Text to table

# Assuming text_data is your text data
text_data <- "Name\tAge\tCity\nJohn\t25\tNew York\nAlice\t30\tLos Angeles"

# Reading text data into a data frame
df <- read.table(text = text_data, header = TRUE, sep = "\t")

# Print the resulting data frame
print(df)

Python

import pandas as pd

# Assuming text_data is your text data
text_data = "Name\tAge\tCity\nJohn\t25\tNew York\nAlice\t30\tLos Angeles"

# Reading text data into a data frame
df = pd.read_csv(pd.compat.StringIO(text_data), sep='\t')

# Print the resulting data frame
print(df)

Both of these examples assume that your text data is structured with a delimiter (tab \t in this case) that separates columns. Adjust the sep parameter accordingly if your data is separated by a different character.

I want to use a dictionary-like, like this:
Subject1
Item1:
Item2:
Subject2
Item1:
Item2:

If you have a hierarchical structure where each subject has items underneath it, and you want to convert this structure into a table or a data frame, you can use a nested dictionary in Python. Here’s an example:

data = {
    'Subject1': {
        'Item1': None,
        'Item2': None
    },
    'Subject2': {
        'Item1': None,
        'Item2': None
    }
}

# Convert nested dictionary to a data frame
df = pd.json_normalize(data, sep='_')

# Print the resulting data frame
print(df)

This will produce a data frame with columns like Subject1_Item1, Subject1_Item2, Subject2_Item1, Subject2_Item2. Each row will represent a combination of subject and item.

In R, you can achieve a similar result with a nested list:

data <- list(
  Subject1 = list(Item1 = NULL, Item2 = NULL),
  Subject2 = list(Item1 = NULL, Item2 = NULL)
)

# Convert nested list to a data frame
df <- as.data.frame(do.call(rbind, lapply(data, function(x) unlist(x))))

# Print the resulting data frame
print(df)

This will give you a data frame with columns like Subject1_Item1, Subject1_Item2, Subject2_Item1, Subject2_Item2 in R.

Adjust the structure of the nested dictionary or list according to your specific hierarchy and data.

If you want the output to be a matrix where columns represent items and rows represent subjects, you can transpose the resulting data frame. Here’s how you can do it in both Python and R:

Python

import pandas as pd

data = {
    'Subject1': {
        'Item1': None,
        'Item2': None
    },
    'Subject2': {
        'Item1': None,
        'Item2': None
    }
}

# Convert nested dictionary to a data frame
df = pd.json_normalize(data, sep='_')

# Transpose the data frame
df_transposed = df.transpose()

# Print the resulting transposed data frame (matrix)
print(df_transposed)

data <- list(
  Subject1 = list(Item1 = NULL, Item2 = NULL),
  Subject2 = list(Item1 = NULL, Item2 = NULL)
)

# Convert nested list to a data frame
df <- as.data.frame(do.call(rbind, lapply(data, function(x) unlist(x))))

# Transpose the data frame
df_transposed <- t(df)

# Print the resulting transposed data frame (matrix)
print(df_transposed)
In both cases, the resulting transposed matrix will have subjects as rows and items as columns. Adjust the code according to your specific data structure if needed.

In both cases, the resulting transposed matrix will have subjects as rows and items as columns. Adjust the code according to your specific data structure if needed.