Primeiramente, todos os arquivos devem ser salvos em uma pasta.
pasta_ctd=('/home/mauricio/Dropbox/Projetos/Programacao em Python e R - 2021/Analise Dados/CTD/csv')
Dois pacotes devem ser carregados. Se der erro tem que instalar os pacotes.
library(readr)
library(lattice)
Armazenando os caminhos dos arquivos na variável arquivos_csv
arquivos_csv=list.files(pasta_ctd,pattern = '*.Csv',full.names = T)
Criando dataframes vazios.
arquivos_txt=c()
dadosF=c()
Lendo cada arquivo Csv e escrevendo uma tabela com a datahora.
for (i in 1:length(arquivos_csv)) {
ArqCabeca=read_lines(arquivos_csv[i],skip=0,n_max=16)
datahora=substr(ArqCabeca[15],11,29) #Tira da linha 15 o valor
ArqDados=read_lines(arquivos_csv[i], skip=43)
arquivos_txt[i]=gsub('Csv','txt',arquivos_csv[i])
write_lines(ArqDados,arquivos_txt[i])
dados=read.csv(arquivos_txt[i], header = T)
dados$datahora=datahora
dados$tempo=as.POSIXct(dados$datahora,format = "%Y/%m/%d %H:%M:%S")
if (i==1) {
dadosF = dados
} else {
dadosF = rbind(dadosF,dados)
}
}
Quem faz a mágica é o pacote Lattice. Há como melhorar a interpolação usando o próprio pacote.
levelplot(dadosF$Sal..... ~ as.numeric(format(dadosF$tempo,"%H"))*-dadosF$Depth..m.,
data = dadosF,
xlab = "X Coordinate (feet)", ylab = "Y Coordinate (feet)",
main = "Surface elevation data",
col.regions = colorRampPalette(c("blue","red"))(30))

Plotando gráficos de perfil de cada campanha.
dados=subset(dadosF,
as.numeric(format(dadosF$tempo,'%H'))==10)
par(mfrow=c(1,5))
plot(dados$Sal.....,-dados$Depth..m.,ty='l',
xlab='Salinity (psu)',
ylab='Depth (m)')
plot(dados$Temp...deg.C.,-dados$Depth..m.,ty='l',
xlab='Temperature (oC)',
ylab='')
plot(dados$Chl.a..ug.l.,-dados$Depth..m.,ty='l',
xlab='Chlorophyl (ug/L)',
ylab='')
plot(dados$Turb.M..FTU.,-dados$Depth..m.,ty='l',
xlab='Turbidity (FTU)',
ylab='')
plot(dados$DO....,-dados$Depth..m.,ty='l',
xlab='Oxygen (%)',
ylab='')

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