diff --git a/DESCRIPTION b/DESCRIPTION
index ba3c629..6ff2b6f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -46,7 +46,8 @@
     glue,
     tools,
     webshot,
-    digest
+    digest,
+    graphics
 Suggests:
     testthat,
     magick,
diff --git a/NAMESPACE b/NAMESPACE
index 59c7f51..eda523b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -41,8 +41,11 @@
 export(save_kable)
 export(scroll_box)
 export(spec_angle)
+export(spec_boxplot)
 export(spec_color)
 export(spec_font_size)
+export(spec_hist)
+export(spec_image)
 export(spec_popover)
 export(spec_tooltip)
 export(text_spec)
@@ -53,6 +56,12 @@
 importFrom(digest,digest)
 importFrom(glue,glue)
 importFrom(grDevices,col2rgb)
+importFrom(grDevices,png)
+importFrom(grDevices,svg)
+importFrom(graphics,boxplot)
+importFrom(graphics,hist)
+importFrom(graphics,par)
+importFrom(graphics,text)
 importFrom(knitr,asis_output)
 importFrom(knitr,include_graphics)
 importFrom(knitr,kable)
@@ -69,6 +78,8 @@
 importFrom(rvest,html_table)
 importFrom(scales,rescale)
 importFrom(stats,ave)
+importFrom(stats,density)
+importFrom(stats,median)
 importFrom(stringr,fixed)
 importFrom(stringr,str_count)
 importFrom(stringr,str_detect)
diff --git a/R/column_spec.R b/R/column_spec.R
index fd33b4e..bdc8655 100644
--- a/R/column_spec.R
+++ b/R/column_spec.R
@@ -51,6 +51,7 @@
 #' to build a popover is through `spec_popover()`. If you only provide a text
 #' string, it will be used as content. Note that You have to enable this
 #' bootstrap module manually. Read the package vignette to see how.
+#' @param image Vector of image paths.
 #'
 #' @details Use `latex_column_spec` in a LaTeX table to change or
 #' customize the column specification.  Because of the way it is handled
@@ -70,7 +71,7 @@
                         extra_css = NULL, include_thead = FALSE,
                         latex_column_spec = NULL, latex_valign = 'p',
                         link = NULL, new_tab = TRUE,
-                        tooltip = NULL, popover = NULL) {
+                        tooltip = NULL, popover = NULL, image = NULL) {
   if (!is.numeric(column)) {
     stop("column must be numeric. ")
   }
@@ -89,7 +90,7 @@
                             border_left, border_right,
                             width_min, width_max,
                             extra_css, include_thead,
-                            link, new_tab, tooltip, popover))
+                            link, new_tab, tooltip, popover, image))
   }
   if (kable_format == "latex") {
     return(column_spec_latex(kable_input, column, width,
@@ -98,7 +99,7 @@
                              color, background,
                              border_left, border_right,
                              latex_column_spec, latex_valign, include_thead,
-                             link))
+                             link, image))
   }
 }
 
@@ -109,7 +110,7 @@
                              border_left, border_right,
                              width_min, width_max,
                              extra_css, include_thead,
-                             link, new_tab, tooltip, popover) {
+                             link, new_tab, tooltip, popover, image) {
   kable_attrs <- attributes(kable_input)
   kable_xml <- read_kable_as_xml(kable_input)
   kable_tbody <- xml_tpart(kable_xml, "tbody")
@@ -163,6 +164,7 @@
   new_tab <- ensure_len_html(new_tab, nrows, "new_tab")
   tooltip <- ensure_len_html(tooltip, nrows, "tooltip")
   popover <- ensure_len_html(popover, nrows, "popover")
+  image <- ensure_len_html(image, nrows, "image")
 
   for (i in all_contents_rows) {
     for (j in column) {
@@ -173,7 +175,7 @@
         color[i], background[i], border_left, border_right,
         border_l_css, border_r_css,
         extra_css,
-        link[i], new_tab[i], tooltip[i], popover[i]
+        link[i], new_tab[i], tooltip[i], popover[i], image[i]
       )
     }
   }
@@ -199,7 +201,7 @@
                                   border_left, border_right,
                                   border_l_css, border_r_css,
                                   extra_css,
-                                  link, new_tab, tooltip, popover) {
+                                  link, new_tab, tooltip, popover, image) {
   if (is.na(xml_attr(target_cell, "style"))) {
     xml_attr(target_cell, "style") <- ""
   }
@@ -259,6 +261,24 @@
                                              extra_css)
   }
 
+  if (!is.null(image)) {
+    image <- image[[1]]
+    if (class(image) == "kableExtraInlinePlots") {
+      if (!is.null(image$svg_text)) {
+        xml_add_child(target_cell, xml2::read_xml(image$svg_text))
+      } else {
+        img_text <- paste0('<img src="', image$path, '" width="',
+        image$width / image$res * 96, '" height="',
+        image$height / image$res * 96,
+        '"></img>')
+        xml_add_child(target_cell, xml2::read_html(img_text))
+      }
+    } else {
+      img_text <- paste0('<img src="', image, '"></img>')
+      xml_add_child(target_cell, xml2::read_html(img_text))
+    }
+  }
+
   # favor popover over tooltip
   if (!is.null(popover)) {
     if (class(popover) != "ke_popover") popover <- spec_popover(popover)
@@ -273,29 +293,6 @@
       xml_attr(target_cell, t) <- tooltip_list[t]
     }
   }
-  # if (!is.null(popover)) {
-  #   if (class(popover) != "ke_popover") popover <- spec_popover(popover)
-  #   popover_list <- attr(popover, 'list')
-  #   span_node <- xml2::read_xml(paste0(
-  #     '<span>', xml_text(target_cell), '</span>'
-  #   ))
-  #   for (p in names(popover_list)) {
-  #     xml_attr(span_node, p) <- popover_list[p]
-  #   }
-  #   xml_add_child(target_cell, span_node)
-  #   xml_text(target_cell) <- ""
-  # } else if (!is.null(tooltip)) {
-  #   if (class(tooltip) != "ke_tooltip") tooltip <- spec_tooltip(tooltip)
-  #   tooltip_list <- attr(tooltip, 'list')
-  #   span_node <- xml2::read_xml(paste0(
-  #     '<span>', xml_text(target_cell), '</span>'
-  #   ))
-  #   for (t in names(tooltip_list)) {
-  #     xml_attr(span_node, t) <- tooltip_list[t]
-  #   }
-  #   xml_add_child(target_cell, span_node)
-  #   xml_text(target_cell) <- ""
-  # }
 
   if (!is.null(link)) {
     href_node <- xml2::read_xml(paste0(
@@ -316,7 +313,7 @@
                               color, background,
                               border_left, border_right,
                               latex_column_spec, latex_valign, include_thead,
-                              link) {
+                              link, image) {
   table_info <- magic_mirror(kable_input)
   if (!is.null(table_info$collapse_rows)) {
     message("Usually it is recommended to use column_spec before collapse_rows,",
@@ -368,6 +365,7 @@
   background <- ensure_len_latex(background, nrows, off, include_thead, "white",
                               "background")
   link <- ensure_len_latex(link, nrows, off, include_thead, "#", "link")
+  image <- ensure_len_latex(image, nrows, off, include_thead, "", "image")
 
   if (include_thead) {
     rows <- seq(1, nrows)
@@ -380,7 +378,7 @@
     new_row <- latex_cell_builder(
       target_row, column, table_info,
       bold[i], italic[i], monospace[i], underline[i],
-      strikeout[i], color[i], background[i], link[i]
+      strikeout[i], color[i], background[i], link[i], image[i]
       # font_size, angle
       )
     temp_sub <- ifelse(i == 1 & (table_info$tabular == "longtable" |
@@ -494,7 +492,7 @@
 latex_cell_builder <- function(target_row, column, table_info,
                                bold, italic, monospace,
                                underline, strikeout,
-                               color, background, link
+                               color, background, link, image
                                # font_size, angle
                                ) {
   new_row <- latex_row_cells(target_row)[[1]]
@@ -538,6 +536,30 @@
                               new_row[column], "\\}")
   }
 
+  if (!is.null(image)) {
+    image <- image[[1]]
+    if (class(image) == "kableExtraInlinePlots") {
+      new_row[column] <- paste0(
+        new_row[column],
+        '\\\\includegraphics\\[width=',
+        # '\\\\raisebox\\{-\\\\totalheight\\}\\{\\\\includegraphics\\[width=',
+        round(image$width / image$res, 2), 'in, height=',
+        round(image$height / image$res, 2), 'in\\]\\{',
+        image$path,
+        '\\}'
+        # '\\}\\}'
+        )
+    } else {
+      if (!is.null(image) && !is.na(image) && image != "") {
+        new_row[column] <- paste0(
+          new_row[column],
+          '\\\\includegraphics\\{',
+          image, '\\}'
+        )
+      }
+    }
+  }
+
   new_row <- paste(new_row, collapse = " & ")
 
