Nothing
inst/doc/introduction-to-rPandas.R
In rPandas: Translating from R to Python's Pandas Package
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(eval=FALSE,
collapse = TRUE,
comment = "#>"
)
## ----setup, include=FALSE-----------------------------------------------------
# library(rPandas)
## -----------------------------------------------------------------------------
# reticulate::py_install("pandas")
## -----------------------------------------------------------------------------
# # Run this if you have connection issues
# rp_check_env()
## -----------------------------------------------------------------------------
# reticulate::conda_list()
## -----------------------------------------------------------------------------
# # Replace with the path from conda_list()
# reticulate::use_python(python = reticulate::conda_list()$python[1], required = TRUE)
#
# # Or, if you prefer to use a conda environment by name:
# reticulate::use_condaenv("your_environment_name", required = TRUE)
## -----------------------------------------------------------------------------
# reticulate::use_python("/usr/local/bin/python3", required = TRUE)
## ----eval=TRUE----------------------------------------------------------------
# Make sure ggplot2 is installed to access the data
data(diamonds, package = "ggplot2")
head(diamonds)
## -----------------------------------------------------------------------------
# # Simple condition
# v1 <- rp_filter(diamonds, carat > 1)
# print(head(v1))
# #> carat cut color clarity depth table price x y z
# #> 1 1.17 Very Good J I1 60.2 61 2774 6.83 6.90 4.13
# #> 2 1.01 Premium F I1 61.8 60 2781 6.39 6.36 3.94
# #> 3 1.01 Fair E I1 64.5 58 2788 6.29 6.21 4.03
# #> 4 1.01 Premium H SI2 62.7 59 2788 6.31 6.22 3.93
# #> 5 1.05 Very Good J SI2 63.2 56 2789 6.49 6.45 4.09
# #> 6 1.05 Fair J SI2 65.8 59 2789 6.41 6.27 4.18
#
# # AND: multiple conditions
# v2 <- rp_filter(diamonds, carat > 1 & cut == "Ideal")
# print(head(v2))
# #> carat cut color clarity depth table price x y z
# #> 1 1.01 Ideal I I1 61.5 57 2844 6.45 6.46 3.97
# #> 2 1.02 Ideal H SI2 61.6 55 2856 6.49 6.43 3.98
# #> 3 1.02 Ideal I I1 61.7 56 2872 6.44 6.49 3.99
# #> 4 1.02 Ideal J SI2 60.3 54 2879 6.53 6.50 3.93
# #> 5 1.01 Ideal I I1 61.5 57 2896 6.46 6.45 3.97
# #> 6 1.02 Ideal I I1 61.7 56 2925 6.49 6.44 3.99
#
# # OR: use | (pipe)
# v3 <- rp_filter(diamonds, color == "D" | color == "E")
# print(head(v3))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
# #> 5 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
# #> 6 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
#
#
# # NOT: use !
# v4 <- rp_filter(diamonds, !(price > 10000))
# print(head(v4))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#
#
# # %in% operator
# v5 <- rp_filter(diamonds, color %in% c("D", "E", "F"))
# print(head(v5))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
# #> 5 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
# #> 6 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
#
# # %notin% (if implemented)
# v6 <- rp_filter(diamonds, color %notin% c("D", "E", "F"))
# print(head(v6))
# #> carat cut color clarity depth table price x y z
# #> 1 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 2 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 3 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> 4 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
