Teaching Culture Perception Survey (TCPS) Project

The goal of tcps package is to provide functionality to tidy and visualize the results of running the teaching culture perception survey at an institution.

Using the tcps package

The package is freely available and requires R to be installed as well. R is a free open-source software available at https://www.r-project.org/. It is a command line program, but there are many high quality and free interactive development environments (IDE) one can use. Rstudio https://www.rstudio.com is a highly recommended IDE to download and install.

To recap:

1. Download & Install R https://www.r-project.org/
2. Download & Install RStudio https://www.rstudio.com
3. Launch RStudio (which will start up an instance of R as well)

Installation

You can install tcps from GitHub, by first installing and loading the devtools package

install.packages("remotes")

remotes::install_github("jkaupp/tcps")

Using this package

There is a vignette in the package that walks through the use of the tcps package

vignette("using_the_tcps")

Quick Features

Importing your data

Reading in properly prepared output from survey platforms, and creating the levers.

library(tcps)

# Load some sample data!
file <- tcps_sample("staff_sample.xlsx")

tcps_data <- tcps_read_excel(file)

head(tcps_data)
#> # A tibble: 6 x 42
#>   part_num scale survey lever1 lever1_q1 lever1_q2 lever1_q3 lever1_q4 lever1_q5
#>      <int> <chr> <chr>   <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#> 1        1 agre… staff   NA           NA        NA        NA        NA        NA
#> 2        1 impo… staff   NA           NA        NA        NA        NA        NA
#> 3        2 agre… staff    3.33         4         4         4         3         2
#> 4        2 impo… staff   NA           NA        NA        NA        NA        NA
#> 5        3 agre… staff   NA           NA        NA        NA        NA        NA
#> 6        3 impo… staff    4.5          4         5         4         4         5
#> # … with 33 more variables: lever1_q6 <dbl>, lever2 <dbl>, lever2_q2 <dbl>,
#> #   lever2_q3 <dbl>, lever2_q4 <dbl>, lever2_q5 <dbl>, lever3 <dbl>,
#> #   lever3_q1 <dbl>, lever3_q3 <dbl>, lever3_q4 <dbl>, lever3_q5 <dbl>,
#> #   lever3_q6 <dbl>, lever3_q7 <dbl>, lever4 <dbl>, lever4_q1 <dbl>,
#> #   lever4_q2 <dbl>, lever4_q3 <dbl>, lever4_q4 <dbl>, lever4_q5 <dbl>,
#> #   lever4_q6 <dbl>, lever5 <dbl>, lever5_q1 <dbl>, lever5_q2 <dbl>,
#> #   lever5_q3 <dbl>, lever5_q4 <dbl>, lever5_q5 <dbl>, lever5_q6 <dbl>,
#> #   lever6 <dbl>, lever6_q1 <dbl>, lever6_q2 <dbl>, lever6_q3 <dbl>,
#> #   lever6_q4 <dbl>, lever6_q6 <dbl>

Visualizing Levers & Scales

Provides functions to visualize the TCPS levers….

tcps_lever_ridgeline(tcps_data, name = "Prestigious Institution", lever = "lever1")
#> Picking joint bandwidth of 0.261

as well as functionality to visualize the scales/questions that comprise the lever.

tcps_lever_scale(tcps_data, choice =  "lever1", name = "Prestigious Institution")

Summary Statistics

You can obtain simple summary statistics by scale, survey type, lever and question.

tcps_summary_stats(tcps_data)
#> # A tibble: 10 x 42
#> # Groups:   scale, survey [2]
#>    scale survey measure lever1 lever1_q1 lever1_q2 lever1_q3 lever1_q4 lever1_q5
#>    <chr> <chr>  <chr>    <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#>  1 agre… staff  mean     3.52      3.75      3.83      3.42      3.71      2.96 
#>  2 agre… staff  median   3.67      4         4         3         3.5       3    
#>  3 agre… staff  n       13         5         6         6         5         6    
#>  4 agre… staff  sd       0.638     0.989     1.52      1.10      0.999     1.12 
#>  5 agre… staff  sem      0.177     0.442     0.622     0.449     0.447     0.458
#>  6 impo… staff  mean     4.37      4.5       4.44      4.46      4.23      4.42 
#>  7 impo… staff  median   4.42      4.5       5         5         4         5    
#>  8 impo… staff  n       13         4         5         3         4         4    
#>  9 impo… staff  sd       0.510     0.648     1.00      0.647     0.863     0.809
#> 10 impo… staff  sem      0.141     0.324     0.449     0.373     0.431     0.404
#> # … with 33 more variables: lever1_q6 <dbl>, lever2 <dbl>, lever2_q2 <dbl>,
#> #   lever2_q3 <dbl>, lever2_q4 <dbl>, lever2_q5 <dbl>, lever3 <dbl>,
#> #   lever3_q1 <dbl>, lever3_q3 <dbl>, lever3_q4 <dbl>, lever3_q5 <dbl>,
#> #   lever3_q6 <dbl>, lever3_q7 <dbl>, lever4 <dbl>, lever4_q1 <dbl>,
#> #   lever4_q2 <dbl>, lever4_q3 <dbl>, lever4_q4 <dbl>, lever4_q5 <dbl>,
#> #   lever4_q6 <dbl>, lever5 <dbl>, lever5_q1 <dbl>, lever5_q2 <dbl>,
#> #   lever5_q3 <dbl>, lever5_q4 <dbl>, lever5_q5 <dbl>, lever5_q6 <dbl>,
#> #   lever6 <dbl>, lever6_q1 <dbl>, lever6_q2 <dbl>, lever6_q3 <dbl>,
#> #   lever6_q4 <dbl>, lever6_q6 <dbl>

Reporting

There is also functions to produce a boilerplate word document report (across all groups: Staff, Faculty and Students) simply by providing a path to a directory where each prepared file resides.

path <- dirname(tcps_sample("faculty_sample.xlsx", full.names = TRUE))

tcps_report(path_to_data = path, name_of_school = "Prestigious Institution", format = "word")

Using the GUI

To help with those new to R, a GUI interface application (built with the shiny package) can be used by calling tcps_gui() at the command line after loading the tcps package.