   return(new_row)
diff --git a/R/kableExtra-package.R b/R/kableExtra-package.R
index 35617e1..415b5b0 100644
--- a/R/kableExtra-package.R
+++ b/R/kableExtra-package.R
@@ -68,13 +68,14 @@
 #' @importFrom utils read.csv head capture.output
 #' @importFrom scales rescale
 #' @importFrom viridisLite viridis
-#' @importFrom stats ave
-#' @importFrom grDevices col2rgb
+#' @importFrom stats ave density median
+#' @importFrom grDevices col2rgb svg png
 #' @importFrom rstudioapi isAvailable viewer
 #' @importFrom glue glue
 #' @importFrom tools file_ext file_path_sans_ext
 #' @importFrom webshot webshot
 #' @importFrom digest digest
+#' @importFrom graphics par text hist boxplot
 #' @import htmltools
 #' @name kableExtra-package
 #' @aliases kableExtra
diff --git a/R/light_themes.R b/R/light_themes.R
index e4ed2a3..a37e541 100644
--- a/R/light_themes.R
+++ b/R/light_themes.R
@@ -35,7 +35,7 @@
 #' @export
 kable_minimal <- function(
   kable_input, lightable_options = "basic",
-  html_font = '"Trebuchet MS", verdana, calibri, sans-serif', ...) {
+  html_font = '"Trebuchet MS", verdana, sans-serif', ...) {
   kable_light(kable_input, "lightable-minimal",
               lightable_options, html_font, ...)
 }
diff --git a/R/mini_plots.R b/R/mini_plots.R
new file mode 100644
index 0000000..f6ed31d
--- /dev/null
+++ b/R/mini_plots.R
@@ -0,0 +1,187 @@
+#' Helper functions to generate inline sparklines
+#'
+#' @description These functions helps you quickly generate sets of sparkline
+#' style plots using base R plotting system. Currently, we support histogram
+#' and boxplot. You can use them together with `column_spec` to
+#' generate inline plot in tables. By default, this function will save images
+#' in a folder called "kableExtra" and return the address of the file.
+#'
+#' @param x Vector of values or List of vectors of values.
+#' @param width The width of the plot in pixel
+#' @param height The height of the plot in pixel
+#' @param res The resolution of the plot. Default is 300.
+#' @param same_lim T/F. If x is a list of vectors, should all the plots be
+#' plotted in the same range? Default is True.
+#' @param lim Manually specify plotting range in the form of `c(0, 10)`.
+#' @param xaxt On/Off for xaxis text
+#' @param yaxt On/Off for yaxis text
+#' @param ann On/Off for annotations (titles and axis titles)
+#' @param col Color for the fill of the histogram bar/boxplot box.
+#' @param border Color for the border.
+#' @param dir Directory of where the images will be saved.
+#' @param file File name. If not provided, a random name will be used
+#' @param file_type Graphic device. Support `png` or `svg`. SVG is recommended
+#' for HTML output
+#' @param add_label For boxplot. T/F to add labels for min, mean and max.
+#' @param label_digits If T for add_label, rounding digits for the label.
+#' Default is 2.
+#' @param boxlty Boxplot - box boarder type
+#' @param medcol Boxplot - median line color
+#' @param medlwd Boxplot - median line width
+#'
+#' @inheritParams graphics::hist
+#' @inheritParams graphics::boxplot
+#' @export
+spec_hist <- function(x, width = 200, height = 50, res = 300,
+                      breaks = "Sturges",
+                      same_lim = TRUE, lim = NULL,
+                      xaxt = 'n', yaxt = 'n', ann = FALSE,
+                      col = "lightgray", border = NULL,
+                      dir = if (is_latex()) rmd_files_dir() else tempdir(),
+                      file = NULL,
+                      file_type = if (is_latex()) "png" else "svg", ...) {
+  if (is.list(x)) {
+    if (same_lim & is.null(lim)) {
+      lim <- base::range(unlist(x))
+    }
+    return(lapply(x, function(x_) {spec_hist(
+      x = x_, width = width, height = height,
+      breaks = breaks, same_lim = same_lim, lim = lim,
+      xaxt = xaxt, yaxt = yaxt, ann = ann, col = col, border = border,
+      dir = dir, file = file, file_type = file_type, ...
+    )}))
+  }
+
+  if (is.null(lim)) {
+    lim <- base::range(x)
+  }
+
+  file_type <- match.arg(file_type, c("svg", "png"))
+
+  if (!dir.exists(dir)) {
+    dir.create(dir)
+  }
+
+  if (is.null(file)) {
+    file <- tempfile("hist", dir, paste0('.', file_type))
+  }
+
+  if (file_type == "svg") {
+    grDevices::svg(filename = file, width = width / res, height = height / res,
+                   bg = 'transparent')
+  } else {
+    grDevices::png(filename = file, width = width, height = height, res = res,
+                   bg = 'transparent')
+  }
+
+  graphics::par(mar = c(0, 0, 0.2, 0), lwd=0.5)
+  graphics::hist(x, breaks = breaks, xlim = lim, border = border,
+                 xaxt = xaxt, yaxt = yaxt, ann = ann, col = col, ...)
+  grDevices::dev.off()
+
+  if (file_type == "svg") {
+    svg_xml <- xml2::read_xml(file)
+    svg_text <- as.character(svg_xml)
+    unlink(file)
+  } else {
+    svg_text <- NULL
+  }
+  out <- list(path = file, dev = file_type, type = "hist",
+              width = width, height = height, res = res,
+              svg_text = svg_text)
+
+  class(out) <- "kableExtraInlinePlots"
+  return(out)
+}
+
+#' @rdname spec_hist
+#' @export
+spec_boxplot <- function(x, width = 200, height = 50, res = 300,
+                         add_label = FALSE, label_digits = 2,
+                         same_lim = TRUE, lim = NULL,
+                         xaxt = 'n', yaxt = 'n', ann = FALSE,
+                         col = "lightgray", border = NULL,
+                         boxlty = 0, medcol = "red", medlwd = 1,
+                         dir = if (is_latex()) rmd_files_dir() else tempdir(),
+                         file = NULL,
+                         file_type = if (is_latex()) "png" else "svg", ...) {
+  if (is.list(x)) {
+    if (same_lim & is.null(lim)) {
+      lim <- base::range(unlist(x))
+    }
+    return(lapply(x, function(x_) {spec_boxplot(
+      x = x_, width = width, height = height,
+      add_label = add_label, same_lim = same_lim, lim = lim,
+      xaxt = xaxt, yaxt = yaxt, ann = ann,
+      col = col, border = border,
+      boxlty = boxlty, medcol = medcol, medlwd = medlwd,
+      dir = dir, file = file, file_type = file_type, ...
+    )}))
+  }
+
+  if (is.null(lim)) {
+    lim <- base::range(x)
+    lim[1] <- lim[1] - (lim[2] - lim[1]) / 10
+    lim[2] <- (lim[2] - lim[1]) / 10 + lim[2]
+  }
+
+  file_type <- match.arg(file_type, c("svg", "png"))
+
+  if (!dir.exists(dir)) {
+    dir.create(dir)
+  }
+
+  if (is.null(file)) {
+    file <- tempfile("hist", dir, paste0('.', file_type))
+  }
+
+  if (file_type == "svg") {
+    grDevices::svg(filename = file, width = width / res, height = height / res,
+                   bg = 'transparent')
+  } else {
+    grDevices::png(filename = file, width = width, height = height, res = res,
+                   bg = 'transparent')
+  }
+
+  graphics::par(mar = c(0, 0, 0, 0))
+
+  graphics::boxplot(x, horizontal = TRUE, ann = ann, frame = FALSE, bty = 'n', ylim = lim,
+          col = col, border = border,
+          boxlty = boxlty, medcol = medcol, medlwd = medlwd,
+          axes = FALSE, outcex = 0.2, whisklty = 1,
+          ...)
+  if (add_label) {
+    x_median <- round(median(x, na.rm = T), label_digits)
+    x_min <- round(min(x, na.rm = T), label_digits)
+    x_max <- round(max(x, na.rm = T), label_digits)
+    graphics::text(x_median, y = 1.4, labels = x_median, cex = 0.5)
+    graphics::text(x_min, y = 0.6, labels = x_min, cex = 0.5)
+    graphics::text(x_max, y = 0.6, labels = x_max, cex = 0.5)
+  }
+  grDevices::dev.off()
+
+  if (file_type == "svg") {
+    svg_xml <- xml2::read_xml(file)
+    svg_text <- as.character(svg_xml)
+    unlink(file)
+  } else {
+    svg_text <- NULL
+  }
+  out <- list(path = file, dev = file_type, type = "boxplot",
+              width = width, height = height, res = res,
+              svg_text = svg_text)
+  class(out) <- "kableExtraInlinePlots"
+  return(out)
+}
+
+is_latex <- knitr::is_latex_output
+
+rmd_files_dir <- function(create = TRUE) {
+  curr_file_name <- sub("\\.[^\\.]*$", "", knitr::current_input())
+  dir_name <- paste0(curr_file_name, "_files")
+  if (!dir.exists(dir_name) & create) dir.create(dir_name)
+  fig_dir_name <- file.path(dir_name, "figure-latex")
+  if (!dir.exists(fig_dir_name) & create) dir.create(fig_dir_name)
+  return(fig_dir_name)
+}
+
diff --git a/R/spec_tools.R b/R/spec_tools.R
index 350925b..4537ada 100644
--- a/R/spec_tools.R
+++ b/R/spec_tools.R
@@ -164,3 +164,36 @@
   attr(popover_options, 'list') <- popover_options_list
   return(popover_options)
 }
+
+#' Setup image path, size, etc
+#'
+#' @description Users can directly provide image file path to column spec.
+#' However, if you need to specify the size of the image, you will need this
+#' function.
+#'
+#' @param path file path(s)
+#' @param width image width in pixel
+#' @param height image height in pixel
+#' @param res image resolution.
+#' @param svg_text If you have the raw text for SVG. Put them here
+#'
+#' @export
+spec_image <- function(path, width, height, res = 300, svg_text = NULL) {
+  if (length(path) > 1) {
+    return(lapply(path, function(p) {
+      return(spec_image(p, width, height, res, svg_text))
+    }))
+  }
+  if (!is.null(svg_text)) {
+    out <- list(path = NULL, dev = NULL, type = "image",
+                width = NULL, height = NULL, res = NULL,
+                svg_text = svg_text)
+    class(out) <- "kableExtraInlinePlots"
+    return(out)
+  }
+  out <- list(path = path, dev = "external", type = "image",
+              width = width, height = height, res = res,
+              svg_text = svg_text)
+  class(out) <- "kableExtraInlinePlots"
+  return(out)
+}
diff --git a/README.md b/README.md
index b46a973..c4f5d31 100644
--- a/README.md
+++ b/README.md
@@ -73,4 +73,4 @@
 
 
 ## Acknowledgement
-I would like to thank colleagues at [Hebrew SeniorLife Institute for Aging Research](https://www.instituteforagingresearch.org/) and the [Boston Pepper Center](http://pepper.bwh.harvard.edu/) for their input. I also would like to appreciate the mentorship from Tom Travison ([@tgt75](https://twitter.com/tgt75)) and all the efforts from the open source community, which help this package keep getting better.
+I would like to thank colleagues at Hebrew SeniorLife [Marcus Institute for Aging Research](https://www.marcusinstituteforaging.org/) and the [Boston Pepper Center](https://pepper.bwh.harvard.edu/) for their input. I also would like to appreciate the mentorship from Tom Travison ([@tgt75](https://twitter.com/tgt75)) and all the efforts from the open source community, which help this package keep getting better.
diff --git a/docs/awesome_table_in_html.Rmd b/docs/awesome_table_in_html.Rmd
index 71a3331..bf9ea7d 100644
--- a/docs/awesome_table_in_html.Rmd
+++ b/docs/awesome_table_in_html.Rmd
@@ -22,7 +22,7 @@
 
 <img src="kableExtra_sm.png" align="right" alt="logo" width="80" height = "93" style = "border: none; float: right;">
 
-> Please see the package [documentation site](http://haozhu233.github.io/kableExtra/) for how to use this package in LaTeX.
+> Please see the package [documentation site](https://haozhu233.github.io/kableExtra/) for how to use this package in LaTeX.
 
