Merging two files based on dates with missing values

Merging two files based on dates with missing values - r

I am trying to merge two files together based on dates:
However, there are two issues:
I cannot use the left join function due to one of the files having the dates set as characters, and the other one set as dates. Changing one of the files values to dates does not fix the issue as it creates a list instead
Using the merge function leads to a lot of missing values, as one of my dataset does not contain data for each date
jointdataset <- merge(group_df, group_tweet, by = 'date', all.x= TRUE)
View(jointdataset)
jointdataset <- dplyr::left_join(group_df, group_tweet)
Here is how my data looks like
> dput(group_tweet)
structure(list(date = structure(c(18628, 18629, 18630, 18631,
18632, 18633, 18634, 18635, 18636, 18637, 18638, 18639, 18640,
18641, 18642, 18643, 18644, 18645, 18646, 18647, 18648, 18649,
18650, 18651, 18652, 18653, 18654, 18655, 18656, 18657, 18658,
18659, 18660, 18661, 18662, 18663, 18664, 18665, 18666, 18667,
18668, 18669, 18670, 18671, 18672, 18673, 18674, 18675, 18676,
18677, 18678, 18679, 18680, 18681, 18682, 18683, 18684, 18685,
18686, 18687, 18688, 18689, 18690, 18691, 18692, 18693, 18694,
18695, 18696, 18697, 18698, 18699, 18700, 18701, 18702, 18703,
18704, 18705, 18706, 18707, 18708, 18709, 18710, 18711, 18712,
18713, 18714, 18715, 18716, 18717, 18718, 18719, 18720, 18721,
18722, 18723, 18724, 18725, 18726, 18727, 18728, 18729, 18730,
18731, 18732, 18733, 18734, 18735, 18736, 18737, 18738, 18739,
18740, 18741, 18742, 18743, 18744, 18745, 18746, 18747, 18748,
18749, 18750, 18751, 18752, 18753, 18754, 18755, 18756, 18757,
18758, 18759, 18760, 18761, 18762, 18763, 18764, 18765, 18766,
18767, 18768, 18769, 18770, 18771, 18772, 18773, 18774, 18775,
18776, 18777, 18778, 18779, 18780, 18781, 18782, 18783, 18784,
18785, 18786, 18787, 18788, 18789, 18790, 18791, 18792, 18793,
18794, 18795, 18796, 18797, 18798, 18799, 18800, 18801, 18802,
18803, 18804, 18805, 18806, 18807, 18808), class = "Date"), `length(text)` = c(1324L,
1548L, 1297L, 1585L, 1636L, 1583L, 1492L, 1676L, 1745L, 1389L,
1718L, 1781L, 1858L, 1798L, 1714L, 1808L, 1315L, 1644L, 1603L,
1820L, 1770L, 1843L, 1885L, 1390L, 1763L, 1875L, 1912L, 1812L,
1764L, 2117L, 1641L, 1914L, 1963L, 2092L, 1968L, 2021L, 2023L,
1331L, 1557L, 1555L, 1904L, 2034L, 1850L, 2067L, 1507L, 1738L,
1915L, 2057L, 1931L, 1859L, 2010L, 1525L, 1835L, 1850L, 1958L,
1848L, 1929L, 2015L, 1449L, 1823L, 1796L, 1902L, 1888L, 1875L,
2078L, 1442L, 1757L, 1877L, 2104L, 1926L, 1949L, 2175L, 1604L,
2030L, 1918L, 2011L, 1978L, 1959L, 2171L, 1528L, 1936L, 1889L,
2132L, 1907L, 2173L, 2233L, 1581L, 1793L, 1986L, 2128L, 2030L,
1805L, 1954L, 1459L, 1691L, 1967L, 2049L, 1835L, 1948L, 2246L,
1581L, 1950L, 1904L, 2245L, 2053L, 1877L, 1913L, 1571L, 1932L,
2004L, 2058L, 2087L, 1989L, 2180L, 1567L, 1865L, 1995L, 2144L,
2169L, 2148L, 2318L, 1606L, 1856L, 1948L, 2036L, 1887L, 2021L,
2132L, 1390L, 1717L, 1872L, 1919L, 1867L, 1994L, 2083L, 1509L,
1786L, 1808L, 1860L, 1854L, 1813L, 2102L, 1513L, 1890L, 1877L,
2063L, 1857L, 1827L, 2059L, 1413L, 1614L, 2153L, 1859L, 1920L,
1877L, 2106L, 1458L, 1822L, 1851L, 2005L, 1984L, 2097L, 2396L,
1607L, 2106L, 2256L, 2398L, 2245L, 2239L, 2287L, 1564L, 1880L,
1991L, 2053L, 2017L, 2012L, 1998L, 1361L, 1663L, 1778L, 1987L
)), row.names = c(NA, -181L), class = c("tbl_df", "tbl", "data.frame"
))
dput(group_df)
structure(list(date = c("01/01/2021", "01/05/2021", "01/06/2021",
"01/07/2021", "01/11/2021", "01/12/2021", "01/14/2021", "01/15/2021",
"01/18/2021", "01/19/2021", "01/20/2021", "01/21/2021", "01/22/2021",
"01/23/2021", "01/26/2021", "01/27/2021", "01/28/2021", "01/29/2021",
"01/31/2021", "02/01/2021", "02/02/2021", "02/04/2021", "02/08/2021",
"02/09/2021", "02/10/2021", "02/11/2021", "02/12/2021", "02/15/2021",
"02/16/2021", "02/18/2021", "02/19/2021", "02/22/2021", "02/23/2021",
"02/24/2021", "02/25/2021", "02/27/2021", "03/01/2021", "03/02/2021",
"03/04/2021", "03/05/2021", "03/06/2021", "03/07/2021", "03/08/2021",
"03/09/2021", "03/11/2021", "03/14/2021", "03/15/2021", "03/16/2021",
"03/17/2021", "03/18/2021", "03/19/2021", "03/20/2021", "03/21/2021",
"03/22/2021", "03/23/2021", "03/24/2021", "03/25/2021", "03/26/2021",
"03/28/2021", "03/29/2021", "03/30/2021", "03/31/2021", "04/01/2021",
"04/02/2021", "04/03/2021", "04/04/2021", "04/05/2021", "04/06/2021",
"04/07/2021", "04/08/2021", "04/09/2021", "04/10/2021", "04/11/2021",
"04/12/2021", "04/13/2021", "04/14/2021", "04/15/2021", "04/16/2021",
"04/17/2021", "04/18/2021", "04/19/2021", "04/20/2021", "04/21/2021",
"04/22/2021", "04/23/2021", "04/24/2021", "04/26/2021", "04/27/2021",
"04/28/2021", "04/29/2021", "04/30/2021", "05/01/2021", "05/02/2021",
"05/03/2021", "05/04/2021", "05/05/2021", "05/06/2021", "05/07/2021",
"05/08/2021", "05/10/2021", "05/11/2021", "05/12/2021", "05/13/2021",
"05/14/2021", "05/15/2021", "05/18/2021", "05/19/2021", "05/20/2021",
"05/21/2021", "05/22/2021", "05/23/2021", "05/25/2021", "05/26/2021",
"05/27/2021", "05/29/2021", "05/31/2021", "06/01/2021", "06/02/2021",
"06/03/2021", "06/04/2021", "06/05/2021", "06/06/2021", "06/07/2021",
"06/08/2021", "06/09/2021", "06/10/2021", "06/11/2021", "06/12/2021",
"06/13/2021", "06/14/2021", "06/15/2021", "06/16/2021", "06/17/2021"
), `length(category)` = c(4L, 8L, 4L, 4L, 4L, 12L, 8L, 8L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 9L, 25L, 10L, 5L, 10L,
5L, 20L, 5L, 5L, 19L, 10L, 20L, 5L, 5L, 5L, 2L, 12L, 22L, 14L,
2L, 4L, 6L, 9L, 5L, 37L, 5L, 4L, 10L, 12L, 21L, 4L, 4L, 6L, 6L,
9L, 16L, 9L, 6L, 13L, 2L, 19L, 24L, 20L, 8L, 25L, 16L, 5L, 24L,
27L, 24L, 6L, 9L, 15L, 8L, 25L, 35L, 26L, 8L, 2L, 5L, 14L, 12L,
20L, 19L, 7L, 9L, 5L, 4L, 17L, 13L, 15L, 8L, 10L, 7L, 5L, 16L,
11L, 16L, 10L, 6L, 3L, 22L, 10L, 10L, 3L, 1L, 7L, 4L, 6L, 4L,
5L, 3L, 6L, 3L, 3L, 1L, 3L, 9L, 6L, 4L, 2L, 1L, 1L, 3L, 5L, 2L,
4L, 2L, 2L, 1L, 5L, 5L, 6L, 3L), `sum(usd_pledged)` = c(50278.64,
366279.590415302, 172073.0471292, 230.537553792, 304353.5676352,
285277.861423738, 931521.92, 62322.104033708, 292739.37663744,
97895.025306156, 20538.4758468, 5716, 69712, 15248.7519561728,
1257502.99126928, 102268, 32151.762183708, 70322.17520884, 27549.00453216,
8371657.96195552, 1420782.4068818, 137818.171860595, 1175436.75496273,
1770825.83285245, 1070, 178761.8127624, 10016.918409372, 146129.2,
610608.234267955, 676175.367448825, 1274147.86429578, 45595.6660422,
1776940, 4198.748196, 17.02446963, 55272.18380506, 490750.12364435,
571800.5, 227.26420984, 619.23335154, 5942.151506976, 1098.48977709,
87300, 1879564.14902818, 57100, 3.628861728, 715993, 228885.860968739,
1005412.33040269, 32978.39955816, 1956.52590528, 5789.84841572,
508.6539266268, 192238.643979976, 278988.70418106, 32470.60072344,
10653.5543364, 333900.00289616, 2467.35065664, 698082.326436438,
581461.49769354, 170032.27513805, 24516.65281874, 11530.3738156855,
20060.6125168384, 154030.2061095, 215919.1704622, 1320696.42919177,
2000470.11990896, 1414.108082664, 12429.5108974052, 80676.567104964,
3211.42501648, 69994.39317561, 827188.797715076, 51349.427891072,
47925.216359587, 42, 28391.25545206, 6199141.75469484, 16078.9170341546,
724307.969231123, 238317.166592813, 243887.2812338, 3856.910710253,
5982.1359855268, 1986.4520326, 180186.734055936, 25818.301703542,
