R/ttscrape.R
In EnricoTomelleri/ttalkR: For download and standard processing of TreeTalker data
Defines functions
ttScrape
Documented in
ttScrape
ttScrape
#' @export
ttScrape <- function(ID, subset_days) {
if(missing(subset_days)) {
subset_days = "all"
}
library(tidyr)
library(ggplot2)
library(tidyverse)
library(signal)
library(zoo)
library(prospectr)
#Specifying the url for desired website to be scraped
url <-
paste("http://naturetalkers.altervista.org/",
ID,
"/ttcloud.txt",
sep = "")
if (RCurl::url.exists(url)==T){
#Reading the HTML code from the website
#mydata0 <- read.csv(url,
# sep = ";",
# header = FALSE,
# fill = TRUE)
mydata0 <- data.table::fread(url,
sep = ";",
header = FALSE,
fill = TRUE,
integer64="numeric")
#mydata0
flag0 <- 1
} else {flag0 <- 0}
#Specifying the url for desired website to be scraped
url <-
paste("http://ittn.altervista.org/", ID, "/ttcloud.txt", sep = "")
#Reading the HTML code from the website
#if (ID == "C0200090"){skip <- 10}else{skip <- 0}
if (RCurl::url.exists(url)==T){
#mydata1 <- read.csv(url,
# sep = ";",
# header = FALSE,
# fill = TRUE)
#url <- "~/Work/UNIBZ/People/AnnaCandotti/Treetalkers/ttcloud_90_23_08_2023.txt"
mydata1 <- data.table::fread(url,
sep = ";",
header = FALSE,
fill = TRUE,
#skip = skip,
integer64="numeric")
flag1 <- 1
} else {flag1 <- 0}
if (flag0 == 0){
if (flag1 == 0){stop()}else{mydata <- mydata1}
}else{
if (flag1 == 0){mydata <- mydata0}
else{
#for some sites the number of variables from the old to the new server changes
varnum_mydata0 <- dim(mydata0)[2]
varnum_mydata1 <- dim(mydata1)[2]
varnum <- min(varnum_mydata0, varnum_mydata1)
mydata0 <- mydata0[,1:varnum]
mydata1 <- mydata1[,1:varnum,]
mydata <- rbind(mydata0, mydata1)
}
}
mydata <- mydata[mydata$V1 != 0,]
mydata_sep <-
separate(mydata,
V1,
into = c("Date", "Time", "SN"),
sep = "[ ,]")
#split the dataset
mydata_4D <- mydata_sep[mydata_sep$V3 == "4D", ]
mydata_4D <- mydata_4D[mydata_4D$SN>52000000,]#TreeTalkers v3.2 have and ID higher than 52000000
#remove thos columns with only NAs
mydata_4D <- Filter(function(x)!all(is.na(x)), mydata_4D)
#convert possible integer64 to integer
mydata_4D <- mydata_4D %>% mutate_if(bit64::is.integer64, as.integer)
#barkbeetle experiment: time stamps are incomplete: some have just 3 digits
#if (ID == "C0200090"){
# # value to be added
# k_time <- 1625461000
# # Add 'k' to three-digit values in 'Value' column
# mydata_4D$V4 <- ifelse(nchar(mydata_4D$V4) == 3, mydata_4D$V4 + k_time, mydata_4D$V4)
#}
mydata_49 <- mydata_sep[mydata_sep$V3 == "49", ]
mydata_49 <- mydata_49[mydata_49$SN>52000000,]#TreeTalkers v3.2 have and ID higher than 52000000
#remove those columns with only NAs
mydata_49 <- Filter(function(x)!all(is.na(x)), mydata_49)
#mydata_49[mydata_49>999999999] <- NA
#conditional filtering for integer64
#mydata_49 %>%
# filter(if (bit64::is.integer64) mydata_49>999999999)
#convert possible integer64 to integer
#mydata_49 <- mydata_49 %>% mutate_if(bit64::is.integer64, as.integer)
#convert all the bands to numeric
mydata_49$V5 <- as.numeric(mydata_49$V5)
mydata_49$V6 <- as.numeric(mydata_49$V6)
mydata_49$V7 <- as.numeric(mydata_49$V7)
mydata_49$V8 <- as.numeric(mydata_49$V8)
mydata_49$V9 <- as.numeric(mydata_49$V9)
