Finding increases from 'baseline' in the graph, not sure how to do - r
I want to write an algorithm that spits out the points highlighted by arrows. I've tried using a second derivative but it returns a similar plot to the one above and not sure how to use it.
Hi, sorry about that, I don't want the peaks, I want the point where the graph starts to increase - ie I want the point where the gradient changes from ~0 to something larger, does that make sense
Example data is below.
df = structure(list(X1 = c("2729", "2730", "2731", "2732", "2733",
"2734", "2735", "2736", "2737", "2738", "2739", "2740", "2741",
"2742", "2743", "2744", "2745", "2746", "2747", "2748", "2749",
"2750", "2751", "2752", "2753", "2754", "2755", "2756", "2757",
"2758", "2759", "2760", "2761", "2762", "2763", "2764", "2765",
"2766", "2767", "2768", "2769", "2770", "2771", "2772", "2773",
"2774", "2775", "2776", "2777", "2778", "2779", "2780", "2781",
"2782", "2783", "2784", "2785", "2786", "2787", "2788", "2789",
"2790", "2791", "2792", "2793", "2794", "2795", "2796", "2797",
"2798", "2799", "2800", "2801", "2802", "2803", "2804", "2805",
"2806", "2807", "2808", "2809", "2810", "2811", "2812", "2813",
"2814", "2815", "2816", "2817", "2818", "2819", "2820", "2821",
"2822", "2823", "2824", "2825", "2826", "2827", "2828", "2829",
"2830", "2831", "2832", "2833", "2834", "2835", "2836", "2837",
"2838", "2839", "2840", "2841", "2842", "2843", "2844", "2845",
"2846", "2847", "2848", "2849", "2850", "2851", "2852", "2853",
"2854", "2855", "2856", "2857", "2858", "2859", "2860", "2861",
"2862", "2863", "2864", "2865", "2866", "2867", "2868", "2869",
"2870", "2871", "2872", "2873", "2874", "2875", "2876", "2877",
"2878", "2879", "2880", "2881", "2882", "2883", "2884", "2885",
"2886", "2887", "2888", "2889", "2890", "2891", "2892", "2893",
"2894", "2895", "2896", "2897", "2898", "2899", "2900", "2901",
"2902", "2903", "2904", "2905", "2906", "2907", "2908", "2909",
"2910", "2911", "2912", "2913", "2914", "2915", "2916", "2917",
"2918", "2919", "2920", "2921", "2922", "2923", "2924", "2925",
"2926", "2927", "2928", "2929", "2930", "2931", "2932", "2933",
"2934", "2935", "2936", "2937", "2938", "2939", "2940", "2941",
"2942", "2943", "2944", "2945", "2946", "2947", "2948", "2949",
"2950", "2951", "2952", "2953", "2954", "2955", "2956", "2957",
"2958", "2959", "2960", "2961", "2962", "2963", "2964", "2965",
"2966", "2967", "2968", "2969", "2970", "2971", "2972", "2973",
"2974", "2975", "2976", "2977", "2978", "2979", "2980", "2981",
"2982", "2983", "2984", "2985", "2986", "2987", "2988", "2989",
"2990", "2991", "2992", "2993", "2994", "2995", "2996", "2997",
"2998", "2999", "3000", "3001", "3002", "3003", "3004", "3005",
"3006", "3007", "3008", "3009", "3010", "3011", "3012", "3013",
"3014", "3015", "3016", "3017", "3018", "3019", "3020", "3021",
"3022", "3023", "3024", "3025", "3026", "3027", "3028", "3029",
"3030", "3031", "3032", "3033", "3034", "3035", "3036", "3037",
"3038", "3039", "3040", "3041", "3042", "3043", "3044", "3045",
"3046", "3047", "3048", "3049", "3050", "3051", "3052", "3053",
"3054", "3055", "3056", "3057", "3058", "3059", "3060", "3061",
"3062", "3063", "3064", "3065", "3066", "3067", "3068", "3069",
"3070", "3071", "3072", "3073", "3074", "3075", "3076", "3077",
"3078", "3079", "3080", "3081", "3082", "3083", "3084", "3085",
"3086", "3087", "3088", "3089", "3090", "3091", "3092", "3093",
"3094", "3095", "3096", "3097", "3098", "3099", "3100", "3101",
"3102", "3103", "3104", "3105", "3106", "3107", "3108", "3109",
"3110", "3111", "3112", "3113", "3114", "3115", "3116", "3117",
"3118", "3119", "3120", "3121", "3122", "3123", "3124", "3125",
"3126", "3127", "3128", "3129", "3130", "3131", "3132", "3133",
"3134", "3135", "3136", "3137", "3138", "3139", "3140", "3141",
"3142", "3143", "3144", "3145", "3146", "3147", "3148", "3149",
"3150", "3151", "3152", "3153", "3154", "3155", "3156", "3157",
"3158", "3159", "3160", "3161", "3162", "3163", "3164", "3165",
"3166", "3167", "3168", "3169", "3170", "3171", "3172", "3173",
"3174", "3175", "3176", "3177", "3178", "3179", "3180", "3181",
"3182", "3183", "3184", "3185", "3186", "3187", "3188", "3189",
"3190", "3191", "3192", "3193", "3194", "3195", "3196", "3197",
"3198", "3199", "3200", "3201", "3202", "3203", "3204", "3205",
"3206", "3207", "3208", "3209", "3210", "3211", "3212", "3213",
"3214", "3215", "3216", "3217", "3218", "3219", "3220", "3221",
"3222", "3223", "3224", "3225", "3226", "3227", "3228", "3229",
"3230", "3231", "3232", "3233", "3234", "3235", "3236", "3237",
"3238", "3239", "3240", "3241", "3242", "3243", "3244", "3245",
"3246", "3247", "3248", "3249", "3250", "3251", "3252", "3253",
"3254", "3255", "3256", "3257", "3258", "3259", "3260", "3261",
"3262", "3263", "3264", "3265", "3266", "3267", "3268", "3269",
"3270", "3271", "3272", "3273", "3274", "3275", "3276", "3277",
"3278", "3279", "3280", "3281", "3282", "3283", "3284", "3285",
"3286", "3287", "3288", "3289", "3290", "3291", "3292", "3293",
"3294", "3295", "3296", "3297", "3298", "3299", "3300", "3301",
