Graphs

library(ttdviewer)
library(inspectdf)
library(dplyr)
library(tidyr)

Below are examples of the data visualizations using inspectdf in the Shiny app.

Data

We’ll need data to demonstrate how the inspect_plot() function works. We’ll use the load_tt_data() function to collect four lists of data from the TidyTuesday repo.

"Netflix Titles" has a single dataset:

ttd <- load_tt_data("Netflix Titles")
#> INFO [2025-07-24 06:20:46] Starting import for netflix_titles.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2021/2021-04-20/netflix_titles.csv
#> SUCCESS [2025-07-24 06:20:46] Successfully loaded netflix_titles.csv
purrr::map(.x = ttd, .f = attr, "clean_title")
#> $netflix_titles.csv
#> [1] "netflix_titles"
# for a list with two datasets, we'll use the `"Space Launches"` data
ttd2 <- load_tt_data("Space Launches")
#> INFO [2025-07-24 06:20:46] Starting import for agencies.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-01-15/agencies.csv
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded agencies.csv
#> INFO [2025-07-24 06:20:47] Starting import for launches.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-01-15/launches.csv
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded launches.csv
purrr::map(.x = ttd2, .f = attr, "clean_title")
#> $agencies.csv
#> [1] "space_launches"
#> 
#> $launches.csv
#> [1] "space_launches"
# "Moore's Law" contains three datasets
ttd3 <- load_tt_data("Moore’s Law")
#> INFO [2025-07-24 06:20:47] Starting import for cpu.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/cpu.csv
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded cpu.csv
#> INFO [2025-07-24 06:20:47] Starting import for gpu.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/gpu.csv
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded gpu.csv
#> INFO [2025-07-24 06:20:47] Starting import for ram.csv from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2019/2019-09-03/ram.csv
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded ram.csv
purrr::map(.x = ttd3, .f = attr, "clean_title")
#> $cpu.csv
#> [1] "moores_law"
#> 
#> $gpu.csv
#> [1] "moores_law"
#> 
#> $ram.csv
#> [1] "moores_law"
# finally, we'll download the "LEGO database" data with 12 datasets into the
# ttd_many list:
ttd_many <- load_tt_data("LEGO database")
#> INFO [2025-07-24 06:20:47] Starting import for colors.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/colors.csv.gz
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded colors.csv.gz
#> INFO [2025-07-24 06:20:47] Starting import for elements.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/elements.csv.gz
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded elements.csv.gz
#> INFO [2025-07-24 06:20:47] Starting import for inventories.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/inventories.csv.gz
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded inventories.csv.gz
#> INFO [2025-07-24 06:20:47] Starting import for inventory_minifigs.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/inventory_minifigs.csv.gz
#> SUCCESS [2025-07-24 06:20:47] Successfully loaded inventory_minifigs.csv.gz
#> INFO [2025-07-24 06:20:47] Starting import for inventory_parts.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/inventory_parts.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded inventory_parts.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for inventory_sets.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/inventory_sets.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded inventory_sets.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for minifigs.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/minifigs.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded minifigs.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for part_categories.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/part_categories.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded part_categories.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for part_relationships.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/part_relationships.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded part_relationships.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for parts.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/parts.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded parts.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for sets.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/sets.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded sets.csv.gz
#> INFO [2025-07-24 06:20:48] Starting import for themes.csv.gz from https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/main/data/2022/2022-09-06/themes.csv.gz
#> SUCCESS [2025-07-24 06:20:48] Successfully loaded themes.csv.gz
purrr::map(.x = ttd_many, .f = attr, "clean_title")
#> $colors.csv.gz
#> [1] "lego_database"
#> 
#> $elements.csv.gz
#> [1] "lego_database"
#> 
#> $inventories.csv.gz
#> [1] "lego_database"
#> 
#> $inventory_minifigs.csv.gz
#> [1] "lego_database"
#> 
#> $inventory_parts.csv.gz
#> [1] "lego_database"
#> 
#> $inventory_sets.csv.gz
#> [1] "lego_database"
#> 
#> $minifigs.csv.gz
#> [1] "lego_database"
#> 
#> $part_categories.csv.gz
#> [1] "lego_database"
#> 
#> $part_relationships.csv.gz
#> [1] "lego_database"
#> 
#> $parts.csv.gz
#> [1] "lego_database"
#> 
#> $sets.csv.gz
#> [1] "lego_database"
#> 
#> $themes.csv.gz
#> [1] "lego_database"

