can't draw the grouped value above stacked bar plot in ggplot2 - r

I have a ggplot2 question, I run the code below show the stacked barplot without add value above each bar correctly:
p=ggplot(data=essnn)
p+geom_bar(binwidth=0.5,stat="identity")+ #
aes(x=reorder(classname,-amount,sum), y=amount, label=amount, fill = sort(year))+
theme()
I want add the sum amount grouped by year in each class, and here is my code:
+geom_text(aes(x=classes,y=total,label=total), data=essnnta, fill=NULL, size=3)
But an error message appear:
Error in fill = year, can not find object "year"
That's my problem: why the object "year" can be found when I draw stack bar plot without add the sum amount grouped by year in each class, but when I add the sum amount grouped by year, the error appear?
> str(essnn)
'data.frame': 48619 obs. of 15 variables:
$ id : int 2006051337 2006051337 2006051337 2006051337 2006051337 2006051337 2004070648 2006031360 2006031360 2004070062 ...
$ gender : Factor w/ 3 levels "","F","M": 3 3 3 3 3 3 3 3 3 3 ...
$ age : num 30 30 30 30 30 30 38 43 43 37 ...
$ class : Factor w/ 92 levels "100ab","100aa",..: 18 18 18 18 18 18 18 18 18 18 ...
$ classname: Factor w/ 1136 levels "cad"," Office2010",..: 111 111 111 111 111 111 116 107 107 107 ...
$ grade : num 7 5 6 8 3 4 1 4 3 2 ...
$ year : Factor w/ 6 levels "98","99","100",..: 3 3 3 3 2 2 4 5 5 3 ...
$ ses : num 212 210 211 213 207 208 217 221 220 210 ...
$ date : int 1010421 1001115 1010214 1010701 1000411 1000627 1020424 1030304 1021121 1001108 ...
$ money : num 5800 5800 5800 5800 5200 5200 3000 0 5500 5500 ...
$ discount : num 1160 1160 1160 1160 1040 1040 600 0 275 550 ...
$ amount : num 4640 4640 4640 4640 4160 ...
$ idc : Factor w/ 7 levels "在校生","校友",..: 2 2 2 2 2 2 2 7 7 7 ...
$ mdy : Date, format: "2012-04-21" "2011-11-15" "2012-02-14" "2012-07-01" ...
$ day : num 1123 1281 1190 1052 1499 ...
> str(essnnta)
'data.frame': 10 obs. of 2 variables:
$ classes: Factor w/ 10 levels "JD","JF",..: 1 7 8 4 6 10 3 5 2 9
$ total : num 55603526 43708950 43555010 35649129 33214372 ...

Your problem might be that your x-axes are not the same in the two data frames. So ggplot does not know which value corresponds with which stack. I am not sure about this as I don't understand the way you define your x axis in the original barplot. I also find it a bit strange to define the aes outside of the ggplot function or the geom_bar. But that might just be me be used to a different kind of syntax.
All in all I find it difficult to help you as you do not provide any reproducible example.

Here is a small bit of data, and a plot that sort of works. If you supplement your question with your data (or a subset of it), see if this works. You may also want to position the label at the top of each bar.
essnn <- data.frame(year = c(98,99,100,101,102),
classname = c("a", "b", "c", "d", "e"),
amount = c(1e6, 2e6,3e6,4e6,5e6))
essnnta <- data.frame(total = c(10, 20, 30, 40, 50))
ggplot(data=essnn, aes(x=reorder(classname,-amount, sum), y=amount, fill = year)) +
geom_bar(binwidth=0.5, stat="identity", position = "stack") +
geom_text(aes(x=essnn$classname, y=essnnta$total, label=essnnta$total), size=3) # not "classes"

Related

I was trying to mutate a new numeric column in a dataframe but the compliler is taking it as char's and i am not even able to access it using index

library(dslabs)
data(heights)
library(dplyr)
mutate(heights, ht_cm = height * 2.54, stringsAsFactor = FALSE )
str(heights) # not showing ht_cm as a variable in the data frame
mean(heights$ht_cm) # giving error that argument is not numeric
You just used mutate, but if you want to add the new column in height you need to:
Code
heights <-
heights %>%
mutate(ht_cm = height * 2.54)
Output
str(heights)
'data.frame': 1050 obs. of 3 variables:
$ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 1 1 1 1 2 ...
$ height: num 75 70 68 74 61 65 66 62 66 67 ...
$ ht_cm : num 190 178 173 188 155 ...

