All frequencies of my bar plot are the same height? - r

Trying to barplot a CSV file and the plot shows all categories as the same height despite the frequencies varying from 450-800
Below is the plot I receive
!https://imgur.com/9HZuiaK
I have tried implementing a height=x, width=x
This results in completely removing the labels and does not fix the initial problem.
setwd("~/Desktop")
causes<-read.csv('causes.csv')
head(causes)
table(causes$Intentional.self.harm..suicide)
barplot(table(causes$Intentional.self.harm..suicide))
barplot(table(causes$Intentional.self.harm..suicide), ylab='Frequency',
main='Barplot of Intentional self-harm (suicide)', col='lightblue'
dput(head(causes, 20))
Intentional.self.harm..suicide. = c(535L,
579L, 480L, 541L, 499L, 537L, 466L, 453L, 459L, 494L, 520L, 553L,
525L, 588L, 578L, 631L, 676L, 656L, 757L, 673L)

I believe you are using wrongly the table function since you already calculated the numbers of modalities. So it is not a graphical nor a functional problem. All you have to do is calculate the frequency as follow :
Intentional.self.harm..suicide. = c(535L,
579L, 480L, 541L, 499L, 537L, 466L, 453L, 459L, 494L, 520L, 553L,
525L, 588L, 578L, 631L, 676L, 656L, 757L, 673L)
df <- Intentional.self.harm..suicide.
barplot(height = df) # correct barplot with counts
df_prop <- table(df) # gives a table that count modalities (all unique ie 1)
str(df_prop)
# With a data.frame if you want to include labels
df_prop <- data.frame(
"type" = paste0("t", 1:20),
"freq" = df/sum(df) # alternatively use prop.table(df)
)
# sum(df_prop$freq) # to check
barplot(height = df_prop$freq) # same 'profile' than first barplot
# --- EDIT / Follow up
# looking at documentation of barplot to set labels and ordinate limits
?barplot
barplot(height = df_prop$freq, names.arg = df_prop$type, ylim=c(0, max(df_prop$freq * 1.2)))

Related

Remove middle inconsistent characters from a column header column name with r

`
set.seed(500)
index <- sample(1:nrow(Bands_reflectance_2017),100, replace = FALSE )
Bands_reflectance_2017 <- dput(head(Bands_reflectance_2017[1:100]))
Bands_reflectance_2017 <-
structure(
list(
t2017.01.05T08.25.12.000000000_blue = c(5064L,
5096L, 5072L, 5048L, 5048L, 5064L),
t2017.01.15T08.26.22.000000000_blue = c(418L,
487L, 480L, 449L, 449L, 480L),
t2017.01.25T08.21.38.000000000_blue = c(312L,
414L, 385L, 385L, 385L, 403L),
t2017.02.04T08.27.09.000000000_blue = c(5156L,
5096L, 5204L, 5240L, 5240L, 5112L),
t2017.02.14T08.27.29.000000000_blue = c(2554L,
2896L, 2842L, 2776L, 2776L, 2934L),
t2017.02.24T08.23.38.000000000_blue = c(2662L,
2428L, 2630L, 2644L, 2644L, 2276L),
t2017.03.06T08.24.47.000000000_blue = c(340L,
403L, 409L, 407L, 407L, 391L),
t2017.03.16T08.16.07.000000000_blue = c(188L,
245L, 257L, 239L, 239L, 245L),
t2017.03.26T08.22.43.000000000_blue = c(379L,
397L, 381L, 345L, 345L, 387L),
t2017.04.05T08.23.06.000000000_blue = c(604L,
647L, 639L, 647L, 647L, 631L),
t2017.04.15T08.23.45.000000000_blue = c(311L,
382L, 376L, 379L, 379L, 425L),
t2017.04.25T08.23.17.000000000_blue = c(219L,
318L, 237L, 322L, 322L, 302L),
t2017.05.05T08.23.45.000000000_blue = c(979L,