Utility functions

The inspect_plot() function has the following utility functions:

Check length

Check the length of the ttd3 list with ttd_length():

ttd_length(ttd = ttd3)
#> [1] 3

Check numeric columns

Check if datasets have numerical columns:

check_ttd_num_cols(ttd = ttd3)
#> $cpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $gpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $ram.csv
#> [1] "capacity_bits"        "transistor_count"     "date_of_introduction"
#> [4] "process"              "area"

• If so, do they have two or more?

Check categorical columns

Check if datasets have categorical (character) columns:

check_ttd_cat_cols(ttd3)
#> $cpu.csv
#> [1] "processor" "designer" 
#> 
#> $gpu.csv
#> [1] "processor"      "designer_s"     "manufacturer_s" "ref"           
#> 
#> $ram.csv
#> [1] "chip_name"      "bit_units"      "ram_type"       "manufacturer_s"
#> [5] "ref"

Check list columns

Check list columns:

check_ttd_list_cols(ttd3)
#> $cpu.csv
#> [1] 0
#> 
#> $gpu.csv
#> [1] 0
#> 
#> $ram.csv
#> [1] 0

Check logical columns

Check logical columns:

check_ttd_log_cols(ttd3)
#> $cpu.csv
#> [1] 0
#> 
#> $gpu.csv
#> [1] 0
#> 
#> $ram.csv
#> [1] 0

Check date columns

Check date columns:

check_ttd_date_cols(ttd3)
#> $cpu.csv
#> [1] 0
#> 
#> $gpu.csv
#> [1] 0
#> 
#> $ram.csv
#> [1] 0

check_col_types()

These are wrapped in the check_col_types() function:

identical( # check the
  x = length(check_col_types(ttd = ttd3, type = "cat")),
  y = length(check_ttd_cat_cols(ttd3))
)
#> [1] TRUE

identical(
  x = length(check_col_types(ttd = ttd3, type = "date")),
  y = length(check_ttd_date_cols(ttd3))
)
#> [1] TRUE

identical(
  x = length(check_col_types(ttd = ttd3, type = "log")),
  y = length(check_ttd_log_cols(ttd3))
)
#> [1] TRUE

identical(
  x = length(check_col_types(ttd = ttd3, type = "num")),
  y = length(check_ttd_num_cols(ttd3))
)
#> [1] TRUE

The functions above allow us to collect the following:

1. Determine the number of datasets in the ttd list
   
2. Determine the name of the datasets in the ttd list
   
3. Determine the number of numerical columns in each dataset in the ttd list
   
4. Determine the names of any intersecting numerical columns in each dataset in the ttd list
   
5. Determine the number of categorical columns in each dataset in the ttd list
   
6. Determine the names of any intersecting categorical columns in each dataset in the ttd list
   
7. Determine the number of logical columns in each dataset in the ttd list
   
8. Determine the number of date columns in each dataset in the ttd list
   
9. Determine the names of any date columns in each dataset in the ttd list
   
10. Determine the number of list columns in each dataset in the ttd list
    
11. Determine the names of any list columns in each dataset in the ttd list

Function logic

Using the functions above, write the custom inspect_plot() function and have it adhere to the following rules:

inspect_types()

if (length(ttd) == 1)

If one dataset in ttd:

inspectdf::inspect_types(
  df1 = ttd2[[1]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(ttd = 
    list("agencies.csv" = ttd2[[1]]), 
  plots = "types")
#> INFO [2025-07-24 06:20:49] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:20:49] Beginning plot type 'types'

#> SUCCESS [2025-07-24 06:20:50] Completed plot type 'types'
#> SUCCESS [2025-07-24 06:20:50] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

• check for intersecting columns

• if columns intersect, store column names as intersecting_columns

if (length(intersect(names(ttd2[[1]]), names(ttd2[[2]]))) > 0) {
  intersecting_columns <- intersect(
  x = names(ttd2[[1]]), 
  y = names(ttd2[[2]])
  )
}

