cv.Rmd

---
name: Lampros Sp. Mouselimis
position: ""
address: ""
profilepic: photo.jpg
phone: ""
www: mlampros.github.io/
email: "mouselimislampros@gmail.com"
twitter: lampros_twit
github: mlampros
linkedin: mlampros
googlescholar: JXg3b58AAAAJ
orcid: "0000-0002-8024-1546"
aboutme: "I'm a data & remote sensing analyst and open source author / maintainer of numerous R language packages (CRAN, Github), competent in two programming languages (R, Python) who takes advantage of C++ (in R through the Rcpp and RcppArmadillo packages and in python through Cython) to improve the efficiency of internal functions"
headcolor: 009ACD
date: "`r format(Sys.time(), '%B %Y')`"
output: 
  vitae::awesomecv:
    keep_tex: true
    fig_crop: no
always_allow_html: yes
header-includes:
  - \usepackage{float}
  - \floatplacement{figure}{H}              # make every figure with caption = h
params:
  work_dir:
    value: x
---

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

require(magrittr)

repo_dir = dirname(params$work_dir)            # use 'dirname()' because the '.Rmd' file is executed from inside the 'docs' directory, therefore 'docs' is considered as working directory and I have to move one directory upwards

software_path = file.path(repo_dir, "data", "software.csv")
education_path = file.path(repo_dir, "data", "education.csv")
jobs_path = file.path(repo_dir, "data", "jobs.csv")
training_path = file.path(repo_dir, "data", "training.csv")
spoken_languages = file.path(repo_dir, "data", "languages.csv")
technical_sk_path = file.path(repo_dir, "data", "technical_skills.csv")
geospatial_anal_path = file.path(repo_dir, "data", "geospatial_analysis.csv")
personal_data_path = file.path(repo_dir, "data", "personal_data.csv")

```

<br>

![](human_evolution.jpg)

<br>

R-Programming Packages
======================


```{r programming, include=FALSE}

software = readr::read_csv(software_path) %>%
  dplyr::arrange(dplyr::desc(when))

```


```{r}

software %>% 
  vitae::detailed_entries(what = what, 
                          with = package, 
                          when = when, 
                          why = why, 
                          where = glue::glue("{where} ({release})"))
```


\bigskip
\bigskip


Education
=========

```{r education, include=FALSE}

edu = readr::read_csv(education_path)

```


```{r}

vitae::detailed_entries(edu, 
                        when = glue::glue("{start_date}--{end_date}"),
                        what = degree,
                        where = loc,
                        with = uni,
                        why = details)

```


\bigskip
\bigskip


Experience
==========

```{r work experience, include=FALSE}

jobs = readr::read_csv(jobs_path)

```


```{r}

jobs %>% 
  vitae::detailed_entries(what = role,
                          when = glue::glue("{start_date}--{end_date}"),
                          with = company,
                          where = where,
                          why = details)
```


\bigskip
\bigskip


Post Graduate Training
======================

```{r training, include=FALSE}

training = readr::read_csv(training_path)

```


```{r}

training %>% 
  vitae::detailed_entries(what = institution, 
                          with = glue::glue("{what}  [ {with} ]"), 
                          when = certificate, 
                          where = programming,
                          why = type_education)
```

\bigskip

Spoken Languages
================

```{r spoken languages, include=FALSE}

languages = readr::read_csv(spoken_languages)

```

```{r}

languages %>% 
  kableExtra::kable(booktabs = T, align = 'c', escape = F, format = "latex") %>%
  kableExtra::kable_styling(full_width = F, font_size = 9, latex_options = "HOLD_position", fixed_thead = T) %>% 
  kableExtra::footnote(
    footnote_as_chunk = T,
    general_title = " ",
    general = "\\\\tiny Common European Framework of Reference for Languages: A1/A2: Basic User. B1/B2: Independent User. C1/C2: Proficient User",
    escape = F
  ) %>%
  kableExtra::column_spec(column = 1:6, width = '1.9cm') %>%
  kableExtra::column_spec(1, bold = TRUE, color = 'blue') %>%
  kableExtra::column_spec(6, bold = TRUE, color = 'green') %>%
  kableExtra::column_spec(8, bold = TRUE, color = 'orange') %>%
  kableExtra::row_spec(0, color = 'blue')

```

\pagebreak


\bigskip
\bigskip


Technical Skills
================

\bigskip