1030L, 1021L, 1030L, 1030L, 985L),
t2017.05.15T08.28.11.000000000_blue = c(138L,
219L, 196L, 201L, 201L, 247L),
t2017.05.25T08.23.46.000000000_blue = c(655L,
779L, 736L, 752L, 752L, 777L),
t2017.06.04T08.25.50.000000000_blue = c(318L,
419L, 384L, 343L, 343L, 400L),
t2017.06.14T08.28.06.000000000_blue = c(397L,
387L, 407L, 432L, 432L, 347L),
t2017.06.24T08.26.00.000000000_blue = c(336L,
450L, 402L, 395L, 395L, 388L),
t2017.07.04T08.23.42.000000000_blue = c(502L,
538L, 512L, 495L, 495L, 505L),
t2017.07.09T08.23.09.000000000_blue = c(568L,
597L, 639L, 611L, 611L, 577L),
t2017.07.19T08.23.43.000000000_blue = c(479L,
517L, 536L, 529L, 529L, 528L),
t2017.07.24T08.23.44.000000000_blue = c(409L,
499L, 499L, 473L, 473L, 482L),
t2017.07.29T08.26.12.000000000_blue = c(781L,
801L, 810L, 823L, 823L, 735L),
t2017.08.03T08.26.43.000000000_blue = c(517L,
579L, 560L, 583L, 583L, 564L),
t2017.08.08T08.23.41.000000000_blue = c(575L,
654L, 650L, 650L, 650L, 602L),
t2017.08.13T08.23.44.000000000_blue = c(623L,
679L, 708L, 698L, 698L, 677L),
t2017.08.18T08.25.16.000000000_blue = c(614L,
651L, 648L, 597L, 597L, 651L),
t2017.08.23T08.22.22.000000000_blue = c(554L,
613L, 559L, 524L, 524L, 596L),
t2017.08.28T08.28.01.000000000_blue = c(769L,
814L, 772L, 744L, 744L, 828L),
t2017.09.02T08.23.42.000000000_blue = c(756L,
761L, 763L, 783L, 783L, 742L),
t2017.09.07T08.23.30.000000000_blue = c(807L,
865L, 826L, 838L, 838L, 837L),
t2017.09.12T08.23.35.000000000_blue = c(861L,
869L, 876L, 904L, 904L, 869L),
t2017.09.22T08.23.38.000000000_blue = c(4640L,
3780L, 4340L, 4728L, 4728L, 3060L),
t2017.09.27T08.16.41.000000000_blue = c(778L,
777L, 811L, 839L, 839L, 752L),
t2017.10.02T08.17.41.000000000_blue = c(766L,
868L, 851L, 857L, 857L, 799L),
t2017.10.07T08.24.51.000000000_blue = c(767L,
816L, 839L, 830L, 830L, 753L),
t2017.10.12T08.24.39.000000000_blue = c(678L,
688L, 706L, 750L, 750L, 627L),
t2017.10.17T08.15.32.000000000_blue = c(678L,
769L, 804L, 797L, 797L, 711L),
t2017.10.22T08.21.34.000000000_blue = c(3146L,
3134L, 3128L, 3160L, 3160L, 3118L),
t2017.10.27T08.23.27.000000000_blue = c(612L,
697L, 721L, 697L, 697L, 708L),
t2017.11.01T08.24.41.000000000_blue = c(941L,
982L, 1001L, 1010L, 1010L, 999L),
t2017.11.06T08.20.50.000000000_blue = c(670L,
824L, 836L, 824L, 824L, 785L),
t2017.11.11T08.27.40.000000000_blue = c(720L,
817L, 839L, 807L, 807L, 801L),
t2017.11.16T08.16.16.000000000_blue = c(9824L,
9744L, 9792L, 9744L, 9744L, 9536L),
t2017.11.21T08.17.00.000000000_blue = c(749L,
841L, 838L, 738L, 738L, 830L),
t2017.11.26T08.25.13.000000000_blue = c(735L,
863L, 832L, 713L, 713L, 899L),
t2017.12.01T08.20.22.000000000_blue = c(674L,
836L, 816L, 800L, 800L, 771L),
t2017.12.06T08.19.42.000000000_blue = c(2742L,
2770L, 2742L, 2762L, 2762L, 2798L),
t2017.12.11T08.19.00.000000000_blue = c(582L,
745L, 734L, 654L, 654L, 743L),
t2017.12.16T08.23.19.000000000_blue = c(926L,
1054L, 1001L, 946L, 946L, 1054L),
t2017.12.21T08.20.53.000000000_blue = c(7432L,
7484L, 7456L, 7404L, 7404L, 7484L),
t2017.12.26T08.20.39.000000000_blue = c(629L,
724L, 762L, 738L, 738L, 731L),
t2017.12.31T08.20.04.000000000_blue = c(667L,