284175.24917946, 27486.5134227176, 51837.3569258, 218101.634171675,
543.2343820104, 299634.97422679, 9200.1639420603, 112660.244016819,
27675.2965010449, 5203.03118806, 75650.25, 327968.549088524,
1002.27102748, 37881.2795828444, 2896.1824198269, 596, 206880.064933601,
1688.14367074, 19176.1722666628, 48811.21823157, 4572.83962465,
375.36475346, 126743.686979946, 489.0998, 4042.91231235, 1343.0098244,
2286.711086, 77485.7681307, 25272.4433349128, 11630.0007860946,
377.22065991, 199.9004139, 161, 2667.93172391, 69404.71338983,
23796.67811208, 17174.64072667, 9950.0416377665, 35443.95356773,
184.603426004, 85934.0997877056, 5603.88739935, 2306.02823117,
13.0064795336), `sum(backers_count)` = c(2880L, 6588L, 3528L,
16L, 4204L, 6632L, 15404L, 1672L, 3588L, 416L, 464L, 364L, 1488L,
228L, 17124L, 2284L, 1348L, 2324L, 744L, 185822L, 35095L, 5980L,
24615L, 32525L, 40L, 4650L, 450L, 2640L, 15952L, 9275L, 16404L,
990L, 35075L, 79L, 6L, 1578L, 10705L, 9302L, 12L, 20L, 36L, 91L,
1505L, 40509L, 1890L, 4L, 9684L, 4196L, 16477L, 754L, 62L, 54L,
36L, 3787L, 3809L, 996L, 132L, 13216L, 50L, 12073L, 13826L, 4353L,
220L, 393L, 628L, 3607L, 6712L, 22403L, 30468L, 106L, 136L, 1854L,
102L, 1623L, 14638L, 1396L, 1923L, 6L, 426L, 161556L, 1020L,
6922L, 3575L, 1627L, 84L, 101L, 56L, 2291L, 450L, 2439L, 678L,
864L, 3195L, 17L, 6072L, 215L, 3119L, 1473L, 85L, 2306L, 7622L,
41L, 973L, 117L, 20L, 3127L, 45L, 424L, 819L, 84L, 12L, 3239L,
18L, 66L, 7L, 35L, 1651L, 651L, 242L, 25L, 13L, 6L, 59L, 1747L,
325L, 244L, 231L, 862L, 6L, 1819L, 74L, 66L, 4L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -133L))
dput(group_df)
structure(list(date = c("01/01/2021", "01/05/2021", "01/06/2021",
"01/07/2021", "01/11/2021", "01/12/2021", "01/14/2021", "01/15/2021",
"01/18/2021", "01/19/2021", "01/20/2021", "01/21/2021", "01/22/2021",
"01/23/2021", "01/26/2021", "01/27/2021", "01/28/2021", "01/29/2021",
"01/31/2021", "02/01/2021", "02/02/2021", "02/04/2021", "02/08/2021",
"02/09/2021", "02/10/2021", "02/11/2021", "02/12/2021", "02/15/2021",
"02/16/2021", "02/18/2021", "02/19/2021", "02/22/2021", "02/23/2021",
"02/24/2021", "02/25/2021", "02/27/2021", "03/01/2021", "03/02/2021",
"03/04/2021", "03/05/2021", "03/06/2021", "03/07/2021", "03/08/2021",
"03/09/2021", "03/11/2021", "03/14/2021", "03/15/2021", "03/16/2021",
"03/17/2021", "03/18/2021", "03/19/2021", "03/20/2021", "03/21/2021",
"03/22/2021", "03/23/2021", "03/24/2021", "03/25/2021", "03/26/2021",
"03/28/2021", "03/29/2021", "03/30/2021", "03/31/2021", "04/01/2021",
"04/02/2021", "04/03/2021", "04/04/2021", "04/05/2021", "04/06/2021",
"04/07/2021", "04/08/2021", "04/09/2021", "04/10/2021", "04/11/2021",
"04/12/2021", "04/13/2021", "04/14/2021", "04/15/2021", "04/16/2021",
"04/17/2021", "04/18/2021", "04/19/2021", "04/20/2021", "04/21/2021",
"04/22/2021", "04/23/2021", "04/24/2021", "04/26/2021", "04/27/2021",
"04/28/2021", "04/29/2021", "04/30/2021", "05/01/2021", "05/02/2021",
"05/03/2021", "05/04/2021", "05/05/2021", "05/06/2021", "05/07/2021",
"05/08/2021", "05/10/2021", "05/11/2021", "05/12/2021", "05/13/2021",
"05/14/2021", "05/15/2021", "05/18/2021", "05/19/2021", "05/20/2021",
"05/21/2021", "05/22/2021", "05/23/2021", "05/25/2021", "05/26/2021",
"05/27/2021", "05/29/2021", "05/31/2021", "06/01/2021", "06/02/2021",
"06/03/2021", "06/04/2021", "06/05/2021", "06/06/2021", "06/07/2021",
"06/08/2021", "06/09/2021", "06/10/2021", "06/11/2021", "06/12/2021",
"06/13/2021", "06/14/2021", "06/15/2021", "06/16/2021", "06/17/2021"
), `length(category)` = c(4L, 8L, 4L, 4L, 4L, 12L, 8L, 8L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 9L, 25L, 10L, 5L, 10L,
5L, 20L, 5L, 5L, 19L, 10L, 20L, 5L, 5L, 5L, 2L, 12L, 22L, 14L,
2L, 4L, 6L, 9L, 5L, 37L, 5L, 4L, 10L, 12L, 21L, 4L, 4L, 6L, 6L,
9L, 16L, 9L, 6L, 13L, 2L, 19L, 24L, 20L, 8L, 25L, 16L, 5L, 24L,
27L, 24L, 6L, 9L, 15L, 8L, 25L, 35L, 26L, 8L, 2L, 5L, 14L, 12L,
20L, 19L, 7L, 9L, 5L, 4L, 17L, 13L, 15L, 8L, 10L, 7L, 5L, 16L,
11L, 16L, 10L, 6L, 3L, 22L, 10L, 10L, 3L, 1L, 7L, 4L, 6L, 4L,
5L, 3L, 6L, 3L, 3L, 1L, 3L, 9L, 6L, 4L, 2L, 1L, 1L, 3L, 5L, 2L,
4L, 2L, 2L, 1L, 5L, 5L, 6L, 3L), `sum(usd_pledged)` = c(50278.64,
366279.590415302, 172073.0471292, 230.537553792, 304353.5676352,
285277.861423738, 931521.92, 62322.104033708, 292739.37663744,
97895.025306156, 20538.4758468, 5716, 69712, 15248.7519561728,
1257502.99126928, 102268, 32151.762183708, 70322.17520884, 27549.00453216,
8371657.96195552, 1420782.4068818, 137818.171860595, 1175436.75496273,
1770825.83285245, 1070, 178761.8127624, 10016.918409372, 146129.2,
610608.234267955, 676175.367448825, 1274147.86429578, 45595.6660422,
1776940, 4198.748196, 17.02446963, 55272.18380506, 490750.12364435,
571800.5, 227.26420984, 619.23335154, 5942.151506976, 1098.48977709,
87300, 1879564.14902818, 57100, 3.628861728, 715993, 228885.860968739,
1005412.33040269, 32978.39955816, 1956.52590528, 5789.84841572,
508.6539266268, 192238.643979976, 278988.70418106, 32470.60072344,
10653.5543364, 333900.00289616, 2467.35065664, 698082.326436438,
581461.49769354, 170032.27513805, 24516.65281874, 11530.3738156855,
20060.6125168384, 154030.2061095, 215919.1704622, 1320696.42919177,
2000470.11990896, 1414.108082664, 12429.5108974052, 80676.567104964,
3211.42501648, 69994.39317561, 827188.797715076, 51349.427891072,
47925.216359587, 42, 28391.25545206, 6199141.75469484, 16078.9170341546,
724307.969231123, 238317.166592813, 243887.2812338, 3856.910710253,
5982.1359855268, 1986.4520326, 180186.734055936, 25818.301703542,
284175.24917946, 27486.5134227176, 51837.3569258, 218101.634171675,
543.2343820104, 299634.97422679, 9200.1639420603, 112660.244016819,
27675.2965010449, 5203.03118806, 75650.25, 327968.549088524,
1002.27102748, 37881.2795828444, 2896.1824198269, 596, 206880.064933601,
1688.14367074, 19176.1722666628, 48811.21823157, 4572.83962465,
375.36475346, 126743.686979946, 489.0998, 4042.91231235, 1343.0098244,
2286.711086, 77485.7681307, 25272.4433349128, 11630.0007860946,
377.22065991, 199.9004139, 161, 2667.93172391, 69404.71338983,
23796.67811208, 17174.64072667, 9950.0416377665, 35443.95356773,
184.603426004, 85934.0997877056, 5603.88739935, 2306.02823117,
13.0064795336), `sum(backers_count)` = c(2880L, 6588L, 3528L,
16L, 4204L, 6632L, 15404L, 1672L, 3588L, 416L, 464L, 364L, 1488L,
228L, 17124L, 2284L, 1348L, 2324L, 744L, 185822L, 35095L, 5980L,
24615L, 32525L, 40L, 4650L, 450L, 2640L, 15952L, 9275L, 16404L,
990L, 35075L, 79L, 6L, 1578L, 10705L, 9302L, 12L, 20L, 36L, 91L,
1505L, 40509L, 1890L, 4L, 9684L, 4196L, 16477L, 754L, 62L, 54L,
36L, 3787L, 3809L, 996L, 132L, 13216L, 50L, 12073L, 13826L, 4353L,
220L, 393L, 628L, 3607L, 6712L, 22403L, 30468L, 106L, 136L, 1854L,
102L, 1623L, 14638L, 1396L, 1923L, 6L, 426L, 161556L, 1020L,
6922L, 3575L, 1627L, 84L, 101L, 56L, 2291L, 450L, 2439L, 678L,
864L, 3195L, 17L, 6072L, 215L, 3119L, 1473L, 85L, 2306L, 7622L,
41L, 973L, 117L, 20L, 3127L, 45L, 424L, 819L, 84L, 12L, 3239L,
18L, 66L, 7L, 35L, 1651L, 651L, 242L, 25L, 13L, 6L, 59L, 1747L,
325L, 244L, 231L, 862L, 6L, 1819L, 74L, 66L, 4L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -133L))
My final objective is to get a dataset, where i can get for each date the data, including for the days where there is missing data in one of the data sets