"3302", "3303", "3304", "3305", "3306", "3307", "3308", "3309",
"3310", "3311", "3312", "3313", "3314", "3315", "3316", "3317",
"3318", "3319", "3320", "3321", "3322", "3323", "3324", "3325",
"3326", "3327", "3328", "3329", "3330", "3331", "3332", "3333",
"3334", "3335", "3336", "3337", "3338", "3339", "3340", "3341",
"3342", "3343", "3344", "3345", "3346", "3347", "3348", "3349",
"3350", "3351", "3352", "3353", "3354", "3355", "3356", "3357",
"3358", "3359", "3360", "3361", "3362", "3363", "3364", "3365",
"3366", "3367", "3368", "3369", "3370", "3371", "3372", "3373",
"3374", "3375", "3376", "3377", "3378", "3379", "3380", "3381",
"3382", "3383", "3384", "3385", "3386", "3387", "3388", "3389",
"3390", "3391", "3392", "3393", "3394", "3395", "3396", "3397",
"3398", "3399", "3400", "3401", "3402", "3403", "3404", "3405",
"3406", "3407", "3408", "3409", "3410", "3411", "3412", "3413",
"3414", "3415", "3416", "3417", "3418", "3419", "3420", "3421",
"3422", "3423", "3424", "3425", "3426", "3427", "3428", "3429",
"3430", "3431", "3432", "3433", "3434", "3435", "3436", "3437",
"3438", "3439", "3440", "3441", "3442", "3443", "3444", "3445"
), X2 = c(-0.00385000000001254, -0.0154500000000484, -0.0277600000000007,
-0.0154500000000279, -0.0386000000000704, -0.0154500000000329,
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0.000769999999999304, -0.0231500000000085, -0.0270500000000402,
0.351200000000562, -0.0231500000000833, -0.0270500000000588,
-0.0463500000000216, -0.0139000000000062, -9.23022089465272e-15,
-8.05101707233625e-15, 0.00385000000000546, 0.000759999999998229,
-0.0115500000000395, 0.000769999999999982, -0.011600000000024,
-0.00770000000001206, -0.0540500000001929, -0.0772000000001558,
-0.0656000000000217, -0.0772000000001484, -0.0579000000001128,
-0.0347000000000764, -0.0193000000000461, -0.00385000000000352,
-0.00385000000002122, -0.00696000000000083, 0.000789999999999225,
0.00384999999999834, -0.000800000000000978, -0.0116000000000234,
-0.00775000000001088, -0.0115900000000055, -0.0193000000000218,
-0.0347500000000808, -0.0386000000000897, -0.0501500000000858,
-0.00233999999999881, -0.00385000000000757, 2.00000000009208e-05,
0.308750000000515, 0.092650000000154, 0.0424500000000756, 0.0231500000000227,
0.0154500000000312, -0.00385000000001469, 0.00538999999999237,
0.474750000000936, 0.212300000000357, -0.0030699999999996, -0.0309000000000739,
-0.0115500000000265, -0.0116000000000265, -3.57390000000716,
-0.293350000001048, -0.119650000000226, -0.104200000000194, -0.0926500000001831,
-0.0540500000001096, -0.0694500000002714, -0.0772000000001527,
-0.0965000000001976, -0.0694500000001375, -0.100350000000182,
-0.084950000000289, -0.061750000000121, -0.0425000000000912,
-0.0424500000000662, -0.00770000000002011, -0.0154500000000422,
-0.00307999999999993, -0.00230999999999994, 0.00385000000001447,
-0.00154, -0.00385000000000567, -0.0386000000000747, -0.0695000000002463,
-0.0772000000001664, -0.0849000000002961, -0.0887500000001668,
-0.0193000000000504, -0.0578500000001047, -0.00775000000000708,
-1.2095231788207e-14, 0.00848999999999485, -3.07674029821757e-15,
-0.00541000000000057, -0.00390000000002247, 0.000769999999999981,
-0.0293300000000002, -0.050200000000087, -0.0656000000002546,
-0.0540500000001096, -0.069450000000138, 0.123500000000375, 0.0849000000001387,
0.00384999999999644, 0.023200000000042, 0.0115500000000123, 0.00775000000000473,
0.0115500000000203, 0.00385000000001447, -0.00775000000002506,
0.00466000000000122, -0.0254699999999978, -0.054799999999998,
-0.0231500000000444, 0.0116000000000454, 0.115800000000206, 0.030900000000046,
0.00385000000000331, -0.00153999999999996, 0.00384999999999084,
-0.00385000000000757, 0.00770000000001088, 1.7849988639723e-14,
0.00230999999999994, 0.00385000000001326, -0.00153999999999882,
-0.038600000000126, -0.0309000000000553, -0.00692999999999628,
-0.0154000000000403, -0.0579000000001097, -0.0347500000000678,
-0.0100400000000054, 0.00385000000000023, -0.00385000000001994,
-2.17923926727129e-14, 0.00389999999999028, 0.00390000000001402,
0.00384999999999084, -0.00385000000001751, 0.00770000000001399,
-0.0308500000000632, -0.0502000000001986, -0.0695000000001394,
-0.0501799999999982, -0.0309000000000752, -0.0270500000000557,
-0.0100500000000011, 0.00389999999999596, 0.0116000000000117,
1.89760393249092e-15, 0.0115500000000123, 0.00384999999998841,
-0.00385000000002965, 0.0077000000000078, 0.00385000000000639,
0.00770000000000283, -0.0501500000001132, -0.0617500000002242,
-0.0710100000000004, -0.0810500000000306, -0.0540500000001891,
-0.0386000000000617, -0.019300000000043, 0.00775000000000473,
0.00847000000000282, 0.00462999999999951, -2.11128370304365e-14,
0.00770000000001088, 0.00384999999999858, 9.99999999962123e-06,
-0.00770000000001206, -0.0733000000000254, -0.0656000000001967,
-0.111900000000213, -0.100350000000323, -0.0579000000001141,
-0.0385500000000131, -0.0116000000000215, 0.0193000000000318,
0.00390000000001402, 0.0270000000000452, 0.00770000000000182,
-8.05101707233625e-15)), row.names = c(NA, -717L), class = "data.frame")
As others have said, it is not clear what you are looking for.
