I'm new in R, and I'm trying to display in console several columns from a row when a condition is fulfilled. I've searched through the internet and I couldn't find a proper solution. At the moment, I've tried the R where clause with little success.
Here's my script.
#Coordinates
northing <- 398380.16
easting <- 6873865.89
filePath = '/media/jgm/Toshiba\ HDD/SatelliteData/data/'
file = 'MOD09GQ_2006075.csv'
mydata <- read.table(paste(filePath,file, sep = ""),header=TRUE,sep=",")
mydata$'(x-northing)²' <- (mydata$x-northing)**2
mydata$'(y-easting)²' <- (mydata$y-easting)**2
mydata$'DISTANCE' <- sqrt(mydata$`(x-northing)²`+mydata$`(y-easting)²`)
minDistance <- min(mydata[,10], na.rm = T)
I want to display in console the value of the columns sur_refl_b01, sur_refl_b02, NDVI and NDVI_SCALED when the value of the column DISTANCE is minDistance.
Hope this table output helps.
welcome to SO, try something like :
print(mydata[which(mydata$'DISTANCE'==minDistance),4:7])
Related
I have been struggling with finding a way to create a new data frame using a loop, where the main goal is to filter the data when is >= 0.5.
I´m using Rstudio; however, python is an option too.
Here is how looks like my data frame (csv file) and some lines of the script (incomplete):
df <- read.table(choose.files(), header = T, sep = ",", comment.char = "")
Site,Partition,alpha,beta,omega,alpha=beta,LRT,p-value,Total branch length
1,1,"0.000","0.000","NaN","0.000","0.000","1.000","0.000"
2,1,"0.060","0.046","0.774","0.048","0.049","0.825","0.000"
Then I use select function to take only two columns that interest me:
sdf <- subset(df, select = c("ï..Site", "alpha.beta"))
ï..Site alpha.beta
1 1 0.000
2 2 0.048
...
Then I thought in use a loop to create a new csv file, when the second column has a value >= 0.5 print this value, it doesn´t have a value that satisfies this requisite pass and print a 0.
Here I try differents ways; obviously neither works for me. Here are the last lines that I tried.
for (i in names(sdf1)) {
f_sdf1 <- sdf1[sdf1[, i] >= 0.5]
write.csv(f_sdf1, paste0(i, ".csv"))
}
So in this post I´m looking for some ideas to generate this script. Maybe it´s simple, but in this case, I need to ask how?
You could use subset to filter your data as in
# first get some example data
expl <- data.frame(site = 1:10, alpha.beta = runif(10))
print(expl)
# now do the filtering
expl.filtered = subset(expl, alpha.beta >= .5)
print(expl.filtered)
# Now write.table or write.csv...
I am trying to create a large number of data frames in a for loop using the "assign" function in R. I want to use the colnames function to set the column names in the data frame. The code I am trying to emulate is the following:
county_tmax_min_df <- data.frame(array(NA,c(length(days),67)))
colnames(county_tmax_min_df) <- c('Date',sd_counties$NAME)
county_tmax_min_df$Date <- days
The code I have so far in the loop looks like this:
file_vars = c('file1','file2')
days <- seq(as.Date("1979-01-01"), as.Date("1979-01-02"), "days")
f = 1
for (f in 1:2){
assign(paste0('county_',file_vars[f]),data.frame(array(NA,c(length(days),67))))
}
I need to be able to set the column names similar to how I did in the above statement. How do I do this? I think it needs to be something like this, but I am unsure what goes in the text portion. The end result I need is just a bunch of data frames. Any help would be wonderful. Thank you.
expression(parse(text = ))
You can set the names within assign, like that:
file_vars = c('file1', 'file2')
days <- seq.Date(from = as.Date("1979-01-01"), to = as.Date("1979-01-02"), by = "days")
for (f in seq_along(file_vars)) {
assign(x = paste0('county_', file_vars[f]),
value = {
df <- data.frame(array(NA, c(length(days), 67)))
colnames(df) <- paste0("fancy_column_",
sample(LETTERS, size = ncol(df), replace = TRUE))
df
})
}
When in {} you can use colnames(df) or setNames to assign column names in any manner desired. In your first piece of code you are referring to sd_counties object that is not available but the generic idea should work for you.
