I have data with Utterances by speakers in conversation as well as their gazes to one another. The speakers' gazes are in columns A_aoi, B_aoi, and C_aoi, the gaze durations are in A_aoi_dur, B_aoi_dur, and C_aoi_dur. Here's a reproducible snippet of the data:
df0 <- structure(list(Line = c(105L, 106L, 107L, 109L, 110L, 111L, 112L,
113L, 114L, 115L, 116L), Speaker = c("ID01.A", NA, "ID01.A",
NA, "ID01.B", NA, "ID01.A", NA, "ID01.A", NA, "ID01.C"), Utterance = c("so you've ↑obviously↑ thought about it obviously: (.) have made a decision (.) I'm !head!ing in this door (.) one of the cleaning ladies at the UB !grabb!ed my elbow",
"(0.662)", "and said (.) ~no no no !this! is the !womens'! bathroom~=",
"(0.015)", "=((v: gasps))=", "(0.166)", "=NOW", "(0.622)", "!how! this always plays out ",
"(0.726)", "[when was] that¿="), UttStart = c(163898L, 172500L,
173162L, 176100L, 176115L, 176800L, 176966L, 177372L, 177994L,
179328L, 180054L), UttEnd = c(172500, 173162, 176100, 176115,
176800, 176966, 177372, 177994, 179328, 180054, 180668), UttDur = c(8602,
662, 2938, 15, 685, 166, 406, 622, 1334, 726, 614), A_aoi = c("*B*C*B*C*B*C*B*C*B*C",
"C*", "*B*C*C", "C", "C*", "*", "*C", "C", "C*B", "B*", "*"),
A_aoi_dur = c("21,516,79,333,200,634,233,651,17,2332,33,400,33,518,17,532,33,1900,119,1",
"414,248", "1124,412,116,533,600,153", "15", "616,69", "166",
"153,253", "622", "204,151,979", "219,507", "614"), B_aoi = c("A*A*A*A*A",
"A", "A", "A", "A", "A", "A", "A*", "*A*A", "A*A", "A*A"),
B_aoi_dur = c("475,130,567,137,1983,313,787,1400,2810", "662",
"2938", "15", "685", "166", "406", "398,224", "76,136,284,838",
"108,571,47", "116,270,228"), C_aoi = c("A", "A", "A*A*A",
"A", "A", "A", "A", "A*A", "A", "A*A", "A"), C_aoi_dur = c("8602",
"662", "1058,123,1300,144,313", "15", "685", "166", "406",
"264,351,7", "1334", "125,323,278", "614")), row.names = c(NA,
-11L), class = c("tbl_df", "tbl", "data.frame"))
EDIT: new test data with temporally overlapping Utterances:
df0 <- structure(list(Line = 137:145,
Speaker = c("ID01.A", "ID01.A-Q", NA, "ID01.A", "ID01.A-Q", "ID01.A-Q", "ID01.A-Q", "ID01.A-Q",NA),
Utterance = c("↑she gra:bs my elbow (.) I turn to !look! at her↑ and she's like ~this is a (.) womens' bathroom you can't go in there~",
"~this is a (.) womens' bathroom you can't go in there~", "(0.534)",
"and I'm like ~((silent f: blank stare))~ (.) and she didn't, she was just like ~you can't go in~ (.) I'm like ~I'm a !woman!~ she said ~no you're not you can't go in~",
"~((silent f: blank stare))~", "~you can't go in~", "~I'm a !woman!~",
"~no you're not you can't go in~", "(0.487)"),
UttStart = c(208845L, 211450L, 214136L, 214670L, 215409L, 218307L, 219235L, 220076L, 221368L),
UttEnd = c(214136, 214136, 214670, 221368, 217117, 219050, 219885, 221368, 221855),
UttDur = c(5291, 2686, 534, 6698, 1708, 743, 650, 1292, 487),
A_aoi = c("C*B*C*C*B*C*", "C*B*C*", "*B", "B*C*B*C*C*B*B", "C*B", "C*B", "*", "*B","B"),
A_aoi_dur = c("57,445,1100,135,199,333,866,302,832,33,468,521","530,302,832,33,468,521",
"144,390", "377,235,466,399,1268,132,268,132,433,6,716,1412,854","339,399,970", "73,6,664", "650", "438,854", "487"),
B_aoi = c("A*A","A", "A", "A*A*A*A*A*A", "A", "*A*A", "*A", "A*A", "A"),
B_aoi_dur = c("1691,121,3479", "2686", "534", "53,180,3333,134,253,280,203,534,1296,138,294",
"1708", "63,253,280,147", "405,245", "860,138,294", "487"),
C_aoi = c("A", "A", "A", "A*A", "A", "A*", "A", "A", "A"),
C_aoi_dur = c("5291", "2686", "534", "3766,734,2198",
"1708", "129,614", "650", "1292", "487")),
row.names = c(NA, -9L), class = c("tbl_df", "tbl", "data.frame"))
What I'd like to be able to visualize is who is looking at whom and for how long for each Utterance, roughly like in this schematic representation:
What I can do at present is plot the gazes on a minute-by-minute scale, but just the gazes - not the Utterances: Plotting gaze movements by multiple speakers in a single plot. Starting from the data as above, this can be achieved by multiple transformations (shown below) but the resulting plot does not feature the Utterances and it plots the gazes per minute, whereas I need the gazes per Utterance:
I'm fully aware that this is demanding a lot. Help with it is all the more appreciated.