 # Overview
 The goal of `kableExtra` is to help you build common complex tables and manipulate table styles. It imports the pipe `%>%` symbol from `magrittr` and verbalize all the functions, so basically you can add "layers" to a kable output in a way that is similar with `ggplot2` and `plotly`. 
@@ -44,7 +44,6 @@
 # Getting Started
 Here we are using the first few columns and rows from dataset `mtcars`
 ```{r}
-library(knitr)
 library(kableExtra)
 dt <- mtcars[1:5, 1:6]
 ```
@@ -119,7 +118,7 @@
 `kable_styling` offers a few other ways to customize the look of a HTML table. 
 
 ## Bootstrap table classes
-If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including `striped`, `bordered`, `hover`, `condensed` and `responsive`. If you are not familiar, no worries, you can take a look at their [documentation site](http://getbootstrap.com/css/#tables) to get a sense of how they look like. All of these options are available here. 
+If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including `striped`, `bordered`, `hover`, `condensed` and `responsive`. If you are not familiar, no worries, you can take a look at their [documentation site](https://getbootstrap.com/css/) to get a sense of how they look like. All of these options are available here. 
 
 For example, to add striped lines (alternative row colors) to your table and you want to highlight the hovered row, you can simply type:
 ```{r}
@@ -192,7 +191,7 @@
 )
 
 kbl(text_tbl) %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   column_spec(1, bold = T, border_right = T) %>%
   column_spec(2, width = "30em", background = "yellow")
 ```
@@ -212,12 +211,48 @@
 
 You can still use the `spec_***` helper functions to help you define color. See the documentation [below](#visualize-data-with-viridis-color). 
 
+## Insert Images into Columns
+Technically, we are still talking about `column_spec` here. However, since this topic itself contains its own subtopics, we split it out as a separate section. Since `kableExtra` 1.2, we introduced the feature of adding images to columns of tables. Here is a quick example. 
+
+```{r}
+tbl_img <- data.frame(
+  name = c("kableExtra 1", "kableExtra 2"),
+  logo = ""
+)
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = "kableExtra_sm.png")
+```
+
+If you need to specify the size of the images, you need to do it through `spec_image`. 
+
+```{r}
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_image(
+    c("kableExtra_sm.png", "kableExtra_sm.png"), 50, 50))
+```
+
+`kableExtra` also provides a few inline plotting tools. Right now, there are `spec_hist` and `spec_boxplot`. One key feature is that by default, the limits of every subplots are fixed so you can compare across rows. Note that in html, you can also use package `sparkline` to create some jquery based interactive sparklines. Check out the end of this guide for details. 
+
+```{r}
+mpg_list <- split(mtcars$mpg, mtcars$cyl)
+inline_plot <- data.frame(cyl = c(4, 6, 8), mpg_box = "", mpg_hist = "")
+inline_plot %>% 
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_boxplot(mpg_list)) %>%
+  column_spec(3, image = spec_hist(mpg_list))
+```
+
 ## Row spec
 Similar with `column_spec`, you can define specifications for rows. Currently, you can either bold or italicize an entire row. Note that, similar with other row-related functions in `kableExtra`, for the position of the target row, you don't need to count in header rows or the group labeling rows.
 
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   column_spec(5:7, bold = T) %>%
   row_spec(3:5, bold = T, color = "white", background = "#D7261E")
 ```
@@ -228,7 +263,7 @@
 One special case of `row_spec` is that you can specify the format of the header row via `row_spec(row = 0, ...)`. 
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   row_spec(0, angle = -45)
 ```
 
@@ -271,7 +306,7 @@
 #   ) %>%
 #   select(car, mpg, cyl) %>%
 #   kbl(format = "html", escape = F) %>%
-#   kable_styling("striped", full_width = F)
+#   kable_paper("striped", full_width = F)
 ```
 
 ## Visualize data with Viridis Color
@@ -300,7 +335,7 @@
 #     background = spec_color(1:10, end = 0.9, option = "A", direction = -1)
 #   )) %>%
 #   kable(escape = F, align = "c") %>%
-#   kable_styling(c("striped", "condensed"), full_width = F)
+#   kable_paper(c("striped", "condensed"), full_width = F)
 ```
 
 ## Text Specification
@@ -363,11 +398,11 @@
     position = popover_dt$position
   ))
 kbl(popover_dt, escape = FALSE) %>%
-  kable_styling("striped", full_width = FALSE)
+  kable_paper("striped", full_width = FALSE)
 ```
 
 ## Links
-You can add links to text via `text_spec("Google", link = "https://google.com")`: `r text_spec("Google", link = "https://google.com")`. If you want your hover message to be more obvious, it might be a good idea to put a `#` (go back to the top of the page) or `javascript:void(0)` (literally do nothing) in the `link` option.
+You can add links to text via `text_spec("kableExtra", link = "https://haozhu233.github.io/kableExtra/")`: `r text_spec("kableExtra", link = "https://haozhu233.github.io/kableExtra/")`. If you want your hover message to be more obvious, it might be a good idea to put a `#` (go back to the top of the page) or `javascript:void(0)` (literally do nothing) in the `link` option.
 `text_spec("Hover on me", link = "javascript:void(0)", popover = "Hello")`:
 `r text_spec("Hover on me", link = "javascript:void(0)", popover = "Hello")`
 
@@ -390,7 +425,7 @@
 ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
 
 kbl(ft_dt, escape = F) %>%
-  kable_styling("hover", full_width = F) %>%
+  kable_paper("hover", full_width = F) %>%
   column_spec(5, width = "3cm") %>%
   add_header_above(c(" ", "Hello" = 2, "World" = 2))
 ```
@@ -417,7 +452,7 @@
 Sometimes we want a few rows of the table being grouped together. They might be items under the same topic (e.g., animals in one species) or just different data groups for a categorical variable (e.g., age < 40, age > 40). With the function `group_rows()`/`pack_rows()` in `kableExtra`, this kind of task can be completed in one line. Please see the example below. Note that when you count for the start/end rows of the group, you don't need to count for the header rows nor other group label rows. You only need to think about the row numbers in the "original R dataframe".
 ```{r}
 kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows("Group 1", 4, 7) %>%
   pack_rows("Group 2", 8, 10)
 ```
@@ -426,14 +461,14 @@
 ```{r, eval = F}
 # Not evaluated. This example generates the same table as above.
 kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows(index = c(" " = 3, "Group 1" = 4, "Group 2" = 3))
 ```
 
 For advanced users, you can even define your own css for the group labeling.
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows("Group 1", 3, 5, label_row_css = "background-color: #666; color: #fff;")
 ```
 
@@ -460,7 +495,7 @@
 For advanced users, you can even define your own css for the group labeling.
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   add_indent(c(1, 3, 5))
 ```
 
@@ -473,7 +508,7 @@
                  C3 = 1:15,
                  C4 = sample(c(0,1), 15, replace = TRUE))
 kbl(collapse_rows_dt, align = "c") %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   column_spec(1, bold = T) %>%
   collapse_rows(columns = 1:2, valign = "top")
 ```
@@ -499,7 +534,7 @@
 
 ```{r}
 kbl(dt, align = "c") %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   footnote(general = "Here is a general comments of the table. ",
            number = c("Footnote 1; ", "Footnote 2; "),
            alphabet = c("Footnote A; ", "Footnote B; "),
@@ -521,7 +556,7 @@
 kbl(dt_footnote, align = "c", 
       # Remember this escape = F
       escape = F) %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   footnote(alphabet = "Footnote A; ",
            symbol = "Footnote Symbol 1; ",
            alphabet_title = "Type II: ", symbol_title = "Type III: ",
@@ -536,7 +571,7 @@
 
 ```{r}
 kbl(cbind(mtcars, mtcars)) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   scroll_box(width = "500px", height = "200px")
 ```
 
@@ -547,7 +582,7 @@
 ```{r}
 kbl(cbind(mtcars, mtcars)) %>%
   add_header_above(c("a" = 5, "b" = 18)) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   scroll_box(width = "100%", height = "200px")
 ```
 
@@ -557,7 +592,7 @@
 If you need to save those HTML tables but you don't want to generate them through rmarkdown, you can try to use the `save_kable()` function. You can choose whether to let those HTML files be self contained (default is yes). Self contained files packed CSS into the HTML file so they are quite large when there are many. 
 ```{r, eval=FALSE}
 kbl(mtcars) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   save_kable(file = "table1.html", self_contained = T)
 ```
 
diff --git a/docs/awesome_table_in_html.html b/docs/awesome_table_in_html.html
index e091362..615b2a9 100644
--- a/docs/awesome_table_in_html.html
+++ b/docs/awesome_table_in_html.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Hao Zhu" />
 
-<meta name="date" content="2020-08-20" />
+<meta name="date" content="2020-08-26" />
 