# #> 5 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
# #> 6 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## -----------------------------------------------------------------------------
# # Select three columns
# v4 <- rp_select(diamonds, carat, cut, price)
# print(head(v4))
# #> carat cut price
# #> 1 0.23 Ideal 326
# #> 2 0.21 Premium 326
# #> 3 0.23 Good 327
# #> 4 0.29 Premium 334
# #> 5 0.31 Good 335
# #> 6 0.24 Very Good 336
## -----------------------------------------------------------------------------
# # Sort by price (ascending by default)
# v8 <- rp_sort(diamonds, price)
# print(head(v8))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#
# # Use desc() to sort in descending order
# v9 <- rp_sort(diamonds, desc(price))
# print(head(v9))
# #> carat cut color clarity depth table price x y z
# #> 1 2.29 Premium I VS2 60.8 60 18823 8.50 8.47 5.16
# #> 2 2.00 Very Good G SI1 63.5 56 18818 7.90 7.97 5.04
# #> 3 1.51 Ideal G IF 61.7 55 18806 7.37 7.41 4.56
# #> 4 2.07 Ideal G SI2 62.5 55 18804 8.20 8.13 5.11
# #> 5 2.00 Very Good H SI1 62.8 57 18803 7.95 8.00 5.01
# #> 6 2.29 Premium I SI1 61.8 59 18797 8.52 8.45 5.24
#
# # Sort by multiple columns
# v10 <- rp_sort(diamonds, cut, desc(price))
# print(head(v10))
# #> carat cut color clarity depth table price x y z
# #> 1 2.01 Fair G SI1 70.6 64 18574 7.43 6.64 4.69
# #> 2 2.02 Fair H VS2 64.5 57 18565 8.00 7.95 5.14
# #> 3 4.50 Fair J I1 65.8 58 18531 10.23 10.16 6.72
# #> 4 2.00 Fair G VS2 67.6 58 18515 7.65 7.61 5.16
# #> 5 2.51 Fair H SI2 64.7 57 18308 8.44 8.50 5.48
# #> 6 3.01 Fair I SI2 65.8 56 18242 8.99 8.94 5.90
## -----------------------------------------------------------------------------
#
# # Create a new column
# v11 <- rp_mutate(diamonds, price_per_carat = price / carat)
# print(head(v11))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> price_per_carat
# #> 1 1417.391
# #> 2 1552.381
# #> 3 1421.739
# #> 4 1151.724
# #> 5 1080.645
# #> 6 1400.000
#
# # Create multiple columns
# v12 <- rp_mutate(
# diamonds,
# price_per_carat = price / carat,
# depth_pct = depth / 100
# )
# print(head(v12))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> price_per_carat depth_pct
# #> 1 1417.391 0.615
# #> 2 1552.381 0.598
# #> 3 1421.739 0.569
# #> 4 1151.724 0.624
# #> 5 1080.645 0.633
# #> 6 1400.000 0.628
#
# # Remove one or more columns
# v13 <- rp_mutate(diamonds, to_remove = c("table", "depth"))
# print(head(v13))
# #> carat cut color clarity price x y z
# #> 1 0.23 Ideal E SI2 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 336 3.94 3.96 2.48
## -----------------------------------------------------------------------------
#
# # Summarize the entire data frame
# v14 <- rp_summarize(diamonds, avg_price = mean(price), max_carat = max(carat))
# print(v14)
# #> price carat
# #> 1 3932.8 NaN
# #> 2 NaN 5.01
#
#
# # Group by one column (unquoted)
# v15 <- rp_summarize(diamonds, avg_price = mean(price), .by = cut)
# print(v15)
# #> cut avg_price
# #> 1 Fair 4358.758
# #> 2 Good 3928.864
# #> 3 Very Good 3981.760
# #> 4 Premium 4584.258
# #> 5 Ideal 3457.542
#
# # Group by multiple columns (unquoted)
# v16 <- rp_summarize(
# diamonds,
# avg_price = mean(price),
# count = n(),
# .by = c(cut, color)
# )
# print(head(v16))
# #> cut color avg_price count
# #> 1 Fair D 4291.061 163
# #> 2 Fair E 3682.312 224
# #> 3 Fair F 3827.003 312
# #> 4 Fair G 4239.255 314