# pivot_longer so that all gazes have their own row:
df0 <- df0 %>%
rename_with(~ str_c(., "_AOI"), ends_with("_aoi")) %>%
pivot_longer(cols = contains("_"),
names_to = c("Gaze_by", ".value"), #
names_pattern = "^(.*)_([^_]+$)"
) %>%
mutate(Gaze_by = sub("^(.).*", "\\1", Gaze_by)) %>%
mutate(AOI = str_replace_all(AOI, "(?<=.)(?=.)", ",")) %>%
separate_rows(c(AOI, dur), sep = ",", convert = TRUE)
# compute starttimes and endtimes for gazes:
df1 <- df0 %>%
group_by(Gaze_by) %>%
mutate(
end = cumsum(dur),
start = end - dur
)
View(df1)
# compute minutes:
df2 <- df1 %>%
mutate(
# which minute does the event start in?
minute_start = as.integer(start/60000),
# which minute does the event end in?
minute_end = as.integer(end/60000),
# does the event straddle a minute mark?
straddler = minute_end > minute_start)
View(df2)
# 1st subset of `df2`:
df2_A1 <- df2 %>%
# filter those rows that contain events straddling minute marks:
filter(straddler=="TRUE") %>%
# reduce the endtime to the exact minute mark:
mutate(end = minute_end*60000)
View(df2_A1)
# 2nd subset of `df2`:
df2_A2 <- df2 %>%
# filter those rows that contain events straddling minute marks:
filter(straddler=="TRUE") %>%
# reduce the starttime to the exact minute mark:
mutate(start = minute_end*60000)
View(df2_A2)
# 3rd subset of `df0`:
df2_A3 <- df2 %>%
# filter those rows that do not contain events straddling minute marks:
filter(!straddler == "TRUE")
View(df2_A3)
# row-bind all three subsets:
df4 <- rbind(df2_A1, df2_A2, df2_A3) %>%
arrange(start) %>%
mutate(
minute = as.integer(start/60000),
# reduce total starttimes to starttimes per minute:
start_pm = start - 60000*minute,
# reduce total endtimes to endtimes per minute:
end_pm = end - 60000*minute)
# plot gaze activity for **ALL** speakers:
df4 %>%
ggplot(aes(x = start_pm,
xend = end_pm,
y = minute + scale(as.numeric(as.factor(Gaze_by))) / 6,
yend = minute + scale(as.numeric(as.factor(Gaze_by))) / 6,
color = AOI)) +
# draw segments for AOI:
geom_segment(size = 2) +
# reverse y-axis scale:
scale_y_reverse(breaks = 0:max(df4$minute),
labels = paste(0:max(df4$minute), "min", " Gaze_by_A\n Gaze_by_B\n Gaze_by_C", sep = " "),
name = NULL) +
# define custom colors:
scale_colour_manual(values = c("*" = "lemonchiffon",
"A" = "darkorange",
"B" = "lawngreen",
"C" = "slateblue1")) +
# plot title:
labs(title = "Gaze activity") +
theme(axis.title.x.bottom = element_blank())
Here is a solution that gets close to what you are looking for, making use of facets. It also uses forcats::fct_reorder and stringr::str_wrap (which are both part of the tidyverse).