I assume your key problem is changing eg 01/15/2022 to a date. You can easily use the package lubridates mdy() function for this (install.packages('lubridate').
*adjusted from your comment.
You can run:
library(dplyr)
library(lubridate)
group_df %>%
mutate(date = mdy(date)) %>%
full_join(
group_tweet
) %>%
arrange(date)
Output is:
Joining, by = "date"
# A tibble: 181 × 5
date `length(category)` `sum(usd_pledged)` `sum(backers_count)` `length(text)`
<date> <int> <dbl> <int> <int>
1 2021-01-01 4 50279. 2880 1324
2 2021-01-02 NA NA NA 1548
3 2021-01-03 NA NA NA 1297
4 2021-01-04 NA NA NA 1585
5 2021-01-05 8 366280. 6588 1636
6 2021-01-06 4 172073. 3528 1583
7 2021-01-07 4 231. 16 1492
8 2021-01-08 NA NA NA 1676
9 2021-01-09 NA NA NA 1745
10 2021-01-10 NA NA NA 1389
# … with 171 more rows

Related

Split issue with model_time and timetk in R

I'm using modeltime to forecast 20 time series (not balanced) at once using Modeltime package. However, when I call the function modeltime_calibrate i got the following error:
Error in glubort(): ! Missing 'new_data'. Try adding a test data set
using rsample::testing(splits). See help for more info:
?modeltime_calibrate
Its seems to be related to split, but I couldn't find any explanation:
splits <Analysis/Assess/Total> <887/20/907>
Follow my code.
Thanks,
Rick
splits <- data %>% time_series_split(assess = "3 months", cumulative = TRUE)
splits %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .interactive = TRUE)
rec_obj <- recipe(value ~ ., training(splits)) %>%
step_mutate(ID = droplevels(ID)) %>%
step_timeseries_signature(date) %>%
step_rm(date) %>%
step_zv(all_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE)
summary(prep(rec_obj))
wflw_xgb <- workflow() %>%
add_model(
boost_tree() %>% set_engine("xgboost")
) %>%
add_recipe(rec_obj) %>%
fit(training(splits))
wflw_xgb
model_tbl <- modeltime_table(
wflw_xgb
)
model_tbl
calib_tbl <- model_tbl %>%
modeltime_calibrate(
new_data = testing(splits),
id = "ID",
modeltime_calibrate(quiet = FALSE)
)
calib_tbl
Data:
dput(data2)
structure(list(date = structure(c(1609459200, 1612137600, 1614556800,
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How can I add a legend to this graph?