# #> 5 Fair H 5135.683 303
# #> 6 Fair I 4685.446 175
#
# # Grouping also accepts quoted column names
# v17 <- rp_summarize(
# diamonds,
# avg_price = mean(price),
# .by = c("cut", "color")
# )
# print(head(v17))
# #> cut color avg_price
# #> 1 Fair D 4291.061
# #> 2 Fair E 3682.312
# #> 3 Fair F 3827.003
# #> 4 Fair G 4239.255
# #> 5 Fair H 5135.683
# #> 6 Fair I 4685.446
## -----------------------------------------------------------------------------
#
# # Apply two functions to two columns, grouped by 'cut'
# v13 <- rp_calculate(
# diamonds,
# price, carat,
# the.functions = c("mean", "sd"),
# .by = cut
# )
# print(head(v13))
# #> cut price.mean price.std carat.mean carat.std
# #> 1 Fair 4358.758 3560.387 1.0461366 0.5164043
# #> 2 Good 3928.864 3681.590 0.8491847 0.4540544
# #> 3 Very Good 3981.760 3935.862 0.8063814 0.4594354
# #> 4 Premium 4584.258 4349.205 0.8919549 0.5152616
# #> 5 Ideal 3457.542 3808.401 0.7028370 0.4328763
## -----------------------------------------------------------------------------
# # First 3 rows overall
# v19 <- rp_first_k_rows(diamonds, k = 3)
# print(v19)
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#
# # Last 2 rows per group (cut and clarity)
# v20 <- rp_last_k_rows(diamonds, k = 2, .by = c(cut, clarity))
# print(head(v20))
# #> carat cut color clarity depth table price x y z
# #> 1 0.70 Fair J VVS1 67.6 54 1691 5.56 5.41 3.71
# #> 2 0.50 Fair D VVS1 65.9 64 1792 4.92 5.03 3.28
# #> 3 0.52 Fair F IF 64.6 58 2144 5.04 5.17 3.30
# #> 4 0.47 Fair D IF 60.6 60 2211 5.09 4.98 3.05
# #> 5 0.55 Good F IF 60.8 60 2266 5.26 5.36 3.23
# #> 6 0.54 Premium F IF 61.9 60 2391 5.26 5.21 3.24
#
# # Both quoted and unquoted group specifications work
# v21 <- rp_first_k_rows(diamonds, k = 1, .by = c("cut", "color"))
# print(v21)
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
# #> 7 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47
# #> 8 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53
# #> 9 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49
# #> 10 0.23 Ideal J VS1 62.8 56.0 340 3.93 3.90 2.46
# #> 11 0.22 Premium F SI1 60.4 61.0 342 3.88 3.84 2.33
# #> 12 0.30 Ideal I SI2 62.0 54.0 348 4.31 4.34 2.68
# #> 13 0.30 Good I SI2 63.3 56.0 351 4.26 4.30 2.71
# #> 14 0.23 Very Good E VS2 63.8 55.0 352 3.85 3.92 2.48
# #> 15 0.23 Very Good G VVS2 60.4 58.0 354 3.97 4.01 2.41
# #> 16 0.23 Very Good D VS2 60.5 61.0 357 3.96 3.97 2.40
# #> 17 0.23 Very Good F VS1 60.9 57.0 357 3.96 3.99 2.42
# #> 18 0.23 Good F VS1 58.2 59.0 402 4.06 4.08 2.37
# #> 19 0.31 Good H SI1 64.0 54.0 402 4.29 4.31 2.75
# #> 20 0.26 Good D VS2 65.2 56.0 403 3.99 4.02 2.61
# #> 21 0.23 Ideal G VS1 61.9 54.0 404 3.93 3.95 2.44
# #> 22 0.22 Premium D VS2 59.3 62.0 404 3.91 3.88 2.31
# #> 23 0.30 Premium J SI2 59.3 61.0 405 4.43 4.38 2.61
# #> 24 0.30 Ideal D SI1 62.5 57.0 552 4.29 4.32 2.69
# #> 25 0.31 Premium G SI1 61.8 58.0 553 4.35 4.32 2.68
# #> 26 0.30 Premium H SI1 62.9 59.0 554 4.28 4.24 2.68
# #> 27 0.96 Fair F SI2 66.3 62.0 2759 6.27 5.95 4.07
# #> 28 0.81 Ideal F SI2 58.8 57.0 2761 6.14 6.11 3.60
# #> 29 0.91 Fair H SI2 64.4 57.0 2763 6.11 6.09 3.93
# #> 30 0.77 Ideal H VS2 62.0 56.0 2763 5.89 5.86 3.64
# #> 31 0.72 Good G VS2 59.7 60.5 2776 5.80 5.84 3.47
# #> 32 0.84 Fair G SI1 55.1 67.0 2782 6.39 6.20 3.47
# #> 33 1.05 Fair J SI2 65.8 59.0 2789 6.41 6.27 4.18
# #> 34 0.90 Fair I SI1 67.3 59.0 2804 5.93 5.84 3.96
