LibStats_report_person.Rmd

---
title: "Personal Statistics Report"
params:
  fpath: "C:/Users/briney/Documents/LibStats/reports/data"
  periodStart: "2018-01-01 00:00:00"
  periodEnd: "2018-05-30 23:59:59"
  person: "nbungert" 
output:
  pdf_document: 
    fig_width: 7
    fig_height: 3
---

<!--
## Name: LibStats_report_person.Rmd
## Created: June 2018 by Kristin Briney
## Purpose: This Rmarkdown template will create a personal report using cleaned StatBot data.
##          Staff ePanther ID and date ranges must be specified.
-->

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning=FALSE, message=FALSE)
library(tidyverse)
library(stringr)
library(lubridate)
library(knitr)

  ## Set parameters -- CHANGE AS NECESSARY
  pStart <-  ymd_hms(params$periodStart)
  pEnd <-  ymd_hms(params$periodEnd)
  pers <- params$person 

  
  
  ## Load consultation data
  
  finput <- paste(params$fpath, "Cons.csv", sep="/")
  Cons_all <- read_csv(finput)
  
  Cons <- filter(Cons_all, Cons_all$StaffName==pers & Cons_all$ConsDate>=pStart & 
                   Cons_all$ConsDate<=pEnd)

  ## Load presentation data
  
  finput <- paste(params$fpath, "Pres.csv", sep="/")
  Pres_all <- read_csv(finput)
  
  Pres <- filter(Pres_all, Pres_all$StaffName==pers & Pres_all$PresDateTime>=pStart & 
                   Pres_all$PresDateTime<=pEnd)

  ## Load transaction data
  
  finput <- paste(params$fpath, "Trans.csv", sep="/")
  Trans_all <- read_csv(finput)
  
  Trans <- filter(Trans_all, Trans_all$StaffName==pers &
                    Trans_all$TransDateTime>=pStart & 
                    Trans_all$TransDateTime<=pEnd)
```

### Person: `r pers`

### Time period: `r pStart` to `r pEnd`


## Transactions

__Total `r pers` transactions = `r count(Trans)`__

### Transaction Patron Status

```{r TuserType}
  TuserTypes <- group_by(Trans, UserStatus) %>% mutate(total=n()) %>% 
    select(UserStatus, total) %>% unique() %>% arrange(desc(total))
  
  ggplot(data=TuserTypes, mapping=aes(x=UserStatus, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="User Status", y="Number of Transactions")
```

### Transaction Courses

```{r Tinstruction}
  Tinstruction <- group_by(Trans, Department, Course) %>% mutate(total=n()) %>% 
    select(Department, Course, total) %>% unique() %>% filter(Department != "NA") %>%
    arrange(Department, Course)

  kable(Tinstruction, format="latex")
  
```

### Transaction Type

_Transactions can be coded as multiple types, meaning the sum of the totals
in each category may exceed the total number of transactions_

```{r Ttype}
  Ttype_type <- bind_rows(tibble(Ttype="Directional"), tibble(Ttype="Informational"),
                              tibble(Ttype="Referral"), tibble(Ttype="Reference"))
  Ttype_total <- bind_rows(count(filter(Trans,Directional==TRUE)), 
                               count(filter(Trans, Informational==TRUE)),
                               count(filter(Trans, Referral==TRUE)),
                               count(filter(Trans, Reference==TRUE)))
  Ttype <- bind_cols(Ttype_type, Ttype_total)
  
  ggplot(data=Ttype, mapping=aes(x=Ttype, y=n, label=n)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="Type", y="Number of Transactions")
```

### Transaction Format

```{r Tform}
  Tform <- group_by(Trans, Format) %>% mutate(total=n()) %>% 
    select(Format, total) %>% unique() %>% 
    filter(Format=="in person/face to face" | Format=="on the phone" | Format=="over e-mail") %>% 
    arrange(desc(total))

  ggplot(data=Tform, mapping=aes(x=Format, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="Format", y="Number of Transactions")
```

### Transaction Notes

```{r Tnotes}
  Tnotes <- select(Trans, TransDateTime, Notes) %>% filter(!is.na(Notes)) %>% arrange(TransDateTime)
  
  kable(Tnotes, format="latex")
```



## Consultations

__Total `r pers` consultations = `r count(Cons)`__

__Total number of patrons in consultations = `r tally(Cons, Count)` (average = `r as.integer(tally(Cons, Count)/count(Cons))`)__

### Consultation Patron Status