I have a graph that I would like to add a legend to:
So far I have been able to either add a legend which defaults to its own colours, or one which changes the colours to a gradient fill which isn't what I want.
What am I doing wrong and how can I fix it?
Plot code:
ggplot(df) +
geom_line(aes(date, actual, group = 1),
colour = "forestgreen") +
geom_line(aes(date, plan, group = 1),
colour = "red") +
geom_smooth(aes(date, difference),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y") +
facet_grid(sector ~ ., scales = "free")
dput:
structure(list(date = structure(c(17959, 17959, 17959, 17959,
17959, 17960, 17960, 17960, 17960, 17960, 17961, 17961, 17961,
17961, 17961, 17962, 17962, 17962, 17962, 17962, 17963, 17963,
17963, 17963, 17963, 17964, 17964, 17964, 17964, 17964, 17965,
17965, 17965, 17965, 17965, 17966, 17966, 17966, 17966, 17966,
17967, 17967, 17967, 17967, 17967, 17968, 17968, 17968, 17968,
17968, 17969, 17969, 17969, 17969, 17969, 17970, 17970, 17970,
17970, 17970, 17971, 17971, 17971, 17971, 17971, 17972, 17972,
17972, 17972, 17972, 17973, 17973, 17973, 17973, 17973, 17974,
17974, 17974, 17974, 17974, 17975, 17975, 17975, 17975, 17975,
17976, 17976, 17976, 17976, 17976, 17977, 17977, 17977, 17977,
17977, 17978, 17978, 17978, 17978, 17978, 17979, 17979, 17979,
17979, 17979, 17980, 17980, 17980, 17980, 17980, 17981, 17981,
17981, 17981, 17981, 17982, 17982, 17982, 17982, 17982, 17983,
17983, 17983, 17983, 17983, 17984, 17984, 17984, 17984, 17984,
17985, 17985, 17985, 17985, 17985, 17986, 17986, 17986, 17986,
17986, 17987, 17987, 17987, 17987, 17987, 17988, 17988, 17988,
17988, 17988, 17989, 17989, 17989, 17989, 17989, 17990, 17990,
17990, 17990, 17990, 17991, 17991, 17991, 17991, 17991, 17992,
17992, 17992, 17992, 17992, 17993, 17993, 17993, 17993, 17993
), class = "Date"), sector = structure(c(1L, 2L, 3L, 4L, 5L,
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3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("ADVANCE CLINICIAN",
"CALL HANDLERS", "CLINICIANS", "GP", "PHARMACIST"), class = "factor"),
actual = c(16L, 232L, 104L, 40L, 12L, 23L, 231L, 90L, 47L,
12L, 34L, 245L, 109L, 61L, 0L, 32L, 226L, 99L, 50L, 0L, 25L,
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86L, 44L, 11L, 14L, 276L, 71L, 68L, 7L, 24L, 263L, 93L, 62L,
12L, 42L, 241L, 92L, 42L, 13L, 42L, 242L, 106L, 60L, 8L,
50L, 262L, 101L, 58L, 8L, 38L, 340L, 80L, 42L, 0L, 32L, 312L,
73L, 39L, 8L, 14L, 219L, 81L, 54L, 15L, 19L, 239L, 100L,
50L, 20L, 15L, 245L, 104L, 58L, 13L, 38L, 233L, 90L, 57L,
8L, 50L, 236L, 94L, 41L, 7L, 78L, 370L, 106L, 61L, 8L, 34L,
328L, 91L, 45L, 8L, 16L, 247L, 54L, 57L, 20L, 19L, 263L,
93L, 43L, 15L, 26L, 231L, 88L, 58L, 13L, 50L, 234L, 87L,
61L, 8L, 68L, 245L, 113L, 50L, 15L, 68L, 352L, 89L, 38L,
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208L, 41L, 45L, 8L, 5L, 215L, 48L, 45L, 0L, 53L, 210L, 45L,
55L, 0L, 20L, 220L, 71L, 23L, 8L, 12L, 349L, 80L, 54L, 8L,
2L, 287L, 52L, 35L, 0L), plan = c(43L, 291L, 28L, 32L, 11L,
37L, 262L, 25L, 32L, 11L, 39L, 260L, 22L, 32L, 11L, 34L,
266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L, 66L, 422L,
152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L, 291L, 106L,
32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L, 94L, 32L,
11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L,
66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L,
291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L,
94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L,
32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L,
14L, 43L, 291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L,
39L, 260L, 94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L,
269L, 93L, 32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L,
133L, 53L, 14L, 43L, 283L, 73L, 32L, 11L, 37L, 283L, 99L,
32L, 11L, 39L, 264L, 88L, 32L, 11L, 34L, 260L, 90L, 32L,
11L, 39L, 275L, 98L, 32L, 11L, 66L, 424L, 156L, 61L, 14L,
54L, 360L, 130L, 53L, 14L), difference = c(-27L, -59L, 76L,
8L, 1L, -14L, -31L, 65L, 15L, 1L, -5L, -15L, 87L, 29L, -11L,
-2L, -40L, 9L, 18L, -11L, -14L, -22L, 10L, 26L, -4L, 18L,
-60L, -63L, -4L, -6L, -4L, -40L, -47L, -9L, -3L, -29L, -15L,
-35L, 36L, -4L, -13L, 1L, 3L, 30L, 1L, 3L, -19L, -2L, 10L,
2L, 8L, -24L, 16L, 28L, -3L, 11L, -7L, 8L, 26L, -3L, -28L,
-82L, -72L, -19L, -14L, -22L, -61L, -60L, -14L, -6L, -29L,
-72L, -25L, 22L, 4L, -18L, -23L, 10L, 18L, 9L, -24L, -15L,
10L, 26L, 2L, 4L, -33L, 0L, 25L, -3L, 11L, -33L, 1L, 9L,
-4L, 12L, -52L, -46L, 0L, -6L, -20L, -45L, -42L, -8L, -6L,
-27L, -44L, -52L, 25L, 9L, -18L, 1L, 3L, 11L, 4L, -13L, -29L,
-6L, 26L, 2L, 16L, -32L, -3L, 29L, -3L, 29L, -24L, 20L, 18L,
4L, 2L, -70L, -63L, -23L, -6L, -32L, -85L, -35L, -35L, -3L,
-30L, -82L, -39L, 8L, -3L, -25L, -75L, -58L, 13L, -3L, -34L,
-49L, -40L, 13L, -11L, 19L, -50L, -45L, 23L, -11L, -19L,
-55L, -27L, -9L, -3L, -54L, -75L, -76L, -7L, -6L, -52L, -73L,
-78L, -18L, -14L)), row.names = c(NA, -175L), class = c("tbl_df",
"tbl", "data.frame"))

Place the colour argument in aes and just write the label for it there. You can rename the title with +labs(colour="Legend Title"), and manipulate the colours with scale_fill_manual(values)
ggplot(df) +
geom_line(aes(date, actual, group = 1,colour = "actual")
) +
geom_line(aes(date, plan, group = 1,colour ="plan")) +
geom_smooth(aes(date, difference,colour="difference"),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y")+
facet_grid(sector ~ ., scales = "free")+
labs(colour="Method")+
scale_colour_manual(values=c("forestgreen","red","blue"))
Hope this is helpful!