765L, 762L, 718L, 718L, 765L),
t2017.01.05T08.25.12.000000000_green = c(5224L,
5196L, 5208L, 5152L, 5152L, 5172L),
t2017.01.15T08.26.22.000000000_green = c(837L,
938L, 907L, 858L, 858L, 927L),
t2017.01.25T08.21.38.000000000_green = c(735L,
808L, 770L, 770L, 770L, 836L),
t2017.02.04T08.27.09.000000000_green = c(5424L,
5492L, 5488L, 5536L, 5536L, 5832L),
t2017.02.14T08.27.29.000000000_green = c(3050L,
3094L, 3108L, 3228L, 3228L, 2900L),
t2017.02.24T08.23.38.000000000_green = c(2664L,
2450L, 2598L, 2646L, 2646L, 2340L),
t2017.03.06T08.24.47.000000000_green = c(702L,
735L, 749L, 727L, 727L, 729L),
t2017.03.16T08.16.07.000000000_green = c(632L,
685L, 708L, 685L, 685L, 703L),
t2017.03.26T08.22.43.000000000_green = c(744L,
841L, 806L, 809L, 809L, 818L),
t2017.04.05T08.23.06.000000000_green = c(1030L,
1036L, 1044L, 1050L, 1050L, 1040L),
t2017.04.15T08.23.45.000000000_green = c(634L,
720L, 708L, 699L, 699L, 751L),
t2017.04.25T08.23.17.000000000_green = c(619L,
698L, 716L, 723L, 723L, 687L),
t2017.05.05T08.23.45.000000000_green = c(1340L,
1368L, 1374L, 1404L, 1404L, 1354L),
t2017.05.15T08.28.11.000000000_green = c(525L,
633L, 619L, 612L, 612L, 626L),
t2017.05.25T08.23.46.000000000_green = c(1042L,
1118L, 1078L, 1028L, 1028L, 1148L),
t2017.06.04T08.25.50.000000000_green = c(655L,
778L, 783L, 769L, 769L, 813L),
t2017.06.14T08.28.06.000000000_green = c(772L,
829L, 838L, 810L, 810L, 822L),
t2017.06.24T08.26.00.000000000_green = c(741L,
888L, 848L, 798L, 798L, 865L),
t2017.07.04T08.23.42.000000000_green = c(867L,
918L, 912L, 846L, 846L, 946L),
t2017.07.09T08.23.09.000000000_green = c(936L,
1001L, 1012L, 972L, 972L, 985L),
t2017.07.19T08.23.43.000000000_green = c(848L,
911L, 925L, 915L, 915L, 903L),
t2017.07.24T08.23.44.000000000_green = c(855L,
907L, 947L, 913L, 913L, 937L),
t2017.07.29T08.26.12.000000000_green = c(1096L,
1106L, 1134L, 1150L, 1150L, 1116L),
t2017.08.03T08.26.43.000000000_green = c(987L,
1072L, 1040L, 1030L, 1030L, 1021L),
t2017.08.08T08.23.41.000000000_green = c(996L,
1011L, 1001L, 1011L, 1011L, 1032L),
t2017.08.13T08.23.44.000000000_green = c(1006L,
1100L, 1082L, 1078L, 1078L, 1092L),
t2017.08.18T08.25.16.000000000_green = c(977L,
1034L, 1032L, 976L, 976L, 1020L),
t2017.08.23T08.22.22.000000000_green = c(976L,
1054L, 1044L, 985L, 985L, 1072L),
t2017.08.28T08.28.01.000000000_green = c(1162L,
1176L, 1188L, 1150L, 1150L, 1200L),
t2017.09.02T08.23.42.000000000_green = c(1136L,
1152L, 1158L, 1176L, 1176L, 1130L),
t2017.09.07T08.23.30.000000000_green = c(1122L,
1166L, 1174L, 1194L, 1194L, 1162L),
t2017.09.12T08.23.35.000000000_green = c(1158L,
1170L, 1168L, 1180L, 1180L, 1146L),
t2017.09.22T08.23.38.000000000_green = c(3304L,
3218L, 3072L, 3580L, 3580L, 4148L),
t2017.09.27T08.16.41.000000000_green = c(1172L,
1228L, 1242L, 1224L, 1224L, 1172L),
t2017.10.02T08.17.41.000000000_green = c(1148L,
1224L, 1220L, 1200L, 1200L, 1164L),
t2017.10.07T08.24.51.000000000_green = c(1120L,
1164L, 1160L, 1148L, 1148L, 1114L),
t2017.10.12T08.24.39.000000000_green = c(1124L,
1158L, 1166L, 1144L, 1144L, 1090L),
t2017.10.17T08.15.32.000000000_green = c(1092L,
1190L, 1180L, 1154L, 1154L, 1146L),