```{r CuserType}
  CuserTypes <- group_by(Cons, UserStatus) %>% mutate(total=n()) %>% 
    select(UserStatus, total) %>% unique() %>% arrange(desc(total))
  
  ggplot(data=CuserTypes, mapping=aes(x=UserStatus, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="User Status", y="Number of Consultations")
```

### Consultation Courses

```{r Cinstruction}
  Cinstruction <- filter(Cons, !is.na(Course)) %>% 
    group_by(Department, Course) %>% mutate(total=n()) %>% 
    select(Department, Course, total) %>% unique() %>% arrange(Department, Course)

  kable(Cinstruction, format="latex")
```

### Patron Departments for non-Course Consultations

```{r CuserDept}
  CuserDept <- filter(Cons, is.na(Course)) %>% group_by(UserDept) %>% mutate(total=n()) %>% 
    select(UserDept, total) %>% unique() %>% filter(UserDept != "NA") %>% arrange(desc(total), UserDept)

  kable(CuserDept, format="latex")
  
```

### Consultation Format

```{r Cform}
  Cform <- group_by(Cons, Format) %>% mutate(total=n()) %>% 
    select(Format, total) %>% unique() %>% arrange(desc(total))

  ggplot(data=Cform, mapping=aes(x=Format, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="Format", y="Number of Consultations")
```

### Consultation Notes

```{r Cnotes}
  Cnotes <- select(Cons, ConsDate, Notes) %>% filter(!is.na(Notes)) %>% arrange(ConsDate)
  
  kable(Cnotes, format="latex")
```



## Presentations

__Total `r pers` presentations = `r count(Pres)`__

__Total number of patrons in presentations = `r as.integer(tally(Pres, Count))` (average = `r as.integer(tally(Pres, Count)/count(Pres))`)__

__Total presentation time = `r as.integer(tally(Pres, Length))` minutes (average = `r as.integer(tally(Pres, Length)/count(Pres))` minutes)__

### Presentation Patron Status

```{r PuserType}
  PuserType_type <- bind_rows(tibble(UserType="Undergraduates"), tibble(UserType="Graduates"),
                              tibble(UserType="FacultyStaff"), tibble(UserType="Children"),
                              tibble(UserType="HighSchool"), tibble(UserType="NonUWM"))
  PuserType_total <- bind_rows(count(filter(Pres,Undergraduates==TRUE)), 
                               count(filter(Pres, Graduates==TRUE)),
                               count(filter(Pres, FacultyStaff==TRUE)),
                               count(filter(Pres, Children==TRUE)),
                               count(filter(Pres, HighSchool)),
                               count(filter(Pres, NonUWM==TRUE)))
  PuserType <- bind_cols(PuserType_type, PuserType_total)
  
  ggplot(PuserType, mapping=aes(x=UserType, y=n, label=n)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="User Type", y="Number of Presentations")
```

### Presentation Courses

```{r Pinstruction}
  Pinstruction <- group_by(Pres, Department, Course) %>% mutate(total=n()) %>% 
    select(Department, Course, total) %>% unique() %>% filter(Department != "NA") %>%
    arrange(Department, Course)

  kable(Pinstruction, format="latex")
```

### Presentation Type

```{r Ptype}
  Ptype <- group_by(Pres, PresType) %>% mutate(total=n()) %>% 
    select(PresType, total) %>% unique() %>% arrange(desc(total))

  ggplot(data=Ptype, mapping=aes(x=PresType, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="Presentation Type", y="Number of Presentations")
```

### Presentation Format

```{r Pform}
  Pform <- group_by(Pres, Format) %>% mutate(total=n()) %>% 
    select(Format, total) %>% unique() %>% arrange(desc(total))

  ggplot(data=Pform, mapping=aes(x=Format, y=total, label=total)) +
    theme_light() + 
    theme(legend.position="none") +
    geom_col(fill="#999999") +
    geom_text() +
    labs(x="Format", y="Number of Presentations")
```

### Presentation Notes

```{r Pnotes}
  Pnotes <- select(Pres, PresDateTime, Notes) %>% filter(!is.na(Notes)) %>% arrange(PresDateTime)
  
  kable(Pnotes, format="latex")
```