# #> 35 0.75 Fair D SI2 64.6 57.0 2848 5.74 5.72 3.70
#
## -----------------------------------------------------------------------------
# # Total row count
# v22 <- rp_count(diamonds)
# print(v22)
# #> n
# #> 1 53940
#
# # Count per group
# v23 <- rp_count(diamonds, .by = cut)
# print(v23)
# #> cut n
# #> 1 Fair 1610
# #> 2 Good 4906
# #> 3 Very Good 12082
# #> 4 Premium 13791
# #> 5 Ideal 21551
#
# # Count per combination of multiple groups
# v24 <- rp_count(diamonds, .by = c(cut, color))
# print(head(v24))
# #> cut color n
# #> 1 Fair D 163
# #> 2 Fair E 224
# #> 3 Fair F 312
# #> 4 Fair G 314
# #> 5 Fair H 303
# #> 6 Fair I 175
## -----------------------------------------------------------------------------
# # Load the pipe
# v25 <- diamonds |>
# rp_filter(carat > 1 & color == "D") |>
# rp_mutate(price_per_carat = price / carat) |>
# rp_summarize(avg_ppc = mean(price_per_carat), .by = cut) |>
# rp_sort(desc(avg_ppc))
#
# print(head(v25))
# #> cut avg_ppc
# #> 1 Ideal 7546.163
# #> 2 Very Good 6789.316
# #> 3 Premium 6548.397
# #> 4 Good 5784.918
# #> 5 Fair 5414.87
## -----------------------------------------------------------------------------
# # See the code for a simple filter
# rp_filter(diamonds, carat > 1 & price < 400, return.as = "code")
# #> [1] "df.query('(carat > 1) and (price < 400)')"
#
# # See the code for a mutate
# rp_mutate(diamonds, ppc = price / carat, return.as = "code")
# #> [1] "df.assign(ppc = lambda x: (x['price'] / x['carat']))"
#
# # See the code for a complex summary
# rp_summarize(
# diamonds,
# avg_price = mean(price),
# count = n(),
# .by = c(cut, color),
# return.as = "code"
# )
# #> [1] "df.groupby(['cut', 'color'], as_index=False, observed=True).agg(avg_price = ('price', 'mean'), count = ('price', 'size'))"
## -----------------------------------------------------------------------------
# # Default placeholder
# rp_filter(diamonds, carat > 1, return.as = "code")
# #> [1] "df.query('carat > 1')"
#
# # With custom table name
# rp_filter(diamonds, carat > 1, table_name = "diamonds", return.as = "code")
# #> [1] "diamonds.query('carat > 1')"
#
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## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(eval=FALSE,
collapse = TRUE,
comment = "#>"
)
## ----setup, include=FALSE-----------------------------------------------------
# library(rPandas)
## -----------------------------------------------------------------------------
# reticulate::py_install("pandas")
## -----------------------------------------------------------------------------
# # Run this if you have connection issues
# rp_check_env()
## -----------------------------------------------------------------------------
# reticulate::conda_list()
## -----------------------------------------------------------------------------
# # Replace with the path from conda_list()
# reticulate::use_python(python = reticulate::conda_list()$python[1], required = TRUE)
#
# # Or, if you prefer to use a conda environment by name:
# reticulate::use_condaenv("your_environment_name", required = TRUE)
## -----------------------------------------------------------------------------
# reticulate::use_python("/usr/local/bin/python3", required = TRUE)
## ----eval=TRUE----------------------------------------------------------------
# Make sure ggplot2 is installed to access the data
data(diamonds, package = "ggplot2")
head(diamonds)
