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Merge branch 'main' of https://github.com/best-practice-and-impact/CARS
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@shilohd
shilohd committed Jul 18, 2023
2 parents ee9792c + fbfd99f commit b13cc5e
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134 changes: 87 additions & 47 deletions quarto/templates/summary.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,11 @@ output:
library(magrittr)
load("../../../../temp/data.Rda")
data <- CARS::get_tidy_data_file("2022_data.csv") %>%
CARS::rename_cols() %>%
CARS::apply_skip_logic() %>%
CARS::clean_departments() %>%
CARS::derive_vars()
data <- {{{filter}}}
Expand All @@ -22,13 +26,6 @@ samples <- CARS::sample_sizes(data)
```


## Draft notice

Please note that all outputs are provisional. All findings have been thoroughly quality assured. However, the outputs presented here may change.

While any changes to the data are not likely to be substantial, we are planning to carry out more in-depth analysis and the commentary may change. As such, we recommend linking to these pages rather than sharing this analysis any other way at this stage.


## Coding frequency and tools

### How often do respondents use code
Expand All @@ -37,7 +34,7 @@ We asked respondents "In your current role, how often do you write code to compl

```{r}
plot <- CARS::plot_freqs(tables$code_freq, n = samples$all, xlab = "Coding frequency", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$code_freq, n = samples$all, xlab = "Coding frequency", font_size = 14)
table <- CARS::df_to_table(tables$code_freq, n = samples$all, column_headers = c("Coding frequency", "Percent"))
CARS::wrap_outputs("coding-freq", plot, table)
Expand All @@ -53,7 +50,7 @@ Respondents who don't do the operation at all have been removed.

```{r}
plot <- CARS::plot_stacked(tables$operations, xlab = "Operation", ylab = "%", font_size = 14)
plot <- CARS::plot_stacked(tables$operations, xlab = "Operation", font_size = 14)
table <- CARS::df_to_table(tables$operations, column_headers = c("Operation", "I do some or all of this by coding (%)", "I do this without coding (%)"), crosstab = TRUE)
CARS::wrap_outputs("operations", plot, table)
Expand All @@ -75,7 +72,7 @@ Please note that capability in programming languages is self-reported here and w

```{r}
plot <- CARS::plot_stacked(tables$access, n = samples$all, xlab = "Programming tool", ylab = "%", colour_scale = "3scale", font_size = 14)
plot <- CARS::plot_stacked(tables$access, n = samples$all, xlab = "Programming tool", colour_scale = "3scale", font_size = 14)
table <- CARS::df_to_table(tables$access, n = samples$all, column_headers = c("Programming tool", "Yes", "Don't Know", "No"), crosstab = TRUE)
CARS::wrap_outputs("access", plot, table)
Expand All @@ -86,7 +83,7 @@ CARS::wrap_outputs("access", plot, table)

```{r}
plot <- CARS::plot_stacked(tables$knowledge, n = samples$all, xlab = "Programming tool", ylab = "%", colour_scale = "3scale", font_size = 14)
plot <- CARS::plot_stacked(tables$knowledge, n = samples$all, xlab = "Programming tool", colour_scale = "3scale", font_size = 14)
table <- CARS::df_to_table(tables$knowledge, n = samples$all, column_headers = c("Programming tool", "Yes", "Don't Know", "No"), crosstab = TRUE)
CARS::wrap_outputs("knowledge", plot, table)
Expand All @@ -105,13 +102,41 @@ Respondents who have neither access nor knowledge have been removed.

```{r}
plot <- CARS::plot_stacked(tables$language_status, xlab = "Programming tool", ylab = "%", colour_scale = "3scale", font_size = 14)
plot <- CARS::plot_stacked(tables$language_status, xlab = "Programming tool", colour_scale = "3scale", font_size = 14)
table <- CARS::df_to_table(tables$language_status, column_headers = c("Programming tool", "Access Only", "Both", "Knowledge Only"), crosstab = TRUE)
CARS::wrap_outputs("langauge-status", plot, table)
```

### Access to and knowledge of git

We asked respondents to answer "Yes", "No" or "Don't know" for the following questions;

- Do you know how to use git to version-control your work?
- Is git available to use in your work?

### Access to git

```{r}
plot <- CARS::plot_freqs(tables$git_access, n = samples$all, xlab = "git access", font_size = 14)
table <- CARS::df_to_table(tables$git_access, n = samples$all, column_headers = c("git access", "Percent"))
CARS::wrap_outputs("git-access", plot, table)
```

### Knowledge of git