t2017.10.22T08.21.34.000000000_green = c(3140L,
3124L, 3142L, 3134L, 3134L, 3096L),
t2017.10.27T08.23.27.000000000_green = c(1064L,
1104L, 1116L, 1078L, 1078L, 1098L),
t2017.11.01T08.24.41.000000000_green = c(1298L,
1310L, 1344L, 1344L, 1344L, 1318L),
t2017.11.06T08.20.50.000000000_green = c(1114L,
1240L, 1220L, 1164L, 1164L, 1212L),
t2017.11.11T08.27.40.000000000_green = c(1182L,1278L, 1278L, 1192L, 1192L, 1284L),
t2017.11.16T08.16.16.000000000_green = c(8872L, 8728L, 8816L, 8904L, 8904L, 8600L),
t2017.11.21T08.17.00.000000000_green = c(1166L, 1268L, 1250L, 1158L, 1158L, 1260L),
t2017.11.26T08.25.13.000000000_green = c(1138L, 1272L, 1288L, 1240L, 1240L, 1278L)), row.names = c(NA, 6L), class = "data.frame")
`
I have a dataframe of dates for per specific bands with 534 column headers as follow:
"t2017-12-31T08:20:04.000000000_red_edge_3"
"t2017-02-04T08:27:09.000000000_nir_1"
"t2017-12-31T08:20:04.000000000_swir_2"
Now, I want to remove everything and only remain with the date and the band name e.g in column header one and two, I want to only remain with
"2017-12-31_red_edge_3"
"2017-02-04_nir_1"
I have about 534 columns and most characters are not consistent because each date time is different and more band examples not similar to what is shown here for all the 534 records, so I was only able to remove repetitive characters such as "T08", ":","t" and "000000000" which are available in all the columns. How do I remove the values between the date and the band characters when they vary per each column and so I cannot use :
for ( col in 1:ncol(Bands_reflectance_2017[5:534])){
colnames(Bands_reflectance_2017)[5:534] <- sub(".000000000", "", colnames(Bands_reflectance_2017)[5:534]) #Remove .000000000
}
etc
Also at the end of the day, I want to replace each bandname with a band coding system such as assign "nir-1" as "B8" and "12" as the month of "December" so that for example my first and second column header reads:
B7_December31
B8_February02
Cell 1
Cell 2
Cell 3
Cell 4
"B7_December31", "B8_February02" which are better naming to run in a random forest. Because I am running into problems of
Error in eval(predvars, data, env) : object '"t2017-12-31T08:20:04.000000000_red_edge_3"' not found
if I keep the naming convention in the example
I have the following column header names in my dataframe (Bands_reflectance_2017) of 534 columns :
"t2017-01-25T08:21:38.000000000_blue"
"t2017-08-23T08:22:22.000000000_green"
Cell 1
Cell 2
Cell 3
Cell 4
I want to remove everything except the date and band name e.g "2017_01_25_blue"
I tried:
for ( col in 1:ncol(Bands_reflectance_2017[5:534])){
colnames(Bands_reflectance_2017)[5:534] <- sub("T08", "", colnames(Bands_reflectance_2017)[5:534]) #Remove T08
But as some of the characters I want to remove are unique per each 534 columns, I am not sure how to remove them
I expect this at the end of the day:
2017_01_25_blue
2017_08_23_green
Cell 1
Cell 2
Cell 3
Cell 4
The later
"B2_December31", B3_August23
Cell 1
Cell 3
I also tried this :
substr(colnames(Bands_Reflectance_2017[2:335]),2,11)
What is the best way to do it? I am fairly new to programming and to r.