specifically, it's not clear how high above "baseline" is too high.
Here's a shot at it:
df_prime <- df$X2[-1] - df$X2[-length(df$X2)]
large_rise <- which(df_prime > sd(df_prime) & df$X2[-length(df$X2)] > -sd(df$X2))
df$X1[large_rise]
It's difficult to know from the question, but aren't you just looking for something like this?
spikes <- as.numeric(df$X1[df$X2 > 0.1])
spikes <- spikes[which(diff(c(0, spikes)) > 3)]
spikes
#> [1] 2738 2758 2984 2994 3126 3139 3190 3260 3273 3309 3316 3363 3377
So, for example if you did
plot(df$X1, df$X2, type = "l")
points(spikes, rep(1, length(spikes)), col="red")
You would get
Related
group by year in yearqrt format R
I would like to group values of all other columns by the year in column yearqtr the following data
dput(narepurchasement)
structure(list(Date = structure(c(844128000, 852076800, 859852800,
867715200, 875664000, 883612800, 891388800, 899251200, 907200000,
915148800, 922924800, 930787200, 938736000, 946684800, 954547200,
962409600, 970358400, 978307200, 986083200, 993945600, 1001894400,
1009843200, 1017619200, 1025481600, 1033430400, 1041379200, 1049155200,
1057017600, 1064966400, 1072915200, 1080777600, 1088640000, 1096588800,
1104537600, 1112313600, 1120176000, 1128124800, 1136073600, 1143849600,
1151712000, 1159660800, 1167609600, 1175385600, 1183248000, 1191196800,
1199145600, 1207008000, 1214870400, 1222819200, 1230768000, 1238544000,
1246406400, 1254355200, 1262304000, 1270080000, 1277942400, 1285891200,
1293840000, 1301616000, 1309478400, 1317427200, 1325376000, 1333238400,
1341100800, 1349049600, 1356998400, 1364774400, 1372636800, 1380585600,
1388534400, 1396310400, 1404172800, 1412121600, 1420070400, 1427846400,
1435708800, 1443657600, 1451606400, 1459468800, 1467331200, 1475280000,
1483228800, 1491004800, 1498867200, 1506816000, 1514764800, 1522540800,
1530403200, 1538352000, 1546300800, 1554076800, 1561939200, 1569888000,
1577836800, 1585699200, 1593561600, 1601510400, 1609459200, 1617235200,
1625097600, 1633046400, 1640995200, 1648771200), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), NetIssuance = c("-7450", "-13950",
"-14675", "-22875", "-25875", "-21675", "-17808", "-64840", "-111214",
"-6920", "-76700", "-26188", "-1", "27044", "-50630", "-10731",
"-83887", "-4850", "-14775", "-27350", "-1150", "-2644", "6357",
"-20316", "2098", "-10173", "-3438", "0", "-2055", "-0.802",
"-16823", "-32200", "-70730", "-43031", "-58722", "-90630", "-83784",
"-110795", "-116977", "-107859", "-137542", "-109583", "-149516",
"-162019", "-226618", "-84099", "-38612", "-73533", "-93475",
"-37950", "39311", "20920", "-62302", "-35987", "-35433", "-71238",
"-58295", "-59766", "-101392", "-133088", "-88329", "-49568",
"-99135", "-73428", "-77876", "-38256", "-73497", "-60269", "-105274",
"-101911", "-48493", "-80452", "-71090", "-116963", "-102404",
"-129399", "-104711", "-127487", "-136914", "-150658", "-80792",
"-89438", "-55464", "-119607", "-61042", "-122438", "-225035",
"-79778", "-190075", "-174006", "-46583", "-111504", "-124927",
"-95947", "-14946", "7398", "-67450", "-30403", "-133211", "-218291",
"-237670", "-227868", "-135084"), GrossIssuance = c(35393, 34426,
39963, 36586, 40630, 36993, 57637, 31110, 52737, 52487, 78711,
65846, 95574, 113349, 86067, 75480, 71906, 54552, 64094, 39824,
55322, 43624, 50257, 29329, 35664, 32098, 36084, 42285, 48634,
57955, 47497, 43892, 55599, 48385, 52197, 63692, 63159, 68401,
69557, 63825, 94723, 88627, 97967, 102944, 108022, 86316, 96002,
93730, 75885, 64674, 77307, 62616, 66705, 54873, 57173, 48392,
68703, 64334, 69966, 43637, 55198, 66678, 70380, 68331, 72198,
73702, 83784, 103945, 94138, 89471, 100239, 100418, 111302, 129933,
124281, 116589, 97678, 106734, 118234, 106262, 107965, 122679,
115625, 107485, 112226, 106358, 99560, 101952, 91526, 95447,
118912, 108570, 100615, 107853, 154908, 134115, 154227, 163567,
126579, 112180, 132474, 92327, 80342), GrossRetirement = c(42843,
48376, 54638, 59461, 66505, 58668, 75445, 95950, 163951, 59407,
