Ignoring NA (missing values) - r
I want to calculate the mean of each fund in the dataset below, but I am not able to exclude the na's I put in in XL as an indication of missing values.
I have tried using na.rm and na.omit without getting it working as intended. Does anyone have any suggestions on how to perceed?
dput(funds[1:50,]) from dataset.
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, -0.0371203551212511, -0.0556553503130705,
0.114407946115376, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, -0.0307016857361987, -0.0325795717342283,
-0.0224503689467165, 0.0258370136198041, NA, NA, NA, NA, NA,
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-0.0238208182014747, -0.0339683325366178, -0.0373842599362443,
-0.0617194981455106, 0.0898312482757637, NA, NA, NA, NA, NA,
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NA, NA, NA, -0.0090002245623988, 0.00232763847063611, 0.0666744669374286,
0.0541982646590207, 0.0357777115456177, 0.0112375620619904, 0.0517733147448458,
0.0553272554088993, 0.0964919466161833, -0.183504972082187, -0.080436535906959,
-0.0168779792120786, -0.0189163710105161, 0.0354464282302958,
0.0570771243180614, -0.0121806484943049, 0.00267147721028516,
0.0418138789866271, 0.0239014241737423, -0.0740578606461375,
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0.143359321093154, 0.0311030232934453, 0.025811812769811, 0.0907165482493208,
0.0525623559303523, -0.0227902272359872, 0.0507802414104146,
0.0424427448435787, -0.00672761966958846, -0.10728921498628,
0.0656231227602588, 0.0935154527174278, 0.0441150328862905, 0.062605816978009,
0.0149006622516556, -0.0320388008910873, 0.0818012957006298,
-0.0454549261682008, 0.0451713668988998, -0.109575700589352,
-0.109309649073184, -0.0294151271748013, 0.00115280249886585,
-0.0641372706445755, -0.0362830848886829, 0.0658140759601307,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.055744910128889,
-0.0622543645374518, 0.0807062164274857, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.593015631147388,
-0.0402998181019883, -0.07037351720532, 0.0716671060725897, 0.0593505268768708,
0.0710274224449907, 0.0181377296578442, -0.0767778231316664,
0.0453741580290961, -0.0324597471184895, 0.0261137296639806,
-0.053332098536766, -0.103195814566841, -0.047796283287009, -0.0174817964779793,
-0.0569324478107568, -0.0714571845404417, 0.0434074010950383,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0924237534290786,
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-0.0323317130652527, 0.0573102975873354, -0.0462136481428159,
0.0359245283018867, -0.0885909951916072, -0.0811350919264587,
-0.0662461939973903, -0.0496110308846135, -0.041907656112146,
-0.0541259528316367, 0.0864297690518687, NA, NA, NA, NA, NA,
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-0.0605664407778432, -0.0344016465745368, -0.0786033292732441,
-0.0139023530448577, 0.0811940031726881, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.0609178826615828, 0.0330911715918167,
0.0246199591154059, 0.0387559218497211, -0.0219724959665873,
0.00576292730999128, 0.0607497869923317, 0.0968700634555142,
0.118662582078258, -0.149187455335955, -0.194607764562562, 0.206793882798755,
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-0.0348720817263815, -0.0779735122906021, -0.0699996691626507,
0.122593574168608, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, -0.00338009126246408, 0.0625741902662371,
-0.0197095435684648, 0.0235653235653237, -0.0205574774344905,
0.0211513478402079, 0.0440504114817319, 0.0713605727123872, 0.122338724009241,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 0.0182819486204802, 0.052568368712947,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), .Dim = c(50L, 108L), .Dimnames = list(NULL,
c("AI.NORS2", "AI.AKSJN", "AB.AKSPR", "AI.AKTIV", "GA.KAPIT",
"GA.GAMB", "BF.HUMAN", "AB.NOPEN", "VL.AKNOR", "AI.NORGS",
"AI.NORG", "BF.NORGE", "AI.NORGI", "AI.VEKST", "AC.NWECA",
"AC.NEQCB", "AC.NWECD", "AC.NWECI", "NR.NORGE", "BF.NORG",
"CA.AKSJE", "CL.AKNOR", "FF.AKFOR", "FF.NOIII", "FF.NOAI2",
"FF.NORGE", "FF.NORII", "FF.VEKST", "DF.NORGE", "DF.VEKST",
"DK.PBNOR", "DK.NORGE", "DK.NORII", "DI.RINV", "DK.NORG3",
"DK.NORIV", "DK.NOIVR", "DK.NSEL1", "DK.NSEL2", "DK.NSEL3",
"DI.RVKST", "DI.SMB", "EK.NORGE", "NF.PLUSS", "FT.GNRTR",
"FT.NOFOK", "FF.BARNE", "FK.AKTI2", "FK.SPAR", "FV.NORGE",
"FV.TRNDR", "GA.OPPKJ", "GF.AKSJE", "GF.INVES", "SU.AKTIV",
"SU.GLNO", "SU.NORGE", "HF.NORGE", "HB.HNORG", "HO.NORGE",
"KF.IPA", "KL.AKSJE", "KL.AKSNO", "IS.NORGE", "IS.UTBYT",
"IS.UTBYI", "NF.AKSJE", "KF.AVKAS", "KF.BARNE", "KF.KAP",
"KF.KAPIT", "KF.KAIII", "KF.NOPLS", "KF.AKPEN", "KF.SMB",
"KF.SMBII", "KF.VEKST", "OD.NORGE", "OD.NORGA", "OD.NORGB",
"OD.NORGD", "OD.NORII", "OR.FIN30", "PO.AKTIV", "FO.AKSJE",
"FO.INDX", "PV.VEKST", "NF.RFAKS", "NF.RFPLU", "AI.SKAFS",
"SE.NORGE", "SK.HORIS", "SK.SMB", "SR.NORGA", "SR.NORGB",
"SP.INNLA", "SP.AKSJS", "SP.NORGE", "SP.NORGA", "SP.STNOP",
"SP.NORGI", "SP.NOINS", "SP.OPTIM", "SP.VEKST", "SP.VERDI",
"SP.STVEN", "TF.NORGE", "OD.VÅRAK")))
````
We can also use sapply with mean
sapply(funds, mean, na.rm = TRUE)
Or with apply
apply(funds, 1, mean, na.rm = TRUE)
This way you can get means for every column:
library(tidyverse)
means <- data %>% as_tibble() %>% mutate_all(as.numeric) %>% summarise_all(sum, na.rm = TRUE)
Maybe you can try colMeans with option na.rm = TRUE, e.g.,
res <- colMeans(funds,na.rm = TRUE)
Related
How can I coalesce this dataset in R?
I am an ICU physician conducting research which involves taking lots of patient-related data from the ICU computer system (all ethically approved, etc). As is often the case, getting data out then requires cleaning and wrangling before it can be used properly.
I have obtained a set of data, and have wrangled it as best I can. Of course, my data science skills are pretty rudimentary and despite being an enthusiastic R user, I am at a complete blockage, and am hoping some of you might be able to shed some light on my problem and how to solve it. I absolutely cannot get round this, but suspect it is a commonly encountered issue in time-series work.
At present, my dataset now includes multiple rows for each time point. So, at time X there is an individual row for heart rate, blood pressure, etc. There are 46 observations, and this repeats for every time point (344 in total for this patient). All observations are not recorded at each time point. I have provided a link to screenshots of the way this data is arranged here.
A sample of the data is here, is that helps.
