I have made a data frame consisting of NFL stats. This data frame contains team name, how many yards they have allowed, if this was passing or rushing, and the total of the yards. I want to create a stacked bar chart with the x axis ordered in ascending order. I have used reorder() but for some reason the Panthers values are flipped and appear much higher than the rest (even though they have the lowest total). I have made other graphs this same way with no problems. I tried manually setting the y axis limit but that did not solve my problem. My question is why is this happening and what have I missed?
I am also open on ways to make this more efficient.
library(rvest)
library(data.table)
library(ggplot2)
#Read and organise data
def = read_html("https://www.pro-football-reference.com/years/2021/opp.htm")
defense = html_table(def)
defense = defense[[1]]
colnames(defense) = defense[1,]
defense = defense[c(-1,-34,-35,-36),]
names = c("rank", "team", "games", "points_against", "yards_conceded", "off_plays_faced",
"yards_per_off_play", "TO", "fumbles_recovered", "1stD_faced", "pass_cmp",
"pass_att", "pass_yd_conceded", "pass_td_conceded", "int_recovered",
"net_yd_pass_att", "pass_1stD_against", "rush_att", "rush_yards_conceded",
"rush_td_conceded", "rush_yards_per_att", "rush_1stD_conceded", "pen_against",
"pen_yards_conceded", "pen_1stD", "off_score_percent", "turnover_percent",
"expected_points_conceded")
colnames(defense) = names
# Get passing and rushing totals
passing_def = data.table(defense$team, defense$pass_yd_conceded, rep("pass", 32))
rushing_def = data.table(defense$team, defense$rush_yards_conceded, rep("rush", 32))
sums = rep(rowSums(cbind(as.numeric(passing_def$V2), as.numeric(rushing_def$V2))),2)
# join them and add the total yards
yds_conc = rbind(passing_def, rushing_def)
yds_conc = data.table(yds_conc, sums)
colnames(yds_conc) = c("team", "conceded", "type", "sums")
# Create the plot so that the teams are sorted from left to right by total yards given up
defplot = ggplot(data = yds_conc, aes(fill = type, x = reorder(team, sums),
y = conceded)) +
geom_bar(stat = 'identity') +
scale_fill_manual(name = "Play Type",labels=c("Passing","Rushing"),
values = c("#006aff", "#4fb350")) +
ggtitle('2021 Season Defense: Total Yards Conceded per Play Type') + ylab("Avg Rank") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 60, vjust = 1, hjust=1),
axis.ticks.y = element_blank(), axis.text.y = element_blank())
defplot```
add this
yds_conc$conceded=as.numeric(yds_conc$conceded)
then run the same script will work
I am trying to create a picture that summarises my data. Data is about prevalence of drug use obtained from different practices form different countries. Each practice has contributed with a different amount of data and I want to show all of this in my picture.
Here is a subset of the data to work on:
gr<-data.frame(matrix(0,36))
gr$drug<-c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b")
gr$practice<-c("a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r")
gr$country<-c("c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3","c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3")
gr$prevalence<-c(9.14,5.53,16.74,1.93,8.51,14.96,18.90,11.18,15.00,20.10,24.56,22.29,19.41,20.25,25.01,25.87,29.33,20.76,18.94,24.60,26.51,13.37,23.84,21.82,23.69,20.56,30.53,16.66,28.71,23.83,21.16,24.66,26.42,27.38,32.46,25.34)
gr$prop<-c(0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406,0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406)
gr$low.CI<-c(8.27,4.80,12.35,1.83,7.22,14.53,18.25,10.56,14.28,18.76,24.25,21.72,18.62,19.83,24.36,25.22,28.80,20.20,17.73,23.15,21.06,13.12,21.79,21.32,22.99,19.76,29.60,15.41,28.39,23.25,20.34,24.20,25.76,26.72,31.92,24.73)
gr$high.CI<-c(10.10,6.37,22.31,2.04,10.00,15.40,19.56,11.83,15.74,21.52,24.87,22.86,20.23,20.68,25.67,26.53,29.86,21.34,20.21,26.10,32.79,13.63,26.02,22.33,24.41,21.39,31.48,17.98,29.04,24.43,22.01,25.12,27.09,28.05,33.01,25.95)
The code I wrote is this
p<-ggplot(data=gr, aes(x=factor(drug), y=as.numeric(gr$prevalence), ymax=max(high.CI),position="dodge",fill=practice,width=prop))
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
p + theme_bw()+
geom_bar(stat="identity",position = position_dodge(0.9)) +
labs(x="Drug",y="Prevalence") +
geom_errorbar(ymax=gr$high.CI,ymin=gr$low.CI,position=position_dodge(0.9),width=0.25,size=0.25,colour="black",aes(x=factor(drug), y=as.numeric(gr$prevalence), fill=practice)) +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The figure I obtain is this one where bars are all on top of each other while I want them "dodge".