Observations becoming NA when ordering levels of factors in R with ordered()

Hi have a longitudinal data frame p that contains 4 variables and looks like this:
> head(p)
date.1 County.x providers beds price
1 Jan/2011 essex 258 5545 251593.4
2 Jan/2011 greater manchester 108 3259 152987.7
3 Jan/2011 kent 301 7191 231985.7
4 Jan/2011 tyne and wear 103 2649 143196.6
5 Jan/2011 west midlands 262 6819 149323.9
6 Jan/2012 essex 2 27 231398.5
The structure of my variables is the following:
'data.frame': 259 obs. of 5 variables:
$ date.1 : Factor w/ 66 levels "Apr/2011","Apr/2012",..: 23 23 23 23 23 24 24 24 25 25 ...
$ County.x : Factor w/ 73 levels "avon","bedfordshire",..: 22 24 32 65 67 22 32 67 22 32 ...
$ providers: int 258 108 301 103 262 2 9 2 1 1 ...
$ beds : int 5545 3259 7191 2649 6819 27 185 24 70 13 ...
$ price : num 251593 152988 231986 143197 149324 ...
I want to order date.1 chronologically. Prior to apply ordered(), this variable does not contain NA observations.
> summary(is.na(p$date.1))
Mode FALSE NA's
logical 259 0
However, once I apply my function for ordering the levels corresponding to date.1:
p$date.1 = with(p, ordered(date.1, levels = c("Jun/2010", "Jul/2010",
"Aug/2010", "Sep/2010", "Oct/2010", "Nov/2010", "Dec/2010", "Jan/2011", "Feb/2011",
"Mar/2011","Apr/2011", "May/2011", "Jun/2011", "Jul/2011", "Aug/2011", "Sep/2011",
"Oct/2011", "Nov/2011", "Dec/2011" ,"Jan/2012", "Feb/2012" ,"Mar/2012" ,"Apr/2012",
"May/2012", "Jun/2012", "Jul/2012", "Aug/2012", "Sep/2012", "Oct/2012", "Nov/2012",
"Dec/2012", "Jan/2013", "Feb/2013", "Mar/2013", "Apr/2013", "May/2013",
"Jun/2013", "Jul/2013", "Aug/2013", "Sep/2013", "Oct/2013", "Nov/2013",
"Dec/2013", "Jan/2014",
"Feb/2014", "Mar/2014", "Apr/2014", "May/2014", "Jun/2014", "Jul/2014" ,"Aug/2014",
"Sep/2014", "Oct/2014", "Nov/2014", "Dec/2014", "Jan/2015", "Feb/2015", "Mar/2015",
"Apr/2015","May/2015", "Jun/2015" ,"Jul/2015" ,"Aug/2015", "Sep/2015", "Oct/2015",
"Nov/2015")))
It seems I miss some observations.
> summary(is.na(p$date.1))
Mode FALSE TRUE NA's
logical 250 9 0
Has anyone come across with this problem when using ordered()? or alternatively, is there any other possible solution to group my observations chronologically?
It is possible that one of your p$date.1 doesn't matched to any of the levels. Try this ord.monas the levels.
ord.mon <- do.call(paste, c(expand.grid(month.abb, 2010:2015), sep = "/"))
Then, you can try this to see if there's any mismatch between the two.
p$date.1 %in% ord.mon
Last, You can also sort the data frame after transforming the date.1 columng into Date (Note that you have to add an actual date beforehand)
p <- p[order(as.Date(paste0("01/", p$date.1), "%d/%b/%Y")), ]

How to plot graph with certain requirement of choosing of x-axis in data table in R?

