Filter columns conditionally to another column

Question

Filter columns conditionally to another column

Asked 4 years, 10 months ago

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I would like to select a single value in the dataframe below, according to the following parameters:

1) The values in column nJRS would be the reference;

2) The value of the column nJRS should correspond to the final row number (column n) to be considered as the filter endpoint;

3) The initial limit of the filter would correspond to the value of column n in the same row as the corresponding value of nJR. Thus: (nJRS = 204 --> n = 1; nJRS = 203 --> n = 2);

4) From there, it would be enough to create a mutatis of the accumulated values of the JM column and select the last calculated value as in the example below:

processo %>% 
  filter(n <= processo$nJRS[1]) %>% 
  mutate(JMAcum = cumsum(JM)) %>% 
  select(JMAcum) %>% 
  slice(n())

I’m trying to iterate this, but I’m not getting it.

Follow my data:

structure(list(n = 1:216, Mes = structure(c(11627, 11657, 11688,
11719, 11747, 11778, 11808, 11839, 11869, 11900, 11931, 11961,
11992, 12022, 12053, 12084, 12112, 12143, 12173, 12204, 12234,
12265, 12296, 12326, 12357, 12387, 12418, 12449, 12478, 12509,
12539, 12570, 12600, 12631, 12662, 12692, 12723, 12753, 12784,
12815, 12843, 12874, 12904, 12935, 12965, 12996, 13027, 13057,
13088, 13118, 13149, 13180, 13208, 13239, 13269, 13300, 13330,
13361, 13392, 13422, 13453, 13483, 13514, 13545, 13573, 13604,
13634, 13665, 13695, 13726, 13757, 13787, 13818, 13848, 13879,
13910, 13939, 13970, 14000, 14031, 14061, 14092, 14123, 14153,
14184, 14214, 14245, 14276, 14304, 14335, 14365, 14396, 14426,
14457, 14488, 14518, 14549, 14579, 14610, 14641, 14669, 14700,
14730, 14761, 14791, 14822, 14853, 14883, 14914, 14944, 14975,
15006, 15034, 15065, 15095, 15126, 15156, 15187, 15218, 15248,
15279, 15309, 15340, 15371, 15400, 15431, 15461, 15492, 15522,
15553, 15584, 15614, 15645, 15675, 15706, 15737, 15765, 15796,
15826, 15857, 15887, 15918, 15949, 15979, 16010, 16040, 16071,
16102, 16130, 16161, 16191, 16222, 16252, 16283, 16314, 16344,
16375, 16405, 16436, 16467, 16495, 16526, 16556, 16587, 16617,
16648, 16679, 16709, 16740, 16770, 16801, 16832, 16861, 16892,
16922, 16953, 16983, 17014, 17045, 17075, 17106, 17136, 17167,
17198, 17226, 17257, 17287, 17318, 17348, 17379, 17410, 17440,
17471, 17501, 17532, 17563, 17591, 17622, 17652, 17683, 17713,
17744, 17775, 17805, 17836, 17866, 17897, 17928, 17956, 17987,
18017, 18048, 18078, 18109, 18140, 18170), class = "Date"), Indice = c("INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC", "INPC",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR",
"TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E",
"IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E", "IPCA-E"), Fator = c(1.0129,
1.0074, 1.0107, 1.0031, 1.0062, 1.0068, 1.0009, 1.0061, 1.0115,
1.0086, 1.0083, 1.0157, 1.0339, 1.027, 1.0247, 1.0146, 1.0137,
1.0138, 1.0099, 0.9994, 1.0004, 1.0018, 1.0082, 1.0039, 1.0037,
1.0054, 1.0083, 1.0039, 1.0057, 1.0041, 1.004, 1.005, 1.0073,
1.005, 1.0017, 1.0017, 1.0044, 1.0086, 1.0057, 1.0044, 1.0073,
1.0091, 1.007, 0.9989, 1.0003, 1, 1.0015, 1.0058, 1.0054, 1.004,
1.0038, 1.0023, 1.0027, 1.0012, 1.0013, 0.9993, 1.0011, 0.9998,
1.0016, 1.0043, 1.0042, 1.0062, 1.0049, 1.0042, 1.0044, 1.0026,
1.0026, 1.0031, 1.0032, 1.0059, 1.0025, 1.003, 1.0043, 1.0097,
1.0069, 1.0048, 1.0051, 1.0064, 1.0096, 1.0091, 1.0058, 1.0021,
1.0015, 1.005, 1.0038, 1.0029, 1.0064, 1.0031, 1.002, 1.0055,
1.006, 1.0042, 1.0023, 1.0008, 1.0016, 1.0024, 1.0037, 1.000533,
1, 1, 1.000792, 1, 1.00051, 1.000589, 1.001151, 1.000909, 1.000702,
1.000472, 1.000336, 1.001406, 1.000715, 1.000524, 1.001212, 1.000369,
1.00157, 1.001114, 1.001229, 1.002076, 1.001003, 1.00062, 1.000645,
