After completing this worksheet, you should have a basic familiarity with the most useful types of data structures in R (lists, data frames, and matrices). You should also be able to subset any of these data structures using the common subsetting operators ([, [[, and $).
You may find the chapters on data structures and subsetting from Hadley Wickham’s Advanced R book to be helpful.
In the previous worksheet you learned about vectors. Vectors hold a one-dimensional set of homogenous values. By homogenous we mean that that the values all have to be the same type (integer, numeric, logical, and so on) and you can’t have both, say, numeric and logical values in the same vector. By one-dimensional we mean that there can be more than one element accessed by a single index. For example, R includes a character vector of the letters in the English alphabet, helpfully called, letters.
letters## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
## [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"The letters object is a character vector because everything inside of it is a character vector.
class(letters)## [1] "character"And it has twenty-six elements:
length(letters)## [1] 26We can subset a vector using the [ operator (actually, it’s a function). For instance, we can get the first element like this:
letters[1]## [1] "a"letters[25]## [1] "y"We can get a range of values like so. Notice that we can get all the numbers one to five like this:
1:5## [1] 1 2 3 4 5So, we can get the first five letters like this:
letters[1:5]## [1] "a" "b" "c" "d" "e"letters[10:12]## [1] "j" "k" "l"You can get an arbitrary subset by creating a numeric (or integer) vector. Here we get the first, tenth, and twelfth letters.
letters[c(1, 10, 12)]## [1] "a" "j" "l"We can also do this by creating a variable and using it to do the subsetting.
what_i_want <- c(1, 3, 5, 7)
letters[what_i_want]## [1] "a" "c" "e" "g"letters variable to get the even letters (e.g., the second, fourth, etc.)even_numbers <-c(2, 4, 6, 8)
letters[even_numbers]## [1] "b" "d" "f" "h"seq() function (look it up with ?seq) to get the even letters in a more clever way?letters[seq(2, 26, by = 2)]## [1] "b" "d" "f" "h" "j" "l" "n" "p" "r" "t" "v" "x" "z"In addition to values, vectors can also have names. For example, let’s create a variable with the numbers 1 to 5, then give those values some names.
myvar <- 1:5
names(myvar) <- letters[1:5]
myvar## a b c d e
## 1 2 3 4 5Now we can also subset the vector based on the names:
myvar["c"]## c
## 3song_rankings <- c(10, 8.4, 6, 8.2, 4)
names (song_rankings) <- c( "Rock of Ages", "Dude Looks Like A Lady", "Welcome to the Jungle", "Highway to Hell", "Here I Go Again")
song_rankings## Rock of Ages Dude Looks Like A Lady Welcome to the Jungle
## 10.0 8.4 6.0
## Highway to Hell Here I Go Again
## 8.2 4.0Vectors are one-dimensional and homogeneous. Matrices are two-dimensional and homogenous, so they have the same kind of value, but have rows and columns as well. A matrix can be used for all kinds of problems in digital history. For now, let’s imagine we have four cites, A, B, C, and D, and have measured the distances between them. For instance, the distance from A to B is 2. We can represent those distances as a matrix with 4 columns and 4 rows, where the names of the rows and columns are the cities.
city_distances <- matrix(c(0, 2, 8, 3, 2, 0, 6, 1, 8, 6, 0, 4, 3, 1, 4, 0),
nrow = 4, ncol = 4)
rownames(city_distances) <- LETTERS[1:4]
colnames(city_distances) <- LETTERS[1:4]
city_distances## A B C D
## A 0 2 8 3
## B 2 0 6 1
## C 8 6 0 4
## D 3 1 4 0A matrix can be subsetted in the same way that a vector can be subsetted. (Because it is a vector—just a vector with two dimensions.) For instance, we can get the third element of the matrix.
city_distances[3]## [1] 8city_distances[5]## [1] 2But matrices are more useful when we subset them by row and column. For instance, here is the value contained in the cell for the first row and third column.
city_distances[1, 3]## [1] 8city_distances[3, 1]## [1] 8We are getting the distances for A, which is 8.