mydata_49$V10 <- as.numeric(mydata_49$V10)
mydata_49$V11 <- as.numeric(mydata_49$V11)
mydata_49$V12 <- as.numeric(mydata_49$V12)
mydata_49$V13 <- as.numeric(mydata_49$V13)
mydata_49$V14 <- as.numeric(mydata_49$V14)
mydata_49$V15 <- as.numeric(mydata_49$V15)
mydata_49$V16 <- as.numeric(mydata_49$V16)
mydata_4B <- mydata_sep[mydata_sep$V3 == "4B", ]#the string 4B and 4C contain only TTcloud data
#remove thos columns with only NAs
mydata_4B <- Filter(function(x)!all(is.na(x)), mydata_4B)
#mydata_4B$Battery <- as.numeric(mydata_4B$Battery)
#convert possible integer64 to integer
#mydata_4B <- mydata_4B %>% mutate_if(bit64::is.integer64, as.integer)
mydata_4C <- mydata_sep[mydata_sep$V3 == "4C", ]#the string 4B and 4C contain only TTcloud data
#remove thos columns with only NAs
mydata_4C <- Filter(function(x)!all(is.na(x)), mydata_4C)
header_4D <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"Tref_0",
"Theat_0",
"growt_sens",
"adc_bandgap",
"Bits",
"RH",
"Tair",
"gz_mean",
"gz_sd",
"gy_mean",
"gy_sd",
"gx_mean",
"gx_sd",
"Tref_1",
"Theat_1",
"StWC",
"adc_Vbat"
)
header_49 <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"AS7263_610",
"AS7263_680",
"AS7263_730",
"AS7263_760",
"AS7263_810",
"AS7263_860",
"AS7262_450",
"AS7262_500",
"AS7262_550",
"AS7262_570",
"AS7262_600",
"AS7262_650",
"integration_T",
"gain"
)
header_4B <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"Acc_Recs",
"Recs_to_be_sent",
"MCC_tel_op",
"MNC_tel_op",
"GSM_reg",
"GSM_field",
"Battery",
"Firmware_ver"
)
header_4C <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"TBL_locked",
"n_first_sens",
"RSSI_TT1",
"RSSI_TT2",
"RSSI_TT3",
"RSSI_TT4",
"RSSI_TT5",
"RSSI_TT6",
"RSSI_TT7",
"RSSI_TT8",
"RSSI_TT9",
"RSSI_TT10",
"RSSI_TT11",
"RSSI_TT12",
"RSSI_TT13",
"RSSI_TT14",
"RSSI_TT15",
"RSSI_TT16",
"RSSI_TT17",
"RSSI_TT18",
"RSSI_TT19",
"RSSI_TT20",
"RSSI_TT21",
"RSSI_TT22",
"RSSI_TT23",
"RSSI_TT24"
)
colnames(mydata_4D) <- header_4D
colnames(mydata_49) <- header_49
colnames(mydata_4B) <- header_4B
header_4C <- header_4C[1:(dim(mydata_4C)[2])]
colnames(mydata_4C) <- header_4C
#filter all the dates earlier than 2020-01-01 00:00:00
mydata_4D$Timestamp[mydata_4D$Timestamp < 1685628046] <- NA
#filter all the dates earlier than 2023-01-01 00:00:00
#if (ID == "C0200090"){mydata_4D$Timestamp[mydata_4D$Timestamp < 1672527600] <- NA}
#convert the ids to integers
mydata_4D$TT_ID <- as.integer(mydata_4D$TT_ID)
mydata_49$TT_ID <- as.integer(mydata_49$TT_ID)
mydata_4B$TT_ID <- substr(mydata_4B$TT_ID, 2, 8) #remove the "C" from the IDs
mydata_4B$TT_ID <- as.integer(mydata_4B$TT_ID)
mydata_4B$Acc_Recs <- as.integer(mydata_4B$Acc_Recs)
mydata_4B$MCC_tel_op <- as.integer(mydata_4B$MCC_tel_op)
mydata_4B$MNC_tel_op <- as.integer(mydata_4B$MCC_tel_op )
mydata_4B$GSM_reg <- as.integer(mydata_4B$GSM_reg)
mydata_4B$GSM_field <- as.integer(mydata_4B$GSM_field)
mydata_4B$Battery <- as.integer(mydata_4B$Battery)
mydata_4C$TT_ID <- substr(mydata_4C$TT_ID, 2, 8) #remove the "C" from the IDs
mydata_4C$TT_ID <- as.integer(mydata_4C$TT_ID);
mydata_4D$gz_mean <- as.integer(mydata_4D$gz_mean)
#timestamp conversion
#Timestamp of the router when the individual device send the data string. The timestamp is the number of seconds since 1st January 1970.
