plot time - variable values ggplot2

tidyverse
,
1

Hello!
I have used reprex() but it is difficult to attach my database so I explained previous tidy-actions made

I have a large database created for years in which all measured biological variables are following this notation

measured_variable_ 01 - basal
measured_variable_02 - 2 months
measured_variable_03 - 12 months
measured_variable_04 - 24 months
measured_variable_05 - 36 months

Furthermore I have age and ID variable unique for patient and 2 main factors(sex and diet type)

image
image792×417 18.4 KB

I have a manipulated database in wihich I included time as continuous variable with the intention of plotting it in x-axis for observing possible interactions with time. I intetend to create a scatterplot in y-axis with the measured variables along time

I have several problems with the plots production

1 - Obviously this is not working. Maybe paired values (time_basal, measured_variable_01) , (time_02, measured_variable_02) .... but I did not find anything like this

ggplot(subset_time_horiz, aes(time, c(oxldl01, oxldl02, oxldl03, oxldl04, oxldl05), colour = grup_int)) + geom_point + geom_line
#> Error in ggplot(subset_time_horiz, aes(time, c(oxldl01, oxldl02, oxldl03, : no se pudo encontrar la función "ggplot"

2 - I cannot group measured_variables by patient ID as if they were a cluster of measures along time and connect the in the plot
I got no code in this item

Thank you so much in advance

2

For problem 1, have you loaded the ggplot2 package? Try

library(ggplot2

Otherwise can you post all of your code and some sample data? A handy way to supply sample data is to use the dput() function. See ?dput. If you have a very large data set then something like head(dput(myfile), 100) will likely supply enough data for us to work with.

3

Yes ggplot2 is loaded. I have tried several times before. It is not working,

I have reduced my database for the manipulations, but I'll come back to the orginal

I guess you mean dput(head(mydata, 100))

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43.58, NA, NA, NA, NA, 74.6, NA, NA, NA, NA, 84.68, NA, NA,
NA, NA, 60.27, NA, NA, NA, NA, 70.07, NA, NA, NA, NA, 78.73,
NA, NA, NA, NA, 54.74, NA, NA, NA, NA, 101.7, NA, NA, NA,
NA, 91.72, NA, NA, NA, NA, 60.85, NA, NA, NA, NA, 44.47,
NA, NA, NA, NA, 71.45, NA, NA), oxldl04 = c(NA, NA, NA, 55.06,
NA, NA, NA, NA, 104.94, NA, NA, NA, NA, 76, NA, NA, NA, NA,
59.31, NA, NA, NA, NA, 52.75, NA, NA, NA, NA, 83.61, NA,
NA, NA, NA, 64.2, NA, NA, NA, NA, 67.17, NA, NA, NA, NA,
77.04, NA, NA, NA, NA, 74.6, NA, NA, NA, NA, 99.2, NA, NA,
NA, NA, 65.32, NA, NA, NA, NA, 46.98, NA, NA, NA, NA, 69.71,
NA, NA, NA, NA, 38.98, NA, NA, NA, NA, 106.67, NA, NA, NA,
NA, 57.39, NA, NA, NA, NA, 18.52, NA, NA, NA, NA, 45.77,
NA, NA, NA, NA, 59.45, NA), oxldl05 = c(NA, NA, NA, NA, 55.21,
NA, NA, NA, NA, 76.49, NA, NA, NA, NA, 57.72, NA, NA, NA,
NA, 59.8, NA, NA, NA, NA, 45.86, NA, NA, NA, NA, 32.37, NA,
NA, NA, NA, 46.27, NA, NA, NA, NA, 87.4, NA, NA, NA, NA,
71.71, NA, NA, NA, NA, 94.5, NA, NA, NA, NA, 54.51, NA, NA,
NA, NA, 30.12, NA, NA, NA, NA, 56.82, NA, NA, NA, NA, 54.15,
NA, NA, NA, NA, 39.94, NA, NA, NA, NA, 99.48, NA, NA, NA,
NA, 19.87, NA, NA, NA, NA, 35.41, NA, NA, NA, NA, 67.29,
NA, NA, NA, NA, 48.49)), row.names = c(NA, 100L), class = "data.frame"

