Create a pivot table
pivotr( dataset, cvars = "", nvar = "None", fun = "mean", normalize = "None", tabfilt = "", tabsort = "", nr = Inf, data_filter = "", envir = parent.frame() )
| dataset | Dataset to tabulate |
|---|---|
| cvars | Categorical variables |
| nvar | Numerical variable |
| fun | Function to apply to numerical variable |
| normalize | Normalize the table by row total, column totals, or overall total |
| tabfilt | Expression used to filter the table (e.g., "Total > 10000") |
| tabsort | Expression used to sort the table (e.g., "desc(Total)") |
| nr | Number of rows to display |
| data_filter | Expression used to filter the dataset before creating the table (e.g., "price > 10000") |
| envir | Environment to extract data from |
Create a pivot-table. See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant
#> List of 15 #> $ cni : logi [1:2] FALSE FALSE #> $ cn : chr [1:2] "cut" "n_obs" #> $ tab_freq : tibble [6 × 2] (S3: tbl_df/tbl/data.frame) #> ..$ cut : Factor w/ 6 levels "Fair","Good",..: 1 2 3 4 5 6 #> ..$ n_obs: int [1:6] 101 275 677 771 1176 3000 #> $ tab :'data.frame': 6 obs. of 2 variables: #> ..$ cut : Factor w/ 6 levels "Fair","Good",..: 1 2 3 4 5 6 #> ..$ n_obs: int [1:6] 101 275 677 771 1176 3000 #> ..- attr(*, "radiant_nrow")= num 5 #> $ df_name : chr "diamonds" #> $ fill : int 0 #> $ vars : chr "cut" #> $ cvars : chr "cut" #> $ nvar : chr "n_obs" #> $ fun : chr "mean" #> $ normalize : chr "None" #> $ tabfilt : chr "" #> $ tabsort : chr "" #> $ nr : num Inf #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:2] "pivotr" "list"pivotr(diamonds, cvars = "cut")$tab#> cut n_obs #> 1 Fair 101 #> 2 Good 275 #> 3 Very Good 677 #> 4 Premium 771 #> 5 Ideal 1176 #> 6 Total 3000#> clarity color Fair Good Very_Good Premium Ideal Total #> 1 I1 D 0 1 0 3 0 4 #> 2 I1 E 1 1 2 1 0 5 #> 3 I1 F 2 1 2 2 4 11 #> 4 I1 G 1 1 1 2 0 5 #> 5 I1 H 3 0 1 3 1 8 #> 6 I1 I 4 0 0 1 0 5 #> 7 I1 J 1 0 0 1 0 2 #> 8 SI2 D 8 14 18 13 15 68 #> 9 SI2 E 5 16 30 25 30 106 #> 10 SI2 F 1 11 24 23 34 93 #> 11 SI2 G 5 7 29 35 21 97 #> 12 SI2 H 4 7 14 31 30 86 #> 13 SI2 I 2 2 7 21 14 46 #> 14 SI2 J 3 5 10 9 6 33 #> 15 SI1 D 4 9 21 38 39 111 #> 16 SI1 E 5 22 37 47 36 147 #> 17 SI1 F 4 14 40 25 42 125 #> 18 SI1 G 5 10 23 26 31 95 #> 19 SI1 H 9 13 21 31 43 117 #> 20 SI1 I 1 14 18 20 23 76 #> 21 SI1 J 1 11 11 9 18 50 #> 22 VS2 D 2 3 17 12 55 89 #> 23 VS2 E 2 15 26 41 55 139 #> 24 VS2 F 4 12 23 36 53 128 #> 25 VS2 G 2 8 17 42 50 119 #> 26 VS2 H 2 10 20 25 31 88 #> 27 VS2 I 2 2 16 17 19 56 #> 28 VS2 J 0 2 12 12 16 42 #> 29 VS1 D 0 3 10 13 24 50 #> 30 VS1 E 0 5 9 13 25 52 #> 31 VS1 F 4 8 25 17 29 83 #> 32 VS1 G 2 7 22 30 51 112 #> 33 VS1 H 3 3 19 23 22 70 #> 34 VS1 I 2 5 11 14 18 50 #> 35 VS1 J 2 2 3 10 8 25 #> 36 VVS2 D 0 4 8 9 20 41 #> 37 VVS2 E 1 1 25 7 22 56 #> 38 VVS2 F 0 0 14 6 29 49 #> 39 VVS2 G 1 3 21 10 43 78 #> 40 VVS2 H 0 3 12 5 13 33 #> 41 VVS2 I 0 1 1 4 14 20 #> 42 VVS2 J 0 0 2 2 3 7 #> 43 VVS1 D 1 1 2 4 9 17 #> 44 VVS1 E 0 2 7 7 21 37 #> 45 VVS1 F 1 7 4 4 29 45 #> 46 VVS1 G 0 3 16 9 42 70 #> 47 VVS1 H 0 1 6 11 14 32 #> 48 VVS1 I 0 1 4 3 12 20 #> 49 VVS1 J 0 0 1 1 1 3 #> 50 IF D 0 0 1 0 1 2 #> 51 IF E 0 0 4 3 5 12 #> 52 IF F 1 2 4 6 18 31 #> 53 IF G 0 1 2 2 16 21 #> 54 IF H 0 0 2 3 15 20 #> 55 IF I 0 1 2 3 5 11 #> 56 IF J 0 0 0 1 1 2 #> 57 Total Total 101 275 677 771 1176 3000pivotr(diamonds, cvars = "cut:clarity", nvar = "price")$tab#> clarity Fair Good Very_Good Premium Ideal Total #> 1 I1 2730.167 4333.500 3864.167 4932.231 6078.200 4194.775 #> 2 SI2 5893.964 5280.919 5045.621 5568.019 4435.673 5100.189 #> 3 SI1 4273.069 3757.022 4277.544 4113.811 3758.125 3998.577 #> 4 VS2 3292.000 3925.481 3950.947 4522.914 3306.290 3822.967 #> 5 VS1 5110.769 3740.697 3889.475 4461.333 3189.362 3789.181 #> 6 VVS2 2030.500 4378.167 2525.193 3580.581 3665.181 3337.820 #> 7 VVS1 6761.500 3889.333 1945.875 1426.692 2960.594 2608.460 #> 8 IF 3205.000 817.250 4675.867 2361.333 1961.344 2411.697 #> 9 Total 4505.238 4130.433 3959.916 4369.409 3470.224 3907.186pivotr(diamonds, cvars = "cut", nvar = "price")$tab#> cut price #> 1 Fair 4505.238 #> 2 Good 4130.433 #> 3 Very Good 3959.916 #> 4 Premium 4369.409 #> 5 Ideal 3470.224 #> 6 Total 3907.186pivotr(diamonds, cvars = "cut", normalize = "total")$tab#> cut n_obs #> 1 Fair 0.03366667 #> 2 Good 0.09166667 #> 3 Very Good 0.22566667 #> 4 Premium 0.25700000 #> 5 Ideal 0.39200000 #> 6 Total 1.00000000