If a matrix has row and column names, we can subset the vector by that. For instance, here is the distance between cities B and D.
city_distances["B","D"]## [1] 1city_distances["D","C"]## [1] 4city_distances[c("A", "B", "C", "D")]## [1] NA NA NA NAThe most useful data structure in R is the data frame. Think of a data frame like a spreadsheet that holds any kind of tabular data. It is two dimensional, like a matrix; unlike a matrix, it is homogenous in that the columns can hold different kinds of data. While other langauges have add on libraries that allow for data structures like this, in R data frames are a first class citizen.
Let’s get a data frame from the historydata package. (If we also load dplyr, we will get nicer printing.)
library(historydata)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnaval_promotions## Source: local data frame [5,705 x 5]
##
## id name generation rank date
## (int) (chr) (int) (fctr) (chr)
## 1 1 Abbot, Joel 3 midshipman 1812-06-18
## 2 2 Abbot, Trevett 4 midshipman 1848-10-13
## 3 3 Abbott, Isaac 4 midshipman 1820-05-10
## 4 4 Abbott, J. Francis 4 midshipman 1837-12-27
## 5 5 Abbott, James W. 4 midshipman 1822-05-01
## 6 6 Abbott, Thomas C. 3 midshipman 1814-12-06
## 7 7 Abbott, Walter 3 midshipman 1812-01-01
## 8 8 Abbott, William A. 4 midshipman 1848-10-13
## 9 9 Abercrombie, Alexander R. 4 midshipman 1841-10-19
## 10 10 Abercrombie, J.B. 4 midshipman 1817-01-01
## .. ... ... ... ... ...We can use str() to get a different look at the data.
str(naval_promotions)## Classes 'tbl_df', 'tbl' and 'data.frame': 5705 obs. of 5 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr "Abbot, Joel" "Abbot, Trevett" "Abbott, Isaac" "Abbott, J. Francis" ...
## $ generation: int 3 4 4 4 4 3 3 4 4 4 ...
## $ rank : Factor w/ 5 levels "midshipman","lieutenant",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : chr "1812-06-18" "1848-10-13" "1820-05-10" "1837-12-27" ...There are five rows and five columns in the data frame. The different vectors include the id, name, generation, rank, and date of naval promotions.
We can use the [ subset function to get access to rows and columns, just like we did with matrices. For instance, here we ask for just the first row and the columned named "name":
naval_promotions[1, 5]## Source: local data frame [1 x 1]
##
## date
## (chr)
## 1 1812-06-18naval_promotions[3, "name"]## Source: local data frame [1 x 1]
##
## name
## (chr)
## 1 Abbott, IsaacWe can also ask for the entire first row (note the comma):
naval_promotions[1, ]## Source: local data frame [1 x 5]
##
## id name generation rank date
## (int) (chr) (int) (fctr) (chr)
## 1 1 Abbot, Joel 3 midshipman 1812-06-18naval_promotions[10, "name"]## Source: local data frame [1 x 1]
##
## name
## (chr)
## 1 Abercrombie, J.B.Because data frames are organized by column, it is possible to extract an entire column using the $ function. Here, let’s get just the names of the people. (We’ll limit it using head() so we don’t print out all of them.)