This also wraps any long utterances and keeps the x-scale the same for all facets, rather than allowing them to stretch to fill the width.
df4 %>%
mutate(#add text for y axis labels
Gaze_by = paste0("Gaze_by_", Gaze_by),
#reorder facet panels, add speaker at start, and wrap to 120 characters
Utterance = fct_reorder(str_wrap(paste0(substr(Speaker, 6, 6), ": ",
Utterance),
120),
start_pm),
#set a dummy end point for each utterance based on the longest one
max_x = UttStart - min(UttStart) + max(UttDur)) %>%
ggplot(aes(x = start_pm, xend = end_pm,
y = Gaze_by, yend = Gaze_by, #as discrete variable
color = AOI)) +
geom_segment(size = 3) +
geom_point(aes(x = max_x, y = Gaze_by), alpha = 0) + #plot invisible dummy end points
scale_y_discrete(name = NULL, limits = rev) + #rev to get A at the top
facet_wrap(~Utterance, scales = "free_x", ncol = 1) +
scale_colour_manual(values = c("*" = "lemonchiffon",
"A" = "darkorange",
"B" = "lawngreen",
"C" = "slateblue1")) +
labs(title = "Gaze activity") +
theme_minimal() + #removes a lot of lines etc
theme(strip.text = element_text(color = "blue", hjust = 0), #facet strip text
strip.background = element_rect(fill = "white", color = "white"),
axis.title.x.bottom = element_blank())
To cut the utterances into 4-second chunks, you can do something like this...
df4 %>% group_by(Utterance) %>%
#work out relative durations from start of utterance and create subutterances
mutate(relStart = start_pm - min(start_pm),
relEnd = end_pm - min(start_pm),
subNo = map2(relStart, relEnd, ~seq(.x %/% 4000, .y %/% 4000, 1))) %>%
unnest(subNo) %>% #expand one row per subutterance
mutate(Utterance = paste0(Utterance, " (#", subNo + 1, ")"), #add sub no
subStart = pmax(4000 * subNo, relStart), #limits on subUtt
subEnd = pmin(4000 * (subNo + 1), relEnd), #limits on subUtt
start_pm = min(start_pm) + subStart, #redefine start
end_pm = min(start_pm) + subEnd) %>% #redefine end
group_by(Utterance) %>% #regroup as Utterance has changed!
mutate(max_x = min(start_pm) + 4000) %>% #define dummy end points
ungroup() %>%
mutate(Gaze_by = paste0("Gaze_by_", Gaze_by),
Utterance = fct_reorder(str_wrap(paste0(substr(Speaker, 6, 6), ": ", Utterance),
120), start_pm)) %>%
ggplot(...) #...as per code above from this point
I would like to plot different rows as different lines in the same plot to illustrate the movements of the average development of 3 groups: All, Men and Women. However, I'm not getting one of the lines printed and the legend is not being filled with the rownames.
I'l be glad for a solution, either in matplot or in ggplot.
Thank you!
Code:
matplot(t(Market_Work), type = 'l', xaxt = 'n', xlab = "Time Period", ylab = "Average", main ="Market Work")
legend("right", legend = seq_len(nrow(Market_Work)), fill=seq_len(nrow(Market_Work)))
axis(1, at = 1:6, colnames(Market_Work))
Data:
2003-2005 2006-2008 2009-2010 2011-2013 2014-2016 2017-2018
All 31.48489 32.53664 30.41938 30.53870 31.15550 31.77960
Men 37.38654 38.16698 35.10247 35.65543 36.54855 36.72496
Women 31.48489 32.53664 30.41938 30.53870 31.15550 31.77960
> dput(Market_Work)
structure(list(`2003-2005` = c(31.4848853173555, 37.3865421137,
31.4848853173555), `2006-2008` = c(32.5366433161048, 38.1669798351148,
32.5366433161048), `2009-2010` = c(30.4193794808191, 35.1024661973137,
30.4193794808191), `2011-2013` = c(30.5387012166381, 35.6554329405739,
30.5387012166381), `2014-2016` = c(31.1555032381292, 36.5485451138792,
31.1555032381292), `2017-2018` = c(31.7795953402235, 36.7249638612854,
31.7795953402235)), row.names = c("All", "Men", "Women"), class = "data.frame")
Here is an example with ggplot2. I changed some of your data, as two rows were same in your originial data.