I also obtain the following warning:
ymax not defined: adjusting position using y instead
Warning message:
position_dodge requires non-overlapping x intervals
Ideally I would get each bar near one another, with their error bars in the middle of its bar, all organised by country.
Also should I be concerned about the warning (which I clearly do not fully understand)?
I hope this makes sense. I hope I am close enough, but I don't seem to be going anywhere, some help would be greatly appreciated.
Thank you
ggplot's geom_bar() accepts the width parameter, but doesn't line them up neatly against one another in dodged position by default. The following workaround references the solution here:
library(dplyr)
# calculate x-axis position for bars of varying width
gr <- gr %>%
group_by(drug) %>%
arrange(practice) %>%
mutate(pos = 0.5 * (cumsum(prop) + cumsum(c(0, prop[-length(prop)])))) %>%
ungroup()
x.labels <- gr$practice[gr$drug == "a"]
x.pos <- gr$pos[gr$drug == "a"]
ggplot(gr,
aes(x = pos, y = prevalence,
fill = country, width = prop,
ymin = low.CI, ymax = high.CI)) +
geom_col(col = "black") +
geom_errorbar(size = 0.25, colour = "black") +
facet_wrap(~drug) +
scale_fill_manual(values = c("c1" = "gray79",
"c2" = "gray60",
"c3" = "gray39"),
guide = F) +
scale_x_continuous(name = "Drug",
labels = x.labels,
breaks = x.pos) +
labs(title = "Drug usage by country and practice", y = "Prevalence") +
theme_classic()
There is a lot of information you are trying to convey here - to contrast drug A and drug B across countries using the barplots and accounting for proportions, you might use the facet_grid function. Try this:
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
gr$drug <- paste("Drug", gr$drug)
p<-ggplot(data=gr, aes(x=factor(practice), y=as.numeric(prevalence),
ymax=high.CI,ymin = low.CI,
position="dodge",fill=practice, width=prop))
p + theme_bw()+ facet_grid(drug~country, scales="free") +
geom_bar(stat="identity") +
labs(x="Practice",y="Prevalence") +
geom_errorbar(position=position_dodge(0.9), width=0.25,size=0.25,colour="black") +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The width is too small in the C1 country and as you indicated the one clinic is quite influential.
Also, you can specify your aesthetics with the ggplot(aes(...)) and not have to reset it and it is not needed to include the dataframe objects name in the aes function within the ggplot call.
The graph I'm currently trying to make falls a little between two stools. I want to make a histogram that is composed of stacked and labelled boxes. Here's an example of exactly the sort of thing I'm talking about, taken from a recent article in the New York Times:
http://farm8.staticflickr.com/7109/7026409819_1d2aaacd0a.jpg
Is it possible to achieve this using ggplot2?
To amplify the question somewhat, so far what I have is:
dfr <- data.frame(
name = LETTERS[1:26],
percent = rnorm(26, mean=15)
)
ggplot(dfr, aes(x=percent, fill=name)) + geom_bar() +
stat_bin(geom="text", aes(label=name))
...which I'm clearly doing all wrong. Ultimately what I'd ideally like is something along the lines of the manually-modified graph below, with (say) letters A to M filled one shade and N to Z filled another.
http://farm8.staticflickr.com/7116/7026536711_4df9a1aa12.jpg
Here you go!
set.seed(3421)
# added type to mimick which candidate is supported
dfr <- data.frame(
name = LETTERS[1:26],
percent = rnorm(26, mean=15),
type = sample(c("A", "B"), 26, replace = TRUE)
)
# easier to prepare data in advance. uses two ideas
# 1. calculate histogram bins (quite flexible)
# 2. calculate frequencies and label positions
dfr <- transform(dfr, perc_bin = cut(percent, 5))
dfr <- ddply(dfr, .(perc_bin), mutate,
freq = length(name), pos = cumsum(freq) - 0.5*freq)
# start plotting. key steps are
# 1. plot bars, filled by type and grouped by name
# 2. plot labels using name at position pos
# 3. get rid of grid, border, background, y axis text and lables
ggplot(dfr, aes(x = perc_bin)) +
geom_bar(aes(y = freq, group = name, fill = type), colour = 'gray',
show_guide = F) +
geom_text(aes(y = pos, label = name), colour = 'white') +
scale_fill_manual(values = c('red', 'orange')) +
theme_bw() + xlab("") + ylab("") +
opts(panel.grid.major = theme_blank(), panel.grid.minor = theme_blank(),
axis.ticks = theme_blank(), panel.border = theme_blank(),
axis.text.y = theme_blank())