```{r technical skills, include=FALSE}

technical_skills = readr::read_csv(technical_sk_path)

```

```{r, fig.width = 8, fig.height = 5, fig.align = "left", out.width = "7.6in", out.height = "4.5in"}

technical_skills$level = factor(technical_skills$level, levels = c("Familiar", "Intermediate", "Advanced", "Proficient"))

plt_lev = ggplot2::ggplot(data = technical_skills, ggplot2::aes(x = skill, y = level)) + 
  ggplot2::geom_tile(mapping = ggplot2::aes(fill = level)) + 
  ggplot2::facet_wrap(facets = ~type, scales = 'free') +
  ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE)) +
  ggplot2::theme(plot.title = ggplot2::element_text(size = "8", hjust = 0.5, face = "bold", colour = "blue"),
                 axis.title.x = ggplot2::element_text(size = 8, face = "bold", colour = "blue"),
                 axis.title.y = ggplot2::element_text(size = 8, face = "bold", colour = "blue"),
                 axis.text.x = ggplot2::element_text(size = 8, face = "bold", colour = "black", angle = 35, vjust = 1.0, hjust = 1.0),
                 axis.text.y = ggplot2::element_text(size = 8, face = "bold", colour = "black"),
                 strip.text.x = ggplot2::element_text(size = 8, face = "bold", colour = "black"),
                 legend.text = ggplot2::element_text(size = 8, face = "bold", colour = "black"),
                 legend.position = "top")

plt_lev

```


Geospatial Analysis (Timeline)
==============================


```{r geospatial analysis, include=FALSE}

geospatial_analysis = readr::read_csv(geospatial_anal_path)

```

```{r, fig.width = 8, fig.height = 4, fig.align = "left", out.width = "7.6in", out.height = "4.5in"}

par(oma = c(0,0,0,0))      # set all plot margins to 0 so that the 'geospatial data' can fit in the same page
par(mar = c(0,0,0,0))

geospatial_analysis = geospatial_analysis[, 1:2]

length_downup = seq(from = 0.4, to = 0.9, length = nrow(geospatial_analysis))

timelineS::timelineS(geospatial_analysis, 
                      main = NA, 
                      label.direction = 'downup', 
                      label.length = length_downup,           # customize the height of the lines
                      scale = 'quarter',
                      scale.font = 1.5,
                      line.color = "blue", 
                      label.color = "orange", 
                      point.color = "green",
                      pch = "*",
                      buffer.days = 720,                      # this parameter extends the range of the years (bothsides)
                      labels = geospatial_analysis[[1]],      # keep only text and omit the dates
                      label.cex = 0.6,
                      label.font = 1)

```

\pagebreak


\bigskip
\bigskip


Personal Data
=============

\bigskip


```{r personal data, include=FALSE}

personal_data = readr::read_csv(personal_data_path)

```

```{r}

personal_data %>%
  kableExtra::kable(booktabs = T, align = 'l', escape = F, format = "latex", col.names = NULL) %>%
  kableExtra::kable_styling(full_width = F, font_size = 9, latex_options = "HOLD_position", fixed_thead = T) %>% 
  kableExtra::column_spec(1, bold = TRUE, color = 'blue') %>%
  kableExtra::column_spec(2, width = "30em")

```


\bigskip
\bigskip


Publications
============

```{r google scholar, include=FALSE}

pubs <- scholar::get_publications("JXg3b58AAAAJ") %>% 
  dplyr::mutate(author = author %>% 
                  as.character %>% 
                  stringr::str_trim(),
                journal = journal %>% 
                  replace(journal %in% "bioRxiv", "BioRxiv"), 
                first_author = dplyr::case_when(stringr::str_starts(author, "L Mouselimis") ~ TRUE,
                                         TRUE ~ FALSE),
                preprint = dplyr::case_when(journal %in% c("bioRxiv", "BioRxiv") ~ TRUE,
                                     TRUE ~ FALSE)) %>% 
  dplyr::arrange(desc(year))

```


```{r}

pubs %>% 
  # dplyr::filter(first_author, !preprint) %>% 
  vitae::detailed_entries(
    what = title,
    when = as.character(year),
    with = author,
    where = journal, 
    .protect = TRUE
  )

```