Thanks for sharing your code and data. Most people won't download random files. In the future you can share data with dput(data) or a smaller version with dput(head(data)).
library(stringr)
library(lubridate)
# Using the data frame that you provided with dput, which I call "df1" here
# You'll probably have to adjust the numbers between the [] because your
# data frame is vastly different from what I have and I'm not sure I have
# the write number, but since you said 534 columns, I'm using that.
df1 <- names(df1)[1:534]
band_names <- rep(NA, length(df1))
# This is messy. I'm sure someone who knows stringr or
# regex better has a neater way to do this.
# str_locate will find positions in a string and return the numeric value of the position
# str_sub uses positions to pull substrings
# gsub replaces patterns
# What this does is find the positions of the dates or labels,
# pulls out the substring, replaces things not needed
# (like "-" I used to mark positions), changed the number for date
# to something numeric so that month() can be switched from number to text.
for(i in 1:length(df1)) {
band_names[i] <- paste0(as.character(month(as.numeric(gsub("\\.","",
str_sub(df1[i],str_locate(df1[i],"\\.[0-9]{2}")))),
label=T, abbr = F)),gsub("T","",str_sub(df1[i],str_locate(df1[i],
"\\.[0-9]{2}T"))),"_",
str_sub(df1[i],str_locate(df1[i],"[a-z]{3,}.+")))}
# You can look at the results
band_names
[1] "Dec-12_red_edge_3" "Feb-02_nir_1" "Dec-12_swir_2"
# Split up band_names to replace the band label with number
band_out <- str_sub(band_names, 7)
band_stay <- str_sub(band_names, 1, 6)
# Made data frame up for the few example lines. I'm not downloading the CSV and I'm not going to find out the actual band names, labels, and numbers.
fake_bands <- data.frame(label = c("red_edge_3", "nir_1", "swir_2"), number = c("b1","b3","b2"))
# Change out labels for the numbers
band_replace <- fake_bands[match(band_out, fake_bands$label), "number"]
new_names <- paste0(band_stay, band_replace)
new_name
[1] "Dec-12_b1" "Feb-02_b3" "Dec-12_b2"
# Again, you might have to adjust the numbers in []
names(df1)[1:534] <- new_names
You're going to have to expand/replace the fake_bands data frame I made here with a data frame that has two columns. One column should have the labels, like "red_edge_3", and the other should have the appropriate band number.

Finding change over time from a table in R - fitting a model to the data

this is the table that has the information, it is much larger but I have just included a small part of it
Part 1 : (solved) This might seem quite easy, but I am very stuck on creating code to make a graph which shows the change over time. The table shows infant mortality and I just need a graph showing how the infant mortality of one country has changed over time.
Part 2 : how can I fit a line to the data - for example, for just one country? I need to model the data as a linear response. How can I do this?
my_data <- structure(list(`1800` = c("Afghanistan", "Albania", "Algeria",
"Andorra", "Angola", "Antigua and Barbuda", "Argentina", "Armenia",
"Australia", "Austria"), `1801` = c(469L, 375L, 460L, NA, 486L,
474L, 402L, 371L, 391L, 387L), `1802` = c(469L, 375L, 460L, NA,
486L, 470L, 402L, 371L, 391L, 373L), `1803` = c(469L, 375L, 460L,
NA, 486L, 466L, 402L, 370L, 391L, 359L), `1804` = c(469L, 375L,
460L, NA, 486L, 462L, 402L, 370L, 391L, 346L), `1805` = c(469L,
375L, 460L, NA, 486L, 458L, 402L, 369L, 391L, 333L), `1806` = c(469L,
375L, 460L, NA, 486L, 455L, 402L, 369L, 391L, 321L), `1807` = c(470L,
375L, 460L, NA, 486L, 451L, 402L, 368L, 391L, 309L), `1808` = c(470L,
375L, 460L, NA, 486L, 447L, 402L, 368L, 391L, 325L), `1809` = c(470L,
375L, 460L, NA, 486L, 444L, 402L, 367L, 391L, 316L)), row.names = c(NA,
10L), class = "data.frame")
You can plot what the question asks for with package ggplot2.
