This post was adapted from a section in The Jigsaw Puzzle Pieces - Creating the Graphs with ggplot2, Part II of a series of posts on building Shiny App. There we introduced several ways of grouping in plotting.

First Steps

As usual, before we begin, let’s load all the packages we will need.

library(dplyr)
library(stringr)
library(data.table)
library(ggplot2)
library(gridExtra)
library(grid)

Sample data

## setwd()
load("~/Desktop/r sample files/survey")

head(survey)
## # A tibble: 6 x 17
##   status  country major  Q1.1   Q1.2  Q1.3  top_reason     place_options  
##   <fct>   <fct>   <fct>  <chr>  <chr> <chr> <chr>          <chr>          
## 1 Freshm… U.S.    Undef… Never  Never Never Find a quiet … quiet (occasio…
## 2 Freshm… China   Undef… Occas… Often Often Find a quiet … crowded,focused
## 3 Freshm… U.S.    Undef… Never  Occa… Often Meet up with … (close to) sil…
## 4 Freshm… China   Undef… Occas… Occa… Occa… Meet up with … focused,(close…
## 5 Freshm… U.S.    Undef… Never  Occa… Occa… Find a quiet … (close to) sil…
## 6 Freshm… China   Undef… Occas… Often Often Find a quiet … (close to) sil…
## # ... with 9 more variables: space_lib <chr>, rank_crowded <dbl>,
## #   rank_modpop <dbl>, rank_noisy <dbl>, rank_quiet <dbl>,
## #   rank_silent <dbl>, rank_relaxed <dbl>, rank_focused <dbl>,
## #   workshops <chr>

Color palette

We’ll create a list of a color palette. This will make working with colors more easily later in plotting.

palette <- list(purple = c("#351F39", "#351C4D", "#6c1f55", "#765285", "#8a6899"),
                turquoise = c("#709FB0", "#849974", "#A0C1B8"),
                golden = c("#D1A827", "#f3da4c"))

Visually Grouping Data

Let’s say we want to visualize the distribution of top reason of visiting library (top_reason) by groups. We will use this subsample as a demo to show ways of visually grouping data.

Previously we’ve created a data frame dtset summarizing top reason of visiting library by country. We did quite some work of reshaping (copied and pasted below).

Reason <- c("Work on a class assignment/paper", "Watch video or listen audio", "Use specialized databases \\(e.g. Bloomberg, Wind\\)", "Use a library computer", "Use a group study room", "Print, photocopy, scan", "Other", "Meet up with friends", "Hang out between classes", "Get readings from Course Reserve", "Get help from a librarian", "Find a quiet place to study", "Borrow books and materials", "Attend a library workshop")

r <- data.frame(survey$country)
for (m in 1: length(Reason)){
  r[,Reason[m]] <- str_extract(survey$top_reason, Reason[m])
}

dtset <- data.frame(Reason)
levels(dtset$Reason)[levels(dtset$Reason) == "Use specialized databases \\(e.g. Bloomberg, Wind\\)"] <- "Use specialized databases (e.g. Bloomberg, Wind)"

g <- c("China", "U.S.", "Other")
for (n in 1:length(g)){
  dtset[,g[n]] <- apply(r[r[] == g[n], 2:15], 2, function(x) length(which(!is.na(x))))
}

dtset$Total<- rowSums(dtset[,2:4], na.rm = TRUE, dims = 1)
dtset <- dtset[order(dtset$Total,decreasing = T),]

dtset
##                                              Reason China U.S. Other Total
## 12                      Find a quiet place to study   123   47    50   220
## 6                            Print, photocopy, scan   104   45    52   201
## 1                  Work on a class assignment/paper    89   46    31   166
## 13                       Borrow books and materials    63   15    28   106
## 10                 Get readings from Course Reserve    30    6     8    44
## 4                            Use a library computer    11   20    10    41
## 5                            Use a group study room    32    4     5    41
## 8                              Meet up with friends    23    4     9    36
## 9                          Hang out between classes    16    6     5    27
## 11                        Get help from a librarian    13    2     7    22
## 3  Use specialized databases (e.g. Bloomberg, Wind)    12    3     2    17
## 14                        Attend a library workshop     9    5     3    17
## 2                       Watch video or listen audio     7    2     2    11
## 7                                             Other     2    2     1     5

We will use the data for all demos below.

Coloring

Let’s say we want to plot the distribution of top visits and make the contrast more visible among groups. One way to achieve the goal is to color the levels in groups. Specifically, we decide to group the frequencies as “high”, “medium” and “low”.

We first code the levels.