The best progress I've made is with the following nested for-loop structure. It works for the first set of observations. I have tried a strange while-loop arrangement that fell flat on its face.
# First, add a group to the entire table specifying each time point that
# observations were conducted.
Patient_full$Verification_group <- as.numeric(as.factor(Patient_full$Time))
# Get the number of these groups
observation_times <- max(Patient_full$Verification_group)
# Create the bare bones of an overall table. This is the first row of the table.
patient_obs_final <- Patient_full[1,]
# Next I need to create a loop within loop. The master loop will coerce rows
# that have been created by the sub-loop.
for (i in 1 : observation_times) {
# Isolate the overall observation group you are dealing with
veri_group <- filter(Patient_full, Verification_group == i)
# Start by getting some numbers to run the sub-loop
lowest_obs_time_row <- min(veri_group$Row)
highest_obs_time_row <- max(veri_group$Row)
rows_in_obs_time <- (highest_obs_time_row - lowest_obs_time_row)
# We can run the sub-loop now
obs_at_timepoint <- Patient_full[lowest_obs_time_row, ]
for (j in 1 : (rows_in_obs_time - 1)) {
obs_at_timepoint <- coalesce(obs_at_timepoint, Patient_full[j + 1,])
}
patient_obs_final <- rbind(patient_obs_final, obs_at_timepoint)
}
patient_obs_final
As soon as j goes to 2 the thing seems to fall apart.
So, in the end my goal is to have a separate row for each time point, and for that row to have whatever was recorded/observed at that time. I am at a loss, and can't even tell why my solution isn't working. Any advice would be greatly appreciated.
Try this dplyr solution:
library(dplyr)
dat %>%
group_by(Time) %>%
mutate(
Cardiac.Rhythm = if_else(nzchar(Cardiac.Rhythm), Cardiac.Rhythm, NA_character_),
across(-Row, ~ .[order(is.na(.))])
) %>%
ungroup() %>%
filter(rowSums(!is.na(.)) > 2) %>%
as.data.frame()
# Row Time Base.excess..vt. Glucose.ABG Lactate.ABG PaCO2 PaO2 PH..ABG. Potassium.ABG Sodium.ABG Cardiac.Rhythm Arterial.Pressure.Diastolic Arterial.Pressure.Mean Arterial.Pressure.Systolic Heart.Rate
# 1 1 2017-09-04 17:00:00 -11.4 11.8 10.7 4.42 31.5 7.25 3.9 3.9 ST NA NA NA NA
# 2 10 2017-09-04 17:55:00 NA NA NA NA NA NA NA NA <NA> 54 68 92 123
# 3 14 2017-09-04 18:00:00 NA NA NA NA NA NA NA NA ST 60 71 86 123
# 4 23 2017-09-04 19:00:00 -9.3 10.1 9.7 4.22 15.0 7.30 3.9 3.9 ST 58 70 92 122
# 5 36 2017-09-04 20:00:00 -8.4 8.1 7.2 5.07 16.9 7.27 3.9 3.9 ST 62 80 117 NA
(I truncated the columns pasted here ...)
Walk-through:
For some reason, Cardiac.Rhythm has empty strings instead of NA, the first mutate converts empty strings "" to NA so that later filtering works;
.[order(is.na(.))] orders the non-NA data first within each column;
rowSums(.) ensures that we have at least one non-NA datum on a row (the > 2 takes into account that Row and Time are not NA).
Notes:
I assume that data is one "person" per frame; if you have a patient id in the data, make sure to add it within the group_by(.) as well.
Within a particular Time (and Patient_ID, if present), I assume that the order of rows is not important (ergo the per-column reordering of values).
I do not assume that each column can have only one value per Time; while logically it makes sense that this would be the case, there could also be an error in the data-scraping/aggregation before this point, so I intentionally do not assume that x[!is.na(x)] (when grouped by Time) will always return length 1. This will evidence as two (or more) rows in a specific Time.
I thought about using pivot_longer for this, and it still might be possible, but ... you have both numeric and character data here, so it's a little problematic to sort that out well.
Data
dat <- structure(list(Row = 1:47, Time = c("2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:00:00", "2017-09-04 17:55:00", "2017-09-04 17:55:00", "2017-09-04 17:55:00", "2017-09-04 17:55:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 18:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 19:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00", "2017-09-04 20:00:00" ), Base.excess..vt. = c(-11.4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -9.3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -8.4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Glucose.ABG = c(NA, 11.8, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Lactate.ABG = c(NA, NA, 10.7, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.7, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.2, NA, NA, NA, NA, NA, NA, NA, NA, NA), PaCO2 = c(NA, NA, NA, 4.42, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.22, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5.07, NA, NA, NA, NA, NA, NA, NA, NA), PaO2 = c(NA, NA, NA, NA, 31.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 16.9, NA, NA, NA, NA, NA, NA, NA), PH..ABG. = c(NA, NA, NA, NA, NA, 7.25, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.27, NA, NA, NA, NA, NA, NA), Potassium.ABG = c(NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA), Sodium.ABG = c(NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.9, NA, NA, NA, NA, NA), Cardiac.Rhythm = c("", "", "", "", "", "", "", "", "ST", "", "", "", "", "", "", "", "ST", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "ST", "", "", "", "", "", "", "", "", "", "", "", "", "ST"), Arterial.Pressure.Diastolic = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 54L, NA, NA, NA, 60L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 62L, NA, NA, NA), Arterial.Pressure.Mean = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68L, NA, NA, NA, 71L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 70L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 80L, NA, NA), Arterial.Pressure.Systolic = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 92L, NA, NA, NA, 86L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 92L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 117L, NA ), Heart.Rate = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 123L, NA, NA, NA, NA, 123L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 122L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Non.Invasive.Arterial.Pressure.Diastolic = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 58L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Non.Invasive.Arterial.Pressure.Mean = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 71L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Non.Invasive.Arterial.Pressure.Systolic = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 108L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Tympanic.Temperature = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 37.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Patient.Positioning.ABG = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Central.Venous.Pressure = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Delivered.Percent.O2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Mean.Airway.Pressure.S = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Minute.Volume.expired..S. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Peak.Inspiratory.Pressure.measured..S = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Positive.End.Expiratory.pressure = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), S.Expired.Tidal.vol...breath. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), S.Tidal.Volume.Inspired = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Servo.i.Modes = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.FiO2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.Flow.Trigger.S = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA ), Set.Pause.time.. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.PEEP.Servo = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA ), Set.rate..CMV.or.SIMV. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.Tidal.Volume..servo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.Upper.Pressure.Limit = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Spontaneous.Rate..S = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Cardiac.output..Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), DO2.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), DO2I.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Stroke.Volume.Index.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Stroke.Volume.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Systemic.Vascular.Resistance.Index.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Systemic.Vascular.Resistance.Vigileo. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Ionised.Calcium.ABG = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Set.Pressure.Control.level.above.PEEP.S. = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 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 = "data.frame", row.names = c(NA, -47L))
Don't do it in a loop. Only one summarise_at is enough !!