 <title>Create Awesome HTML Table with knitr::kable and kableExtra</title>
 
@@ -1731,7 +1731,7 @@
 
 <h1 class="title toc-ignore">Create Awesome HTML Table with knitr::kable and kableExtra</h1>
 <h4 class="author">Hao Zhu</h4>
-<h4 class="date">2020-08-20</h4>
+<h4 class="date">2020-08-26</h4>
 
 </div>
 
@@ -1743,7 +1743,7 @@
 </script>
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" align="right" alt="logo" width="80" height="93" style="border: none; float: right;"></p>
 <blockquote>
-<p>Please see the package <a href="http://haozhu233.github.io/kableExtra/">documentation site</a> for how to use this package in LaTeX.</p>
+<p>Please see the package <a href="https://haozhu233.github.io/kableExtra/">documentation site</a> for how to use this package in LaTeX.</p>
 </blockquote>
 <div id="overview" class="section level1">
 <h1>Overview</h1>
@@ -1763,8 +1763,7 @@
 <div id="getting-started" class="section level1">
 <h1>Getting Started</h1>
 <p>Here we are using the first few columns and rows from dataset <code>mtcars</code></p>
-<pre class="r"><code>library(knitr)
-library(kableExtra)
+<pre class="r"><code>library(kableExtra)
 dt &lt;- mtcars[1:5, 1:6]</code></pre>
 <blockquote>
 <p><strong>Key Update:</strong> In the latest version of this package (1.2+), we provide a wrapper funciton <code>kbl</code> to the original <code>kable</code> function with detailed documentation of all the hidden html/latex options. It also does auto-formatting check in every function call instead of relying on the global environement variable. As a result, it also solves an issue for multi-format R Markdown documents. I encourage you start to use the new <code>kbl</code> function for all its convenience but the support for the original <code>kable</code> function is still there. In this doc, we will use <code>kbl</code> instead of <code>kable</code>.</p>
@@ -2518,7 +2517,7 @@
 <pre class="r"><code>dt %&gt;%
   kbl() %&gt;%
   kable_minimal()</code></pre>
-<table class=" lightable-minimal" style="font-family: &quot;Trebuchet MS&quot;, verdana, calibri, sans-serif; margin-left: auto; margin-right: auto;">
+<table class=" lightable-minimal" style="font-family: &quot;Trebuchet MS&quot;, verdana, sans-serif; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -2960,7 +2959,7 @@
 <p><code>kable_styling</code> offers a few other ways to customize the look of a HTML table.</p>
 <div id="bootstrap-table-classes" class="section level2">
 <h2>Bootstrap table classes</h2>
-<p>If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including <code>striped</code>, <code>bordered</code>, <code>hover</code>, <code>condensed</code> and <code>responsive</code>. If you are not familiar, no worries, you can take a look at their <a href="http://getbootstrap.com/css/#tables">documentation site</a> to get a sense of how they look like. All of these options are available here.</p>
+<p>If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including <code>striped</code>, <code>bordered</code>, <code>hover</code>, <code>condensed</code> and <code>responsive</code>. If you are not familiar, no worries, you can take a look at their <a href="https://getbootstrap.com/css/">documentation site</a> to get a sense of how they look like. All of these options are available here.</p>
 <p>For example, to add striped lines (alternative row colors) to your table and you want to highlight the hovered row, you can simply type:</p>
 <pre class="r"><code>kbl(dt) %&gt;%
   kable_styling(bootstrap_options = c(&quot;striped&quot;, &quot;hover&quot;))</code></pre>
@@ -4242,10 +4241,10 @@
 )
 
 kbl(text_tbl) %&gt;%
-  kable_styling(full_width = F) %&gt;%
+  kable_paper(full_width = F) %&gt;%
   column_spec(1, bold = T, border_right = T) %&gt;%
   column_spec(2, width = &quot;30em&quot;, background = &quot;yellow&quot;)</code></pre>
-<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -4562,14 +4561,178 @@
 </table>
 <p>You can still use the <code>spec_***</code> helper functions to help you define color. See the documentation <a href="#visualize-data-with-viridis-color">below</a>.</p>
 </div>
+<div id="insert-images-into-columns" class="section level2">
+<h2>Insert Images into Columns</h2>
+<p>Technically, we are still talking about <code>column_spec</code> here. However, since this topic itself contains its own subtopics, we split it out as a separate section. Since <code>kableExtra</code> 1.2, we introduced the feature of adding images to columns of tables. Here is a quick example.</p>
+<pre class="r"><code>tbl_img &lt;- data.frame(
+  name = c(&quot;kableExtra 1&quot;, &quot;kableExtra 2&quot;),
+  logo = &quot;&quot;
+)
+tbl_img %&gt;%
+  kbl(booktabs = T) %&gt;%
+  kable_paper(full_width = F) %&gt;%
+  column_spec(2, image = &quot;kableExtra_sm.png&quot;)</code></pre>
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;">
+name
+</th>
+<th style="text-align:left;">
+logo
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+kableExtra 1
+</td>
+<td style="text-align:left;">
+<html>
+<body>
+<img 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">
+</body>
+</html>
+</td>
+</tr>
+</tbody>
+</table>
+<p>If you need to specify the size of the images, you need to do it through <code>spec_image</code>.</p>
+<pre class="r"><code>tbl_img %&gt;%
+  kbl(booktabs = T) %&gt;%
+  kable_paper(full_width = F) %&gt;%
+  column_spec(2, image = spec_image(
+    c(&quot;kableExtra_sm.png&quot;, &quot;kableExtra_sm.png&quot;), 50, 50))</code></pre>
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;">
+name
+</th>
+<th style="text-align:left;">
+logo
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+kableExtra 1
+</td>
+<td style="text-align:left;">
+<html>
+<body>
+<img 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" width="16" height="16">
+</body>
+</html>
+</td>
+</tr>
+</tbody>
+</table>
+<p><code>kableExtra</code> also provides a few inline plotting tools. Right now, there are <code>spec_hist</code> and <code>spec_boxplot</code>. One key feature is that by default, the limits of every subplots are fixed so you can compare across rows. Note that in html, you can also use package <code>sparkline</code> to create some jquery based interactive sparklines. Check out the end of this guide for details.</p>
+<pre class="r"><code>mpg_list &lt;- split(mtcars$mpg, mtcars$cyl)
+inline_plot &lt;- data.frame(cyl = c(4, 6, 8), mpg_box = &quot;&quot;, mpg_hist = &quot;&quot;)
+inline_plot %&gt;% 
+  kbl(booktabs = T) %&gt;%
+  kable_paper(full_width = F) %&gt;%
+  column_spec(2, image = spec_boxplot(mpg_list)) %&gt;%
+  column_spec(3, image = spec_hist(mpg_list))</code></pre>
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:right;">
+cyl
+</th>
+<th style="text-align:left;">
+mpg_box
+</th>
+<th style="text-align:left;">
+mpg_hist
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:right;">
+4
+</td>
+<td style="text-align:left;">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="48pt" height="12pt" viewbox="0 0 48 12" version="1.1">
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+</svg>
+</td>
+<td style="text-align:left;">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="48pt" height="12pt" viewBox="0 0 48 12" version="1.1">
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+</tr>
+<tr>
+<td style="text-align:right;">
+6
+</td>
+<td style="text-align:left;">
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+<td style="text-align:left;">
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+</svg>
+</td>
+</tr>
+<tr>
+<td style="text-align:right;">
+8
+</td>
+<td style="text-align:left;">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="48pt" height="12pt" viewbox="0 0 48 12" version="1.1">
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+</svg>
+</td>
+<td style="text-align:left;">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="48pt" height="12pt" viewBox="0 0 48 12" version="1.1">
+<g id="surface26"><path style="fill-rule:nonzero;fill:rgb(82.745098%,82.745098%,82.745098%);fill-opacity:1;stroke-width:0.375;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 1.019531 11.664062 L 4.800781 11.664062 L 4.800781 9.25 L 1.019531 9.25 Z M 1.019531 11.664062 "></path><path style="fill-rule:nonzero;fill:rgb(82.745098%,82.745098%,82.745098%);fill-opacity:1;stroke-width:0.375;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 4.804688 11.664062 L 8.585938 11.664062 L 8.585938 10.457031 L 4.804688 10.457031 Z M 4.804688 11.664062 "></path><path style="fill-rule:nonzero;fill:rgb(82.745098%,82.745098%,82.745098%);fill-opacity:1;stroke-width:0.375;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 8.585938 11.664062 L 12.367188 11.664062 L 12.367188 3.21875 L 8.585938 3.21875 Z M 8.585938 11.664062 "></path><path style="fill-rule:nonzero;fill:rgb(82.745098%,82.745098%,82.745098%);fill-opacity:1;stroke-width:0.375;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 12.367188 11.664062 L 16.148438 11.664062 L 16.148438 9.25 L 12.367188 9.25 Z M 12.367188 11.664062 "></path><path style="fill-rule:nonzero;fill:rgb(82.745098%,82.745098%,82.745098%);fill-opacity:1;stroke-width:0.375;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;" d="M 16.152344 11.664062 L 19.933594 11.664062 L 19.933594 9.25 L 16.152344 9.25 Z M 16.152344 11.664062 "></path></g>
+</svg>
+</td>
+</tr>
+</tbody>
+</table>
+</div>
 <div id="row-spec" class="section level2">
 <h2>Row spec</h2>
 <p>Similar with <code>column_spec</code>, you can define specifications for rows. Currently, you can either bold or italicize an entire row. Note that, similar with other row-related functions in <code>kableExtra</code>, for the position of the target row, you don’t need to count in header rows or the group labeling rows.</p>
 <pre class="r"><code>kbl(dt) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   column_spec(5:7, bold = T) %&gt;%
   row_spec(3:5, bold = T, color = &quot;white&quot;, background = &quot;#D7261E&quot;)</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -4717,9 +4880,9 @@
 <h2>Header Rows</h2>
 <p>One special case of <code>row_spec</code> is that you can specify the format of the header row via <code>row_spec(row = 0, ...)</code>.</p>
 <pre class="r"><code>kbl(dt) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   row_spec(0, angle = -45)</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;-webkit-transform: rotate(-45deg); -moz-transform: rotate(-45deg); -ms-transform: rotate(-45deg); -o-transform: rotate(-45deg); transform: rotate(-45deg);">
@@ -5027,7 +5190,7 @@
 #   ) %&gt;%
 #   select(car, mpg, cyl) %&gt;%
 #   kbl(format = &quot;html&quot;, escape = F) %&gt;%
-#   kable_styling(&quot;striped&quot;, full_width = F)</code></pre>
+#   kable_paper(&quot;striped&quot;, full_width = F)</code></pre>
 </div>
 <div id="visualize-data-with-viridis-color" class="section level2">
 <h2>Visualize data with Viridis Color</h2>
@@ -5247,7 +5410,7 @@
 #     background = spec_color(1:10, end = 0.9, option = &quot;A&quot;, direction = -1)
 #   )) %&gt;%
 #   kable(escape = F, align = &quot;c&quot;) %&gt;%
-#   kable_styling(c(&quot;striped&quot;, &quot;condensed&quot;), full_width = F)</code></pre>
+#   kable_paper(c(&quot;striped&quot;, &quot;condensed&quot;), full_width = F)</code></pre>
 </div>
 <div id="text-specification" class="section level2">
 <h2>Text Specification</h2>
@@ -5297,8 +5460,8 @@
     position = popover_dt$position
   ))
 kbl(popover_dt, escape = FALSE) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = FALSE)</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+  kable_paper(&quot;striped&quot;, full_width = FALSE)</code></pre>
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -5347,7 +5510,7 @@
 </div>
 <div id="links" class="section level2">
 <h2>Links</h2>
-<p>You can add links to text via <code>text_spec(&quot;Google&quot;, link = &quot;https://google.com&quot;)</code>: <a href="https://google.com" style="     ">Google</a>. If you want your hover message to be more obvious, it might be a good idea to put a <code>#</code> (go back to the top of the page) or <code>javascript:void(0)</code> (literally do nothing) in the <code>link</code> option. <code>text_spec(&quot;Hover on me&quot;, link = &quot;javascript:void(0)&quot;, popover = &quot;Hello&quot;)</code>: <a href="javascript:void(0)" style="     " data-toggle="popover" data-container="body" data-trigger="hover" data-placement="right" data-content="Hello">Hover on me</a></p>
+<p>You can add links to text via <code>text_spec(&quot;kableExtra&quot;, link = &quot;https://haozhu233.github.io/kableExtra/&quot;)</code>: <a href="https://haozhu233.github.io/kableExtra/" style="     ">kableExtra</a>. If you want your hover message to be more obvious, it might be a good idea to put a <code>#</code> (go back to the top of the page) or <code>javascript:void(0)</code> (literally do nothing) in the <code>link</code> option. <code>text_spec(&quot;Hover on me&quot;, link = &quot;javascript:void(0)&quot;, popover = &quot;Hello&quot;)</code>: <a href="javascript:void(0)" style="     " data-toggle="popover" data-container="body" data-trigger="hover" data-placement="right" data-content="Hello">Hover on me</a></p>
 </div>
 <div id="integration-with-formattable" class="section level2">
 <h2>Integration with <code>formattable</code></h2>
@@ -5368,21 +5531,21 @@
 ft_dt &lt;- ft_dt[c(&quot;car&quot;, &quot;mpg&quot;, &quot;cyl&quot;, &quot;disp&quot;, &quot;hp&quot;)]
 
