I'm just starting with r, so this may very well be a very simple question but...
I've tried changing the name in 'a' to be more elaborate but this makes no difference
If I try to assign it to a variable
(e.g. baseline <- a %>% filter(Period == "Baseline") %>% group_by(File)%>%
It just tells me:
"Error in a %>% filter(Period == "Baseline") %>% group_by(File) %>% :
could not find function "%>%<-"
I'd really be grateful for any help with this.
It keeps telling me "Error in a(.) : could not find function "a"
and that it is unable to find Baseline_MAP even though it is defined earlier.
in mutate(Delta_MAP = Group_MAP - Baseline_MAP,
a <- read_csv("file.csv")
summary(a)
a %>%
filter(Period == "Baseline") %>%
group_by(File)%>%
summarise(Baseline_MAP = mean(MAP_Mean, na.rm=T),
Baseline_SBP = mean(SBP_Mean, na.rm=T),
Baseline_LaserMc1 = mean(Laser1_Magic, na.rm=T),
Baseline_Laser1 = mean(Laser1_Mean, na.rm=T))%>%
a%>%
filter(Period != "Baseline") %>%
group_by(File)%>%
summarise(Group_MAP = mean(MAP_Mean, na.rm=T),
Group_SBP = mean(SBP_Mean, na.rm=T),
Group_Laser_1Magic = mean(Laser1_Magic, na.rm=T),
Group_Laser_1 = mean(Laser1_Mean, na.rm=T))
a%>%
mutate(Delta_MAP = Group_MAP - Baseline_MAP,
Delta_MAP_Log = log(Group_MAP)-log(Baseline_MAP),
Delta_SBP = Group_SBP - Baseline_SBP,
Delta_SBP_Log = log(Group_SBP)-log(Baseline_SBP),
Delta_Laser1_Magic = Group_Laser_1Magic - Baseline_LaserMc1,
Delta_Laser1_Log = log(Group_Laser_1Magic)-log(Baseline_LaserMc1))
%>% is from the package "dplyr". So make sure you load it, i.e. library(dplyr).
Next, %>% does not assign the result to a variable. I.e.
a %>% mutate(foo=bar(x))
does not alter a. It will just show the result on the console (and none if you are running the script or calling it from a function).
You might be confusing the pipe-operator with %<>% (found in the package magrittr) which uses the left-hand variable as input for the pipe, and overwrites the variable with the modified result.
Finally, when you write
If I try to assign it to a variable (e.g. baseline <- a %>% filter(Period == "Baseline") %>% group_by(File)%>%)
You are assigning the result from the pipeline to a variable baseline -- this however does not modify the variable-names in the data frames (i.e. the column names).
Related
I need to create a line ID column within a dataframe for further pre-processing steps. The code worked fine up until yesterday. Today, however I am facing the error message:
"Error in mutate():
ℹ In argument: line_id = (function (x, y) ....
Caused by error:
! Can't convert y to match type of x ."
Here is my code - the dataframe consists of two character columns:
split_text <- raw_text %>%
mutate(text = enframe(strsplit(text, split = "\n", ))) %>%
unnest(cols = c(text)) %>%
unnest(cols = c(value)) %>%
rename(text_raw = value) %>%
select(-name) %>%
mutate(doc_id = str_remove(doc_id, ".txt")) %>%
# removing empty rows + add line_id
mutate(line_id = row_number())
Besides row_number(), I also tried rowid_to_column, and even c(1:1000) - the length of the dataframe. The error message stays the same.