library(tidyverse)
Patient_01_sample <- read.csv("E:/R/StackOverflow/Patient_01_sample.xlsx - Sheet 1.csv", row.names=1)
f = function(x) ifelse(length(x[!is.na(x)])==0,NA,x[!is.na(x)][1])
Patient = read_csv("Patient_01_sample.xlsx - Sheet 1.csv")
Patient %>% group_by(Time) %>%
summarise_at(vars(3:45), f)
output
# A tibble: 5 x 44
Time `Glucose ABG` `Lactate ABG` PaCO2 PaO2 `PH (ABG)` `Potassium ABG` `Sodium ABG` `Cardiac Rhythm` `Arterial Pressure Di~
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 2017-09-04 17:00:00 11.8 10.7 4.42 31.5 7.25 3.9 3.9 ST NA
2 2017-09-04 17:55:00 NA NA NA NA NA NA NA NA 54
3 2017-09-04 18:00:00 NA NA NA NA NA NA NA ST 60
4 2017-09-04 19:00:00 10.1 9.7 4.22 15 7.3 3.9 3.9 ST 58
5 2017-09-04 20:00:00 8.1 7.2 5.07 16.9 7.27 3.9 3.9 ST 62
# ... with 34 more variables: Arterial Pressure Mean <dbl>, Arterial Pressure Systolic <dbl>, Heart Rate <dbl>,
# Non Invasive Arterial Pressure Diastolic <dbl>, Non Invasive Arterial Pressure Mean <dbl>,
# Non Invasive Arterial Pressure Systolic <dbl>, Tympanic Temperature <dbl>, Patient Positioning ABG <lgl>, Central Venous Pressure <lgl>,
# Delivered Percent O2 <lgl>, Mean Airway Pressure S <lgl>, Minute Volume expired (S) <lgl>, Peak Inspiratory Pressure measured S <lgl>,
# Positive End Expiratory pressure <lgl>, S Expired Tidal vol. (breath) <lgl>, S Tidal Volume Inspired <lgl>, Servo i Modes <lgl>,
# Set FiO2 <lgl>, Set Flow Trigger S <lgl>, Set Pause time % <lgl>, Set PEEP Servo <lgl>, Set rate (CMV or SIMV) <lgl>,
# Set Tidal Volume (servo) <lgl>, Set Upper Pressure Limit <lgl>, Spontaneous Rate S <lgl>, Cardiac output (Vigileo) <lgl>, ...
As #r2evans pointed out, this cal also be done with dplyr::pivot_wider.
As he suggested, we need a little hack, that is, to transform all columns to character, then back to their original classes after changing back to wide with pivot_wider
library(readxl)
library(dplyr)
df<-read.xlsx("Patient_01_sample.xlsx")
df %>% mutate(`Cardiac Rhythm`=replace(`Cardiac Rhythm`, `Cardiac Rhythm`=='', NA)) %>%
select(-1) %>%
mutate(across(-1, as.character)) %>%
pivot_longer(-1)%>%
group_by(name) %>%
filter(!is.na(value)) %>%
ungroup%>%
pivot_wider(id_cols=Time) %>%
mutate(across(-c(1, `Cardiac Rhythm`), as.numeric))
# A tibble: 5 x 18
Time `Base excess (vt)` `Glucose ABG` `Lactate ABG` PaCO2 PaO2 `PH (ABG)` `Potassium ABG`
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2017-09-04 17:00:00 -11.4 11.8 10.7 4.42 31.5 7.25 3.9
2 2017-09-04 17:55:00 NA NA NA NA NA NA NA
3 2017-09-04 18:00:00 NA NA NA NA NA NA NA
4 2017-09-04 19:00:00 -9.3 10.1 9.7 4.22 15 7.3 3.9
5 2017-09-04 20:00:00 -8.4 8.1 7.2 5.07 16.9 7.27 3.9
# … with 10 more variables: Sodium ABG <dbl>, Cardiac Rhythm <chr>, Arterial Pressure Diastolic <dbl>,
# Arterial Pressure Mean <dbl>, Arterial Pressure Systolic <dbl>, Heart Rate <dbl>,
# Non Invasive Arterial Pressure Diastolic <dbl>, Non Invasive Arterial Pressure Mean <dbl>,
# Non Invasive Arterial Pressure Systolic <dbl>, Tympanic Temperature <dbl>
UpsetR - choosing intersections to visualize
I would like to use Upset plot instead of venn diagram to show overlap between specific groups (20 in total). However, one of the group (number 10) is the most important for me and I would like to present how many unique values is in that specific fraction. I would like to present ~25-30 intersections in total on the graph but uniqueness of group 10 has to be also shown.
I know existence of sets function but I would like to present around 25-30 intersections as mentioned and this 1 group additionally.
Any ideas ?
EDIT:
Added reproducible example:
dput(rep_exp)
structure(list(Gr_4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 24.4310955935393, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), Gr_5 = c(NA, NA, NA, 21.8310535918369,
NA, NA, NA, NA, NA, 20.7254228450715, NA, 27.1619253143803, NA,
NA, NA, NA, NA, NA, NA, 26.6027203831498, NA, NA, NA, NA, NA,
NA, 30.8729830402671, NA, NA, NA), Gr_6 = c(28.8390902059829,
24.67734371881, 22.683139406727, 29.1546773298581, NA, NA, 21.9107159172821,
NA, 22.9230495998744, 26.9880437180908, NA, 32.391666051163,
NA, NA, NA, 21.6001415858001, 23.0239282537894, NA, 21.055168555216,
30.7121903523751, NA, NA, NA, NA, 22.0963548474675, NA, 32.513357598066,
NA, NA, 23.7976852708585), Gr_7 = c(21.4265985064224, NA, NA,
23.0695638371137, NA, NA, NA, NA, NA, 20.7903453146324, NA, 28.2499758022535,
NA, NA, NA, NA, NA, NA, NA, 25.9613085520105, NA, NA, NA, NA,
NA, NA, 29.355377815192, NA, NA, 21.1302512982254), Gr_8 = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 21.8730880931062, NA,
NA, NA, NA, NA, NA, NA, 22.4189005564519, NA, NA, NA, NA, NA,
NA, 30.2275670312356, NA, NA, NA), Gr_9 = c(22.195894810917,
25.9203441316619, NA, 23.5317193622031, NA, NA, NA, 20.4526193062251,
NA, 22.357699113594, 19.9767319209274, 29.0184743803346, NA,
NA, NA, NA, 22.1010446624755, NA, NA, 26.3118997535445, NA, NA,
NA, NA, NA, NA, 29.9658173049532, NA, NA, 22.7388204380555),
Gr_10 = c(24.9716280984187, 26.6702013159945, NA, 26.0197313721615,
NA, NA, NA, 22.1233522815746, NA, 24.0516716332837, 22.4063679987568,
30.256761573029, NA, NA, NA, NA, 26.4434318913431, NA, NA,
27.9654320211905, NA, NA, NA, NA, NA, NA, 29.8212398361126,
NA, NA, 24.1442935303143), Gr_11 = c(22.9856008507804, 25.1691705265075,
NA, 26.0689081411402, NA, NA, NA, 21.1400234004731, NA, 24.5711480491199,
23.5402595534611, 29.329649538014, NA, NA, NA, NA, 28.6076666902364,