Extracting overlaping pairs of rows from df with two columns

I would like to find out which pairs overlap between these two tables:
> dput(data1)
structure(list(Name.x = c("MDH1", "MDH1", "IDH2", "IDH2", "IDH2",
"IDH2", "IDH2", "IDH2", "IDH2", "SCOALB", "SCOALB", "CSY4", "CSY4",
"CSY4", "CSY4", "CSY4", "FUM1", "FUM1", "IDH6", "IDH6", "IDH6",
"ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC2-1", "ODC2-1",
"ODC2-1", "ACO2", "IDH1", "IDH1", "IDH1", "IDH1", "ODC2-2"),
Name.y = c("SCOALB", "SCOALA-1", "CSY4", "IDH6", "ODC1-1",
"ODC2-1", "IDH1", "ODC2-2", "ODC1-2", "SCOALA-1", "SCOALA-2",
"IDH6", "SDH2-1", "IDH1", "IDH5", "ICDH", "ODC1-1", "ODC1-2",
"ACO2", "IDH1", "IDH5", "ODC2-1", "IDH1", "IDH5", "ODC2-2",
"ODC1-2", "IDH1", "ODC2-2", "ODC1-2", "IDH1", "IDH5", "SCOALA-2",
"ODC2-2", "ODC1-2", "ODC1-2")), .Names = c("Name.x", "Name.y"
), class = "data.frame", row.names = c(NA, -35L))
> dput(data2)
structure(list(Protein1 = structure(c(3L, 7L, 18L, 19L, 7L, 19L,
6L, 18L, 6L, 18L, 18L, 19L, 9L, 8L, 19L, 18L, 9L, 7L, 18L, 12L,
8L, 19L, 5L, 29L, 12L, 29L, 12L, 18L, 7L, 17L, 6L, 5L, 9L, 19L,
12L, 3L, 19L, 16L, 18L, 17L, 16L, 17L, 9L, 29L, 12L, 7L, 29L,
18L, 16L, 18L, 29L, 8L, 17L, 16L, 17L, 12L, 6L, 8L, 17L, 29L,
9L, 17L, 29L, 19L, 8L, 17L, 29L, 9L, 9L, 16L, 29L, 29L, 19L,
19L, 19L, 29L, 12L, 19L, 17L, 29L, 17L, 16L, 16L, 19L, 16L, 4L,
1L, 5L, 17L, 9L, 18L, 18L, 6L, 4L, 8L, 16L, 16L, 29L, 7L, 12L,
8L, 4L, 29L, 12L, 5L), .Label = c("ACO2", "ACO3", "CSY4", "FUM1",
"ICDH", "IDH1", "IDH2", "IDH5", "IDH6", "LPD1", "LPD2", "MDH1",
"MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2", "ODC2-1", "ODC2-2",
"PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1", "PDC2-2", "SCoALa-1",
"SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1", "SDH2-2", "SDH2-3",
"SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a", "SDH7b", "SDH8"), class = "factor"),
Protein2 = structure(c(1L, 6L, 7L, 17L, 1L, 16L, 3L, 9L,
1L, 5L, 17L, 9L, 8L, 7L, 18L, 18L, 5L, 3L, 16L, 3L, 5L, 8L,
4L, 7L, 5L, 3L, 6L, 6L, 5L, 3L, 5L, 3L, 3L, 6L, 7L, 3L, 7L,
9L, 1L, 8L, 5L, 16L, 7L, 6L, 4L, 7L, 4L, 3L, 3L, 12L, 1L,
1L, 9L, 7L, 7L, 9L, 6L, 6L, 5L, 8L, 1L, 17L, 29L, 3L, 8L,
6L, 9L, 9L, 6L, 12L, 5L, 19L, 12L, 5L, 1L, 16L, 1L, 19L,
4L, 18L, 12L, 1L, 4L, 4L, 6L, 3L, 1L, 1L, 1L, 4L, 4L, 8L,
4L, 1L, 3L, 8L, 16L, 12L, 4L, 12L, 4L, 4L, 17L, 8L, 5L), .Label = c("ACO2",
"ACO3", "CSY4", "FUM1", "ICDH", "IDH1", "IDH2", "IDH5", "IDH6",
"LPD1", "LPD2", "MDH1", "MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2",
"ODC2-1", "ODC2-2", "PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1",
"PDC2-2", "SCoALa-1", "SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1",
"SDH2-2", "SDH2-3", "SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a",
"SDH7b", "SDH8"), class = "factor")), .Names = c("Protein1",
"Protein2"), class = "data.frame", row.names = c(1L, 4L, 6L,
12L, 22L, 25L, 28L, 33L, 44L, 48L, 51L, 52L, 53L, 60L, 68L, 70L,
72L, 76L, 86L, 109L, 110L, 119L, 133L, 144L, 146L, 158L, 170L,
197L, 202L, 206L, 211L, 213L, 226L, 227L, 237L, 271L, 272L, 286L,
290L, 297L, 304L, 305L, 306L, 319L, 323L, 327L, 347L, 348L, 351L,
357L, 370L, 372L, 373L, 378L, 379L, 392L, 406L, 410L, 414L, 417L,
419L, 437L, 442L, 445L, 448L, 455L, 457L, 462L, 471L, 479L, 482L,
483L, 488L, 503L, 509L, 522L, 536L, 563L, 618L, 620L, 623L, 628L,
630L, 644L, 647L, 666L, 668L, 673L, 676L, 678L, 679L, 690L, 691L,
694L, 698L, 703L, 709L, 714L, 715L, 722L, 723L, 724L, 727L, 739L,
740L))
In each of df there are two columns which store strings. Strings overlap between table. However, the order between pairs might be different. One string from the pair might be find in first column of data1 and in second column in data2. How to find what pairs and how many of them overlap between datasets ?