This type of problems generaly has to do with reshaping the data. The format should be the long format and the data is in wide format. See this post on how to reshape the data from long to wide format.
library(dplyr)
library(tidyr)
library(ggplot2)
my_data %>%
rename(country = `1800`) %>%
pivot_longer(
cols = starts_with('18'),
names_to = 'time',
values_to = 'mortality'
) %>%
mutate(time = as.numeric(time)) %>%
ggplot(aes(time, mortality, color = country)) +
geom_point() +
geom_line()
We can use matplot from base R
matplot(t('row.names<-'(as.matrix(my_data[-1]), my_data[[1]])),
type = 'l', xaxt = 'n')
legend("top", my_data[[1]], col = seq_along(my_data[[1]]),
fill = seq_along(my_data[[1]]))

Heat map soccer game in r

I have a dataframe with the position of a player on a pitch.
The bounding box for the area is 0 - 1000 and 0 - 750.
The starting ball position is 375-500 and the starting player position is 637-692.
I was trying using geom_tile but I can't get a heat map. How can I link the variables to make a heatmap?
frames <- structure(list(half = c("1T", "1T", "1T", "1T", "1T", "1T", "1T",
"1T", "1T", "1T", "1T", "1T", "1T", "1T", "1T", "1T", "1T", "1T",
"1T", "1T", "2T", "2T", "2T", "2T"), `ball-X` = c(375L, 375L,
375L, 375L, 375L, 372L, 365L, 358L, 351L, 344L, 338L, 332L, 326L,
320L, 315L, 310L, 305L, 301L, 300L, 309L, 631L, 631L, 631L, 631L
), `ball-Y` = c(500L, 500L, 500L, 500L, 500L, 490L, 470L, 450L,
432L, 414L, 397L, 381L, 365L, 350L, 336L, 322L, 309L, 297L, 302L,
304L, 577L, 582L, 589L, 596L), `L-2-X` = c(637L, 637L, 636L,
636L, 639L, 639L, 641L, 643L, 645L, 648L, 652L, 656L, 660L, 665L,
669L, 672L, 673L, 674L, 673L, 672L, 227L, 230L, 233L, 235L),
`L-2-Y` = c(692L, 692L, 691L, 688L, 685L, 684L, 681L, 678L,
674L, 669L, 663L, 657L, 649L, 641L, 633L, 624L, 615L, 606L,
596L, 587L, 438L, 445L, 452L, 460L)), class = "data.frame", row.names = c(NA,
-24L))
ggplot(frames, aes(x = `L-2-X`, y = `L-2-Y`)) +
scale_x_continuous(limits = c(0,750))+
scale_y_continuous(limits = c(0,1000))+
geom_tile(aes(fill = `L-2-X`)) +
scale_fill_viridis_c(option = "B", direction = -1) +
theme_light()+
facet_grid(~ half)
Not sure about the final result you are trying to achieve. As far as I get it your code works fine. However, your tiles are simply to small for being visible. Only when I zoomed the plot some tiny tiles appeared.
Therefore I would recommend to bin the data to get a nice heatmap. As an example my code below bins the data in squares of size 25 to 25 (cm??). For the fill I simply count the number of obs per square. Another approach would be to use e.g. geom_hex which uses hexagons for the binning.
library(ggplot2)
library(dplyr)
# Bin data
frames_bin <- frames %>%
# Bin data
mutate(l_2_x = cut(`L-2-X`, breaks = seq(0, 750, 25), labels = seq(0, 725, 25), include.lowest = TRUE),
l_2_y = cut(`L-2-Y`, breaks = seq(0, 1000, 25), labels = seq(0, 975, 25), include.lowest = TRUE)) %>%
# Count number of obs per bin
count(half, l_2_x, l_2_y) %>%
# Convert factors to numeric
mutate_at(vars(l_2_x, l_2_y), ~ as.numeric(as.character(.x)))
ggplot(frames_bin) +
scale_x_continuous(limits = c(0, 750)) +
scale_y_continuous(limits = c(0, 1000)) +
geom_tile(aes(x = l_2_x, y = l_2_y, fill = n)) +
scale_fill_viridis_c(option = "B", direction = -1) +
theme_light()+
facet_grid(~ half)
# Out of the box: use geom_hex
ggplot(frames) +
scale_x_continuous(limits = c(0, 750)) +
scale_y_continuous(limits = c(0, 1000)) +
geom_hex(aes(x = `L-2-X`, y = `L-2-Y`, fill = ..ncount..)) +
scale_fill_viridis_c(option = "B", direction = -1) +
theme_light()+
facet_grid(~ half)

fewer data values than degrees of freedom in loess

when trying to perform a loess in R I'm getting the warning
1: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
span too small. fewer data values than degrees of freedom.