Try explicitly specifying the data type of the "line_id" column as an integer using the as.integer() function, like this:
mutate(line_id = as.integer(row_number()))
This code works but is not fully satisfying, since I have to break the pipe:
split_text$line_id <- as.integer(c(1:nrow(split_text)))
library(highcharter)
library(dplyr)
library(viridisLite)
library(forecast)
library(treemap)
data("Groceries", package = "arules")
dfitems <- tbl_df(Groceries#itemInfo)
set.seed(10)
dfitemsg <- dfitems %>%
mutate(category = gsub(" ", "-", level1),
subcategory = gsub(" ", "-", level2)) %>%
group_by(category, subcategory) %>%
summarise(sales = n() ^ 3 ) %>%
ungroup() %>%
sample_n(31)
hctreemap2(group_vars = c("category","subcategory"),
size_var = "sales")%>%
hc_tooltip(pointFormat = "<b>{point.name}</b>:<br>
Pop: {point.value:,.0f}<br>
GNI: {point.colorValue:,.0f}")
the error is the following
Error in hctreemap2(., group_vars = c("category", "subcategory"), size_var = "sales") : Treemap data uses same label at multiple levels.
I tried everything and it doesn't work out, could someone with experience explain to me what is happening?
When I tried your code, it also stated that the function was deprecated and to use data_to_hierarchical. Although, it's never quite that simple, right? I tried multiple ways to get hctreemap2 to work, but wasn't able to discern that issue. From there I turned to the package recommended data_to_hierarchical. Now that worked without an issue--once I figured out the right type, which in hindsight seemed kind-of obvious.
That being said, this is what I've got:
data_to_hierarchical(data = dfitemsg,
group_vars = c(category,subcategory),
size_var = sales) %>%
hchart(type = "treemap") %>%
hc_tooltip(pointFormat = "<b>{point.name}</b>:<br>
Pop: {point.value:,.0f}<br>
GNI: {point.colorValue:,.0f}")
You didn't actually designate a color, so the GNI comes up blank.
Let me know if you run into any issues.
Based on your comment:
I have not found a way to change the color to density, which is what both hctreemap2 and treemap appear to do. The function data_to_heirarchical codes the colors to the first grouping variable or the level 1 variable.
Inadvertently, I did figure out why the function hctreemap2 would not work. It checks to see if any category labels are the same as a subcategory label. I didn't go through all of the data, but I know there is a perfumery perfumery. I don't understand what that's a hard stop. If that is a problem for this call, why wouldn't data_to_heirchical be looking for this issue, as well?
So, I changed the function. First, I called the function itself.
x = hctreemap2
Then I selected it from the environment pane. Alternatively, you can code View(x).
This view is read-only, but it's easier to read than the console. I copied the function and assigned it to its original name with changes. I removed two pieces of the code, which changed nothing structurally speaking to how the chart is created.
I removed the first line of code in the function:
.Deprecated("data_to_hierarchical")
and this code (about a third of the way down)
if (data %>% select(!!!group_syms) %>% map(unique) %>% unlist() %>%
anyDuplicated()) {
stop("Treemap data uses same label at multiple levels.")
}
This left me to recreate the function with this code:
hctreemap2 <- function (data, group_vars, size_var, color_var = NULL, ...)
{
assertthat::assert_that(is.data.frame(data))
assertthat::assert_that(is.character(group_vars))
assertthat::assert_that(is.character(size_var))
if (!is.null(color_var))
assertthat::assert_that(is.character(color_var))
group_syms <- rlang::syms(group_vars)
size_sym <- rlang::sym(size_var)
color_sym <- rlang::sym(ifelse(is.null(color_var), size_var, color_var))
data <- data %>% mutate_at(group_vars, as.character)
name_cell <- function(..., depth) paste0(list(...),
seq_len(depth),
collapse = "")
data_at_depth <- function(depth) {
data %>%
group_by(!!!group_syms) %>%
summarise(value = sum(!!size_sym), colorValue = sum(!!color_sym)) %>%
ungroup() %>%
mutate(name = !!group_syms[[depth]], level = depth) %>%
mutate_at(group_vars, as.character()) %>% {
if (depth == 1) {
mutate(., id = paste0(name, 1))
}
else {
mutate(.,
parent = pmap_chr(list(!!!group_syms[seq_len(depth) - 1]),
name_cell, depth = depth - 1),
id = paste0(parent, name, depth))
}
}
}
treemap_df <- seq_along(group_vars) %>% map(data_at_depth) %>% bind_rows()
data_list <- treemap_df %>% highcharter::list_parse() %>%
purrr::map(~.[!is.na(.)])