## -----------------------------------------------------------------------------
# # Simple condition
# v1 <- rp_filter(diamonds, carat > 1)
# print(head(v1))
# #> carat cut color clarity depth table price x y z
# #> 1 1.17 Very Good J I1 60.2 61 2774 6.83 6.90 4.13
# #> 2 1.01 Premium F I1 61.8 60 2781 6.39 6.36 3.94
# #> 3 1.01 Fair E I1 64.5 58 2788 6.29 6.21 4.03
# #> 4 1.01 Premium H SI2 62.7 59 2788 6.31 6.22 3.93
# #> 5 1.05 Very Good J SI2 63.2 56 2789 6.49 6.45 4.09
# #> 6 1.05 Fair J SI2 65.8 59 2789 6.41 6.27 4.18
#
# # AND: multiple conditions
# v2 <- rp_filter(diamonds, carat > 1 & cut == "Ideal")
# print(head(v2))
# #> carat cut color clarity depth table price x y z
# #> 1 1.01 Ideal I I1 61.5 57 2844 6.45 6.46 3.97
# #> 2 1.02 Ideal H SI2 61.6 55 2856 6.49 6.43 3.98
# #> 3 1.02 Ideal I I1 61.7 56 2872 6.44 6.49 3.99
# #> 4 1.02 Ideal J SI2 60.3 54 2879 6.53 6.50 3.93
# #> 5 1.01 Ideal I I1 61.5 57 2896 6.46 6.45 3.97
# #> 6 1.02 Ideal I I1 61.7 56 2925 6.49 6.44 3.99
#
# # OR: use | (pipe)
# v3 <- rp_filter(diamonds, color == "D" | color == "E")
# print(head(v3))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
# #> 5 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
# #> 6 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
#
#
# # NOT: use !
# v4 <- rp_filter(diamonds, !(price > 10000))
# print(head(v4))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#
#
# # %in% operator
# v5 <- rp_filter(diamonds, color %in% c("D", "E", "F"))
# print(head(v5))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
# #> 5 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
# #> 6 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
#
# # %notin% (if implemented)
# v6 <- rp_filter(diamonds, color %notin% c("D", "E", "F"))
# print(head(v6))
# #> carat cut color clarity depth table price x y z
# #> 1 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 2 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 3 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> 4 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
# #> 5 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
# #> 6 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## -----------------------------------------------------------------------------
# # Select three columns
# v4 <- rp_select(diamonds, carat, cut, price)
# print(head(v4))
# #> carat cut price
# #> 1 0.23 Ideal 326
# #> 2 0.21 Premium 326
# #> 3 0.23 Good 327
# #> 4 0.29 Premium 334
# #> 5 0.31 Good 335
# #> 6 0.24 Very Good 336
## -----------------------------------------------------------------------------
# # Sort by price (ascending by default)
# v8 <- rp_sort(diamonds, price)
# print(head(v8))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#
# # Use desc() to sort in descending order
# v9 <- rp_sort(diamonds, desc(price))
# print(head(v9))
# #> carat cut color clarity depth table price x y z
# #> 1 2.29 Premium I VS2 60.8 60 18823 8.50 8.47 5.16
# #> 2 2.00 Very Good G SI1 63.5 56 18818 7.90 7.97 5.04
# #> 3 1.51 Ideal G IF 61.7 55 18806 7.37 7.41 4.56
# #> 4 2.07 Ideal G SI2 62.5 55 18804 8.20 8.13 5.11
# #> 5 2.00 Very Good H SI1 62.8 57 18803 7.95 8.00 5.01
# #> 6 2.29 Premium I SI1 61.8 59 18797 8.52 8.45 5.24
#
# # Sort by multiple columns
# v10 <- rp_sort(diamonds, cut, desc(price))
# print(head(v10))
# #> carat cut color clarity depth table price x y z
# #> 1 2.01 Fair G SI1 70.6 64 18574 7.43 6.64 4.69
# #> 2 2.02 Fair H VS2 64.5 57 18565 8.00 7.95 5.14
# #> 3 4.50 Fair J I1 65.8 58 18531 10.23 10.16 6.72
# #> 4 2.00 Fair G VS2 67.6 58 18515 7.65 7.61 5.16
# #> 5 2.51 Fair H SI2 64.7 57 18308 8.44 8.50 5.48
# #> 6 3.01 Fair I SI2 65.8 56 18242 8.99 8.94 5.90
## -----------------------------------------------------------------------------
#
# # Create a new column
# v11 <- rp_mutate(diamonds, price_per_carat = price / carat)
# print(head(v11))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> price_per_carat
# #> 1 1417.391
# #> 2 1552.381
# #> 3 1421.739
# #> 4 1151.724
# #> 5 1080.645
# #> 6 1400.000
#
# # Create multiple columns
# v12 <- rp_mutate(
# diamonds,
# price_per_carat = price / carat,
# depth_pct = depth / 100
# )
# print(head(v12))
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# #> price_per_carat depth_pct