I have a data frame as following:
>str(df)
'data.frame': 22673 obs. of 6 variables:
$ V1 : Factor w/ 39 levels "2015-02-09","2015-02-09 ",..: 1 1 1 1 1 1 1 1 1 1 ...
$ V2 : Factor w/ 10465 levels "00:48:26","01:49:26",..: 3949 3956 3964 3985 4196 4254 4262 4268 4275 4309 ...
$ V3 : Factor w/ 3 levels "Admin","AmbassadorSchoolPlayer",..: 3 3 3 3 3 3 3 3 1 3 ...
$ V4 : Factor w/ 104 levels "1builder1","22mAsgarfus",..: 77 77 57 77 48 48 48 48 6 77 ...
$ V5 : Factor w/ 8580 levels ""," - -?"," - 14 1",..: 2306 874 7433 3650 2306 2306 3364 6501 3257 2306 ...
df$V4 is the user_name, and I'd like to plot the graph which takes df$V1 as x-axis, df$V4 as y-axis. But given the number of user is too big, I 'd like to choose the ones(user-name) who appear for more than a threshold times, let's say, 10, in the data frame. How can I do it? I am quite new to R, and I have read several article introducing ggplot2, but did not find the answer. Thank you in advance.
use the table function
count <- table(df$V4)
subset which usernames with more than 10 entries
some_usernames <- names(count[count>10])
then subset your dataframe
df_subset <- df[df$V4 %in% some_usernames, ]
then use ggplot2 or base graphics to do what you want. Hope this helps.