 kbl(ft_dt, escape = F) %&gt;%
-  kable_styling(&quot;hover&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;hover&quot;, full_width = F) %&gt;%
   column_spec(5, width = &quot;3cm&quot;) %&gt;%
   add_header_above(c(&quot; &quot;, &quot;Hello&quot; = 2, &quot;World&quot; = 2))</code></pre>
-<table class="table table-hover" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-hover" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
-<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
+<th style="empty-cells: hide;" colspan="1">
 </th>
-<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
-<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
+<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
+<div style="TRUE">
 Hello
 </div>
 </th>
-<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
-<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
+<th style="padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
+<div style="TRUE">
 World
 </div>
 </th>
@@ -5861,10 +6024,10 @@
 <h2>Group rows via labeling</h2>
 <p>Sometimes we want a few rows of the table being grouped together. They might be items under the same topic (e.g., animals in one species) or just different data groups for a categorical variable (e.g., age &lt; 40, age &gt; 40). With the function <code>group_rows()</code>/<code>pack_rows()</code> in <code>kableExtra</code>, this kind of task can be completed in one line. Please see the example below. Note that when you count for the start/end rows of the group, you don’t need to count for the header rows nor other group label rows. You only need to think about the row numbers in the “original R dataframe”.</p>
 <pre class="r"><code>kbl(mtcars[1:10, 1:6], caption = &quot;Group Rows&quot;) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   pack_rows(&quot;Group 1&quot;, 4, 7) %&gt;%
   pack_rows(&quot;Group 2&quot;, 8, 10)</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <caption>
 Group Rows
 </caption>
@@ -6138,13 +6301,13 @@
 <p>Another way to use <code>pack_rows</code> is to provide an grouping index, similar with <code>add_header_above()</code>. This feature is only available in kableExtra &gt; 0.5.2.</p>
 <pre class="r"><code># Not evaluated. This example generates the same table as above.
 kbl(mtcars[1:10, 1:6], caption = &quot;Group Rows&quot;) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   pack_rows(index = c(&quot; &quot; = 3, &quot;Group 1&quot; = 4, &quot;Group 2&quot; = 3))</code></pre>
 <p>For advanced users, you can even define your own css for the group labeling.</p>
 <pre class="r"><code>kbl(dt) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   pack_rows(&quot;Group 1&quot;, 3, 5, label_row_css = &quot;background-color: #666; color: #fff;&quot;)</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -6309,9 +6472,9 @@
 <h2>Row indentation</h2>
 <p>Unlike <code>pack_rows()</code>, which will insert a labeling row, sometimes we want to list a few sub groups under a total one. In that case, <code>add_indent()</code> is probably more apporiate. For advanced users, you can even define your own css for the group labeling.</p>
 <pre class="r"><code>kbl(dt) %&gt;%
-  kable_styling(&quot;striped&quot;, full_width = F) %&gt;%
+  kable_paper(&quot;striped&quot;, full_width = F) %&gt;%
   add_indent(c(1, 3, 5))</code></pre>
-<table class="table table-striped" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper lightable-striped" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -6463,10 +6626,10 @@
                  C3 = 1:15,
                  C4 = sample(c(0,1), 15, replace = TRUE))
 kbl(collapse_rows_dt, align = &quot;c&quot;) %&gt;%
-  kable_styling(full_width = F) %&gt;%
+  kable_paper(full_width = F) %&gt;%
   column_spec(1, bold = T) %&gt;%
   collapse_rows(columns = 1:2, valign = &quot;top&quot;)</code></pre>
-<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:center;">
@@ -6495,7 +6658,7 @@
 1
 </td>
 <td style="text-align:center;">
-0
+1
 </td>
 </tr>
 <tr>
@@ -6511,7 +6674,7 @@
 3
 </td>
 <td style="text-align:center;">
-0
+1
 </td>
 </tr>
 <tr>
@@ -6554,7 +6717,7 @@
 8
 </td>
 <td style="text-align:center;">
-0
+1
 </td>
 </tr>
 <tr>
@@ -6562,7 +6725,7 @@
 9
 </td>
 <td style="text-align:center;">
-1
+0
 </td>
 </tr>
 <tr>
@@ -6570,7 +6733,7 @@
 10
 </td>
 <td style="text-align:center;">
-1
+0
 </td>
 </tr>
 <tr>
@@ -6584,7 +6747,7 @@
 11
 </td>
 <td style="text-align:center;">
-1
+0
 </td>
 </tr>
 <tr>
@@ -6592,7 +6755,7 @@
 12
 </td>
 <td style="text-align:center;">
-0
+1
 </td>
 </tr>
 <tr>
@@ -6611,7 +6774,7 @@
 14
 </td>
 <td style="text-align:center;">
-0
+1
 </td>
 </tr>
 <tr>
@@ -6827,7 +6990,7 @@
 </table>
 <p>You can also specify title for each category by using the <code>***_title</code> arguments. Default value for <code>general_title</code> is “Note:” and &quot;&quot; for the rest three. You can also change the order using <code>footnote_order</code>. You can even display footnote as chunk texts (default is as a list) using <code>footnote_as_chunk</code>. The font format of the titles are controlled by <code>title_format</code> with options including “italic” (default), “bold” and “underline”.</p>
 <pre class="r"><code>kbl(dt, align = &quot;c&quot;) %&gt;%
-  kable_styling(full_width = F) %&gt;%
+  kable_paper(full_width = F) %&gt;%
   footnote(general = &quot;Here is a general comments of the table. &quot;,
            number = c(&quot;Footnote 1; &quot;, &quot;Footnote 2; &quot;),
            alphabet = c(&quot;Footnote A; &quot;, &quot;Footnote B; &quot;),
@@ -6836,7 +6999,7 @@
            alphabet_title = &quot;Type II: &quot;, symbol_title = &quot;Type III: &quot;,
            footnote_as_chunk = T, title_format = c(&quot;italic&quot;, &quot;underline&quot;)
            )</code></pre>
-<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -7010,12 +7173,12 @@
 kbl(dt_footnote, align = &quot;c&quot;, 
       # Remember this escape = F
       escape = F) %&gt;%
-  kable_styling(full_width = F) %&gt;%
+  kable_paper(full_width = F) %&gt;%
   footnote(alphabet = &quot;Footnote A; &quot;,
            symbol = &quot;Footnote Symbol 1; &quot;,
            alphabet_title = &quot;Type II: &quot;, symbol_title = &quot;Type III: &quot;,
            footnote_as_chunk = T)</code></pre>
-<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;">
@@ -7178,10 +7341,10 @@
 <p>If you have a huge table and you don’t want to reduce the font size to unreadable, you may want to put your HTML table in a scroll box, of which users can pick the part they like to read. Note that scroll box isn’t printer friendly, so be aware of that when you use this feature.</p>
 <p>When you use <code>scroll_box</code>, you can specify either <code>height</code> or <code>width</code>. When you specify <code>height</code>, you will get a vertically scrollable box and vice versa. If you specify both, you will get a two-way scrollable box.</p>
 <pre class="r"><code>kbl(cbind(mtcars, mtcars)) %&gt;%
-  kable_styling() %&gt;%
+  kable_paper() %&gt;%
   scroll_box(width = &quot;500px&quot;, height = &quot;200px&quot;)</code></pre>
 <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:500px; ">
-<table class="table" style="margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
@@ -9534,10 +9697,10 @@
 <p>You can also specify width using a percentage.</p>
 <pre class="r"><code>kbl(cbind(mtcars, mtcars)) %&gt;%
   add_header_above(c(&quot;a&quot; = 5, &quot;b&quot; = 18)) %&gt;%
-  kable_styling() %&gt;%
+  kable_paper() %&gt;%
   scroll_box(width = &quot;100%&quot;, height = &quot;200px&quot;)</code></pre>
 <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; ">
-<table class="table" style="margin-left: auto; margin-right: auto;">
+<table class=" lightable-paper" style="font-family: &quot;Arial Narrow&quot;, arial, helvetica, sans-serif; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; position: sticky; top:0; background-color: #FFFFFF;" colspan="5">
@@ -11904,7 +12067,7 @@
 <h2>Save HTML table directly</h2>
 <p>If you need to save those HTML tables but you don’t want to generate them through rmarkdown, you can try to use the <code>save_kable()</code> function. You can choose whether to let those HTML files be self contained (default is yes). Self contained files packed CSS into the HTML file so they are quite large when there are many.</p>
 <pre class="r"><code>kbl(mtcars) %&gt;%
-  kable_styling() %&gt;%
+  kable_paper() %&gt;%
   save_kable(file = &quot;table1.html&quot;, self_contained = T)</code></pre>
 </div>
 <div id="use-it-with-sparkline" class="section level2">
diff --git a/docs/awesome_table_in_pdf.Rmd b/docs/awesome_table_in_pdf.Rmd
index 4a2a67f..4046d9e 100644
--- a/docs/awesome_table_in_pdf.Rmd
+++ b/docs/awesome_table_in_pdf.Rmd
@@ -20,6 +20,7 @@
   - \usepackage{threeparttablex}
   - \usepackage[normalem]{ulem}
   - \usepackage{makecell}
+  - \usepackage{xcolor}
 vignette: >
   %\VignetteIndexEntry{Create Awesome PDF Table with knitr::kable and kableExtra}
   %\VignetteEngine{knitr::rmarkdown}
@@ -238,6 +239,41 @@
 