Reason2 <- c("Work on a class assignment/paper", "Watch video or listen audio", "Use specialized databases (e.g. Bloomberg, Wind)", "Use a library computer",  "Use a group study room", "Print, photocopy, scan",  "Other", "Meet up with friends", "Hang out between classes", "Get readings from Course Reserve", "Get help from a librarian", "Find a quiet place to study", "Borrow books and materials", "Attend a library workshop")

l <- data.frame(Reason = Reason2, Level = c("High","Low","Low","Medium","Medium","High","Low","Medium","Low","Medium","Low","High","High","Low"))
## label the frequency level
## the standard may be a bit arbitrary

dtset <- dtset %>% left_join(l, by = "Reason")

The rest is standard plotting.

ggplot(dtset, aes(x = reorder(Reason, Total), y = Total, fill = factor(Level, levels = c("High","Medium","Low")))) + 
  geom_bar(stat = "identity", alpha = 0.75) + 
  scale_fill_manual(values = c(palette[[1]][4], palette[[2]][1], palette[[3]][1]), name="Level of\nFrequency") +
##  \n in "Level of\nFrequency" breaks the line
  coord_flip() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12, margin = margin(0,3,0,0)),
        axis.title.y = element_blank(),
        axis.title.x = element_text(size = 12, margin = margin(15,0,0,0)),
        axis.ticks.x = element_line(size = 0),
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 12),
        plot.margin = unit(c(0,0,1,0), "cm"))

Facets

Now let’s say we want to plot the distribution of top visits by country. This can be done with “facets” - we will get panels within one plot for each subgroup.

To support the graphing needs, we need to reshape the dtset data frame into long format.

dtset2 <- dtset %>% melt()
dtset2
##                                              Reason  Level variable value
## 1                       Find a quiet place to study   High    China   123
## 2                            Print, photocopy, scan   High    China   104
## 3                  Work on a class assignment/paper   High    China    89
## 4                        Borrow books and materials   High    China    63
## 5                  Get readings from Course Reserve Medium    China    30
## 6                            Use a library computer Medium    China    11
## 7                            Use a group study room Medium    China    32
## 8                              Meet up with friends Medium    China    23
## 9                          Hang out between classes    Low    China    16
## 10                        Get help from a librarian    Low    China    13
## 11 Use specialized databases (e.g. Bloomberg, Wind)    Low    China    12
## 12                        Attend a library workshop    Low    China     9
## 13                      Watch video or listen audio    Low    China     7
## 14                                            Other    Low    China     2
## 15                      Find a quiet place to study   High     U.S.    47
## 16                           Print, photocopy, scan   High     U.S.    45
## 17                 Work on a class assignment/paper   High     U.S.    46
## 18                       Borrow books and materials   High     U.S.    15
## 19                 Get readings from Course Reserve Medium     U.S.     6
## 20                           Use a library computer Medium     U.S.    20
## 21                           Use a group study room Medium     U.S.     4
## 22                             Meet up with friends Medium     U.S.     4
## 23                         Hang out between classes    Low     U.S.     6
## 24                        Get help from a librarian    Low     U.S.     2
## 25 Use specialized databases (e.g. Bloomberg, Wind)    Low     U.S.     3
## 26                        Attend a library workshop    Low     U.S.     5
## 27                      Watch video or listen audio    Low     U.S.     2
## 28                                            Other    Low     U.S.     2
## 29                      Find a quiet place to study   High    Other    50
## 30                           Print, photocopy, scan   High    Other    52
## 31                 Work on a class assignment/paper   High    Other    31
## 32                       Borrow books and materials   High    Other    28
## 33                 Get readings from Course Reserve Medium    Other     8
## 34                           Use a library computer Medium    Other    10
## 35                           Use a group study room Medium    Other     5
## 36                             Meet up with friends Medium    Other     9
## 37                         Hang out between classes    Low    Other     5
## 38                        Get help from a librarian    Low    Other     7
## 39 Use specialized databases (e.g. Bloomberg, Wind)    Low    Other     2
## 40                        Attend a library workshop    Low    Other     3
## 41                      Watch video or listen audio    Low    Other     2
## 42                                            Other    Low    Other     1
## 43                      Find a quiet place to study   High    Total   220
## 44                           Print, photocopy, scan   High    Total   201
## 45                 Work on a class assignment/paper   High    Total   166
## 46                       Borrow books and materials   High    Total   106
## 47                 Get readings from Course Reserve Medium    Total    44
## 48                           Use a library computer Medium    Total    41
## 49                           Use a group study room Medium    Total    41
## 50                             Meet up with friends Medium    Total    36
## 51                         Hang out between classes    Low    Total    27
## 52                        Get help from a librarian    Low    Total    22
## 53 Use specialized databases (e.g. Bloomberg, Wind)    Low    Total    17
## 54                        Attend a library workshop    Low    Total    17
## 55                      Watch video or listen audio    Low    Total    11
## 56                                            Other    Low    Total     5