155411, 92034, 96134, 86305, 136697, 86211, 155793, 59402, 78869,
67174, 56472, 46268, 43900, 49645, 33566, 42271, 39522, 42226,
50689, 58757, 64320, 76092, 126329, 91416, 110919, 154322, 146943,
179196, 186534, 171684, 232265, 198210, 247483, 264963, 334640,
170415, 134614, 167263, 169360, 102624, 37996, 41696, 129007,
90860, 92606, 119630, 126998, 124100, 171358, 176725, 143527,
116246, 169515, 141759, 150074, 111958, 157281, 164214, 199412,
191382, 148732, 180870, 182392, 246896, 226685, 245988, 202389,
234221, 255148, 256920, 188757, 212117, 171089, 227092, 173268,
228796, 324595, 181730, 281601, 269453, 165495, 220074, 225542,
203800, 169854, 126717, 221677, 193970, 259790, 330471, 370144,
320195, 215426), Repurchases = c(22263, 22638, 23514, 25005,
34369, 26643, 29082, 41095, 27253, 31805, 30779, 29350, 35972,
38084, 22859, 24761, 30152, 25245, 26623, 27689, 24038, 20954,
27243, 27314, 18885, 20208, 22000, 25993, 34329, 31567, 34011,
42358, 46643, 52980, 63201, 66599, 90778, 76295, 97243, 91990,
96248, 92541, 121025, 121251, 98213, 94359, 75799, 80943, 45745,
26459, 17862, 24888, 33600, 40277, 59624, 57199, 62624, 66172,
73022, 96186, 74495, 64511, 83483, 65770, 86040, 77135, 100169,
97375, 105120, 124551, 99652, 108215, 106062, 113685, 100343,
122057, 107005, 123418, 99546, 75010, 89025, 93073, 81638, 84879,
87762, 143170, 138764, 134874, 148169, 137193, 107400, 108922,
119371, 143785, 79929, 88312, 110984, 128796, 141252, 154680,
195502, 220050, 120000), MA = c(20579, 25738, 31124, 34456, 32136,
32025, 46364, 54855, 136698, 27602, 124632, 62684, 60162, 48221,
113837, 61450, 125641, 34157, 52246, 39486, 32434, 25314, 16657,
22331, 14681, 22063, 17522, 16233, 16360, 27191, 30309, 33735,
79686, 38436, 47718, 87723, 56166, 102901, 89291, 79694, 136016,
105669, 126458, 143711, 236427, 76055, 58816, 86320, 123615,
76166, 20134, 16809, 95407, 50583, 32982, 62430, 64373, 57928,
98336, 80539, 69032, 51735, 86032, 75988, 64033, 34823, 57112,
66838, 94292, 66831, 49079, 72655, 76330, 133211, 126342, 123931,
95384, 110803, 155602, 181911, 99732, 119044, 89451, 142213,
85506, 85626, 185832, 46856, 133432, 132260, 58095, 111152, 106172,
60015, 89925, 38404, 110693, 65174, 118539, 175791, 174642, 100146,
95426), GDP = c(8259.771, 8362.655, 8518.825, 8662.823, 8765.907,
8866.48, 8969.699, 9121.097, 9293.991, 9411.682, 9526.21, 9686.626,
9900.169, 10002.179, 10247.72, 10318.165, 10435.744, 10470.231,
10599, 10598.02, 10660.465, 10783.5, 10887.46, 10984.04, 11061.433,
11174.129, 11312.766, 11566.669, 11772.234, 11923.447, 12112.815,
12305.307, 12527.214, 12767.286, 12922.656, 13142.642, 13324.204,
13599.16, 13753.424, 13870.188, 14039.56, 14215.651, 14402.082,
14564.117, 14715.058, 14706.538, 14865.701, 14898.999, 14608.208,
14430.901, 14381.236, 14448.882, 14651.248, 14764.611, 14980.193,
15141.605, 15309.471, 15351.444, 15557.535, 15647.681, 15842.267,
16068.824, 16207.13, 16319.54, 16420.386, 16629.05, 16699.551,
16911.068, 17133.114, 17144.281, 17462.703, 17743.227, 17852.54,
17991.348, 18193.707, 18306.96, 18332.079, 18425.306, 18611.617,
18775.459, 18968.041, 19148.194, 19304.506, 19561.896, 19894.75,
20155.486, 20470.197, 20687.278, 20819.269, 21013.085, 21272.448,
21531.839, 21706.532, 21538.032, 19636.731, 21362.428, 21704.706,
22313.85, 23046.934, 23550.42, 24349.121, 24740.48, 25248.476
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-103L))
I don't know how exactly...
We may use library(dplyr) library(zoo) library(lubridate) narepurchasement %>% mutate(yearqtr = as.yearqtr(Date)) %>% group_by(year = year(yearqtr))
Updating dataframe column value by referring to another dataframe
My first data frame (df) contains Entrydate and ExitDate columns. Another dataframe (n1) has all trading dates. I need a new column in first dataframe calculated as number of days as calculated from the second dataframe. How do I call this dayCount function for each row of df. When I try to use mapply, I am unable to pass n1 as a parameter.