NA, NA, 26.5597151498881, NA, NA, 25.8334491330428, NA, NA,
NA, 29.7854239060885, NA, NA, 21.751849665826), Gr_12 = c(28.2942576160509,
28.4109042369708, NA, 30.2938411874268, 28.1159976488766,
26.6893919055319, NA, 20.2236435193017, NA, 31.1236147481775,
27.1394614209655, 33.7497512742728, NA, NA, NA, 22.1620175455317,
32.740995072413, NA, 23.2685659859292, 31.9204662366898,
NA, NA, 30.7601811119423, NA, NA, 22.8704941623247, 31.3416488641037,
NA, NA, 28.6773773257387), Gr_13 = c(27.9415091276483, 27.0299165363222,
NA, 30.7110417097659, 28.7379570773404, 25.5882365428802,
NA, NA, NA, 32.2667076588073, 27.2933369287433, 34.7079501935325,
NA, 22.8206916170467, NA, NA, 32.5779472688676, NA, NA, 32.6317048040664,
NA, NA, 30.1389490092958, 23.8308408919424, 23.0679896658325,
26.164689687244, 30.2006952484736, NA, 24.447772868487, 29.5606883639626
), Gr_14 = c(27.4616237005853, 26.7750499947566, NA, 30.3932526396929,
31.1062446290124, 27.2595253359549, NA, NA, NA, 33.6656430607522,
27.734214173453, 35.0800848727354, NA, NA, NA, 23.151279208873,
33.2366327906614, NA, NA, 33.4932145181405, 22.9608977649923,
NA, 31.8193222893087, 24.7850652730265, NA, 24.9920915833786,
29.0239557410047, 25.2744788247811, 26.6821750741598, 29.7891764054099
), Gr_15 = c(27.2029382158867, 25.3112934881725, NA, 29.1103329989503,
29.514275096105, NA, NA, NA, NA, 31.6854120776358, 28.5249970429603,
35.9001903675862, 22.4465240056921, NA, NA, NA, 31.8450938083269,
25.5788830788713, NA, 34.7663358707296, 25.6549086895753,
26.2291635318221, 31.9466351025545, 26.715548983008, NA,
25.6752720211283, 28.4457302899793, 27.2647239196348, 25.0412216502086,
31.6489022687779), Gr_16 = c(25.1843096821522, NA, NA, 26.444459119903,
23.8302606418847, NA, NA, NA, NA, 27.987230611469, 27.8591095189136,
32.8816869988268, NA, NA, 24.8165571469754, NA, 28.7689442058935,
25.2395434664377, NA, 32.829999906694, 23.6787411063596,
NA, 27.8325560998723, 25.9582137297807, NA, NA, 25.6769403745901,
25.3048339598422, 23.7070405817542, 29.8423911570548), Gr_17 = c(23.2209751780558,
NA, NA, 24.6434488773652, 22.5225058653221, NA, NA, NA, NA,
27.0216809889885, 26.6607134339159, 31.099676534797, NA,
NA, 26.93077937966, NA, 27.8090060912948, 26.7795654758791,
NA, 32.3731900255852, 24.9494014193233, NA, 24.5609834789349,
26.086325932043, NA, NA, 25.5082418618407, 23.6504233402429,
23.8014399755019, 28.7791270904749), Gr_18 = c(NA, NA, NA,
NA, NA, NA, 26.0401348427439, NA, NA, 24.3341543275568, 24.7556235529872,
30.4889365348298, NA, NA, 26.9888022043666, NA, 25.7387173773674,
27.1316334308385, NA, 31.571451882524, NA, NA, NA, 25.745888266175,
NA, NA, 23.2997781674234, NA, NA, 23.2402643606836), Gr_19 = c(NA,
NA, NA, NA, NA, NA, 24.3940790216008, NA, NA, NA, 21.4222413790374,
25.7991932672173, NA, NA, 25.9372380266141, NA, 22.9217973627502,
20.5334552143032, NA, 28.7776543930148, NA, NA, NA, 23.9298543509444,
NA, NA, 24.3614522942989, NA, NA, NA), Gr_20 = c(NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 25.6961748124338, NA,
NA, NA, NA, NA, NA, NA, 26.4582321196234, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA), Gr_21 = c(NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 22.8258042878256, NA, NA, NA, NA, NA,
NA, NA, 25.1317511650203, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA)), row.names = c(NA, 30L), class = "data.frame")
It is a code which has been used to generate a plot:
for (i in 1:nrow(rep_exp)) { rep_exp[i, ][is.na(rep_exp[i, ]) == F] = rownames(rep_exp)[i]}
n_sizescale=nrow(rep_exp)*1.2
p1 <- { upset( fromList(rep_exp),
nsets = ncol(rep_exp), nintersects = 35, order.by = "freq", #degree, freq
empty.intersections = "on", number.angles = 0, mb.ratio = c(0.55, 0.45), point.size = 2.5, line.size = 0.8,
text.scale = c(1.3, 1.3, 1, 1, 1, 1), mainbar.y.label = "Number of groups",
sets.x.label = "Groups",
show.numbers = "yes", keep.order = TRUE,
set_size.show = TRUE,
set_size.scale_max = n_sizescale)
}
If I understood correctly the idea of dots below barplot they indicate how many groups can be found in specific intersections and a single dot gives a number for that specific Gr ? Am I right ?
How to force and algorithm to show couple of "interesting" groups as a single dot (show uniqueness of this group) and other as intersections.
Can you (#krassowski) rewrite a code for the package you mentioned ?
Counting observations and adding them to data frame in R
I have the return og 108 mutual funds and over from 1987 to 2019. I want to count the number of observations in total (excluding NA) over the existence of the funds.
I have been able to get the kurtosis, skewness, etc using the following codes:
kurt <- apply(funds, 2, kurtosis, na.rm = TRUE)
skew <- apply(funds, 2, skewness, na.rm = TRUE)
max <- apply(funds, 2, max, na.rm = TRUE)
min <- apply(funds, 2, min, na.rm = TRUE)
sd <- apply(funds, 2, sd, na.rm = TRUE)
m <- apply(funds, 2, mean, na.rm = TRUE)
Then trying to the same with the number of observations and not succeeding:
obs <- apply(funds, 2, count, na.rm = TRUE)
Getting this error:
Error in UseMethod("group_by_") :
no applicable method for 'group_by_' applied to an object of class "c('double', 'numeric')"
The first 10 lines from the data set is here (funds). It is much longer but this should be sufficiently illustrative. As you can see there are a lot of NA in the first lines. Number of observations here would result in 0, and if one looks at the fund "DK.NORGE" the number of observations in the first 10 lines would be 10.