I show the str()

data.frame': 335 obs. of 30 variables:
id : num 50202067 50202067 50202067 50202067 50202067 ... sexo : chr "mujer" "mujer" "mujer" "mujer" ...
grup_int: chr "dieta baja en grasa" "dieta baja en grasa" "dieta baja en grasa" "dieta baja en grasa" ... edad0 : num 72 72 72 72 72 70 70 70 70 70 ...
time : num 0 2 12 24 36 0 2 12 24 36 ... ca_col_1: num 181 NA NA NA NA 212 NA NA NA NA ...
ca_col_3: num NA NA 184 NA NA NA NA 180 NA NA ... ca_col_4: num NA NA NA 187 NA NA NA NA NA NA ...
ca_col_5: num NA NA NA NA 186 NA NA NA NA NA ... ca_hdl_1: num 45.8 NA NA NA NA ...
ca_hdl_3: num NA NA 48.2 NA NA ... ca_hdl_4: num NA NA NA 45 NA ...
ca_hdl_5: num NA NA NA NA 46.6 ... ca_tg_1 : num 184 NA NA NA NA ...
ca_tg_3 : num NA NA 141 NA NA ... ca_tg_4 : num NA NA NA 108 NA ...
ca_tg_5 : num NA NA NA NA 110 ... icoles02: num NA 180 NA NA NA NA 198 NA NA NA ...
ihdl02 : num NA 47.7 NA NA NA ... itrig02 : num NA 179 NA NA NA NA 119 NA NA NA ...
ldlcalc1: num 98.3 NA NA NA NA ... ldlcalc2: num NA 96.5 NA NA NA ...
ldlcalc3: num NA NA 108 NA NA ... ldlcalc4: num NA NA NA 120 NA ...
ldlcalc5: num NA NA NA NA 117 ... oxldl01 : num 42.1 NA NA NA NA ...
oxldl02 : num NA 41.7 NA NA NA ... oxldl03 : num NA NA 75.5 NA NA ...
oxldl04 : num NA NA NA 55.1 NA ... oxldl05 : num NA NA NA NA 55.2 ...

4

Great, thanks for the data. You have some syntax mistakes, for example you cannot say

 geom_point + geom_line

It must be

 geom_point() + geom_line()

Also you need to rearrange your data from wide to long format. I have done a quick and dirty example that will actually give you a plot but I do not think it is what you want but it is late here and I must go.

What I have done is call your sample data dat1 created a subset of the variables, called dat2and then transformed them into a long format dat3 using the reshape2 package and then graphed them. As I said I don't think I have it correct but it may help a bit.

library(tidyverse) 
library(reshape2)
dat2 <-   dat1[, c("time", "oxldl01", "oxldl02", "oxldl03", "oxldl04", "oxldl05")]

dat3 <-   melt(dat2, id.vars = "time")

ggplot(dat3, aes(time, value, colour = variable)) +
       geom_point() + geom_line()

By the way your paste of dput() missed the last )