# #> 1 1417.391 0.615
# #> 2 1552.381 0.598
# #> 3 1421.739 0.569
# #> 4 1151.724 0.624
# #> 5 1080.645 0.633
# #> 6 1400.000 0.628
#
# # Remove one or more columns
# v13 <- rp_mutate(diamonds, to_remove = c("table", "depth"))
# print(head(v13))
# #> carat cut color clarity price x y z
# #> 1 0.23 Ideal E SI2 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 336 3.94 3.96 2.48
## -----------------------------------------------------------------------------
#
# # Summarize the entire data frame
# v14 <- rp_summarize(diamonds, avg_price = mean(price), max_carat = max(carat))
# print(v14)
# #> price carat
# #> 1 3932.8 NaN
# #> 2 NaN 5.01
#
#
# # Group by one column (unquoted)
# v15 <- rp_summarize(diamonds, avg_price = mean(price), .by = cut)
# print(v15)
# #> cut avg_price
# #> 1 Fair 4358.758
# #> 2 Good 3928.864
# #> 3 Very Good 3981.760
# #> 4 Premium 4584.258
# #> 5 Ideal 3457.542
#
# # Group by multiple columns (unquoted)
# v16 <- rp_summarize(
# diamonds,
# avg_price = mean(price),
# count = n(),
# .by = c(cut, color)
# )
# print(head(v16))
# #> cut color avg_price count
# #> 1 Fair D 4291.061 163
# #> 2 Fair E 3682.312 224
# #> 3 Fair F 3827.003 312
# #> 4 Fair G 4239.255 314
# #> 5 Fair H 5135.683 303
# #> 6 Fair I 4685.446 175
#
# # Grouping also accepts quoted column names
# v17 <- rp_summarize(
# diamonds,
# avg_price = mean(price),
# .by = c("cut", "color")
# )
# print(head(v17))
# #> cut color avg_price
# #> 1 Fair D 4291.061
# #> 2 Fair E 3682.312
# #> 3 Fair F 3827.003
# #> 4 Fair G 4239.255
# #> 5 Fair H 5135.683
# #> 6 Fair I 4685.446
## -----------------------------------------------------------------------------
#
# # Apply two functions to two columns, grouped by 'cut'
# v13 <- rp_calculate(
# diamonds,
# price, carat,
# the.functions = c("mean", "sd"),
# .by = cut
# )
# print(head(v13))
# #> cut price.mean price.std carat.mean carat.std
# #> 1 Fair 4358.758 3560.387 1.0461366 0.5164043
# #> 2 Good 3928.864 3681.590 0.8491847 0.4540544
# #> 3 Very Good 3981.760 3935.862 0.8063814 0.4594354
# #> 4 Premium 4584.258 4349.205 0.8919549 0.5152616
# #> 5 Ideal 3457.542 3808.401 0.7028370 0.4328763
## -----------------------------------------------------------------------------
# # First 3 rows overall
# v19 <- rp_first_k_rows(diamonds, k = 3)
# print(v19)
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#
# # Last 2 rows per group (cut and clarity)
# v20 <- rp_last_k_rows(diamonds, k = 2, .by = c(cut, clarity))
# print(head(v20))
# #> carat cut color clarity depth table price x y z
# #> 1 0.70 Fair J VVS1 67.6 54 1691 5.56 5.41 3.71
# #> 2 0.50 Fair D VVS1 65.9 64 1792 4.92 5.03 3.28
# #> 3 0.52 Fair F IF 64.6 58 2144 5.04 5.17 3.30
# #> 4 0.47 Fair D IF 60.6 60 2211 5.09 4.98 3.05
# #> 5 0.55 Good F IF 60.8 60 2266 5.26 5.36 3.23
# #> 6 0.54 Premium F IF 61.9 60 2391 5.26 5.21 3.24
#
# # Both quoted and unquoted group specifications work
# v21 <- rp_first_k_rows(diamonds, k = 1, .by = c("cut", "color"))
# print(v21)
# #> carat cut color clarity depth table price x y z
# #> 1 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
# #> 2 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
# #> 3 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
# #> 4 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
# #> 5 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