The facet_wrap() option allows us to plot by groups.

ggplot(dtset2, aes(value, reorder(Reason, value))) +
  geom_segment(aes(x = 0, y = reorder(Reason, value), xend = value, yend = reorder(Reason, value)), size = 0.3, color = "grey50") +
  geom_point(color = palette[[1]][4], size = 2) +
  facet_wrap(~variable, nrow = 2) +
## produces the facets in two rows  
  theme(axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 10, margin = margin(0,5,0,0)),
        axis.title.y = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_line(size = 0),
        strip.text = element_text(size=12))

Combining graphs

Another scenario of grouping plots is “combining graphs”. Facets work on subgroups, but combining puts plots of different topics and types into one graph rather than plots subgroups on one graph.

For instance, we can combine “top reason of visiting library” and “distribution of survey participants”; but we will use faceting for plotting “top reason of visiting library (U.S.)” and “top reason of visiting library (other international students)”.

Below we will create a combined plot on student submissions of “My favorite place to study in Library” with user preference on study environment.

rank_status <- 
  data.frame(survey %>% group_by(status) %>% summarise(rank_crowded = round(mean(rank_crowded, na.rm =TRUE),2))) %>% 
  left_join(data.frame(survey %>% group_by(status) %>% summarise(rank_modpop = round(mean(rank_modpop, na.rm =TRUE),2)))) %>%
  left_join(data.frame(survey %>% group_by(status) %>% summarise(rank_noisy = round(mean(rank_noisy, na.rm =TRUE),2)))) %>%
  left_join(data.frame(survey %>% group_by(status) %>% summarise(rank_quiet = round(mean(rank_quiet, na.rm =TRUE),2)))) %>%
  left_join(data.frame(survey %>% group_by(status) %>% summarise(rank_silent = round(mean(rank_silent, na.rm =TRUE),2)))) %>%
  rename(`crowded` = rank_crowded, `moderately populated` = rank_modpop, `noisy` = rank_noisy, `quiet (occasional whispers)` = rank_quiet, `(close to) silent` = rank_silent) 

rank_status <- rank_status %>% melt()

c1 <-
      ggplot(rank_status, aes(x = variable, y = status)) +
        geom_tile(aes(fill = value), alpha = 0.95)+
        scale_fill_gradient(high = "white", low = palette[[2]][1]) +
        geom_text(aes(label = value), size = 2.5, alpha = 0.9)+
        scale_y_discrete(limits = rev(levels(rank_status$status))) +
        theme(axis.text.x = element_text(size = 12, angle = 90, hjust = 1),
              axis.text.y = element_text(size = 12, margin = margin(0,3,0,0)),
              axis.title.y = element_blank(),
              axis.title.x = element_blank(),
              axis.ticks.x = element_line(size = 0),
              plot.subtitle = element_text(size = 10, margin = margin(0,0,15,0)),
              legend.title = element_blank(),
              legend.position = "none",
              plot.margin = unit(c(1,1,1,1), "cm")) +
        labs(subtitle = "  * Average scores of ranking (1 to 6). Note: limited cases in each group.") 

c1

lib <- unlist(strsplit(survey$space_lib, ","))
lib <- str_trim(lib, side = "both")
lib <- lib[!lib %in% c("NULL")]
lib <- data.frame(lib) %>% count(lib) %>% data.frame() %>% arrange(-n)

c2 <-
  ggplot(lib, aes(n, reorder(lib, -n), label = n)) +
  geom_segment(aes(x = 0, y = reorder(lib, -n), xend = n, yend = reorder(lib, -n)), 
               size = 0.5, color = "grey50") +
  geom_point(size = 5) +
  geom_text(color = "white", size = 2) +
  coord_flip() +
  theme(axis.text.x = element_text(size = 12, angle = 90, hjust = 1),
        axis.text.y = element_text(size = 12),
        axis.title.y = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x = element_line(size = 0))

c2

grid.arrange(c1, c2, nrow = 1, top = textGrob("Study Space Preference", gp = gpar(fontsize = 10)))

As you may say, the combination is a bit arbitrary and the combined plot is not so beautiful. But I want to show you the rationale of using “combining graphs” rather than “facets”.