• Then use intersecting_columns to subset ttd for the inspect_cat() function:

inspectdf::inspect_types(
  df1 = ttd2[[1]][intersecting_columns],
  df2 = ttd2[[2]][intersecting_columns],
) |>
  inspectdf::show_plot(text_labels = TRUE)

if (length(ttd) >= 3)

If more than two datasets in ttd:

inspectdf::inspect_types(
  df1 = ttd3[[1]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("cpu.csv" = ttd3[[1]]),
  plots = "types"
  )
#> INFO [2025-07-24 06:20:53] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:20:53] Beginning plot type 'types'

#> SUCCESS [2025-07-24 06:20:53] Completed plot type 'types'
#> SUCCESS [2025-07-24 06:20:53] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_types(
  df1 = ttd3[[2]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("gpu.csv" = ttd3[[2]]), 
  plots = "types"
  )
#> INFO [2025-07-24 06:20:55] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:20:55] Beginning plot type 'types'

#> SUCCESS [2025-07-24 06:20:56] Completed plot type 'types'
#> SUCCESS [2025-07-24 06:20:56] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_types(
  df1 = ttd3[[3]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("ram.csv" = ttd3[[3]]), 
  plots = "types"
  )
#> INFO [2025-07-24 06:20:58] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:20:58] Beginning plot type 'types'

#> SUCCESS [2025-07-24 06:20:58] Completed plot type 'types'
#> SUCCESS [2025-07-24 06:20:58] inspect_plot(): all requested plots finished
#> NULL

inspect_mem()

if (length(ttd) == 1)

If one dataset in ttd:

inspectdf::inspect_mem(
  df1 = ttd[[1]],
  df2 = NULL) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("netflix_titles.csv" = ttd[[1]]), 
  plots = "mem"
  )
#> INFO [2025-07-24 06:21:00] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:00] Beginning plot type 'mem'

#> SUCCESS [2025-07-24 06:21:00] Completed plot type 'mem'
#> SUCCESS [2025-07-24 06:21:00] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

inspectdf::inspect_mem(
  df1 = ttd2[[1]], 
  df2 = ttd2[[2]]) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list(
    "agencies.csv" = ttd2[[1]],
    "launches.csv" = ttd2[[2]]
    ), 
  plots = "mem")
#> INFO [2025-07-24 06:21:02] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:02] Beginning plot type 'mem'

#> SUCCESS [2025-07-24 06:21:03] Completed plot type 'mem'
#> SUCCESS [2025-07-24 06:21:03] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) >= 3)

If more than two datasets in ttd:

inspectdf::inspect_mem(
  df1 = ttd3[[1]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("cpu.csv" = ttd3[[1]]), 
  plots = "mem"
  )
#> INFO [2025-07-24 06:21:05] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:05] Beginning plot type 'mem'

#> SUCCESS [2025-07-24 06:21:05] Completed plot type 'mem'
#> SUCCESS [2025-07-24 06:21:05] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_mem(
  df1 = ttd3[[2]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("gpu.csv" = ttd3[[2]]), 
  plots = "mem"
  )
#> INFO [2025-07-24 06:21:07] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:07] Beginning plot type 'mem'

#> SUCCESS [2025-07-24 06:21:07] Completed plot type 'mem'
#> SUCCESS [2025-07-24 06:21:07] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_mem(
  df1 = ttd3[[3]],
  df2 = NULL
) |>
  inspectdf::show_plot(text_labels = TRUE)

# ... for all datasets in ttd list

inspect_plot(
  ttd = list("ram.csv" = ttd3[[3]]), 
  plots = "mem"
  )
#> INFO [2025-07-24 06:21:08] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:08] Beginning plot type 'mem'

#> SUCCESS [2025-07-24 06:21:09] Completed plot type 'mem'
#> SUCCESS [2025-07-24 06:21:09] inspect_plot(): all requested plots finished
#> NULL

inspect_cor()

if (length(ttd) == 1)

If one dataset in ttd:

