On reading in multiple files and applying functions to each simultaneously

[ASandy]

I have a project in which I am trying to read in 9 GIS shapefiles into R Studio IDE. I was able to scour the internet and initiate the file.path function to read the shapefiles at once as a list. Past this point is where I begin to have problems.

I'm hoping to get guidance on how to build a comprehensive workflow to perform the same analysis on these shapefiles to efficiently save time, of course. I had trouble even being able to "read" the shapefiles as demonstrated in the reproducible code with different attempts. "St_read" was not effective and neither was "readOGR" along with the "lapply" function, which I believe is the crux of this enterprise.

I'm looking forward to any assistance that can help me efficiently evaluate multiple shapefiles at once.

getwd()
[1] "C:/Users/Akil/Desktop/SpatialDataAnalysis_EHailData/DistanceAnalyses"
#Arranging libraries necessary for loading
BarbaraGordon <- c("rgdal","tidyverse","purrr", "maptools", "ggplot2", "ggmap", "DescTools",

•                "dplyr", "tidyr", "tmap", "sp", "sf", "jaccard", "datatable",
  

•                "raster", "GISTools", "ggspatial", "here")
  

#Loading all libraries at once
lapply(BarbaraGordon, library, character.only = TRUE)
Error: package or namespace load failed for ‘ggmap’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):
there is no package called ‘jpeg’
(bwayne <- file.path("C:/SpatialDataAnalysis_EHailData/DistanceAnalyses", c("JunoDOs_dist_JunoPUs.shp","JunoDO_distLyftPUs.shp",

•                                                                     "JunoDO_dist_UberPUs.shp","LyftDOs_dist_LyftPUs.shp",
  

•                                                                     "LyftDOs_dist_JunoPUs.shp",
  

•                                                                      "LyftDOs_dist_UberPUs.shp", "UberDOs_dist_UberPUs.shp",
  

•                                                                      "UberDOs_distJunoPUs.shp", "UberDOs_dist_LyftPUs.shp")))
  

[1] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/JunoDOs_dist_JunoPUs.shp"
[2] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/JunoDO_distLyftPUs.shp"
[3] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/JunoDO_dist_UberPUs.shp"
[4] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/LyftDOs_dist_LyftPUs.shp"
[5] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/LyftDOs_dist_JunoPUs.shp"
[6] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/LyftDOs_dist_UberPUs.shp"
[7] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/UberDOs_dist_UberPUs.shp"
[8] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/UberDOs_distJunoPUs.shp"
[9] "C:/SpatialDataAnalysis_EHailData/DistanceAnalyses/UberDOs_dist_LyftPUs.shp"

alfred <- lapply(bwayne, st_read)
Error in match.fun(FUN) : object 'st_read' not found
alfred <- lapply(bwayne, FUN = st_read)
Error in match.fun(FUN) : object 'st_read' not found
list.files(bwayne)
character(0)
listOfShp <- lapply(bwayne, st_read)
Error in match.fun(FUN) : object 'st_read' not found
listOfShp <- lapply(bwayne, readOGR)
Error in ogrListLayers(dsn = dsn) : Cannot open data source

Thank you

[woodward]

lapply is good but I think using a loop is easier for some things.

mylist <- ... # my list of objects
for (i in seq_along(mylist)){
   # do stuff to mylist[[i]]
}

You can also start off with an empty list and build the list inside the loop.

mylist <- vector("list", number_of_items) # empty list of objects 
for (i in seq_along(mylist)){
   mylist[[i]] <- ... # read in or create mylist[[i]]
   # do stuff to mylist[[i]]
}

[woodward]

That's an awkward method of loading libraries.

When I installed jaccard I couldn't find a dependency called qvalue. I had to get it from here:

qvalue

Did you mean "data.table" with a dot? I can't find "datatable"

st_read is not found because the sf package has not been successfully loaded.

[ASandy]

OK, thank you for your response. As for the library loading, I'm still very new to programming, in RStudio and in general. I learned this method from an online data science source that I can't name now, but I always thought it was very effective. It appears with this troubleshooting that you have alerted me to an inefficiency. I will edit accordingly.