```{r}
plot <- CARS::plot_freqs(tables$git_knowledge, n = samples$all, xlab = "git knowledge", font_size = 14)
table <- CARS::df_to_table(tables$git_knowledge, n = samples$all, column_headers = c("git knowledge", "Percent"))
CARS::wrap_outputs("git-knowledge", plot, table)
```

## Coding capability

Expand All @@ -123,7 +148,7 @@ This question was only asked of respondents with coding experience outside of th

```{r}
plot <- CARS::plot_freqs(tables$ability_change, n = samples$other_code_experience, xlab = "Ability change", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$ability_change, n = samples$other_code_experience, xlab = "Ability change", font_size = 14)
table <- CARS::df_to_table(tables$ability_change, n = samples$other_code_experience, column_headers = c("Ability change", "Percent"))
CARS::wrap_outputs("ability-change", plot, table)
Expand All @@ -138,7 +163,7 @@ These data only show where people first learned to code. They do not show all th

```{r}
plot <- CARS::plot_freqs(tables$where_learned, n = samples$can_code, xlab = "Where learned", ylab = "%", font_size = 14, orientation = "h")
plot <- CARS::plot_freqs(tables$where_learned, n = samples$can_code, xlab = "Where learned", font_size = 14, orientation = "h")
table <- CARS::df_to_table(tables$where_learned, n = samples$can_code, column_headers = c("Where learned", "Percent"))
CARS::wrap_outputs("where-learned", plot, table)
Expand All @@ -155,8 +180,8 @@ We asked respondents who said they currently use code in their work, how often t
```{r}
plot <- CARS::plot_likert(tables$coding_practices, n = samples$code_at_work,
mid = 5, neutral_mid = FALSE, break_q_names_col = "name",
max_lines = 2, xlab = "%", font_size = 14, height = 800, width = "100%")
mid = 5, neutral_mid = FALSE, break_q_names_col = "name",
max_lines = 2, font_size = 14, height = 800, width = "100%")
table <- CARS::df_to_table(tables$coding_practices,
column_headers = c(
"Statement",
Expand All @@ -180,19 +205,19 @@ CARS::wrap_outputs("good-practices", plot, table)

```{r}
plot <- CARS::plot_likert(tables$doc, n = samples$code_at_work, mid = 5, neutral_mid = FALSE, xlab = "%", font_size = 14, height = 600, width = "100%")
plot <- CARS::plot_likert(tables$doc, n = samples$code_at_work, mid = 5, neutral_mid = FALSE, font_size = 14, height = 600, width = "100%")
table <- CARS::df_to_table(tables$doc,
column_headers = c(
"Statement",
"I don't understand this question (%)",
"Never (%)",
"Rarely (%)",
"Sometimes (%)",
"Regularly (%)",
"All the time (%)"
),
n = samples$code_at_work,
crosstab = TRUE)
column_headers = c(
"Statement",
"I don't understand this question (%)",
"Never (%)",
"Rarely (%)",
"Sometimes (%)",
"Regularly (%)",
"All the time (%)"
),
n = samples$code_at_work,
crosstab = TRUE)
CARS::wrap_outputs("doc", plot, table)
Expand All @@ -208,7 +233,7 @@ We provided examples of tools that may be used for dependency management:

```{r}
plot <- CARS::plot_freqs(tables$dependency_management, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use dependency management software", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$dependency_management, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use dependency management software", font_size = 14)
table <- CARS::df_to_table(tables$dependency_management, n = samples$code_at_work, column_headers = c("Use dependency management software", "Percent"))
CARS::wrap_outputs("dependency-management", plot, table)
Expand All @@ -225,7 +250,7 @@ As above, respondents were provided with examples of continuous integration tech

```{r}
plot <- CARS::plot_freqs(tables$ci, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use continuous integration", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$ci, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use continuous integration", font_size = 14)
table <- CARS::df_to_table(tables$ci, n = samples$code_at_work, column_headers = c("Use continuous integration", "Percent"))
CARS::wrap_outputs("ci", plot, table)
Expand All @@ -241,7 +266,7 @@ Respondents were asked whether they use reproducible workflow packages. Responde