head(naval_promotions$name, 10)## [1] "Abbot, Joel" "Abbot, Trevett"
## [3] "Abbott, Isaac" "Abbott, J. Francis"
## [5] "Abbott, James W." "Abbott, Thomas C."
## [7] "Abbott, Walter" "Abbott, William A."
## [9] "Abercrombie, Alexander R." "Abercrombie, J.B."The function unique() gives you, well, all the unique values in a vector. For instance:
unique(c(1, 1, 1, 2, 3))## [1] 1 2 3naval_promotions dataset? (Hint: use unique().)unique(naval_promotions$rank, 10)## [1] midshipman lieutenant master_commandant captain
## [5] left_service
## 5 Levels: midshipman lieutenant master_commandant ... left_servicenaval_promotions dataset?unique(naval_promotions$id, 10)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
## [14] 14 15 16 17 18 19 20 21 22 23 24 25 26
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## [2471] 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575
## [2484] 2576 2577 2578 2579 2581 2582 2583 2584 2585 2586 2587 2588 2590
## [2497] 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603
## [2510] 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2615 2616 2617
## [2523] 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630
## [2536] 2631 2632 2633 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644
## [2549] 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657
## [2562] 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670
## [2575] 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683
## [2588] 2684 2685 2686 2687 2688 2689 2690 2691 2693 2695 2696 2697 2698
## [2601] 2699 2700 2701 2702 2703 2705 2706 2707 2708 2709 2710 2711 2712
## [2614] 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725
## [2627] 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738
## [2640] 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751
## [2653] 2752 2753 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765
## [2666] 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778
## [2679] 2779 2780 2781 2782 2783 2784 2785 2786 2787 2789 2790 2791 2792
## [2692] 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805
## [2705] 2806 2807 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819
## [2718] 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832
## [2731] 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845
## [2744] 2846 2847 2849 2850 2851 2853 2854 2855 2856 2857 2858 2859 2860
## [2757] 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873
## [2770] 2874 2875 2876 2877 2878 2879 2881 2882 2883 2884 2885 2886 2887
## [2783] 2888 2890 2891 2892 2893 2895 2896 2897 2898 2899 2900 2901 2902
## [2796] 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915
## [2809] 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928
## [2822] 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942
## [2835] 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955
## [2848] 2956 2957 2958 2959 2960 2961 2964 2965 2966 2967 2969 2970 2971
## [2861] 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984
## [2874] 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997
## [2887] 2998 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011
## [2900] 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024
## [2913] 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037
## [2926] 3038 3039 3040 3041 3043 3044 3045 3046 3047 3048 3049 3050 3051
## [2939] 3052 3053 3054 3055 3056 3057 3058 3060 3062 3063 3064 3065 3066
## [2952] 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079
## [2965] 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092
## [2978] 3094 3095 3096 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107
## [2991] 3108 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121
## [3004] 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134
## [3017] 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148
## [3030] 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3162
## [3043] 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175
## [3056] 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188
## [3069] 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201
## [3082] 3202 3203 3204 3205 3206 3208 3210 3211 3212 3213 3214 3215 3216
## [3095] 3217 3218 3219 3220 3221 3222 3223 3224 3226 3227 3228 3229 3230
## [3108] 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243
## [3121] 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256
## [3134] 3258 3259 3260 3261 3262 3263 3264 3266 3267 3268 3269 3270 3271
## [3147] 3272 3273 3274 3275 3276 3277 3278 3279 3281 3282 3283 3284 3285
## [3160] 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298
## [3173] 3299 3300 3301 3302 3303 3304 3305 3306 3307 3309 3310 3311 3313
## [3186] 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3327
## [3199] 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3340 3341
## [3212] 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354
## [3225] 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367
## [3238] 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380
## [3251] 3381 3382 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394
## [3264] 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407
## [3277] 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420
## [3290] 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433
## [3303] 3434 3435 3436 3437 3438 3439 3440 76 117 127 141 168 180
## [3316] 259 434 453 474 525 545 584 591 616 618 666 747 764
## [3329] 779 935 966 991 1005 1009 1062 1090 1219 1260 1403 1406 1474
## [3342] 1514 1522 1549 1569 1604 1649 1666 1698 1722 1760 1810 1852 1870
## [3355] 1912 1940 1946 1955 1963 1981 2005 2024 2115 2127 2131 2132 2158
## [3368] 2242 2252 2284 2293 2357 2366 2376 2377 2433 2441 2455 2589 2614
## [3381] 2634 2692 2704 2754 2788 2808 2852 2880 2889 2894 2929 2962 2968
## [3394] 2999 3042 3061 3109 3135 3161 3207 3209 3225 3257 3265 3280 3308
## [3407] 3312 3339 3383 472 1708 2184 2848 161 170 173 292 387 542
## [3420] 703 743 799 819 1096 1768 2001 2105 2139 2213 2349 2374 2580
## [3433] 2694 2963 3059 3093 3097 3326 10Because we only asked R for the first ten rows of data. By asking for the id using the unique function, we were able to se all of the people who were in the naval_promotions dataset. Which is 3433.
