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Error: Names must be unique. x These names are duplicated: * "particle_size" at locations 91, 122, and 243. Run rlang::last_error() to see where the error occurred.

I am attempting to combine multiple csv files exported from the nanosight. When I add use the nanocombine function, all the data is merged into one DF, but it pulls particle size from each csv resulting in redundant columns names. I believe this is incompatible with the subsequent nanotidy function.

Create a function or argument to determine surface area and volume from Nanosight data mentioned in #3

Hi there,

First of all, thanks for creating this useful package.

I was having trouble getting the nanoShapiro function to work. When I was using it on a total exosome count table similar to Fig 6A in your preprint it would throw the following error.

Error in mutate_impl(.data, dots) : 
  Column `broom::glance` is of unsupported type function

I made a tiny adjustment to the function which fixed the issue.

Original nanoShapiro

function (df, ..., value) {
  group_var <- rlang::quos(...)
  value <- enquo(value)

  df %>% 
    dplyr::group_by(!!! group_var) %>% 
    tidyr::nest() %>% 
    dplyr::mutate(
        Shapiro = purrr::map(data, ~stats::shapiro.test(pull(.x, quo_name(value)))), 
        glance = purrr::map(Shapiro, broom::glance)) %>% 
    tidyr::unnest(broom::glance, .drop = TRUE) %>% 
    dplyr::mutate(Normal_dist = dplyr::case_when(p.value > 0.05 ~ TRUE, 
                                                 p.value < 0.05 ~ FALSE), 
                  Statistical_test = dplyr::case_when(Normal_dist == TRUE ~ "Perform parametric test",
                                                      Normal_dist == FALSE ~ "Perform non-parametric test"))
}

Adjusted nanoShapiro

function (df, ..., value) {
  group_var <- rlang::quos(...)
  value <- enquo(value)

  df <- df %>% 
    dplyr::group_by(!!! group_var) %>% 
    tidyr::nest() %>% 
    dplyr::mutate(
        Shapiro = purrr::map(data, ~stats::shapiro.test(pull(.x, quo_name(value)))), 
        glance = purrr::map(Shapiro, broom::glance)) %>% 
    tidyr::unnest(broom::glance, .drop = TRUE) %>% 
    dplyr::mutate(Normal_dist = dplyr::case_when(p.value > 0.05 ~ TRUE, 
                                                 p.value < 0.05 ~ FALSE), 
                  Statistical_test = dplyr::case_when(Normal_dist == TRUE ~ "Perform parametric test",
                                                      Normal_dist == FALSE ~ "Perform non-parametric test"))
  return(df)
}

Hopefully that helps anyone else having a similar problem.

Cheers,
Charlotte

Hi there, thanks for writing the package.

I installed and followed your instructions, trying the example first which imports the data like it should. When I go to import my own data i get the following error:

data.file <- "~/Caseinate_NTA/Caseinates_Nanosight/A1A1/A1_35.csv"
data <- nanoimport(data.file)
NTA version: 3
Sample name: Sample 35 caseinates 1500x dil
Error in .f(.x[[i]], ...) : object '[Data Included]' not found

I checked my file and it looks exactly like your example file, and the [Data Included] line is
my file. Help!

I attach my file as a .txt so I could upload it. Just change it back to .csv to use.

A1_35 - Copy.txt

Hello,

I am having a problem while using nanotidy() function. My samples are uploaded with nano import (but each sample on which 5 trials were done on NTA) is uploaded separately and then i merged them. I am having a problem of seeing the dilution, technical replicate information, etc. when i upload these files.

do you have a more detailed vignette or more explanations on what parameters could be used in these two functions with some examples?
In the publication & the Readme file, the uploaded .csv file contains all the samples, whereas normally, typical NTA data comes in separate .csv file for each sample.
thank you
best