dat1 <-   structure(list(id = c(50202067, 50202067, 50202067, 50202067, 
50202067, 60101001, 60101001, 60101001, 60101001, 60101001, 60101002, 
60101002, 60101002, 60101002, 60101002, 60101003, 60101003, 60101003, 
60101003, 60101003, 60101008, 60101008, 60101008, 60101008, 60101008, 
60101010, 60101010, 60101010, 60101010, 60101010, 60101011, 60101011, 
60101011, 60101011, 60101011, 60101012, 60101012, 60101012, 60101012, 
60101012, 60101013, 60101013, 60101013, 60101013, 60101013, 60101014, 
60101014, 60101014, 60101014, 60101014, 60101015, 60101015, 60101015, 
60101015, 60101015, 60101016, 60101016, 60101016, 60101016, 60101016, 
60101017, 60101017, 60101017, 60101017, 60101017, 60101018, 60101018, 
60101018, 60101018, 60101018, 60102001, 60102001, 60102001, 60102001, 
60102001, 60102004, 60102004, 60102004, 60102004, 60102004, 60102005, 
60102005, 60102005, 60102005, 60102005, 60102006, 60102006, 60102006, 
60102006, 60102006, 60102007, 60102007, 60102007, 60102007, 60102007, 
60102009, 60102009, 60102009, 60102009, 60102009), sexo = c("mujer", 
"mujer", "mujer", "mujer", "mujer", "hombre", "hombre", "hombre", 
"hombre", "hombre", "hombre", "hombre", "hombre", "hombre", "hombre", 
"mujer", "mujer", "mujer", "mujer", "mujer", "mujer", "mujer", 
"mujer", "mujer", "mujer", "mujer", "mujer", "mujer", "mujer", 
"mujer", "mujer", "mujer", "mujer", "mujer", "mujer", "hombre", 
"hombre", "hombre", "hombre", "hombre", "hombre", "hombre", "hombre", 
"hombre", "hombre", "mujer", "mujer", "mujer", "mujer", "mujer", 
"mujer", "mujer", "mujer", "mujer", "mujer", "hombre", "hombre", 
"hombre", "hombre", "hombre", "mujer", "mujer", "mujer", "mujer", 
"mujer", "hombre", "hombre", "hombre", "hombre", "hombre", "hombre", 
"hombre", "hombre", "hombre", "hombre", "hombre", "hombre", "hombre", 
"hombre", "hombre", "mujer", "mujer", "mujer", "mujer", "mujer", 
"hombre", "hombre", "hombre", "hombre", "hombre", "hombre", "hombre", 
"hombre", "hombre", "hombre", "mujer", "mujer", "mujer", "mujer", 
"mujer"), grup_int = c("dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"frutos secos", "frutos secos", "frutos secos", "frutos secos", 
"frutos secos", "frutos secos", "frutos secos", "frutos secos", 
"frutos secos", "frutos secos", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "frutos secos", 
"frutos secos", "frutos secos", "frutos secos", "frutos secos", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "dieta baja en grasa", "dieta baja en grasa", 
"dieta baja en grasa", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "frutos secos", "frutos secos", "frutos secos", 
"frutos secos", "frutos secos", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva", 
"aceite de oliva", "aceite de oliva", "aceite de oliva", "aceite de oliva"
), edad0 = c(72, 72, 72, 72, 72, 70, 70, 70, 70, 70, 66, 66, 
66, 66, 66, 71, 71, 71, 71, 71, 67, 67, 67, 67, 67, 71, 71, 71, 
