I have a matrix table that looks like this:
="£" & FormatNumber(Sum(Fields!CalculatedFee.Value *
Fields!Unit.Value), 2, False, False, True)
When the report is run, it looks like this:
As you can see, it populates this year's and the previous year's data, the year is a group also.
What I would like to achieve is to colour the cell of this year's net when it is lower than the previous.
For example:
I have tried many many ways to make this work but I'm stuck. My latest incarnation is this, which fails:
=IIF(Fields!Year.Value = Year(now) and Sum(Fields!CalculatedFee.Value *
Fields!Unit.Value) < IIF(Fields!Year.Value = Year(now)-1 and
Sum(Fields!CalculatedFee.Value * Fields!Unit.Value), true, false) ,
"Red", "")
Can someone please help.
Method 1:
Try something like this.
=IIF(Fields!Year.Value = Year(now)
and
SUM(iif(Fields!Year.Value = Year(now), Fields!CalculatedFee.Value * Fields!Unit.Value, 0.0), "YourDataSetName" )
< SUM(iif(Fields!Year.Value = Year(now)-1, Fields!CalculatedFee.Value * Fields!Unit.Value, 0.0), "YourDataSetName" )
,"Red", Nothing)
If you specify SUM(Fields!CalculatedFee.Value * Fields!Unit.Value) SSRS will sum it up in your current dataset scope.
Method 2:
It depends on how your dataset is laid out. You may be able to use lookup function.
=IIF(Fields!Year.Value = Year(now)
and
Lookup(Year(now), Fields!Year.Value, Fields!CalculatedFee.Value * Fields!Unit.Value, "YourDataSetName" )
< Lookup(Year(now)-1, Fields!Year.Value, Fields!CalculatedFee.Value * Fields!Unit.Value, "YourDataSetName" )
,"Red", Nothing)
Method 3:
If you can change the report output to show 2013 first and 2014 next. In that case you can use Previous function to set the color.
Method 4:
You can change the dataset query to set a flag for color. In other words do your compaision in the query and use the flag to set the color in report.
Related
I can create an index vs the previous year when I have just one item, but I'm trying to figure out how to do this when I have multiple items. Here is my data set:
rng = pd.date_range('1/1/2011', periods=3, freq='Y')
rng = np.repeat(rng,3)
country = ["USA","Brazil","Japan"]*3
df = pd.DataFrame({'Country':country,'date':rng,'value':range(20,29)})
If I only had one item/country I can do something like this:
df['pct_iya'] = 100*(df['value'].pct_change()+1)
I'm trying to get this to work with multiple items. Here is the expected result:
Maybe this could work with a groupby, but my attempt did not work...
df['pct_iya2'] = df.groupby(['Country','date'])['value'].pct_change()
Answer: Use a group by (excluding date) and than add one to the percent change (ex +15percent goes from .15 to 1.15), then multiple you 100.
df['pct_iya'] = 100*(df.groupby(['Country'])['value'].pct_change()+1)
I'm trying to get the lowest low of a series of candles after a condition, but it always returns the last candle of the condition. I try with min(), lowest() and a for loop but it doesn't work. Also try using blackCandle[] and min(ThreeinARow)/lowest(ThreeinARow) and sometimes it returns the last candle and other times it gives me compilation error.
blackCandle = close < open
ThreeinARow = blackCandle[3] and blackCandle[2] and blackCandle[1]
SL = ThreeinARow ? min(low[1], low[2], low[3]) : na
//#version=4
study("Help (low after 3DownBar)", overlay=true, max_bars_back=100)
blackCandle = close < open
ThreeinARow = blackCandle[3] and blackCandle[2] and blackCandle[1]
bar_ind = barssince(ThreeinARow)
//SL = lowest(max(1, nz(bar_ind))) // the lowest low of a series of candles after the condition
SL = lowest(max(1, nz(bar_ind)+1)) // the lowest low of a series of candles since the condition
plot(SL, style=plot.style_cross, linewidth=3)
bgcolor(ThreeinARow ? color.silver : na)
See also the second solution which is in the commented line
It seems that I was misinterpreting it. Using min() does return the minimum of a series of candles. The detail is that I must enter the specific number of candles that I will use to calculate the minimum, which, for now, does not generate any problem for me. In the end, this is how I ended up writing it:
blackCandle = close < open
ThreeinARow = blackCandle[3] and blackCandle[2] and blackCandle[1]
Lowest_Low = if ThreeinARow
min(low[1], low[2], low[3])
plot(Lowest_Low, color=color.red)
I want to combine the CSV files from the Johns Hopkins Covid Data (e.g. https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/05-10-2020.csv & https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-23-2020.csv).
I already managed to load the files into a DataFrame as well as sanitizing the header (_ vs. / in some names). Now I want to pick one column (e.g. Confirmed), rename it to the day of the file and then combine those CSV files to get a progress over time.
This merge needs to be done by state_province. In both frames, the key may not be present. How can I do this? I experimented with rightjoin and outerjoin, but didn't have any success. Can someone point me the right way please?