> data1$combine = as.character(interaction(data1$Name.x, data1$Name.y))
> data2$combine = as.character(interaction(data2$Protein1, data2$Protein2))
>
> dat.overlap = data1[complete.cases(match(data2$combine, data1$combine)),]
> dat.overlap
Name.x Name.y combine
2 MDH1 SCOALA-1 MDH1.SCOALA-1
4 IDH2 IDH6 IDH2.IDH6
11 SCOALB SCOALA-2 SCOALB.SCOALA-2
13 CSY4 SDH2-1 CSY4.SDH2-1
18 FUM1 ODC1-2 FUM1.ODC1-2
28 ODC2-1 ODC2-2 ODC2-1.ODC2-2
data1[complete.cases(match(data1$combine, data2$combine)),]
Name.x Name.y combine
3 IDH2 CSY4 IDH2.CSY4
7 IDH2 IDH1 IDH2.IDH1
19 IDH6 ACO2 IDH6.ACO2
20 IDH6 IDH1 IDH6.IDH1
21 IDH6 IDH5 IDH6.IDH5
23 ODC1-1 IDH1 ODC1-1.IDH1
24 ODC1-1 IDH5 ODC1-1.IDH5
27 ODC2-1 IDH1 ODC2-1.IDH1
29 ODC2-1 ODC1-2 ODC2-1.ODC1-2
35 ODC2-2 ODC1-2 ODC2-2.ODC1-2

Sort row-wise and make a key by pasting, then merge:
data1$key <- apply(data1, 1, function(i) paste(sort(i), collapse = "_"))
data2$key <- apply(data2, 1, function(i) paste(sort(i), collapse = "_"))
res <- merge(data1, data2, by = "key")
head(res)
# key Name.x Name.y Protein1 Protein2
# 1 ACO2_IDH1 ACO2 IDH1 IDH1 ACO2
# 2 ACO2_IDH6 IDH6 ACO2 IDH6 ACO2
# 3 CSY4_ICDH CSY4 ICDH ICDH CSY4
# 4 CSY4_IDH1 CSY4 IDH1 IDH1 CSY4
# 5 CSY4_IDH2 IDH2 CSY4 IDH2 CSY4
# 6 CSY4_IDH5 CSY4 IDH5 IDH5 CSY4