1.000937, 1.000864, 1, 1.001068, 1.000227, 1.000468, 1, 1.000144,
1.000123, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.000209, 1, 1.000079,
1.00092, 1.000207, 1.000494, 1.001126, 1.000537, 1.000266, 1.000459,
1.000604, 1.000465, 1.001054, 1.000602, 1.000873, 1.001038, 1.000483,
1.001053, 1.000878, 1.000168, 1.001296, 1.0107, 1.006, 1.0099,
1.0059, 1.0043, 1.0039, 1.0066, 1.0085, 1.0118, 1.0092, 1.0142,
1.0043, 1.0051, 1.0086, 1.004, 1.0054, 1.0045, 1.0023, 1.0019,
1.0026, 1.0019, 1.0031, 1.0054, 1.0015, 1.0021, 1.0024, 1.0016,
1.0018, 1.0035, 1.0011, 1.0034, 1.0032, 1.0035, 1.0039, 1.0038,
1.001, 1.0021, 1.0014, 1.0111, 1.0064, 1.0013, 1.0009, 1.0058,
1.0019, 1.0016, 1.003, 1.0034, 1.0054, 1.0072, 1.0035, 1.0006,
1.0009, 1.0008, 1.0009, 1.09), Fac = c(1.0129, 1.02039546, 1.031313691422,
1.03451076386541, 1.04092473060137, 1.04800301876946, 1.04894622148636,
1.05534479343742, 1.06748125856195, 1.07666159738559, 1.08559788864389,
1.10264177549559, 1.1400213316849, 1.17080190764039, 1.1997207147591,
1.21723663719459, 1.23391277912415, 1.25094077547607, 1.26332508915328,
1.26256709409979, 1.26307212093743, 1.26534565075512, 1.27572148509131,
1.28069679888316, 1.28543537703903, 1.29237672807504, 1.30310345491806,
1.30818555839225, 1.31564221607508, 1.32103634916099, 1.32632049455763,
1.33295209703042, 1.34268264733874, 1.34939606057544, 1.35169003387842,
1.35398790693601, 1.35994545372653, 1.37164098462857, 1.37945933824096,
1.38552895932922, 1.39564332073232, 1.40834367495099, 1.41820208067564,
1.4166420583869, 1.41706705100442, 1.41706705100442, 1.41919265158092,
1.42742396896009, 1.43513205839248, 1.44087258662605, 1.44634790245522,
1.44967450263087, 1.45358862378798, 1.45533293013652, 1.4572248629457,
1.45620480554164, 1.45780663082773, 1.45751506950157, 1.45984709361277,
1.4661244361153, 1.47228215874699, 1.48141030813122, 1.48866921864106,
1.49492162935936, 1.50149928452854, 1.50540318266831, 1.50931723094325,
1.51399611435917, 1.51884090192512, 1.52780206324648, 1.5316215684046,
1.53621643310981, 1.54282216377218, 1.55778753876077, 1.56853627277822,
1.57606524688756, 1.58410317964668, 1.59424143999642, 1.60954615782039,
1.62419302785655, 1.63361334741812, 1.6370439354477, 1.63949950135087,
1.64769699885763, 1.65395824745328, 1.6587547263709, 1.66937075661967,
1.67454580596519, 1.67789489757712, 1.6871233195138, 1.69724605943088,
1.70437449288049, 1.70829455421412, 1.70966118985749, 1.71239664776126,
1.71650639971589, 1.72285747339483, 1.72377575642815, 1.72377575642815,
1.72377575642815, 1.72514098682724, 1.72514098682724, 1.72602080873053,
1.72703743498687, 1.72902525507454, 1.7305969390314, 1.7318118180826,
1.73262923326074, 1.73321139668311, 1.73564829190685, 1.73688928043556,
1.73779941041851, 1.73990562330394, 1.74054764847894, 1.74328030828705,
1.74522232255048, 1.74736720078489, 1.75099473509372, 1.75275098281302,
1.75383768842237, 1.7549689137314, 1.75661331960357, 1.7581310335117,
1.7581310335117, 1.76000871745549, 1.76040823943436, 1.76123211049041,
1.76123211049041, 1.76148572791432, 1.76170239065886, 1.76170239065886,
1.76170239065886, 1.76170239065886, 1.76170239065886, 1.76170239065886,
1.76170239065886, 1.76170239065886, 1.76170239065886, 1.76170239065886,
1.76170239065886, 1.7620705864585, 1.7620705864585, 1.76220979003483,
1.76383102304167, 1.76419613606344, 1.76506764895465, 1.76705511512737,
1.7680040237242, 1.76847431279451, 1.76928604250408, 1.77035469127375,
1.7711779062052, 1.77304472771834, 1.77411210064442, 1.77566090050828,
1.77750403652301, 1.77836257097265, 1.78023518675989, 1.78179823325386,
1.78209757535705, 1.78440717381471, 1.80350033057453, 1.81432133255798,
1.8322831137503, 1.84309358412143, 1.85101888653315, 1.85823786019063,
1.87050223006789, 1.88640149902346, 1.90866103671194, 1.92622071824969,
1.95357305244884, 1.96197341657437, 1.97197948099889, 1.98893850453549,