71, 71, 76, 76, 76, 76, 76, 61, 61, 61, 61, 61, 72, 72, 72, 72, 
72, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 61, 61, 61, 61, 61, 
59, 59, 59, 59, 59, 68, 68, 68, 68, 68, 65, 65, 65, 65, 65, 59, 
59, 59, 59, 59, 64, 64, 64, 64, 64, 68, 68, 68, 68, 68, 64, 64, 
64, 64, 64, 60, 60, 60, 60, 60), time = c(0, 2, 12, 24, 36, 0, 
2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 
24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 
0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 
24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 
0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 24, 36, 0, 2, 12, 
24, 36, 0, 2, 12, 24, 36), ca_col_1 = c(181, NA, NA, NA, NA, 
212, NA, NA, NA, NA, 165, NA, NA, NA, NA, 212, NA, NA, NA, NA, 
173, NA, NA, NA, NA, 163, NA, NA, NA, NA, 165, NA, NA, NA, NA, 
216, NA, NA, NA, NA, 157, NA, NA, NA, NA, 237, NA, NA, NA, NA, 
209, NA, NA, NA, NA, 172, NA, NA, NA, NA, 184, NA, NA, NA, NA, 
179, NA, NA, NA, NA, 196, NA, NA, NA, NA, 277, NA, NA, NA, NA, 
235, NA, NA, NA, NA, 159, NA, NA, NA, NA, 174, NA, NA, NA, NA, 
210, NA, NA, NA, NA), ca_col_3 = c(NA, NA, 184, NA, NA, NA, NA, 
180, NA, NA, NA, NA, 148, NA, NA, NA, NA, 180, NA, NA, NA, NA, 
180, NA, NA, NA, NA, 161, NA, NA, NA, NA, 171, NA, NA, NA, NA, 
190, NA, NA, NA, NA, 154, NA, NA, NA, NA, 198, NA, NA, NA, NA, 
182, NA, NA, NA, NA, 168, NA, NA, NA, NA, 223, NA, NA, NA, NA, 
200, NA, NA, NA, NA, 216, NA, NA, NA, NA, 252, NA, NA, NA, NA, 
194, NA, NA, NA, NA, 163, NA, NA, NA, NA, 159, NA, NA, NA, NA, 
231, NA, NA), ca_col_4 = c(NA, NA, NA, 187, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 174, NA, NA, NA, NA, 194, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 180, NA, NA, NA, NA, NA, NA, NA, NA, NA, 177, NA, 
NA, NA, NA, 171, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 172, NA), ca_col_5 = c(NA, 
NA, NA, NA, 186, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 178, NA, NA, NA, NA, 160, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 167, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 203, NA, NA, 
NA, NA, 261, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 154, NA, NA, NA, NA, 211), ca_hdl_1 = c(45.77, NA, NA, 
NA, NA, 46.65, NA, NA, NA, NA, 39.79, NA, NA, NA, NA, 45.33, 
NA, NA, NA, NA, 40.82, NA, NA, NA, NA, 41.01, NA, NA, NA, NA, 
59.15, NA, NA, NA, NA, 40.82, NA, NA, NA, NA, 53.89, NA, NA, 
NA, NA, 50.13, NA, NA, NA, NA, 54.55, NA, NA, NA, NA, 44.68, 
NA, NA, NA, NA, 44.39, NA, NA, NA, NA, 34.71, NA, NA, NA, NA, 
56.71, NA, NA, NA, NA, 47.68, NA, NA, NA, NA, 60.09, NA, NA, 
NA, NA, 42.8, NA, NA, NA, NA, 40.73, NA, NA, NA, NA, 61.88, NA, 
NA, NA, NA), ca_hdl_3 = c(NA, NA, 48.17, NA, NA, NA, NA, 41.48, 
NA, NA, NA, NA, 34.99, NA, NA, NA, NA, 45.43, NA, NA, NA, NA, 
45.24, NA, NA, NA, NA, 36.4, NA, NA, NA, NA, 53.42, NA, NA, NA, 
NA, 45.99, NA, NA, NA, NA, 54.08, NA, NA, NA, NA, 50, NA, NA, 