• check if there are at least 2 numeric columns in the datasets in the ttd list
  • Plot the dataset with at least two numeric columns

if (ttd_length(ttd2) == 2) { # check for two datasets
  # check for two numerical columns in ttd[[1]]
  if (length(check_ttd_num_cols(ttd = ttd2)[[1]]) == 2) {
    # plot first dataset
    inspectdf::inspect_cor(
      df1 = ttd2[[1]],
      df2 = NULL,
      method = "pearson",
      with_col = NULL,
      alpha = 0.05
    ) |>
      inspectdf::show_plot(text_labels = TRUE)
    # check for two numerical columns in ttd[[2]]
  } else if (length(check_ttd_num_cols(ttd = ttd2)[[2]]) >= 2) {
    # plot second dataset
    inspectdf::inspect_cor(
      df1 = ttd2[[2]],
      df2 = NULL,
      method = "pearson",
      with_col = NULL,
      alpha = 0.05
    ) |>
      inspectdf::show_plot(text_labels = TRUE)
  } else {
    logr_msg("Error", level = "INFO")
  }
}

inspect_plot(
  ttd = list(
    "launches.csv" = ttd2[[2]]
    ), 
  plots = "cor"
  )
#> INFO [2025-07-24 06:21:10] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:10] Beginning plot type 'cor'

#> SUCCESS [2025-07-24 06:21:10] Completed plot type 'cor'
#> SUCCESS [2025-07-24 06:21:10] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

• check if there is at least 1 numeric column in each dataset from the ttd list

check_col_types(ttd2, type = "num")
#> $agencies.csv
#> [1] "count"
#> 
#> $launches.csv
#> [1] "JD"          "launch_year"

check_col_types(ttd3, type = "num")
#> $cpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $gpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $ram.csv
#> [1] "capacity_bits"        "transistor_count"     "date_of_introduction"
#> [4] "process"              "area"

• Run inspectdf::inspect_cor()if at least one numerical column is in each dataset:

inspectdf::inspect_cor(
  df1 = ttd3[[1]],
  df2 = ttd3[[2]],
  method = "pearson",
  with_col = NULL,
  alpha = 0.05
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list(
    "cpu.csv" = ttd3[[1]],
    "gpu.csv" = ttd3[[2]]
  ), 
  plots = "cor"
  )
#> INFO [2025-07-24 06:21:12] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:12] Beginning plot type 'cor'

#> SUCCESS [2025-07-24 06:21:12] Completed plot type 'cor'
#> SUCCESS [2025-07-24 06:21:12] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) >= 3)

If more than two datasets in ttd:

• check if there is at least 1 numeric column in each of the datasets from the ttd list:

check_col_types(ttd3, type = "num")
#> $cpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $gpu.csv
#> [1] "transistor_count"     "date_of_introduction" "process"             
#> [4] "area"                
#> 
#> $ram.csv
#> [1] "capacity_bits"        "transistor_count"     "date_of_introduction"
#> [4] "process"              "area"

• Run the following on all datasets with at least two numerical columns.

inspectdf::inspect_cor(
  df1 = ttd3[[1]],
  df2 = NULL,
  method = "pearson",
  with_col = NULL,
  alpha = 0.05
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("cpu.csv" = ttd3[[1]]), 
  plots = "cor")
#> INFO [2025-07-24 06:21:14] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:14] Beginning plot type 'cor'

#> SUCCESS [2025-07-24 06:21:14] Completed plot type 'cor'
#> SUCCESS [2025-07-24 06:21:14] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_cor(
  df1 = ttd3[[2]],
  df2 = NULL,
  method = "pearson",
  with_col = NULL,
  alpha = 0.05
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("gpu.csv" = ttd3[[2]]), 
  plots = "cor")
#> INFO [2025-07-24 06:21:16] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:16] Beginning plot type 'cor'

#> SUCCESS [2025-07-24 06:21:16] Completed plot type 'cor'
#> SUCCESS [2025-07-24 06:21:16] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_cor(
  df1 = ttd3[[3]],
  df2 = NULL,
  method = "pearson",
  with_col = NULL,
  alpha = 0.05
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("ram.csv" = ttd3[[3]]), 
  plots = "cor")
#> INFO [2025-07-24 06:21:18] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:18] Beginning plot type 'cor'

#> SUCCESS [2025-07-24 06:21:18] Completed plot type 'cor'
#> SUCCESS [2025-07-24 06:21:18] inspect_plot(): all requested plots finished
#> NULL
# for all other dataset in the list

inspect_imb()

if (length(ttd) == 1)

If one dataset in ttd:

inspectdf::inspect_imb(
  df1 = ttd[[1]],
  df2 = NULL,
  include_na = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("netflix_titles.csv" = ttd[[1]]), 
  plots = "imb")
#> INFO [2025-07-24 06:21:20] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:20] Beginning plot type 'imb'