sort(), head(), tail(), range(), and as.Date() to be useful. Don’t forget about na.rm = TRUE as appropriate.)range(naval_promotions$date, na.rm = TRUE)## [1] "1794-06-04" "unknown"We will work much, much more with data frames.
Another very useful kind of data structure is the list. A list can hold values of any type, including vectors, data frames, and even other lists. For instance, we can create a list that holds several different kinds of information:
our_class <- list(
title = "Intro to R",
year = 2016,
books = c("Basics of R", "Get Awesome at R"),
students = c("Adam", "Betsy", "Cynthia", "David")
)
str(our_class)## List of 4
## $ title : chr "Intro to R"
## $ year : num 2016
## $ books : chr [1:2] "Basics of R" "Get Awesome at R"
## $ students: chr [1:4] "Adam" "Betsy" "Cynthia" "David"We can get just part of the list using the [ function that we’ve become used to. For instance, to get just the title:
our_class["title"]## $title
## [1] "Intro to R"But notice that the returned value is a list, not a character vector
is.list(our_class["title"])## [1] TRUEis.character(our_class["title"])## [1] FALSER has another subset operator, [[. The single bracket ([) gives us what we asked for inside a list; the double bracket ([[) simplifies the list to give us the vector (or whatever) we asked for.
our_class[["title"]]## [1] "Intro to R"is.list(our_class[["title"]])## [1] FALSEis.character(our_class[["title"]])## [1] TRUEour_class list, get just the year (as a list). Get it as a numeric vector.our_class["year"]## $year
## [1] 2016is.list(our_class[["year"]])## [1] FALSEis.numeric(our_class[["year"]])## [1] TRUEour_class list, get the class title, the book list, and the year (as a list). (Hint: remember the different kinds of subsetting we did with [.)R also lets us use the $ operator we used to get columns of a data frame.
our_class$title## [1] "Intro to R"our_class:our_class$students## [1] "Adam" "Betsy" "Cynthia" "David"$ equivalent to [ or [[?$ is equivalent to a single bracket []. This is because the single bracket lists out the data we are looking for, whereas the double brackets only answer TRUE or FALSE.
ameri_rev <- list(
event = "American Revolution",
year = 1765,
books = c("Ideological Origins", "The British Americans"),
figures = c("John", "Thomas", "George", "Abigail")
)
str(ameri_rev)## List of 4
## $ event : chr "American Revolution"
## $ year : num 1765
## $ books : chr [1:2] "Ideological Origins" "The British Americans"
## $ figures: chr [1:4] "John" "Thomas" "George" "Abigail"ameri_rev$figures## [1] "John" "Thomas" "George" "Abigail"$, [, and [[ on both data frames and lists. What is the relationship between a data frame and a list? (Hint: use is.list() and is.data.frame() on a list and a data frame.)a list includes homogeneous data of a vector. A data frame inclues heterogeneous.