colorVals <- treemap_df %>%
filter(level == length(group_vars)) %>% pull(colorValue)
highchart() %>%
hc_add_series(data = data_list, type = "treemap",
allowDrillToNode = TRUE, ...) %>%
hc_colorAxis(min = min(colorVals), max = max(colorVals), enabled = TRUE)
}
Now your code, as originally written will work. You did not change the highcharter package by doing this. So if you think you'll use it in the future save the function code, as well. You will need the library purrr, since you already called dplyr (where most, if any conflicts occur), you could just call tidyverse (which calls several libraries at one time, including both dplyr and purrr).
This is what it will look like with set.seed(10):
If you drill down on the largest block:
It looks odd to me, but I'm guessing that's what you were looking for to begin with.
I have made a function and then returning an object named final, However when I try to access the object outside of the function it give me error object not found.
I am not sure where I am getting wrong this seems to be fairly simple and correct, when I try to exclucde the function and just run the statements, I am able to access the final object only when trying to return the object I am not able to do so.
I am not sure why this is happening.
myfunction <- function(lo,X_train,y_train,X_test,y_test,pred){
loan_number<-as.numeric(testing$lo)
xgb.train = xgb.DMatrix(data=X_train,label=y_train)
xgb.test = xgb.DMatrix(data=X_test,label=y_test)
explainer = buildExplainer(xgb,xgb.train, type="binary", base_score = 0.5, trees = NULL)
pred.breakdown = explainPredictions(xgb, explainer, X_test)
pred.breakdown<-as.data.frame(pred.breakdown)
pred.breakdown <- pred.breakdown %>% do(.[!duplicated(names(.))])
pred_break<-pred.breakdown %>%
#Create an id by row
dplyr::mutate(id=1:n()) %>%
#Reshape
pivot_longer(cols = -id) %>%
#Arrange
arrange(id,-value) %>%
#Filter top 5
group_by(id) %>%
dplyr::mutate(Var=1:n()) %>%
filter(Var<=5) %>%
select(-c(value,Var)) %>%
#Format
dplyr::mutate(Var=paste0('Attribute',1:n())) %>%
pivot_wider(names_from = Var,values_from=name) %>%
ungroup() %>%
select(-id)
pred_break<-as.data.frame(pred_break)
prop_score<-pred
final<-as.data.frame(cbind(loan_number,prop_score,pred_break))
print("final exec")
return(final)
}
myfunction(loan_number,X_train,y_train,X_test,y_test,pred)
final<-as.data.frame(final)
Printing final exec to check if everything is working or not , Apparently it's weird that I am not able to access the final object which is passed to return statement.
R is primarily a functional programming language with lexical scoping. This line:
myfunction(loan_number,X_train,y_train,X_test,y_test,pred)
runs your function and returns the VALUE of final, but it's returning to the console. The function's return needs to be assigned to another variable in order to be used, like #Duck suggests:
final <- myfunction(loan_number,X_train,y_train,X_test,y_test,pred)
This is different than final in your function. That final is inaccessible outside of the function.
I'm new to R and I don't know all basic concepts yet. The task is to produce a one merged table with multiple response sets. I am trying to do this using expss library and a loop.
This is the code in R without a loop (works fine):
#libraries
#blah, blah...
#path
df.path = "C:/dataset.sav"
#dataset load
df = read_sav(df.path)
#table
table_undropped1 = df %>%
tab_cells(mdset(q20s1i1 %to% q20s1i8)) %>%
tab_total_row_position("none") %>%
tab_stat_cpct() %>%
tab_pivot()
There are 10 multiple response sets therefore I need to create 10 tables in a manner shown above. Then I transpose those tables and merge. To simplify the code (and learn something new) I decided to produce tables using a loop. However nothing works. I'd looked for a solution and I think the most close to correct one is:
#this generates a message: '1' not found
for(i in 1:10) {
assign(paste0("table_undropped",i),1) = df %>%
tab_cells(mdset(assign(paste0("q20s",i,"i1"),1) %to% assign(paste0("q20s",i,"i8"),1)))
tab_total_row_position("none") %>%
tab_stat_cpct() %>%
tab_pivot()
}
Still it causes an error described above the code.