```{r}
plot <- CARS::plot_freqs(tables$rep_workflow, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use reproduciable workflow packages", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$rep_workflow, n = samples$code_at_work, break_q_names_col = "value", xlab = "Use reproduciable workflow packages", font_size = 14)
table <- CARS::df_to_table(tables$rep_workflow, n = samples$code_at_work, column_headers = c("Use reproduciable workflow packages", "Percent"))
CARS::wrap_outputs("rep-workflow", plot, table)
Expand All @@ -264,31 +289,46 @@ Respondents who have neither access nor knowledge have been removed.

```{r}
plot <- CARS::plot_freqs(tables$rap_knowledge, n = samples$not_RAP_champ, break_q_names_col = "value", max_lines = 3, xlab = "Knowledge", ylab = "%", font_size = 14, orientation = "h")
plot <- CARS::plot_freqs(tables$rap_knowledge, n = samples$not_RAP_champ, break_q_names_col = "value", max_lines = 3, xlab = "Knowledge", font_size = 14, orientation = "h")
table <- CARS::df_to_table(tables$rap_knowledge, n = samples$not_RAP_champ, column_headers = c("Knowledge", "Percent"))
CARS::wrap_outputs("rap-knowledge", plot, table)
```

### Knowledge of RAP strategy

We asked the respondents "What is your familiarity with the Analysis Function reproducible analytical pipelines (RAP) strategy?".

```{r}
plot <- CARS::plot_freqs(tables$strategy_knowledge, n = samples$heard_of_RAP,
break_q_names_col = "value", max_lines = 3,
xlab = "RAP strategy knowledge", font_size = 14)
table <- CARS::df_to_table(tables$strategy_knowledge, n = samples$heard_of_RAP, column_headers = c("RAP strategy knowledge", "Percent"))
CARS::wrap_outputs("strategy-knowledge", plot, table)
```

### Opinions on RAP

We asked respondents who had heard of RAP whether they agreed with a series of statements.

```{r}
plot <- CARS::plot_likert(tables$rap_opinions, n = samples$heard_of_RAP, mid = 3, break_q_names_col = "name", max_lines = 2, xlab = "%", font_size = 14, height = 575, width = "100%")
plot <- CARS::plot_likert(tables$rap_opinions, n = samples$heard_of_RAP, mid = 3, break_q_names_col = "name", max_lines = 2, font_size = 14, height = 575, width = "100%")
table <- CARS::df_to_table(tables$rap_opinions,
column_headers = c(
"Statement",
"Strongly Disagree (%)",
"Disagree (%)",
"Neutral (%)",
"Agree (%)",
"Strongly Agree (%)"
),
n = samples$heard_of_RAP,
crosstab = TRUE)
column_headers = c(
"Statement",
"Strongly Disagree (%)",
"Disagree (%)",
"Neutral (%)",
"Agree (%)",
"Strongly Agree (%)"
),
n = samples$heard_of_RAP,
crosstab = TRUE)
CARS::wrap_outputs("rap-opinions", plot, table)
Expand All @@ -308,7 +348,7 @@ RAP scores are then calculated for each respondent as the total of their positiv

```{r}
plot <- CARS::plot_grouped(tables$rap_components, n = samples$code_at_work, orientation = "h", ylab = "%", xlab = "RAP component", font_size = 14)
plot <- CARS::plot_grouped(tables$rap_components, n = samples$code_at_work, orientation = "h", xlab = "RAP component", font_size = 14)
table <- CARS::df_to_table(tables$rap_components, n = samples$code_at_work, column_headers = c("RAP component", "Type", "Percentage of analysts who code in their work"))
CARS::wrap_outputs("rap-comp", plot, table)
Expand All @@ -321,7 +361,7 @@ CARS::wrap_outputs("rap-comp", plot, table)
bar_colour <- CARS::get_2colour_scale(2)[2][[1]]
plot <- CARS::plot_freqs(tables$basic_rap_scores, n = samples$code_at_work, bar_colour = bar_colour, xlab = "Basic RAP scores", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$basic_rap_scores, n = samples$code_at_work, bar_colour = bar_colour, xlab = "Basic RAP scores", font_size = 14)
table <- CARS::df_to_table(tables$basic_rap_scores, n = samples$code_at_work, column_headers = c("Basic RAP scores", "Percent"))
CARS::wrap_outputs("basic-rap-scores", plot, table)
Expand All @@ -332,7 +372,7 @@ CARS::wrap_outputs("basic-rap-scores", plot, table)

```{r}
plot <- CARS::plot_freqs(tables$advanced_rap_scores, n = samples$code_at_work, xlab = "Advanced RAP scores", ylab = "%", font_size = 14)
plot <- CARS::plot_freqs(tables$advanced_rap_scores, n = samples$code_at_work, xlab = "Advanced RAP scores", font_size = 14)
table <- CARS::df_to_table(tables$advanced_rap_scores, n = samples$code_at_work, column_headers = c("Advanced RAP scores", "Percent"))
CARS::wrap_outputs("advanced-rap-scores", plot, table)
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