mydata_4D$Timestamp <-
as.POSIXct(mydata_4D$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_4B$Timestamp <-
as.POSIXct(mydata_4B$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_49$Timestamp <-
as.POSIXct(mydata_49$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_4C$Timestamp <-
as.POSIXct(mydata_4C$Timestamp, origin = "1970-01-01", tz="GMT")
#create a color index
#id_col <- mydata_4D$TT_ID
#id_col[id_col == max(id_col, na.rm = T)] <- 21
#id_col[id_col != 21] <- id_col[id_col != 21] - max(id_col, na.rm = T)
#mydata_4D$id_col <- abs(id_col)
if (subset_days != "all"){
tt_begin <- mydata_4B$Timestamp[length(mydata_4B$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4B$Timestamp[length(mydata_4B$Timestamp)]
mydata_4B <- mydata_4B[mydata_4B$Timestamp > tt_begin,]
mydata_4B <- mydata_4B[is.na(mydata_4B$Timestamp) == FALSE,] #remove NAs
} else {mydata_4B <- mydata_4B}
mydata_4B <- mydata_4B %>% drop_na(Timestamp, TT_ID)
mydata_4B <- mydata_4B %>% distinct(TT_ID, Timestamp, .keep_all = TRUE) #remove duplicates
.GlobalEnv$mydata_4B <- mydata_4B
if (subset_days != "all"){
tt_begin <- mydata_4D$Timestamp[length(mydata_4D$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4D$Timestamp[length(mydata_4D$Timestamp)]
mydata_4D <- mydata_4D[mydata_4D$Timestamp > tt_begin,]
mydata_4D <- mydata_4D[is.na(mydata_4D$Timestamp) == FALSE,] #remove NAs
} else {mydata_4D <- mydata_4D}
mydata_4D <- mydata_4D %>% drop_na(Timestamp, TT_ID)
mydata_4D <- mydata_4D %>% distinct(TT_ID, Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_4D <- mydata_4D
if (subset_days != "all"){
tt_begin <- mydata_49$Timestamp[length(mydata_49$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_49$Timestamp[length(mydata_49$Timestamp)]
mydata_49 <- mydata_49[mydata_49$Timestamp > tt_begin,]
mydata_49 <- mydata_49[is.na(mydata_49$Timestamp) == FALSE,] #remove NAs
} else {mydata_49 <- mydata_49}
mydata_49 <- mydata_49 %>% drop_na(Timestamp, TT_ID)
mydata_49 <- mydata_49 %>% distinct(TT_ID, Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_49 <- mydata_49
if (subset_days != "all"){
tt_begin <- mydata_4C$Timestamp[length(mydata_4C$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4C$Timestamp[length(mydata_4C$Timestamp)]
mydata_4C <- mydata_4C[mydata_4C$Timestamp > tt_begin,]
mydata_4C <- mydata_4C[is.na(mydata_4C$Timestamp) == FALSE,] #remove NAs
} else {mydata_4C <- mydata_4C}
mydata_4C <- mydata_4C %>% drop_na(Timestamp, TT_ID)
mydata_4C <- mydata_4C %>% distinct(Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_4C <- mydata_4C
}
EnricoTomelleri/ttalkR documentation built on Sept. 27, 2024, 2:49 p.m.