dayCount <- function (startDate, endDate, n1) {
return (nrow(subset(n1, Date >= startDate & Date <= endDate)))
}
df<- structure(list(EntryDate = structure(c(11355, 11418, 11436, 11449,
11520, 11523, 11548, 11620, 11768, 11773), class = "Date"), ExitDate = structure(c(11360,
11422, 11438, 11457, 11522, 11526, 11554, 11625, 11772, 11778
), class = "Date")), row.names = c(22L, 65L, 76L, 84L, 135L,
138L, 155L, 204L, 305L, 307L), class = "data.frame")
n1<- structure(c(11354, 11355, 11358, 11359, 11360, 11361, 11362,
11365, 11366, 11367, 11368, 11369, 11372, 11373, 11374, 11375,
11376, 11379, 11380, 11381, 11382, 11383, 11386, 11388, 11389,
11390, 11393, 11394, 11395, 11396, 11397, 11400, 11401, 11402,
11403, 11404, 11407, 11408, 11409, 11410, 11411, 11414, 11415,
11416, 11418, 11421, 11422, 11423, 11424, 11428, 11429, 11430,
11431, 11432, 11435, 11436, 11437, 11438, 11439, 11442, 11444,
11445, 11446, 11449, 11450, 11451, 11452, 11453, 11456, 11457,
11458, 11459, 11460, 11463, 11464, 11465, 11466, 11467, 11470,
11471, 11472, 11473, 11474, 11477, 11478, 11479, 11480, 11481,
11484, 11485, 11486, 11487, 11488, 11491, 11492, 11493, 11494,
11495, 11498, 11499, 11500, 11501, 11502, 11505, 11506, 11507,
11508, 11509, 11512, 11513, 11514, 11515, 11516, 11519, 11520,
11521, 11522, 11523, 11526, 11527, 11528, 11529, 11530, 11533,
11534, 11535, 11536, 11537, 11540, 11541, 11542, 11543, 11544,
11547, 11548, 11550, 11551, 11554, 11555, 11557, 11558, 11561,
11562, 11563, 11564, 11565, 11568, 11569, 11570, 11571, 11572,
11575, 11576, 11577, 11578, 11579, 11582, 11583, 11584, 11585,
11586, 11589, 11590, 11591, 11592, 11593, 11596, 11598, 11599,
11600, 11603, 11604, 11605, 11606, 11607, 11610, 11611, 11612,
11613, 11614, 11617, 11618, 11619, 11620, 11624, 11625, 11626,
11627, 11628, 11631, 11632, 11633, 11634, 11635, 11638, 11639,
11640, 11641, 11645, 11646, 11647, 11648, 11649, 11652, 11653,
11654, 11655, 11659, 11660, 11661, 11662, 11663, 11666, 11667,
11668, 11669, 11670, 11674, 11675, 11676, 11677, 11680, 11682,
11683, 11684, 11687, 11688, 11689, 11690, 11691, 11694, 11695,
11696, 11697, 11698, 11701, 11702, 11703, 11704, 11705, 11708,
11709, 11710, 11711, 11712, 11715, 11716, 11717, 11718, 11719,
11722, 11723, 11724, 11725, 11726, 11729, 11730, 11731, 11732,
11733, 11736, 11737, 11738, 11739, 11740, 11743, 11744, 11745,
11746, 11747, 11750, 11751, 11752, 11753, 11754, 11757, 11758,
11759, 11760, 11761, 11764, 11765, 11766, 11767, 11768, 11772,
11773, 11774, 11778), class = "Date")
You can use %in% to count number of days in n1 between each EntryDate and ExitDate. df$dayCount <- colSums(mapply(function(x, y) n1 %in% seq(x, y, by = '1 day'), df$EntryDate, df$ExitDate)) df # EntryDate ExitDate dayCount #22 2001-02-02 2001-02-07 4 #65 2001-04-06 2001-04-10 3 #76 2001-04-24 2001-04-26 3 #84 2001-05-07 2001-05-15 7 #135 2001-07-17 2001-07-19 3 #138 2001-07-20 2001-07-23 2 #155 2001-08-14 2001-08-20 4 #204 2001-10-25 2001-10-30 3 #305 2002-03-22 2002-03-26 2 #307 2002-03-27 2002-04-01 3
What is the format of "{123, affdsf, 223, 22, dgbwa, 33333}"?
I have the following format, please advise how to convert it to a list in R?
"{1948, 2507, 2510, 7030, 7110, 9009, 00027, 00206, 00399, 00717, 00814, 00828, 00848, 00917, 01050, 01105, 01144, 02130, 02768, 03037, 03752, 03754, 04070, 04110, 05050, 05255, 05289, 05564, 05595, 06100, 06330, 06671, 07041, 07119, 07137, 07273, 07313, 07454, 07871, 08104, 08714, 08726, 08995, 09059, 09073, 09525, 09949, 09981, 10092, 10439, 10782, 11185, 11507, 11712, 11806, 11858, 11980, 12067, 12113, 12139, 12643, 13820, 14534, 15007, 15014, 15549, 15953, 16151, 16174, 16634, 16733, 16888, 17111, 17207, 17377, 17721, 17900, 18118, 18400, 18686, 18880, 19080, 19342, 19444, 19772, 19790, 19891, 20091, 20245, 20402, 20811, 21114, 21345, 21811, 21881, 22222, 22311, 22320, 22831, 22969, 23251, 23572, 23734, 23862, 23889, 24034, 24463, 25172, 25688, 26143, 26221, 26803, 26850, 26898, 27497, 28291, 28343, 29411, 29419, 30024, 30561, 30923, 31345, 31351, 31555, 31927, 32198, 32861, 33020, 33040, 33095, 33188, 33311, 33368, 33377, 33475, 33519, 33574, 33592, 34207, 34235, 34272, 34484, 34854, 34872, 34875, 34876, 34880, 35222, 35292, 35344, 36177, 36266, 37038, 37060, 37548, 37686, 37700, 38139, 39368, 39369, 39633, 40132, 40698, 40704, 40744, 40819, 41311, 41971, 42102, 42616, 43055, 43211, 43234, 43428, 43494, 43934, 44117, 44252, 44272, 44301, 44336, 44619, 44866, 44888, 45049, 45197, 45412, 45718, 46694, 46736, 47000, 48046, 48540, 49078, 49109, 49216, 49388, 49464, 50056, 50155, 50217, 50477, 50692, 51122, 51445, 51946, 52475, 52537, 52982, 54011, 54031, 54160, 54963, 55000, 55537, 56080, 56163, 56282, 56760, 56787, 57102, 57727, 57871, 58101, 58558, 58882, 59902, 60225, 60397, 