Subsetting two corresponding variables if both of them are true

I'm trying to create a vector with two columns that contain the following strings given that the data in BOTH columns are true. I tried, unsuccessfully with:
CrimesAndLocation <- table(c(Crimes_Data$Primary.Type=="ARSON","ASSAULT","BATTERY","BURGLARY","HOMICIDE","HUMAN TRAFFICKING","KIDNAPPING","ROBBERY",Crimes_Data$Location.Description=="RESIDENCE")))
I'm trying to get an output where:
Primary.Type, is one of the 8 specific felonies listed above. Thus, it should not show all 32 possible felonies, just out of the 8 listed above
Location.Description, is RESIDENCE
This is the goal of what I'm trying to do:
COLUMN 1 COLUMN 2
"ARSON" "RESIDENCE"
"KIDNAPPING" "RESIDENCE"
"BATTERY" "RESIDENCE"
"HOMICIDE" "RESIDENCE"
"ASSAULT" "RESIDENCE"
...
UPDATE: > str(Crimes_Data) :
'data.frame': 293036 obs. of 22 variables:
$ ID : int 10248194 10251162 10248198 10248242 10248228 10248223 10248192 10248157 10249529 10252453 ...
$ Case.Number : Factor w/ 293015 levels "F218264","HA168845",..: 292354 292350 292363 292359 292368 292366 292351 292348 292364 292816 ...
$ Date : Factor w/ 124573 levels "01/01/2015 01:00:00 AM",..: 94544 94542 94539 94536 94535 94535 94535 94535 94529 94528 ...
$ Block : Factor w/ 27983 levels "0000X E 100TH PL",..: 13541 7650 22635 1317 13262 9623 12854 8232 24201 14279 ...
$ IUCR : Factor w/ 334 levels "0110","0130",..: 49 139 321 33 251 82 38 282 97 38 ...
$ Primary.Type : Factor w/ 32 levels "ARSON","ASSAULT",..: 3 7 24 3 18 31 3 13 17 3 ...
$ Description : Factor w/ 313 levels "$500 AND UNDER",..: 111 281 119 35 131 1 260 193 274 260 ...
$ Location.Description: Factor w/ 121 levels "","ABANDONED BUILDING",..: 95 19 110 48 97 110 106 110 110 99 ...
$ Arrest : Factor w/ 2 levels "false","true": 1 1 2 1 2 2 1 2 2 1 ...
$ Domestic : Factor w/ 2 levels "false","true": 2 1 1 1 1 1 1 1 1 1 ...
$ Beat : int 835 333 733 634 1121 1432 1024 735 414 2535 ...
$ District : int 8 3 7 6 11 14 10 7 4 25 ...
$ Ward : int 18 5 6 21 27 1 22 17 7 26 ...
$ Community.Area : int 70 43 68 49 23 22 30 67 46 23 ...
$ FBI.Code : Factor w/ 26 levels "01A","01B","02",..: 11 17 26 6 21 8 11 25 9 11 ...
$ X.Coordinate : int 1154209 1190610 1172166 1176493 1153156 1159961 1154332 1163770 1193570 NA ...
$ Y.Coordinate : int 1852321 1856955 1858813 1841948 1904451 1915955 1887190 1857568 1852889 NA ...
$ Year : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
$ Updated.On : Factor w/ 442 levels "01/01/2015 12:39:07 PM",..: 288 288 288 288 288 288 288 288 288 288 ...
$ Latitude : num 41.8 41.8 41.8 41.7 41.9 ...
$ Longitude : num -87.7 -87.6 -87.6 -87.6 -87.7 ...
$ Location : Factor w/ 173646 levels "","(41.644604096, -87.610728247)",..: 31318 40835 45858 15601 116871 140063 84837 42961 32176 1 ...
This is a good job for the dplyr package. The filter function will filter a data frame according to any number of logical expressions that you feed it. The following should work for you:
library(dplyr)
filter(
Crimes_Data,
Primary.Type %in% c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "HOMICIDE", "HUMAN TRAFFICKING",
"KIDNAPPING", "ROBBERY"),
Location.Description == "RESIDENCE"
)
If you'd rather not use dplyr, you can do it the old fashioned way with base R, like this:
type.bool <- Crimes_Data$Primary.Type %in% c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "HOMICIDE",
"HUMAN TRAFFICKING", "KIDNAPPING",
"ROBBERY")
location.bool <- Crimes_Data$Location.Description == "RESIDENCE"
Crimes_Data[type.bool & location.bool, ]
Instead of an integer vector of indices, the [ subsetting operator can take a boolean vector. In that case, it will only return the rows of the data frame for which the corresponding elements of the boolean vector are TRUE.
Thanks for the str() aka "structure" output update, it makes it clearer to be able to help you.
To obtain a list of observations where
these eight felonies : "ARSON","ASSAULT","BATTERY","BURGLARY","HOMICIDE","HUMAN TRAFFICKING","KIDNAPPING","ROBBERY"
occurred at RESIDENCE
Try breaking up the task into slightly smaller parts:
Step 1:
ViolentCrimes = subset(Crimes_Data, Primary.Type == "ARSON" | Primary.Type == "ASSAULT" | Primary.Type == "BATTERY" | Primary.Type == "BURGLARY" | Primary.Type == "HOMICIDE" | Primary.Type == "HUMAN TRAFFICKING" | Primary.Type == "KIDNAPPING" | Primary.Type == "ROBBERY")
Step 2:
ViolentCrimesResidence = subset(ViolentCrimes, Location.Description == "RESIDENCE", select = c(Primary.Type, Location.Description))
Result:
ViolentCrimesResidence holds two columns with Column 1 being a list of Primary.Type and column 2 is Location.Description, where Column 1 only has values from the eight felonies of interest and column2 only "RESIDENCE"
Explanation
Step 1:
From R website's examples about subset and OR condition:
PineTreeGrade3Data<-subset(StudentData, SchoolName=="Pine Tree Elementary" | Grade==3)
Whereas we have:
ViolentCrimes = subset(Crimes_Data, Primary.Type == "ARSON" |
we use the subset() function
Crimes_Data is the existing data frame as input
next are the conditions. Which simply take the pattern of VectorName == "Some string", in this casePrimary.Type == "ARSON"`
But we want observations for the other types too, so use the "or" condition to include them
in R, "or" is written with | symbol. So we use this repeatedly to include each of the other felonies of interest
the equal sign = is synonymous with <- and assigns, saves this subset result, into to a new data frame we call ViolentCrimes.
note I prefer using = because it is less keystrokes to type than <-, either is correct
Step 2:
ViolentCrimesResidence = subset(ViolentCrimes, Location.Description == "RESIDENCE", select = c(Primary.Type, Location.Description))
the input is ViolentCrimes data frame we made previously which contains only the eight violent crimes , the eight felonies "ARSON", "ASSAULT"...
now we are interested in, out of all these violent crimes, which ones occurred at home, so use condition Location.Description == "RESIDENCE"
but a further option of subset() we didn't use before, is the select = ... option
we do a select = c(Variable1, Variable2) to choose just the Primary.Type and Location.Description vectors
note that if you actually don't want to limit to the columns aka Variables, you simply omit this , select ... option
thus it saves this new subset into ViolentCrimesResidence
So, now in R when you:
ViolentCrimesResidence
You will see a two-column output you wanted of the eight felonies of interest, that happened in RESIDENCE.