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Defines functions ttScrape
Documented in ttScrape ttScrape
#' @export
ttScrape <- function(ID, subset_days) {
if(missing(subset_days)) {
subset_days = "all"
}
library(tidyr)
library(ggplot2)
library(tidyverse)
library(signal)
library(zoo)
library(prospectr)
#Specifying the url for desired website to be scraped
url <-
paste("http://naturetalkers.altervista.org/",
ID,
"/ttcloud.txt",
sep = "")
if (RCurl::url.exists(url)==T){
#Reading the HTML code from the website
#mydata0 <- read.csv(url,
# sep = ";",
# header = FALSE,
# fill = TRUE)
mydata0 <- data.table::fread(url,
sep = ";",
header = FALSE,
fill = TRUE,
integer64="numeric")
#mydata0
flag0 <- 1
} else {flag0 <- 0}
#Specifying the url for desired website to be scraped
url <-
paste("http://ittn.altervista.org/", ID, "/ttcloud.txt", sep = "")
#Reading the HTML code from the website
#if (ID == "C0200090"){skip <- 10}else{skip <- 0}
if (RCurl::url.exists(url)==T){
#mydata1 <- read.csv(url,
# sep = ";",
# header = FALSE,
# fill = TRUE)
#url <- "~/Work/UNIBZ/People/AnnaCandotti/Treetalkers/ttcloud_90_23_08_2023.txt"
mydata1 <- data.table::fread(url,
sep = ";",
header = FALSE,
fill = TRUE,
#skip = skip,
integer64="numeric")
flag1 <- 1
} else {flag1 <- 0}
if (flag0 == 0){
if (flag1 == 0){stop()}else{mydata <- mydata1}
}else{
if (flag1 == 0){mydata <- mydata0}
else{
#for some sites the number of variables from the old to the new server changes
varnum_mydata0 <- dim(mydata0)[2]
varnum_mydata1 <- dim(mydata1)[2]
varnum <- min(varnum_mydata0, varnum_mydata1)
mydata0 <- mydata0[,1:varnum]
mydata1 <- mydata1[,1:varnum,]
mydata <- rbind(mydata0, mydata1)
}
}
mydata <- mydata[mydata$V1 != 0,]
mydata_sep <-
separate(mydata,
V1,
into = c("Date", "Time", "SN"),
sep = "[ ,]")
#split the dataset
mydata_4D <- mydata_sep[mydata_sep$V3 == "4D", ]
mydata_4D <- mydata_4D[mydata_4D$SN>52000000,]#TreeTalkers v3.2 have and ID higher than 52000000
#remove thos columns with only NAs
mydata_4D <- Filter(function(x)!all(is.na(x)), mydata_4D)
#convert possible integer64 to integer
mydata_4D <- mydata_4D %>% mutate_if(bit64::is.integer64, as.integer)
#barkbeetle experiment: time stamps are incomplete: some have just 3 digits
#if (ID == "C0200090"){
# # value to be added
# k_time <- 1625461000
# # Add 'k' to three-digit values in 'Value' column
# mydata_4D$V4 <- ifelse(nchar(mydata_4D$V4) == 3, mydata_4D$V4 + k_time, mydata_4D$V4)
#}
mydata_49 <- mydata_sep[mydata_sep$V3 == "49", ]
mydata_49 <- mydata_49[mydata_49$SN>52000000,]#TreeTalkers v3.2 have and ID higher than 52000000
#remove those columns with only NAs
mydata_49 <- Filter(function(x)!all(is.na(x)), mydata_49)
#mydata_49[mydata_49>999999999] <- NA
#conditional filtering for integer64
#mydata_49 %>%
# filter(if (bit64::is.integer64) mydata_49>999999999)
#convert possible integer64 to integer
#mydata_49 <- mydata_49 %>% mutate_if(bit64::is.integer64, as.integer)
#convert all the bands to numeric
mydata_49$V5 <- as.numeric(mydata_49$V5)
mydata_49$V6 <- as.numeric(mydata_49$V6)
mydata_49$V7 <- as.numeric(mydata_49$V7)
mydata_49$V8 <- as.numeric(mydata_49$V8)
mydata_49$V9 <- as.numeric(mydata_49$V9)
mydata_49$V10 <- as.numeric(mydata_49$V10)