NA, NA, 45.15, NA, NA, NA, NA, 47.59, NA, NA, NA, NA, 47.21, 
NA, NA, NA, NA, 32.08, NA, NA, NA, NA, 58.02, NA, NA, NA, NA, 
47.5, NA, NA, NA, NA, 56.71, NA, NA, NA, NA, 49.75, NA, NA, NA, 
NA, 37.44, NA, NA, NA, NA, 56.43, NA, NA), ca_hdl_4 = c(NA, NA, 
NA, 44.97, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 34.43, NA, NA, NA, 
NA, 58.49, NA, NA, NA, NA, NA, NA, NA, NA, NA, 54.17, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 43.17, NA, NA, NA, NA, 47.12, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 58.21, NA), ca_hdl_5 = c(NA, NA, NA, 
NA, 46.57, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 48.62, NA, NA, NA, NA, 48.15, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 47.31, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 56.71, NA, NA, NA, 
NA, 47.31, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 36.4, NA, NA, NA, NA, 62.63), ca_tg_1 = c(184.406734702019, 
NA, NA, NA, NA, 114, NA, NA, NA, NA, 141, NA, NA, NA, NA, 119, 
NA, NA, NA, NA, 96, NA, NA, NA, NA, 237, NA, NA, NA, NA, 166, 
NA, NA, NA, NA, 128, NA, NA, NA, NA, 74, NA, NA, NA, NA, 178, 
NA, NA, NA, NA, 268, NA, NA, NA, NA, 105, NA, NA, NA, NA, 121, 
NA, NA, NA, NA, 167, NA, NA, NA, NA, 85, NA, NA, NA, NA, 212, 
NA, NA, NA, NA, 127, NA, NA, NA, NA, 86, NA, NA, NA, NA, 112, 
NA, NA, NA, NA, 92, NA, NA, NA, NA), ca_tg_3 = c(NA, NA, 141.290108444337, 
NA, NA, NA, NA, 67, NA, NA, NA, NA, 121, NA, NA, NA, NA, 91, 
NA, NA, NA, NA, 103, NA, NA, NA, NA, 130, NA, NA, NA, NA, 142, 
NA, NA, NA, NA, 101, NA, NA, NA, NA, 84, NA, NA, NA, NA, 109, 
NA, NA, NA, NA, 167, NA, NA, NA, NA, 122, NA, NA, NA, NA, 187, 
NA, NA, NA, NA, 312, NA, NA, NA, NA, 81, NA, NA, NA, NA, 149, 
NA, NA, NA, NA, 92, NA, NA, NA, NA, 53, NA, NA, NA, NA, 131, 
NA, NA, NA, NA, 140, NA, NA), ca_tg_4 = c(NA, NA, NA, 107.893970823895, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 168, NA, NA, NA, NA, 145, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 62.9, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 140, NA, NA, NA, NA, 76, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 48, NA), ca_tg_5 = c(NA, NA, NA, NA, 109.75957654523, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 94.9, NA, 
NA, NA, NA, 97.2, 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, 100.9, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 65.8, NA, NA, NA, NA, 80, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 105.9, NA, NA, 
NA, NA, 58.4), icoles02 = c(NA, 180, NA, NA, NA, NA, 198, NA, 
NA, NA, NA, 183, NA, NA, NA, NA, 212, NA, NA, NA, NA, 179, NA, 
NA, NA, NA, 188, NA, NA, NA, NA, 189, NA, NA, NA, NA, 189, NA, 
NA, NA, NA, 157, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 176, NA, NA, NA, NA, NA, NA, NA, NA, NA, 188, NA, 
NA, NA, NA, 199, NA, NA, NA, NA, 246, NA, NA, NA, NA, 185, NA, 
NA, NA, NA, 157, NA, NA, NA, NA, 173, NA, NA, NA, NA, 227, NA, 