 You can still use the `spec_***` helper functions to help you define color. See the documentation [below](#visualize-data-with-viridis-color). 
 
+## Insert Images into Columns
+Technically, we are still talking about `column_spec` here. However, since this topic itself contains its own subtopics, we split it out as a separate section. Since `kableExtra` 1.2, we introduced the feature of adding images to columns of tables. Here is a quick example. 
+
+```{r}
+tbl_img <- data.frame(
+  name = c("kableExtra 1", "kableExtra 2"),
+  logo = ""
+)
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = "kableExtra_sm.png")
+```
+
+If you need to specify the size of the images, you need to do it through `spec_image`. 
+
+```{r}
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_image(
+    c("kableExtra_sm.png", "kableExtra_sm.png"), 50, 50))
+```
+
+`kableExtra` also provides a few inline plotting tools. Right now, there are `spec_hist` and `spec_boxplot`. One key feature is that by default, the limits of every subplots are fixed so you can compare across rows. 
+
+```{r}
+mpg_list <- split(mtcars$mpg, mtcars$cyl)
+inline_plot <- data.frame(cyl = c(4, 6, 8), mpg_box = "", mpg_hist = "")
+inline_plot %>% 
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_boxplot(mpg_list)) %>%
+  column_spec(3, image = spec_hist(mpg_list))
+```
 
 ## Row spec
 Similar with `column_spec`, you can define specifications for rows. Currently, you can either bold or italicize an entire row. Note that, similar to other row-related functions in `kableExtra`, for the position of the target row, you don't need to count in header rows or the group labeling rows.
diff --git a/docs/awesome_table_in_pdf.pdf b/docs/awesome_table_in_pdf.pdf
index 1b60d92..a841c2a 100644
--- a/docs/awesome_table_in_pdf.pdf
+++ b/docs/awesome_table_in_pdf.pdf
Binary files differ
diff --git a/docs/awesome_table_in_pdf.toc b/docs/awesome_table_in_pdf.toc
index c3af8ce..682098c 100644
--- a/docs/awesome_table_in_pdf.toc
+++ b/docs/awesome_table_in_pdf.toc
@@ -1,37 +1,38 @@
-\contentsline {section}{Overview}{2}{section*.2}%
-\contentsline {section}{Installation}{2}{section*.3}%
-\contentsline {section}{Getting Started}{2}{section*.4}%
-\contentsline {subsection}{LaTeX packages used in this package}{3}{section*.5}%
-\contentsline {subsection}{Plain LaTeX}{3}{section*.6}%
-\contentsline {subsection}{LaTeX table with booktabs}{4}{section*.7}%
-\contentsline {section}{Table Styles}{4}{section*.8}%
-\contentsline {subsection}{LaTeX options}{4}{section*.9}%
-\contentsline {subsubsection}{Striped}{4}{section*.10}%
-\contentsline {subsubsection}{Hold position}{5}{section*.11}%
-\contentsline {subsubsection}{Scale down}{5}{section*.12}%
-\contentsline {subsubsection}{Repeat header in longtable}{6}{section*.13}%
-\contentsline {subsection}{Full width?}{8}{section*.14}%
-\contentsline {subsection}{Position}{8}{section*.15}%
-\contentsline {subsection}{Font Size}{9}{section*.16}%
-\contentsline {section}{Column / Row Specification}{9}{section*.17}%
-\contentsline {subsection}{Column spec}{9}{section*.18}%
-\contentsline {subsection}{Row spec}{10}{section*.19}%
-\contentsline {subsection}{Header Rows}{11}{section*.20}%
-\contentsline {section}{Cell/Text Specification}{11}{section*.21}%
-\contentsline {subsection}{Conditional logic}{11}{section*.22}%
-\contentsline {subsection}{Visualize data with Viridis Color}{12}{section*.23}%
-\contentsline {subsection}{Text Specification}{13}{section*.24}%
-\contentsline {section}{Grouped Columns / Rows}{14}{section*.25}%
-\contentsline {subsection}{Add header rows to group columns}{14}{section*.26}%
-\contentsline {subsection}{Group rows via labeling}{14}{section*.27}%
-\contentsline {subsection}{Row indentation}{16}{section*.28}%
-\contentsline {subsection}{Group rows via multi-row cell}{16}{section*.29}%
-\contentsline {section}{Table Footnote}{20}{section*.30}%
-\contentsline {section}{LaTeX Only Features}{23}{section*.31}%
-\contentsline {subsection}{Linebreak processor}{23}{section*.32}%
-\contentsline {subsection}{Table on a Landscape Page}{23}{section*.33}%
-\contentsline {subsection}{Decimal Alignment}{25}{section*.34}%
-\contentsline {subsection}{Use LaTeX table in HTML or Word}{25}{section*.35}%
-\contentsline {section}{From other packages}{25}{section*.36}%
-\contentsline {subsection}{\texttt {tables}}{25}{section*.37}%
-\contentsline {subsection}{\texttt {xtable}}{26}{section*.38}%
+\contentsline {section}{Overview}{3}{section*.2}%
+\contentsline {section}{Installation}{3}{section*.3}%
+\contentsline {section}{Getting Started}{3}{section*.4}%
+\contentsline {subsection}{LaTeX packages used in this package}{4}{section*.5}%
+\contentsline {subsection}{Plain LaTeX}{4}{section*.6}%
+\contentsline {subsection}{LaTeX table with booktabs}{5}{section*.7}%
+\contentsline {section}{Table Styles}{5}{section*.8}%
+\contentsline {subsection}{LaTeX options}{5}{section*.9}%
+\contentsline {subsubsection}{Striped}{5}{section*.10}%
+\contentsline {subsubsection}{Hold position}{6}{section*.11}%
+\contentsline {subsubsection}{Scale down}{6}{section*.12}%
+\contentsline {subsubsection}{Repeat header in longtable}{7}{section*.13}%
+\contentsline {subsection}{Full width?}{9}{section*.14}%
+\contentsline {subsection}{Position}{9}{section*.15}%
+\contentsline {subsection}{Font Size}{10}{section*.16}%
+\contentsline {section}{Column / Row Specification}{10}{section*.17}%
+\contentsline {subsection}{Column spec}{10}{section*.18}%
+\contentsline {subsection}{Insert Images into Columns}{11}{section*.19}%
+\contentsline {subsection}{Row spec}{13}{section*.20}%
+\contentsline {subsection}{Header Rows}{13}{section*.21}%
+\contentsline {section}{Cell/Text Specification}{13}{section*.22}%
+\contentsline {subsection}{Conditional logic}{14}{section*.23}%
+\contentsline {subsection}{Visualize data with Viridis Color}{14}{section*.24}%
+\contentsline {subsection}{Text Specification}{15}{section*.25}%
+\contentsline {section}{Grouped Columns / Rows}{16}{section*.26}%
+\contentsline {subsection}{Add header rows to group columns}{16}{section*.27}%
+\contentsline {subsection}{Group rows via labeling}{17}{section*.28}%
+\contentsline {subsection}{Row indentation}{18}{section*.29}%
+\contentsline {subsection}{Group rows via multi-row cell}{18}{section*.30}%
+\contentsline {section}{Table Footnote}{22}{section*.31}%
+\contentsline {section}{LaTeX Only Features}{25}{section*.32}%
+\contentsline {subsection}{Linebreak processor}{25}{section*.33}%
+\contentsline {subsection}{Table on a Landscape Page}{25}{section*.34}%
+\contentsline {subsection}{Decimal Alignment}{27}{section*.35}%
+\contentsline {subsection}{Use LaTeX table in HTML or Word}{27}{section*.36}%
+\contentsline {section}{From other packages}{27}{section*.37}%
+\contentsline {subsection}{\texttt {tables}}{27}{section*.38}%
+\contentsline {subsection}{\texttt {xtable}}{28}{section*.39}%
diff --git a/docs/experimental/mini_plots.R b/docs/experimental/mini_plots.R
deleted file mode 100644
index 400746d..0000000
--- a/docs/experimental/mini_plots.R
+++ /dev/null
@@ -1,19 +0,0 @@
-mini_hist <- function(x, width = 60, height = 20,
-                      dir = "kableExtra", file = NULL,
-                      xaxt = 'n', yaxt = 'n', ann = FALSE, col = "gray",
-                      ...) {
-  if (!dir.exists(dir)) {
-    dir.create(dir)
-  }
-
-  if (is.null(file)) {
-    file <- tempfile("hist", dir, ".png")
-  }
-
-  grDevices::png(filename = file, width = width, height = height,
-                 bg = 'transparent')
-  graphics::par(mar = c(0, 0, 0, 0))
-  graphics::hist(x, xaxt = xaxt, yaxt = yaxt, ann = ann, col = col, ...)
-  grDevices::dev.off()
-  return(file)
-}
diff --git a/inst/NEWS.md b/inst/NEWS.md
index b3d9fb2..c786df1 100644
--- a/inst/NEWS.md
+++ b/inst/NEWS.md
@@ -10,7 +10,9 @@
 * Added a few alternative HTML table themes. See https://haozhu233.github.io/kableExtra/awesome_table_in_html.html#Alternative_themes. (#451)
 