mydata_49$V11 <- as.numeric(mydata_49$V11)
mydata_49$V12 <- as.numeric(mydata_49$V12)
mydata_49$V13 <- as.numeric(mydata_49$V13)
mydata_49$V14 <- as.numeric(mydata_49$V14)
mydata_49$V15 <- as.numeric(mydata_49$V15)
mydata_49$V16 <- as.numeric(mydata_49$V16)
mydata_4B <- mydata_sep[mydata_sep$V3 == "4B", ]#the string 4B and 4C contain only TTcloud data
#remove thos columns with only NAs
mydata_4B <- Filter(function(x)!all(is.na(x)), mydata_4B)
#mydata_4B$Battery <- as.numeric(mydata_4B$Battery)
#convert possible integer64 to integer
#mydata_4B <- mydata_4B %>% mutate_if(bit64::is.integer64, as.integer)
mydata_4C <- mydata_sep[mydata_sep$V3 == "4C", ]#the string 4B and 4C contain only TTcloud data
#remove thos columns with only NAs
mydata_4C <- Filter(function(x)!all(is.na(x)), mydata_4C)
header_4D <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"Tref_0",
"Theat_0",
"growt_sens",
"adc_bandgap",
"Bits",
"RH",
"Tair",
"gz_mean",
"gz_sd",
"gy_mean",
"gy_sd",
"gx_mean",
"gx_sd",
"Tref_1",
"Theat_1",
"StWC",
"adc_Vbat"
)
header_49 <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"AS7263_610",
"AS7263_680",
"AS7263_730",
"AS7263_760",
"AS7263_810",
"AS7263_860",
"AS7262_450",
"AS7262_500",
"AS7262_550",
"AS7262_570",
"AS7262_600",
"AS7262_650",
"integration_T",
"gain"
)
header_4B <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"Acc_Recs",
"Recs_to_be_sent",
"MCC_tel_op",
"MNC_tel_op",
"GSM_reg",
"GSM_field",
"Battery",
"Firmware_ver"
)
header_4C <-
c(
"SDate",
"STime",
"TT_ID",
"Rec_Nr",
"Dev_Ty",
"Timestamp",
"TBL_locked",
"n_first_sens",
"RSSI_TT1",
"RSSI_TT2",
"RSSI_TT3",
"RSSI_TT4",
"RSSI_TT5",
"RSSI_TT6",
"RSSI_TT7",
"RSSI_TT8",
"RSSI_TT9",
"RSSI_TT10",
"RSSI_TT11",
"RSSI_TT12",
"RSSI_TT13",
"RSSI_TT14",
"RSSI_TT15",
"RSSI_TT16",
"RSSI_TT17",
"RSSI_TT18",
"RSSI_TT19",
"RSSI_TT20",
"RSSI_TT21",
"RSSI_TT22",
"RSSI_TT23",
"RSSI_TT24"
)
colnames(mydata_4D) <- header_4D
colnames(mydata_49) <- header_49
colnames(mydata_4B) <- header_4B
header_4C <- header_4C[1:(dim(mydata_4C)[2])]
colnames(mydata_4C) <- header_4C
#filter all the dates earlier than 2020-01-01 00:00:00
mydata_4D$Timestamp[mydata_4D$Timestamp < 1685628046] <- NA
#filter all the dates earlier than 2023-01-01 00:00:00
#if (ID == "C0200090"){mydata_4D$Timestamp[mydata_4D$Timestamp < 1672527600] <- NA}
#convert the ids to integers
mydata_4D$TT_ID <- as.integer(mydata_4D$TT_ID)
mydata_49$TT_ID <- as.integer(mydata_49$TT_ID)
mydata_4B$TT_ID <- substr(mydata_4B$TT_ID, 2, 8) #remove the "C" from the IDs
mydata_4B$TT_ID <- as.integer(mydata_4B$TT_ID)
mydata_4B$Acc_Recs <- as.integer(mydata_4B$Acc_Recs)
mydata_4B$MCC_tel_op <- as.integer(mydata_4B$MCC_tel_op)
mydata_4B$MNC_tel_op <- as.integer(mydata_4B$MCC_tel_op )
mydata_4B$GSM_reg <- as.integer(mydata_4B$GSM_reg)
mydata_4B$GSM_field <- as.integer(mydata_4B$GSM_field)
mydata_4B$Battery <- as.integer(mydata_4B$Battery)
mydata_4C$TT_ID <- substr(mydata_4C$TT_ID, 2, 8) #remove the "C" from the IDs
mydata_4C$TT_ID <- as.integer(mydata_4C$TT_ID);
mydata_4D$gz_mean <- as.integer(mydata_4D$gz_mean)
#timestamp conversion
#Timestamp of the router when the individual device send the data string. The timestamp is the number of seconds since 1st January 1970.