library(tidyverse)
df <- structure(list(`2003-2005` = c(31.4848853173555, 37.3865421137,
30.4848853173555), `2006-2008` = c(32.5366433161048, 38.1669798351148,
30.5366433161048), `2009-2010` = c(30.4193794808191, 35.1024661973137,
33.4193794808191), `2011-2013` = c(30.5387012166381, 35.6554329405739,
33.5387012166381), `2014-2016` = c(31.1555032381292, 36.5485451138792,
30.1555032381292), `2017-2018` = c(31.7795953402235, 36.7249638612854,
30.7795953402235)), row.names = c("All", "Men", "Women"), class = "data.frame")
df2 <- as.data.frame(t(df))
df2$Year <- rownames(df2)
df2%>% pivot_longer( c(All,Men,Women), names_to = "Category") %>%
ggplot(aes(x = Year, y = value)) + geom_line(aes(group = Category, color = Category))
I am trying to put on a Italian geographical map a dot reporting the provenience ('provincia') of our patients. Ideally, the dot size should be proportional to the number of patients coming from that 'provincia'. An example of the list I would like to plot is the following.
MI 8319
CO 537
MB 436
VA 338
BG 310
PV 254
CR 244
NO 210
RM 189
CS 179
In the first column there is the 'provincia' code: MI (Milano), CO (Como), MB (Monza-Brianza), etc. In the second column there is the number of patients from that 'provincia'. So the output should be an Italian political map where the biggest dot is around the city of Milano (MI), the second biggest dot is near the city of Como (CO), the third one is around the city of Monza-Brianza (MB),etc.
Is there any package that could do the plot I am looking for? I found a tool that could do the job here, but apparently they expect that I load the geographical coordinates in order to do the plot.
https://www.littlemissdata.com/blog/maps
Thanks in advance.
Here is one way to handle your task. You have the abbreviations for Italian province. You want to use them to merge your data with polygon data. If you download Italy's polygons from GADM, you can obtain data that contain the abbreviations. Specifically, the column, HASC_2 is the one. You need to merge your data with the polygon data. Then, you want to create another data set which contains centroid. You can draw a map with the two data sets.
library(tidyverse)
library(sf)
library(ggthemes)
# Get the sf file from https://gadm.org/download_country_v3.html
# and import it in R.
mysf <- readRDS("gadm36_ITA_2_sf.rds")
# This is your data, which is called mydata.
mydata <- structure(list(abbs = c("MI", "CO", "MB", "VA", "BG", "PV", "CR",
"NO", "RM", "CS"), value = c(8319L, 537L, 436L, 338L, 310L, 254L,
244L, 210L, 189L, 179L)), class = "data.frame", row.names = c(NA,
-10L))
abbs value
1 MI 8319
2 CO 537
3 MB 436
4 VA 338
5 BG 310
6 PV 254
7 CR 244
8 NO 210
9 RM 189
10 CS 179
# Abbreviations are in HASC_2 in mysf. Manipulate strings so that
# I can join mydata with mysf with the abbreviations. I also get
# longitude and latitude with st_centroid(). This data set is for
# geom_point().
mysf2 <- mutate(mysf, HASC_2 = sub(x = HASC_2, pattern = "^IT.", replacement = "")) %>%
left_join(mydata, by = c("HASC_2" = "abbs")) %>%
mutate(lon = map_dbl(geometry, ~st_centroid(.x)[[1]]),
lat = map_dbl(geometry, ~st_centroid(.x)[[2]]))
# Draw a map
ggplot() +
geom_sf(data = mysf) +
geom_point(data = mysf2, aes(x = lon, y = lat, size = value)) +
theme_map()
UPDATE ON INSET MAP