60501, 60619, 60703, 60890, 61075, 61894, 61944, 62322, 62337, 62380, 62413, 62729, 62766, 62923, 63010, 63234, 63977, 64127, 65359, 65428, 65542, 65750, 65863, 66184, 66636, 66712, 67201, 67439, 67953, 68133, 68854, 69251, 69959, 70107, 70725, 70768, 71081, 71099, 71948, 72013, 72377, 72400, 72420, 72735, 73000, 73015, 73142, 73223, 73455, 73717, 74049, 74492, 74854, 74941, 75142, 75399, 75464, 75587, 75618, 75642, 75887, 76357, 76651, 77199, 77302, 77456, 77579, 77601, 77649, 77668, 77694, 77745, 78006, 78010, 78178, 78335, 78656, 78729, 78808, 78824, 78844, 78945, 79416, 79471, 79915, 80077, 80111, 80189, 80262, 80409, 80470, 80529, 80539, 80838, 81272, 81513, 81658, 81740, 81743, 81762, 81843, 82001, 82070, 82106, 82342, 82472, 82719, 83670, 84009, 84151, 84299, 84430, 84450, 84460, 84945, 86411, 86443, 86446, 86668, 86942, 87286, 87317, 87624, 87785, 88023, 88517, 88696, 88787, 88868, 88977, 89206, 90108, 90440, 90734, 90802, 90849, 90920, 90931, 91011, 91031, 91133, 91777, 91949, 92162, 92494, 93012, 93172, 94300, 94517, 95142, 95410, 95559, 95859, 96112, 97255, 97787, 97986, 98240, 98817, 99050, 99198, 99222, 99241, 99295, 99326, 99335, 99503, 99603, 99643, 99803, 99968}"
THIS IS NOT A DUPLICATE OF convert json to list in a vectorized way in R
IT'S COMPLETELY DIFFERENT BECAUSE THE FORMAT IS ABSOLUTELY DIFFERENT.
Try this one line code:
as.numeric(sapply(strsplit(substr(j,2,nchar(j)-1),split = ","),trimws))
[1] 1948 2507 2510 7030 7110 9009 27 206 399 717 814 828 848 917 1050 1105 1144
[18] 2130 2768 3037 3752 3754 4070 4110 5050 5255 5289 5564 5595 6100 6330 6671 7041 7119
[35] 7137 7273 7313 7454 7871 8104 8714 8726 8995 9059 9073 9525 9949 9981 10092 10439 10782
[52] 11185 11507 11712 11806 11858 11980 12067 12113 1213 ..
Your input:
j<-"{1948, 2507, 2510, 7030, 7110, 9009, 00027, 00206, 00399, 00717, 00814, 00828, 00848, 00917, 01050, 01105, 01144, 02130, 02768, 03037, 03752, 03754, 04070, 04110, 05050, 05255, 05289, 05564, 05595, 06100, 06330, 06671, 07041, 07119, 07137, 07273, 07313, 07454, 07871, 08104, 08714, 08726, 08995, 09059, 09073, 09525, 09949, 09981, 10092, 10439, 10782, 11185, 11507, 11712, 11806, 11858, 11980, 12067, 12113, 12139, 12643, 13820, 14534, 15007, 15014, 15549, 15953, 16151, 16174, 16634, 16733, 16888, 17111, 17207, 17377, 17721, 17900, 18118, 18400, 18686, 18880, 19080, 19342, 19444, 19772, 19790, 19891, 20091, 20245, 20402, 20811, 21114, 21345, 21811, 21881, 22222, 22311, 22320, 22831, 22969, 23251, 23572, 23734, 23862, 23889, 24034, 24463, 25172, 25688, 26143, 26221, 26803, 26850, 26898, 27497, 28291, 28343, 29411, 29419, 30024, 30561, 30923, 31345, 31351, 31555, 31927, 32198, 32861, 33020, 33040, 33095, 33188, 33311, 33368, 33377, 33475, 33519, 33574, 33592, 34207, 34235, 34272, 34484, 34854, 34872, 34875, 34876, 34880, 35222, 35292, 35344, 36177, 36266, 37038, 37060, 37548, 37686, 37700, 38139, 39368, 39369, 39633, 40132, 40698, 40704, 40744, 40819, 41311, 41971, 42102, 42616, 43055, 43211, 43234, 43428, 43494, 43934, 44117, 44252, 44272, 44301, 44336, 44619, 44866, 44888, 45049, 45197, 45412, 45718, 46694, 46736, 47000, 48046, 48540, 49078, 49109, 49216, 49388, 49464, 50056, 50155, 50217, 50477, 50692, 51122, 51445, 51946, 52475, 52537, 52982, 54011, 54031, 54160, 54963, 55000, 55537, 56080, 56163, 56282, 56760, 56787, 57102, 57727, 57871, 58101, 58558, 58882, 59902, 60225, 60397, 60501, 60619, 60703, 60890, 61075, 61894, 61944, 62322, 62337, 62380, 62413, 62729, 62766, 62923, 63010, 63234, 63977, 64127, 65359, 65428, 65542, 65750, 65863, 66184, 66636, 66712, 67201, 67439, 67953, 68133, 68854, 69251, 69959, 70107, 70725, 70768, 71081, 71099, 71948, 72013, 72377, 72400, 72420, 72735, 73000, 73015, 73142, 73223, 73455, 73717, 74049, 74492, 74854, 74941, 75142, 75399, 75464, 75587, 75618, 75642, 75887, 76357, 76651, 77199, 77302, 77456, 77579, 77601, 77649, 77668, 77694, 77745, 78006, 78010, 78178, 78335, 78656, 78729, 78808, 78824, 78844, 78945, 79416, 79471, 79915, 80077, 80111, 80189, 80262, 80409, 80470, 80529, 80539, 80838, 81272, 81513, 81658, 81740, 81743, 81762, 81843, 82001, 82070, 82106, 82342, 82472, 82719, 83670, 84009, 84151, 84299, 84430, 84450, 84460, 84945, 86411, 86443, 86446, 86668, 86942, 87286, 87317, 87624, 87785, 88023, 88517, 88696, 88787, 88868, 88977, 89206, 90108, 90440, 90734, 90802, 90849, 90920, 90931, 91011, 91031, 91133, 91777, 91949, 92162, 92494, 93012, 93172, 94300, 94517, 95142, 95410, 95559, 95859, 96112, 97255, 97787, 97986, 98240, 98817, 99050, 99198, 99222, 99241, 99295, 99326, 99335, 99503, 99603, 99643, 99803, 99968}"
This code removes first and last character of the string ("{" and "}" characters), splits values by "," and removes whitespaces using trimws. After that it moves the format to number.