1.99689425855363, 2.00767748754982, 2.01671203624379, 2.02135047392715,
2.02519103982761, 2.03045653653116, 2.03431440395057, 2.04062077860282,
2.05164013080728, 2.05471759100349, 2.05903249794459, 2.06397417593966,
2.06727653462117, 2.07099763238348, 2.07824612409683, 2.08053219483333,
2.08760600429577, 2.09428634350951, 2.1016163457118, 2.10981264946007,
2.11782993752802, 2.11994776746555, 2.12439965777723, 2.12737381729811,
2.15098766667012, 2.16475398773681, 2.16756816792087, 2.169518979272,
2.18210218935178, 2.18624818351154, 2.18974618060516, 2.19631541914698,
2.20378289157208, 2.21568331918657, 2.23163623908471, 2.23944696592151,
2.24079063410106, 2.24280734567175, 2.24460159154829, 2.24662173298068,
2.44881768894894), JM = c(0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.00478916666666667, 0.005, 0.00478916666666667, 0.00426416666666667,
0.005, 0.004865, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.00464333333333333, 0.00494083333333333, 0.005, 0.00389666666666667,
0.00441583333333333, 0.00396666666666667, 0.00396666666666667,
0.00434, 0.00352333333333333, 0.00396666666666667, 0.00441583333333333,
0.00434, 0.00441583333333333, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.00464333333333333, 0.00464333333333333, 0.00411833333333333,
0.00389666666666667, 0.00419416666666667, 0.0033775, 0.00382083333333333,
0.003745, 0.003745, 0.003745, 0.00389666666666667, 0.00411833333333333,
0.0033775, 0.00389666666666667, 0.00352333333333333, 0.00352333333333333,
0.00389666666666667, 0.00352333333333333, 0.0033775, 0.003745,
0.00389666666666667, 0.0033775, 0.00411833333333333, 0.00359916666666667,
0.00330166666666667, 0.0034475), Nominal1 = c(100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100, 100, 100, 100, 100, 100, 100, 100, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Nominal1Acum = c(100,
200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300,
1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400,
2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500,
3600, 3700, 3800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), nCM = c(216L, 215L, 214L, 213L,
212L, 211L, 210L, 209L, 208L, 207L, 206L, 205L, 204L, 203L, 202L,
201L, 200L, 199L, 198L, 197L, 196L, 195L, 194L, 193L, 192L, 191L,
190L, 189L, 188L, 187L, 186L, 185L, 184L, 183L, 182L, 181L, 180L,
179L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), nJRS = c(204L, 203L, 202L, 201L, 200L, 199L,
198L, 197L, 196L, 195L, 194L, 193L, 192L, 191L, 190L, 189L, 188L,
187L, 186L, 185L, 184L, 183L, 182L, 181L, 180L, 179L, 178L, 177L,
176L, 175L, 174L, 173L, 172L, 171L, 170L, 169L, 168L, 167L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), Corrigido1 = c(241.763025861284, 239.987121164665, 237.446444211601,
236.712635042968, 235.254059871763, 233.665136940567, 233.455027415893,
232.039585941649, 229.401469047601, 227.445438278407, 225.573180877127,
222.086424020013, 214.80454978239, 209.157302611869, 204.115646151917,
201.178440914564, 198.459545146063, 195.758083592487, 193.839076732832,
193.955450002834, 193.877898843296, 193.529545661106, 191.95551047521,
191.209792285297, 190.504924066252, 189.48172276333, 187.921970408936,
187.191921913473, 186.130975353955, 185.370954440748, 184.632424741781,
183.713855464459, 182.382463481047, 181.475088040843, 181.167103964104,
180.859642571732, 180.06734624824, 178.531971295102, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), Corrigido1Acum = c(241.76, 481.75, 719.2, 955.91, 1191.16,
1424.83, 1658.28, 1890.32, 2119.72, 2347.17, 2572.74, 2794.83,
3009.63, 3218.79, 3422.91, 3624.09, 3822.55, 4018.3, 4212.14,
4406.1, 4599.98, 4793.51, 4985.46, 5176.67, 5367.18, 5556.66,
5744.58, 5931.77, 6117.9, 6303.27, 6487.91, 6671.62, 6854, 7035.48,
7216.64, 7397.5, 7577.57, 7756.1, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -216L))