 * `column_spec` now takes vectorized input so it's easier to do conditional 
-formatting without using `cell_spec`
+formatting without using `cell_spec`.
+
+* Added tools to include images and inline plots in `column_spec`. 
 
 # Minor Change
 
diff --git a/man/column_spec.Rd b/man/column_spec.Rd
index aeff8ec..8d19fc7 100644
--- a/man/column_spec.Rd
+++ b/man/column_spec.Rd
@@ -26,7 +26,8 @@
   link = NULL,
   new_tab = TRUE,
   tooltip = NULL,
-  popover = NULL
+  popover = NULL,
+  image = NULL
 )
 }
 \arguments{
@@ -99,6 +100,8 @@
 to build a popover is through \code{spec_popover()}. If you only provide a text
 string, it will be used as content. Note that You have to enable this
 bootstrap module manually. Read the package vignette to see how.}
+
+\item{image}{Vector of image paths.}
 }
 \description{
 This function allows users to select a column and then specify
diff --git a/man/kable_classic.Rd b/man/kable_classic.Rd
index 7192342..1883eb9 100644
--- a/man/kable_classic.Rd
+++ b/man/kable_classic.Rd
@@ -26,7 +26,7 @@
 kable_minimal(
   kable_input,
   lightable_options = "basic",
-  html_font = "\\"Trebuchet MS\\", verdana, calibri, sans-serif",
+  html_font = "\\"Trebuchet MS\\", verdana, sans-serif",
   ...
 )
 
diff --git a/man/spec_hist.Rd b/man/spec_hist.Rd
new file mode 100644
index 0000000..9deb046
--- /dev/null
+++ b/man/spec_hist.Rd
@@ -0,0 +1,119 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mini_plots.R
+\name{spec_hist}
+\alias{spec_hist}
+\alias{spec_boxplot}
+\title{Helper functions to generate inline sparklines}
+\usage{
+spec_hist(
+  x,
+  width = 200,
+  height = 50,
+  res = 300,
+  breaks = "Sturges",
+  same_lim = TRUE,
+  lim = NULL,
+  xaxt = "n",
+  yaxt = "n",
+  ann = FALSE,
+  col = "lightgray",
+  border = NULL,
+  dir = if (is_latex()) rmd_files_dir() else tempdir(),
+  file = NULL,
+  file_type = if (is_latex()) "png" else "svg",
+  ...
+)
+
+spec_boxplot(
+  x,
+  width = 200,
+  height = 50,
+  res = 300,
+  add_label = FALSE,
+  label_digits = 2,
+  same_lim = TRUE,
+  lim = NULL,
+  xaxt = "n",
+  yaxt = "n",
+  ann = FALSE,
+  col = "lightgray",
+  border = NULL,
+  boxlty = 0,
+  medcol = "red",
+  medlwd = 1,
+  dir = if (is_latex()) rmd_files_dir() else tempdir(),
+  file = NULL,
+  file_type = if (is_latex()) "png" else "svg",
+  ...
+)
+}
+\arguments{
+\item{x}{Vector of values or List of vectors of values.}
+
+\item{width}{The width of the plot in pixel}
+
+\item{height}{The height of the plot in pixel}
+
+\item{res}{The resolution of the plot. Default is 300.}
+
+\item{breaks}{one of:
+    \itemize{
+      \item a vector giving the breakpoints between histogram cells,
+      \item a function to compute the vector of breakpoints,
+      \item a single number giving the number of cells for the histogram,
+      \item a character string naming an algorithm to compute the
+      number of cells (see \sQuote{Details}),
+      \item a function to compute the number of cells.
+    }
+    In the last three cases the number is a suggestion only; as the
+    breakpoints will be set to \code{\link{pretty}} values, the number
+    is limited to \code{1e6} (with a warning if it was larger).  If
+    \code{breaks} is a function, the \code{x} vector is supplied to it
+    as the only argument (and the number of breaks is only limited by
+    the amount of available memory).
+  }
+
+\item{same_lim}{T/F. If x is a list of vectors, should all the plots be
+plotted in the same range? Default is True.}
+
+\item{lim}{Manually specify plotting range in the form of \code{c(0, 10)}.}
+
+\item{xaxt}{On/Off for xaxis text}
+
+\item{yaxt}{On/Off for yaxis text}
+
+\item{ann}{On/Off for annotations (titles and axis titles)}
+
+\item{col}{Color for the fill of the histogram bar/boxplot box.}
+
+\item{border}{Color for the border.}
+
+\item{dir}{Directory of where the images will be saved.}
+
+\item{file}{File name. If not provided, a random name will be used}
+
+\item{file_type}{Graphic device. Support \code{png} or \code{svg}. SVG is recommended
+for HTML output}
+
+\item{...}{further arguments and \link[graphics]{graphical parameters} passed to
+    \code{\link[graphics]{plot.histogram}} and thence to \code{\link[graphics]{title}} and
+    \code{\link[graphics]{axis}} (if \code{plot = TRUE}).}
+
+\item{add_label}{For boxplot. T/F to add labels for min, mean and max.}
+
+\item{label_digits}{If T for add_label, rounding digits for the label.
+Default is 2.}
+
+\item{boxlty}{Boxplot - box boarder type}
+
+\item{medcol}{Boxplot - median line color}
+
+\item{medlwd}{Boxplot - median line width}
+}
+\description{
+These functions helps you quickly generate sets of sparkline
+style plots using base R plotting system. Currently, we support histogram
+and boxplot. You can use them together with \code{column_spec} to
+generate inline plot in tables. By default, this function will save images
+in a folder called "kableExtra" and return the address of the file.
+}
diff --git a/man/spec_image.Rd b/man/spec_image.Rd
new file mode 100644
index 0000000..a4638e5
--- /dev/null
+++ b/man/spec_image.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/spec_tools.R
+\name{spec_image}
+\alias{spec_image}
+\title{Setup image path, size, etc}
+\usage{
+spec_image(path, width, height, res = 300, svg_text = NULL)
+}
+\arguments{
+\item{path}{file path(s)}
+
+\item{width}{image width in pixel}
+
+\item{height}{image height in pixel}
+
+\item{res}{image resolution.}
+
+\item{svg_text}{If you have the raw text for SVG. Put them here}
+}
+\description{
+Users can directly provide image file path to column spec.
+However, if you need to specify the size of the image, you will need this
+function.
+}
diff --git a/vignettes/awesome_table_in_html.Rmd b/vignettes/awesome_table_in_html.Rmd
index 71a3331..bf9ea7d 100644
--- a/vignettes/awesome_table_in_html.Rmd
+++ b/vignettes/awesome_table_in_html.Rmd
@@ -22,7 +22,7 @@
 
 <img src="kableExtra_sm.png" align="right" alt="logo" width="80" height = "93" style = "border: none; float: right;">
 
-> Please see the package [documentation site](http://haozhu233.github.io/kableExtra/) for how to use this package in LaTeX.
+> Please see the package [documentation site](https://haozhu233.github.io/kableExtra/) for how to use this package in LaTeX.
 
 # Overview
 The goal of `kableExtra` is to help you build common complex tables and manipulate table styles. It imports the pipe `%>%` symbol from `magrittr` and verbalize all the functions, so basically you can add "layers" to a kable output in a way that is similar with `ggplot2` and `plotly`. 
@@ -44,7 +44,6 @@
 # Getting Started
 Here we are using the first few columns and rows from dataset `mtcars`
 ```{r}
-library(knitr)
 library(kableExtra)
 dt <- mtcars[1:5, 1:6]
 ```
@@ -119,7 +118,7 @@
 `kable_styling` offers a few other ways to customize the look of a HTML table. 
 
 ## Bootstrap table classes
-If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including `striped`, `bordered`, `hover`, `condensed` and `responsive`. If you are not familiar, no worries, you can take a look at their [documentation site](http://getbootstrap.com/css/#tables) to get a sense of how they look like. All of these options are available here. 
+If you are familiar with twitter bootstrap, you probably have already known its predefined classes, including `striped`, `bordered`, `hover`, `condensed` and `responsive`. If you are not familiar, no worries, you can take a look at their [documentation site](https://getbootstrap.com/css/) to get a sense of how they look like. All of these options are available here. 
 
