I have collected data from two instruments, one is collected @10 Hz and the other is @100Hz. I would like to increase the data from 10Hz to 100Hz in one dataframe to then align and merge the two dataframes together
The example data frame is:
| DeltaT | Speed | Acc | HR | Player |
|---|---|---|---|---|
| 48860,7 | 0,03 | -0,05 | 0 | Player1 |
| 48860,8 | 0,02 | -0,05 | 0 | Player1 |
| 48860,9 | 0,02 | -0,04 | 0 | Player1 |
| 48861,0 | 0,02 | -0,03 | 0 | Player1 |
| 48861,1 | 0,01 | -0,02 | 0 | Player1 |
Is there a package function that can help me create data between two points?
CodePudding user response：
Manually with the approx function:
dt<- read.table(text=gsub(",", ".", 'DeltaT Speed Acc HR Player
48860,7 0,03 -0,05 0 Player1
48860,8 0,02 -0,05 0 Player1
48860,9 0,02 -0,04 0 Player1
48861,0 0,02 -0,03 0 Player1
48861,1 0,01 -0,02 0 Player1', fixed = TRUE),header=T)
upsampleDeltaT=seq(from=min(dt$DeltaT),to=max(dt$DeltaT),by=.01)
Speed<-approx(dt$DeltaT,dt$Speed,upsampleDeltaT)$y
Acc<-approx(dt$DeltaT,dt$Acc,upsampleDeltaT)$y
HR<-approx(dt$DeltaT,dt$HR,upsampleDeltaT)$y
Player <- rep(dt$Player,c(rep(10,nrow(dt)-1),1))
data.frame(upsampleDeltaT,Speed,Acc,HR,Player)
#> upsampleDeltaT Speed Acc HR Player
#> 1 48860.70 0.030 -0.050 0 Player1
#> 2 48860.71 0.029 -0.050 0 Player1
#> 3 48860.72 0.028 -0.050 0 Player1
#> 4 48860.73 0.027 -0.050 0 Player1
#> 5 48860.74 0.026 -0.050 0 Player1
#> 6 48860.75 0.025 -0.050 0 Player1
#> 7 48860.76 0.024 -0.050 0 Player1
#> 8 48860.77 0.023 -0.050 0 Player1
#> 9 48860.78 0.022 -0.050 0 Player1
#> 10 48860.79 0.021 -0.050 0 Player1
#> 11 48860.80 0.020 -0.050 0 Player1
#> 12 48860.81 0.020 -0.049 0 Player1
#> 13 48860.82 0.020 -0.048 0 Player1
#> 14 48860.83 0.020 -0.047 0 Player1
#> 15 48860.84 0.020 -0.046 0 Player1
#> 16 48860.85 0.020 -0.045 0 Player1
#> 17 48860.86 0.020 -0.044 0 Player1
#> 18 48860.87 0.020 -0.043 0 Player1
#> 19 48860.88 0.020 -0.042 0 Player1
#> 20 48860.89 0.020 -0.041 0 Player1
#> 21 48860.90 0.020 -0.040 0 Player1
#> 22 48860.91 0.020 -0.039 0 Player1
#> 23 48860.92 0.020 -0.038 0 Player1
#> 24 48860.93 0.020 -0.037 0 Player1
#> 25 48860.94 0.020 -0.036 0 Player1
#> 26 48860.95 0.020 -0.035 0 Player1
#> 27 48860.96 0.020 -0.034 0 Player1
#> 28 48860.97 0.020 -0.033 0 Player1
#> 29 48860.98 0.020 -0.032 0 Player1
#> 30 48860.99 0.020 -0.031 0 Player1
#> 31 48861.00 0.020 -0.030 0 Player1
#> 32 48861.01 0.019 -0.029 0 Player1
#> 33 48861.02 0.018 -0.028 0 Player1
#> 34 48861.03 0.017 -0.027 0 Player1
#> 35 48861.04 0.016 -0.026 0 Player1
#> 36 48861.05 0.015 -0.025 0 Player1
#> 37 48861.06 0.014 -0.024 0 Player1
#> 38 48861.07 0.013 -0.023 0 Player1
#> 39 48861.08 0.012 -0.022 0 Player1
#> 40 48861.09 0.011 -0.021 0 Player1
#> 41 48861.10 0.010 -0.020 0 Player1
CodePudding user response：
library(data.table)
library(zoo)
set.seed(123)
# 10Hz and 100Hz sample data
DT10 <- data.table(time = seq(0,1, by = 0.1), value = sample(1:10, 11, replace = TRUE))
DT100 <- data.table(time = seq(0,1, by = 0.01), value = sample(1:10, 101, replace = TRUE))
# you should use setDT() if your data is not already data.table format
# join the DT10 to DT100
DT100[DT10, value2 := i.value, on = .(time)]
# intyerpolate NA-values
DT100[, value2_inter := zoo::na.approx(value2)]
#output
head(DT100, 31)
# time value value2 value2_inter
# 1: 0.00 3 3 3.0
# 2: 0.01 9 NA 3.0
# 3: 0.02 9 NA 3.0
# 4: 0.03 9 NA 3.0
# 5: 0.04 3 NA 3.0
# 6: 0.05 8 NA 3.0
# 7: 0.06 10 NA 3.0
# 8: 0.07 7 NA 3.0
# 9: 0.08 10 NA 3.0
# 10: 0.09 9 NA 3.0
# 11: 0.10 3 3 3.0
# 12: 0.11 4 NA 3.7
# 13: 0.12 1 NA 4.4
# 14: 0.13 7 NA 5.1
# 15: 0.14 5 NA 5.8
# 16: 0.15 10 NA 6.5
# 17: 0.16 7 NA 7.2
# 18: 0.17 9 NA 7.9
# 19: 0.18 9 NA 8.6
# 20: 0.19 10 NA 9.3
# 21: 0.20 7 10 10.0
# 22: 0.21 5 NA 9.8
# 23: 0.22 7 NA 9.6
# 24: 0.23 5 NA 9.4
# 25: 0.24 6 NA 9.2
# 26: 0.25 9 NA 9.0
# 27: 0.26 2 NA 8.8
# 28: 0.27 5 NA 8.6
# 29: 0.28 8 NA 8.4
# 30: 0.29 2 NA 8.2
# 31: 0.30 1 NA 8.0
# time value value2 value2_inter
CodePudding user response：
Have a look at the approx function. It interpolates data for new points by.