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0090002245623988, 0.00232763847063611,
0.0666744669374286, 0.0541982646590207, 0.0357777115456177, 0.0112375620619904,
0.0517733147448458, 0.0553272554088993, 0.0964919466161833, -0.183504972082187,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, -0.0132758821474321, 0.0246370824973443,
0.0436835891381346, 0.0356472795497187, 0.000293052003410121,
-0.0158201720510295, 0.0677617514139583, 0.0710647033479483,
0.0996190340976313, -0.26700522906759, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.0609178826615828, 0.0330911715918167,
0.0246199591154059, 0.0387559218497211, -0.0219724959665873,
0.00576292730999128, 0.0607497869923317, 0.0968700634555142,
0.118662582078258, -0.149187455335955, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.00338009126246408,
0.0625741902662371, -0.0197095435684648, 0.0235653235653237,
-0.0205574774344905, 0.0211513478402079, 0.0440504114817319,
0.0713605727123872, 0.122338724009241, -0.193811951737024, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.0182819486204802, 0.052568368712947,
0.0223478709564888, 0.0430931528662419, 0.00418444259680784,
0.0149102804245731, 0.0891504229496138, 0.101929676995524, 0.0713342508037151,
-0.184479046400599, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), .Dim = c(10L, 108L), .Dimnames = list(
NULL, c("AI.NORS2", "AI.AKSJN", "AB.AKSPR", "AI.AKTIV", "GA.KAPIT",
"GA.GAMB", "BF.HUMAN", "AB.NOPEN", "VL.AKNOR", "AI.NORGS",
"AI.NORG", "BF.NORGE", "AI.NORGI", "AI.VEKST", "AC.NWECA",
"AC.NEQCB", "AC.NWECD", "AC.NWECI", "NR.NORGE", "BF.NORG",
"CA.AKSJE", "CL.AKNOR", "FF.AKFOR", "FF.NOIII", "FF.NOAI2",
"FF.NORGE", "FF.NORII", "FF.VEKST", "DF.NORGE", "DF.VEKST",
"DK.PBNOR", "DK.NORGE", "DK.NORII", "DI.RINV", "DK.NORG3",
"DK.NORIV", "DK.NOIVR", "DK.NSEL1", "DK.NSEL2", "DK.NSEL3",
"DI.RVKST", "DI.SMB", "EK.NORGE", "NF.PLUSS", "FT.GNRTR",
"FT.NOFOK", "FF.BARNE", "FK.AKTI2", "FK.SPAR", "FV.NORGE",
"FV.TRNDR", "GA.OPPKJ", "GF.AKSJE", "GF.INVES", "SU.AKTIV",
"SU.GLNO", "SU.NORGE", "HF.NORGE", "HB.HNORG", "HO.NORGE",
"KF.IPA", "KL.AKSJE", "KL.AKSNO", "IS.NORGE", "IS.UTBYT",
"IS.UTBYI", "NF.AKSJE", "KF.AVKAS", "KF.BARNE", "KF.KAP",
"KF.KAPIT", "KF.KAIII", "KF.NOPLS", "KF.AKPEN", "KF.SMB",
"KF.SMBII", "KF.VEKST", "OD.NORGE", "OD.NORGA", "OD.NORGB",
"OD.NORGD", "OD.NORII", "OR.FIN30", "PO.AKTIV", "FO.AKSJE",
"FO.INDX", "PV.VEKST", "NF.RFAKS", "NF.RFPLU", "AI.SKAFS",
"SE.NORGE", "SK.HORIS", "SK.SMB", "SR.NORGA", "SR.NORGB",
"SP.INNLA", "SP.AKSJS", "SP.NORGE", "SP.NORGA", "SP.STNOP",
"SP.NORGI", "SP.NOINS", "SP.OPTIM", "SP.VEKST", "SP.VERDI",
"SP.STVEN", "TF.NORGE", "OD.VÅRAK")))
Any feedback is appreciated. Thank you.
count is not the right function here. To count number of non-NA value in each column use is.na with sum. obs <- apply(funds, 2, function(x) sum(!is.na(x))) However, a better option is colSums which can take input as complete dataframe or matrix. colSums(!is.na(funds))
Boxplot/ Box & Whisker help in ggplot2 (R) Need to remove duplicates while also plotting on one plot
So I would like to create a plot of 6 different boxplots (so all in one graphic). I am however running into some trouble.
First, there are many NA's and they are randomly admist the dataset so I am unsure how to plot all the variables on one plot with this issue.
Secondly, ggplot requires a y variable.... I am fairly new to the tidyverse but am unsure how to create a y variable that will allow for each plot to be represented.
Here is the code I have thus far as well as some sample data, just getting some nonsense...:
library(readr)
library(ggplot2)
library(tidyr)
Box_Whiskers_Plot <- read_delim("C:/Users/johnt/Downloads/Box & Whiskers Plot.txt",
"\t", escape_double = FALSE, trim_ws = TRUE)
box_tidy <- gather(data = Box_Whiskers_Plot,
key = Concern,
value = Value)
ggplot(data = box_tidy, mapping = aes(x = Concern, y = Value)) +
geom_boxplot()
Sample data:
structure(list(`1 concern` = c(NA, NA, NA, NA, "4.7051072361071977E-2",
"0.19811079686050914", "0.15241809445883892", "9.3784616216209704E-2",
NA, NA, "0.12902642667986841", NA, NA, NA, "-2.7995766112836051E-3",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.16257072914439274",
NA, NA, NA, NA, NA, NA, NA, "-0.32189822523240785", NA, NA, "8.8779492146409344E-2",
NA, NA, NA, NA, "0.25876167614411516", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.12140341771652063", NA,
NA, NA, NA, NA, NA, NA, "7.8315099203373872E-2", NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "-2.4887790087301243E-2", NA, NA, "0.17817816702345479",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-0.45715764794257374",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.1727380710391988",
NA, NA, NA, NA, NA, "-", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "0.11810158539718096", NA, NA, NA,
"0.27340288238873622", NA, NA, "0.31222498045287939", NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "1 concern", "8.3059150913641758E-2"
), `2 concerns` = c(NA, "0.30750786698302712", NA, NA, NA, NA,
NA, NA, "0.19491094633578943", "0.14347068243793348", NA, NA,
NA, NA, NA, "9.4003202704330935E-2", NA, NA, NA, NA, NA, NA,
"5.8682039323707746E-2", NA, NA, NA, NA, NA, NA, "0.38837474884084",
NA, NA, "9.9772158663856914E-2", NA, NA, NA, NA, "0.15369966808838376",
NA, "-9.7591933707396827E-2", "7.5799891559719335E-2", NA, "0.74069094176638783",
"0.18455079764897997", "0.35878241180217119", NA, NA, "9.7671065222774578E-2",
NA, "-1", "1.9762661406333537E-2", NA, NA, NA, NA, NA, NA, "0.12110279127050561",
"-8.8073972864920469E-2", NA, NA, "-5.3063552654085022E-2", "-0.19524178703281547",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.19324044960582598",
NA, NA, NA, NA, NA, "0.19558769095890249", "9.8480653359761305E-2",
"-2.7258845509566809E-2", NA, NA, "4.2377241471322602E-2", NA,
NA, NA, "-0.31089100169922018", NA, NA, "9.4259642624681561E-2",
NA, NA, NA, NA, NA, NA, NA, NA, "2.9465956237787916E-2", "0.36028868638565514",
"0.28696166852692623", "0.16026874768911181", NA, NA, NA, NA,
NA, NA, NA, "0.17495242646710829", NA, NA, NA, "8.0174590835183634E-2",
NA, NA, NA, NA, NA, "0.3741514609038552", NA, NA, NA, NA, NA,
NA, NA, NA, NA, "0.18203421025448296", NA, NA, NA, "0.18032116561517086",
NA, NA, "-0.24673024063961035", "8.3759133449436751E-2", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-0.12597421585178167",
NA, NA, NA, "0.19902495793991903", NA, NA, NA, NA, "-9.2408051470944635E-2",