NA, NA), ihdl02 = c(NA, 47.68, NA, NA, NA, NA, 44.86, NA, NA, 
NA, NA, 42.89, NA, NA, NA, NA, 49, NA, NA, NA, NA, 44.02, NA, 
NA, NA, NA, 36.87, NA, NA, NA, NA, 62.72, NA, NA, NA, NA, 44.77, 
NA, NA, NA, NA, 53.98, NA, NA, NA, NA, NA, NA, NA, NA, NA, 40.73, 
NA, NA, NA, NA, 48.53, NA, NA, NA, NA, NA, NA, NA, NA, NA, 31.8, 
NA, NA, NA, NA, 54.73, NA, NA, NA, NA, 52.85, NA, NA, NA, NA, 
52.1, NA, NA, NA, NA, 42.14, NA, NA, NA, NA, 40.45, NA, NA, NA, 
NA, 58.31, NA, NA, NA), itrig02 = c(NA, 179, NA, NA, NA, NA, 
119, NA, NA, NA, NA, 147, NA, NA, NA, NA, 122, NA, NA, NA, NA, 
87, NA, NA, NA, NA, 163, NA, NA, NA, NA, 116, NA, NA, NA, NA, 
84, NA, NA, NA, NA, 73, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 74, NA, NA, NA, NA, NA, NA, NA, NA, NA, 187, 
NA, NA, NA, NA, 81, NA, NA, NA, NA, 130, NA, NA, NA, NA, 95, 
NA, NA, NA, NA, 75, NA, NA, NA, NA, 88, NA, NA, NA, NA, 92, NA, 
NA, NA), ldlcalc1 = c(98.3486530595962, NA, NA, NA, NA, 142.55, 
NA, NA, NA, NA, 97.01, NA, NA, NA, NA, 142.87, NA, NA, NA, NA, 
112.98, NA, NA, NA, NA, 74.59, NA, NA, NA, NA, 72.65, NA, NA, 
NA, NA, 149.58, NA, NA, NA, NA, 88.31, NA, NA, NA, NA, 151.27, 
NA, NA, NA, NA, 100.85, NA, NA, NA, NA, 106.32, NA, NA, NA, NA, 
115.41, NA, NA, NA, NA, 110.89, NA, NA, NA, NA, 122.29, NA, NA, 
NA, NA, 186.92, NA, NA, NA, NA, 149.51, NA, NA, NA, NA, 99, NA, 
NA, NA, NA, 110.87, NA, NA, NA, NA, 129.72, NA, NA, NA, NA), 
    ldlcalc2 = c(NA, 96.52, NA, NA, NA, NA, 129.34, NA, NA, NA, 
    NA, 110.71, NA, NA, NA, NA, 138.6, NA, NA, NA, NA, 117.58, 
    NA, NA, NA, NA, 118.53, NA, NA, NA, NA, 103.08, NA, NA, NA, 
    NA, 127.43, NA, NA, NA, NA, 88.42, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 112.67, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 118.8, NA, NA, NA, NA, 128.07, NA, NA, NA, 
    NA, 167.15, NA, NA, NA, NA, 113.9, NA, NA, NA, NA, 99.86, 
    NA, NA, NA, NA, 114.95, NA, NA, NA, NA, 150.29, NA, NA, NA
    ), ldlcalc3 = c(NA, NA, 107.571978311133, NA, NA, NA, NA, 
    125.12, NA, NA, NA, NA, 88.81, NA, NA, NA, NA, 116.37, NA, 
    NA, NA, NA, 114.16, NA, NA, NA, NA, 98.6, NA, NA, NA, NA, 
    89.18, NA, NA, NA, NA, 123.81, NA, NA, NA, NA, 83.12, NA, 
    NA, NA, NA, 126.2, NA, NA, NA, NA, 103.45, NA, NA, NA, NA, 
    96.01, NA, NA, NA, NA, 138.39, NA, NA, NA, NA, 105.52, NA, 
    NA, NA, NA, 141.78, NA, NA, NA, NA, 174.7, NA, NA, NA, NA, 
    118.89, NA, NA, NA, NA, 102.65, NA, NA, NA, NA, 95.36, NA, 
    NA, NA, NA, 146.57, NA, NA), ldlcalc4 = c(NA, NA, NA, 120.451205835221, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, 105.97, NA, NA, NA, NA, 
    106.51, NA, NA, NA, NA, NA, NA, NA, NA, NA, 113.25, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 105.83, NA, NA, NA, NA, 108.68, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, 104.19, NA), ldlcalc5 = c(NA, 