If it happens your data actually is json, stick with the rjson package. This answer is assuming your data is not json (since rjson::fromjson throws an error on your data)
Try:
string <- "{1948, 2507, 2510, 7030, 7110, 9009, 00027, 00206, 00399, 00717, 00814, 00828, 00848, 00917, 01050, 01105, 01144, 02130, 02768, 03037, 03752, 03754, 04070, 04110, 05050, 05255, 05289, 05564, 05595, 06100, 06330, 06671, 07041, 07119, 07137, 07273, 07313, 07454, 07871, 08104, 08714, 08726, 08995, 09059, 09073, 09525, 09949, 09981, 10092, 10439, 10782, 11185, 11507, 11712, 11806, 11858, 11980, 12067, 12113, 12139, 12643, 13820, 14534, 15007, 15014, 15549, 15953, 16151, 16174, 16634, 16733, 16888, 17111, 17207, 17377, 17721, 17900, 18118, 18400, 18686, 18880, 19080, 19342, 19444, 19772, 19790, 19891, 20091, 20245, 20402, 20811, 21114, 21345, 21811, 21881, 22222, 22311, 22320, 22831, 22969, 23251, 23572, 23734, 23862, 23889, 24034, 24463, 25172, 25688, 26143, 26221, 26803, 26850, 26898, 27497, 28291, 28343, 29411, 29419, 30024, 30561, 30923, 31345, 31351, 31555, 31927, 32198, 32861, 33020, 33040, 33095, 33188, 33311, 33368, 33377, 33475, 33519, 33574, 33592, 34207, 34235, 34272, 34484, 34854, 34872, 34875, 34876, 34880, 35222, 35292, 35344, 36177, 36266, 37038, 37060, 37548, 37686, 37700, 38139, 39368, 39369, 39633, 40132, 40698, 40704, 40744, 40819, 41311, 41971, 42102, 42616, 43055, 43211, 43234, 43428, 43494, 43934, 44117, 44252, 44272, 44301, 44336, 44619, 44866, 44888, 45049, 45197, 45412, 45718, 46694, 46736, 47000, 48046, 48540, 49078, 49109, 49216, 49388, 49464, 50056, 50155, 50217, 50477, 50692, 51122, 51445, 51946, 52475, 52537, 52982, 54011, 54031, 54160, 54963, 55000, 55537, 56080, 56163, 56282, 56760, 56787, 57102, 57727, 57871, 58101, 58558, 58882, 59902, 60225, 60397, 60501, 60619, 60703, 60890, 61075, 61894, 61944, 62322, 62337, 62380, 62413, 62729, 62766, 62923, 63010, 63234, 63977, 64127, 65359, 65428, 65542, 65750, 65863, 66184, 66636, 66712, 67201, 67439, 67953, 68133, 68854, 69251, 69959, 70107, 70725, 70768, 71081, 71099, 71948, 72013, 72377, 72400, 72420, 72735, 73000, 73015, 73142, 73223, 73455, 73717, 74049, 74492, 74854, 74941, 75142, 75399, 75464, 75587, 75618, 75642, 75887, 76357, 76651, 77199, 77302, 77456, 77579, 77601, 77649, 77668, 77694, 77745, 78006, 78010, 78178, 78335, 78656, 78729, 78808, 78824, 78844, 78945, 79416, 79471, 79915, 80077, 80111, 80189, 80262, 80409, 80470, 80529, 80539, 80838, 81272, 81513, 81658, 81740, 81743, 81762, 81843, 82001, 82070, 82106, 82342, 82472, 82719, 83670, 84009, 84151, 84299, 84430, 84450, 84460, 84945, 86411, 86443, 86446, 86668, 86942, 87286, 87317, 87624, 87785, 88023, 88517, 88696, 88787, 88868, 88977, 89206, 90108, 90440, 90734, 90802, 90849, 90920, 90931, 91011, 91031, 91133, 91777, 91949, 92162, 92494, 93012, 93172, 94300, 94517, 95142, 95410, 95559, 95859, 96112, 97255, 97787, 97986, 98240, 98817, 99050, 99198, 99222, 99241, 99295, 99326, 99335, 99503, 99603, 99643, 99803, 99968}"
string as list of characters:
string_as_list_char <- as.list(strsplit(gsub('\\{|\\}', '', string), ", "))[[1]]
or converted to numeric:
string_as_list_num <- as.list(as.numeric(strsplit(gsub('\\{|\\}', '', string), ", ")[[1]]))
R forecast function not picking up seasonality
I am having trouble picking up the seasonality the seems to be implied in the data. I think (though its just a guess that its using additive and not multiplicative seasonality). I am using the forecast function and thought it would automatically pick what I need based on a lecture from Dr. Hyndman. The following snipet of code plots the chart and I would have expected the forecast to be higher then it is. Am I missing a model parameter or something? Any help would be appreciated. sw<-c(2280, 1754, 1667, 1359, 1285, 1379, 2166, 1053, 1076, 1149, 1277, 1577, 1639, 1719, 1592, 2306, 3075, 2897, 1875, 1966, 2927, 3528, 2948, 2890, 3947, 3913, 3885, 4148, 5293, 5752, 6001, 7719, 5512, 6782, 6320, 6425, 6406, 7237, 8655, 9269, 12447, 13470, 13469, 13949, 