mydata_4D$Timestamp <-
as.POSIXct(mydata_4D$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_4B$Timestamp <-
as.POSIXct(mydata_4B$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_49$Timestamp <-
as.POSIXct(mydata_49$Timestamp, origin = "1970-01-01", tz="GMT")
mydata_4C$Timestamp <-
as.POSIXct(mydata_4C$Timestamp, origin = "1970-01-01", tz="GMT")
#create a color index
#id_col <- mydata_4D$TT_ID
#id_col[id_col == max(id_col, na.rm = T)] <- 21
#id_col[id_col != 21] <- id_col[id_col != 21] - max(id_col, na.rm = T)
#mydata_4D$id_col <- abs(id_col)
if (subset_days != "all"){
tt_begin <- mydata_4B$Timestamp[length(mydata_4B$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4B$Timestamp[length(mydata_4B$Timestamp)]
mydata_4B <- mydata_4B[mydata_4B$Timestamp > tt_begin,]
mydata_4B <- mydata_4B[is.na(mydata_4B$Timestamp) == FALSE,] #remove NAs
} else {mydata_4B <- mydata_4B}
mydata_4B <- mydata_4B %>% drop_na(Timestamp, TT_ID)
mydata_4B <- mydata_4B %>% distinct(TT_ID, Timestamp, .keep_all = TRUE) #remove duplicates
.GlobalEnv$mydata_4B <- mydata_4B
if (subset_days != "all"){
tt_begin <- mydata_4D$Timestamp[length(mydata_4D$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4D$Timestamp[length(mydata_4D$Timestamp)]
mydata_4D <- mydata_4D[mydata_4D$Timestamp > tt_begin,]
mydata_4D <- mydata_4D[is.na(mydata_4D$Timestamp) == FALSE,] #remove NAs
} else {mydata_4D <- mydata_4D}
mydata_4D <- mydata_4D %>% drop_na(Timestamp, TT_ID)
mydata_4D <- mydata_4D %>% distinct(TT_ID, Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_4D <- mydata_4D
if (subset_days != "all"){
tt_begin <- mydata_49$Timestamp[length(mydata_49$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_49$Timestamp[length(mydata_49$Timestamp)]
mydata_49 <- mydata_49[mydata_49$Timestamp > tt_begin,]
mydata_49 <- mydata_49[is.na(mydata_49$Timestamp) == FALSE,] #remove NAs
} else {mydata_49 <- mydata_49}
mydata_49 <- mydata_49 %>% drop_na(Timestamp, TT_ID)
mydata_49 <- mydata_49 %>% distinct(TT_ID, Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_49 <- mydata_49
if (subset_days != "all"){
tt_begin <- mydata_4C$Timestamp[length(mydata_4C$Timestamp)] - (24*60*60*subset_days)
#tt_end <- mydata_4C$Timestamp[length(mydata_4C$Timestamp)]
mydata_4C <- mydata_4C[mydata_4C$Timestamp > tt_begin,]
mydata_4C <- mydata_4C[is.na(mydata_4C$Timestamp) == FALSE,] #remove NAs
} else {mydata_4C <- mydata_4C}
mydata_4C <- mydata_4C %>% drop_na(Timestamp, TT_ID)
mydata_4C <- mydata_4C %>% distinct(Timestamp, .keep_all = TRUE)#remove duplicates
.GlobalEnv$mydata_4C <- mydata_4C
}
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