I'm having to generate a graph using different internet links. As the data input has very close values, the graph has practically no differences.
Is there a way to make this difference more apparent?
Here is the code used:
generateGraphic <- function(gFile, wifiFile, wifigFile){
gData <- read.csv(file=gFile, header=FALSE)
wifiData <- read.csv(file=wifiFile, header=FALSE)
wifigData <- read.csv(file=wifigFile, header=FALSE)
x = gData[,1]
y1 = gData[,2]
print(y1)
y2 = wifiData[,2]
print(y2)
y3 = wifigData[,2]
print(y3)
plot(x, y1, type="b", pch = 16, xlab="Tempo (s)", ylab="Probabilidade", xaxt="n", yaxt="n")
axis(1, at = seq(0, 4, by = 0.4), las=2)
axis(2, at = seq(0, 1, by = 0.1), las=2)
lines(x, y2, type="b", col="dark gray", pch = 15)
lines(x, y3, type="b", col="light gray", pch = 8)
legend("topleft",
legend = c("4G", "WiFi", "WiFi4G"),
col = c("black", "dark gray", "light gray"),
pch = c(16, 15, 8))
}
Here is a example of the input data:
Wifi Link
0 0.0
0.1 0.20326429999999998
0.2 0.4248706
0.3 0.5867006
0.4 0.7030588999999999
0.5 0.7866605
0.6 0.8467247999999999
0.7 0.8898784999999999
0.8 0.9208824999999999
0.9 0.9431575999999999
1 0.9591613
1.1 0.9706591999999999
1.2 0.9789199999999999
1.3 0.9848549999999999
1.4 0.9891190999999999
1.5 0.9921825999999999
1.6 0.9943835999999999
1.7 0.9959648999999999
1.8 0.9971009999999999
1.9 0.9979172
2 0.9985035999999999
4G Link
0 0.0
0.1 0.2032832
0.2 0.4249017
0.3 0.5867356
0.4 0.703093
0.5 0.7866915
0.6 0.8467515999999999
0.7 0.8899009999999999
0.8 0.9209010999999999
0.9 0.9431725999999999
1 0.9591732
1.1 0.9706686
1.2 0.9789274
1.3 0.9848606999999999
1.4 0.9891234999999999
1.5 0.9921859
1.6 0.9943862
1.7 0.9959669
1.8 0.9971025
1.9 0.9979184
2 0.9985044999999999
2.1 0.9989256
2.2 0.9992281
2.3 0.9994455
2.4 0.9996016
CodePudding user response：
You could plot the differences to 4G as reference.
I would put the value columns into a list, and adapt their lengths by filling with NA.
values <- list(gData=gData$V2, wifiData=wifiData$V2, wifigData=wifigData$V2)
After that you may cbind to a matrix m.
m <- do.call(cbind, lapply(values, `length<-`, max(lengths(values))))
Since 4G is in first column, subtract iot from the other columns
m <- m[, 2:3] - m[, 1]
and use matplot.
matplot(m, type='b', xaxt='n', pch=c(15, 8), col=1, xlab='Tempo (s)',
ylab='Probabilidade', main='Diferencias')
sq <- seq.int(0, by=.1, length.out=nrow(m))
axis(1, sq[seq_along(sq) %% 4 == 1], at=seq_along(sq)[seq_along(sq) %% 4 == 1])
abline(h=0, col=8)
mtext('4G', 4, -2, at=-.5e-5, las=2, col=8, font=2)
legend("bottomright", leg=c("WiFi", "WiFi4G"), horiz=T, pch=c(15, 8), bty='n')
You could also turn it around of course.
m <- do.call(cbind, lapply(values, `length<-`, max(lengths(values))))
m <- m[, 1] - m[, 2:3]
matplot(m, type='b', xaxt='n', pch=c(15, 8), col=1, xlab='Tempo (s)',
ylab='Probabilidade', main='Diferencias')
sq <- seq.int(0, by=.1, length.out=nrow(m))
axis(1, sq[seq_along(sq) %% 4 == 1], at=seq_along(sq)[seq_along(sq) %% 4 == 1])
legend("bottomright", leg=c("4G vs. WiFi", "4G vs. WiFi4G"), horiz=T, pch=c(15, 8),
bty='n')
Data:
gData <- structure(list(V1 = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1,
2.2, 2.3, 2.4), V2 = c(0, 0.203385231442384, 0.425002665444611,
0.586833487363752, 0.703191551616986, 0.7867914923001, 0.846852450869833,
0.889999103106634, 0.921001598894712, 0.943272154043885, 0.959273034150037,
0.970768887392538, 0.979028541813682, 0.984959224443878, 0.989223699201295,
0.992284899754003, 0.994485588797307, 0.996068229156018, 0.997202959924694,
0.998019756784877, 0.998605023394967, 0.999025567967921, 0.999327477772277,
0.999546146749613, 0.999701921229212)), row.names = c(NA, -25L
), class = "data.frame")
wifiData <- structure(list(V1 = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2), V2 = c(0,
0.2032643, 0.4248706, 0.5867006, 0.7030589, 0.7866605, 0.8467248,
0.8898785, 0.9208825, 0.9431576, 0.9591613, 0.9706592, 0.97892,
0.984855, 0.9891191, 0.9921826, 0.9943836, 0.9959649, 0.997101,
0.9979172, 0.9985036)), class = "data.frame", row.names = c(NA,
-21L))
wifigData <- structure(list(V1 = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1,
2.2, 2.3, 2.4), V2 = c(0, 0.2032832, 0.4249017, 0.5867356, 0.703093,
0.7866915, 0.8467516, 0.889901, 0.9209011, 0.9431726, 0.9591732,
0.9706686, 0.9789274, 0.9848607, 0.9891235, 0.9921859, 0.9943862,
0.9959669, 0.9971025, 0.9979184, 0.9985045, 0.9989256, 0.9992281,
0.9994455, 0.9996016)), class = "data.frame", row.names = c(NA,
-25L))
CodePudding user response：
Since you are dealing with probabilities, you can convert them to odds very easily:
odds_wifi <- wifiData[[2]] / (1-wifiData[[2]])
odds_gData <- gData[[2]] / (1-gData[[2]])
And depending on your set-up, it might make sense to compare them with an odds ratio:
OR <- odds_wifi / odds_gData
plot(x, OR, type="b", pch = 16, xlab="Tempo (s)",
ylab="Relación de probabilidades")