This is an update following different suggestion on using inset maps, which I think it would be the best solution for yout question and comments:
library(sf)
library(cartography)
EU = st_read("~/R/mapslib/EUROSTAT/NUTS_RG_03M_2016_3035_LEVL_3.geojson")
IT = subset(EU, CNTR_CODE == "IT")
mydata <-
structure(list(
abbs = c("MI", "CO", "MB", "VA", "BG", "PV", "CR",
"NO", "RM", "CS"),
value = c(8319L, 537L, 436L, 338L, 310L, 254L,
244L, 210L, 189L, 179L),
nuts = c("ITC4C","ITC42","ITC4D","ITC41",
"ITC46", "ITC48","ITC4A","ITC15",
"ITI43","ITF61")
),
class = "data.frame",
row.names = c(NA, -10L))
patients = merge(IT, mydata, by.x = "id", by.y = "nuts")
#Get breaks for map
br=getBreaks(patients$value)
#Delimit zone
#Based on NUTS1, Nortwest Italy
par(mar=c(0,0,0,0))
ghostLayer(IT[grep("ITC",IT$NUTS_ID),], bg="lightblue")
plot(st_geometry(EU), col="grey90", add=TRUE)
plot(st_geometry(IT), col = "#FEFEE9", border = "#646464", add=TRUE)
choroLayer(
patients,
var = "value",
breaks = br,
col = carto.pal(pal1 = "red.pal", n1 = length(br)-1),
legend.pos = "topleft",
legend.title.txt = "Total patients",
add = TRUE,
legend.frame = TRUE
)
labelLayer(patients,txt="abbs", halo=TRUE, overlap = FALSE)
#Inset
par(
fig = c(0, 0.4, 0.01, 0.4),
new = TRUE
)
inset=patients[patients$abbs %in% c("RM","CS"),]
ghostLayer(inset, bg="lightblue")
plot(st_geometry(EU), col="grey90", add=TRUE)
plot(st_geometry(IT), col = "#FEFEE9", border = "#646464", add=TRUE)
choroLayer(
patients,
var = "value",
breaks = br,
col = carto.pal(pal1 = "red.pal", n1 = length(br)-1),
legend.pos = "n",
add = TRUE
)
labelLayer(patients,txt="abbs", halo=TRUE, overlap = FALSE)
box(which = "figure", lwd = 1)
#RESTORE PLOT
par(fig=c(0,1,0,1))
OLD ANSWER
Following my comment on plotting labels, maybe with circles is not the best option for your map, given the concentration. I suggest you to use another kind of map for that, as chorolayer, I leveraged on https://stackoverflow.com/users/3304471/jazzurro for the dataframe.
library(sf)
library(cartography)
EU = st_read("~/R/mapslib/EUROSTAT/NUTS_RG_03M_2016_3035_LEVL_3.geojson")
IT = subset(EU, CNTR_CODE == "IT")
mydata <-
structure(list(
abbs = c("MI", "CO", "MB", "VA", "BG", "PV", "CR",
"NO", "RM", "CS"),
value = c(8319L, 537L, 436L, 338L, 310L, 254L,
244L, 210L, 189L, 179L),
nuts = c("ITC4C","ITC42","ITC4D","ITC41",
"ITC46", "ITC48","ITC4A","ITC15",
"ITI43","ITF61")
),
class = "data.frame",
row.names = c(NA, -10L))
patients = merge(IT, mydata, by.x = "id", by.y = "nuts")
#Options1 - With circles
par(mar = c(0, 0, 0, 0))
plot(st_geometry(IT), col = "#FEFEE9", border = "#646464")
propSymbolsLayer(
x = patients,
var = "value",
col = carto.pal(pal1 = "red.pal", n1 = 6),
legend.title.txt = "Total patients",
add = TRUE
)
#Option 2 - Chorolayer with labels
par(mar = c(0, 0, 0, 0))
plot(st_geometry(IT), col = "#FEFEE9", border = "#646464")
choroLayer(
patients,
var = "value",
col = carto.pal(pal1 = "red.pal", n1 = 6),
legend.title.txt = "Total patients",
add = TRUE
)
#Create labels
patients$label = paste(patients$abbs, patients$value, sep = " - ")
labelLayer(
patients,
txt = "label",
overlap = FALSE,
halo = TRUE,
show.lines = TRUE,
)
Data from
https://ec.europa.eu/eurostat/cache/GISCO/distribution/v2/nuts/nuts-2016-files.html
I'm having some problems to change the legend in a plot. Since I'm plotting symbols, I want my legend also with symbols. It should be simple, but I don't know what it's the best way.