    NA, NA, NA, 117.478084690954, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 110.4, NA, NA, NA, NA, 92.41, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 99.51, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, 133.13, NA, NA, NA, NA, 197.69, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 96.42, NA, 
    NA, NA, NA, 136.69), oxldl01 = c(42.11, NA, NA, NA, NA, 121.12, 
    NA, NA, NA, NA, 52.93, NA, NA, NA, NA, 73.81, NA, NA, NA, 
    NA, 62.49, NA, NA, NA, NA, 34.34, NA, NA, NA, NA, 67.97, 
    NA, NA, NA, NA, 114.03, NA, NA, NA, NA, 47.28, NA, NA, NA, 
    NA, 59.99, NA, NA, NA, NA, 93.03, NA, NA, NA, NA, 73.42, 
    NA, NA, NA, NA, 92.18, NA, NA, NA, NA, 90.61, NA, NA, NA, 
    NA, 85.16, NA, NA, NA, NA, 78.4, NA, NA, NA, NA, 103.93, 
    NA, NA, NA, NA, 44.23, NA, NA, NA, NA, 45.94, NA, NA, NA, 
    NA, 65.41, NA, NA, NA, NA), oxldl02 = c(NA, 41.7, NA, NA, 
    NA, NA, 44.25, NA, NA, NA, NA, 15.56, NA, NA, NA, NA, 105.89, 
    NA, NA, NA, NA, 57.9, NA, NA, NA, NA, 132.27, NA, NA, NA, 
    NA, 73.02, NA, NA, NA, NA, 69.74, NA, NA, NA, NA, 16.16, 
    NA, NA, NA, NA, 76.63, NA, NA, NA, NA, 81.68, NA, NA, NA, 
    NA, 36.32, NA, NA, NA, NA, 61.32, NA, NA, NA, NA, 74.4, NA, 
    NA, NA, NA, 92.18, NA, NA, NA, NA, 50.49, NA, NA, NA, NA, 
    98.09, NA, NA, NA, NA, 61, NA, NA, NA, NA, 98.48, NA, NA, 
    NA, NA, 60.33, NA, NA, NA), oxldl03 = c(NA, NA, 75.45, NA, 
    NA, NA, NA, 81.03, NA, NA, NA, NA, 55.19, NA, NA, NA, NA, 
    56.31, NA, NA, NA, NA, 57.89, NA, NA, NA, NA, 68.82, NA, 
    NA, NA, NA, 60.54, NA, NA, NA, NA, 72.63, NA, NA, NA, NA, 
    43.58, NA, NA, NA, NA, 74.6, NA, NA, NA, NA, 84.68, NA, NA, 
    NA, NA, 60.27, NA, NA, NA, NA, 70.07, NA, NA, NA, NA, 78.73, 
    NA, NA, NA, NA, 54.74, NA, NA, NA, NA, 101.7, NA, NA, NA, 
    NA, 91.72, NA, NA, NA, NA, 60.85, NA, NA, NA, NA, 44.47, 
    NA, NA, NA, NA, 71.45, NA, NA), oxldl04 = c(NA, NA, NA, 55.06, 
    NA, NA, NA, NA, 104.94, NA, NA, NA, NA, 76, NA, NA, NA, NA, 
    59.31, NA, NA, NA, NA, 52.75, NA, NA, NA, NA, 83.61, NA, 
    NA, NA, NA, 64.2, NA, NA, NA, NA, 67.17, NA, NA, NA, NA, 
    77.04, NA, NA, NA, NA, 74.6, NA, NA, NA, NA, 99.2, NA, NA, 
    NA, NA, 65.32, NA, NA, NA, NA, 46.98, NA, NA, NA, NA, 69.71, 
    NA, NA, NA, NA, 38.98, NA, NA, NA, NA, 106.67, NA, NA, NA, 
    NA, 57.39, NA, NA, NA, NA, 18.52, NA, NA, NA, NA, 45.77, 
    NA, NA, NA, NA, 59.45, NA), oxldl05 = c(NA, NA, NA, NA, 55.21, 
    NA, NA, NA, NA, 76.49, NA, NA, NA, NA, 57.72, NA, NA, NA, 
    NA, 59.8, NA, NA, NA, NA, 45.86, NA, NA, NA, NA, 32.37, NA, 
    NA, NA, NA, 46.27, NA, NA, NA, NA, 87.4, NA, NA, NA, NA, 
    71.71, NA, NA, NA, NA, 94.5, NA, NA, NA, NA, 54.51, NA, NA, 
    NA, NA, 30.12, NA, NA, NA, NA, 56.82, NA, NA, NA, NA, 54.15, 
    NA, NA, NA, NA, 39.94, NA, NA, NA, NA, 99.48, NA, NA, NA, 
    NA, 19.87, NA, NA, NA, NA, 35.41, NA, NA, NA, NA, 67.29, 
    NA, NA, NA, NA, 48.49)), row.names = c(NA, 100L), class = "data.frame")
5