#> SUCCESS [2025-07-24 06:21:20] Completed plot type 'imb'
#> SUCCESS [2025-07-24 06:21:20] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

inspectdf::inspect_imb(
  df1 = ttd2[[1]],
  df2 = ttd2[[2]],
  include_na = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list(
    "agencies.csv" = ttd2[[1]],
    "launches.csv" = ttd2[[2]]
    ), 
  plots = "imb")
#> INFO [2025-07-24 06:21:22] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:22] Beginning plot type 'imb'

#> SUCCESS [2025-07-24 06:21:23] Completed plot type 'imb'
#> SUCCESS [2025-07-24 06:21:23] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) >= 3)

If more than two datasets in ttd:

inspectdf::inspect_imb(
  df1 = ttd3[[1]],
  df2 = NULL,
  include_na = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("cpu.csv" = ttd3[[1]]), 
  plots = "imb")
#> INFO [2025-07-24 06:21:24] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:24] Beginning plot type 'imb'

#> SUCCESS [2025-07-24 06:21:24] Completed plot type 'imb'
#> SUCCESS [2025-07-24 06:21:24] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_imb(
  df1 = ttd3[[2]],
  df2 = NULL,
  include_na = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("gpu.csv" = ttd3[[2]]), 
  plots = "imb")
#> INFO [2025-07-24 06:21:26] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:26] Beginning plot type 'imb'

#> SUCCESS [2025-07-24 06:21:26] Completed plot type 'imb'
#> SUCCESS [2025-07-24 06:21:26] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_imb(
  df1 = ttd3[[3]],
  df2 = NULL,
  include_na = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("ram.csv" = ttd3[[3]]), 
  plots = "imb")
#> INFO [2025-07-24 06:21:28] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:28] Beginning plot type 'imb'

#> SUCCESS [2025-07-24 06:21:28] Completed plot type 'imb'
#> SUCCESS [2025-07-24 06:21:28] inspect_plot(): all requested plots finished
#> NULL
# ... for all datasets in ttd list

inspect_num()

if (length(ttd) == 1)

If one dataset in ttd:

• check if there is at least 1 numeric column in the dataset in the ttd list

check_ttd_num_cols(ttd2)
#> $agencies.csv
#> [1] "count"
#> 
#> $launches.csv
#> [1] "JD"          "launch_year"

• if so, run:

inspectdf::inspect_num(
  df1 = ttd2[[1]],
  df2 = NULL,
  breaks = 20,
  include_int = TRUE
) |>
  inspectdf::show_plot(text_labels = TRUE)

BUG: inspect_plot() doesn’t seem to identify when a list has a dataset with single numerical column.

inspect_plot(
  ttd = list("agencies.csv" = ttd2[[1]]), 
  plots = "num")
#> INFO [2025-07-24 06:21:30] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:30] Beginning plot type 'num'
#> SUCCESS [2025-07-24 06:21:30] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:30] inspect_plot(): all requested plots finished
#> NULL

If will work if the list has two numeric columns:

inspect_plot(
  ttd = list("launches.csv" = ttd2[[2]]), 
  plots = "num")
#> INFO [2025-07-24 06:21:30] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:30] Beginning plot type 'num'

#> SUCCESS [2025-07-24 06:21:30] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:30] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

• check if there is at least 1 numeric column in each of the two datasets in the ttd list

  • if so, run:

inspectdf::inspect_num(
  df1 = ttd2[[1]],
  df2 = ttd2[[2]],
  breaks = 20,
  include_int = TRUE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list(
  "agencies.csv" = ttd2[[1]],
  "launches.csv" = ttd2[[2]]),
  plots = "num")
#> INFO [2025-07-24 06:21:32] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:32] Beginning plot type 'num'

#> SUCCESS [2025-07-24 06:21:33] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:33] inspect_plot(): all requested plots finished
#> NULL

• if there is only one numeric column, run the code above on the dataset with the numeric column.

if (length(ttd) >= 3)

If more than two datasets in ttd:

inspectdf::inspect_num(
  df1 = ttd3[[1]],
  df2 = NULL,
  breaks = 20,
  include_int = TRUE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("cpu.csv" = ttd3[[1]]), 
  plots = "num")
#> INFO [2025-07-24 06:21:35] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:35] Beginning plot type 'num'

