## [78] 1.7777778 1.7229437 1.6681097 1.6132756 1.5584416 1.5036075 1.4487734
## [85] 1.3939394 1.3391053 1.2842713 1.2294372 1.1746032 1.1197691 1.0649351
## [92] 1.0101010 0.9552670 0.9004329 0.8455988 0.7907648 0.7359307 0.6810967
## [99] 0.6262626 0.5714286length(sequence)## [1] 1##9.What is the class of the following object a <- seq(1, 10, 0.5)?
a <- seq(1, 10, 0.5)
a## [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
## [16] 8.5 9.0 9.5 10.0##10. What is the class of the following object a <- seq(1, 10)?
a <- seq(1, 10)
a## [1] 1 2 3 4 5 6 7 8 9 10##11.The class of class(a<-1) is numeric, not integer. R defaults to numeric and to force an integer, youneed to add the letter L. Confirm that the class of 1L is integer.
class(a <- 1)## [1] "numeric"class(b <- 1L)## [1] "integer"##12.Define the following vector:x <- c(“1”, “3”, “5”)and coerce it to get integers.
x <- c("1", "3", "5")
x <- as.integer(x)
x## [1] 1 3 5