NA, NA, NA, NA, NA, "1.3515493037121162E-2", NA, NA, "-2.2110562098653141E-2",
NA, NA, NA, NA, "3.5029098159769845E-2", NA, NA, NA, NA, NA,
NA, NA, "-0.38542680544884389", "0.2673937214255111", NA, NA,
NA, NA, NA, NA, "0.1514695655588354", NA, "-0.19290183848262321",
"0.19830470195985717", "0.25974088161209186", "0", "0.12635072134014091",
"4.3529572197642308E-2", NA, "-2.811733193779542E-2", "5.2999441490886978E-2",
"-1.5294438792050502E-2", "-0.1092036064257218", NA, NA, NA,
NA, NA, NA, NA, "-8.4682877918448418E-2", NA, NA, NA, NA, NA,
NA, "0.33060935555613358", "-0.26950721703104663", NA, NA, NA,
NA, NA, NA, NA, NA, NA, "2 concerns", "6.5143247152538983E-2"
), `3 concerns` = c(NA, NA, NA, "-6.2005384615384615E-2", NA,
NA, NA, NA, NA, NA, NA, NA, "0.16466373149154445", "0.14529429748819767",
NA, NA, "3.3101080910433733E-3", NA, NA, "-0.14716333286324612",
NA, NA, NA, "0.101855405108354", NA, NA, NA, "1.5624661137794593E-2",
"4.089776650666388E-2", NA, "-", "0.14868399697158718", NA, NA,
"8.4936940656134663E-2", "-7.3275278911856751E-5", NA, NA, "0.16209406140402915",
NA, NA, NA, NA, NA, NA, "7.5733790938149026E-2", "7.7802906849214093E-2",
NA, "0.29092905402715896", NA, NA, NA, "0.2433591777340911",
NA, NA, "0.16878584978409417", "0.23450765393402495", NA, NA,
NA, NA, NA, NA, "1.4972641033242029E-2", NA, NA, "0.15914858376902719",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "0.23167642917280462",
"-0.12014200114033269", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "0.23296355648900113", "9.2737675544091028E-2",
"-1.135676252608786E-2", "-2.5231545331790839E-3", "-1.831276418414618E-2",
NA, "3.700270212564627E-2", NA, NA, NA, NA, NA, NA, NA, "0.12864133565206637",
NA, "0.2713309994071611", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.33081997450170131", NA, NA, NA, NA, NA, NA, "0.10916148370698719",
NA, NA, NA, NA, NA, NA, NA, NA, "-1", "-0.10648202245319915",
NA, NA, NA, NA, NA, NA, "7.6583001375218229E-2", "0.11923826063359644",
"0.1382325704168097", "4.411629139778972E-2", NA, NA, "-2.7571494462436785E-2",
"-8.1186210791162505E-2", "0.36815766123347382", "0.21997253864625099",
"9.5269593575127098E-2", NA, "0.40386694165317971", "0.1317061317077115",
"8.4533840305895946E-2", NA, NA, NA, "7.064976326243011E-2",
"8.2533081202996961E-2", NA, NA, NA, NA, "-6.5935523766861404E-2",
"0.15935278497831473", NA, "0.1159060020401923", NA, NA, "0.11817005685670501",
"6.1029901139001863E-2", "0.12692362698225845", "3.4415424790262605E-2",
NA, NA, "0.23155179134453707", "0.14332216947092591", "7.4795242229677816E-2",
"0", NA, NA, NA, NA, NA, NA, "0.14534924839754271", "0.27815354547396853",
"0.19493428600637031", "0.1283055485269069", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-2.3773331360783301E-2",
NA, "0.20660830748524073", NA, "0.23154616465669209", NA, "-0.80937062468163068",
NA, NA, "0.41853447897377194", NA, NA, NA, "9.4089917760579844E-2",
NA, NA, "6.3552512454774224E-3", "-0.43971479670164443", "0.15974143122420936",
"-0.16029537031373786", NA, "3 concerns", "7.198593957320798E-2"
), `4 concerns` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "6.4352691220779912E-2", NA, NA,
"0.21279729530946834", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, "-9.3690492677869663E-2", NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, "-7.1185121289991993E-2", NA, NA, "6.569732863463229E-2",
NA, NA, "9.7222332805540157E-2", NA, NA, NA, NA, NA, NA, NA,
"8.5074456366478923E-2", NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "1.7663351832379881E-3", NA, NA, NA, "0.20859715043286409",
"-4.0588246304824382E-2", NA, NA, NA, NA, NA, NA, NA, NA, "3.1799587621662351E-2",
NA, "8.6166092731043253E-2", NA, NA, NA, NA, NA, NA, NA, "4.869038187032948E-2",
"0.18071545075957585", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "4.2986578596766911E-2",
NA, NA, NA, NA, "9.4277092317434086E-3", NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, "3.5496813684543493E-2", "-8.1501862554191895E-2",
NA, NA, "9.9940934380241986E-2", NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, "4 concerns", "5.1777127158001604E-2"), `5 concerns` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "8.9612836579635591E-2", "8.1063923186028175E-2",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, "8.4668999169687842E-2", NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, "6.0739595493825904E-2", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, "9.0340384993987888E-2", NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "6.1693228854984072E-2", NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
"-0.20631919750140182", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, "5 concerns", "3.7399967253821095E-2"), `6 concerns` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.32874505543754307", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, "0.15408216010209475", NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "-4.8467807432570065E-2",
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, "6 concerns", "0.14478646936902259"
)), row.names = c(NA, -238L), class = c("tbl_df", "tbl", "data.frame"
))
ggplot requires your data to be "tidy" (see here for details https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). In your case just use the gather function from tidyr package. As your numeric values are actually character and not numeric you also ned to convert them to numeric before plotting them. ggplot will drop NA values (with a warning) so you don't need to worry about them. So the code is as follows: library(tidyverse) Box_Whiskers_Plot <- Box_Whiskers_Plot %>% gather(key = "concerns", "value") %>% mutate(value = as.numeric(value)) ggplot(Box_Whiskers_Plot, mapping = aes(x = concerns, y = value)) + geom_boxplot() Resulting in:
singularity error regressing lag in R
I am trying to run a forecasting model where I simply regress earnings on lagged earnings
$Y_{i,t+1}=a+bY_{i,t}$ (edit: the mathematical formulas do not seem to work in so?)
Y(i,t) = a + bY(i,t-1)
Doing so gives me however the following error:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
NA/NaN/Inf in 'x'
What surprises me is that I was not getting an error message when my model was larger (where I included other variables such as X(i,t).
What can I do to solve this issue? I tried to copy my data with dput but it was too long.
How can i only paste the first 100 values of a vector? as in dput(A[1:100,])?