Stuck on a "recycled leaving remainder" warning

I am having a tough time with a problem I keep encountering with the following code:
#require(gdata)
require(jfreels)
require(mondate)
require(lubridate)
require(gdata)
# source this file
asof=as.Date('2017-05-31')
gm_quarter_value<-quarter(asof)
gm_year_value<-year(asof)
# load in and format data ----- the block of code where error occurs.
history_original<-
read.xls("//mhistory.xlsx",sheet='Allocation',na.strings="N/A")
mh<-data.table(history_original)
mh$date<-as.Date(mh$date)
setkey(mh,'date','mani')
mh[mani!='Cash',vami:=vami(return),by=fixed.name]
#load(file='data/history/mh.rda')
I have attempted to fix the error by reading up online but nothing seems to work as all explanations infer I am creating a data table (dt. ) rather than importing a data table through an Excel file.
The warning messages that popup from my code are the following:
Warning messages:
1: In `[.data.table`(cgmh, strategy != "Cash", `:=`(vami, vami(return)), :
Supplied 145 items to be assigned to group 3 of size 146 in column 'vami' (recycled leaving remainder of 1 items).
2: In `[.data.table`(mh, mani != "Cash", `:=`(vami, vami(return)), :
Supplied 71 items to be assigned to group 21 of size 72 in column 'vami' (recycled leaving remainder of 1 items).
3: In `[.data.table`(mh, mani != "Cash", `:=`(vami, vami(return)), :
Supplied 44 items to be assigned to group 40 of size 45 in column 'vami' (recycled leaving remainder of 1 items).
4: In `[.data.table`(mh, mani!= "Cash", `:=`(vami, vami(return)), :
Supplied 27 items to be assigned to group 41 of size 28 in column 'vami' (recycled leaving remainder of 1 items).
Here is some of the data from dput(head(mh, 50))
structure(list(date = structure(c(12752, 12752, 12752, 12752,
12752, 12752, 12752, 12752, 12752, 12783, 12783, 12783, 12783,
12783, 12783, 12783, 12783, 12783, 12783, 12783, 12814, 12814,
12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814, 12814,
12814, 12842, 12842, 12842, 12842, 12842, 12842, 12842, 12842,
12842, 12842, 12842, 12842, 12873, 12873, 12873, 12873, 12873,
12873), class = "Date"), fixed.name = structure(c(17L, 18L, 46L,
47L, 70L, 4L, 60L, 59L, 69L, 17L, 18L, 8L, 46L, 47L, 70L, 4L,
24L, 60L, 59L, 69L, 17L, 17L, 8L, 46L, 47L, 70L, 4L, 24L, 60L,
59L, 69L, 73L, 17L, 17L, 8L, 46L, 47L, 70L, 4L, 24L, 60L, 59L,
69L, 73L, 17L, 17L, 60L, 8L, 46L, 47L), .Label = c("1",
"2", "3", "4", "5", "6", "7", etc. class = "factor"),
manager = structure(c(24L, 44L, 62L, 70L, 71L, 5L, 43L, 85L,
99L, 24L, 44L, 12L, 62L, 70L, 71L, 5L, 32L, 43L, 85L, 99L,
24L, 27L, 12L, 62L, 70L, 71L, 7L, 32L, 43L, 85L, 101L, 107L,
24L, 27L, 12L, 62L, 70L, 71L, 7L, 32L, 43L, 85L, 101L, 107L,
24L, 27L, 43L, 12L, 62L, 70L), .Label = c(lots of data here ), class = "factor"), strategy = structure(c(1L,
1L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 1L, 2L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 4L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 2L, 2L, 2L), .Label = c("Cash", "Discretionary", "Quantitative",
"Trend Follower"), class = "factor"), gross_allocation = c(NA,
NA, 0.142404915838961, 0.0474683052796535, 0.0474683052796535,
0.189873221118614, 0.113923932671168, 0.261393169848591,
0.189873221118614, NA, NA, 0.0732346506007515, 0.127227083260399,
0.0504539213350938, 0.0500494951829262, 0.150598347942872,
0.097646200801002, 0.102648545851004, 0.139406844436169,
0.146469301201503, NA, NA, 0.0712445227169108, 0.123964691130067,
0.0476800374705463, 0.0485758686645857, 0.145697372074475,
0.0928750257506969, 0.0983016698067595, 0.13403145511268,
0.142043313516008, 0.0946955423440056, NA, NA, 0.0835168624939674,
0.106588512365316, 0.0643739492830623, 0.066140094839865,
0.120819106420193, 0.079432110728629, 0.0819557073526206,
0.11538840144924, 0.166281136586948, 0.127042633304666, NA,
NA, NA, 0.105856694204315, 0.106336463704097, 0.0628120217517739
), net_allocation = c(0, 0, 0.143494748241449, 0.0478315827471496,
0.0478315827471496, 0.191326330988599, 0.114795798593159,
0.263393625693896, 0.191326330988599, 0, 0, 0.0780974349815385,
0.135674967809812, 0.0538040641787497, 0.0533727841103034,
0.160598085609968, 0.104129913308718, 0.109464414314636,
0.148663465723197, 0.156194869963077, 0, 0, 0.0713080226118237,
0.124075180253433, 0.047722534454923, 0.0486191640989275,
0.145827231430156, 0.0929578048072517, 0.0983892856046405,
0.134150916693587, 0.142169916027154, 0.0947799440181028,
0, 0, 0.082564194313903, 0.10537266827038, 0.0636396423266499,
0.0653856416444407, 0.11944093541624, 0.0785260368878884,
0.0810208471071804, 0.114072177933095, 0.164384384924578,
0.125593471175645, 0, 0, NA, 0.0983666777386705, 0.0988125005761266,
0.0583676823484184), attribution = c(-0.000443266946085618,
0, 0.010051776300819, 0.00317113437469883, 0.00238472696113082,
0.00802893915484529, 0.00972706935855351, 0.00968370136548388,
0, -0.000440070107589608, 0.000121585143389376, 0.000229810333585158,
0.000600276113576124, -0.00128820140991525, 3.99695193738757e-05,
-0.000361129826732395, -0.00187724283985821, -0.00128385273636259,
-0.0011990465227359, 0, -0.00036254125256041, 0.000633512231325974,
-0.000512960563561758, -0.00347960664253897, 0.00141283713223096,
0.00251341429314942, -0.00912636717243622, -0.00308688292194134,
-0.00566091419913372, -0.00359918817341097, -0.0014308969925891,
0.00156261612460105, -0.000632849158692302, 2.08436840409153e-05,
0.00133540685104398, -0.000313375712697167, -0.00159815288576456,
-0.00473252239700671, -0.0113934901937967, 0.000777818917761139,
-0.0012305250368858, 0.00126422585853451, 0.00915133063638428,
0.00468022553426879, -0.000688569622908879, 0.00112718749151131,
NA, -0.000994649965118036, 0.00325597011568602, 0.00780374962611487
), cash_change = structure(c(2143L, 1161L, 1202L, 1967L,
1777L, 2512L, 1181L, 1178L, 1161L, 22L, 1771L, 2164L, 1294L,
496L, 2492L, 1000L, 686L, 493L, 460L, 1161L, 755L, 1348L,
47L, 995L, 1895L, 2235L, 338L, 942L, 92L, 1018L, 566L, 1960L,
802L, 2200L, 1936L, 1010L, 678L, 61L, 515L, 1589L, 550L,
1899L, 1693L, 1236L, 2100L, 1819L, 1L, 438L, 2460L, 1592L
), .Label = c("", "-0.179999999993015", "-0.309999999999945",
"-1", "-1.61999999999898", "-1.74622982740402e-010", "-100068.974874801",
"-100235.36", "-100276", "-100319", "-100330.48", "-100416.569999998",
"-100940.009999999", "-1014043.74000001", "-101446.620000001",
"-101573.619999999", "-101748.387580004", "-1018471.4", "-101919.760000002",
"-102261.04", "-102748.5", "-102775.78", "-103320.27", "-104101.220000001",
"-104371.649999999", "-104377.770000001", "-104541.310000001",
"-104628.85", "-104824.74", "-10491.2799999993", "-104981.119999999",
"-10503.46", "-1050639.386", "-105344.909999999", "-105556.61794",
"-105789.020000001", "-105974.700000001", "-106033.25", "-1064338.6",
"-106699.800000001", "-107116.92676", "-107245", "-107366.670000001",
"-1075850.947517", "-108219.72", "-10825.3", "-10833.8904",
"-1086.06", "-109098.68", "-109781", "-110030", "-110055.35",
"-110720.6", "-110939.54", "-111091.300000001", "-111159.81",
"-112.5", "-112523.399999999", "-112567.65", "-112924.3754",
"-112983.76", "-113459", "-11359.29", "-1137.7200000002",
"-113724.59", "-114064.922", "-114153.33", "-114339", "-1148311.78",
"-11499.2599999998", "-115498", "-115633.560000002", "-115669",
"-116181.349999999", "-116263.77", "-116345.49", "-116450.220000001",
"-1165174.434", "-116681.39", "-1169.39000000013", "-117.260000000359",
"-117.820000000007", "-117650.72", "-117976", "-118088.83",
"-118127", "-118366.880000001", "-118910.431612", "-11904.6899999995",
"-119076.620000001", "-119227.389999999", "-119560.31", "-119656",
"-119847.8272", "-12015", "-120228.99", "-121215.200000001",
"-12130.3399999999", "-121305.060000001", "-121359.48", "-121372",
"-12180.6916476834", "-121957.979999999", "-122016.87", "-122204",
"-122583.029999999", "-122763.76", "-12296.059", "-1231586.02",
"-12317.6600000001", "-123233.93", "-123985.18", "-12435.3400000001",
"-1243584.30698176", "-124386.42", "-124440.090000002", "-124663.23",
"-124749.83", "-124793.56", "-125286.95", "-125752.57", "-125991.149999999",
"-126087.13", "-126237.84", "-12664.3299999998", "-12678.8099999996",
"-127087.609999999", "-127365.219999999", "-127413.159999999",
"-127539", "-127599.52", "-127817", "-127918.43", "-128162.890000001",
"-128527.47", "-128537.842920001", "-12891.7100000009", "-1289347.356",
"-130006.02", "-1303381.25", "-13075.7300000004", "-132065.08",
"-132862", "-1332167", "-133229.1", "-133275.790000001",
"-133338.311793", "-133503.7", "-1335110.79028567", "-1337.90000000037",
"-13387.7999999998", "-133920.21", "-134013.22", "-13407",
"-134145.310000001", "-134485", "-134609", "-134663.096483",
"-134869.25", "-134924.48", "-13493", "-135069.81", "-13507.7385",
"-135282.92", "-135678", "-135838.550345", "-135966.47",
"-135980.300000001", "-136106.209999999", "-136170.24", "-136216.82",
"-13623.1699999999", "-136498.119999999", "-136566.42", "-13666.98",
"-13692.48", "-136986.177008999", "-137760.81", "-137788.919999999",
"-13814.404000001", "-138491.18", "-138764.109999999", "-139008.85",
"-139111.35", "-139225.75", "-14007.25", "-140087.68", "-140461",
"-140480.869999999", "-140559.73", "-140789.98", "-141094",
"-141172.939999999", "-141442.25", "-14176.7831290001", "-142115.64",
"-1422133.03", "-142394.86", "-14317.4900000002", "-143382.365999999",
"-143496.060000001", "-143889.82", "-144339.269999998", "-145025.73",
"-145032.54", "-145230.810000001", "-14555.0599999996", "-1463.14999999991",
"-1467083.61", "-147.699999999721", "-147371", "-1478130.07",
"-148036.200000003", "-1484752.36627733", "-148638", "-14909.0599999996",
"-1495.34999999963", "-149909.27", "-150808.970000001", "-151104",
"-151515", "-151554.5", "-151600.35", "-151761.93", "-152666.139999999",
"-1527.53999999957", "-153321.930489", "-153351.430000002",
"-153826.57", "-154261.23", "-154344.79", "-154366.819999998",
"-155071.159999999", "-1552270", "-155300.539999999", "-155664.83",
"-156528.430000002", "-157231.1", "-157309.49", "-157588.36",
"-157756.130000001", "-15811.5100000007", "-158116", "-158301.819999998",
"-15851.169999999", "-158916.79", "-15900.6599999992", "-159261.640000001",
"-159841.81", "-16028.8700000005", "-16041.0600000005", "-160788.28",
"-160931.31", "-161112.47", "-161391.54", "-161571.82", "-162772",
"-163080.23", "-1631776.18961502", "-16370.216", "-163878.1142",
"-16407.5000000009", "-164132.29", "-164310.990000002", "-16477.2000000002",
"-164922", "-1654145.89", "-16681.0199999996", "-166813.67",
"-167432", "-16806.3499999996", "-168587.42", "-168676.27",
"-168736.3815498", "-169282.720000001", "-169582.54", "-169808.25",
"-169880.369999999", "-17057.9499999993", "-171058.639999999",
"-17214.3399999999", "-172574", "-1727156.3", "-17324.3899999999",
"-1733011.377417", "-173827.036639996", "-173844.989699999",
"-174051.67", "-174113", "-174613.67", "-174662.9", "-175170.82",
"-175250.83", "-175571.09", "-176099.709999999", "-176272.018000001",
"-176316.539999999", "-176970.13", "-177463.85", "-177831.319999999",
"-178211.209999999", "-178706.12", "-179790.199999997", "-180461.18",
"-18086.96", "-18128.4466440002", "-181300.99", "-181974.76",
"-182119.993906", "-183227.55", "-18338", "-18377", "-184355.62",
"-184513.06", "-1851550.63567736", "-185359.37", "-186026.2",
"-18608.7799999993", "-18620.5700000003", "-186253.428",
"-186376", "-187126.5761172", "-187427", "-18747", "-18759.1000000006",
"-18766.4999999981", "-18767.1799999997", "-189286.97", "-1895.0700000003",
"-190057.699999999", "-190305.23", "-19074", "-190989.26",
"-191427.800000001", "-192097.08", "-192347.11", "-192681.0327519",
"-192751.78", "-193205.17", "-194234.0916", "-1943373.03",
"-194383", "-195419.41", "-19568.4000000004", "-196017.789999999",
"-196032.99", "-196138.35", "-196535.11", "-196541.58", "-196720",
"-196810.279999999", "-197097.19", "-197401.92", "-1982330.27000001",
"-19880", "-19903.3842000002", "-199137.73", "-199310.61",
"-200074.399999999", "-20135.3700000001", "-201950.129999999",...... NA,
NA, NA, 1.00175057961823, 1.07428786780012, 1.1734795592287
)), .Names = c("date", "name", "job", "mani",
"gross_allocation", "net_allocation", "attribution", "cash_change",
"return", "vami"), sorted = c("date", "strategy"), class = c("data.table",
"data.frame"), row.names = c(NA, -50L), .internal.selfref = <pointer: 0x0000000000120788>)
I am fairly new to coding and would appreciate any feedback to help learn more about this warning. Is it the underlying Excel sheet or the code that is the problem?