We have done subsetting above using numeric and character vectors. We can also do subsetting using logical vectors. Let’s create a sample dataset of the heights of soldiers.
set.seed(3929)
heights <- rnorm(20, mean = 69)
names(heights) <- letters[1:20]
heights## a b c d e f g h
## 67.82345 69.04801 69.59076 68.86947 68.54834 68.13089 68.82323 67.90888
## i j k l m n o p
## 71.88773 66.51703 68.58429 68.23702 70.36570 68.33046 69.48776 69.86949
## q r s t
## 69.78694 68.90278 68.82545 69.47962We can find all the soldiers who are taller than average like this. First let’s compare all the heights to the mean height.
heights > mean(heights)## a b c d e f g h i j k l
## FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## m n o p q r s t
## TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUENotice that we get a logical vector as a result. We can use that within the [ operator to get just the soldiers who are taller than average.
heights[heights > mean(heights)]## b c i m o p q t
## 69.04801 69.59076 71.88773 70.36570 69.48776 69.86949 69.78694 69.47962heights[heights > 70]## i m
## 71.88773 70.36570This kind of subsetting also works for data frames. Here we get all the officers from the first generation. (Notice the comma.)
first_gen <- naval_promotions[naval_promotions$generation == 1, ]
head(first_gen, 10)## Source: local data frame [10 x 5]
##
## id name generation rank date
## (int) (chr) (int) (fctr) (chr)
## 1 76 Archer, John 1 lieutenant 1798-11-08
## 2 117 Bainbridge, William 1 lieutenant 1798-08-03
## 3 127 Baker, Thomas 1 lieutenant 1798-05-25
## 4 141 Ballard, John 1 lieutenant 1798-10-02
## 5 168 Barron, James 1 lieutenant 1798-03-09
## 6 180 Barton, Jeremiah 1 lieutenant 1798-06-08
## 7 259 Blair, George 1 lieutenant 1799-03-13
## 8 434 Burns, James 1 lieutenant 1798-10-29
## 9 453 Byrne, Gerald 1 lieutenant 1799-06-17
## 10 474 Campbell, James 1 lieutenant 1799-09-20head(naval_promotions, "captain")## Source: local data frame [5,705 x 5]
##
## id name generation rank date
## (int) (chr) (int) (fctr) (chr)
## 1 1 Abbot, Joel 3 midshipman 1812-06-18
## 2 2 Abbot, Trevett 4 midshipman 1848-10-13
## 3 3 Abbott, Isaac 4 midshipman 1820-05-10
## 4 4 Abbott, J. Francis 4 midshipman 1837-12-27
## 5 5 Abbott, James W. 4 midshipman 1822-05-01
## 6 6 Abbott, Thomas C. 3 midshipman 1814-12-06
## 7 7 Abbott, Walter 3 midshipman 1812-01-01
## 8 8 Abbott, William A. 4 midshipman 1848-10-13
## 9 9 Abercrombie, Alexander R. 4 midshipman 1841-10-19
## 10 10 Abercrombie, J.B. 4 midshipman 1817-01-01
## .. ... ... ... ... ...naval_promotions$date <- as.Date(naval_promotions$date)
naval_promotions[naval_promotions$date >= as.Date("1800-01-01") & naval_promotions$date < as.Date("1800-12-31"), ]## Source: local data frame [189 x 5]
##
## id name generation rank date
## (int) (chr) (int) (fctr) (date)
## 1 31 Aldrick, Samuel 2 midshipman 1800-07-07
## 2 45 Allen, Samuel 2 midshipman 1800-12-13
## 3 47 Allen, William H. 2 midshipman 1800-04-28
## 4 66 Anderson, Thomas O. 2 midshipman 1800-04-14
## 5 72 Angier, Charles 2 midshipman 1800-09-03
## 6 154 Barker, William W. 2 midshipman 1800-03-26
## 7 195 Beale, Thomas T. 2 midshipman 1800-02-17
## 8 203 Beck, Daniel 2 midshipman 1800-11-24
## 9 227 Bennett, Edward 2 midshipman 1800-01-03
## 10 233 Bentley, Peter E. 2 midshipman 1800-04-12
## .. ... ... ... ... ...