Plot time(x axis) and time of day & duration(y axis) of episodes

I am measuring the duration of an event, and I would like to plot the duration, and time the event takes place in each observation day.
My dataset is the following
> str(question.stack)
'data.frame': 398 obs. of 6 variables:
$ id : Factor w/ 1 level "AA11": 1 1 1 1 1 1 1 1 1 1 ...
$ begin.recording : Factor w/ 1 level "8/15/2007": 1 1 1 1 1 1 1 1 1 1 ...
$ begin.of.episode: Factor w/ 111 levels "1/1/2009","1/11/2009",..: 86 86 86 87 88 90 90 96 96 103 ...
$ episode.day : int 12 12 12 13 14 15 15 17 17 18 ...
$ start.time : Factor w/ 383 levels "0:06:01","0:17:12",..: 324 15 18 179 269 320 379 281 287 298 ...
$ duration : num 278 14 1324 18 428 ...
I would like in the x axis the episode.day. The y axis should go from 00:00 to 23:59:59 (start.time). For example, for the second entry of the dataset, i would like a black bar starting at (x=12,y=10:55:12) till (x=12, y=11:09:12) denoting a 14 minute episode duration on day 12. An episode can span between more than 1 days.
Is this possible with R? If possible please only baseR solutions
Something similar is Plot dates on the x axis and time on the y axis with ggplot2 but not exactly what I am looking.
Many thanks
Ok I finally found it.
On the x axis I wanted to plot dates either as POSIXct or as number of day of recording (integer). On the y axis I wanted the time of day so that the graph would present a dark bar on each day (x-axis) and between the time (y-axis) that the episode take place.
R can plot POSIX, but in my case the episode start and end time (for the y-axis) should be date-"less"
I did this like this
#Cleaning the Dataset
qs<-question.stack
qs$id<-as.character(qs$id)
qs$begin.recording<-as.character(qs$begin.recording)
qs$begin.of.episode<-as.character(qs$begin.of.episode)
qs$start.time<-as.character(qs$start.time)
qs$start<-as.character(paste(qs$begin.of.episode,qs$start.time))
qs$duration<-round(qs$duration,0)
#Convert time and dates to POSIXct
qs$start<-as.POSIXct(qs$start,format="%m/%d/%Y %H:%M:%S",tz="UTC")
qs$start<-round(qs$start,"mins")
qs$end<-as.POSIXct(qs$start+qs$duration*60)
qs$start<-as.POSIXct(qs$start)
Now we have
str(qs)
'data.frame': 398 obs. of 8 variables:
$ id : chr "AA11" "AA11" "AA11" "AA11" ...
$ begin.recording : chr "8/15/2007" "8/15/2007" "8/15/2007" "8/15/2007" ...
$ begin.of.episode: chr "8/27/2007" "8/27/2007" "8/27/2007" "8/28/2007" ...
$ episode.day : int 12 12 12 13 14 15 15 17 17 18 ...
$ start.time : chr "6:15:12" "10:55:12" "11:15:12" "18:19:12" ...
$ duration : num 278 14 1324 18 428 ...
$ start : POSIXct, format: "2007-08-27 06:15:00" "2007-08-27 10:55:00" ...
$ end : POSIXct, format: "2007-08-27 10:53:00" "2007-08-27 11:09:00" ...
The following makes a vector which includes all minutes that there was an episode. One can fine tune it by seconds or upscale it by hours
tmp<-do.call(c, apply(qs, 1, function(x) seq(from=as.POSIXct(x[7]), to=as.POSIXct(x[8]),by="mins")))
The following makes a data frame. The switch of the time of day from POSIX to (date-"less") and then back to POSIX garantees that there will be the same date in all times of time.of.day. Perhaps one can also do it with the origin argument.
ep <- data.frame(sqs=tmp, date=as.Date(tmp,"%Y-%m-%d"),time.of.day=as.POSIXct(as.character(format(tmp,"%H:%M")),format="%H:%M"))
Plot
plot(ep$date, ep$time.of.day,pch=".")

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