#> SUCCESS [2025-07-24 06:21:35] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:35] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_num(
  df1 = ttd3[[2]],
  df2 = NULL,
  breaks = 20,
  include_int = TRUE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("gpu.csv" = ttd3[[2]]), 
  plots = "num")
#> INFO [2025-07-24 06:21:37] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:37] Beginning plot type 'num'

#> SUCCESS [2025-07-24 06:21:37] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:37] inspect_plot(): all requested plots finished
#> NULL

inspectdf::inspect_num(
  df1 = ttd3[[3]],
  df2 = NULL,
  breaks = 20,
  include_int = TRUE
) |>
  inspectdf::show_plot(text_labels = TRUE)

# ... for all datasets in ttd list

inspect_plot(
  ttd = list("ram.csv" = ttd3[[3]]), 
  plots = "num")
#> INFO [2025-07-24 06:21:39] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:39] Beginning plot type 'num'

#> SUCCESS [2025-07-24 06:21:40] Completed plot type 'num'
#> SUCCESS [2025-07-24 06:21:40] inspect_plot(): all requested plots finished
#> NULL

inspect_cat()

if (length(ttd) == 1)

If one dataset in ttd:

• check if there is at least 1 categorical column in the dataset in the ttd list
  • if so, run:

inspectdf::inspect_cat(
  df1 = ttd[[1]],
  df2 = NULL,
  include_int = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("netflix_titles.csv" = ttd[[1]]), 
  plots = "cat")
#> INFO [2025-07-24 06:21:43] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:43] Beginning plot type 'cat'

#> SUCCESS [2025-07-24 06:21:45] Completed plot type 'cat'
#> SUCCESS [2025-07-24 06:21:45] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) == 2)

If two datasets in ttd:

• check for intersecting columns in the two datasets in the ttd list

length(
  intersect(
    x = check_ttd_cat_cols(ttd2)[[1]],
    y = check_ttd_cat_cols(ttd2)[[2]]
  )
) > 0
#> [1] TRUE

• If TRUE, store intersecting_columns

intersecting_columns <- intersect(x = names(ttd2[[1]]), y = names(ttd2[[2]]))

• Use intersecting_columns to subset each dataset in ttd list.

inspectdf::inspect_cat(
  df1 = ttd2[[1]][intersecting_columns],
  df2 = ttd2[[2]][intersecting_columns],
  include_int = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list(
    "agencies.csv" = ttd2[[1]][intersecting_columns],
    "launches.csv" = ttd2[[2]][intersecting_columns]
    ), 
  plots = "cat")
#> INFO [2025-07-24 06:21:47] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:47] Beginning plot type 'cat'

#> SUCCESS [2025-07-24 06:21:48] Completed plot type 'cat'
#> SUCCESS [2025-07-24 06:21:48] inspect_plot(): all requested plots finished
#> NULL

• if there aren’t any intersecting categorical columns, run the following on the dataset with the categorical column ttd[[?]]:

inspectdf::inspect_cat(
  df1 = ttd[[1]],
  df2 = NULL,
  include_int = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("netflix_titles.csv" = ttd[[1]]), 
  plots = "cat")
#> INFO [2025-07-24 06:21:51] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:51] Beginning plot type 'cat'

#> SUCCESS [2025-07-24 06:21:53] Completed plot type 'cat'
#> SUCCESS [2025-07-24 06:21:53] inspect_plot(): all requested plots finished
#> NULL

if (length(ttd) >= 3)

If more than two datasets in ttd:

inspectdf::inspect_cat(
  df1 = ttd_many[[1]],
  df2 = NULL,
  include_int = FALSE
) |>
  inspectdf::show_plot(text_labels = TRUE)

inspect_plot(
  ttd = list("colors.csv.gz" = ttd_many[[1]]), 
  plots = "cat")
#> INFO [2025-07-24 06:21:55] inspect_plot(): starting analysis
#> INFO [2025-07-24 06:21:55] Beginning plot type 'cat'

#> SUCCESS [2025-07-24 06:21:55] Completed plot type 'cat'
#> SUCCESS [2025-07-24 06:21:55] inspect_plot(): all requested plots finished
#> NULL