lm(I(inc.plus1/csout)~I(inc/csout), data=df)
where inc.plus1 is the lagged income (lag+1) and csout are the number of shares outstanding
A represents the first 100 entries of my vector I(inc.plus1/csout):
dput(head(A,600))
structure(c(NA, NA, 1.446, 0.9995, 1.999, 2.902, 3.657, 3.96875,
4.10175, 4.0565, 3.44475, 5.7205, 61.93475, -3.85725, -4.5245390070922,
5.62880429175694, 2.47738918119605, 2.96300124018189, 2.73025552646406,
8.07212115316016, 10.5535326434138, 15.9591327947488, 1.96747660017018,
2.22629738160507, 3.58404764906088, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.37888779527559,
-1.41959644816633, 1.0763348800732, 2.68114979918346, 2.80325842125764,
2.43790873353744, -2.15764063396585, 1.4799008091882, NA, NA,
NA, 1.37544255734004, 1.9209810662425, 2.80281184257786, 4.19364769870183,
4.92729231874391, 2.12704602596336, 0.595823284929961, 2.11401303299297,
2.72348504284468, 2.52902356618847, 2.32582482425984, 0.346862332597876,
1.82379803991995, 2.08507363127918, 5.08163579455077, 5.94934078367083,
7.23445185266149, 5.8378412444986, 4.63755947383151, -2.17174787920901,
1.40756281631882, -4.05886251253207, -0.0212408027324169, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, -0.479369880443708, -0.293425018839488, 0.0929350592321281,
0.123835254518112, 0.12706259774046, 0.116354410972905, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.44976515683205,
4.48091561842034, 4.96571829617233, 1.65772899046248, 0.680583048407414,
1.98560374302681, 4.02555335612741, 4.81968688141083, 0.762689075630252,
1.88469387755102, 3.33593220338983, 0.309443507588533, 3.51697478991597,
2.86402247341315, 2.03178679830559, 5.51008520836107, 3.87780527915746,
2.40556264606919, 5.07700077828744, 4.83989720998532, 4.53742961245445,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -1.98851777777778,
0.0187844444444444, 0.128071111111111, 0.699172682926829, 0.907074545454545,
0.795127607650579, 0.357956423686356, -0.463226859004222, 0.60042762366109,
0.855410812777128, 1.70155389507976, NA, NA, NA, 6.28810975609756,
5.65225290697674, 5.50109011627907, 5.46905424200278, 6.82666666666667,
9.63833333333333, 8.50433333333333, 7.02166666666667, 7.61833333333333,
8.26466666666667, 9.84033333333333, 9.239, 8.05, 6.783, 7.553,
8.29666724227249, 11.525749819631, 14.2648420619405, 11.2771348353078,
-14.1189900160153, -17.6900879135628, 14.6432951757972, 17.706462585034,
NA, NA, NA, 2.54016638089795, 2.28029063187611, -1.70405905251086,
-13.3982068926821, 1.08612445944267, 1.02272906761858, -4.90830977239342,
4.93192336244886, 2.71279872376048, 3.58730158730159, 2.67460317460317,
-14.7646639032994, -10.1970204908481, -2.43234807690238, 1.50090714167904,
0.777890139686844, 1.00461845428288, -0.577281452731578, 0.916700774289767,
0.693536467511796, 1.02936271521732, 0.885562723018554, 0.533956763933385,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 6.33737983599889, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1.97118644067797, 2.74399323181049,
3.00780780780781, 3.58680659670165, 2.81889504170007, 2.50917809245523,
2.79962476547842, 2.3177358490566, 2.98221831785893, 3.53841135956795,
4.84714071243347, 4.42075856720104, 3.43539749078812, 2.10469553058082,
0.698847262247839, 1.15489967560666, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 16.48, 23.5307692307692, 26.2953846153846, 29.1553846153846,
34.8107692307692, 34.1, 31.7707692307692, 35.66, 42.6569230769231,
50.8294573643411, 60.6072371486256, 71.8920605209796, 73.1694942127795,
81.0144689246958, 101.277860326894, 84.9637442320369, 99.4780896110291,
NA, NA, NA, 3.21418291525857, 3.47403755478572, 2.82309202411355,
1.88046287028157, 2.54218477118243, 2.86466949503918, 2.7850953423992,
3.71485649429788, 3.57246064220529, 3.59200878943474, 3.39917935379717,
4.36734916933839, 3.47052845528455, 2.10217797766436, 2.50138966092273,
2.53216008085335, 5.6740159729317, 7.12959606279318, 3.0704500763493,
-9.36316494785294, 2.98453249387051, 0.15134546114962, -0.30269092229924,
NA, NA, NA, 2.5918904109589, 1.72054794520548, 3.37534246575342,
3.15616438356164, 2.44266666666667, 3.72987012987013, 4.66193853427896,
5.6548463356974, -6.70449172576832, 5.53191489361702, 7.55555555555556,
4.56737588652482, -0.699029126213592, 3.77435897435897, 5.6053580628542,
8.82009465467434, 9.75106700157242, 17.3575865849202, 6.79550870390565,
-10.0748330068806, 2.45326283957664, 12.7237136465324, 15.6632796497851,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 6.74737416450689, 3.73783734165596, 3.56847945896463,
4.01606499861509, 4.31513436031426, NA, NA, NA, 1.18216666666667,
1.15616666666667, 0.718833333333333, 0.846666666666667, 0.949833333333333,
0.855, 0.947, 1.362, 1.29283333333333, -0.4095, -1.71166666666667,
-6.73979933110368, -1.07073578595318, 0.439966555183946, 0.889597315436242,
1.29455445544554, 0.888846453280844, 2.07047744964451, 1.42014282853901,
-5.14692281267341, 2.76534467725315, 2.98839786207796, 2.61554688592088,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.3883876937312,
0.0920378175190796, 1.80967558338076, 4.26608220425823, 4.22229845626072,
8.08242880047986, -2.31627512828963, 7.1832993650083, 1.45599924522962,
-1.63071337035455, 2.12153047215424, -14.241522334182, -13.2118418381361,
NA, NA, NA, 115.623529411765, 210, 224.705882352941, 234.117647058824,
217.714285714286, 235.555555555556, 240, 244.878048780488, 305.853658536585,
316.818181818182, 346.363636363636, 419.724770642202, 467.43119266055,
561.467889908257, 683.48623853211, 792.201834862385, 953.902327704245,
1135.53943789665, 1172.64573991031, 854.803010181496, 1066.1084178052,
1080.66637439719, 1076.95690413369, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, -8.00416281221091, -10.8834412580944, 7.80989824236818,
-6.48704902867715, -27.7520814061055, 4.79648473635523, 8.26732673267327,
4.09595559080095, 4.86207928837189, -9.03804256745433, -3.14144062935344,
0.0326826252097871, -0.859022222222222, 2.15602936527451, NA,
NA, NA, NA, NA, NA, NA, Inf, 1.16978683393883, 1.51189430424169,
3.52039622666496, 3.18081603714028, 3.61737627328914, 3.10188513592364,
-7.09439807225699, -3.69752722803501, 0.707971643687819, 0.327086826731375,
1.23655832779557, 0.575014101013737, 0.473355975527496, -0.122513039981693,
-0.851168008718768, 0.840333176705788, 0.978143386038959, 0.767234041909876,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 3.9974572801517, 3.81505135065159, 3.59021071115013,
2.22077609062425, 1.36322005302483, 2.3571531896427, NA, NA,
NA, NA, NA, NA, NA, NA, 1.37809917355372, 5.96900826446281, 9.65909090909091,
8.60330578512397, 12.426, 21.7577639751553, 22.3953974895398,
12.6962809917355, 2.77066115702479, 5.46201232032854, 13.7374488657494,
4.1607203533809, 9.63248867259607, 12.3481116584565, -16.9937258042985,
9.91789166997749, 13.2756799577502, 7.46975276170437, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
0.564217391304348, -0.212196078431373, 0.0218077803203661, 1.11794583661086,
0.440747736316973, 0.363348739557059, 0.641371480161689, 1.61685900721797,
NA, NA, NA), class = "AsIs")
and B: I(inc/csout)
dput(head(B,600))
structure(c(NA, NA, 1.6175, 1.446, 0.9995, 1.999, 2.902, 3.657,