I want to rearrange the data in the dataframe which is originally in following format
3 rows for one project
I have extracted this using "rvest" package
library(rvest)
library(plyr)
library(dplyr)
projects<-NULL
thepage = read_html("https://www.99acres.com/search/project/buy/residential/pune?search_type=QS&search_location=SH&lstAcn=NPSEARCH&lstAcnId=9753976212484323&src=CLUSTER&preference=S&city=19&res_com=R&selected_tab=3&isvoicesearch=N&keyword=pune&strEntityMap=IiI%3D&refine_results=Y&Refine_Localities=Refine%20Localities&action=%2Fdo%2Fquicksearch%2Fsearch&searchform=1&price_min=null&price_max=null")
table = data.frame(html_table(x = thepage, fill = TRUE))
table = as.data.frame(t(table))
ResidentialProjects<-rbind(projects,setNames(table, names(table)))
I want all the details about 1 project (Real Estate Project Name) in one row.
I tried making a code for it as
newdf<-data.frame(matrix(ncol = 10),stringsAsFactors = FALSE)
df=ResidentialProjects
projectName=""
count<-0
for(n in 1:nrow(df)){
if(df[n,]$V1!=projectName){
count = count+1
projectName=df[n,]$V1
newdf[count,c(1,2,3,4)]=df[n,c(1,2,3,4)]
newdf[count,c(5,6,7)]=df[n+1,c(2,3,4)]
newdf[count,c(8,9,10)]=df[n+2,c(2,3,4)]
}else{
print(n)
next
}
}
But its giving me a table of numbers like
Output newdf
what is the problem? or any better option??
Trying to figure why when I run this code all the information from the columns is being written to the first file only. What I want is only the data from the columns unique to a MO number to be written out. I believe the problem is in the third line, but am not sure how to divide the data by each unique number.
Thanks for the help,
for (i in 1:nrow(MOs_InterestDF1)) {
MO = MOs_InterestDF1[i,1]
df = MOs_Interest[MOs_Interest$MO_NUMBER == MO, c("ITEM_NUMBER", "OPER_NO", "OPER_DESC", "STDRUNHRS", "ACTRUNHRS","Difference", "Sum")]
submit.df <- data.frame(df)
filename = paste("Variance", "Report",MO, ".csv", sep="")
write.csv(submit.df, file = filename, row.names = FALSE)}
If you are trying to write out a separate csv for each unique MO number, then something like this may work to accomplish that.
unique.mos <- unique(MOs_Interest$MO_NUMBER)
for (mo in unique.mos){
submit.df <- MOs_Interest[MOs_Interest$MO_NUMBER == mo, c("ITEM_NUMBER", "OPER_NO", "OPER_DESC", "STDRUNHRS", "ACTRUNHRS","Difference", "Sum")]
filename <- paste("Variance", "Report", mo, ".csv", sep="")
write.csv(submit.df, file = filename, row.names = FALSE)
}
It's hard to answer fully without example data (what are the columns of MOs_InterestDF1?) but I think your issue is in the df line. Are you trying to subset the dataframe to only the data matching the MO? If so, try which as in df = MOs_Interest[which(MOs_Interest$MO_NUMBER == MO),].
I wasn't sure if you actually had two separate dfs (MOs_Interest and MOs_InterestDF1); if not, make sure the df line points to the correct data frame.
I tried to create some simplified sample data:
MOs_InterestDF1 <- data.frame("MO_NUMBER" = c(1,2,3), "Item_No" = c(142,423,214), "Desc" = c("Plate","Book","Table"))
for (i in 1:nrow(MOs_InterestDF1)) {
MO = MOs_InterestDF1[i,1]
mydf = data.frame(MOs_InterestDF1[which(MOs_InterestDF1$MO_NUMBER == MO),])
filename = paste("This is number ",MO,".csv", sep="")
write.csv(mydf, file = filename, row.names=FALSE)
}
This output three different csv files, each with exactly one row of data. For example, "This is number 1.csv" had the following data:
MOs Item_No Desc
1 142 Plate
In a dbf I make the new field xyz then attempt to sum existing item1 and item2 fields and replace field xyz with sum and then create a new dbf-- but does not work. All working without the for loop. I hope someone can help. Thank you.
library(foreign)
setwd("C:/temp")
dbfdata <- read.dbf("sldu_500ka.dbf", as.is = TRUE)
dbfdata$xyz <- 1:nrow(dbfdata)
for(i in 1:nrow(dbfdata)) {
row <- dbfdata[i,]
dbfdata$xyz <- dbfdata$item1 + dbfdata$item2
}
write.dbf(dbfdata, "sldu_500k1.dbf")
I'm not sure whether I understand you correctly, but
library(foreign)
setwd("C:/temp")
dbfdata <- read.dbf("sldu_500ka.dbf", as.is = TRUE)
dbfdata$xyz <- dbfdata$item1 + dbfdata$item2
write.dbf(dbfdata, "sldu_500k1.dbf")
should do the job. Instead of looping overall rows, you can add the entire column at once.