The fitted values obtained are identical (except first and last) to the observed values.
I feel however that as I'm using a degree 2 regression I'd only need three points to fit a regression. As I have a span = 0.16 and 24 values, that would give 3.84 rounding up to 4, so more than enough.
ctg <- c(49L, 33L, 23L, 35L, 49L, 134L, 261L, 415L, 466L, 432L, 442L, 499L, 420L, 412L, 504L, 493L, 554L, 623L, 571L, 620L, 584L, 415L,
329L, 193L)
span <- 0.16
h <- c(0, 1.04347826086957, 2.08695652173913, 3.1304347826087, 4.17391304347826,
5.21739130434783, 6.26086956521739, 7.30434782608696, 8.34782608695652,
9.39130434782609, 10.4347826086957, 11.4782608695652, 12.5217391304348,
13.5652173913043, 14.6086956521739, 15.6521739130435, 16.695652173913,
17.7391304347826, 18.7826086956522, 19.8260869565217, 20.8695652173913,
21.9130434782609, 22.9565217391304, 24)
loess(ctg ~ h , span = span)

dotplot in R with lattice: display of vertical axis and error bars

I'm trying to do a dotplot with the libraries lattice and latticeExtra in R. However, no proper representation of the values on the vertical y-axis is done. Instead of choosing the actual values of the numeric variable, R plots the rank of the value. That is, there are values [375, 500, 625, 750, ..., 3000] and R plots their ranks [1,2,3,4,...23] and chooses the scale accordingly. Has someone experienced a problem like this? How can I manage the get a proper representation with ticks like (0, 500, 1000, 1500, ...) on the vertical y-scale?
Here the program code so far:
df.dose <- read.table("data.csv", sep=",", header=TRUE)
library(lattice); library(latticeExtra)
useOuterStrips(dotplot(z ~ sample.size | as.factor(effect.size)*as.factor(true.dose),
groups=as.factor(type), data=df.dose, as.table=TRUE))
(Added from comment below): Also, can error bars be added to the graph? I thought of the following (to be added to the call), but it doesn't seem to work. Is it possible somehow?
up=z+se, lo=z-se, panel.groups=function(x,y,..., up, lo, subscripts){
up <- up[subscripts]
lo <- lo[subscripts]
panel.segments(lo, as.numeric(y), up, as.numeric(y), ...)
}
Here's my data: https://www.dropbox.com/s/egy25cj00rhum40/data.csv
Added: here's the relevant portion of the data using expand.grid and dput:
df.dose <- expand.grid(effect.size=c(-.5, -.625, -0.75),
sample.size=c(40L, 60L, 80L),
true.dose=c(375L, 500L, 750L, 1125L),
type=c("dose", "categ", "FP2", "FP1"))
df.dose$z <- c(875L, 875L, 750L, 750L, 750L, 625L, 625L, 625L, 625L, 875L,
875L, 750L, 1000L, 1000L, 1000L, 1125L, 1000L, 875L, 1000L, 1000L,
875L, 1000L, 1000L, 875L, 1125L, 1000L, 1000L, 1250L, 1125L,
1000L, 1250L, 1250L, 1125L, 1250L, 1000L, 1000L, 500L, 500L,
500L, 500L, 500L, 500L, 500L, 500L, 500L, 625L, 625L, 625L, 625L,
625L, 625L, 625L, 625L, 625L, 750L, 750L, 625L, 750L, 750L, 750L,
750L, 750L, 750L, 875L, 875L, 750L, 750L, 875L, 875L, 875L, 875L,
875L, 2500L, 1500L, 1125L, 2000L, 1000L, 1750L, 250L, 500L, 500L,
1250L, 750L, 625L, 875L, 500L, 500L, 875L, 500L, 375L, 1250L,
875L, 750L, 1000L, 625L, 625L, 875L, 500L, 500L, 1125L, 1000L,
875L, 1125L, 875L, 625L, 1125L, 1000L, 625L, 2500L, 2125L, 2375L,
2000L, 750L, 2625L, 250L, 625L, 250L, 875L, 875L, 500L, 625L,
500L, 625L, 1000L, 500L, 375L, 1000L, 875L, 625L, 875L, 500L,
500L, 875L, 500L, 500L, 1250L, 1125L, 875L, 1125L, 875L, 750L,
1250L, 1000L, 625L)
You need to makez a factor: dotplot(factor(z) ~ ...