1

Could provide a sample of the data with dput(head(dados, 30))?

– neves

2020/01/25 at 05:35
dput of the data added. Thank you for the reply

– Fernando Vieira

2020/01/25 at 15:04
Thanks for the data, but what @neves meant was to copy and paste these outputs to the question, the images do not allow us to copy to an R session.

– Rui Barradas

2020/01/25 at 16:35
I did that friend, pasted the data in the text of the question. Did not solve? If I did wrong, I apologize in advance.

– Fernando Vieira

2020/01/25 at 19:55

1 answer

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by user2332849 • **246** points · Answer 1 · 2020-03-01T17:21:41+00:00

If I understand correctly, you want, for each row of the table, a cumulative sum for that row. That is, for the first line, you want the sum of JM from 1 to 204. For the second line, the sum of JM from 2 to 203, and so on. The lines from 39 onwards do not have nJRS filled (they are with NA), so they do not need to be computed. That’s right?

The problem is that R operates with vectors, but within each mutate, you want to operate with a new vector. This generates a bidimensionality, which R does not treat well, the way it is written.

I’ll try to make a code, if I understand what you want, and then you tell me if it’s correct.

processo %>% 
  filter(!is.na(nJRS)) %>%
  mutate(JMAcum = mapply(function(n, nJRS) sum(processo$JM[n:nJRS]), n, nJRS)) %>% 
  select(JMAcum)