 For example, to add striped lines (alternative row colors) to your table and you want to highlight the hovered row, you can simply type:
 ```{r}
@@ -192,7 +191,7 @@
 )
 
 kbl(text_tbl) %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   column_spec(1, bold = T, border_right = T) %>%
   column_spec(2, width = "30em", background = "yellow")
 ```
@@ -212,12 +211,48 @@
 
 You can still use the `spec_***` helper functions to help you define color. See the documentation [below](#visualize-data-with-viridis-color). 
 
+## Insert Images into Columns
+Technically, we are still talking about `column_spec` here. However, since this topic itself contains its own subtopics, we split it out as a separate section. Since `kableExtra` 1.2, we introduced the feature of adding images to columns of tables. Here is a quick example. 
+
+```{r}
+tbl_img <- data.frame(
+  name = c("kableExtra 1", "kableExtra 2"),
+  logo = ""
+)
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = "kableExtra_sm.png")
+```
+
+If you need to specify the size of the images, you need to do it through `spec_image`. 
+
+```{r}
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_image(
+    c("kableExtra_sm.png", "kableExtra_sm.png"), 50, 50))
+```
+
+`kableExtra` also provides a few inline plotting tools. Right now, there are `spec_hist` and `spec_boxplot`. One key feature is that by default, the limits of every subplots are fixed so you can compare across rows. Note that in html, you can also use package `sparkline` to create some jquery based interactive sparklines. Check out the end of this guide for details. 
+
+```{r}
+mpg_list <- split(mtcars$mpg, mtcars$cyl)
+inline_plot <- data.frame(cyl = c(4, 6, 8), mpg_box = "", mpg_hist = "")
+inline_plot %>% 
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_boxplot(mpg_list)) %>%
+  column_spec(3, image = spec_hist(mpg_list))
+```
+
 ## Row spec
 Similar with `column_spec`, you can define specifications for rows. Currently, you can either bold or italicize an entire row. Note that, similar with other row-related functions in `kableExtra`, for the position of the target row, you don't need to count in header rows or the group labeling rows.
 
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   column_spec(5:7, bold = T) %>%
   row_spec(3:5, bold = T, color = "white", background = "#D7261E")
 ```
@@ -228,7 +263,7 @@
 One special case of `row_spec` is that you can specify the format of the header row via `row_spec(row = 0, ...)`. 
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   row_spec(0, angle = -45)
 ```
 
@@ -271,7 +306,7 @@
 #   ) %>%
 #   select(car, mpg, cyl) %>%
 #   kbl(format = "html", escape = F) %>%
-#   kable_styling("striped", full_width = F)
+#   kable_paper("striped", full_width = F)
 ```
 
 ## Visualize data with Viridis Color
@@ -300,7 +335,7 @@
 #     background = spec_color(1:10, end = 0.9, option = "A", direction = -1)
 #   )) %>%
 #   kable(escape = F, align = "c") %>%
-#   kable_styling(c("striped", "condensed"), full_width = F)
+#   kable_paper(c("striped", "condensed"), full_width = F)
 ```
 
 ## Text Specification
@@ -363,11 +398,11 @@
     position = popover_dt$position
   ))
 kbl(popover_dt, escape = FALSE) %>%
-  kable_styling("striped", full_width = FALSE)
+  kable_paper("striped", full_width = FALSE)
 ```
 
 ## Links
-You can add links to text via `text_spec("Google", link = "https://google.com")`: `r text_spec("Google", link = "https://google.com")`. If you want your hover message to be more obvious, it might be a good idea to put a `#` (go back to the top of the page) or `javascript:void(0)` (literally do nothing) in the `link` option.
+You can add links to text via `text_spec("kableExtra", link = "https://haozhu233.github.io/kableExtra/")`: `r text_spec("kableExtra", link = "https://haozhu233.github.io/kableExtra/")`. If you want your hover message to be more obvious, it might be a good idea to put a `#` (go back to the top of the page) or `javascript:void(0)` (literally do nothing) in the `link` option.
 `text_spec("Hover on me", link = "javascript:void(0)", popover = "Hello")`:
 `r text_spec("Hover on me", link = "javascript:void(0)", popover = "Hello")`
 
@@ -390,7 +425,7 @@
 ft_dt <- ft_dt[c("car", "mpg", "cyl", "disp", "hp")]
 
 kbl(ft_dt, escape = F) %>%
-  kable_styling("hover", full_width = F) %>%
+  kable_paper("hover", full_width = F) %>%
   column_spec(5, width = "3cm") %>%
   add_header_above(c(" ", "Hello" = 2, "World" = 2))
 ```
@@ -417,7 +452,7 @@
 Sometimes we want a few rows of the table being grouped together. They might be items under the same topic (e.g., animals in one species) or just different data groups for a categorical variable (e.g., age < 40, age > 40). With the function `group_rows()`/`pack_rows()` in `kableExtra`, this kind of task can be completed in one line. Please see the example below. Note that when you count for the start/end rows of the group, you don't need to count for the header rows nor other group label rows. You only need to think about the row numbers in the "original R dataframe".
 ```{r}
 kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows("Group 1", 4, 7) %>%
   pack_rows("Group 2", 8, 10)
 ```
@@ -426,14 +461,14 @@
 ```{r, eval = F}
 # Not evaluated. This example generates the same table as above.
 kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows(index = c(" " = 3, "Group 1" = 4, "Group 2" = 3))
 ```
 
 For advanced users, you can even define your own css for the group labeling.
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   pack_rows("Group 1", 3, 5, label_row_css = "background-color: #666; color: #fff;")
 ```
 
@@ -460,7 +495,7 @@
 For advanced users, you can even define your own css for the group labeling.
 ```{r}
 kbl(dt) %>%
-  kable_styling("striped", full_width = F) %>%
+  kable_paper("striped", full_width = F) %>%
   add_indent(c(1, 3, 5))
 ```
 
@@ -473,7 +508,7 @@
                  C3 = 1:15,
                  C4 = sample(c(0,1), 15, replace = TRUE))
 kbl(collapse_rows_dt, align = "c") %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   column_spec(1, bold = T) %>%
   collapse_rows(columns = 1:2, valign = "top")
 ```
@@ -499,7 +534,7 @@
 
 ```{r}
 kbl(dt, align = "c") %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   footnote(general = "Here is a general comments of the table. ",
            number = c("Footnote 1; ", "Footnote 2; "),
            alphabet = c("Footnote A; ", "Footnote B; "),
@@ -521,7 +556,7 @@
 kbl(dt_footnote, align = "c", 
       # Remember this escape = F
       escape = F) %>%
-  kable_styling(full_width = F) %>%
+  kable_paper(full_width = F) %>%
   footnote(alphabet = "Footnote A; ",
            symbol = "Footnote Symbol 1; ",
            alphabet_title = "Type II: ", symbol_title = "Type III: ",
@@ -536,7 +571,7 @@
 
 ```{r}
 kbl(cbind(mtcars, mtcars)) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   scroll_box(width = "500px", height = "200px")
 ```
 
@@ -547,7 +582,7 @@
 ```{r}
 kbl(cbind(mtcars, mtcars)) %>%
   add_header_above(c("a" = 5, "b" = 18)) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   scroll_box(width = "100%", height = "200px")
 ```
 
@@ -557,7 +592,7 @@
 If you need to save those HTML tables but you don't want to generate them through rmarkdown, you can try to use the `save_kable()` function. You can choose whether to let those HTML files be self contained (default is yes). Self contained files packed CSS into the HTML file so they are quite large when there are many. 
 ```{r, eval=FALSE}
 kbl(mtcars) %>%
-  kable_styling() %>%
+  kable_paper() %>%
   save_kable(file = "table1.html", self_contained = T)
 ```
 
diff --git a/vignettes/awesome_table_in_pdf.Rmd b/vignettes/awesome_table_in_pdf.Rmd
index 4a2a67f..4046d9e 100644
--- a/vignettes/awesome_table_in_pdf.Rmd
+++ b/vignettes/awesome_table_in_pdf.Rmd
@@ -20,6 +20,7 @@
   - \usepackage{threeparttablex}
   - \usepackage[normalem]{ulem}
   - \usepackage{makecell}
+  - \usepackage{xcolor}
 vignette: >
   %\VignetteIndexEntry{Create Awesome PDF Table with knitr::kable and kableExtra}
   %\VignetteEngine{knitr::rmarkdown}
@@ -238,6 +239,41 @@
 
 You can still use the `spec_***` helper functions to help you define color. See the documentation [below](#visualize-data-with-viridis-color). 
 
+## Insert Images into Columns
+Technically, we are still talking about `column_spec` here. However, since this topic itself contains its own subtopics, we split it out as a separate section. Since `kableExtra` 1.2, we introduced the feature of adding images to columns of tables. Here is a quick example. 
+
+```{r}
+tbl_img <- data.frame(
+  name = c("kableExtra 1", "kableExtra 2"),
+  logo = ""
+)
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = "kableExtra_sm.png")
+```
+
+If you need to specify the size of the images, you need to do it through `spec_image`. 
+
+```{r}
+tbl_img %>%
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_image(
+    c("kableExtra_sm.png", "kableExtra_sm.png"), 50, 50))
+```
+
+`kableExtra` also provides a few inline plotting tools. Right now, there are `spec_hist` and `spec_boxplot`. One key feature is that by default, the limits of every subplots are fixed so you can compare across rows. 
+
+```{r}
+mpg_list <- split(mtcars$mpg, mtcars$cyl)
+inline_plot <- data.frame(cyl = c(4, 6, 8), mpg_box = "", mpg_hist = "")
+inline_plot %>% 
+  kbl(booktabs = T) %>%
+  kable_paper(full_width = F) %>%
+  column_spec(2, image = spec_boxplot(mpg_list)) %>%
+  column_spec(3, image = spec_hist(mpg_list))
+```
 
 ## Row spec
 Similar with `column_spec`, you can define specifications for rows. Currently, you can either bold or italicize an entire row. Note that, similar to other row-related functions in `kableExtra`, for the position of the target row, you don't need to count in header rows or the group labeling rows.