17753, 17653, 14531, 14496, 13643, 12652, 12665, 10629, 8962, 8198, 6833, 5027, 4407, 4449, 4399, 5896, 6589, 3786, 4386, 4847, 5597, 5407, 4800, 7803, 9255, 10423, 5523, 8121, 6944, 8434, 9847, 9292, 9794, 10195, 10124, 11310, 12245, 12798, 14611, 15402, 13532, 16154, 15101, 14755, 17139, 16475, 19935, 19980, 25173, 28568, 27839, 28991, 27073, 29615, 25849, 27910, 27067, 21303, 20544, 15188, 13706, 9277, 10815, 7228, 4608, 4409, 9866, 8471, 8223, 6445, 6641, 6833, 11421, 8945, 8127, 10380, 12005, 13272, 9431, 12144, 14934, 14052, 11712, 14888, 15824, 17275, 18067, 19839, 21192, 22763, 22976, 23721, 22681, 20131, 19965, 20539, 19517, 22022, 23076, 30574, 40247, 43111, 39577, 40724, 44982, 44388, 46372, 43153, 36821, 32258, 31256, 27153, 23180, 18252, 16381, 13220, 12500, 10727, 9636, 8892, 8644, 9482, 9170, 10937, 12299, 15781, 11477, 16524, 16752, 18072, 14776, 13388, 18056, 19815, 21263, 22046, 26415, 24247, 25403, 30058, 26331, 32533, 31891, 35973, 27558, 24554, 25692, 25955, 24284, 24930, 28354, 34840, 40055, 42099, 42768, 48279, 50086, 56466, 42244, 51451, 44583, 39091, 33391, 29452, 25533) swts <- ts(sw, frequency=52, start=c(2006,30)) swfc <- forecast(swts,h=52) plot(swfc)
Did you data have multiple seasonal periods? If so you could check the tbats function. Anyway, your seasonal period is greater than 12, so forecast is using a stl decomposition to adjust your seasonal data. Maybe you wanna check ?stlf for more info on what parameters you can change, or try a BoxCox transformation: lambda <- BoxCox.lambda(sw) swfc <- forecast(swts,h=52, lambda = lambda, robust = TRUE) plot(swfc)
Calculating Time Weighted Rate of Return in R
Is there an R function or library that will give me the monthly (or any other specified timeframe) time weighted rate of return (twrr) for my portfolio?
I am including a dput dump of sample data below of the date and portfolio ending balance below. Not sure why the dates were dput'ed the way they were, but the first date 12053 is '2003-01-01' and the last date 12195 is '2003-05-23'.
portfolio.df <- structure(
list(
Date = structure(c(12053, 12054, 12055, 12058,
12059, 12060, 12061, 12062, 12065, 12066, 12067, 12068, 12069,
12073, 12074, 12075, 12076, 12079, 12080, 12081, 12082, 12083,
12086, 12087, 12088, 12089, 12090, 12093, 12094, 12095, 12096,
12097, 12101, 12102, 12103, 12104, 12107, 12108, 12109, 12110,
12111, 12114, 12115, 12116, 12117, 12118, 12121, 12122, 12123,
12124, 12125, 12128, 12129, 12130, 12131, 12132, 12135, 12136,
12137, 12138, 12139, 12142, 12143, 12144, 12145, 12146, 12149,
12150, 12151, 12152, 12153, 12156, 12157, 12158, 12159, 12163,
12164, 12165, 12166, 12167, 12170, 12171, 12172, 12173, 12174,
12177, 12178, 12179, 12180, 12181, 12184, 12185, 12186, 12187,
12188, 12191, 12192, 12193, 12194, 12195),
class = "Date"),
Ending_Balance = c(56250000L,
56852500L, 57080000L, 57355000L, 57477500L, 56817500L, 57885000L,
57810000L, 57732500L, 57670000L, 57520000L, 57285000L, 57270000L,
56655000L, 55802500L, 56337500L, 55642500L, 54510000L, 54987500L,
55802500L, 56065000L, 56865000L, 56635000L, 56497500L, 56640000L,
56155000L, 55757500L, 55972500L, 55865000L, 55535000L, 55885000L,
56840000L, 56902500L, 56945000L, 56622500L, 57012500L, 57200000L,
58072500L, 57612500L, 57447500L, 57157500L, 57032500L, 57405000L,
57502500L, 56785000L, 57007500L, 56342500L, 55697500L, 56655000L,
56900000L, 57002500L, 57465000L, 57467500L, 57382500L, 57982500L,
56562500L, 58065000L, 58935000L, 58502500L, 58200000L, 57767500L,
57757500L, 58055000L, 58305000L, 58277500L, 58295000L, 59047500L,
58907500L, 59125000L, 59072500L, 59107500L, 59315000L, 59690000L,
58957500L, 59407500L, 59385000L, 59965000L, 60297500L, 59890000L,
59822500L, 60367500L, 60407500L, 60380000L, 60815000L, 61155000L,
61080000L, 61132500L, 61265000L, 60912500L, 61107500L, 61445000L,
61345000L, 61137500L, 61035000L, 60707500L, 61340000L, 61365000L,
61402500L, 61640000L, 61675000L)),
.Names = c("Date", "Ending_Balance"),
row.names = c(NA, 100L),
class = "data.frame")