Apply function to data grouped by cut()

I would like some help with summarising data using cut. I have been successful in less complicated situations, but now I am stuck.
The data:
> dput(sumsq)
structure(list(part_no = c(10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), ratperc = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0, 0, 0, 0,
0, 0, 75.6, 0, 89.6, 24.8, -100, -100, 75.6, 100, 100, -100,
-100, -100, -100, -100, -100, 75.6, 98.4, 98.4, -51.2, -51.2,
0.8, 0.8, 0.4, 0.4, 0.4, 0.4, 75.2, -100, -100, -100, 1.2, -0.4,
-0.4, -0.4, -0.4, 100, 100, -1.6, 0, 0, 0, 0, -100, 0.4, 100,
0.4, 0.4, 100, -0.4, -78.4, 0.4, 100, 100, 100, 100, -100, 23.6,
61.2, 61.2, 69.2, 75.6, 75.6, 75.6, 75.6, 75.6, 98, 98, 98, -75.2,
-75.2, 47.2, 47.2, 47.2, 47.2, 76.8, 97.6, -71.6, -71.6, -71.6,
-71.6, 24, 52, 52, 52, 75.2, 75.2, -77.6, 25.2, 47.2, 76.4, 76.4,
76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4,
76.4, -73.2, -73.2, -73.2, -73.2, 0.8, 0.8, 75.2, 75.2, 75.2,
75.2, 75.2, 75.2, 0.4, 0.4, 0.4, 0.4, 0.4, -100, -100, -100,
-100, -100, 73.2, 2, -0.8, -0.8, -0.8, -100, -0.4, -0.4, 50.4,
50.4, 50.4, 50.4, 50.4, 50.4, -76.4, 99.6, 99.6, -76.4, 100,
100, 50.4, 1.2, 28, -1.2, 93.6, 41.2, 1.6, 24.8, -1.6, 0, 0,
24.8, -24, 26, 50.8, 2, 28, 36.4, 24, -43.6, 33.6, 61.2, 81.2,
86.8, 34, -51.6, -2, 28.4, 2, 82, 41.6, 25.6, 82, 0.8, 92, 1.2,
86.4, 54, 96, 0.4, -54.4, 1.2, -93.2, -49.2, -98.4, -2, -77.2,
93.2, 23.6, 78.8, 42.4, 0.4, 2.8, 70.8, 24.4, 2.4, 62, 92.8,
16.4, -61.2, 24.4, -77.2, -0.4, 74.8, 3.6, 82, 82, 18, 54, 9.2,
55.2, 96.4, 96.4, 90, 90, -84.4, -84.4, -2.8, -2, -90.4, 2.4,
34.8, 24, -1.6, -16.8, 2.8, 2.4, -83.2, 22.4, 22.4, -1.6, -1.6,
60, -2.4, 2.4, 2, 0.8, -22.8, 2, -1.6, 25.2, 2, 2, -52.8, -1.2,
-1.2, 3.2, -74.4, 3.2, 3.2, -78.4, 0.4, -2.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, -79.2, -0.8, -0.8, -0.8, -0.8, -0.8, -3.2, 41.2,
-0.8, -0.8, -0.8, -0.8, -83.2, -1.6, -1.6, 0.4, 0.4, 0.4, -90,
-1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6,
77.6, -79.6, 80.8, -81.6, -93.2, -100, 8.4, 75.6, 82.8, 67.2,
-27.2, 78.8, 65.6, 84.8, 73.6, 46.8, -62.4, 57.2, 74, 13.6, -0.8,
32.8, -27.2, 6.4, -67.2, 79.2, -64, 58, -40.4, 64, 8, 60, 76.8,
-24.8, -52.4, 56.8, 75.6, 38.4, -50.4, -72.8, -83.6, 24, 34.8,
54.4, -54, 67.6, 78.4, -41.6, -64.4, -83.6, -93.6, 76.8, -2.4,
-19.2, -54, -38, 5.2, 52.4, 64.8, 42.4, 77.6, -46.4, -74.8, -60.4,
-83.2, -56.4, -34.8, -16.8, 21.2, 40, 59.2, 0.4, -17.6, 24.4,
-14.4, 35.2, -26.8, 42, 44, -1.2, -35.6, 10.8, -19.6, -35.2,
22.4, -18.4, 27.6, -9.6, 43.2, -31.2, 45.2, 23.6, -16.4, 28.8,
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2250L), class = "data.frame")
using the vector:
timevec1 = as.vector(ggplot2:::breaks(sumsq$diffdist, "n", n=8))
I normally summarise the data using xtabs and cutusing:
bb1 = data.frame(xtabs(~ratperc +cut(diffdist, timevec1 ), dat=sumsq))
colnames(bb1) = c("rating", "range", "freq", "id")
While this solution is not idea for what I wanted it, I was able to then summarise the values for each cut using ddply.
However now I need to preserve the part_no too, but I can't seem to be able to pass more than one column to cut.
The question is, is there any way to do everything in one step? Basically get for each participant the mean of all the ratings for each cut? In other words, part_no as rows, ranges as columns and the intersection being the mean of ratings for the values that below there.

If you just want the mean rating for each part_no and interval from cut(diffdist, timevec1 ) I would just do something like this:
#Add cut variable as new column
sumsq$range <- cut(sumsq$diffdist,timevec1)
#Summarise using ddply
ddply(sumsq,.(part_no,range),summarise,val = mean(ratperc))

I didn't get if you want the mean for each participant and interval or the cumulative mean along the intervals for each participant.
If you want the normal mean you can get it with
sapply(split(sumsq, cut(sumsq$diffdist, timevec1)), function(ss)
sapply(split(ss$ratperc, ss$part_no), mean))
If you want the cumulative you can rephrase it as
t(sapply(split(sumsq, sumsq$part_no), function(ss){
sapply(timevec1[-1], function(tc) mean(ss$ratperc[ss$diffdist <= tc]))
}))

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