# #> 6 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48
# #> 7 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47
# #> 8 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53
# #> 9 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49
# #> 10 0.23 Ideal J VS1 62.8 56.0 340 3.93 3.90 2.46
# #> 11 0.22 Premium F SI1 60.4 61.0 342 3.88 3.84 2.33
# #> 12 0.30 Ideal I SI2 62.0 54.0 348 4.31 4.34 2.68
# #> 13 0.30 Good I SI2 63.3 56.0 351 4.26 4.30 2.71
# #> 14 0.23 Very Good E VS2 63.8 55.0 352 3.85 3.92 2.48
# #> 15 0.23 Very Good G VVS2 60.4 58.0 354 3.97 4.01 2.41
# #> 16 0.23 Very Good D VS2 60.5 61.0 357 3.96 3.97 2.40
# #> 17 0.23 Very Good F VS1 60.9 57.0 357 3.96 3.99 2.42
# #> 18 0.23 Good F VS1 58.2 59.0 402 4.06 4.08 2.37
# #> 19 0.31 Good H SI1 64.0 54.0 402 4.29 4.31 2.75
# #> 20 0.26 Good D VS2 65.2 56.0 403 3.99 4.02 2.61
# #> 21 0.23 Ideal G VS1 61.9 54.0 404 3.93 3.95 2.44
# #> 22 0.22 Premium D VS2 59.3 62.0 404 3.91 3.88 2.31
# #> 23 0.30 Premium J SI2 59.3 61.0 405 4.43 4.38 2.61
# #> 24 0.30 Ideal D SI1 62.5 57.0 552 4.29 4.32 2.69
# #> 25 0.31 Premium G SI1 61.8 58.0 553 4.35 4.32 2.68
# #> 26 0.30 Premium H SI1 62.9 59.0 554 4.28 4.24 2.68
# #> 27 0.96 Fair F SI2 66.3 62.0 2759 6.27 5.95 4.07
# #> 28 0.81 Ideal F SI2 58.8 57.0 2761 6.14 6.11 3.60
# #> 29 0.91 Fair H SI2 64.4 57.0 2763 6.11 6.09 3.93
# #> 30 0.77 Ideal H VS2 62.0 56.0 2763 5.89 5.86 3.64
# #> 31 0.72 Good G VS2 59.7 60.5 2776 5.80 5.84 3.47
# #> 32 0.84 Fair G SI1 55.1 67.0 2782 6.39 6.20 3.47
# #> 33 1.05 Fair J SI2 65.8 59.0 2789 6.41 6.27 4.18
# #> 34 0.90 Fair I SI1 67.3 59.0 2804 5.93 5.84 3.96
# #> 35 0.75 Fair D SI2 64.6 57.0 2848 5.74 5.72 3.70
#
## -----------------------------------------------------------------------------
# # Total row count
# v22 <- rp_count(diamonds)
# print(v22)
# #> n
# #> 1 53940
#
# # Count per group
# v23 <- rp_count(diamonds, .by = cut)
# print(v23)
# #> cut n
# #> 1 Fair 1610
# #> 2 Good 4906
# #> 3 Very Good 12082
# #> 4 Premium 13791
# #> 5 Ideal 21551
#
# # Count per combination of multiple groups
# v24 <- rp_count(diamonds, .by = c(cut, color))
# print(head(v24))
# #> cut color n
# #> 1 Fair D 163
# #> 2 Fair E 224
# #> 3 Fair F 312
# #> 4 Fair G 314
# #> 5 Fair H 303
# #> 6 Fair I 175
## -----------------------------------------------------------------------------
# # Load the pipe
# v25 <- diamonds |>
# rp_filter(carat > 1 & color == "D") |>
# rp_mutate(price_per_carat = price / carat) |>
# rp_summarize(avg_ppc = mean(price_per_carat), .by = cut) |>
# rp_sort(desc(avg_ppc))
#
# print(head(v25))
# #> cut avg_ppc
# #> 1 Ideal 7546.163
# #> 2 Very Good 6789.316
# #> 3 Premium 6548.397
# #> 4 Good 5784.918
# #> 5 Fair 5414.87
## -----------------------------------------------------------------------------
# # See the code for a simple filter
# rp_filter(diamonds, carat > 1 & price < 400, return.as = "code")
# #> [1] "df.query('(carat > 1) and (price < 400)')"
#
# # See the code for a mutate
# rp_mutate(diamonds, ppc = price / carat, return.as = "code")
# #> [1] "df.assign(ppc = lambda x: (x['price'] / x['carat']))"
#
# # See the code for a complex summary
# rp_summarize(
# diamonds,
# avg_price = mean(price),
# count = n(),
# .by = c(cut, color),
# return.as = "code"
# )
# #> [1] "df.groupby(['cut', 'color'], as_index=False, observed=True).agg(avg_price = ('price', 'mean'), count = ('price', 'size'))"
## -----------------------------------------------------------------------------
# # Default placeholder
# rp_filter(diamonds, carat > 1, return.as = "code")
# #> [1] "df.query('carat > 1')"
#
# # With custom table name
# rp_filter(diamonds, carat > 1, table_name = "diamonds", return.as = "code")
# #> [1] "diamonds.query('carat > 1')"
#
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