3.96875, 4.10175, 4.0565, 3.44475, 5.7205, 61.93475, -4.37702127659574,
-5.48471405481619, 5.62880429175694, 2.48174176657021, 2.85056176051437,
2.67146609592373, 7.95068353136579, 10.4269755112345, 15.9375059090479,
1.96710464127044, 2.22664817849489, 3.33193683532066, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.46958661417323,
3.21177447300029, -1.28516110058166, 1.0933050087961, 2.73595610276559,
2.99856886650604, 2.66279506915451, -2.03313196852743, 1.54029613242514,
NA, NA, 1.00559289855816, 1.37544255734004, 1.9209810662425,
2.80281184257786, 4.19364769870183, 4.92729231874391, 2.12704602596336,
0.595823284929961, 2.11401303299297, 2.79331431759097, 2.4658012212017,
2.32582482425984, 0.346862332597876, 1.82379803991995, 2.08507363127918,
5.71682560206885, 5.63252814515248, 6.95230192325872, 5.36379165580937,
4.63755947383151, -2.23752373944775, 0.801826142613582, -4.05886251253207,
-0.0212111270744482, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.0609118506239777, -0.46619168322258,
-0.273575584586673, 0.0868731271713617, 0.121252977886906, 0.126940380912401,
0.116359932757648, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.295874906555075,
NA, NA, NA, NA, 2.71176375317173, 3.44976515683205, 4.48091561842034,
1.65520964549217, 1.65772899046248, 0.680583048407414, 1.98560374302681,
4.02555335612741, 4.50134453781513, 0.771768707482993, 1.87830508474576,
3.31905564924115, 0.308403361344538, 3.49909705036452, 2.85647576798639,
2.02752629476767, 5.49992524792771, 3.86932091296348, 2.40325224792595,
4.50212922173275, 2.72935574693607, 4.27727254986144, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.189564444444444,
-1.98851777777778, 0.0187844444444444, 0.112452682926829, 0.651501818181818,
0.916795915967128, 0.794619441852948, 0.35890755018202, -0.473067607413648,
0.627831250675921, 0.749854780188053, 1.53659424970853, NA, NA,
5.64291158536585, 5.99563953488372, 5.65225290697674, 5.26390820584145,
5.243, 6.82666666666667, 9.63833333333333, 8.50433333333333,
7.02166666666667, 7.61833333333333, 8.26466666666667, 9.84033333333333,
9.239, 8.05, 6.783, 7.82559143498532, 8.25334112069262, 11.5059848406901,
14.2409094021776, 11.2229529423791, -13.903429300047, -17.9612973562279,
14.619387755102, 17.706462585034, NA, NA, 2.478958140989, 2.18773270954903,
2.18862560772722, -1.69130819883693, -13.3982068926821, 1.07011419816462,
1.02272906761858, -4.90830977239342, 4.93192336244886, 2.72619047619048,
3.58730158730159, 2.49220723035908, -14.3167028199566, -10.2029468004578,
-2.79564571994062, 1.0112571815929, 0.781369908886687, 1.00884137370567,
-0.577493454139731, 0.929452560968677, 0.701576822662082, 1.03605050601517,
0.8831933500736, 0.536452684873194, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0.492431541310724, 6.37958532695375, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1.18508474576271, 1.9678510998308, 2.43498498498498,
3.00329835082459, 3.58538211954771, 2.81853600059939, 2.51332082551595,
2.81547169811321, 2.32570467867258, 2.98696865802967, 3.53706989369607,
4.81367137790562, 4.3791117807647, 3.42566215683504, 2.10174099052259,
0.698447811272854, 1.15344409599431, NA, NA, NA, NA, NA, NA,
NA, NA, 14.96, 16.48, 23.5307692307692, 26.2953846153846, 29.1553846153846,
34.8107692307692, 34.1, 31.7707692307692, 35.66, 42.9875968992248,
50.9162913495884, 60.8719388550928, 73.0775328999524, 75.8763564616902,
81.3488525672775, 101.094264996704, 84.6151321188249, 99.3965234503116,
NA, NA, 2.3167169493513, 2.94633564772966, 3.08802835253036,
2.72227364294659, 1.88046287028157, 2.54218477118243, 2.86466949503918,
2.7850953423992, 3.71485649429788, 3.8251978743918, 3.59200878943474,
3.45198192946663, 4.8729674796748, 3.45141240082874, 2.10217797766436,
2.71891375276011, 2.53216008085335, 5.6740159729317, 7.12959606279318,
3.25700635087106, -9.72848624269758, 2.98453249387051, 0.15134546114962,
-0.30269092229924, NA, NA, 2.53928767123288, 2.5918904109589,
1.72054794520548, 3.37534246575342, 3.072, 2.37922077922078,
3.39479905437352, 4.66193853427896, 5.6548463356974, -6.70449172576832,
5.53191489361702, 7.55555555555556, 4.68932038834951, -0.738461538461539,
3.79185986604843, 5.57274273187322, 8.79127611345953, 9.6938568354379,
17.2664485641585, 6.76008236653106, -10.0433576656954, 2.44686800894855,
12.7916783806579, 15.9669198174039, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.89339789933161,
4.54057279236277, 3.04295028677116, 3.52709814421568, 3.94625587429463,
0.843121058173297, NA, NA, 1.16633333333333, 1.18216666666667,
1.15616666666667, 0.718833333333333, 0.846666666666667, 0.949833333333333,
0.855, 0.947, 1.362, 1.29283333333333, -0.4095, -1.71739130434783,
-6.73979933110368, -1.07073578595318, 0.441442953020134, 0.874917491749175,
1.15237157924115, 0.816345803596724, 2.04858840018688, 1.40567608276299,
-5.14637884112347, 2.72871855038456, 2.91316668996454, 2.59330191769775,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.880985426787,
9.24615559858754, 0.0919749573136027, 1.81008766936127, 4.28461978273299,
4.25916740682639, 8.24690457134787, -2.3184854446709, 7.19083897445574,
1.46494850253928, -1.64066137408225, 2.12416777231924, -14.2285966776933,
-13.1682814768967, NA, NA, 121.211764705882, 115.623529411765,
210, 224.705882352941, 227.428571428571, 211.666666666667, 217.435897435897,
228.292682926829, 244.878048780488, 285, 316.818181818182, 349.54128440367,
419.724770642202, 467.43119266055, 561.467889908257, 683.48623853211,
788.224554997718, 947.416137805984, 1123.31838565022, 1157.59185480301,
851.035698545615, 1060.4997807979, 1083.99296394019, 1079.33010136624,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5.07169287696577,
-8.00416281221091, -10.8834412580944, 7.80989824236818, -6.48704902867715,
-27.7520814061055, 2.01320132013201, 4.82440240172199, 3.09241756831886,
4.76328138092844, -8.83881012865689, -3.3861849659924, 0.0263111111111111,
-0.84864238294285, 2.13807997770656, NA, NA, NA, NA, NA, NA,
Inf, 1.13667965939339, 1.16978683393883, 1.51189430424169, 1.93641281650334,
3.18037779732132, 3.60374959261304, 2.95168386947919, -7.1346652146591,
-3.73879239931005, 0.618398531789007, 0.302060049537848, 1.15002820202747,
0.574163266612055, 0.472550297072245, -0.122848990949101, -0.853532127125251,
0.849206303333824, 0.831485168572279, 0.722226135762859, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 4.87792789880837, 4.00254595667558, 3.88103599648815,
3.94239575801398, 2.22077609062425, 1.36361444621245, 2.33424853766265,
NA, NA, NA, NA, NA, NA, NA, 2.1797520661157, 1.37809917355372,
5.96900826446281, 9.65909090909091, 8.328, 12.8633540372671,
21.9853556485356, 22.1177685950413, 12.6962809917355, 2.75359342915811,
4.94607660840461, 12.5518178729188, 4.10974995804665, 8.56844305120167,
11.0425844346549, -16.8586942126871, 9.88777396355955, 13.2233035244608,
7.5351552135845, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, -1.47473913043478, 0.508901960784314,
-0.0619107551487414, 0.0142787579128741, 1.07581896303132, 0.445689416361701,
0.34090023049512, 0.649568445989942, 1.70371965372768, NA, NA
), class = "AsIs")