Alternatively, an SPSS macro for that would be (published only to better express the problem because I have to avoid SPSS):
define macro1 (x = !tokens (1)
/y = !tokens (1))
!do !i = !x !to !y.
mrsets
/mdgroup name = !concat($SET_,!i)
variables = !concat("q20s",!i,"i1") to !concat("q20s",!i,"i8")
value = 1.
ctables
/table !concat($SET_,!i) [colpct.responses.count pct40.0].
!doend
!enddefine.
*** MACRO CALL.
macro1 x = 1 y = 10.
In other words I am looking for a working substitute of !concat() in R.
%to% is not suited for parametric variable selection. There is a set of special functions for parametric variable selection and assignment. One of them is mdset_t:
for(i in 1:10) {
table_name = paste0("table_undropped",i)
..$table_name = df %>%
tab_cells(mdset_t("q20s{i}i{1:8}")) %>% # expressions in the curly brackets will be evaluated and substituted
tab_total_row_position("none") %>%
tab_stat_cpct() %>%
tab_pivot()
}
However, it is not good practice to store all tables as separate variables in the global environment. Better approach is to save all tables in the list:
all_tables = lapply(1:10, function(i)
df %>%
tab_cells(mdset_t("q20s{i}i{1:8}")) %>%
tab_total_row_position("none") %>%
tab_stat_cpct() %>%
tab_pivot()
)
UPDATE.
Generally speaking, there is no need to merge. You can do all your work with tab_*:
my_big_table = df %>%
tab_total_row_position("none")
for(i in 1:10) {
my_big_table = my_big_table %>%
tab_cells(mdset_t("q20s{i}i{1:8}")) %>% # expressions in the curly brackets will be evaluated and substituted
tab_stat_cpct()
}
my_big_table = my_big_table %>%
tab_pivot(stat_position = "inside_columns") # here we say that we need combine subtables horizontally
I am trying to follow an Vignette "How to make a Markov Chain" (http://datafeedtoolbox.com/attribution-theory-the-two-best-models-for-algorithmic-marketing-attribution-implemented-in-apache-spark-and-r/).
This tutorial is interesting, because it is using the same data source as I use. But, a part of the code is using "Spark SQL code" (what I got back from my previous question Concat_ws() function in Sparklyr is missing).
My question: I googled a lot and tried to solve this by myself. But I have no idea how, since I don't know exactly what the data should look like (the author didn't gave an example of his DF before and after the function).
How can I transform this piece of code into "normal" R code (without using Spark) (especially: the concat_ws & collect_list functions are causing trouble
He is using this line of code:
channel_stacks = data_feed_tbl %>%
group_by(visitor_id, order_seq) %>%
summarize(
path = concat_ws(" > ", collect_list(mid_campaign)),
conversion = sum(conversion)
) %>% ungroup() %>%
group_by(path) %>%
summarize(
conversion = sum(conversion)
) %>%
filter(path != "") %>%
collect()
From my previous question, I know that we can replace a part of the code:
concat_ws() can be replaced the paste() function
But again, another part of code is jumping in:
collect_list() # describtion: Aggregate function: returns a list of objects with duplicates.
I hope that I described this question as clear as possible.
paste has the ability to collapse the string vector with a separator that is provided with the collapse parameter.
This can act as a drop in replacement for concat_ws(" > ", collect_list(mid_campaign))
channel_stacks = data_feed_tbl %>%
group_by(visitor_id, order_seq) %>%
summarize(
path = paste(mid_campaign, collapse = " > "),
conversion = sum(conversion)
) %>% ungroup() %>%
group_by(path) %>%
summarize(
conversion = sum(conversion)
) %>%
filter(path != "")