I attach a sample of my data:
dat <- dput(dmam[1:3,])
structure(list(A = c(20.3962664097025, 23.1851978051035, 24.100067818374
), C = c(21.3308712003524, 18.9836301631049, 18.0438507268565
), E = c(4.65577231445595, 5.31932828758369, 7.38133268738002
), N = c(7.27444462203666, 7.05405386380459, 6.86223246170241
), NE = c(3.17580210229611, 3.61284920419648, 4.61289092127168
), NW = c(9.24999105407481, 7.474673429406, 5.15097610969423),
S = c(7.5472564395321, 7.66985191633565, 8.38928522440817
), SE = c(2.93704709971095, 2.86345108676898, 4.23877287952622
), SW = c(6.66026053678963, 6.92699394482923, 5.47572225923119
), U = c(3.66624356005668, 5.29918120412581, 8.3455794646421
), W = c(13.1060448390873, 11.6107893469172, 7.3992896902522
), mod = c("BNU-ESM", "CCSM4", "CESM1-CAM5"), id = 1:3), .Names = c("A",
"C", "E", "N", "NE", "NW", "S", "SE", "SW", "U", "W", "mod",
"id"), row.names = c("BNU-ESM", "CCSM4", "CESM1-CAM5"), class = "data.frame")
What I did:
df <- melt(dat,id=c('mod','id'))
s <- ggplot(df, aes(variable,value))
s <- s + geom_point(aes(shape = id,colour=mod), size = 3) +
scale_shape_identity()
But the legend is not showing the symbols...
Make id into a factor and drop the scale_shape_identity, then it will draw a legend.
s <- ggplot(df, aes(variable, value))
s <- s + geom_point(aes(shape = as.factor(id), colour = mod), size = 3)
s
I have a data frame like this.
ID read1 read2 read3 read4 class
1 5820350 0.3791915 0.3747022 0.3729779 0.3724259 1
2 5820364 0.3758676 0.3711775 0.3695976 0.3693112 2
3 5820378 0.3885081 0.3823900 0.3804273 0.3797707 2
4 5820392 0.3779945 0.3729582 0.3714910 0.3709072 1
5 5820425 0.2954782 0.2971604 0.2973882 0.2973216 3
6 5820426 0.3376101 0.3368173 0.3360203 0.3359517 3
Each row represents one sample with four values,and the last column is the classification of this sample. I want to visualize each sample curve and set the class as the color.
I tried to reshape the data frame, but I then lost the class feature which I need.
Could you please give me some hint or show me how to do that in R?
Thanks in advance.
You are going to want to tidy your data first (shown below with tidyr::gather). Then, when you plot, you will want to set your group = ID and color = factor(class) (for discrete colors):
library(tidyr)
library(ggplot2)
df <- structure(list(ID = c(5820350L, 5820364L, 5820378L, 5820392L, 5820425L, 5820426L),
read1 = c(0.3791915, 0.3758676, 0.3885081, 0.3779945, 0.2954782, 0.3376101),
read2 = c(0.3747022, 0.3711775, 0.38239, 0.3729582, 0.2971604, 0.3368173),
read3 = c(0.3729779, 0.3695976, 0.3804273, 0.371491, 0.2973882, 0.3360203),
read4 = c(0.3724259, 0.3693112, 0.3797707, 0.3709072, 0.2973216, 0.3359517),
class = c(1L, 2L, 2L, 1L, 3L, 3L)),
.Names = c("ID", "read1", "read2", "read3", "read4", "class"),
class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6"))
df <- gather(df, reading, value, -c(ID, class))
ggplot(df, aes(x = reading, y = value, color = factor(class))) +
geom_line(aes(group = ID))
Here's a function that may do what you want:
PlotMultiCurve = function(x, classes, cols = NULL, colSet = "Set1", ...) {
if(!is.factor(classes)) classes = as.factor(classes)
nClasses = length(levels(classes))
if(is.null(cols)) cols = brewer.pal(nClasses, colSet)
plot(1:ncol(x), x[1,], col = cols[classes[1]], type = "l",
ylim = range(x), xaxt = "n", ...)
axis(1, 1:ncol(x), 1:ncol(x))
for(i in 2:nrow(x)) {
par(new = T)
plot(1:ncol(x), x[i,], col = cols[classes[i]], type = "l",
ylim = range(x), axes = F, xlab = "", ylab = "")
}
}
It uses chooses colors automatically from the RColorBrewer package unless you provide the colors. I copied your data directly into a text file and then ran the following:
# Prepare data
require(RColorBrewer)
myData = read.table("Data.2016-05-03.txt")
x = myData[,2:5]
classes = as.factor(myData$class)
# Plot into PNG file[![enter image description here][1]][1]
png("Plot.2016-05-03.png", width = 1000, height = 1000, res = 300)
par(cex = 0.8)
PlotMultiCurve(x = x, classes = classes, xlab = "Read", ylab = "Response")
dev.off()