I initially didn't want to share the code that I have so far because I didn't want to guide to a specific solution - but here it is. It is copied together from several Jupyter cells.
using Dates
start = Dates.Date(2020,1,22) #begin of recording
now = Dates.Date(Dates.now())- Dates.Day(1) #today
date_range = collect(start:Dates.Day(1):now) #create a date range with 1 element per day
prefix = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/"
suffix = ".csv"
function create_url(date)
return prefix * Dates.format(date, "mm-dd-YYYY") * suffix
end
function cleanup_column_names(name)
if name == "Country/Region" || name == "Country_Region"
return "country"
elseif name == "Province/State" || name == "Province_State"
return "state"
else
return name
end
end
using CSV
using HTTP
using DataFrames
selected_data = "Confirmed"
date = date_range[1]
data = DataFrame(CSV.File(HTTP.get(create_url(date)).body))
DataFrames.rename!(cleanup_column_names, data)
DataFrames.select!(data,["state", "country", selected_data])
DataFrames.rename!(data, 3 => Dates.format(date, "YYYY-mm-dd"))
Regards
Tobias
I am relatively new to Julia, so take my answer with a bit of scepticism:
First, we wrap the DataFrame creation into a function:
function prepare_date_df(date)
data = DataFrame(CSV.File(HTTP.get(create_url(date)).body))
DataFrames.rename!(cleanup_column_names, data)
DataFrames.select!(data,["state", "country", selected_data])
DataFrames.rename!(data, 3 => Dates.format(date, "YYYY-mm-dd"))
return data
end
Let's create our first Dataframe:
df = prepare_date_df(date_range[1])
Now, let's iterate over all the other dates, create a dataframe for each date and merge this with our first dataframe:
for date in date_range[2:end]
df_new = prepare_date_df(date)
df = outerjoin(df, df_new, on = [:state, :country])
end
This works fine for the first two months, but with the growing Dataframes, it suddenly gets very slow (and even hangs?). So I would be very interested in a more performative answer!
I have a network diagram that looks like this:
I made it using ggraph and added the labels using geom_nodelabel_repel() from ggnetwork:
( ggraph_plot <- ggraph(layout) +
geom_edge_fan(aes(color = as.factor(responses), edge_width = as.factor(responses))) +
geom_node_point(aes(color = as.factor(group)), size = 10) +
geom_nodelabel_repel(aes(label = name, x=x, y=y), segment.size = 1, segment.color = "black", size = 5) +
scale_color_manual("Group", values = c("#2b83ba", "#d7191c", "#fdae61")) +
scale_edge_color_manual("Frequency of Communication", values = c("Once a week or more" = "#444444","Monthly" = "#777777",
"Once every 3 months" = "#888888", "Once a year" = "#999999"),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
scale_edge_width_manual("Frequency of Communication", values = c("Once a week or more" = 3,"Monthly" = 2,
"Once every 3 months" = 1, "Once a year" = 0.25),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
theme_void() +
theme(legend.text = element_text(size=16, face="bold"),
legend.title = element_text(size=16, face="bold")) )
I want to have the labels on the left side of the plot be off to the left, and the labels on the right side of the plot to be off to the right. I want to do this because the actual labels are quite long (organization names) and they get in the way of the lines in the actual plot.
How can I do this using geom_nodelabel_repel()? i've tried different combinations of box_padding and point_padding, as well as h_just and v_just but these apply to all labels and it doesn't seem like there is a way to subset or position specific points.
Apologies for not providing a reproducible example but I wasn't sure how to do this without compromising the identities of respondents from my survey.
Well, there is always the manually-intensive, yet effective method of separately adding the geom_node_label_repel function for the nodes on the "left" vs. the "right" of the plot. It's not at all elegant and probably bad coding practice, but I've done similar things myself when I can't figure out an elegant solution. It works really well when you don't have a very large dataset to begin with and if you are not planning to make the same plot over and over again. Basically, it would entail:
Identifying if there exists a property in your dataset that places points on the "left" vs. the "right". In this case, it doesn't look like it, so you would just have to create a list manually of those entries on the "left" vs. "right" of your plot.
Using separate calls to geom_node_label_repel with different nudge_x values. Use any reasonable method to subset the "left" and "right datapoints. You can create a new column in the dataset, or use formatting in-line like data = subset(your.data.frame, property %in% left.list)
For example, if you created a column called subset.side, being either "left" or "right" in your data.frame (here: your.data.frame), your calls to geom_node_label_repel might look something like:
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='left'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=-10
) +
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='right'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=10
) +
Alternatively, you can create a list based on the label name itself--let's say you called those lists names.left and names.right, where you can subset accordingly by swapping in as represented in the pseudo code below:
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.left),...
nudge_x = -10, ...
) +
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.right),...
nudge_x = 10, ...
)
To be fair, I have not worked with the node geoms before, so I am assuming here that the positioning of the labels will not affect the mapping (as it would not with other geoms).
I have problem with a chart in vb.net. The problem is that line and bar are not synced in the chart area. I've attached a picture to make it clear what I mean
Here is the code where I populate the chart. I´m getting the data from a database.
Dim theDate As Date
For i As Integer = Count - 1 To 0 Step -1
'Chart1.Series("serRxTime").Points.AddY(dv(i)(0) / 60)
theDate = dv(i)(1)
Chart1.Series("serTime").Points.AddXY(theDate.ToString("dd-MMM HH:MM", enUS), dv(i)(0) / 60)
Chart1.Series("serAdd").Points.AddY(dv(i)(2))
Next
Line and column series have the same XValues that's why their centres are aligned. You would need to generate different XValues for the two series. XValues that are offset by a small margin. Something like this:
Chart1.Series("serTime").XValues = {0.8, 1.8, 2.8, 3.8,,...,count - 0.2}
Chart1.Series("serAdd").XValues = {1, 2, 3, 4,..., count}
I used 0.2 difference, but this will be different in your case (especially since it seems you have date axis set?). This would push the line series to the left.
I created an example for you. On the first picture you can see the data for the columns. Their x values are 1,2,3,4,...,12 and their y values are marked with blue.
And this is the values for the XY chart. As you can see I moved the x values by 0.2 to the left.