Package consisting of a set of helper functions for doing data science with iNZight. These functions are designed to work well with a graphical user interface (GUI), but many[1] are functional for direct use through R.
The current release version is available on CRAN:
install.packages("iNZightTools")The development version can be downloaded from GitHub:
remotes::install_github("iNZightVIT/iNZightTools@dev")The package itself doesn’t have any one specific use, but the functions can be broken down into various workflows.
library(iNZightTools)
#>
#> Attaching package: 'iNZightTools'
#> The following object is masked from 'package:stats':
#>
#> filterMost of the functions return not only the resulting data, but attach the tidyverse code used to generate it. This is useful for GUIs that display code history (e.g., iNZight) or when learning to code.
Importing data is done using the smart_read() function, which can read
CSV, Excel, Stata, SAS, RData, and a few other formats based on the file
extension.
data <- smart_read(system.file("extdata/cas500.xls", package = "iNZightTools"))
str(data)
#> tibble [500 × 10] (S3: tbl_df/tbl/data.frame)
#> $ cellsource: Factor w/ 5 levels "job","NA","other",..: 5 4 4 5 5 4 4 5 4 3 ...
#> $ rightfoot : num [1:500] 20 25 21 20 23 19 23 35 22 30 ...
#> $ travel : Factor w/ 6 levels "bike","bus","motor",..: 6 4 3 6 4 3 3 3 3 6 ...
#> $ getlunch : Factor w/ 7 levels "dairy","friend",..: 3 2 3 3 3 3 3 7 3 7 ...
#> $ height : num [1:500] 152 153 137 115 165 137 164 150 150 123 ...
#> $ gender : Factor w/ 2 levels "female","male": 2 1 2 2 1 1 1 1 1 2 ...
#> $ age : num [1:500] 12 11 10 9 14 11 12 15 12 14 ...
#> $ year : num [1:500] 7 6 6 5 10 7 8 11 8 9 ...
#> $ armspan : num [1:500] 150 152 132 130 160 50 164 100 152 23 ...
#> $ cellcost : num [1:500] 30 50 55 60 20 50 10 20 10 0 ...
#> - attr(*, "code")= chr "readxl::read_excel(\"/home/tom/R/x86_64-pc-linux-gnu-library/4.2/iNZightTools/extdata/cas500.xls\") %>% dplyr::"| __truncated__
#> - attr(*, "available.sheets")= chr "Census at School-500"
tidy_all_code(code(data))
#> Loading required namespace: styler
#> [1] "readxl::read_excel(\"/home/tom/R/x86_64-pc-linux-gnu-library/4.2/iNZightTools/extdata/cas500.xls\") %>%"
#> [2] " dplyr::mutate_at("
#> [3] " c("
#> [4] " \"cellsource\","
#> [5] " \"travel\","
#> [6] " \"getlunch\","
#> [7] " \"gender\""
#> [8] " ),"
#> [9] " as.factor"
#> [10] " ) %>%"
#> [11] " dplyr::mutate_at("
#> [12] " c("
#> [13] " \"rightfoot\","
#> [14] " \"height\","
#> [15] " \"age\","
#> [16] " \"armspan\","
#> [17] " \"cellcost\""
#> [18] " ),"
#> [19] " as.numeric"
#> [20] " )"Being an important but tricker data type to work with, iNZightTools includes methods for easily importing surveys using a specification format. For details, check out https://inzight.nz/docs/survey-specification.html
There are many other data manipulation-focussed functions, such as filter, renaming variables, etc.
filter_num(data, "height", "<", 150)
#> # A tibble: 127 × 10
#> cellsource rightfoot travel getlunch height gender age year armspan
#> * <fct> <dbl> <fct> <fct> <dbl> <fct> <dbl> <dbl> <dbl>
#> 1 parent 21 motor home 137 male 10 6 132
#> 2 pocket 20 walk home 115 male 9 5 130
#> 3 parent 19 motor home 137 female 11 7 50
#> 4 other 30 walk tuckshop 123 male 14 9 23
#> 5 parent 11 bike home 129 male 10 5 165
#> 6 other 23 motor home 145 male 10 6 144
#> 7 parent 19 motor home 146 female 9 4 140
#> 8 pocket 22 bus home 146 female 12 8 136
#> 9 job 19 motor home 130 female 9 6 130
#> 10 parent 21 motor home 135 female 11 6 137
#> # ℹ 117 more rows
#> # ℹ 1 more variable: cellcost <dbl>- with others being modified in time