Also you probably want some jitter in the plot to prevent overlap; try adding jitter.x=TRUE or jitter.y=TRUE, or both.
Judging by your comment below and looking at the data again, I think you're plotting the dotplot the wrong way. I think you want the lines to be for the sample sizes, not for the z's. If you really want z on the vertical axis, you then need to add horizontal=TRUE. You could also swap what is on the horizontal and vertical axes.
useOuterStrips(dotplot(z ~ factor(sample.size) |
as.factor(effect.size)*as.factor(true.dose),
groups=as.factor(type), data=df.dose,
as.table=TRUE, horizontal=FALSE, jitter.x=TRUE))
To add an error bar, it's a little more complicated because you have groups within the panels, so you need to use a panel.groups function; additionally, so that the lines don't overlap, you probably want to jitter them from side to side a little, which is best done in a custom panel function.
df.dose$se <- 200
df.dose$type <- factor(df.dose$type)
df.dose$sample.size <- factor(df.dose$sample.size)
panel.groups.mydotplot <- function(x, y, subscripts, up, lo,
col=NA, col.line=NA, ...) {
panel.points(x, y, ...)
panel.segments(x, lo[subscripts], x, up[subscripts], col=col.line, ...)
}
panel.mydotplot <- function(x, y, subscripts, groups, ..., jitter=0.1) {
jitter <- seq(-1,1,len=nlevels(groups))*jitter
xx <- as.numeric(x) + jitter[as.numeric(groups[subscripts])]
panel.dotplot(x, y, groups=groups, subscripts=subscripts, pch=NA, ...)
panel.superpose(xx, y, groups=groups, subscripts=subscripts,
panel.groups=panel.groups.mydotplot, ...)
}
pp <- dotplot(z ~ sample.size | as.factor(effect.size)*as.factor(true.dose),
groups=type, data=df.dose, as.table=TRUE, horizontal=FALSE,
up=df.dose$z + df.dose$se, lo=df.dose$z - df.dose$se,
panel=panel.mydotplot, auto.key=list(space="right"))
useOuterStrips(pp)
I'm not sure if I understand the problem and you asked for a lattice solution but I thought it may be helpful to see this done with ggplot2:
ggplot(data=df.dose, aes(x=sample.size, y=as.factor(z), colour=type)) +
geom_point() + facet_grid(true.dose~effect.size)
Yields:
Or we can free the scales with:
ggplot(data=df.dose, aes(x=sample.size, y=as.factor(z), colour=type)) +
geom_point() + facet_grid(true.dose~effect.size, scales="free")
Yielding:
You can also use xYplot from the package Hmisc, to achieve solution similar to #Aaron, although it might be a bit tricky to get the same jitter he got:
a <- xYplot(Cbind(z, z-se, z+se) ~ sample.size | as.factor(effect.size) * as.factor(true.dose),
groups=as.factor(type), data=df.dose, as.table=TRUE, auto.key=list(space="top"))
useOuterStrips(a)
But is really informative plot? Does it show your data effects well, highlights your comparisons? Does it explore any trends in the data? To better see all the factors you want to plot, I would first make lines connections between your groups, to better see individual effects within different sample.size.
key.variety <- list(space = "top",
text = list(levels(df.dose$type)),
points = list(pch = 0:3, col = "black"))
a <- xyplot(z ~ as.factor(sample.size) | as.factor(effect.size)*as.factor(true.dose),
df.dose, type = "o", as.table=TRUE, groups = type, key = key.variety,
lty = 1, pch = 0:3, col.line = "darkgrey", col.symbol = "black")
useOuterStrips(a)
But there is something hiding there and there is still too much noise because of the density of data. Let's get rid of the effect.size and plot regression line, although it's probably a sin to do with so few data points.
a <- xyplot(z ~ as.factor(sample.size) | as.factor(type)*as.factor(true.dose),
data=df.dose, as.table=TRUE,
panel = function(x, y){
panel.xyplot(x, y, jitter.x = T, col=1);
panel.lmline(x, y, col=1, lwd=1.5);
})
useOuterStrips(a)
I know I might have not convinced you, but sometimes it's better to unload a plot from too many factors to get better look at the data. Sometimes it might be more accessible visually if you show the factors separated.

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