r/RStudio

Hi everyone,

I’m looking to see if anyone has experience integrating AI models directly in RStudio. I really want something that works natively inside RStudio and can access the loaded dataframes, libraries, and variables, so I don’t have to keep explaining what each variable is called, like I need to do when using the LLM in the browser.

- I’ve used Autopilot before, but I found the autocompletes unhelpful.

- chattr just seems to be a chat window inside RStudio without access to the current script or dataframes

- I recently saw that RStudio offers a new native AI, but it’s $20 a month, which is pretty pricey—especially as opposed to other LLMs you can't use it for other tasks like text or image generation.

- ClaudeR seems to do what I'm looking for. Any experience on how fast the tokens of the pro model are maxed out using this?

So: is there something that natively integrates into RStudio? A big plus would be if it integrates with Gemini, since that’s currently my favorite model.

Extra: I’d also love to hear if there is something like fully autonomous models—ones that just analyze the data on their own without a lot of guardrails. You give it the dataframe, information on key variables and what your hypothesis is and it iterates and debugs the code, tries out various tests and so on. When seeing reports on openclaw I just thought, this would be something viable today?

https://preview.redd.it/3hg1pzxci52h1.png?width=1094&format=png&auto=webp&s=bdec36e242db6aa1232374fdd0146a813541c0e2

Hi all,

I am trying to make a histogram of some data I collected. I am trying to stratify my "baseline" data by biological replicate and all other data (a separate category called ExM) by gel. I want it to look something like what I've drawn below. This is the code I am currently working with and the current output. Any recommendations will be greatly appreciated!

# mean + SD per gel × tissue_type

gel_stats <- DAPI_Quant %>%

group_by(gel, tissue_type) %>%

summarise(

mean_avg = mean(ratio, na.rm = TRUE),

sd_avg = sd(ratio, na.rm = TRUE),

.groups = "drop"

)

# Convert gel to character

gel_stats$gel <- as.character(gel_stats$gel)

# Create color palette

unique_gels <- unique(gel_stats$gel)

# ExM palette (Dark2)

exm_palette <- scales::brewer_pal(type = "qual", palette = "Dark2")(length(unique_gels))

gel_colors <- setNames(exm_palette, unique_gels)

# Baseline override (curve color)

gel_colors["baseline"] <- "grey30"

# plot styling + axes

p <- ggplot() +

theme_classic(base_size = 10) +

theme(

legend.position = "right",

axis.title = element_text(size = 10),

axis.text = element_text(size = 10),

plot.margin = margin(10, 10, 10, 10)

) +

xlim(0.75,1.35) + ylim(0, 15)+

labs(

x = "XY Ratio",

y = "Density"

)

# histogram per gel

for (g in unique_gels) {

df_gel <- DAPI_Quant %>% filter(gel == g)

# Baseline bars grey, ExM bars keep their original color

hist_fill <- ifelse(g == "baseline", "grey30", gel_colors[g])

p <- p +

geom_histogram(

data = df_gel,

aes(x = ratio, y = after_stat(density)),

bins = 150,

fill = hist_fill,

alpha = 0.25,

color = NA

)

}

for (i in 1:nrow(gel_stats)) {

row <- gel_stats[i, ]

g <- row$gel

# Baseline curve grey, ExM curves keep their colors

curve_col <- ifelse(row$tissue_type == "baseline", "grey30", gel_colors[g])

p <- p + stat_function(

fun = dnorm,

args = list(mean = row$mean_avg, sd = row$sd_avg),

color = curve_col,

linewidth = ifelse(row$tissue_type == "baseline", 1.4, 1.1),

lineend = "round",

inherit.aes = FALSE

)

}

p

https://preview.redd.it/nhd1e4poh52h1.png?width=1132&format=png&auto=webp&s=2c9832379f59bc7543282334da02870e7623a6ad

Update: Has been solved, thank you for all the responses

Hi everyone,

everyone has been so kind and helpful so I am asking one last question, that the internet, unfortunately, could not answer for me...

I would like to create a stacked bar chart with a complex dataset. My dataset looks a little like this:

Work Group 1a Group 1b Group 2a ...(up to 9)
yes 0 1 0 ...
no 1 0 0 ...
...

I have tried to use this explanation online, but I am unsure what to add for "points" in the code.