Along the lines of jrkrideaus' suggestions, I think you can restructure your data to get this plot to work nicely with the help of a few packages.

# for plotting
library(ggplot2)
# for melting
library(reshape2)
# for updating
library(dplyr)

First, let's do some clean-up:

new_data <- subset_time_horiz %>%
  # Melt the data, but keep sample information as id.vars
  melt(id.vars = c("id","sexo","edad0","grup_int","time"),
       variable.name = "medicion") %>%
  # Remove missing values
  filter(!is.na(value)) %>%
  # Make a new column for assay without separation by which measurement
  # Helpful for grouping later
  mutate(ensayo = sub("[0-9|_]+$","",medicion))

Let's check out what we have now:

> head(new_data)
        id   sexo edad0            grup_int time medicion value ensayo
1 50202067  mujer    72 dieta baja en grasa    0 ca_col_1   181 ca_col
2 60101001 hombre    70     aceite de oliva    0 ca_col_1   212 ca_col
3 60101002 hombre    66     aceite de oliva    0 ca_col_1   165 ca_col
4 60101003  mujer    71     aceite de oliva    0 ca_col_1   212 ca_col
5 60101008  mujer    67     aceite de oliva    0 ca_col_1   173 ca_col
6 60101010  mujer    71        frutos secos    0 ca_col_1   163 ca_col

Now, let's plot one of your measurements, oxldl:

oxldl_data <- new_data %>%
  filter(ensayo == "oxldl")

With this, we can generate a plot that connects each subject by id using geom_point() and geom_line():

ggplot(oxldl_data) + 
  geom_point(aes(x = time,
                 y = value,
                 colour = grup_int)) + 
  geom_line(aes(x = time,
                y = value,
                color = grup_int,
                group = id)) +
  scale_y_continuous("OXLDL") +
  scale_x_continuous("Epoca (Meses)")

Alternately, we can make a whole panel of plots using facet_wrap() and the whole new_data df:

ggplot(new_data) + 
  geom_point(aes(x = time,
                 y = value,
                 colour = grup_int)) + 
  geom_line(aes(x = time,
                y = value,
                color = grup_int,
                group = id)) +
  scale_y_continuous("Valor") +
  scale_x_continuous("Time (Meses)") +
  facet_wrap(~ ensayo, 
             ncol = 2,
             scale = "free_y")

Apologies if the language is not correct! I tried to match based on some of the columns (and guessed Spanish), but am not a Spanish speaker.

6

Lovely.

I knew I was wrong and I totally missed the need for

  melt(id.vars = c("id","sexo","edad0","grup_int","time")

I thought it was a syntax error. How to be embarrassed.

7

Incredible. Thank you so much. The Spanish is totally perfect
I guess I need to learn a lot about data manipulation

  mutate(ensayo = sub("[0-9|_]+$","",medicion))

I do not really understand this code ( "[0-9|_]" function? and "+ $" function?

If you can recommend me some material, because the classical books (ggplot2, R for data science ...) accomplish the first approach function. Afterwards I need something else

Thank u again

8

Thanks for all! @ lucasgraybuck has completed what you began

9

I have one more question because the data manipulation I have done previously, I did it in R, just one step that I did it manually in excel. This step is adding the time column and adding to the variable the correspondant time (following notation 01 - 0, 02 - 2 months, 03 - 12 months....)

The problem is when I repeteadly knit the RMarkdown document the dataset is not working because it has not the time column in the xlsx document imported. Because this step is not saved in the RStudio document, and xlsx is overwritten again and again

My question is, is it possible to add time column in R according the notation I followed?

10

I am not sure why this is happening, but rerunning the first part of the code I get the next error. However, the graphics remain correctly.

"Error in sub("[0-9|_]+$", "", medicion) : objeto 'medicion' no encontrado"

If I modify the last medicion and exchange it for "medicion", with the quoting, then the first graphic disappeared.

Thank you in advance

11

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