#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each
=3),
                 position=rep(c('Guard', 'Forward', 'Center'), times
=3),
                 points=c(14, 8, 8, 16, 3, 7, 17, 22, 26))

#view data frame
df

  team position points
1    A    Guard     14
2    A  Forward      8
3    A   Center      8
4    B    Guard     16
5    B  Forward      3
6    B   Center      7
7    C    Guard     17
8    C  Forward     22
9    C   Center     26



library
(ggplot2)

ggplot(df, aes
(fill=position, y=points, x=team)) + 
  geom_bar(position='stack', stat='identity')

Further explanation:

I am trying to map which students have time for leisure so the dataset looks as follows:
'Work' answers the question "Do you work?" with Yes or no
Group 1a would be: Yes I have time for leisure and my parents support me
Group 1b would be: Yes I have time for leisure and my parents don't support me --> if a person falls into this category I assigned a 1, if they don't a 0 --> this counts for all the groups (up to 9).

I would like to have all the groups on the x-Axis and the answers to "do you work" stacked for each group.

Would the best approach be, to group the yes or no answers and count the values for each group and then based off of that do the stacked bar chart?

Unfortunately, since it has taken me a while to relearn a lot about R and there were a lot of data to present and organise, I am by now in a bit of a time crunch, so I only have today to finish all my graphs and I don't have as much time as I would like to try out different approaches. I'd appreciate any help you can give me.

I'm doing a thesis on understudied defensive effects on win percentages and salary in the WNBA and NBA. I found an R program package called wehoop (WNBA) and hoopR (NBA) that had load functions for all the stats I wanted to use (shown in the code blocks). I went to run them today and on the women's side it says the package doesn't exist in the 3.0.0 version of wehoop and makes blank data tables in the 2.0.0 version. The hoopR version is also creating blank tables with all zeroes.

Does anyone know how to access the old functions and data? I think the functions were disabled or something in the newer versions and I can't get the old versions to work.

wnba_boxscorehustlev2

nba_leaguehustlestatsplayer

Hi!

I'm making summary tables for a set of linear mixed models using the function msummary and the package KableExtra. My problem is that the p-values given by the msummary function I use to build my tables are not the same that the ones in my models. I understood that msummary has a different was of calculating the p-values than the summary(lmer) but I really need the p-values from my actual models and I don't manage to figure out how to get msummary to calculate/extract that. Does someone has an idea about what I could do to fix that?

Here's my code:

modeltable=msummary(models,
                    output = "kableExtra",
                    statistic = c(
                      "SE = {std.error}"),
                    stars = c('*' = .05, '**' = .01, '***' = .001),
                    coef_map = coef_map,
                    gof_map = NA,
                    add_rows = add_rows,
                    fmt = 3,
                    escape = FALSE)

Many thanks!

To pass my R studio class I need to finish this project, it’s pretty straight forward but I’m too lost and it’s too far gone to learn now. Please help!! I’ll pay

Hi everyone,

thank you for your help last time with finding the problem in my code for plotly. I am struggling with calculating the percentages and receiving a tidy usable table using the mutate() function. Unfortunately, all the tutorials online do not seem to work for me and I don't understand what I am doing wrong.

```

surveyresponses_Freizeit_Master_count=surveyresponses_Freizeit_Master%>% count(.$`Bleibt genug Zeit für Freizeit?`)

```

After this I receive a table where all the answers per group have been calculated and what I would need is an additional column in which I have the percentages adding up to 100% and I am not sure how to get there... Could anyone please help? I would really like to learn how to do it and to truly understand it, because while I could do it by hand, I do have two more datasets I need to do this for. I appreciate any help.

https://preview.redd.it/ks6vlv7xyn0h1.png?width=874&format=png&auto=webp&s=0a81cc3ef285846578dbd28c60a9f586736983d5

hey guys, i’m writing some code to generate a trend line, but when i use this code, the line becomes misaligned and appears to be offset (idk why). i’d also like to know if there’s a command or method for creating a trend line similar to the one shown in excel. i used the next code:

lm_c1<-subset(c1,Time %in% c(2,3,6))

C1<-ggplot(c1,

aes(x=Time, y=LnOP))+geom_smooth(data=lm_c1,method ="lm", se = FALSE, color = "#082E8B", linewidth = 0.8)+geom_line(color="#8B6508")+geom_point(shape=21,fill="#EEAD0E",size=1.5,color="#CD950C",stroke=1.5)+scale_y_continuous(labels = function(x) sprintf("%.3f", x))+scale_x_continuous( breaks = seq(0, 60, by = 2))+theme_few()+theme(axis.title.y=element_text(margin=margin(r=35)), plot.margin = margin(10, 5, 5, 5),text= element_text(family = "Times New Roman"))

Hi. I heard the company mading RStudio also made Positron. Both are free code editor. My question: how compagnies like this one is making a livibg ? What is their business model when everything seems free ?

thx